[{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-Effectiveness Acceptability Curve Plots","text":"intention vignette show plot different styles cost-effectiveness acceptability curves using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"two-interventions-only","dir":"Articles","previous_headings":"","what":"Two interventions only","title":"Cost-Effectiveness Acceptability Curve Plots","text":"simplest case, usually alternative intervention (\\(=1\\)) versus status-quo (\\(=0\\)). plot show probability alternative intervention cost-effective willingness pay, \\(k\\), \\[ p(NB_1 \\geq NB_0 | k) \\mbox{   } NB_i = ke - c \\] Using set \\(N\\) posterior samples, approximated \\[ \\frac{1}{N} \\sum_j^N \\mathbb{} (k \\Delta e^j - \\Delta c^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code","dir":"Articles","previous_headings":"Two interventions only","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"calculate BCEA use bcea() function.  plot defaults base R plotting. Type plot can set explicitly using graph argument.   plotting arguments can specified title, line colours theme.","code":"data(\"Vaccine\")  he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. # str(he)  ceac.plot(he) ceac.plot(he, graph = \"base\") ceac.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(color = \"green\"),           theme = theme_dark())"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Cost-Effectiveness Acceptability Curve Plots","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status-quo, pair-wise comparisons.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"against-a-fixed-reference-intervention","dir":"Articles","previous_headings":"Multiple interventions","what":"Against a fixed reference intervention","title":"Cost-Effectiveness Acceptability Curve Plots","text":"Without loss generality, assume interested intervention \\(=1\\), wish calculate \\[ p(NB_1 \\geq NB_s | k) \\;\\; \\exists \\; s \\S \\] Using set \\(N\\) posterior samples, approximated \\[ \\frac{1}{N} \\sum_j^N \\mathbb{} (k \\Delta e_{1,s}^j - \\Delta c_{1,s}^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions > Against a fixed reference intervention","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"default plot ceac.plot() simply follow steps new data set.    Reposition legend.          Define colour palette.","code":"data(\"Smoking\")  he <- bcea(eff, cost, ref = 4) # str(he) ceac.plot(he) ceac.plot(he,           graph = \"base\",           title = \"my title\",           line = list(color = \"green\")) ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(color = \"green\")) ceac.plot(he, pos = FALSE) # bottom right ceac.plot(he, pos = c(0, 0)) ceac.plot(he, pos = c(0, 1)) ceac.plot(he, pos = c(1, 0)) ceac.plot(he, pos = c(1, 1)) ceac.plot(he, graph = \"ggplot2\", pos = c(0, 0)) ceac.plot(he, graph = \"ggplot2\", pos = c(0, 1)) ceac.plot(he, graph = \"ggplot2\", pos = c(1, 0)) ceac.plot(he, graph = \"ggplot2\", pos = c(1, 1)) mypalette <- RColorBrewer::brewer.pal(3, \"Accent\")  ceac.plot(he,           graph = \"base\",           title = \"my title\",           line = list(color = mypalette),           pos = FALSE) ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(color = mypalette),           pos = FALSE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"pair-wise-comparisons","dir":"Articles","previous_headings":"Multiple interventions","what":"Pair-wise comparisons","title":"Cost-Effectiveness Acceptability Curve Plots","text":", without loss generality, assume interested intervention \\(=1\\), wish calculate \\[ p(NB_1 = \\max\\{NB_i : \\S\\} | k) \\] can approximated following. \\[ \\frac{1}{N} \\sum_j^N \\prod_{\\S} \\mathbb{} (k \\Delta e_{1,}^j - \\Delta c_{1,}^j) \\]","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceac.html","id":"r-code-2","dir":"Articles","previous_headings":"Multiple interventions > Pair-wise comparisons","what":"R code","title":"Cost-Effectiveness Acceptability Curve Plots","text":"BCEA first must determine combinations paired interventions using multi.ce() function. can use plotting calls .e.¬†ceac.plot() BCEA deal pairwise situation appropriately. Note case probabilities given willingness pay sum 1.     line width can changes either single value change lines thickness value .","code":"he <- multi.ce(he) ceac.plot(he, graph = \"base\") ceac.plot(he,           graph = \"base\",           title = \"my title\",           line = list(color = \"green\"),           pos = FALSE) mypalette <- RColorBrewer::brewer.pal(4, \"Dark2\")  ceac.plot(he,           graph = \"base\",           title = \"my title\",           line = list(color = mypalette),           pos = c(0,1)) ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(color = mypalette),           pos = c(0,1)) ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(size = 2)) ceac.plot(he,           graph = \"ggplot2\",           title = \"my title\",           line = list(size = c(1,2,3)))"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-Effectiveness Efficiency Frontier","text":"line connecting successive points cost-effectiveness plane represent effect cost associated different treatment alternatives. gradient line segment represents ICER treatment comparison two alternatives represented segment. cost-effectiveness frontier consists set points corresponding treatment alternatives considered cost-effective different values cost-effectiveness threshold. steeper gradient successive points frontier, higher ICER treatment alternatives expensive alternative considered cost-effective high value cost-effectiveness threshold assumed. Points lying cost-effectiveness frontier represent treatment alternatives considered cost-effective value cost-effectiveness threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"r-code","dir":"Articles","previous_headings":"","what":"R code","title":"Cost-Effectiveness Efficiency Frontier","text":"create plots BCEA first call bcea() function. base R       ggplot       Check legend position argument:","code":"data(Smoking)  treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) # all interventions ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness   Costs Increase slope Increase angle #> Self-help               0.28824  45.733         158.66         1.5645 #> Group counselling       0.72252 143.301         224.67         1.5663 #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness  Costs     Dominance type #> No intervention              0.00000  0.000 Extended dominance #> Individual counselling       0.48486 94.919 Extended dominance # subset setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # check numbering and legend setComparisons(bcea_smoke) <- c(3,1) ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness  Costs     Dominance type #> No intervention       0.48486 94.919 Extended dominance setComparisons(bcea_smoke) <- c(3,2) ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness  Costs Increase slope Increase angle #> Individual counselling       0.43428 97.568         224.67         1.5663 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness  Costs     Dominance type #> No intervention       0.19662 49.186 Extended dominance #> Self-help             0.00000  0.000 Extended dominance setComparisons(bcea_smoke) <- 1 ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>           Effectiveness Costs Increase slope Increase angle #> Self-help       0.72252 143.3         198.33         1.5658 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # add interventions back in setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)  # all interventions ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness   Costs Increase slope Increase angle #> Self-help               0.28824  45.733         158.66         1.5645 #> Group counselling       0.72252 143.301         224.67         1.5663 #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness  Costs     Dominance type #> No intervention              0.00000  0.000 Extended dominance #> Individual counselling       0.48486 94.919 Extended dominance # subset setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # check numbering and legend setComparisons(bcea_smoke) <- c(3,1) ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness  Costs     Dominance type #> No intervention       0.48486 94.919 Extended dominance setComparisons(bcea_smoke) <- c(3,2) ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness  Costs Increase slope Increase angle #> Individual counselling       0.43428 97.568         224.67         1.5663 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness  Costs     Dominance type #> No intervention       0.19662 49.186 Extended dominance #> Self-help             0.00000  0.000 Extended dominance setComparisons(bcea_smoke) <- 1 ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>           Effectiveness Costs Increase slope Increase angle #> Self-help       0.72252 143.3         198.33         1.5658 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # add interventions back in setComparisons(bcea_smoke) <- c(1,3) ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # base R ceef.plot(bcea_smoke, pos = c(1,0)) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = c(1,1)) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = TRUE) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = FALSE) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = \"topleft\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = \"topright\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = \"bottomleft\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, pos = \"bottomright\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance # ggplot2 ceef.plot(bcea_smoke, graph = \"ggplot\", pos = c(1,0)) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = c(1,1)) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = TRUE) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = FALSE) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"top\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"bottom\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"left\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"ggplot\", pos = \"right\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"flipping-plot","dir":"Articles","previous_headings":"R code","what":"Flipping plot","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"ceef.plot(bcea_smoke,           flip = TRUE,           dominance = FALSE,           start.from.origins = FALSE,           print.summary = FALSE,           graph = \"base\") ceef.plot(bcea_smoke,           dominance = TRUE,           start.from.origins = FALSE,           pos = TRUE,           print.summary = FALSE,           graph = \"ggplot2\")"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"start-from-origin-or-smallest-ec-","dir":"Articles","previous_headings":"R code","what":"Start from origin or smallest (e,c).","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"ceef.plot(bcea_smoke,           flip = TRUE,           dominance = TRUE,           start.from.origins = TRUE,           print.summary = FALSE,           graph = \"base\") ceef.plot(bcea_smoke,           dominance = TRUE,           start.from.origins = TRUE,           pos = TRUE,           print.summary = FALSE,           graph = \"ggplot2\")"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceef.html","id":"negative-cost-or-effectiveness","dir":"Articles","previous_headings":"R code","what":"Negative cost or effectiveness","title":"Cost-Effectiveness Efficiency Frontier","text":"","code":"data(\"Smoking\")  cost[, 4] <- -cost[, 4] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500)  # all interventions ceef.plot(bcea_smoke, graph = \"ggplot\") #> Costs are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #> Costs are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = \"ggplot\") #> Costs are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = \"base\") #> Costs are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance setComparisons(bcea_smoke) <- c(1,2) ceef.plot(bcea_smoke, graph = \"ggplot\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness  Costs Increase slope Increase angle #> Self-help                    0.28824 45.733         158.66         1.5645 #> Individual counselling       0.48486 94.919         250.16         1.5668 #>  #> Interventions not on the efficiency frontier: #>              Effectiveness Costs     Dominance type #> No treatment             0     0 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness  Costs Increase slope Increase angle #> Self-help                    0.28824 45.733         158.66         1.5645 #> Individual counselling       0.48486 94.919         250.16         1.5668 #>  #> Interventions not on the efficiency frontier: #>              Effectiveness Costs     Dominance type #> No treatment             0     0 Extended dominance eff[, 3] <- -eff[, 3] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500) ceef.plot(bcea_smoke, graph = \"ggplot\") #> Costs and benefits are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance ceef.plot(bcea_smoke, graph = \"base\") #> Costs and benefits are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness  Costs Increase slope Increase angle #> Group counselling         1.133 -143.3             NA             NA #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness    Costs     Dominance type #> No treatment                 0.41051    0.000 Absolute dominance #> Self-help                    0.69875   45.733 Absolute dominance #> Individual counselling       1.13303 -143.301 Extended dominance data(\"Smoking\") eff[, 3] <- -eff[, 3] bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500) ceef.plot(bcea_smoke, graph = \"ggplot\") #> Benefits are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Individual counselling      -0.89536  94.919             NA             NA #> Group counselling            1.13303 143.301         23.852         1.5289 #>  #> Interventions not on the efficiency frontier: #>              Effectiveness  Costs     Dominance type #> No treatment       0.41051  0.000 Absolute dominance #> Self-help          0.69875 45.733 Absolute dominance ceef.plot(bcea_smoke, graph = \"base\") #> Benefits are negative, the frontier will not start from the origins #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Individual counselling      -0.89536  94.919             NA             NA #> Group counselling            1.13303 143.301         23.852         1.5289 #>  #> Interventions not on the efficiency frontier: #>              Effectiveness  Costs     Dominance type #> No treatment       0.41051  0.000 Absolute dominance #> Self-help          0.69875 45.733 Absolute dominance"},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Cost-effectiveness plane","text":"intention vignette show plot different styles cost-effectiveness acceptability curves using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"r-code","dir":"Articles","previous_headings":"Introduction","what":"R code","title":"Cost-effectiveness plane","text":"calculate BCEA use bcea() function. plot defaults base R plotting. Type plot can set explicitly using graph argument.   plotting arguments can specified title, line colours theme.  mean point can suppress sample points passing size NA.","code":"data(\"Vaccine\")  he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"base\") ceplane.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") ceplane.plot(he,              graph = \"ggplot2\",              title = \"my title\",              line = list(color = \"green\", size = 3),              point = list(color = \"blue\", shape = 10, size = 5),              icer = list(color = \"orange\", size = 5),              area = list(fill = \"grey\"),              theme = theme_linedraw()) ceplane.plot(he,              graph = \"ggplot2\",              point = list(size = NA),              icer = list(size = 5)) #> Warning: Removed 1000 rows containing missing values (`geom_point()`)."},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Cost-effectiveness plane","text":"situation two interventions consider.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/ceplane.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Cost-effectiveness plane","text":"Reposition legend.","code":"data(\"Smoking\")  he <- bcea(eff, cost, ref = 4) # str(he) ceplane.plot(he) ceplane.plot(he, graph = \"ggplot2\") ceplane.plot(he,              graph = \"ggplot2\",              title = \"my title\",              line = list(color = \"red\", size = 1),              point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2),              icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5)) ceplane.plot(he, pos = FALSE) # bottom right ceplane.plot(he, pos = c(0, 0)) ceplane.plot(he, pos = c(0, 1)) ceplane.plot(he, pos = c(1, 0)) ceplane.plot(he, pos = c(1, 1))"},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Contour Plots","text":"intention vignette show plot different styles contour plot using BCEA package contour() contour2() functions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"r-code","dir":"Articles","previous_headings":"Introduction","what":"R code","title":"Contour Plots","text":"calculate BCEA use bcea() function. plot defaults base R plotting. Type plot can set explicitly using graph argument.   User-defined contour levels can provided. levels nlevels arguments specify quantiles number levels. base R levels arguments kept back-compatibility ggplot2 style arguments used associated plot.   plotting arguments can specified title, line colour thickness type point.   Alternatively, contour2() function essentially wrapper ceplane.plot() addition contour lines.   plotting arguments can specified exactly way .","code":"data(\"Vaccine\") he <- bcea(eff, cost, ref = 2) contour(he, graph = \"base\") contour(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") contour(he, levels = c(0.2, 0.8)) contour(he, graph = \"ggplot2\", contour = list(breaks = c(0.2, 0.8))) contour(he,         graph = \"ggplot2\",         title = \"my title\",         point = list(color = \"blue\", shape = 2, size = 5),         contour = list(size = 2)) contour(he,         graph = \"base\",         title = \"my title\",         point = list(color = \"blue\", shape = 2, size = 2),         contour = list(size = 2)) contour2(he, graph = \"base\") contour2(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") contour2(he,          graph = \"ggplot2\",          title = \"my title\",          point = list(color = \"blue\", shape = 10, size = 5),          contour = list(size = 2)) contour2(he,          graph = \"base\",          title = \"my title\",          point = list(color = \"blue\", shape = 2, size = 3),          contour = list(size = 4))"},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Contour Plots","text":"situation two interventions consider.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/contour.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Contour Plots","text":"multiple groups quadrant annotation omitted.   scale argument determines smoothness contours.   quantiles number levels.       , applies contour2() version contour plot .   styling plot multiple comparisons can specifically change colour point type comparison.   Reposition legend.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4) # str(he) contour(he) contour(he, graph = \"ggplot2\") contour(he, scale = 0.9) contour(he, graph = \"ggplot2\", scale = 0.9)  ##TODO: what is the equivalent ggplot2 argument? contour(he, nlevels = 10) contour(he, graph = \"ggplot2\", contour = list(bins = 10)) contour(he, levels = c(0.2, 0.8)) contour(he, graph = \"ggplot2\", contour = list(breaks = c(0.2, 0.8))) contour(he,         graph = \"ggplot2\",         title = \"my title\",         line = list(color = \"red\", size = 1),         point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2),         icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5),         contour = list(size = 2)) contour(he,         graph = \"base\",         title = \"my title\",         line = list(color = \"red\", size = 1),         point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2),         icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5),         contour = list(size = 4)) contour2(he, wtp = 250) contour2(he, wtp = 250, graph = \"ggplot2\") contour2(he, wtp = 250,          graph = \"ggplot2\",          title = \"my title\",          line = list(color = \"red\", size = 1),          point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2),          icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5),          contour = list(size = 2)) contour2(he, wtp = 250,          graph = \"base\",          title = \"my title\",          line = list(color = \"red\", size = 1),          point = list(color = c(\"plum\", \"tomato\", \"springgreen\"), shape = 3:5, size = 2),          icer = list(color = c(\"red\", \"orange\", \"black\"), size = 5),          contour = list(size = 4)) contour(he, pos = FALSE)    # bottom right contour(he, pos = c(0, 0)) contour(he, pos = c(0, 1)) contour(he, pos = c(1, 0)) contour(he, pos = c(1, 1))"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Expected Incremental Benefit Plot","text":"intention vignette show plot different styles expected incremental benefit (EIB) plots using BCEA package.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"two-interventions-only","dir":"Articles","previous_headings":"","what":"Two interventions only","title":"Expected Incremental Benefit Plot","text":"simplest case, usually alternative intervention (\\(=1\\)) versus status-quo (\\(=0\\)). plot based incremental benefit function willingness pay \\(k\\). \\[ IB(\\theta) = k \\Delta_e - \\Delta_c \\] Using set \\(S\\) posterior samples, EIB approximated \\[ \\frac{1}{S} \\sum_s^S IB(\\theta_s) \\] \\(\\theta_s\\) realised configuration parameters \\(\\theta\\) correspondence \\(s\\)-th simulation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"r-code","dir":"Articles","previous_headings":"Two interventions only","what":"R code","title":"Expected Incremental Benefit Plot","text":"calculate BCEA use bcea() function. default EIB plot gives single diagonal line using base R.  vertical line represents break-even value corresponding \\(k^*\\) indicating threshold alternative treatment cost-effective status-quo. \\[ k^* = \\min\\{ k : \\mbox{EIB} > 0 \\} \\] point curve crosses x-axis. plot defaults base R plotting. Type plot can set explicitly using graph argument.   plotting arguments can specified title, line colours theme.  Credible interval can also plotted using plot.cri logical argument.","code":"data(Vaccine)  he <-   bcea(eff, cost,                          ref = 2,                        interventions = treats,         Kmax = 50000,        plot = FALSE) eib.plot(he) eib.plot(he, graph = \"base\") eib.plot(he, graph = \"ggplot2\") # ceac.plot(he, graph = \"plotly\") eib.plot(he,          graph = \"ggplot2\",          main = \"my title\",          line = list(color = \"green\"),          theme = theme_dark()) eib.plot(he, plot.cri = FALSE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Expected Incremental Benefit Plot","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status quo, comparisons simultaneously. curves pair-wise comparisons status-quo vertical lines k* annotation simultaneous comparisons. Without loss generality, assume status quo intervention \\(=0\\), wish calculate \\[ \\frac{1}{S} \\sum_s^S IB(\\theta^{i0}_s) \\;\\; \\mbox{} \\; \\] break-even points represent preference two best interventions \\(k\\). \\[ k^*_i = \\min\\{ k : \\mbox{EIB}(\\theta^) > \\mbox{EIB}(\\theta^j) \\} \\] right-curves cross x-axis.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions","what":"R code","title":"Expected Incremental Benefit Plot","text":"default plot eib.plot() simply follow steps new data set.  example, can change main title EIB line colours green.   Credible interval can also plotted . isn‚Äôt recommended case since hard understand many lines.","code":"data(Smoking)  treats <- c(\"No intervention\", \"Self-help\",             \"Individual counselling\", \"Group counselling\") he <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)  eib.plot(he) eib.plot(he,          graph = \"base\",          main = \"my title\",          line = list(color = \"green\")) eib.plot(he,          graph = \"ggplot2\",          main = \"my title\",          line = list(color = \"green\")) eib.plot(he, plot.cri = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/articles/eib.html","id":"repositioning-the-legend-","dir":"Articles","previous_headings":"Multiple interventions > R code","what":"Repositioning the legend.","title":"Expected Incremental Benefit Plot","text":"base R,      ggplot2,     Define colour palette different colour EIB line.","code":"eib.plot(he, pos = FALSE) # bottom right eib.plot(he, pos = c(0, 0)) eib.plot(he, pos = c(0, 1)) eib.plot(he, pos = c(1, 0)) eib.plot(he, pos = c(1, 1)) ##TODO: eib.plot(he, graph = \"ggplot2\", pos = c(0, 0)) eib.plot(he, graph = \"ggplot2\", pos = c(0, 1)) eib.plot(he, graph = \"ggplot2\", pos = c(1, 0)) eib.plot(he, graph = \"ggplot2\", pos = c(1, 1)) mypalette <- RColorBrewer::brewer.pal(3, \"Accent\")  eib.plot(he,          graph = \"base\",          line = list(color = mypalette)) eib.plot(he,          graph = \"ggplot2\",          line = list(color = mypalette))"},{"path":"https://n8thangreen.github.io/BCEA/articles/info-rank.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Info-rank plot","text":"smaller number top variables can selected using howManyPars argument.","code":"data(\"Vaccine\") m <- bcea(e, c) inp <- createInputs(vaccine, print_is_linear_comb = FALSE) info.rank(m, inp) info.rank(m, inp, graph = \"base\") info.rank(m, inp, graph = \"plotly\") info.rank(m, inp, graph = \"base\", howManyPars = 10) info.rank(m, inp, graph = \"plotly\", howManyPars = 10)"},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Paired vs Multiple Comparisons","text":"intention vignette show plot CEAC EIB plots depending whether consider interventions simultaneously pair-wise reference.","code":""},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"multiple-interventions","dir":"Articles","previous_headings":"","what":"Multiple interventions","title":"Paired vs Multiple Comparisons","text":"situation two interventions consider. Incremental values can obtained either always fixed reference intervention, status-quo, comparisons simultaneously. call paired comparison multiple comparison.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"r-code","dir":"Articles","previous_headings":"Multiple interventions > Against a fixed reference intervention","what":"R code","title":"Paired vs Multiple Comparisons","text":"default plot ceac.plot() simply follow steps new data set.","code":"data(\"Smoking\") he <- bcea(eff, cost, ref = 4, Kmax = 500) par(mfrow = c(2,1)) ceac.plot(he) abline(h = 0.5, lty = 2) abline(v = c(160, 225), lty = 3) eib.plot(he, plot.cri = FALSE)"},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/paired_vs_multiple_comps.html","id":"r-code-1","dir":"Articles","previous_headings":"Multiple interventions > Pair-wise comparisons","what":"R code","title":"Paired vs Multiple Comparisons","text":"BCEA first must determine combinations paired interventions using multi.ce() function.","code":"he.multi <- multi.ce(he) par(mfrow = c(2, 1)) ceac.plot(he.multi) abline(h = 0.5, lty = 2) abline(v = c(160, 225), lty = 3) eib.plot(he, plot.cri = FALSE)"},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/articles/R_journal_examples.html","id":"smoking-cessation-example","dir":"Articles","previous_headings":"","what":"Smoking cessation example","title":"R journal examples","text":"grid plots   individual plots cost-effectiveness plane   plots      multiple simultaneous comparisons   mixed strategy  risk aversion","code":"data(Smoking)  treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") bcea_smoke <- bcea(e, c, ref = 4, interventions = treats, Kmax = 500) library(ggplot2)  plot(bcea_smoke) plot(bcea_smoke, graph = \"ggplot2\", wtp = 250, pos = TRUE, size = rel(2), ICER.size = 2) ceplane.plot(bcea_smoke, comparison = 2, wtp = 250) setComparisons(bcea_smoke) <- c(1,3)  ceplane.plot(bcea_smoke, wtp = 250, graph = \"ggplot2\") eib.plot(bcea_smoke) contour(bcea_smoke) ceac.plot(bcea_smoke) ib.plot(bcea_smoke) #> NB: k (wtp) is defined in the interval [0 - 500] ceef.plot(bcea_smoke) #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                        Effectiveness   Costs Increase slope Increase angle #> Self-help                    0.48486  94.919         195.77         1.5657 #> Individual counselling       0.72252 143.301         203.57         1.5659 #>  #> Interventions not on the efficiency frontier: #>                 Effectiveness Costs     Dominance type #> No intervention             0     0 Extended dominance bcea_smoke <- multi.ce(bcea_smoke)  ceac.plot(bcea_smoke, pos = \"topright\") ceaf.plot(bcea_smoke) mixedAn(bcea_smoke) <- c(0.4, 0.3, 0.2, 0.1) summary(bcea_smoke, wtp = 250) #>  #> Analysis of mixed strategy for willingness to pay parameter k = 250 #>  #> Reference intervention: Group counselling (10.00% market share) #> Comparator intervention(s): No intervention (40.00% market share) #>                           : Individual counselling (20.00% market share) #>  #> Loss in the expected value of information = 34.43 evi.plot(bcea_smoke, graph = \"ggplot\", pos = \"b\") r <- c(0, 0.005, 0.020, 0.035) CEriskav(bcea_smoke) <- r  plot(bcea_smoke)"},{"path":"https://n8thangreen.github.io/BCEA/articles/R_journal_examples.html","id":"influenza-vaccine-data","dir":"Articles","previous_headings":"","what":"Influenza vaccine data","title":"R journal examples","text":"grid plots  summary output risk aversion  value information","code":"data(Vaccine)  treats <- c(\"Status quo\", \"Vaccination\") bcea_vacc <- bcea(e, c, ref = 2, interventions = treats) plot(bcea_vacc) summary(bcea_vacc, wtp = 10000) #>  #> Cost-effectiveness analysis summary  #>  #> Reference intervention:  Vaccination #> Comparator intervention: Status quo #>  #> Optimal decision: choose Status quo for k < 20100 and Vaccination for k >= 20100 #>  #>  #> Analysis for willingness to pay parameter k = 10000 #>  #>             Expected utility #> Status quo           -36.054 #> Vaccination          -34.826 #>  #>                              EIB  CEAC  ICER #> Vaccination vs Status quo 1.2284 0.529 20098 #>  #> Optimal intervention (max expected utility) for k = 10000: Status quo #>             #> EVPI 3.0287 head(sim_table(bcea_vacc, wtp = 25000)$Table) #>          U1        U2        U*      IB2_1       OL         VI #> 1 -36.57582 -38.71760 -36.57582 -2.1417866 2.141787  -1.135907 #> 2 -27.92514 -27.67448 -27.67448  0.2506573 0.000000   7.765431 #> 3 -28.03024 -33.37394 -28.03024 -5.3436963 5.343696   7.409665 #> 4 -53.28408 -47.13734 -47.13734  6.1467384 0.000000 -11.697432 #> 5 -43.58389 -40.40469 -40.40469  3.1791976 0.000000  -4.964782 #> 6 -42.37456 -33.08547 -33.08547  9.2890987 0.000000   2.354444 r <- c(0, 0.005, 0.020, 0.035) CEriskav(bcea_vacc) <- r  plot(bcea_vacc) evi.plot(bcea_vacc) inp <- createInputs(vaccine_mat, print_is_linear_comb = FALSE) info.rank(bcea_vacc, inp) EVPPI <- evppi(bcea_vacc, c(\"beta.1.\", \"beta.2.\"), inp$mat) #> [1] \"method: GAM\" \"method: GAM\" #>  #> Calculating fitted values for the GAM regression  #>  #> Calculating fitted values for the GAM regression  #> Calculating EVPPI plot(EVPPI)"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"changing-reference-group","dir":"Articles","previous_headings":"","what":"Changing Reference Group","title":"Set bcea() Parameters: Constructor and Setters","text":"Load cost-effectiveness data. first create bcea object using constructor function 2 different reference groups.  Alternatively, can modifying first output.","code":"data(Vaccine) he_ref1 <- bcea(eff, cost,                 ref = 1,                            interventions = treats,                 Kmax = 50000)   str(he_ref1) #> List of 24 #>  $ n_sim        : int 1000 #>  $ n_comparators: num 2 #>  $ n_comparisons: int 1 #>  $ delta_e      :'data.frame':   1000 obs. of  1 variable: #>   ..$ Vaccination: num [1:1000] -0.000148 -0.000152 -0.000192 -0.000504 -0.000394 ... #>  $ delta_c      :'data.frame':   1000 obs. of  1 variable: #>   ..$ Vaccination: num [1:1000] -5.84 -3.54 -10.15 -6.45 -6.68 ... #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Vaccination\" #>  $ Kmax         : num 50000 #>  $ k            : num [1:501] 0 100 200 300 400 500 600 700 800 900 ... #>  $ ceac         : num [1:501, 1] 0.98 0.978 0.977 0.977 0.977 0.976 0.976 0.975 0.975 0.973 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ k   : NULL #>   .. ..$ ints: chr \"Vaccination\" #>  $ ib           : num [1:501, 1:1000, 1] 5.84 5.83 5.81 5.8 5.78 ... #>   ..- attr(*, \"dimnames\")=List of 3 #>   .. ..$ k   : NULL #>   .. ..$ sims: NULL #>   .. ..$ ints: chr \"Vaccination\" #>  $ eib          : num [1:501, 1] 5.04 5.01 4.99 4.96 4.94 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ k   : NULL #>   .. ..$ ints: chr \"Vaccination\" #>  $ kstar        : num 20100 #>  $ best         : int [1:501] 1 1 1 1 1 1 1 1 1 1 ... #>  $ U            : num [1:1000, 1:501, 1:2] -10.41 -5.83 -5.78 -12.21 -9.79 ... #>   ..- attr(*, \"dimnames\")=List of 3 #>   .. ..$ sims: NULL #>   .. ..$ k   : NULL #>   .. ..$ ints: chr [1:2] \"Status Quo\" \"Vaccination\" #>  $ vi           : num [1:1000, 1:501] -0.754 3.821 3.871 -2.553 -0.131 ... #>  $ Ustar        : num [1:1000, 1:501] -10.41 -5.83 -5.78 -12.21 -9.79 ... #>  $ ol           : num [1:1000, 1:501] 0 0 0 0 0 0 0 0 0 0 ... #>  $ evi          : num [1:501] 0.0562 0.0573 0.0586 0.0598 0.0611 ... #>  $ ref          : int 1 #>  $ comp         : int 2 #>  $ step         : num 100 #>  $ interventions: chr [1:2] \"Status Quo\" \"Vaccination\" #>  $ e            : num [1:1000, 1:2] -0.001047 -0.000884 -0.00089 -0.001643 -0.001352 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : NULL #>   .. ..$ : chr [1:2] \"Status Quo\" \"Vaccination\" #>  $ c            : num [1:1000, 1:2] 10.41 5.83 5.78 12.21 9.79 ... #>   ..- attr(*, \"dimnames\")=List of 2 #>   .. ..$ : NULL #>   .. ..$ : chr [1:2] \"Status Quo\" \"Vaccination\" #>  - attr(*, \"class\")= chr [1:2] \"bcea\" \"list\"  ceplane.plot(he_ref1) he_ref2 <- bcea(eff, cost,                 ref = 2,                            interventions = treats,                 Kmax = 50000)    str(he_ref2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Status Quo\" #>  $ ref          : int 2 #>  $ comp         : int 1 setReferenceGroup(he_ref1) <- 2  str(he_ref1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Status Quo\" #>  $ ref          : int 2 #>  $ comp         : int 1"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"changing-kmax","dir":"Articles","previous_headings":"","what":"Changing Kmax","title":"Set bcea() Parameters: Constructor and Setters","text":"way can change Kmax 2 equivalent ways.","code":"he_Kmax1 <- bcea(eff, cost,                  ref = 1,                             interventions = treats,                  Kmax = 50000)    str(he_Kmax1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Vaccination\" #>  $ ref          : int 1 #>  $ comp         : int 2 #>  $ Kmax         : num 50000 he_Kmax2 <- bcea(eff, cost,                  ref = 2,                             interventions = treats,                  Kmax = 2000)    str(he_Kmax2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Status Quo\" #>  $ ref          : int 2 #>  $ comp         : int 1 #>  $ Kmax         : num 2000 setKmax(he_Kmax1) <- 2000  str(he_Kmax1[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\", \"Kmax\")]) #> List of 5 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 20098 #>   ..- attr(*, \"names\")= chr \"Vaccination\" #>  $ ref          : int 1 #>  $ comp         : int 2 #>  $ Kmax         : num 2000"},{"path":"https://n8thangreen.github.io/BCEA/articles/Set_bcea_parameters.html","id":"change-comparison-groups","dir":"Articles","previous_headings":"","what":"Change Comparison Groups","title":"Set bcea() Parameters: Constructor and Setters","text":"Lets load data two groups. Defaults groups case 2, 3 4.  Let us compare groups 2.  can achieve thing using appropriate setter.  can select multiple comparison groups . Let us compare groups 2 4.   , bcea object comparison groups can passed functions ceplane.plot ceac.plot comparison argument, modifications using functions internally instead.","code":"data(Smoking) he_comp234 <- bcea(eff, cost,                    ref = 1,                               interventions = treats,                    Kmax = 50000)    str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 4 #>  $ ICER         : Named num [1:3] 159 196 198 #>   ..- attr(*, \"names\")= chr [1:3] \"Self-help\" \"Individual counselling\" \"Group counselling\" #>  $ ref          : int 1 #>  $ comp         : int [1:3] 2 3 4  ceplane.plot(he_comp234, wtp = 2000) he_comp2 <- bcea(eff, cost,                  ref = 1,                  .comparison = 2,                  interventions = treats,                  Kmax = 2000)    str(he_comp2[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 159 #>   ..- attr(*, \"names\")= chr \"Self-help\" #>  $ ref          : int 1 #>  $ comp         : num 2  ceplane.plot(he_comp2, wtp = 2000) setComparisons(he_comp234) <- 2  str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 2 #>  $ ICER         : Named num 159 #>   ..- attr(*, \"names\")= chr \"Self-help\" #>  $ ref          : int 1 #>  $ comp         : num 2  ceplane.plot(he_comp234, wtp = 2000) he_comp24 <- bcea(eff, cost,                   ref = 1,                   .comparison = c(2,4),                   interventions = treats,                   Kmax = 2000)   str(he_comp24[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 3 #>  $ ICER         : Named num [1:2] 159 198 #>   ..- attr(*, \"names\")= chr [1:2] \"Self-help\" \"Group counselling\" #>  $ ref          : int 1 #>  $ comp         : num [1:2] 2 4  ceplane.plot(he_comp24, wtp = 2000) setComparisons(he_comp234) <- c(2,4)  str(he_comp234[c(\"n_comparators\", \"ICER\", \"ref\", \"comp\")]) #> List of 4 #>  $ n_comparators: num 3 #>  $ ICER         : Named num [1:2] 159 198 #>   ..- attr(*, \"names\")= chr [1:2] \"Self-help\" \"Group counselling\" #>  $ ref          : int 1 #>  $ comp         : num [1:2] 2 4  ceplane.plot(he_comp24, wtp = 2000) ceplane.plot(he_comp234, comparison = 2, wtp = 2000) ceplane.plot(he_comp234, comparison = c(2,4), wtp = 2000)"},{"path":"https://n8thangreen.github.io/BCEA/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Gianluca Baio. Author, maintainer, copyright holder. Andrea Berardi. Author. Anna Heath. Author. Nathan Green. Author.","code":""},{"path":"https://n8thangreen.github.io/BCEA/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Baio et al (2017). Bayesian Cost Effectiveness Analysis R package BCEA. Springer, New York, NY. doi: 10.1007/978-3-319-55718-2, URL: https://link.springer.com/book/10.1007/978-3-319-55718-2","code":"@Book{,   title = {Bayesian Cost-Effectiveness Analysis with the R package {BCEA}},   author = {{Baio} and {G} and {Berardi} and {A} and {Heath} and {A}},   year = {2017},   publisher = {Springer},   month = {Jul},   address = {New York, NY},   url = {https://link.springer.com/book/10.1007/978-3-319-55718-2},   doi = {10.1007/978-3-319-55718-2},   isbn = {978-3-319-55718-2}, }"},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_BACKUP_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_BASE_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_LOCAL_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONDUCT_REMOTE_1725.html","id":null,"dir":"","previous_headings":"","what":"Contributor Code of Conduct","title":"Contributor Code of Conduct","text":"contributors maintainers project, pledge respect people contribute reporting issues, posting feature requests, updating documentation, submitting pull requests patches, activities. committed making participation project harassment-free experience everyone, regardless level experience, gender, gender identity expression, sexual orientation, disability, personal appearance, body size, race, ethnicity, age, religion. Examples unacceptable behaviour participants include use sexual language imagery, derogatory comments personal attacks, trolling, public private harassment, insults, unprofessional conduct. Project maintainers right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct. Project maintainers follow Code Conduct may removed project team. Instances abusive, harassing, otherwise unacceptable behaviour may reported opening issue contacting one project maintainers. Code Conduct adapted Contributor Covenant (https://contributor-covenant.org), version 1.0.0, available https://contributor-covenant.org/version/1/0/0/","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to BCEA","title":"Contributing to BCEA","text":"Thank taking time contribute development BCEA. find following guidelines useful making contributions. start: important valid GitHub account. Trivial changes comments documentation require creating new issue.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"did-you-find-a-bug","dir":"","previous_headings":"","what":"Did you find a bug?","title":"Contributing to BCEA","text":"Make sure bug already reported Github Issues. Open issue clearly describe issue much information possible. code sample executable test case recommended.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"did-you-plan-to-write-a-patch-that-fixes-a-bug","dir":"","previous_headings":"","what":"Did you plan to write a patch that fixes a bug?","title":"Contributing to BCEA","text":"Open issue clearly describe problem discuss solution affect BCEA. Fork repository GitHub work patch. Get touch maintainer refine prioritize issue.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"making-changes-and-pull-requests","dir":"","previous_headings":"","what":"Making changes and Pull requests","title":"Contributing to BCEA","text":"Start work fork repository. haven‚Äôt done , try using usethis::create_from_github(\"n8thangreen/BCEA\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). Create Git branch pull request (PR). may want use usethis::pr_init(\"brief-description--change\"). Check unnecessary whitespace git diff --check format code. Commit messages descriptive, mentioning changed , also reference relevant issue number. Ensure add necessary tests changes (testthat preferably). Run tests assure nothing else accidentally broken, also keep eye test coverage metric. Commit git, create PR running usethis::pr_push(), following prompts browser","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"copyright-issues","dir":"","previous_headings":"","what":"Copyright issues","title":"Contributing to BCEA","text":"submission, crucial PR includes following statement: copyright code contributed, hereby grant BCEA repo cph unlimited license use code version future version BCEA. reserve rights code. may advisable contribute third party codes project. Useful suggestions nonetheless welcomed. PRs thereafter reviewed, feedback communicated soon possible.","code":""},{"path":"https://n8thangreen.github.io/BCEA/CONTRIBUTING.html","id":"additional-resources","dir":"","previous_headings":"","what":"Additional Resources","title":"Contributing to BCEA","text":"General GitHub documentation pull requests","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"bcea-bayesian-cost-effectiveness-analysis-","dir":"","previous_headings":"","what":"Bayesian Cost Effectiveness Analysis","title":"Bayesian Cost Effectiveness Analysis","text":"Perform Bayesian Cost-Effectiveness Analysis R. üöÄ Version 2.4.5 development now! Check release notes .","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"contents","dir":"","previous_headings":"","what":"Contents","title":"Bayesian Cost Effectiveness Analysis","text":"Overview Features Installation Articles details","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Bayesian Cost Effectiveness Analysis","text":"Given results Bayesian model (possibly based MCMC) form simulations posterior distributions suitable variables costs clinical benefits two interventions, produces health economic evaluation. Compares one interventions (‚Äúreference‚Äù) others (‚Äúcomparators‚Äù).","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"Bayesian Cost Effectiveness Analysis","text":"Main features BCEA include: Cost-effectiveness analysis plots, CE planes CEAC Summary statistics tables EVPPI calculations plots","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Bayesian Cost Effectiveness Analysis","text":"Install released version CRAN stable version (can updated quickly) can installed using GitHub repository. Windows machines, need install dependencies, including Rtools first, e.g.¬†running installing package using remotes: Linux MacOS, sufficient install package via remotes:","code":"install.packages(\"BCEA\") pkgs <- c(\"MASS\", \"Rtools\", \"remotes\") repos <- c(\"https://cran.rstudio.com\", \"https://inla.r-inla-download.org/R/stable/\")  install.packages(pkgs, repos=repos, dependencies = \"Depends\") remotes::install_github(\"giabaio/BCEA\") install.packages(\"remotes\") remotes::install_github(\"giabaio/BCEA\")"},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"articles","dir":"","previous_headings":"","what":"Articles","title":"Bayesian Cost Effectiveness Analysis","text":"Examples using specific functions different arguments given articles: Get Started Set bcea() Parameters: Constructor Setters Cost-Effectiveness Acceptability Curve Plots Cost-Effectiveness Efficiency Frontier Risk Aversion Analysis Expected Incremental Benefit Plot Paired vs Multiple Comparisons","code":""},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"further-details","dir":"","previous_headings":"","what":"Further details","title":"Bayesian Cost Effectiveness Analysis","text":"pkgdown site . details BCEA available book Bayesian Cost-Effectiveness Analysis R Package BCEA (published UseR! Springer series). Also, details package, including references links pdf presentation posts blog) given .","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/index.html","id":"contributing","dir":"","previous_headings":"","what":"Contributing","title":"Bayesian Cost Effectiveness Analysis","text":"Please submit contributions Pull Requests, following contributing guidelines. report issues /seek support, please file new ticket issue tracker. Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU General Public License","title":"GNU General Public License","text":"Version 3, 29 June 2007Copyright ¬© 2007 Free Software Foundation, Inc.¬†<https://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU General Public License","text":"GNU General Public License free, copyleft license software kinds works. licenses software practical works designed take away freedom share change works. contrast, GNU General Public License intended guarantee freedom share change versions program‚Äìmake sure remains free software users. , Free Software Foundation, use GNU General Public License software; applies also work released way authors. can apply programs, . speak free software, referring freedom, price. 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Definitions","title":"GNU General Public License","text":"‚ÄúLicense‚Äù refers version 3 GNU General Public License. ‚ÄúCopyright‚Äù also means copyright-like laws apply kinds works, semiconductor masks. ‚ÄúProgram‚Äù refers copyrightable work licensed License. licensee addressed ‚Äú‚Äù. ‚ÄúLicensees‚Äù ‚Äúrecipients‚Äù may individuals organizations. ‚Äúmodify‚Äù work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called ‚Äúmodified version‚Äù earlier work work ‚Äúbased ‚Äù earlier work. ‚Äúcovered work‚Äù means either unmodified Program work based Program. ‚Äúpropagate‚Äù work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. ‚Äúconvey‚Äù work means kind propagation enables parties make receive copies. 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Source Code","title":"GNU General Public License","text":"‚Äúsource code‚Äù work means preferred form work making modifications . ‚ÄúObject code‚Äù means non-source form work. ‚ÄúStandard Interface‚Äù means interface either official standard defined recognized standards body, , case interfaces specified particular programming language, one widely used among developers working language. ‚ÄúSystem Libraries‚Äù executable work include anything, work whole, () included normal form packaging Major Component, part Major Component, (b) serves enable use work Major Component, implement Standard Interface implementation available public source code form. ‚ÄúMajor Component‚Äù, context, means major essential component (kernel, window system, ) specific operating system () executable work runs, compiler used produce work, object code interpreter used run . ‚ÄúCorresponding Source‚Äù work object code form means source code needed generate, install, (executable work) run object code modify work, including scripts control activities. 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Termination","title":"GNU General Public License","text":"may propagate modify covered work except expressly provided License. attempt otherwise propagate modify void, automatically terminate rights License (including patent licenses granted third paragraph section 11). However, cease violation License, license particular copyright holder reinstated () provisionally, unless copyright holder explicitly finally terminates license, (b) permanently, copyright holder fails notify violation reasonable means prior 60 days cessation. Moreover, license particular copyright holder reinstated permanently copyright holder notifies violation reasonable means, first time received notice violation License (work) copyright holder, cure violation prior 30 days receipt notice. 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Use with the GNU Affero General Public License","title":"GNU General Public License","text":"Notwithstanding provision License, permission link combine covered work work licensed version 3 GNU Affero General Public License single combined work, convey resulting work. terms License continue apply part covered work, special requirements GNU Affero General Public License, section 13, concerning interaction network apply combination .","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_14-revised-versions-of-this-license","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"14. Revised Versions of this License","title":"GNU General Public License","text":"Free Software Foundation may publish revised /new versions GNU General Public License time time. new versions similar spirit present version, may differ detail address new problems concerns. version given distinguishing version number. Program specifies certain numbered version GNU General Public License ‚Äúlater version‚Äù applies , option following terms conditions either numbered version later version published Free Software Foundation. Program specify version number GNU General Public License, may choose version ever published Free Software Foundation. Program specifies proxy can decide future versions GNU General Public License can used, proxy‚Äôs public statement acceptance version permanently authorizes choose version Program. Later license versions may give additional different permissions. However, additional obligations imposed author copyright holder result choosing follow later version.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_15-disclaimer-of-warranty","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"15. Disclaimer of Warranty","title":"GNU General Public License","text":"WARRANTY PROGRAM, EXTENT PERMITTED APPLICABLE LAW. EXCEPT OTHERWISE STATED WRITING COPYRIGHT HOLDERS /PARTIES PROVIDE PROGRAM ‚Äú‚Äù WITHOUT WARRANTY KIND, EITHER EXPRESSED IMPLIED, INCLUDING, LIMITED , IMPLIED WARRANTIES MERCHANTABILITY FITNESS PARTICULAR PURPOSE. ENTIRE RISK QUALITY PERFORMANCE PROGRAM . PROGRAM PROVE DEFECTIVE, ASSUME COST NECESSARY SERVICING, REPAIR CORRECTION.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_16-limitation-of-liability","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"16. Limitation of Liability","title":"GNU General Public License","text":"EVENT UNLESS REQUIRED APPLICABLE LAW AGREED WRITING COPYRIGHT HOLDER, PARTY MODIFIES /CONVEYS PROGRAM PERMITTED , LIABLE DAMAGES, INCLUDING GENERAL, SPECIAL, INCIDENTAL CONSEQUENTIAL DAMAGES ARISING USE INABILITY USE PROGRAM (INCLUDING LIMITED LOSS DATA DATA RENDERED INACCURATE LOSSES SUSTAINED THIRD PARTIES FAILURE PROGRAM OPERATE PROGRAMS), EVEN HOLDER PARTY ADVISED POSSIBILITY DAMAGES.","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"id_17-interpretation-of-sections-15-and-16","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"17. Interpretation of Sections 15 and 16","title":"GNU General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://n8thangreen.github.io/BCEA/LICENSE.html","id":"how-to-apply-these-terms-to-your-new-programs","dir":"","previous_headings":"","what":"How to Apply These Terms to Your New Programs","title":"GNU General Public License","text":"develop new program, want greatest possible use public, best way achieve make free software everyone can redistribute change terms. , attach following notices program. safest attach start source file effectively state exclusion warranty; file least ‚Äúcopyright‚Äù line pointer full notice found. Also add information contact electronic paper mail. program terminal interaction, make output short notice like starts interactive mode: hypothetical commands show w show c show appropriate parts General Public License. course, program‚Äôs commands might different; GUI interface, use ‚Äúbox‚Äù. also get employer (work programmer) school, , sign ‚Äúcopyright disclaimer‚Äù program, necessary. information , apply follow GNU GPL, see <http://www.gnu.org/licenses/>. GNU General Public License permit incorporating program proprietary programs. program subroutine library, may consider useful permit linking proprietary applications library. want , use GNU Lesser General Public License instead License. first, please read <http://www.gnu.org/philosophy/--lgpl.html>.","code":"<one line to give the program's name and a brief idea of what it does.> Copyright (C) 2020 G Baio  This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.  This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.  You should have received a copy of the GNU General Public License along with this program.  If not, see <http://www.gnu.org/licenses/>. BCEA Copyright (C) 2020 G Baio This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'. This is free software, and you are welcome to redistribute it under certain conditions; type 'show c' for details."},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"contents","dir":"","previous_headings":"","what":"Contents","title":"BCEA ‚Äî development version ","text":"Overview Features Installation details","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"BCEA ‚Äî development version ","text":"Perform Bayesian Cost-Effectiveness Analysis R. Given results Bayesian model (possibly based MCMC) form simulations posterior distributions suitable variables costs clinical benefits two interventions, produces health economic evaluation. Compares one interventions (‚Äúreference‚Äù) others (‚Äúcomparators‚Äù).","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"features","dir":"","previous_headings":"","what":"Features","title":"BCEA ‚Äî development version ","text":"Main features BCEA include: Summary statistics tables Cost-effectiveness analysis plots, CE planes CEAC EVPPI calculations plots development version BCEA (currently 2.4). contains major refactoring code streamline functions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"BCEA ‚Äî development version ","text":"development version can installed using GitHub repository. Windows machines, need install dependencies, including Rtools first, e.g.¬†running installing package using remotes: Linux MacOS, sufficient install package via remotes:","code":"pkgs <- c(\"MASS\", \"Rtools\", \"remotes\") repos <- c(\"https://cran.rstudio.com\", \"https://inla.r-inla-download.org/R/stable/\")  install.packages(pkgs, repos=repos, dependencies = \"Depends\") remotes::install_github(\"giabaio/BCEA\", ref=\"dev\") install.packages(\"remotes\") remotes::install_github(\"giabaio/BCEA\", ref=\"dev\")"},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"further-details","dir":"","previous_headings":"","what":"Further details","title":"BCEA ‚Äî development version ","text":"pkgdown site . details BCEA available book Bayesian Cost-Effectiveness Analysis R Package BCEA (published UseR! Springer series). Also, details package, including references links pdf presentation posts blog) given .","code":""},{"path":"https://n8thangreen.github.io/BCEA/README_dev.html","id":"licence","dir":"","previous_headings":"","what":"Licence","title":"BCEA ‚Äî development version ","text":"GPL-3 ¬© G Baio. Please note project released Contributor Code Conduct. participating project agree abide terms.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Contours to Base R Plot ‚Äî add_contours","title":"Add Contours to Base R Plot ‚Äî add_contours","text":"Add Contours Base R Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Contours to Base R Plot ‚Äî add_contours","text":"","code":"add_contours(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Contours to Base R Plot ‚Äî add_contours","text":"bcea object containing results Bayesian modelling economic evaluation. params List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contours.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Contours to Base R Plot ‚Äî add_contours","text":"plot side effect","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":null,"dir":"Reference","previous_headings":"","what":"Add Contour Quadrants ‚Äî add_contour_quadrants","title":"Add Contour Quadrants ‚Äî add_contour_quadrants","text":"Add Contour Quadrants","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add Contour Quadrants ‚Äî add_contour_quadrants","text":"","code":"add_contour_quadrants(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add Contour Quadrants ‚Äî add_contour_quadrants","text":"bcea object containing results Bayesian modelling economic evaluation. params List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/add_contour_quadrants.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add Contour Quadrants ‚Äî add_contour_quadrants","text":"Plot side effect","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":null,"dir":"Reference","previous_headings":"","what":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"functions listed deprecated defunct near future. possible, alternative functions similar functionality also mentioned. Help pages deprecated functions available help(\"<function>-deprecated\").","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"","code":"plot.mixedAn(x, y.limits=NULL, pos=c(0,1), graph=c(\"base\",\"ggplot2\"),...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"x object class mixedAn, given output call function mixedAn(). y.limits Range y-axis graph. default value NULL, case maximum range optimal mixed analysis scenarios considered. pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(0,1), topleft corner inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"evi ggplot object containing plot. Returned graph=\"ggplot2\". function produces graph showing difference ''optimal'' version EVPI (cost-effective intervention included market) mixed strategy one (one intervention considered market).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"plot-mixedan","dir":"Reference","previous_headings":"","what":"plot.mixedAn","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"plot.mixedAn, use evi.plot(). Summary plot health economic analysis mixed analysis considered Compares optimal scenario mixed case terms EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-deprecated.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Deprecated functions in package BCEA. ‚Äî BCEA-deprecated","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":null,"dir":"Reference","previous_headings":"","what":"BCEA: Bayesian Cost Effectiveness Analysis ‚Äî BCEA-package","title":"BCEA: Bayesian Cost Effectiveness Analysis ‚Äî BCEA-package","text":"Produces economic evaluation sample suitable variables cost effectiveness / utility two interventions, e.g. Bayesian model form MCMC simulations. package computes cost-effective alternative produces graphical summaries probabilistic sensitivity analysis, see Baio et al (2017) doi:10.1007/978-3-319-55718-2 .","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/BCEA-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"BCEA: Bayesian Cost Effectiveness Analysis ‚Äî BCEA-package","text":"Maintainer: Gianluca Baio g.baio@ucl.ac.uk (ORCID) [copyright holder] Authors: Andrea Berardi .berardi@ucl.ac.uk (ORCID) Anna Heath anna.heath@sickkids.ca (ORCID) Nathan Green n.green@ucl.ac.uk (ORCID)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"Cost-effectiveness analysis based results simulation model variable clinical benefits (e) costs (c). Produces results post-processed give health economic analysis. output stored object class \"bcea\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"","code":"bcea(   eff,   cost,   ref = 1,   interventions = NULL,   .comparison = NULL,   Kmax = 50000,   k = NULL,   plot = FALSE,   ... )  # S3 method for default bcea(   eff,   cost,   ref = NULL,   interventions = NULL,   .comparison = NULL,   Kmax = 50000,   k = NULL,   plot = FALSE,   ... )  # S3 method for rjags bcea(eff, ...)  # S3 method for rstan bcea(eff, ...)  # S3 method for bugs bcea(eff, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"eff object containing nsim simulations variable clinical effectiveness intervention considered. general matrix nsim rows nint columns. partially matched e' previous version BCEA` back-compatibility. cost object containing nsim simulations variable cost intervention considered. general matrix nsim rows nint columns. partially matched c' previous version BCEA` back-compatibility. ref Defines intervention (columns eff cost) considered reference strategy. default value ref = 1 means intervention associated first column eff cost reference one(s) associated column(s) () comparators. interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\". .comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison = 2). Kmax Maximum value willingness pay considered. Default value k = 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k = NULL (default). k (n optional) vector values willingness pay grid. length > 1 otherwise plots empty. specified BCEA construct grid 501 values 0 Kmax. option useful performing intensive computations (e.g. EVPPI). changed wtp previous versions consistency functions deprecated future. plot logical value indicating whether function produce summary plot . ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"object class \"bcea\" containing following elements n_sim Number simulations produced Bayesian model n_comparators Number interventions analysed n_comparisons Number possible pairwise comparisons delta.e possible comparison, differential effectiveness measure delta.c possible comparison, differential cost measure ICER value Incremental Cost-Effectiveness Ratio Kmax maximum value assumed willingness pay threshold k vector values grid approximation willingness pay ceac value Cost-Effectiveness Acceptability Curve, function willingness pay ib distribution Incremental Benefit, given willingness pay eib value Expected Incremental Benefit, function willingness pay kstar grid approximation break-even point(s) best vector containing numeric label intervention cost-effective value willingness pay selected grid approximation U array including value expected utility simulation Bayesian model, value grid approximation willingness pay intervention considered vi array including value information simulation Bayesian model value grid approximation willingness pay Ustar array including maximum \"known-distribution\" utility simulation Bayesian model value grid approximation willingness pay ol array including opportunity loss simulation Bayesian model value grid approximation willingness pay evi vector values Expected Value Information, function willingness pay interventions vector labels interventions considered ref numeric index associated intervention used reference analysis comp numeric index(es) associated intervention(s) used comparator(s) analysis step step size used form grid approximation willingness pay e eff matrix used generate object (see Arguments) c cost matrix used generate object (see Arguments)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"Gianluca Baio, Andrea Berardi, Nathan Green","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create Bayesian Cost-Effectiveness Analysis Object ‚Äî bcea","text":"","code":"# See Baio (2013), Baio (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(       e=eff,       c=cost,               # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax)       plot=TRUE             # plots the results )   # Creates a summary table summary(       m,         # uses the results of the economic evaluation                   #  (a \"bcea\" object)       wtp=25000  # selects the particular value for k  ) #>  #> Cost-effectiveness analysis summary  #>  #> Reference intervention:  Vaccination #> Comparator intervention: Status Quo #>  #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #>  #>  #> Analysis for willingness to pay parameter k = 25000 #>  #>             Expected net benefit #> Status Quo               -36.054 #> Vaccination              -34.826 #>  #>                              EIB  CEAC  ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #>  #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #>             #> EVPI 2.4145  # \\donttest{  # Plots the cost-effectiveness plane using base graphics ceplane.plot(       m,             # plots the Cost-Effectiveness plane       comparison=1,  # if more than 2 interventions, selects the                      #  pairwise comparison        wtp=25000,     # selects the relevant willingness to pay                       #  (default: 25,000)        graph=\"base\"   # selects base graphics (default) )   # Plots the cost-effectiveness plane using ggplot2 if (requireNamespace(\"ggplot2\")) { ceplane.plot(       m,             # plots the Cost-Effectiveness plane       comparison=1,  # if more than 2 interventions, selects the                      #  pairwise comparison        wtp=25000,     # selects the relevant willingness to pay                       #  (default: 25,000)        graph=\"ggplot2\"# selects ggplot2 as the graphical engine )  # Some more options ceplane.plot(       m,       graph=\"ggplot2\",       pos=\"top\",       size=5,       ICER_size=1.5,       label.pos=FALSE,       opt.theme=ggplot2::theme(text=ggplot2::element_text(size=8)) ) }   # Plots the contour and scatterplot of the bivariate  # distribution of (Delta_e,Delta_c) contour(       m,            # uses the results of the economic evaluation                      #  (a \"bcea\" object)       comparison=1, # if more than 2 interventions, selects the                      #  pairwise comparison        nlevels=4,    # selects the number of levels to be                      #  plotted (default=4)       levels=NULL,  # specifies the actual levels to be plotted                      #  (default=NULL, so that R will decide)       scale=0.5,    # scales the bandwidths for both x- and                      #  y-axis (default=0.5)       graph=\"base\"  # uses base graphics to produce the plot )   # Plots the contour and scatterplot of the bivariate  #   distribution of (Delta_e,Delta_c) contour2(       m,          # uses the results of the economic evaluation                    #  (a \"bcea\" object)       wtp=25000,  # selects the willingness-to-pay threshold )   # Using ggplot2 if (requireNamespace(\"ggplot2\")) { contour2(       m,              # uses the results of the economic evaluation                        #  (a \"bcea\" object)       graph=\"ggplot2\",# selects the graphical engine       wtp=25000,      # selects the willingness-to-pay threshold       label.pos=FALSE # alternative position for the wtp label ) }   # Plots the Expected Incremental Benefit for the \"bcea\" object m eib.plot(m)   # Plots the distribution of the Incremental Benefit ib.plot(     m,            # uses the results of the economic evaluation                    #  (a \"bcea\" object)     comparison=1, # if more than 2 interventions, selects the                    #  pairwise comparison      wtp=25000,    # selects the relevant willingness                    #  to pay (default: 25,000)     graph=\"base\"  # uses base graphics )   # Produces a plot of the CEAC against a grid of values for the  # willingness to pay threshold ceac.plot(m)   # Plots the Expected Value of Information for the \"bcea\" object m evi.plot(m)  # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":null,"dir":"Reference","previous_headings":"","what":"Optimal intervention ‚Äî best_interv_given_k","title":"Optimal intervention ‚Äî best_interv_given_k","text":"Select best option value willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Optimal intervention ‚Äî best_interv_given_k","text":"","code":"best_interv_given_k(eib, ref, comp)"},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Optimal intervention ‚Äî best_interv_given_k","text":"eib Expected incremental benefit ref Reference group number comp Comparison group number(s)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/best_interv_given_k.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Optimal intervention ‚Äî best_interv_given_k","text":"Group index","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"Produces plot Cost-Effectiveness Acceptability Curve (CEAC) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"","code":"# S3 method for bcea ceac.plot(   he,   comparison = NULL,   pos = c(1, 0),   graph = c(\"base\", \"ggplot2\", \"plotly\"),   ... )  ceac.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional arguments: line = list(color): specifies line colour(s) - graph types. line = list(type): specifies line type(s) lty numeric values - graph types. line = list(size): specifies line width(s) numeric values - graph types. currency: Currency prefix willingness pay values - ggplot2 . area_include: logical, include area CEAC curves - plotly . area_color: specifies AUC colour - plotly .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"ceac graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. function produces plot cost-effectiveness acceptability curve discrete grid possible values willingness pay parameter. Values CEAC closer 1 indicate uncertainty cost-effectiveness reference intervention low. Similarly, values CEAC closer 0 indicate uncertainty cost-effectiveness comparator low.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"CEAC estimates probability cost-effectiveness, respect given willingness pay threshold. CEAC used mainly evaluate uncertainty associated decision-making process, since enables quantification preference compared interventions, defined terms difference utilities. Formally, CEAC defined : $$\\textrm{CEAC} = P(\\textrm{IB}(\\theta) > 0)$$ net benefit function used utility function, definition can re-written $$\\textrm{CEAC} = P(k \\cdot \\Delta_e - \\Delta_c > 0)$$ effectively depending willingness pay value \\(k\\).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot ‚Äî ceac.plot.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceac.plot(he)  ceac.plot(he, graph = \"base\")  ceac.plot(he, graph = \"ggplot2\")  ceac.plot(he, graph = \"plotly\")  {\"x\":{\"visdat\":{\"4cac41787eda\":[\"function () \",\"plotlyVisDat\"]},\"cur_data\":\"4cac41787eda\",\"attrs\":{\"4cac41787eda\":{\"x\":{},\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"y\":{},\"type\":\"scatter\",\"mode\":\"lines\",\"fill\":\"none\",\"name\":{},\"color\":{},\"colors\":\"black\",\"linetype\":{},\"linetypes\":1,\"inherit\":true}},\"layout\":{\"margin\":{\"b\":40,\"l\":60,\"t\":25,\"r\":10},\"title\":\"Cost Effectiveness Acceptability Curve\",\"xaxis\":{\"domain\":[0,1],\"automargin\":true,\"hoverformat\":\".2f\",\"title\":\"Willingness to pay\"},\"yaxis\":{\"domain\":[0,1],\"automargin\":true,\"title\":\"Probability of cost 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(p, registerFrames = TRUE) \",\"{\",\"    UseMethod(\\\"plotly_build\\\")\",\"}\"],\"jsHooks\":[]},\"type\":\"scatter\",\"mode\":\"lines\",\"fill\":\"none\",\"name\":\"intervention 1 vs intervention 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graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceac_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Curve (CEAC) Plot By Graph Device ‚Äî ceac_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position graph_params Aesthetic ggplot parameters ... Additional arguments ceac ceac index ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"Produces plot Cost-Effectiveness Acceptability Frontier (CEAF) willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"","code":"# S3 method for pairwise ceaf.plot(mce, graph = c(\"base\", \"ggplot2\"), ...)  ceaf.plot(mce, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"mce output call function multi.ce() graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"ceaf ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceaf.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Acceptability Frontier (CEAF) plot ‚Äî ceaf.plot.pairwise","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(       e=eff,       c=cost,               # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax)       plot=FALSE            # inhibits graphical output )  # \\donttest{ mce <- multi.ce(m)          # uses the results of the economic analysis  # }  # \\donttest{ ceaf.plot(mce)              # plots the CEAF   # }  # \\donttest{ ceaf.plot(mce, graph = \"g\") # uses ggplot2   # }  # \\donttest{ # Use the smoking cessation dataset data(Smoking) m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE) mce <- multi.ce(m) ceaf.plot(mce)  # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"line connecting successive points cost-effectiveness plane represent effect cost associated different treatment alternatives. gradient line segment represents ICER treatment comparison two alternatives represented segment. cost-effectiveness frontier consists set points corresponding treatment alternatives considered cost-effective different values cost-effectiveness threshold. steeper gradient successive points frontier, higher ICER treatment alternatives expensive alternative considered cost-effective high value cost-effectiveness threshold assumed. Points lying cost-effectiveness frontier represent treatment alternatives considered cost-effective value cost-effectiveness threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"","code":"# S3 method for bcea ceef.plot(   he,   comparators = NULL,   pos = c(1, 1),   start.from.origins = TRUE,   threshold = NULL,   flip = FALSE,   dominance = TRUE,   relative = FALSE,   print.summary = TRUE,   graph = c(\"base\", \"ggplot2\"),   print.plot = TRUE,   ... )  ceef.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparators Vector specifying comparators included frontier analysis. must length > 1. Default NULL includes available comparators. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. start..origins Logical. frontier start origins axes? argument reset FALSE average effectiveness /costs least one comparator negative. threshold Specifies efficiency defined based willingness--pay threshold value. set NULL (default), conditions included slope increase. positive value passed argument, efficient intervention also requires ICER comparison versus last efficient strategy greater specified threshold value. negative value ignored warning. flip Logical. axes plane inverted? dominance Logical. dominance regions included plot? relative Logical. plot display absolute measures (default FALSE) differential outcomes versus reference comparator? print.summary Logical. efficiency frontier summary printed along graph?  See Details additional information. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". print.plot Logical. efficiency frontier plotted? ... graph_type=\"ggplot2\" named theme object supplied, added ggplot object. Ignored graph_type=\"base\". Setting optional argument include.ICER TRUE print ICERs summary tables, produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"ceplane ggplot object containing plot. Returned graph_type=\"ggplot2\". function produces plot cost-effectiveness efficiency frontier. dots show simulated values intervention-specific distributions effectiveness costs. circles indicate average bivariate distribution, numbers referring included intervention. numbers inside circles black intervention included frontier grey otherwise. option dominance set TRUE, dominance regions plotted, indicating areas dominance. Interventions areas dominance region frontier situation extended dominance.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"Back compatibility BCEA previous versions: bcea objects include generating e c matrices BCEA versions <2.1-0. function compatible objects created previous versions. matrices can appended bcea objects obtained using previous versions, making sure class object remains unaltered. argument print.summary allows printing brief summary efficiency frontier, default TRUE. Two tables plotted, one interventions included frontier one dominated interventions. average costs clinical benefits included intervention. frontier table includes slope increase frontier non-frontier table displays dominance type dominated intervention. Please note slopes defined increment costs unit increment benefits even flip = TRUE consistency ICER definition. angle increase radians depends definition axes, .e. value given flip argument. argument relative set TRUE, graph display absolute measures costs benefits. Instead axes represent differential costs benefits compared reference intervention (indexed ref bcea() function).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"Baio G (2013). Bayesian Methods Health Economics. CRC. IQWIG (2009). ‚ÄúGeneral Methods Assessment Relation Benefits Cost, Version 1.0.‚Äù Institute Quality Efficiency Health Care (IQWIG).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"Andrea Berardi, Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Efficiency Frontier (CEEF) Plot ‚Äî ceef.plot.bcea","text":"","code":"## create the bcea object m for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)  ## produce plot ceef.plot(m, graph = \"base\") #>  #> Cost-effectiveness efficiency frontier summary  #>  #> Interventions on the efficiency frontier: #>                   Effectiveness   Costs Increase slope Increase angle #> Self-help               0.28824  45.733         158.66         1.5645 #> Group counselling       0.72252 143.301         224.67         1.5663 #>  #> Interventions not on the efficiency frontier: #>                        Effectiveness  Costs     Dominance type #> No treatment                 0.00000  0.000 Extended dominance #> Individual counselling       0.48486 94.919 Extended dominance   # \\donttest{ ## tweak the options ## flip axis ceef.plot(m,           flip = TRUE,           dominance = FALSE,           start.from.origins = FALSE,           print.summary = FALSE,           graph = \"base\")             ## or use ggplot2 instead if(require(ggplot2)){ ceef.plot(m,           dominance = TRUE,           start.from.origins = FALSE,           pos = TRUE,           print.summary = FALSE,           graph = \"ggplot2\")  } #> Loading required package: ggplot2  # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary table for CEEF ‚Äî ceef.summary","title":"Summary table for CEEF ‚Äî ceef.summary","text":"Summary table CEEF","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary table for CEEF ‚Äî ceef.summary","text":"","code":"ceef.summary(he, frontier_data, frontier_params, include.ICER = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary table for CEEF ‚Äî ceef.summary","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters include.ICER include ICER? default: FALSE ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef.summary.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary table for CEEF ‚Äî ceef.summary","text":"Summary printed console","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Efficiency Frontier Plot By Graph Device ‚Äî ceef_plot_graph","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device ‚Äî ceef_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device ‚Äî ceef_plot_graph","text":"","code":"ceef_plot_ggplot(he, frontier_data, frontier_params, ...)  ceef_plot_base(he, frontier_data, frontier_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceef_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Efficiency Frontier Plot By Graph Device ‚Äî ceef_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. frontier_data Frontier data frontier_params Frontier parameters ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"Produces scatter plot cost-effectiveness plane, together sustainability area, function selected willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"","code":"# S3 method for bcea ceplane.plot(   he,   comparison = NULL,   wtp = 25000,   pos = c(0, 1),   graph = c(\"base\", \"ggplot2\", \"plotly\"),   ... )  ceplane.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison = c(1,3) comparison = 2). wtp value willingness pay parameter. used graph = \"base\" multiple comparisons. pos Parameter set position legend; single comparison plot, ICER legend position. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. Default value c(1,1), topright corner inside plot area. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... graph = \"ggplot2\" named theme object supplied, passed ggplot2 object. usual ggplot2 syntax used. Additional graphical arguments: label.pos = FALSE: place willingness pay label different  position bottom graph - base ggplot2 (label plotly). line = list(color): colour specifying colour willingness--pay line. point = list(color): vector colours specifying colour(s) associated cloud points. length 1 equal number comparisons. point = list(size): vector colours specifying size(s) points. length 1 equal number comparisons. point = list(shape): vector shapes specifying type(s) points. length 1 equal number comparisons. icer = list(color): vector colours specifying colour(s) ICER points. length 1 equal number comparisons. icer = list(size): vector colours specifying size(s) ICER points. length 1 equal number comparisons. area_include: logical, include exclude cost-effectiveness acceptability area (default TRUE). area = list(color): colour specifying colour cost-effectiveness acceptability area. currency: Currency prefix cost differential values - ggplot2 . icer_annot: Annotate ICER point text label - ggplot2 .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"graph = \"ggplot2\" ggplot object, graph = \"plotly\" plotly object containing requested plot. Nothing returned graph = \"base\", default. Grey dots show simulated values joint distribution effectiveness cost differentials. larger red dot shows ICER grey area identifies sustainability area, .e. part plan simulated values willingness pay threshold. proportion points sustainability area effectively represents CEAC given value willingness pay. comparators 2 pairwise comparison specified, scatterplots graphed using different colours.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"plotly version, point_colors, ICER_colors area_color can also specified rgba colours using either [plotly]toRGB function rgba colour string, e.g. 'rgba(1, 1, 1, 1)'.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Plane Plot ‚Äî ceplane.plot.bcea","text":"","code":"## create the bcea object for the smoking cessation example data(Smoking)  m <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)  ## produce the base plot ceplane.plot(m, wtp = 200, graph = \"base\")   ## select only one comparator ceplane.plot(m, wtp = 200, graph = \"base\", comparison = 3)   ## use ggplot2 if (requireNamespace(\"ggplot2\")) {    ceplane.plot(m, wtp = 200, pos = \"right\", icer = list(size = 2), graph = \"ggplot2\") }   ## plotly ceplane.plot(m, wtp = 200, graph = \"plotly\")  {\"x\":{\"visdat\":{\"4cac2d3f4dc6\":[\"function () \",\"plotlyVisDat\"],\"4cac51be4d1f\":[\"function () \",\"data\"],\"4cac5d531d22\":[\"function () \",\"data\"],\"4cac662524bc\":[\"function () \",\"data\"]},\"cur_data\":\"4cac662524bc\",\"attrs\":{\"4cac51be4d1f\":{\"colors\":[\"#4D4D4DFF\",\"#8B8B8BFF\",\"#B3B3B3FF\"],\"alpha_stroke\":1,\"sizes\":[10,100],\"spans\":[1,20],\"type\":\"scatter\",\"mode\":\"markers\",\"y\":{},\"x\":{},\"color\":{},\"hoverinfo\":\"name+x+y\",\"inherit\":true},\"4cac5d531d22\":{\"type\":\"scatter\",\"mode\":\"lines\",\"x\":{},\"y\":{},\"line\":{\"color\":[]},\"showlegend\":false,\"inherit\":false},\"4cac662524bc\":{\"x\":{},\"y\":{},\"type\":\"scatter\",\"fill\":\"toself\",\"mode\":\"lines\",\"fillcolor\":\"rgba(190,190,190,0.5)\",\"line\":{\"color\":\"transparent\"},\"opacity\":0.29999999999999999,\"name\":\"CEA 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Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","title":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","text":"","code":"# S3 method for bcea ceplane_plot_base(he, wtp = 25000, pos_legend, graph_params, ...)  ceplane_plot_base(he, ...)  # S3 method for bcea ceplane_plot_ggplot(he, wtp = 25000, pos_legend, graph_params, ...)  ceplane_plot_ggplot(he, ...)  # S3 method for bcea ceplane_plot_plotly(he, wtp = 25000, pos_legend, graph_params, ...)  ceplane_plot_plotly(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay threshold; default 25,000 pos_legend Legend position graph_params Graph parameters ggplot2 format ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","text":"base R returns plot ggplot2 returns ggplot2 object plotly returns plot Viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ceplane_plot_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Plane Plot By Graph Device ‚Äî ceplane_plot_graph","text":"","code":"# single comparator data(Vaccine, package = \"BCEA\")  he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ceplane.plot(he, graph = \"base\")   if (FALSE) { # need to provide all the defaults because thats what # ceplane.plot() does  graph_params <-  list(xlab = \"x-axis label\",                       ylab = \"y-axis label\",                       title = \"my title\",                       xlim = c(-0.002, 0.001),                       ylim = c(-13, 5),                       point = list(sizes = 1,                                    colors = \"darkgrey\"),                       area = list(color = \"lightgrey\"))                        he$delta_e <- as.matrix(he$delta_e) he$delta_c <- as.matrix(he$delta_c)  BCEA::ceplane_plot_base(he, graph_params = graph_params)  ## single non-default comparator   ## multiple comparators data(Smoking)  graph_params <-  list(xlab = \"x-axis label\",                       ylab = \"y-axis label\",                       title = \"my title\",                       xlim = c(-1, 2.5),                       ylim = c(-1, 160),                       point = list(sizes = 0.5,                                    colors = grey.colors(3, start = 0.1, end = 0.7)),                       area = list(color = \"lightgrey\"))                                     he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)  BCEA::ceplane_plot_base(he,                         wtp = 200,                         pos_legend = FALSE,                         graph_params = graph_params) }   data(Vaccine) he <- bcea(eff, cost) #> No reference selected. Defaulting to first intervention.  ceplane.plot(he, graph = \"ggplot2\")  ceplane.plot(he, wtp=10000, graph = \"ggplot2\",              point = list(colors = \"blue\", sizes = 2),              area = list(col = \"springgreen3\"))   data(Smoking) he <- bcea(eff, cost, ref = 4, Kmax = 500, interventions = treats)  ceplane.plot(he, graph = \"ggplot2\")   ceplane.plot(he,              wtp = 200,              pos = \"right\",              ICER_size = 2,              graph = \"ggplot2\")      ceplane.plot(he,              wtp = 200,              pos = TRUE,              graph = \"ggplot2\")   ceplane.plot(he,              graph = \"ggplot2\",              wtp=200,              theme = ggplot2::theme_linedraw())"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"Extends standard cost-effectiveness analysis modify utility function risk aversion decision maker explicitly accounted . Default vector risk aversion parameters: 1e-11, 2.5e-6, 5e-6","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"","code":"CEriskav(he) <- value  # S3 method for bcea CEriskav(he) <- value  # S3 method for default CEriskav(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value vector values risk aversion parameter. NULL, default values assigned R. first (smallest) value (r -> 0) produces standard analysis risk aversion.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"object class CEriskav containing following elements: Ur array containing simulated values ''known-distribution'' utilities interventions, values willingness pay parameter possible values r Urstar array containing simulated values maximum ''known-distribution'' expected utility values willingness pay parameter possible values r IBr array containing simulated values distribution Incremental Benefit values willingness pay possible values r eibr array containing Expected Incremental Benefit value willingness pay parameter possible values r vir array containing simulations Value Information value willingness pay parameter possible values r evir array containing Expected Value Information value willingness pay parameter possible values r R number possible values parameter risk aversion r r vector containing possible values parameter risk aversion r","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_assign.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis Including a Parameter of Risk Aversion ‚Äî CEriskav_assign","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(e=eff,c=cost,     # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000            # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax) )  # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035)   # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{ # uses the results of the economic evaluation  # if more than 2 interventions, selects the  #  pairwise comparison  CEriskav(m) <- r # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Plot Including a Parameter of Risk Aversion ‚Äî CEriskav_plot_graph","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion ‚Äî CEriskav_plot_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion ‚Äî CEriskav_plot_graph","text":"","code":"CEriskav_plot_base(he, pos_legend)  CEriskav_plot_ggplot(he, pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/CEriskav_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Plot Including a Parameter of Risk Aversion ‚Äî CEriskav_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness summary statistics table ‚Äî ce_table","title":"Cost-effectiveness summary statistics table ‚Äî ce_table","text":"commonly shown journal paper.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness summary statistics table ‚Äî ce_table","text":"","code":"ce_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness summary statistics table ‚Äî ce_table","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness pay ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ce_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness summary statistics table ‚Äî ce_table","text":"","code":"data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(       e=eff,       c=cost,               # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax) ) ce_table(m) #>                  cost          eff  delta.c      delta.e     ICER      INB #> Vaccination 14.691446 -0.000805370       NA           NA       NA       NA #> Status Quo   9.655464 -0.001055946 5.035983 0.0002505764 20097.59 1.228428"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI ‚Äî compute.evppi","title":"Compute EVPPI ‚Äî compute.evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI ‚Äî compute.evppi","text":"","code":"# S3 method for evppi compute(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI ‚Äî compute.evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI ‚Äî compute.evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Curve ‚Äî compute_CEAC","title":"Compute Cost-Effectiveness Acceptability Curve ‚Äî compute_CEAC","text":"Compute Cost-Effectiveness Acceptability Curve","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Curve ‚Äî compute_CEAC","text":"","code":"compute_CEAC(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Curve ‚Äî compute_CEAC","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_CEAC.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Cost-Effectiveness Acceptability Curve ‚Äî compute_CEAC","text":"Array dimensions (interv x k)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Cost-Effectiveness Acceptability Frontier ‚Äî compute_ceaf","title":"Compute Cost-Effectiveness Acceptability Frontier ‚Äî compute_ceaf","text":"Compute Cost-Effectiveness Acceptability Frontier","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Cost-Effectiveness Acceptability Frontier ‚Äî compute_ceaf","text":"","code":"compute_ceaf(p_best_interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ceaf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Cost-Effectiveness Acceptability Frontier ‚Äî compute_ceaf","text":"p_best_interv Probability best intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Incremental Benefit ‚Äî compute_EIB","title":"Compute Expected Incremental Benefit ‚Äî compute_EIB","text":"summary measure useful assess potential changes decision different scenarios.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Incremental Benefit ‚Äî compute_EIB","text":"","code":"compute_EIB(ib)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Incremental Benefit ‚Äî compute_EIB","text":"ib Incremental benefit","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Incremental Benefit ‚Äî compute_EIB","text":"Array dimensions (interv x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EIB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Expected Incremental Benefit ‚Äî compute_EIB","text":"considering pairwise comparison (e.g. simple case reference intervention \\(t = 1\\) comparator, status quo, \\(t = 0\\)), defined difference expected utilities two alternatives: $$eib := \\mbox{E}[u(e,c;1)] - \\mbox{E}[u(e,c;0)] = \\mathcal{U}^1 - \\mathcal{U}^0.$$ Analysis expected incremental benefit describes decision changes different values threshold. EIB marginalises uncertainty, incorporate describe explicitly uncertainty outcomes. overcome problem tool choice CEAC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Credible Intervals ‚Äî compute_eib_cri","title":"Calculate Credible Intervals ‚Äî compute_eib_cri","text":"expected incremental benefit plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Credible Intervals ‚Äî compute_eib_cri","text":"","code":"compute_eib_cri(he, alpha_cri = 0.05, cri.quantile = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Credible Intervals ‚Äî compute_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. alpha_cri Significance level, 0 - 1 cri.quantile Credible interval quantile?; logical","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_eib_cri.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Credible Intervals ‚Äî compute_eib_cri","text":"cri","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Expected Value of Information ‚Äî compute_EVI","title":"Compute Expected Value of Information ‚Äî compute_EVI","text":"Compute Expected Value Information","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Expected Value of Information ‚Äî compute_EVI","text":"","code":"compute_EVI(ol)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Expected Value of Information ‚Äî compute_EVI","text":"ol Opportunity loss","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_EVI.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Expected Value of Information ‚Äî compute_EVI","text":"EVI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute EVPPI ‚Äî compute_evppi","title":"Compute EVPPI ‚Äî compute_evppi","text":"Compute EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute EVPPI ‚Äî compute_evppi","text":"","code":"compute_evppi(he, fit.full)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute EVPPI ‚Äî compute_evppi","text":"bcea object containing results Bayesian modelling economic evaluation. fit.full fit.full","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute EVPPI ‚Äî compute_evppi","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Benefit ‚Äî compute_IB","title":"Compute Incremental Benefit ‚Äî compute_IB","text":"Sample incremental net monetary benefit willingness--pay threshold, \\(k\\), comparator.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Benefit ‚Äî compute_IB","text":"","code":"compute_IB(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Benefit ‚Äî compute_IB","text":"df_ce Dataframe cost effectiveness deltas k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Benefit ‚Äî compute_IB","text":"Array dimensions (k x sim x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_IB.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Benefit ‚Äî compute_IB","text":"Defined : $$IB = u(e,c; 1) - u(e,c; 0).$$ net benefit function used utility function, definition can re-written $$IB = k\\cdot\\Delta_e - \\Delta_c.$$","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","title":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","text":"Defined ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","text":"","code":"compute_ICER(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","text":"ICER comparisons","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ICER.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Incremental Cost-Effectiveness Ratio ‚Äî compute_ICER","text":"$$ICER = \\Delta_c/\\Delta_e$$","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute k^* ‚Äî compute_kstar","title":"Compute k^* ‚Äî compute_kstar","text":"Find willingness--pay threshold optimal decision changes.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute k^* ‚Äî compute_kstar","text":"","code":"compute_kstar(k, best, ref)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute k^* ‚Äî compute_kstar","text":"k Willingness--pay grid approximation budget willing invest (vector) best Best intervention k (int) ref Reference intervention (int)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute k^* ‚Äî compute_kstar","text":"integer representing intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_kstar.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute k^* ‚Äî compute_kstar","text":"$$k^* := \\min\\{k : IB < 0 \\}$$ value break-even point corresponds ICER quantifies point decision-maker indifferent two options.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Opportunity Loss ‚Äî compute_ol","title":"Compute Opportunity Loss ‚Äî compute_ol","text":"difference maximum utility computed current parameter configuration (e.g. current simulation) \\(U^*\\) current utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Opportunity Loss ‚Äî compute_ol","text":"","code":"compute_ol(Ustar, U, best)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Opportunity Loss ‚Äî compute_ol","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv) best Best intervention given willingness--pay (k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Opportunity Loss ‚Äî compute_ol","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_ol.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Opportunity Loss ‚Äî compute_ol","text":"mathematical notation, $$\\textrm{OL}(\\theta) := U^*(\\theta) - U(\\theta^\\tau)$$ \\(\\tau\\) intervention associated overall maximum utility \\(U^*(\\theta)\\) maximum utility value among comparators given simulation. opportunity loss non-negative quantity, since \\(U(\\theta^\\tau)\\leq U^*(\\theta)\\). simulations intervention cost-effective (.e. incremental benefit positive), \\(\\textrm{OL}(\\theta) = 0\\) opportunity loss, parameter configuration one obtained current simulation.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Probability Best Intervention ‚Äî compute_p_best_interv","title":"Compute Probability Best Intervention ‚Äî compute_p_best_interv","text":"Compute Probability Best Intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Probability Best Intervention ‚Äî compute_p_best_interv","text":"","code":"compute_p_best_interv(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_p_best_interv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Probability Best Intervention ‚Äî compute_p_best_interv","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute U Statistic ‚Äî compute_U","title":"Compute U Statistic ‚Äî compute_U","text":"Sample net (monetary) benefit willingness--pay threshold intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute U Statistic ‚Äî compute_U","text":"","code":"compute_U(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute U Statistic ‚Äî compute_U","text":"df_ce Cost-effectiveness dataframe k Willingness pay vector","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_U.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute U Statistic ‚Äî compute_U","text":"Array dimensions (sim x k x ints)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute NB for mixture of interventions ‚Äî compute_Ubar","title":"Compute NB for mixture of interventions ‚Äî compute_Ubar","text":"Compute NB mixture interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute NB for mixture of interventions ‚Äî compute_Ubar","text":"","code":"compute_Ubar(he, value)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ubar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute NB for mixture of interventions ‚Äî compute_Ubar","text":"bcea object containing results Bayesian modelling economic evaluation. value Mixture weights","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Ustar Statistic ‚Äî compute_Ustar","title":"Compute Ustar Statistic ‚Äî compute_Ustar","text":"maximum utility value among comparators, indicating intervention produced benefits simulation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Ustar Statistic ‚Äî compute_Ustar","text":"","code":"compute_Ustar(U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Ustar Statistic ‚Äî compute_Ustar","text":"U Net monetary benefit (sim x k x intervs)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_Ustar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Ustar Statistic ‚Äî compute_Ustar","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute Value of Information ‚Äî compute_vi","title":"Compute Value of Information ‚Äî compute_vi","text":"difference maximum utility computed current parameter configuration \\(U^*\\) utility intervention associated maximum utility overall.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute Value of Information ‚Äî compute_vi","text":"","code":"compute_vi(Ustar, U)"},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute Value of Information ‚Äî compute_vi","text":"Ustar Maximum utility value (sim x k) U Net monetary benefit (sim x k x interv)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute Value of Information ‚Äî compute_vi","text":"Array dimensions (sim x k)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/compute_vi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Compute Value of Information ‚Äî compute_vi","text":"value obtaining additional information parameter \\(\\theta\\) reduce uncertainty decisional process. defined : $$\\textrm{VI}(\\theta) := U^*(\\theta) - \\mathcal{U}^*$$ \\(U^*(\\theta)\\) maximum utility value given simulation among comparators \\(\\mathcal{U}^*(\\theta)\\) expected utility gained adoption cost-effective intervention.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":null,"dir":"Reference","previous_headings":"","what":"Comparison Names From ‚Äî comp_names_from_","title":"Comparison Names From ‚Äî comp_names_from_","text":"Comparison Names ","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Comparison Names From ‚Äî comp_names_from_","text":"","code":"comp_names_from_(df_ce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/comp_names_from_.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Comparison Names From ‚Äî comp_names_from_","text":"df_ce Cost-effectiveness dataframe","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"Contour method objects class bcea. Produces scatterplot cost-effectiveness plane, contour-plot bivariate density differentials cost (y-axis) effectiveness (x-axis).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"","code":"# S3 method for bcea contour(   he,   pos = c(0, 1),   graph = c(\"base\", \"ggplot2\"),   comparison = NULL,   ... )  contour(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). ... Additional graphical arguments. usual ggplot2 syntax used regardless graph type. xlim: range plot along x-axis. NULL (default) determined range simulated values delta_e ylim: range plot along y-axis. NULL (default) determined range simulated values delta_c scale: Scales plot function observed standard deviation. levels: Numeric vector levels draw contour lines. Quantiles 0<p<1. nlevels: Number levels plotted contour.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"ceplane ggplot object containing plot. Returned graph=\"ggplot2\". Plots cost-effectiveness plane scatterplot simulated values (posterior) bivariate distribution (\\(\\Delta_e, \\Delta_c\\)), differentials effectiveness costs; superimposes contour distribution prints estimated value probability quadrant (combination positive/negative values \\(\\Delta_e\\) \\(\\Delta_c\\))","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Contour Plots for the Cost-Effectiveness Plane ‚Äî contour.bcea","text":"","code":"data(Vaccine)  # run the health economic evaluation using BCEA m <- bcea(e=eff,           c=cost,           # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax)       plot=TRUE             # plots the results )   contour(m)  contour(m, graph = \"ggplot2\")   contour(m,          # uses the results of the economic evaluation                      #  (a \"bcea\" object)       comparison=1, # if more than 2 interventions, selects the                      #  pairwise comparison        nlevels=10,   # selects the number of levels to be                      #  plotted (default=4)       levels=NULL,  # specifies the actual levels to be plotted                      #  (default=NULL, so that R will decide)       scale=1,      # scales the bandwidths for both x- and                      #  y-axis (default=0.5)       graph=\"base\"  # uses base graphics to produce the plot )   # use the smoking cessation dataset data(Smoking) m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE) contour(m)  contour(m, graph = \"ggplot2\")"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":null,"dir":"Reference","previous_headings":"","what":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"Produces scatterplot cost-effectiveness plane, contour-plot bivariate density differentials cost (y-axis) effectiveness (x-axis).  Also adds sustainability area (.e. selected value willingness--pay threshold).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"","code":"# S3 method for bcea contour2(   he,   comparison = NULL,   wtp = 25000,   graph = c(\"base\", \"ggplot2\"),   pos = c(0, 1),   ... )  contour2(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison comparison plotted. Default NULL graph_type=\"ggplot2\" default value choose possible comparisons. subset possible comparisons can selected (e.g., comparison=c(1,3)). wtp selected value willingness--pay. Default 25000. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. ... Arguments passed ceplane.plot(). See relative manual page details.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"contour ggplot item containing requested plot. Returned graph_type=\"ggplot2\". Plots cost-effectiveness plane scatterplot simulated values (posterior) bivariate distribution (\\(\\Delta_e, \\Delta_c\\)), differentials effectiveness costs; superimposes contour distribution prints value ICER, together sustainability area.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour2.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Specialised CE-plane Contour Plot ‚Äî contour2.bcea","text":"","code":"## create the bcea object m for the smoking cessation example data(Smoking) m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)  ## produce the plot contour2(m,          wtp = 200,          graph_type = \"base\")  # \\donttest{ ## or use ggplot2 to plot multiple comparisons contour2(m,          wtp = 200,          ICER_size = 2,          graph_type = \"ggplot2\")  # }  ## vaccination example data(Vaccine) treats = c(\"Status quo\", \"Vaccination\") m <- bcea(eff, cost, ref = 2, interventions = treats, Kmax = 50000) contour2(m)  contour2(m, wtp = 100)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_ggplot_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour ggplot Parameters ‚Äî contour_ggplot_params","title":"Contour ggplot Parameters ‚Äî contour_ggplot_params","text":"Contour ggplot Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_ggplot_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour ggplot Parameters ‚Äî contour_ggplot_params","text":"","code":"contour_ggplot_params(he, graph_params, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_ggplot_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour ggplot Parameters ‚Äî contour_ggplot_params","text":"bcea object containing results Bayesian modelling economic evaluation. graph_params graphical parameters ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Contour Cost-Effectiveness Plane ‚Äî contour_graph","title":"Contour Cost-Effectiveness Plane ‚Äî contour_graph","text":"Choice base R, ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Contour Cost-Effectiveness Plane ‚Äî contour_graph","text":"","code":"contour_base(he, pos_legend, graph_params, ...)  contour_ggplot(he, pos_legend, graph_params, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/contour_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Contour Cost-Effectiveness Plane ‚Äî contour_graph","text":"bcea object containing results Bayesian modelling economic evaluation. pos_legend Legend position graph_params Plot parameters; list ... Additional arguments","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/convert_pts_to_mm.html","id":null,"dir":"Reference","previous_headings":"","what":"Use from Base  R to ggplot ‚Äî convert_pts_to_mm","title":"Use from Base  R to ggplot ‚Äî convert_pts_to_mm","text":"Use Base  R ggplot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/convert_pts_to_mm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use from Base  R to ggplot ‚Äî convert_pts_to_mm","text":"","code":"convert_pts_to_mm(x)"},{"path":"https://n8thangreen.github.io/BCEA/reference/convert_pts_to_mm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use from Base  R to ggplot ‚Äî convert_pts_to_mm","text":"x points","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/createInputs.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","title":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","text":"Creates object containing matrix parameters simulated using MCMC procedure (using JAGS, BUGS Stan) vector parameters (strings) can used perform expected value partial information analysis. process, createInputs also checks linear dependency among columns PSA samples columns constant values removes leave fundamental parameters (run VoI analysis). also deals simulations stored .csv .txt file (e.g. obtained using bootstrapping non-Bayesian model).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/createInputs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","text":"","code":"# S3 method for default createInputs(inputs, print_is_linear_comb = TRUE)  createInputs(inputs, print_is_linear_comb = TRUE)  # S3 method for rjags createInputs(inputs, print_is_linear_comb = TRUE)  # S3 method for bugs createInputs(inputs, print_is_linear_comb = TRUE)  # S3 method for stanfit createInputs(inputs, print_is_linear_comb = TRUE)  # S3 method for data.frame createInputs(inputs, print_is_linear_comb = TRUE)  # S3 method for numeric createInputs(inputs, print_is_linear_comb = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/createInputs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","text":"inputs rjags, bugs stanfit object, containing results call either JAGS, (using R2jags), BUGS (using R2WinBUGS, Stan (using rstan). print_is_linear_comb Logical indicator. set TRUE (default) prints output procedure trying assess whether parameters linear combination others (case removed).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/createInputs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","text":"mat Data.frame containing simulations monitored parameters parameters Character vectors names monitored parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/createInputs.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Create Inputs for EVPI Calculation ‚Äî createInputs.default","text":"Gianluca Baio, Anna Heath Mark Strong","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"function produces either residual plot comparing fitted values INLA-SPDE Gaussian Process regression residuals. scatter plot residuals y axis fitted values (estimated responses) x axis. plot used detect non-linearity, unequal error variances, outliers. well-behaved residual plot supporting appropriateness simple linear regression model following characteristics: residuals bounce randomly around 0 line. suggests assumption relationship linear reasonable. residuals roughly form horizontal band around 0 line. suggests variances error terms equal. None residual stands basic random pattern residuals. suggests outliers.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"","code":"diag.evppi(evppi, he, plot_type = c(\"residuals\", \"qqplot\"), interv = 1)"},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"evppi evppi object obtained running function evppi bcea model. bcea object containing results Bayesian modelling economic evaluation. plot_type type diagnostics performed. can 'residual plot' (residuals) Q-Q (quantile-quantile) plot (qqplot). interv Specifies interventions diagnostic tests performed (many options compared)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"second possible diagnostic Q-Q plot fitted value. graphical method comparing fitted values distributions assumed underlying normal distribution plotting quantiles . First, set intervals quantiles chosen. point (x,y) plot corresponds one quantiles second distribution (y-coordinate) plotted quantile first distribution (x-coordinate). two distributions compared identical, Q-Q plot follows 45 degrees line.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/diag.evppi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Diagnostic Plots For The Results Of The EVPPI ‚Äî diag.evppi","text":"Gianluca Baio, Anna Heath","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"Produces plot Expected Incremental Benefit (EIB) function willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"","code":"# S3 method for bcea eib.plot(   he,   comparison = NULL,   pos = c(1, 0),   size = NULL,   plot.cri = NULL,   graph = c(\"base\", \"ggplot2\", \"plotly\"),   ... )  eib.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. size Value (millimetres) size willingness pay label. Used graph=\"ggplot2\", otherwise ignored message. set NA, break-even point line(s) label(s) suppressed, base graphics ggplot2. plot.cri Logical value. credible intervals plotted along expected incremental benefit? Default NULL draws 95\\ include multiple comparisons.  Setting plot.cri=TRUE plot.cri=FALSE forces function add intervals . level intervals can also set, see ... details. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". plotting functions \"plotly\" implementation yet. ... graph=\"ggplot2\" named theme object supplied, added ggplot object. Additional arguments: alpha can used set CrI level plot.cri=TRUE, default value alpha=0.05. cri.quantile controls method calculation credible intervals. default value cri.quantile=TRUE defines CrI interval alpha/2-th 1-alpha/2-th quantiles IB distribution. Setting cri.quantile=FALSE use normal approximation IB distribution calculate intervals. line = list(color): specifies line colour(s) - graph types. line = list(type): specifies line type(s) lty numeric values - graph types. area_include: include area EIB curve - plotly . area_color: specifies AUC curve - plotly .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"eib graph=\"ggplot2\" ggplot object, graph=\"plotly\" plotly object containing requested plot. Nothing returned graph=\"base\", default. function produces plot Expected Incremental Benefit function discrete grid approximation willingness pay parameter. break even point (.e. point EIB = 0, .e. optimal decision changes one intervention another) also showed default. value k* discrete grid approximation ICER.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expected Incremental Benefit (EIB) Plot ‚Äî eib.plot.bcea","text":"","code":"data(Vaccine)   # Runs the health economic evaluation using BCEA m <- bcea(       e=eff,       c=cost,               # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax)       plot=FALSE             # plots the results ) eib.plot(m)  eib.plot(m, graph = \"ggplot2\") + ggplot2::theme_linedraw()   data(Smoking) treats <- c(\"No intervention\", \"Self-help\",             \"Individual counselling\", \"Group counselling\") m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) eib.plot(m)"},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_base.html","id":null,"dir":"Reference","previous_headings":"","what":"EIB parameters specific to base R plot ‚Äî eib_params_base","title":"EIB parameters specific to base R plot ‚Äî eib_params_base","text":"EIB parameters specific base R plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_base.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EIB parameters specific to base R plot ‚Äî eib_params_base","text":"","code":"eib_params_base(he, graph_params, cri_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_base.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"EIB parameters specific to base R plot ‚Äî eib_params_base","text":"bcea object containing results Bayesian modelling economic evaluation. graph_params Type plot device cri_params Credible interval parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_base.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"EIB parameters specific to base R plot ‚Äî eib_params_base","text":"list","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"eib_params_cri ‚Äî eib_params_cri","title":"eib_params_cri ‚Äî eib_params_cri","text":"eib_params_cri EIB Parameters CrI","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_params_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"eib_params_cri ‚Äî eib_params_cri","text":"","code":"eib_params_cri(he, graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Incremental Benefit Plot By Graph Device ‚Äî eib_plot_graph","title":"Expected Incremental Benefit Plot By Graph Device ‚Äî eib_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Incremental Benefit Plot By Graph Device ‚Äî eib_plot_graph","text":"","code":"eib_plot_base(he, graph_params, ...)  eib_plot_ggplot(he, graph_params, ...)  eib_plot_plotly(he, graph_params, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/eib_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Incremental Benefit Plot By Graph Device ‚Äî eib_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. graph_params Graph parameters ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/estimate.hyperparams.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate hyperparameters ‚Äî estimate.hyperparams","title":"Estimate hyperparameters ‚Äî estimate.hyperparams","text":"Estimate hyperparameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/estimate.hyperparams.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate hyperparameters ‚Äî estimate.hyperparams","text":"","code":"estimate.hyperparams(x, input.matrix, parameter, n.sim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/estimate.hyperparams.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate hyperparameters ‚Äî estimate.hyperparams","text":"x x input.matrix Input matrix parameter Parameters n.sim Number simulations","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"Plots Expected Value Information (EVI) willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"","code":"# S3 method for bcea evi.plot(he, graph = c(\"base\", \"ggplot2\", \"plotly\"), ...)  evi.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. graph string used select graphical engine use plotting. (partial-)match three options \"base\", \"ggplot2\" \"plotly\". Default value \"base\". ... Additional graphical arguments: line_colors specify EVPI line colour - graph types. line_types specify line type (lty) - graph types. area_include specify whether include area EVPI curve - plotly . area_color specify area colour curve - plotly .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"eib graph=\"ggplot2\" ggplot object, graph=\"plotly\" plotly object containing requested plot. Nothing returned graph=\"base\", default. function produces plot Expected Value Information function discrete grid approximation willingness pay parameter. break even point(s) (.e. point EIB=0, ie optimal decision changes one intervention another) () also showed.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expected Value of Information (EVI) Plot ‚Äî evi.plot.bcea","text":"","code":"data(Vaccine) m <- bcea(       e=eff,       c=cost,               # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax)       plot=FALSE            # plots the results ) evi.plot(m)   data(Smoking) treats <- c(\"No intervention\", \"Self-help\",             \"Individual counselling\", \"Group counselling\") m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) evi.plot(m)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":null,"dir":"Reference","previous_headings":"","what":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"Compares optimal scenario mixed case terms EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"","code":"# S3 method for mixedAn evi.plot(he, y.limits = NULL, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"object class mixedAn, subclass bcea, given output call function mixedAn(). y.limits Range y-axis graph. default value NULL, case maximum range optimal mixed analysis scenarios considered. pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"evi ggplot object containing plot. Returned graph=\"ggplot2\". function produces graph showing difference ''optimal'' version EVPI (cost-effective intervention included market) mixed strategy one (one intervention considered market).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"Baio G, Russo P (2009). ‚Äúdecision-theoretic framework application cost-effectiveness analysis regulatory processes.‚Äù Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi.plot.mixedAn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"EVI Plot of the Health Economic Analysis For Mixed Analysis ‚Äî evi.plot.mixedAn","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem # # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax)       plot=FALSE            # inhibits graphical output )  mixedAn(m) <- NULL      # uses the results of the mixed strategy                          #  analysis (a \"mixedAn\" object)                         # the vector of market shares can be defined                          #  externally. If NULL, then each of the T                          #  interventions will have 1/T market share                         # produces the plots evi.plot(m)   evi.plot(m, graph=\"base\")   # Or with ggplot2 if (require(ggplot2)) {    evi.plot(m, graph=\"ggplot2\") }"},{"path":"https://n8thangreen.github.io/BCEA/reference/evi_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Value of Information Plot By Graph Device ‚Äî evi_plot_graph","title":"Expected Value of Information Plot By Graph Device ‚Äî evi_plot_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evi_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Value of Information Plot By Graph Device ‚Äî evi_plot_graph","text":"","code":"evi_plot_base(he, data.psa, plot_aes, plot_annotations)  evi_plot_ggplot(he, data.psa, plot_aes, plot_annotations)  evi_plot_plotly(data.psa, plot_aes, plot_annotations)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evi_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Value of Information Plot By Graph Device ‚Äî evi_plot_graph","text":"bcea object containing results Bayesian modelling economic evaluation. data.psa Data plot_aes Aesthetic parameters plot_annotations Plot parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"Calculates Expected Value Perfect Partial Information (EVPPI) subsets parameters. Uses GAM non-parametric regression single parameter EVPPI SPDE-INLA method larger parameter subsets.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"","code":"evppi(he, param_idx, input, N = NULL, plot = FALSE, residuals = TRUE, ...)  # S3 method for default evppi(he, ...)  # S3 method for bcea evppi(   he,   param_idx = NULL,   input,   N = NULL,   plot = FALSE,   residuals = TRUE,   method = NULL,   ... )"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"bcea object containing results Bayesian modelling economic evaluation. param_idx vector parameters EVPPI calculated. can given string (vector strings) names numeric vector, corresponding column numbers important parameters. input matrix containing simulations parameters monitored call JAGS BUGS. matrix column names matching names parameters values vector parameter match least one values. N number PSA simulations used calculate EVPPI. default uses available samples. plot logical value indicating whether triangular mesh SPDE-INLA plotted. Default set FALSE. residuals logical value indicating whether fitted values SPDE-INLA method outputted. Default set TRUE. ... Additional arguments. Details methods compute EVPPI additional arguments : single-parameter: Generalized additive model (GAM) (default). method Strong & Oakley use method string . user needs also specify number \"blocks\" (e.g. n.blocks=20). Note multi-parameter version method deprecated. method Sadatsafavi et al. method takes value string either sad sal. possible also specify number \"separators\" (e.g. n.seps=3). none specified, default value n.seps=1 used. Note multi-parameter version method deprecated. multi-parameter: INLA/SPDE (default). Gaussian process regression method gp. method Character string select method use. default methods recommended. However, possible (mainly backward compatibility) use different methods.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"Object class evppi: evppi computed values evppi values parameter willingness pay. index numerical vector index associated parameters EVPPI calculated. k vector values willingness pay. evi vector values overall EVPPI. fitted.costs fitted values costs. fitted.effects fitted values effects. parameters single string containing names parameters EVPPI calculated, used plotting EVPPI. time Computational time (seconds). fit.c object produced model fit costs. fit.e object produced model fit effects. formula formula used fit model. method string indicating method used estimate EVPPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"single parameter EVPPI calculated using non-parametric GAM regression developed Strong et al. (2014). multi-parameter EVPPI calculated using SPDE-INLA regression method Gaussian Process regression developed Heath et al. (2015). function completely changed restructured make possible change regression method. method argument can now given list. first element element list vector giving regression method effects. second gives regression method costs. method' argument can also given uses regression method curves. extra_argscan given .int.ordcan updated using list formulation give interactions different curve. formula argument GAM can given , eitherte()ors() + s()` computational reasons rather aid fit. can still plot INLA mesh elements output meshes.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"gam-regression","dir":"Reference","previous_headings":"","what":"GAM regression","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"multi-parameter, user can select 3 possible methods. method = \"GAM\" (BCEA accept also \"gam\", \"G\" \"g\"), computations based GAM regression. user can also specify formula regression. default option use tensor product (e.g. two main parameters, p1 p2, amounts setting formula = \"te(p1,p2)\", indicates two parameters interact). Alternatively, possible specify model parameters independent using notation formula = \"s(p1) + s(p2)\". may lead worse accuracy estimates.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"strong-et-al-gp-regression","dir":"Reference","previous_headings":"","what":"Strong et al. GP regression","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"used method=\"GP\" (BCEA also accept specification method=\"gp\"). case, user can also specify number PSA runs used estimate hyperparameters model (e.g. n.sim=100). value set default 500.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"inla-related-options","dir":"Reference","previous_headings":"","what":"INLA-related options","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"rather technical described detail Baio et al. (2017). optional parameter vector int.ord can take integer values (c(1,1) default) force predictor include interactions: int.ord = c(k, h), k-way interactions used effects h-way interactions used costs. Also, user can specify feature mesh \"spatial\" part model. optional parameter cutoff (default 0.3) controls density points inside mesh. Acceptable values typically interval (0.1, 0.5), lower values implying points (thus better approximation greater computational time). construction boundaries mesh can controlled optional inputs convex.inner (default = -0.4) convex.outer (default = -0.7). negative values can decreased (say -0.7 -1, respectively) increase distance points outer boundary, also increases precision computational time. optional argumentrobust can set TRUE, case INLA use t prior distribution coefficients linear predictor. Finally, user can control accuracy INLA grid-search estimation hyperparameters. done setting value h.value (default = 0.00005). Lower values imply refined search (hence better accuracy), expense computational speed. method argument can also given list allowing different regression methods effects costs, different incremental decisions. first list element contain vector methods incremental effects second costs, example method = list(c(\"GAM\"), c(\"INLA\")). int.ord argument can also given list give different interaction levels regression curve. default, method specified user, evppi use GAM number parameters <5 INLA otherwise.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"Strong M, Oakley JE, Brennan (2014). ‚ÄúEstimating Multiparameter Partial Expected Value Perfect Information Probabilistic Sensitivity Analysis Sample : Nonparametric Regression Approach.‚Äù Medical Decision Making, 311--326. doi:10.1177/0272989X13505910 . Sadatsafavi M, Bansback N, Zafari Z, Najafzadeh M, Marra C (2013). ‚ÄúNeed speed: efficient algorithm calculation single-parameter expected value partial perfect information.‚Äù Value Heal., 16(2), 438--448. ISSN 10983015, doi:10.1016/j.jval.2012.10.018 , http://dx.doi.org/10.1016/j.jval.2012.10.018. Baio G (2013). Bayesian Methods Health Economics. CRC. Baio, Gianluca, Berardi, Andrea, Heath (2017). Bayesian Cost-Effectiveness Analysis R package BCEA. Springer International Publishing. https://link.springer.com/book/10.1007/978-3-319-55718-2. Heath , Manolopoulou , Baio G (2016). ‚ÄúEstimating expected value partial perfect information health economic evaluations using integrated nested Laplace approximation.‚Äù Stat. Med., 35(23), 4264--4280. ISSN 0277-6715, doi:10.1002/sim.6983 , 1504.05436, https://pubmed.ncbi.nlm.nih.gov/27189534/.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"Anna Heath, Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expected Value of Perfect Partial Information (EVPPI) for Selected\r\nParameters ‚Äî evppi","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  if (FALSE) { # Load the post-processed results of the MCMC simulation model # original JAGS output is can be downloaded from here # https://gianluca.statistica.it/book/bcea/code/vaccine.RData  data(Vaccine, package = \"BCEA\") treats <- c(\"Status quo\", \"Vaccination\")  # Run the health economic evaluation using BCEA m <- bcea(e.pts, c.pts, ref = 2, interventions = treats)  # Compute the EVPPI for a bunch of parameters inp <- createInputs(vaccine_mat)  EVPPI <- evppi(m, c(\"beta.1.\" , \"beta.2.\"), inp$mat)  plot(EVPPI)  # deprecated (single parameter) methods EVPPI.so <- evppi(m, c(\"beta.1.\", \"beta.2.\"), inp$mat, method = \"so\", n.blocks = 50) EVPPI.sad <- evppi(m, c(\"beta.1.\", \"beta.2.\"), inp$mat, method = \"sad\", n.seps = 1)  plot(EVPPI.so) plot(EVPPI.sad)   # Compute the EVPPI using INLA/SPDE if (require(\"INLA\"))   x_inla <- evppi(he = m, 39:40, input = inp$mat)  # using GAM regression x_gam <- evppi(he = m, 39:40, input = inp$mat, method = \"GAM\")  # using Strong et al GP regression x_gp <- evppi(he = m, 39:40, input = inp$mat, method = \"GP\")  # plot results if (require(\"INLA\")) plot(x_inla) points(x_inla$k, x_inla$evppi, type = \"l\", lwd = 2, lty = 2) points(x_gam$k, x_gam$evppi, type = \"l\", col = \"red\") points(x_gp$k, x_gp$evppi, type = \"l\", col = \"blue\")  if (require(\"INLA\")) {   plot(x_inla$k, x_inla$evppi, type = \"l\", lwd = 2, lty = 2)   points(x_gam$k, x_gam$evppi, type = \"l\", col = \"red\")   points(x_gp$k, x_gp$evppi, type = \"l\", col = \"blue\") }  data(Smoking) treats <- c(\"No intervention\", \"Self-help\", \"Individual counselling\", \"Group counselling\") m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500) inp <- createInputs(smoking_output) EVPPI <- evppi(m, c(2,3), inp$mat, h.value = 0.0000005) plot(EVPPI) }  data(Vaccine, package = \"BCEA\") treats <- c(\"Status quo\", \"Vaccination\") bcea_vacc <- bcea(e.pts, c.pts, ref = 2, interventions = treats) inp <- createInputs(vaccine_mat) #>  [1] \"14 \\nLinear dependence: removing column pi.2.2.\" #>  [2] \"15 \\nLinear dependence: removing column pi.2.2.\" #>  [3] \"16 \\nLinear dependence: removing column pi.2.2.\" #>  [4] \"17 \\nLinear dependence: removing column pi.2.2.\" #>  [5] \"18 \\nLinear dependence: removing column pi.2.2.\" #>  [6] \"19 \\nLinear dependence: removing column pi.2.2.\" #>  [7] \"20 \\nLinear dependence: removing column pi.2.2.\" #>  [8] \"21 \\nLinear dependence: removing column pi.2.2.\" #>  [9] \"22 \\nLinear dependence: removing column pi.2.2.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.2.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.2.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.2.\" #> [13] \"46 \\nLinear dependence: removing column pi.2.2.\" #> [14] \"47 \\nLinear dependence: removing column pi.2.2.\" #>  [1] \"14 \\nLinear dependence: removing column pi.2.1.\" #>  [2] \"15 \\nLinear dependence: removing column pi.2.1.\" #>  [3] \"16 \\nLinear dependence: removing column pi.2.1.\" #>  [4] \"17 \\nLinear dependence: removing column pi.2.1.\" #>  [5] \"18 \\nLinear dependence: removing column pi.2.1.\" #>  [6] \"19 \\nLinear dependence: removing column pi.2.1.\" #>  [7] \"20 \\nLinear dependence: removing column pi.2.1.\" #>  [8] \"21 \\nLinear dependence: removing column pi.2.1.\" #>  [9] \"22 \\nLinear dependence: removing column pi.2.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.2.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.2.1.\" #> [12] \"45 \\nLinear dependence: removing column pi.2.1.\" #>  [1] \"14 \\nLinear dependence: removing column pi.1.1.\" #>  [2] \"15 \\nLinear dependence: removing column pi.1.1.\" #>  [3] \"16 \\nLinear dependence: removing column pi.1.1.\" #>  [4] \"17 \\nLinear dependence: removing column pi.1.1.\" #>  [5] \"18 \\nLinear dependence: removing column pi.1.1.\" #>  [6] \"19 \\nLinear dependence: removing column pi.1.1.\" #>  [7] \"20 \\nLinear dependence: removing column pi.1.1.\" #>  [8] \"21 \\nLinear dependence: removing column pi.1.1.\" #>  [9] \"22 \\nLinear dependence: removing column pi.1.1.\" #> [10] \"29 \\nLinear dependence: removing column pi.1.1.\" #> [11] \"44 \\nLinear dependence: removing column pi.1.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [3] \"16 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [4] \"17 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [5] \"18 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [6] \"19 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [7] \"20 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [8] \"21 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [9] \"22 \\nLinear dependence: removing column Repeat.GP.2.2.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [2] \"15 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [3] \"17 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [4] \"18 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [5] \"20 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [6] \"21 \\nLinear dependence: removing column Repeat.GP.2.1.\" #> [1] \"14 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [2] \"17 \\nLinear dependence: removing column Repeat.GP.1.1.\" #> [3] \"20 \\nLinear dependence: removing column Repeat.GP.1.1.\" evppi(bcea_vacc, c(\"beta.1.\", \"beta.2.\"), inp$mat) #> $evppi #>   [1] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>   [6] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>  [11] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>  [16] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>  [21] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>  [26] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 #>  [31] 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 2.457218e-05 #>  [36] 9.285676e-05 1.611413e-04 2.294259e-04 2.977105e-04 3.659951e-04 #>  [41] 4.342796e-04 5.025642e-04 5.708488e-04 6.391333e-04 7.074179e-04 #>  [46] 8.359579e-04 9.696656e-04 1.103373e-03 1.237081e-03 1.370789e-03 #>  [51] 1.504497e-03 1.670549e-03 1.859133e-03 2.047718e-03 2.236302e-03 #>  [56] 2.424887e-03 2.613472e-03 2.802056e-03 2.990641e-03 3.179226e-03 #>  [61] 3.388697e-03 3.632914e-03 3.877131e-03 4.139327e-03 4.557299e-03 #>  [66] 5.018772e-03 5.518913e-03 6.019054e-03 6.519195e-03 7.072839e-03 #>  [71] 7.631472e-03 8.238981e-03 8.914107e-03 9.667252e-03 1.050133e-02 #>  [76] 1.136910e-02 1.233047e-02 1.330277e-02 1.435958e-02 1.545712e-02 #>  [81] 1.665844e-02 1.791987e-02 1.926646e-02 2.069755e-02 2.215719e-02 #>  [86] 2.365858e-02 2.536231e-02 2.721777e-02 2.926248e-02 3.142167e-02 #>  [91] 3.366613e-02 3.594727e-02 3.828947e-02 4.066173e-02 4.308486e-02 #>  [96] 4.554495e-02 4.810142e-02 5.076759e-02 5.354944e-02 5.645627e-02 #> [101] 5.953500e-02 6.272953e-02 6.605710e-02 6.946561e-02 7.299518e-02 #> [106] 7.661519e-02 8.044882e-02 8.454452e-02 8.880028e-02 9.316356e-02 #> [111] 9.757211e-02 1.020631e-01 1.066899e-01 1.115583e-01 1.165761e-01 #> [116] 1.217236e-01 1.269848e-01 1.324089e-01 1.379159e-01 1.435288e-01 #> [121] 1.492557e-01 1.550713e-01 1.610079e-01 1.670532e-01 1.733199e-01 #> [126] 1.797311e-01 1.862892e-01 1.929569e-01 1.998673e-01 2.069650e-01 #> [131] 2.141277e-01 2.214818e-01 2.290093e-01 2.366830e-01 2.444225e-01 #> [136] 2.522186e-01 2.601369e-01 2.681379e-01 2.761958e-01 2.844056e-01 #> [141] 2.927010e-01 3.011088e-01 3.096150e-01 3.182103e-01 3.270021e-01 #> [146] 3.359932e-01 3.451267e-01 3.543843e-01 3.637561e-01 3.733013e-01 #> [151] 3.829607e-01 3.927341e-01 4.026574e-01 4.127028e-01 4.228980e-01 #> [156] 4.332371e-01 4.437379e-01 4.544137e-01 4.651813e-01 4.761334e-01 #> [161] 4.872191e-01 4.984766e-01 5.098566e-01 5.214073e-01 5.330834e-01 #> [166] 5.449266e-01 5.569304e-01 5.690002e-01 5.812412e-01 5.936338e-01 #> [171] 6.061169e-01 6.187243e-01 6.314361e-01 6.442246e-01 6.570961e-01 #> [176] 6.700288e-01 6.830269e-01 6.960982e-01 7.092648e-01 7.225512e-01 #> [181] 7.358751e-01 7.492499e-01 7.627448e-01 7.763942e-01 7.901216e-01 #> [186] 8.039977e-01 8.179865e-01 8.320791e-01 8.462536e-01 8.605366e-01 #> [191] 8.749299e-01 8.893719e-01 9.039347e-01 9.186171e-01 9.334328e-01 #> [196] 9.483216e-01 9.632936e-01 9.783535e-01 9.935304e-01 1.008831e+00 #> [201] 1.024234e+00 1.039745e+00 1.055422e+00 1.071224e+00 1.084066e+00 #> [206] 1.074809e+00 1.065660e+00 1.056641e+00 1.047729e+00 1.038873e+00 #> [211] 1.030071e+00 1.021314e+00 1.012653e+00 1.004120e+00 9.956920e-01 #> [216] 9.872935e-01 9.789248e-01 9.706356e-01 9.623851e-01 9.542037e-01 #> [221] 9.461404e-01 9.381714e-01 9.302605e-01 9.224375e-01 9.147457e-01 #> [226] 9.071125e-01 8.995306e-01 8.919891e-01 8.844713e-01 8.770460e-01 #> [231] 8.697233e-01 8.625451e-01 8.554401e-01 8.483837e-01 8.413724e-01 #> [236] 8.344054e-01 8.275071e-01 8.206365e-01 8.138200e-01 8.071060e-01 #> [241] 8.004723e-01 7.938925e-01 7.873783e-01 7.809368e-01 7.745497e-01 #> [246] 7.682354e-01 7.619523e-01 7.557112e-01 7.495586e-01 7.434515e-01 #> [251] 7.374292e-01 7.314887e-01 7.256113e-01 7.197572e-01 7.139554e-01 #> [256] 7.081910e-01 7.024605e-01 6.967964e-01 6.912702e-01 6.858125e-01 #> [261] 6.804006e-01 6.750311e-01 6.697037e-01 6.644020e-01 6.591274e-01 #> [266] 6.539432e-01 6.488189e-01 6.437494e-01 6.387192e-01 6.337315e-01 #> [271] 6.287676e-01 6.238712e-01 6.190358e-01 6.143003e-01 6.096329e-01 #> [276] 6.050127e-01 6.004741e-01 5.960047e-01 5.915586e-01 5.871403e-01 #> [281] 5.827532e-01 5.783884e-01 5.740675e-01 5.697920e-01 5.655458e-01 #> [286] 5.613352e-01 5.571441e-01 5.529855e-01 5.488534e-01 5.447722e-01 #> [291] 5.406968e-01 5.366213e-01 5.325726e-01 5.285423e-01 5.245240e-01 #> [296] 5.205150e-01 5.165845e-01 5.126795e-01 5.088076e-01 5.049948e-01 #> [301] 5.012110e-01 4.975225e-01 4.938624e-01 4.902186e-01 4.865747e-01 #> [306] 4.829573e-01 4.794062e-01 4.758743e-01 4.723592e-01 4.688739e-01 #> [311] 4.654132e-01 4.619793e-01 4.585647e-01 4.551584e-01 4.517521e-01 #> [316] 4.483486e-01 4.449656e-01 4.416163e-01 4.383141e-01 4.350518e-01 #> [321] 4.318246e-01 4.286196e-01 4.254631e-01 4.223070e-01 4.191951e-01 #> [326] 4.161302e-01 4.131096e-01 4.101110e-01 4.071347e-01 4.041827e-01 #> [331] 4.012589e-01 3.983743e-01 3.955212e-01 3.927254e-01 3.899432e-01 #> [336] 3.871855e-01 3.844689e-01 3.817796e-01 3.791394e-01 3.765335e-01 #> [341] 3.739600e-01 3.714164e-01 3.688934e-01 3.663704e-01 3.638535e-01 #> [346] 3.613474e-01 3.588413e-01 3.563576e-01 3.539089e-01 3.514843e-01 #> [351] 3.490616e-01 3.466548e-01 3.442652e-01 3.418779e-01 3.395314e-01 #> [356] 3.372204e-01 3.349244e-01 3.326430e-01 3.303822e-01 3.281223e-01 #> [361] 3.258784e-01 3.236347e-01 3.214071e-01 3.191795e-01 3.169807e-01 #> [366] 3.148009e-01 3.126353e-01 3.105210e-01 3.084279e-01 3.063603e-01 #> [371] 3.043336e-01 3.023203e-01 3.003220e-01 2.983323e-01 2.963581e-01 #> [376] 2.944046e-01 2.924985e-01 2.906183e-01 2.887512e-01 2.869099e-01 #> [381] 2.850765e-01 2.832430e-01 2.814097e-01 2.796030e-01 2.778181e-01 #> [386] 2.760596e-01 2.743042e-01 2.725626e-01 2.708210e-01 2.690877e-01 #> [391] 2.673614e-01 2.656351e-01 2.639213e-01 2.622214e-01 2.605248e-01 #> [396] 2.588283e-01 2.571434e-01 2.554615e-01 2.537810e-01 2.521252e-01 #> [401] 2.504935e-01 2.488892e-01 2.472959e-01 2.457275e-01 2.441923e-01 #> [406] 2.426613e-01 2.411526e-01 2.396684e-01 2.382291e-01 2.367951e-01 #> [411] 2.353736e-01 2.339666e-01 2.325611e-01 2.311557e-01 2.297563e-01 #> [416] 2.283653e-01 2.269863e-01 2.256259e-01 2.242938e-01 2.229744e-01 #> [421] 2.216551e-01 2.203358e-01 2.190165e-01 2.176971e-01 2.163804e-01 #> [426] 2.150968e-01 2.138344e-01 2.125824e-01 2.113339e-01 2.100859e-01 #> [431] 2.088515e-01 2.076339e-01 2.064492e-01 2.052776e-01 2.041179e-01 #> [436] 2.029666e-01 2.018338e-01 2.007256e-01 1.996347e-01 1.985587e-01 #> [441] 1.974892e-01 1.964197e-01 1.953502e-01 1.942885e-01 1.932456e-01 #> [446] 1.922157e-01 1.911862e-01 1.901613e-01 1.891454e-01 1.881421e-01 #> [451] 1.871387e-01 1.861354e-01 1.851410e-01 1.841523e-01 1.831884e-01 #> [456] 1.822613e-01 1.813469e-01 1.804353e-01 1.795236e-01 1.786235e-01 #> [461] 1.777250e-01 1.768265e-01 1.759280e-01 1.750408e-01 1.741682e-01 #> [466] 1.733006e-01 1.724417e-01 1.715828e-01 1.707240e-01 1.698655e-01 #> [471] 1.690192e-01 1.681822e-01 1.673487e-01 1.665152e-01 1.656817e-01 #> [476] 1.648482e-01 1.640322e-01 1.632500e-01 1.624679e-01 1.616857e-01 #> [481] 1.609060e-01 1.601366e-01 1.593672e-01 1.585978e-01 1.578284e-01 #> [486] 1.570590e-01 1.562896e-01 1.555302e-01 1.547736e-01 1.540170e-01 #> [491] 1.532604e-01 1.525038e-01 1.517472e-01 1.509906e-01 1.502340e-01 #> [496] 1.494882e-01 1.487427e-01 1.479973e-01 1.472519e-01 1.465064e-01 #> [501] 1.457650e-01 #>  #> $index #> [1] \"beta.1.\" \"beta.2.\" #>  #> $k #>   [1]     0   100   200   300   400   500   600   700   800   900  1000  1100 #>  [13]  1200  1300  1400  1500  1600  1700  1800  1900  2000  2100  2200  2300 #>  [25]  2400  2500  2600  2700  2800  2900  3000  3100  3200  3300  3400  3500 #>  [37]  3600  3700  3800  3900  4000  4100  4200  4300  4400  4500  4600  4700 #>  [49]  4800  4900  5000  5100  5200  5300  5400  5500  5600  5700  5800  5900 #>  [61]  6000  6100  6200  6300  6400  6500  6600  6700  6800  6900  7000  7100 #>  [73]  7200  7300  7400  7500  7600  7700  7800  7900  8000  8100  8200  8300 #>  [85]  8400  8500  8600  8700  8800  8900  9000  9100  9200  9300  9400  9500 #>  [97]  9600  9700  9800  9900 10000 10100 10200 10300 10400 10500 10600 10700 #> [109] 10800 10900 11000 11100 11200 11300 11400 11500 11600 11700 11800 11900 #> [121] 12000 12100 12200 12300 12400 12500 12600 12700 12800 12900 13000 13100 #> [133] 13200 13300 13400 13500 13600 13700 13800 13900 14000 14100 14200 14300 #> [145] 14400 14500 14600 14700 14800 14900 15000 15100 15200 15300 15400 15500 #> [157] 15600 15700 15800 15900 16000 16100 16200 16300 16400 16500 16600 16700 #> [169] 16800 16900 17000 17100 17200 17300 17400 17500 17600 17700 17800 17900 #> [181] 18000 18100 18200 18300 18400 18500 18600 18700 18800 18900 19000 19100 #> [193] 19200 19300 19400 19500 19600 19700 19800 19900 20000 20100 20200 20300 #> [205] 20400 20500 20600 20700 20800 20900 21000 21100 21200 21300 21400 21500 #> [217] 21600 21700 21800 21900 22000 22100 22200 22300 22400 22500 22600 22700 #> [229] 22800 22900 23000 23100 23200 23300 23400 23500 23600 23700 23800 23900 #> [241] 24000 24100 24200 24300 24400 24500 24600 24700 24800 24900 25000 25100 #> [253] 25200 25300 25400 25500 25600 25700 25800 25900 26000 26100 26200 26300 #> [265] 26400 26500 26600 26700 26800 26900 27000 27100 27200 27300 27400 27500 #> [277] 27600 27700 27800 27900 28000 28100 28200 28300 28400 28500 28600 28700 #> [289] 28800 28900 29000 29100 29200 29300 29400 29500 29600 29700 29800 29900 #> [301] 30000 30100 30200 30300 30400 30500 30600 30700 30800 30900 31000 31100 #> [313] 31200 31300 31400 31500 31600 31700 31800 31900 32000 32100 32200 32300 #> [325] 32400 32500 32600 32700 32800 32900 33000 33100 33200 33300 33400 33500 #> [337] 33600 33700 33800 33900 34000 34100 34200 34300 34400 34500 34600 34700 #> [349] 34800 34900 35000 35100 35200 35300 35400 35500 35600 35700 35800 35900 #> [361] 36000 36100 36200 36300 36400 36500 36600 36700 36800 36900 37000 37100 #> [373] 37200 37300 37400 37500 37600 37700 37800 37900 38000 38100 38200 38300 #> [385] 38400 38500 38600 38700 38800 38900 39000 39100 39200 39300 39400 39500 #> [397] 39600 39700 39800 39900 40000 40100 40200 40300 40400 40500 40600 40700 #> [409] 40800 40900 41000 41100 41200 41300 41400 41500 41600 41700 41800 41900 #> [421] 42000 42100 42200 42300 42400 42500 42600 42700 42800 42900 43000 43100 #> [433] 43200 43300 43400 43500 43600 43700 43800 43900 44000 44100 44200 44300 #> [445] 44400 44500 44600 44700 44800 44900 45000 45100 45200 45300 45400 45500 #> [457] 45600 45700 45800 45900 46000 46100 46200 46300 46400 46500 46600 46700 #> [469] 46800 46900 47000 47100 47200 47300 47400 47500 47600 47700 47800 47900 #> [481] 48000 48100 48200 48300 48400 48500 48600 48700 48800 48900 49000 49100 #> [493] 49200 49300 49400 49500 49600 49700 49800 49900 50000 #>  #> $evi #>   [1] 0.03705361 0.03785587 0.03869327 0.03957261 0.04053147 0.04149032 #>   [7] 0.04249547 0.04357790 0.04468983 0.04580177 0.04696679 0.04820919 #>  [13] 0.04945159 0.05071720 0.05204083 0.05341006 0.05479360 0.05628177 #>  [19] 0.05790349 0.05968408 0.06163395 0.06371142 0.06579055 0.06796376 #>  [25] 0.07022097 0.07273816 0.07532847 0.07795127 0.08057406 0.08320268 #>  [31] 0.08589638 0.08868468 0.09152601 0.09437325 0.09732932 0.10029796 #>  [37] 0.10336608 0.10650386 0.10969266 0.11291187 0.11623517 0.11961608 #>  [43] 0.12301796 0.12655367 0.13016897 0.13389347 0.13771150 0.14164319 #>  [49] 0.14562582 0.14972633 0.15408068 0.15855398 0.16308971 0.16773032 #>  [55] 0.17246620 0.17733774 0.18240585 0.18763480 0.19302758 0.19870097 #>  [61] 0.20455777 0.21058793 0.21671040 0.22296970 0.22947367 0.23630862 #>  [67] 0.24330447 0.25039355 0.25766800 0.26518678 0.27299674 0.28116086 #>  [73] 0.28956168 0.29817600 0.30710155 0.31640030 0.32590075 0.33562984 #>  [79] 0.34549466 0.35556390 0.36586862 0.37636890 0.38722042 0.39819986 #>  [85] 0.40931334 0.42067277 0.43210667 0.44377365 0.45566760 0.46785113 #>  [91] 0.48014364 0.49246093 0.50499804 0.51762457 0.53037623 0.54325794 #>  [97] 0.55623809 0.56931969 0.58247875 0.59587792 0.60962413 0.62343508 #> [103] 0.63739430 0.65146528 0.66560388 0.67977458 0.69403487 0.70842482 #> [109] 0.72293925 0.73755255 0.75234332 0.76723566 0.78221180 0.79721725 #> [115] 0.81232276 0.82758674 0.84296480 0.85844709 0.87398274 0.88966269 #> [121] 0.90550552 0.92141819 0.93740388 0.95345109 0.96960001 0.98586393 #> [127] 1.00217636 1.01852827 1.03494208 1.05142297 1.06797820 1.08468101 #> [133] 1.10149448 1.11850610 1.13565841 1.15294406 1.17038384 1.18787278 #> [139] 1.20544370 1.22316318 1.24098583 1.25890098 1.27685790 1.29494468 #> [145] 1.31305675 1.33121282 1.34951452 1.36794968 1.38653595 1.40518347 #> [151] 1.42398997 1.44291220 1.46187667 1.48090091 1.49998386 1.51913333 #> [157] 1.53842038 1.55785893 1.57742357 1.59708499 1.61685411 1.63671449 #> [163] 1.65661087 1.67656286 1.69652816 1.71649345 1.73649008 1.75651452 #> [169] 1.77658853 1.79668980 1.81684907 1.83704353 1.85732385 1.87768519 #> [175] 1.89813142 1.91868758 1.93931951 1.95998689 1.98070096 2.00151878 #> [181] 2.02248963 2.04353027 2.06465898 2.08582184 2.10699995 2.12828009 #> [187] 2.14966652 2.17114009 2.19267665 2.21423926 2.23585252 2.25753717 #> [193] 2.27924027 2.30098203 2.32278791 2.34466165 2.36659499 2.38854408 #> [199] 2.41055686 2.43261442 2.45475044 2.47693925 2.49917875 2.52143631 #> [205] 2.54070684 2.53787999 2.53506303 2.53229036 2.52958762 2.52697130 #> [211] 2.52440752 2.52185173 2.51932424 2.51682773 2.51436495 2.51199912 #> [217] 2.50972006 2.50748163 2.50529882 2.50313412 2.50097250 2.49883204 #> [223] 2.49676430 2.49474058 2.49271685 2.49069313 2.48867961 2.48671075 #> [229] 2.48488026 2.48307698 2.48130197 2.47958992 2.47791258 2.47625725 #> [235] 2.47464086 2.47306603 2.47152238 2.46999548 2.46856835 2.46717635 #> [241] 2.46580518 2.46446047 2.46314796 2.46185416 2.46056499 2.45930632 #> [247] 2.45807492 2.45694841 2.45588064 2.45485168 2.45384951 2.45290828 #> [253] 2.45201256 2.45115357 2.45034345 2.44954816 2.44877788 2.44801321 #> [259] 2.44724855 2.44648588 2.44575366 2.44502992 2.44434475 2.44368189 #> [265] 2.44304942 2.44246395 2.44190999 2.44138249 2.44092506 2.44048420 #> [271] 2.44005376 2.43966976 2.43930692 2.43896298 2.43867630 2.43841126 #> [277] 2.43815833 2.43792271 2.43770326 2.43750768 2.43734001 2.43717648 #> [283] 2.43701295 2.43685240 2.43672731 2.43660228 2.43649001 2.43639935 #> [289] 2.43632504 2.43627307 2.43624400 2.43625683 2.43628988 2.43635282 #> [295] 2.43644101 2.43653657 2.43664089 2.43676890 2.43691328 2.43709723 #> [301] 2.43728708 2.43747693 2.43766678 2.43785663 2.43804648 2.43823633 #> [307] 2.43844428 2.43869049 2.43895177 2.43921999 2.43948822 2.43975645 #> [313] 2.44002468 2.44029291 2.44056114 2.44082937 2.44109760 2.44137953 #> [319] 2.44170391 2.44205164 2.44241982 2.44282017 2.44323328 2.44364639 #> [325] 2.44409249 2.44457061 2.44507864 2.44562174 2.44616484 2.44670794 #> [331] 2.44725127 2.44781211 2.44838171 2.44895677 2.44954877 2.45015945 #> [337] 2.45077012 2.45139695 2.45203779 2.45268564 2.45333350 2.45398136 #> [343] 2.45462922 2.45528430 2.45595980 2.45663861 2.45733166 2.45803623 #> [349] 2.45876134 2.45951256 2.46027210 2.46104954 2.46184788 2.46270278 #> [355] 2.46356630 2.46442982 2.46529334 2.46616454 2.46705682 2.46794910 #> [361] 2.46884707 2.46975739 2.47066771 2.47157803 2.47250829 2.47345963 #> [367] 2.47441097 2.47536231 2.47631365 2.47726499 2.47821633 2.47916766 #> [373] 2.48011900 2.48108604 2.48205474 2.48302345 2.48399401 2.48498194 #> [379] 2.48596986 2.48696203 2.48797773 2.48900137 2.49002501 2.49106195 #> [385] 2.49210191 2.49314802 2.49419569 2.49526209 2.49633230 2.49740252 #> [391] 2.49847482 2.49956060 2.50065377 2.50174694 2.50284011 2.50393327 #> [397] 2.50502644 2.50613166 2.50724137 2.50835109 2.50946173 2.51058409 #> [403] 2.51171324 2.51285383 2.51399442 2.51515263 2.51631514 2.51748307 #> [409] 2.51865224 2.51982141 2.52100290 2.52218565 2.52336840 2.52455115 #> [415] 2.52573390 2.52692109 2.52811219 2.52933476 2.53058895 2.53185552 #> [421] 2.53312209 2.53441819 2.53575671 2.53709574 2.53846228 2.53987875 #> [427] 2.54131648 2.54276301 2.54421702 2.54567692 2.54713737 2.54861863 #> [433] 2.55010724 2.55161064 2.55311404 2.55461744 2.55612084 2.55762424 #> [439] 2.55912764 2.56063104 2.56213444 2.56364595 2.56516720 2.56668845 #> [445] 2.56821847 2.56975283 2.57129950 2.57284964 2.57442273 2.57600088 #> [451] 2.57757903 2.57916437 2.58076660 2.58238097 2.58400753 2.58565356 #> [457] 2.58730489 2.58896026 2.59062565 2.59229225 2.59396413 2.59565871 #> [463] 2.59736472 2.59909129 2.60083599 2.60258363 2.60433128 2.60607892 #> [469] 2.60782656 2.60957421 2.61132185 2.61306949 2.61482472 2.61658627 #> [475] 2.61835210 2.62011955 2.62188699 2.62365443 2.62542187 2.62718931 #> [481] 2.62896830 2.63074890 2.63253189 2.63431941 2.63611797 2.63793732 #> [487] 2.63975777 2.64157823 2.64339903 2.64522609 2.64705546 2.64888724 #> [493] 2.65071902 2.65255080 2.65440805 2.65627824 2.65815205 2.66002586 #> [499] 2.66189967 2.66377348 2.66564729 #>  #> $parameters #> [1] \"beta.1. and beta.2.\" #>  #> $time #> $time$`Fitting for Effects` #> NULL #>  #> $time$`Fitting for Costs` #> NULL #>  #> $time$`Calculating EVPPI` #> NULL #>  #>  #> $method #> $method$`Methods for Effects` #> [1] \"gam\" #>  #> $method$`Methods for Costs` #> [1] \"gam\" #>  #>  #> $fitted.costs #>             ...1   #>    [1,] 5.539060 0 #>    [2,] 5.042096 0 #>    [3,] 5.420907 0 #>    [4,] 5.738414 0 #>    [5,] 5.469780 0 #>    [6,] 5.552250 0 #>    [7,] 3.622860 0 #>    [8,] 6.049415 0 #>    [9,] 5.829584 0 #>   [10,] 4.978065 0 #>   [11,] 4.429175 0 #>   [12,] 5.407992 0 #>   [13,] 6.090357 0 #>   [14,] 6.110306 0 #>   [15,] 4.868019 0 #>   [16,] 5.264807 0 #>   [17,] 5.938002 0 #>   [18,] 4.435298 0 #>   [19,] 4.992994 0 #>   [20,] 5.138636 0 #>   [21,] 5.692185 0 #>   [22,] 5.451911 0 #>   [23,] 5.696216 0 #>   [24,] 5.172949 0 #>   [25,] 4.411596 0 #>   [26,] 4.540321 0 #>   [27,] 6.219929 0 #>   [28,] 5.880996 0 #>   [29,] 5.089494 0 #>   [30,] 5.046960 0 #>   [31,] 5.684144 0 #>   [32,] 3.806804 0 #>   [33,] 5.209105 0 #>   [34,] 5.301564 0 #>   [35,] 5.106181 0 #>   [36,] 5.813065 0 #>   [37,] 4.163367 0 #>   [38,] 5.291885 0 #>   [39,] 5.057783 0 #>   [40,] 5.640054 0 #>   [41,] 5.212866 0 #>   [42,] 5.686964 0 #>   [43,] 5.509276 0 #>   [44,] 4.560746 0 #>   [45,] 5.257646 0 #>   [46,] 5.586814 0 #>   [47,] 5.714918 0 #>   [48,] 5.374325 0 #>   [49,] 5.181640 0 #>   [50,] 6.092522 0 #>   [51,] 5.040795 0 #>   [52,] 3.921548 0 #>   [53,] 6.035031 0 #>   [54,] 5.881642 0 #>   [55,] 5.361438 0 #>   [56,] 6.353782 0 #>   [57,] 5.481254 0 #>   [58,] 5.536473 0 #>   [59,] 5.249361 0 #>   [60,] 5.351821 0 #>   [61,] 4.919147 0 #>   [62,] 5.741087 0 #>   [63,] 4.555483 0 #>   [64,] 5.829663 0 #>   [65,] 4.456022 0 #>   [66,] 4.756325 0 #>   [67,] 5.156087 0 #>   [68,] 4.299859 0 #>   [69,] 4.859611 0 #>   [70,] 4.520524 0 #>   [71,] 4.270351 0 #>   [72,] 5.854351 0 #>   [73,] 4.204380 0 #>   [74,] 5.162071 0 #>   [75,] 5.889816 0 #>   [76,] 4.742549 0 #>   [77,] 5.483039 0 #>   [78,] 4.585330 0 #>   [79,] 5.076819 0 #>   [80,] 4.929809 0 #>   [81,] 5.851112 0 #>   [82,] 6.150576 0 #>   [83,] 5.039752 0 #>   [84,] 4.184469 0 #>   [85,] 5.557155 0 #>   [86,] 3.080087 0 #>   [87,] 5.447201 0 #>   [88,] 5.299291 0 #>   [89,] 4.586296 0 #>   [90,] 5.248812 0 #>   [91,] 3.693466 0 #>   [92,] 4.814863 0 #>   [93,] 5.685345 0 #>   [94,] 4.713743 0 #>   [95,] 4.499628 0 #>   [96,] 3.619984 0 #>   [97,] 4.879671 0 #>   [98,] 4.647593 0 #>   [99,] 5.920827 0 #>  [100,] 6.020268 0 #>  [101,] 4.651883 0 #>  [102,] 5.377532 0 #>  [103,] 5.025746 0 #>  [104,] 4.793158 0 #>  [105,] 3.982976 0 #>  [106,] 4.550494 0 #>  [107,] 5.395733 0 #>  [108,] 5.616294 0 #>  [109,] 6.198823 0 #>  [110,] 6.239986 0 #>  [111,] 4.160499 0 #>  [112,] 5.684216 0 #>  [113,] 6.312563 0 #>  [114,] 4.996849 0 #>  [115,] 4.215937 0 #>  [116,] 4.857364 0 #>  [117,] 4.417169 0 #>  [118,] 3.308411 0 #>  [119,] 4.758624 0 #>  [120,] 5.334703 0 #>  [121,] 5.117974 0 #>  [122,] 5.452513 0 #>  [123,] 5.616120 0 #>  [124,] 5.031824 0 #>  [125,] 5.906992 0 #>  [126,] 5.201315 0 #>  [127,] 5.199154 0 #>  [128,] 6.321913 0 #>  [129,] 5.327667 0 #>  [130,] 4.656508 0 #>  [131,] 5.418207 0 #>  [132,] 5.610628 0 #>  [133,] 4.035627 0 #>  [134,] 4.090451 0 #>  [135,] 5.323000 0 #>  [136,] 5.161108 0 #>  [137,] 5.628860 0 #>  [138,] 6.002209 0 #>  [139,] 5.940414 0 #>  [140,] 5.951488 0 #>  [141,] 4.155158 0 #>  [142,] 4.305728 0 #>  [143,] 5.811871 0 #>  [144,] 3.968335 0 #>  [145,] 4.683313 0 #>  [146,] 4.813944 0 #>  [147,] 5.717913 0 #>  [148,] 3.841804 0 #>  [149,] 5.448253 0 #>  [150,] 6.119106 0 #>  [151,] 5.366319 0 #>  [152,] 5.886763 0 #>  [153,] 6.044123 0 #>  [154,] 5.484369 0 #>  [155,] 5.156000 0 #>  [156,] 6.495019 0 #>  [157,] 5.582958 0 #>  [158,] 4.897245 0 #>  [159,] 5.068719 0 #>  [160,] 4.308136 0 #>  [161,] 4.848514 0 #>  [162,] 4.541059 0 #>  [163,] 5.500579 0 #>  [164,] 5.744604 0 #>  [165,] 4.803044 0 #>  [166,] 4.975764 0 #>  [167,] 5.303851 0 #>  [168,] 5.046976 0 #>  [169,] 5.036179 0 #>  [170,] 4.239431 0 #>  [171,] 6.274060 0 #>  [172,] 4.101257 0 #>  [173,] 6.202505 0 #>  [174,] 4.269867 0 #>  [175,] 5.798713 0 #>  [176,] 4.612728 0 #>  [177,] 5.447640 0 #>  [178,] 5.744765 0 #>  [179,] 4.769053 0 #>  [180,] 5.942330 0 #>  [181,] 4.969693 0 #>  [182,] 5.259084 0 #>  [183,] 5.637955 0 #>  [184,] 5.069238 0 #>  [185,] 6.448760 0 #>  [186,] 5.282090 0 #>  [187,] 4.093503 0 #>  [188,] 4.514099 0 #>  [189,] 6.195264 0 #>  [190,] 5.580690 0 #>  [191,] 5.291420 0 #>  [192,] 5.241205 0 #>  [193,] 4.601338 0 #>  [194,] 4.753936 0 #>  [195,] 4.537685 0 #>  [196,] 3.874407 0 #>  [197,] 6.430400 0 #>  [198,] 5.854666 0 #>  [199,] 3.407401 0 #>  [200,] 4.170614 0 #>  [201,] 5.680480 0 #>  [202,] 5.705507 0 #>  [203,] 4.802852 0 #>  [204,] 5.122291 0 #>  [205,] 5.146346 0 #>  [206,] 4.268045 0 #>  [207,] 5.107833 0 #>  [208,] 6.245946 0 #>  [209,] 4.418758 0 #>  [210,] 4.432187 0 #>  [211,] 4.216319 0 #>  [212,] 5.158100 0 #>  [213,] 5.057736 0 #>  [214,] 4.340614 0 #>  [215,] 5.221258 0 #>  [216,] 4.560710 0 #>  [217,] 5.550095 0 #>  [218,] 6.486105 0 #>  [219,] 5.039445 0 #>  [220,] 5.324935 0 #>  [221,] 5.279964 0 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0 #>  [726,] 2.228909e-04 0 #>  [727,] 3.337844e-04 0 #>  [728,] 1.053994e-04 0 #>  [729,] 1.871177e-04 0 #>  [730,] 2.518188e-04 0 #>  [731,] 4.384977e-04 0 #>  [732,] 5.243590e-04 0 #>  [733,] 2.018604e-04 0 #>  [734,] 2.395444e-04 0 #>  [735,] 3.241952e-04 0 #>  [736,] 1.130585e-04 0 #>  [737,] 2.450688e-04 0 #>  [738,] 2.493143e-05 0 #>  [739,] 2.882067e-04 0 #>  [740,] 4.044332e-04 0 #>  [741,] 1.149805e-04 0 #>  [742,] 3.730648e-04 0 #>  [743,] 1.283070e-04 0 #>  [744,] 3.997110e-04 0 #>  [745,] 1.822030e-04 0 #>  [746,] 2.716948e-04 0 #>  [747,] 1.137194e-04 0 #>  [748,] 3.509345e-04 0 #>  [749,] 2.534691e-04 0 #>  [750,] 1.623516e-04 0 #>  [751,] 3.234420e-04 0 #>  [752,] 1.390159e-04 0 #>  [753,] 3.052435e-04 0 #>  [754,] 2.163745e-04 0 #>  [755,] 1.824000e-04 0 #>  [756,] 2.204347e-04 0 #>  [757,] 2.936416e-04 0 #>  [758,] 2.374341e-04 0 #>  [759,] 1.472234e-04 0 #>  [760,] 4.303334e-04 0 #>  [761,] 3.104332e-04 0 #>  [762,] 3.033111e-04 0 #>  [763,] 2.722330e-04 0 #>  [764,] 3.945617e-04 0 #>  [765,] 2.630174e-04 0 #>  [766,] 1.966927e-04 0 #>  [767,] 3.844374e-04 0 #>  [768,] 3.157664e-04 0 #>  [769,] 4.335259e-05 0 #>  [770,] 3.141318e-04 0 #>  [771,] 1.654519e-04 0 #>  [772,] 3.228501e-04 0 #>  [773,] 3.028235e-04 0 #>  [774,] 2.356557e-04 0 #>  [775,] 2.774814e-04 0 #>  [776,] 2.157424e-04 0 #>  [777,] 1.091757e-04 0 #>  [778,] 8.044973e-05 0 #>  [779,] 8.345155e-05 0 #>  [780,] 1.081871e-04 0 #>  [781,] 2.364508e-04 0 #>  [782,] 1.332829e-04 0 #>  [783,] 2.488744e-04 0 #>  [784,] 1.152727e-04 0 #>  [785,] 2.535754e-04 0 #>  [786,] 3.100772e-04 0 #>  [787,] 4.785662e-04 0 #>  [788,] 3.031556e-04 0 #>  [789,] 2.736506e-04 0 #>  [790,] 1.996877e-04 0 #>  [791,] 1.275504e-04 0 #>  [792,] 1.853950e-04 0 #>  [793,] 1.734803e-04 0 #>  [794,] 2.672030e-04 0 #>  [795,] 7.667651e-05 0 #>  [796,] 2.112453e-04 0 #>  [797,] 3.736929e-04 0 #>  [798,] 5.560811e-04 0 #>  [799,] 1.790343e-04 0 #>  [800,] 1.746465e-04 0 #>  [801,] 2.447328e-04 0 #>  [802,] 1.693475e-04 0 #>  [803,] 1.674992e-04 0 #>  [804,] 6.930171e-05 0 #>  [805,] 8.983587e-05 0 #>  [806,] 2.634435e-04 0 #>  [807,] 2.582486e-04 0 #>  [808,] 2.556380e-04 0 #>  [809,] 1.995702e-04 0 #>  [810,] 2.323220e-04 0 #>  [811,] 2.857051e-04 0 #>  [812,] 1.570781e-04 0 #>  [813,] 2.510821e-04 0 #>  [814,] 1.558716e-04 0 #>  [815,] 2.963195e-04 0 #>  [816,] 4.929957e-05 0 #>  [817,] 2.629739e-04 0 #>  [818,] 3.690959e-04 0 #>  [819,] 3.209644e-04 0 #>  [820,] 5.537454e-04 0 #>  [821,] 1.403553e-04 0 #>  [822,] 1.646922e-04 0 #>  [823,] 4.237362e-04 0 #>  [824,] 2.988110e-04 0 #>  [825,] 3.215684e-04 0 #>  [826,] 2.762804e-04 0 #>  [827,] 1.348681e-04 0 #>  [828,] 3.215862e-04 0 #>  [829,] 1.280123e-04 0 #>  [830,] 1.836707e-04 0 #>  [831,] 3.014668e-04 0 #>  [832,] 2.678450e-04 0 #>  [833,] 3.514266e-04 0 #>  [834,] 1.878796e-04 0 #>  [835,] 2.105712e-04 0 #>  [836,] 3.305988e-04 0 #>  [837,] 6.751337e-05 0 #>  [838,] 3.685690e-04 0 #>  [839,] 1.003287e-04 0 #>  [840,] 1.851440e-04 0 #>  [841,] 1.603199e-04 0 #>  [842,] 1.468121e-04 0 #>  [843,] 3.060096e-04 0 #>  [844,] 3.520104e-04 0 #>  [845,] 5.563269e-04 0 #>  [846,] 1.859809e-04 0 #>  [847,] 1.993243e-04 0 #>  [848,] 4.688514e-04 0 #>  [849,] 2.369772e-04 0 #>  [850,] 3.941052e-04 0 #>  [851,] 1.504409e-04 0 #>  [852,] 2.960572e-04 0 #>  [853,] 2.108035e-04 0 #>  [854,] 2.246083e-04 0 #>  [855,] 2.852168e-04 0 #>  [856,] 1.423209e-04 0 #>  [857,] 3.518317e-04 0 #>  [858,] 2.797742e-04 0 #>  [859,] 2.222195e-04 0 #>  [860,] 2.281903e-04 0 #>  [861,] 1.316371e-04 0 #>  [862,] 3.027514e-04 0 #>  [863,] 2.942336e-04 0 #>  [864,] 3.196133e-04 0 #>  [865,] 2.178491e-04 0 #>  [866,] 3.078019e-04 0 #>  [867,] 1.659707e-04 0 #>  [868,] 1.574956e-04 0 #>  [869,] 2.561883e-04 0 #>  [870,] 3.813491e-04 0 #>  [871,] 1.841328e-04 0 #>  [872,] 1.878828e-04 0 #>  [873,] 2.876409e-04 0 #>  [874,] 2.834307e-04 0 #>  [875,] 2.445162e-04 0 #>  [876,] 1.625989e-04 0 #>  [877,] 3.593802e-04 0 #>  [878,] 1.990678e-04 0 #>  [879,] 1.946178e-04 0 #>  [880,] 1.917611e-04 0 #>  [881,] 1.932140e-04 0 #>  [882,] 2.130751e-04 0 #>  [883,] 2.530181e-04 0 #>  [884,] 1.422756e-04 0 #>  [885,] 2.391618e-04 0 #>  [886,] 2.545553e-04 0 #>  [887,] 2.307330e-04 0 #>  [888,] 7.217122e-05 0 #>  [889,] 4.158472e-04 0 #>  [890,] 1.999728e-04 0 #>  [891,] 2.276761e-04 0 #>  [892,] 3.742574e-04 0 #>  [893,] 2.460132e-04 0 #>  [894,] 2.837380e-04 0 #>  [895,] 2.418482e-04 0 #>  [896,] 4.452901e-04 0 #>  [897,] 2.472185e-04 0 #>  [898,] 1.484630e-04 0 #>  [899,] 2.565810e-04 0 #>  [900,] 3.116468e-04 0 #>  [901,] 2.057377e-04 0 #>  [902,] 2.248702e-04 0 #>  [903,] 2.476694e-04 0 #>  [904,] 2.393110e-04 0 #>  [905,] 1.758681e-04 0 #>  [906,] 3.539754e-04 0 #>  [907,] 2.837831e-04 0 #>  [908,] 2.018909e-04 0 #>  [909,] 2.859341e-04 0 #>  [910,] 1.315055e-04 0 #>  [911,] 3.526117e-04 0 #>  [912,] 1.404419e-04 0 #>  [913,] 1.636013e-04 0 #>  [914,] 3.183048e-04 0 #>  [915,] 1.109403e-04 0 #>  [916,] 2.805868e-04 0 #>  [917,] 4.248834e-05 0 #>  [918,] 2.641218e-04 0 #>  [919,] 1.591183e-04 0 #>  [920,] 5.045417e-04 0 #>  [921,] 1.781811e-04 0 #>  [922,] 4.753054e-04 0 #>  [923,] 2.873764e-04 0 #>  [924,] 2.030911e-04 0 #>  [925,] 1.672600e-04 0 #>  [926,] 2.856257e-04 0 #>  [927,] 4.057556e-04 0 #>  [928,] 2.847504e-04 0 #>  [929,] 1.221909e-04 0 #>  [930,] 2.410461e-04 0 #>  [931,] 1.670921e-04 0 #>  [932,] 2.497373e-04 0 #>  [933,] 9.214140e-05 0 #>  [934,] 5.533825e-04 0 #>  [935,] 2.583420e-04 0 #>  [936,] 1.731495e-04 0 #>  [937,] 1.590001e-04 0 #>  [938,] 2.612118e-04 0 #>  [939,] 1.852760e-04 0 #>  [940,] 3.501000e-04 0 #>  [941,] 2.984257e-04 0 #>  [942,] 2.376755e-04 0 #>  [943,] 3.153702e-04 0 #>  [944,] 3.773165e-04 0 #>  [945,] 3.644880e-04 0 #>  [946,] 2.543904e-04 0 #>  [947,] 2.732824e-04 0 #>  [948,] 2.906559e-04 0 #>  [949,] 1.327584e-04 0 #>  [950,] 3.356992e-04 0 #>  [951,] 3.028427e-04 0 #>  [952,] 1.893701e-04 0 #>  [953,] 1.358843e-04 0 #>  [954,] 7.866640e-05 0 #>  [955,] 3.341807e-04 0 #>  [956,] 2.154368e-04 0 #>  [957,] 3.409731e-04 0 #>  [958,] 4.846245e-04 0 #>  [959,] 2.034264e-04 0 #>  [960,] 2.416117e-04 0 #>  [961,] 3.012140e-04 0 #>  [962,] 4.464723e-04 0 #>  [963,] 3.008839e-04 0 #>  [964,] 2.210974e-04 0 #>  [965,] 2.549875e-04 0 #>  [966,] 2.730135e-04 0 #>  [967,] 4.464490e-04 0 #>  [968,] 3.302712e-04 0 #>  [969,] 2.318027e-04 0 #>  [970,] 3.287092e-04 0 #>  [971,] 1.561973e-04 0 #>  [972,] 2.966892e-04 0 #>  [973,] 2.479396e-04 0 #>  [974,] 3.828799e-04 0 #>  [975,] 2.450978e-04 0 #>  [976,] 1.585412e-04 0 #>  [977,] 3.925257e-04 0 #>  [978,] 1.406725e-04 0 #>  [979,] 2.428278e-04 0 #>  [980,] 1.963504e-04 0 #>  [981,] 2.996787e-04 0 #>  [982,] 2.225087e-04 0 #>  [983,] 3.079070e-04 0 #>  [984,] 2.144225e-04 0 #>  [985,] 3.831465e-04 0 #>  [986,] 2.136507e-04 0 #>  [987,] 9.502069e-05 0 #>  [988,] 2.147885e-04 0 #>  [989,] 2.199898e-04 0 #>  [990,] 1.990254e-04 0 #>  [991,] 2.568355e-04 0 #>  [992,] 2.764570e-04 0 #>  [993,] 1.339279e-04 0 #>  [994,] 2.515579e-04 0 #>  [995,] 3.253564e-04 0 #>  [996,] 3.340186e-04 0 #>  [997,] 3.056571e-04 0 #>  [998,] 3.111801e-04 0 #>  [999,] 2.623249e-04 0 #> [1000,] 2.901752e-04 0 #>  #> $select #>    [1]    1    2    3    4    5    6    7    8    9   10   11   12   13   14 #>   [15]   15   16   17   18   19   20   21   22   23   24   25   26   27   28 #>   [29]   29   30   31   32   33   34   35   36   37   38   39   40   41   42 #>   [43]   43   44   45   46   47   48   49   50   51   52   53   54   55   56 #>   [57]   57   58   59   60   61   62   63   64   65   66   67   68   69   70 #>   [71]   71   72   73   74   75   76   77   78   79   80   81   82   83   84 #>   [85]   85   86   87   88   89   90   91   92   93   94   95   96   97   98 #>   [99]   99  100  101  102  103  104  105  106  107  108  109  110  111  112 #>  [113]  113  114  115  116  117  118  119  120  121  122  123  124  125  126 #>  [127]  127  128  129  130  131  132  133  134  135  136  137  138  139  140 #>  [141]  141  142  143  144  145  146  147  148  149  150  151  152  153  154 #>  [155]  155  156  157  158  159  160  161  162  163  164  165  166  167  168 #>  [169]  169  170  171  172  173  174  175  176  177  178  179  180  181  182 #>  [183]  183  184  185  186  187  188  189  190  191  192  193  194  195  196 #>  [197]  197  198  199  200  201  202  203  204  205  206  207  208  209  210 #>  [211]  211  212  213  214  215  216  217  218  219  220  221  222  223  224 #>  [225]  225  226  227  228  229  230  231  232  233  234  235  236  237  238 #>  [239]  239  240  241  242  243  244  245  246  247  248  249  250  251  252 #>  [253]  253  254  255  256  257  258  259  260  261  262  263  264  265  266 #>  [267]  267  268  269  270  271  272  273  274  275  276  277  278  279  280 #>  [281]  281  282  283  284  285  286  287  288  289  290  291  292  293  294 #>  [295]  295  296  297  298  299  300  301  302  303  304  305  306  307  308 #>  [309]  309  310  311  312  313  314  315  316  317  318  319  320  321  322 #>  [323]  323  324  325  326  327  328  329  330  331  332  333  334  335  336 #>  [337]  337  338  339  340  341  342  343  344  345  346  347  348  349  350 #>  [351]  351  352  353  354  355  356  357  358  359  360  361  362  363  364 #>  [365]  365  366  367  368  369  370  371  372  373  374  375  376  377  378 #>  [379]  379  380  381  382  383  384  385  386  387  388  389  390  391  392 #>  [393]  393  394  395  396  397  398  399  400  401  402  403  404  405  406 #>  [407]  407  408  409  410  411  412  413  414  415  416  417  418  419  420 #>  [421]  421  422  423  424  425  426  427  428  429  430  431  432  433  434 #>  [435]  435  436  437  438  439  440  441  442  443  444  445  446  447  448 #>  [449]  449  450  451  452  453  454  455  456  457  458  459  460  461  462 #>  [463]  463  464  465  466  467  468  469  470  471  472  473  474  475  476 #>  [477]  477  478  479  480  481  482  483  484  485  486  487  488  489  490 #>  [491]  491  492  493  494  495  496  497  498  499  500  501  502  503  504 #>  [505]  505  506  507  508  509  510  511  512  513  514  515  516  517  518 #>  [519]  519  520  521  522  523  524  525  526  527  528  529  530  531  532 #>  [533]  533  534  535  536  537  538  539  540  541  542  543  544  545  546 #>  [547]  547  548  549  550  551  552  553  554  555  556  557  558  559  560 #>  [561]  561  562  563  564  565  566  567  568  569  570  571  572  573  574 #>  [575]  575  576  577  578  579  580  581  582  583  584  585  586  587  588 #>  [589]  589  590  591  592  593  594  595  596  597  598  599  600  601  602 #>  [603]  603  604  605  606  607  608  609  610  611  612  613  614  615  616 #>  [617]  617  618  619  620  621  622  623  624  625  626  627  628  629  630 #>  [631]  631  632  633  634  635  636  637  638  639  640  641  642  643  644 #>  [645]  645  646  647  648  649  650  651  652  653  654  655  656  657  658 #>  [659]  659  660  661  662  663  664  665  666  667  668  669  670  671  672 #>  [673]  673  674  675  676  677  678  679  680  681  682  683  684  685  686 #>  [687]  687  688  689  690  691  692  693  694  695  696  697  698  699  700 #>  [701]  701  702  703  704  705  706  707  708  709  710  711  712  713  714 #>  [715]  715  716  717  718  719  720  721  722  723  724  725  726  727  728 #>  [729]  729  730  731  732  733  734  735  736  737  738  739  740  741  742 #>  [743]  743  744  745  746  747  748  749  750  751  752  753  754  755  756 #>  [757]  757  758  759  760  761  762  763  764  765  766  767  768  769  770 #>  [771]  771  772  773  774  775  776  777  778  779  780  781  782  783  784 #>  [785]  785  786  787  788  789  790  791  792  793  794  795  796  797  798 #>  [799]  799  800  801  802  803  804  805  806  807  808  809  810  811  812 #>  [813]  813  814  815  816  817  818  819  820  821  822  823  824  825  826 #>  [827]  827  828  829  830  831  832  833  834  835  836  837  838  839  840 #>  [841]  841  842  843  844  845  846  847  848  849  850  851  852  853  854 #>  [855]  855  856  857  858  859  860  861  862  863  864  865  866  867  868 #>  [869]  869  870  871  872  873  874  875  876  877  878  879  880  881  882 #>  [883]  883  884  885  886  887  888  889  890  891  892  893  894  895  896 #>  [897]  897  898  899  900  901  902  903  904  905  906  907  908  909  910 #>  [911]  911  912  913  914  915  916  917  918  919  920  921  922  923  924 #>  [925]  925  926  927  928  929  930  931  932  933  934  935  936  937  938 #>  [939]  939  940  941  942  943  944  945  946  947  948  949  950  951  952 #>  [953]  953  954  955  956  957  958  959  960  961  962  963  964  965  966 #>  [967]  967  968  969  970  971  972  973  974  975  976  977  978  979  980 #>  [981]  981  982  983  984  985  986  987  988  989  990  991  992  993  994 #>  [995]  995  996  997  998  999 1000 #>  #> $formula #> [1] \"te( beta.1.,beta.2., bs='cr')\" #>  #> $pars #>   [1] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>   [5] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>   [9] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [13] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [17] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [21] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [25] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [29] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [33] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [37] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [41] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [45] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [49] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [53] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [57] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [61] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [65] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [69] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [73] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [77] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [81] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [85] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [89] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [93] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #>  [97] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [101] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [105] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [109] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [113] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [117] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [121] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [125] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [129] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [133] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [137] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [141] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [145] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [149] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [153] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [157] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [161] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [165] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [169] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [173] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [177] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [181] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [185] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [189] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [193] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [197] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [201] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [205] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [209] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [213] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [217] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [221] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [225] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [229] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [233] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [237] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [241] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [245] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [249] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [253] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [257] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [261] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [265] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [269] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [273] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [277] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [281] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [285] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [289] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [293] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [297] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [301] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [305] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [309] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [313] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [317] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [321] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [325] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [329] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [333] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [337] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [341] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [345] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [349] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [353] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [357] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [361] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [365] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [369] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [373] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [377] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [381] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [385] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [389] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [393] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [397] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [401] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [405] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [409] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [413] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [417] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [421] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [425] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [429] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [433] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [437] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [441] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [445] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [449] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [453] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [457] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [461] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [465] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [469] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [473] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [477] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [481] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [485] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [489] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [493] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [497] \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" \"beta.1.,beta.2.\" #> [501] \"beta.1.,beta.2.\" #>  #> $res #>                pars     k        evppi #> 1   beta.1.,beta.2.     0 0.000000e+00 #> 2   beta.1.,beta.2.   100 0.000000e+00 #> 3   beta.1.,beta.2.   200 0.000000e+00 #> 4   beta.1.,beta.2.   300 0.000000e+00 #> 5   beta.1.,beta.2.   400 0.000000e+00 #> 6   beta.1.,beta.2.   500 0.000000e+00 #> 7   beta.1.,beta.2.   600 0.000000e+00 #> 8   beta.1.,beta.2.   700 0.000000e+00 #> 9   beta.1.,beta.2.   800 0.000000e+00 #> 10  beta.1.,beta.2.   900 0.000000e+00 #> 11  beta.1.,beta.2.  1000 0.000000e+00 #> 12  beta.1.,beta.2.  1100 0.000000e+00 #> 13  beta.1.,beta.2.  1200 0.000000e+00 #> 14  beta.1.,beta.2.  1300 0.000000e+00 #> 15  beta.1.,beta.2.  1400 0.000000e+00 #> 16  beta.1.,beta.2.  1500 0.000000e+00 #> 17  beta.1.,beta.2.  1600 0.000000e+00 #> 18  beta.1.,beta.2.  1700 0.000000e+00 #> 19  beta.1.,beta.2.  1800 0.000000e+00 #> 20  beta.1.,beta.2.  1900 0.000000e+00 #> 21  beta.1.,beta.2.  2000 0.000000e+00 #> 22  beta.1.,beta.2.  2100 0.000000e+00 #> 23  beta.1.,beta.2.  2200 0.000000e+00 #> 24  beta.1.,beta.2.  2300 0.000000e+00 #> 25  beta.1.,beta.2.  2400 0.000000e+00 #> 26  beta.1.,beta.2.  2500 0.000000e+00 #> 27  beta.1.,beta.2.  2600 0.000000e+00 #> 28  beta.1.,beta.2.  2700 0.000000e+00 #> 29  beta.1.,beta.2.  2800 0.000000e+00 #> 30  beta.1.,beta.2.  2900 0.000000e+00 #> 31  beta.1.,beta.2.  3000 0.000000e+00 #> 32  beta.1.,beta.2.  3100 0.000000e+00 #> 33  beta.1.,beta.2.  3200 0.000000e+00 #> 34  beta.1.,beta.2.  3300 0.000000e+00 #> 35  beta.1.,beta.2.  3400 2.457218e-05 #> 36  beta.1.,beta.2.  3500 9.285676e-05 #> 37  beta.1.,beta.2.  3600 1.611413e-04 #> 38  beta.1.,beta.2.  3700 2.294259e-04 #> 39  beta.1.,beta.2.  3800 2.977105e-04 #> 40  beta.1.,beta.2.  3900 3.659951e-04 #> 41  beta.1.,beta.2.  4000 4.342796e-04 #> 42  beta.1.,beta.2.  4100 5.025642e-04 #> 43  beta.1.,beta.2.  4200 5.708488e-04 #> 44  beta.1.,beta.2.  4300 6.391333e-04 #> 45  beta.1.,beta.2.  4400 7.074179e-04 #> 46  beta.1.,beta.2.  4500 8.359579e-04 #> 47  beta.1.,beta.2.  4600 9.696656e-04 #> 48  beta.1.,beta.2.  4700 1.103373e-03 #> 49  beta.1.,beta.2.  4800 1.237081e-03 #> 50  beta.1.,beta.2.  4900 1.370789e-03 #> 51  beta.1.,beta.2.  5000 1.504497e-03 #> 52  beta.1.,beta.2.  5100 1.670549e-03 #> 53  beta.1.,beta.2.  5200 1.859133e-03 #> 54  beta.1.,beta.2.  5300 2.047718e-03 #> 55  beta.1.,beta.2.  5400 2.236302e-03 #> 56  beta.1.,beta.2.  5500 2.424887e-03 #> 57  beta.1.,beta.2.  5600 2.613472e-03 #> 58  beta.1.,beta.2.  5700 2.802056e-03 #> 59  beta.1.,beta.2.  5800 2.990641e-03 #> 60  beta.1.,beta.2.  5900 3.179226e-03 #> 61  beta.1.,beta.2.  6000 3.388697e-03 #> 62  beta.1.,beta.2.  6100 3.632914e-03 #> 63  beta.1.,beta.2.  6200 3.877131e-03 #> 64  beta.1.,beta.2.  6300 4.139327e-03 #> 65  beta.1.,beta.2.  6400 4.557299e-03 #> 66  beta.1.,beta.2.  6500 5.018772e-03 #> 67  beta.1.,beta.2.  6600 5.518913e-03 #> 68  beta.1.,beta.2.  6700 6.019054e-03 #> 69  beta.1.,beta.2.  6800 6.519195e-03 #> 70  beta.1.,beta.2.  6900 7.072839e-03 #> 71  beta.1.,beta.2.  7000 7.631472e-03 #> 72  beta.1.,beta.2.  7100 8.238981e-03 #> 73  beta.1.,beta.2.  7200 8.914107e-03 #> 74  beta.1.,beta.2.  7300 9.667252e-03 #> 75  beta.1.,beta.2.  7400 1.050133e-02 #> 76  beta.1.,beta.2.  7500 1.136910e-02 #> 77  beta.1.,beta.2.  7600 1.233047e-02 #> 78  beta.1.,beta.2.  7700 1.330277e-02 #> 79  beta.1.,beta.2.  7800 1.435958e-02 #> 80  beta.1.,beta.2.  7900 1.545712e-02 #> 81  beta.1.,beta.2.  8000 1.665844e-02 #> 82  beta.1.,beta.2.  8100 1.791987e-02 #> 83  beta.1.,beta.2.  8200 1.926646e-02 #> 84  beta.1.,beta.2.  8300 2.069755e-02 #> 85  beta.1.,beta.2.  8400 2.215719e-02 #> 86  beta.1.,beta.2.  8500 2.365858e-02 #> 87  beta.1.,beta.2.  8600 2.536231e-02 #> 88  beta.1.,beta.2.  8700 2.721777e-02 #> 89  beta.1.,beta.2.  8800 2.926248e-02 #> 90  beta.1.,beta.2.  8900 3.142167e-02 #> 91  beta.1.,beta.2.  9000 3.366613e-02 #> 92  beta.1.,beta.2.  9100 3.594727e-02 #> 93  beta.1.,beta.2.  9200 3.828947e-02 #> 94  beta.1.,beta.2.  9300 4.066173e-02 #> 95  beta.1.,beta.2.  9400 4.308486e-02 #> 96  beta.1.,beta.2.  9500 4.554495e-02 #> 97  beta.1.,beta.2.  9600 4.810142e-02 #> 98  beta.1.,beta.2.  9700 5.076759e-02 #> 99  beta.1.,beta.2.  9800 5.354944e-02 #> 100 beta.1.,beta.2.  9900 5.645627e-02 #> 101 beta.1.,beta.2. 10000 5.953500e-02 #> 102 beta.1.,beta.2. 10100 6.272953e-02 #> 103 beta.1.,beta.2. 10200 6.605710e-02 #> 104 beta.1.,beta.2. 10300 6.946561e-02 #> 105 beta.1.,beta.2. 10400 7.299518e-02 #> 106 beta.1.,beta.2. 10500 7.661519e-02 #> 107 beta.1.,beta.2. 10600 8.044882e-02 #> 108 beta.1.,beta.2. 10700 8.454452e-02 #> 109 beta.1.,beta.2. 10800 8.880028e-02 #> 110 beta.1.,beta.2. 10900 9.316356e-02 #> 111 beta.1.,beta.2. 11000 9.757211e-02 #> 112 beta.1.,beta.2. 11100 1.020631e-01 #> 113 beta.1.,beta.2. 11200 1.066899e-01 #> 114 beta.1.,beta.2. 11300 1.115583e-01 #> 115 beta.1.,beta.2. 11400 1.165761e-01 #> 116 beta.1.,beta.2. 11500 1.217236e-01 #> 117 beta.1.,beta.2. 11600 1.269848e-01 #> 118 beta.1.,beta.2. 11700 1.324089e-01 #> 119 beta.1.,beta.2. 11800 1.379159e-01 #> 120 beta.1.,beta.2. 11900 1.435288e-01 #> 121 beta.1.,beta.2. 12000 1.492557e-01 #> 122 beta.1.,beta.2. 12100 1.550713e-01 #> 123 beta.1.,beta.2. 12200 1.610079e-01 #> 124 beta.1.,beta.2. 12300 1.670532e-01 #> 125 beta.1.,beta.2. 12400 1.733199e-01 #> 126 beta.1.,beta.2. 12500 1.797311e-01 #> 127 beta.1.,beta.2. 12600 1.862892e-01 #> 128 beta.1.,beta.2. 12700 1.929569e-01 #> 129 beta.1.,beta.2. 12800 1.998673e-01 #> 130 beta.1.,beta.2. 12900 2.069650e-01 #> 131 beta.1.,beta.2. 13000 2.141277e-01 #> 132 beta.1.,beta.2. 13100 2.214818e-01 #> 133 beta.1.,beta.2. 13200 2.290093e-01 #> 134 beta.1.,beta.2. 13300 2.366830e-01 #> 135 beta.1.,beta.2. 13400 2.444225e-01 #> 136 beta.1.,beta.2. 13500 2.522186e-01 #> 137 beta.1.,beta.2. 13600 2.601369e-01 #> 138 beta.1.,beta.2. 13700 2.681379e-01 #> 139 beta.1.,beta.2. 13800 2.761958e-01 #> 140 beta.1.,beta.2. 13900 2.844056e-01 #> 141 beta.1.,beta.2. 14000 2.927010e-01 #> 142 beta.1.,beta.2. 14100 3.011088e-01 #> 143 beta.1.,beta.2. 14200 3.096150e-01 #> 144 beta.1.,beta.2. 14300 3.182103e-01 #> 145 beta.1.,beta.2. 14400 3.270021e-01 #> 146 beta.1.,beta.2. 14500 3.359932e-01 #> 147 beta.1.,beta.2. 14600 3.451267e-01 #> 148 beta.1.,beta.2. 14700 3.543843e-01 #> 149 beta.1.,beta.2. 14800 3.637561e-01 #> 150 beta.1.,beta.2. 14900 3.733013e-01 #> 151 beta.1.,beta.2. 15000 3.829607e-01 #> 152 beta.1.,beta.2. 15100 3.927341e-01 #> 153 beta.1.,beta.2. 15200 4.026574e-01 #> 154 beta.1.,beta.2. 15300 4.127028e-01 #> 155 beta.1.,beta.2. 15400 4.228980e-01 #> 156 beta.1.,beta.2. 15500 4.332371e-01 #> 157 beta.1.,beta.2. 15600 4.437379e-01 #> 158 beta.1.,beta.2. 15700 4.544137e-01 #> 159 beta.1.,beta.2. 15800 4.651813e-01 #> 160 beta.1.,beta.2. 15900 4.761334e-01 #> 161 beta.1.,beta.2. 16000 4.872191e-01 #> 162 beta.1.,beta.2. 16100 4.984766e-01 #> 163 beta.1.,beta.2. 16200 5.098566e-01 #> 164 beta.1.,beta.2. 16300 5.214073e-01 #> 165 beta.1.,beta.2. 16400 5.330834e-01 #> 166 beta.1.,beta.2. 16500 5.449266e-01 #> 167 beta.1.,beta.2. 16600 5.569304e-01 #> 168 beta.1.,beta.2. 16700 5.690002e-01 #> 169 beta.1.,beta.2. 16800 5.812412e-01 #> 170 beta.1.,beta.2. 16900 5.936338e-01 #> 171 beta.1.,beta.2. 17000 6.061169e-01 #> 172 beta.1.,beta.2. 17100 6.187243e-01 #> 173 beta.1.,beta.2. 17200 6.314361e-01 #> 174 beta.1.,beta.2. 17300 6.442246e-01 #> 175 beta.1.,beta.2. 17400 6.570961e-01 #> 176 beta.1.,beta.2. 17500 6.700288e-01 #> 177 beta.1.,beta.2. 17600 6.830269e-01 #> 178 beta.1.,beta.2. 17700 6.960982e-01 #> 179 beta.1.,beta.2. 17800 7.092648e-01 #> 180 beta.1.,beta.2. 17900 7.225512e-01 #> 181 beta.1.,beta.2. 18000 7.358751e-01 #> 182 beta.1.,beta.2. 18100 7.492499e-01 #> 183 beta.1.,beta.2. 18200 7.627448e-01 #> 184 beta.1.,beta.2. 18300 7.763942e-01 #> 185 beta.1.,beta.2. 18400 7.901216e-01 #> 186 beta.1.,beta.2. 18500 8.039977e-01 #> 187 beta.1.,beta.2. 18600 8.179865e-01 #> 188 beta.1.,beta.2. 18700 8.320791e-01 #> 189 beta.1.,beta.2. 18800 8.462536e-01 #> 190 beta.1.,beta.2. 18900 8.605366e-01 #> 191 beta.1.,beta.2. 19000 8.749299e-01 #> 192 beta.1.,beta.2. 19100 8.893719e-01 #> 193 beta.1.,beta.2. 19200 9.039347e-01 #> 194 beta.1.,beta.2. 19300 9.186171e-01 #> 195 beta.1.,beta.2. 19400 9.334328e-01 #> 196 beta.1.,beta.2. 19500 9.483216e-01 #> 197 beta.1.,beta.2. 19600 9.632936e-01 #> 198 beta.1.,beta.2. 19700 9.783535e-01 #> 199 beta.1.,beta.2. 19800 9.935304e-01 #> 200 beta.1.,beta.2. 19900 1.008831e+00 #> 201 beta.1.,beta.2. 20000 1.024234e+00 #> 202 beta.1.,beta.2. 20100 1.039745e+00 #> 203 beta.1.,beta.2. 20200 1.055422e+00 #> 204 beta.1.,beta.2. 20300 1.071224e+00 #> 205 beta.1.,beta.2. 20400 1.084066e+00 #> 206 beta.1.,beta.2. 20500 1.074809e+00 #> 207 beta.1.,beta.2. 20600 1.065660e+00 #> 208 beta.1.,beta.2. 20700 1.056641e+00 #> 209 beta.1.,beta.2. 20800 1.047729e+00 #> 210 beta.1.,beta.2. 20900 1.038873e+00 #> 211 beta.1.,beta.2. 21000 1.030071e+00 #> 212 beta.1.,beta.2. 21100 1.021314e+00 #> 213 beta.1.,beta.2. 21200 1.012653e+00 #> 214 beta.1.,beta.2. 21300 1.004120e+00 #> 215 beta.1.,beta.2. 21400 9.956920e-01 #> 216 beta.1.,beta.2. 21500 9.872935e-01 #> 217 beta.1.,beta.2. 21600 9.789248e-01 #> 218 beta.1.,beta.2. 21700 9.706356e-01 #> 219 beta.1.,beta.2. 21800 9.623851e-01 #> 220 beta.1.,beta.2. 21900 9.542037e-01 #> 221 beta.1.,beta.2. 22000 9.461404e-01 #> 222 beta.1.,beta.2. 22100 9.381714e-01 #> 223 beta.1.,beta.2. 22200 9.302605e-01 #> 224 beta.1.,beta.2. 22300 9.224375e-01 #> 225 beta.1.,beta.2. 22400 9.147457e-01 #> 226 beta.1.,beta.2. 22500 9.071125e-01 #> 227 beta.1.,beta.2. 22600 8.995306e-01 #> 228 beta.1.,beta.2. 22700 8.919891e-01 #> 229 beta.1.,beta.2. 22800 8.844713e-01 #> 230 beta.1.,beta.2. 22900 8.770460e-01 #> 231 beta.1.,beta.2. 23000 8.697233e-01 #> 232 beta.1.,beta.2. 23100 8.625451e-01 #> 233 beta.1.,beta.2. 23200 8.554401e-01 #> 234 beta.1.,beta.2. 23300 8.483837e-01 #> 235 beta.1.,beta.2. 23400 8.413724e-01 #> 236 beta.1.,beta.2. 23500 8.344054e-01 #> 237 beta.1.,beta.2. 23600 8.275071e-01 #> 238 beta.1.,beta.2. 23700 8.206365e-01 #> 239 beta.1.,beta.2. 23800 8.138200e-01 #> 240 beta.1.,beta.2. 23900 8.071060e-01 #> 241 beta.1.,beta.2. 24000 8.004723e-01 #> 242 beta.1.,beta.2. 24100 7.938925e-01 #> 243 beta.1.,beta.2. 24200 7.873783e-01 #> 244 beta.1.,beta.2. 24300 7.809368e-01 #> 245 beta.1.,beta.2. 24400 7.745497e-01 #> 246 beta.1.,beta.2. 24500 7.682354e-01 #> 247 beta.1.,beta.2. 24600 7.619523e-01 #> 248 beta.1.,beta.2. 24700 7.557112e-01 #> 249 beta.1.,beta.2. 24800 7.495586e-01 #> 250 beta.1.,beta.2. 24900 7.434515e-01 #> 251 beta.1.,beta.2. 25000 7.374292e-01 #> 252 beta.1.,beta.2. 25100 7.314887e-01 #> 253 beta.1.,beta.2. 25200 7.256113e-01 #> 254 beta.1.,beta.2. 25300 7.197572e-01 #> 255 beta.1.,beta.2. 25400 7.139554e-01 #> 256 beta.1.,beta.2. 25500 7.081910e-01 #> 257 beta.1.,beta.2. 25600 7.024605e-01 #> 258 beta.1.,beta.2. 25700 6.967964e-01 #> 259 beta.1.,beta.2. 25800 6.912702e-01 #> 260 beta.1.,beta.2. 25900 6.858125e-01 #> 261 beta.1.,beta.2. 26000 6.804006e-01 #> 262 beta.1.,beta.2. 26100 6.750311e-01 #> 263 beta.1.,beta.2. 26200 6.697037e-01 #> 264 beta.1.,beta.2. 26300 6.644020e-01 #> 265 beta.1.,beta.2. 26400 6.591274e-01 #> 266 beta.1.,beta.2. 26500 6.539432e-01 #> 267 beta.1.,beta.2. 26600 6.488189e-01 #> 268 beta.1.,beta.2. 26700 6.437494e-01 #> 269 beta.1.,beta.2. 26800 6.387192e-01 #> 270 beta.1.,beta.2. 26900 6.337315e-01 #> 271 beta.1.,beta.2. 27000 6.287676e-01 #> 272 beta.1.,beta.2. 27100 6.238712e-01 #> 273 beta.1.,beta.2. 27200 6.190358e-01 #> 274 beta.1.,beta.2. 27300 6.143003e-01 #> 275 beta.1.,beta.2. 27400 6.096329e-01 #> 276 beta.1.,beta.2. 27500 6.050127e-01 #> 277 beta.1.,beta.2. 27600 6.004741e-01 #> 278 beta.1.,beta.2. 27700 5.960047e-01 #> 279 beta.1.,beta.2. 27800 5.915586e-01 #> 280 beta.1.,beta.2. 27900 5.871403e-01 #> 281 beta.1.,beta.2. 28000 5.827532e-01 #> 282 beta.1.,beta.2. 28100 5.783884e-01 #> 283 beta.1.,beta.2. 28200 5.740675e-01 #> 284 beta.1.,beta.2. 28300 5.697920e-01 #> 285 beta.1.,beta.2. 28400 5.655458e-01 #> 286 beta.1.,beta.2. 28500 5.613352e-01 #> 287 beta.1.,beta.2. 28600 5.571441e-01 #> 288 beta.1.,beta.2. 28700 5.529855e-01 #> 289 beta.1.,beta.2. 28800 5.488534e-01 #> 290 beta.1.,beta.2. 28900 5.447722e-01 #> 291 beta.1.,beta.2. 29000 5.406968e-01 #> 292 beta.1.,beta.2. 29100 5.366213e-01 #> 293 beta.1.,beta.2. 29200 5.325726e-01 #> 294 beta.1.,beta.2. 29300 5.285423e-01 #> 295 beta.1.,beta.2. 29400 5.245240e-01 #> 296 beta.1.,beta.2. 29500 5.205150e-01 #> 297 beta.1.,beta.2. 29600 5.165845e-01 #> 298 beta.1.,beta.2. 29700 5.126795e-01 #> 299 beta.1.,beta.2. 29800 5.088076e-01 #> 300 beta.1.,beta.2. 29900 5.049948e-01 #> 301 beta.1.,beta.2. 30000 5.012110e-01 #> 302 beta.1.,beta.2. 30100 4.975225e-01 #> 303 beta.1.,beta.2. 30200 4.938624e-01 #> 304 beta.1.,beta.2. 30300 4.902186e-01 #> 305 beta.1.,beta.2. 30400 4.865747e-01 #> 306 beta.1.,beta.2. 30500 4.829573e-01 #> 307 beta.1.,beta.2. 30600 4.794062e-01 #> 308 beta.1.,beta.2. 30700 4.758743e-01 #> 309 beta.1.,beta.2. 30800 4.723592e-01 #> 310 beta.1.,beta.2. 30900 4.688739e-01 #> 311 beta.1.,beta.2. 31000 4.654132e-01 #> 312 beta.1.,beta.2. 31100 4.619793e-01 #> 313 beta.1.,beta.2. 31200 4.585647e-01 #> 314 beta.1.,beta.2. 31300 4.551584e-01 #> 315 beta.1.,beta.2. 31400 4.517521e-01 #> 316 beta.1.,beta.2. 31500 4.483486e-01 #> 317 beta.1.,beta.2. 31600 4.449656e-01 #> 318 beta.1.,beta.2. 31700 4.416163e-01 #> 319 beta.1.,beta.2. 31800 4.383141e-01 #> 320 beta.1.,beta.2. 31900 4.350518e-01 #> 321 beta.1.,beta.2. 32000 4.318246e-01 #> 322 beta.1.,beta.2. 32100 4.286196e-01 #> 323 beta.1.,beta.2. 32200 4.254631e-01 #> 324 beta.1.,beta.2. 32300 4.223070e-01 #> 325 beta.1.,beta.2. 32400 4.191951e-01 #> 326 beta.1.,beta.2. 32500 4.161302e-01 #> 327 beta.1.,beta.2. 32600 4.131096e-01 #> 328 beta.1.,beta.2. 32700 4.101110e-01 #> 329 beta.1.,beta.2. 32800 4.071347e-01 #> 330 beta.1.,beta.2. 32900 4.041827e-01 #> 331 beta.1.,beta.2. 33000 4.012589e-01 #> 332 beta.1.,beta.2. 33100 3.983743e-01 #> 333 beta.1.,beta.2. 33200 3.955212e-01 #> 334 beta.1.,beta.2. 33300 3.927254e-01 #> 335 beta.1.,beta.2. 33400 3.899432e-01 #> 336 beta.1.,beta.2. 33500 3.871855e-01 #> 337 beta.1.,beta.2. 33600 3.844689e-01 #> 338 beta.1.,beta.2. 33700 3.817796e-01 #> 339 beta.1.,beta.2. 33800 3.791394e-01 #> 340 beta.1.,beta.2. 33900 3.765335e-01 #> 341 beta.1.,beta.2. 34000 3.739600e-01 #> 342 beta.1.,beta.2. 34100 3.714164e-01 #> 343 beta.1.,beta.2. 34200 3.688934e-01 #> 344 beta.1.,beta.2. 34300 3.663704e-01 #> 345 beta.1.,beta.2. 34400 3.638535e-01 #> 346 beta.1.,beta.2. 34500 3.613474e-01 #> 347 beta.1.,beta.2. 34600 3.588413e-01 #> 348 beta.1.,beta.2. 34700 3.563576e-01 #> 349 beta.1.,beta.2. 34800 3.539089e-01 #> 350 beta.1.,beta.2. 34900 3.514843e-01 #> 351 beta.1.,beta.2. 35000 3.490616e-01 #> 352 beta.1.,beta.2. 35100 3.466548e-01 #> 353 beta.1.,beta.2. 35200 3.442652e-01 #> 354 beta.1.,beta.2. 35300 3.418779e-01 #> 355 beta.1.,beta.2. 35400 3.395314e-01 #> 356 beta.1.,beta.2. 35500 3.372204e-01 #> 357 beta.1.,beta.2. 35600 3.349244e-01 #> 358 beta.1.,beta.2. 35700 3.326430e-01 #> 359 beta.1.,beta.2. 35800 3.303822e-01 #> 360 beta.1.,beta.2. 35900 3.281223e-01 #> 361 beta.1.,beta.2. 36000 3.258784e-01 #> 362 beta.1.,beta.2. 36100 3.236347e-01 #> 363 beta.1.,beta.2. 36200 3.214071e-01 #> 364 beta.1.,beta.2. 36300 3.191795e-01 #> 365 beta.1.,beta.2. 36400 3.169807e-01 #> 366 beta.1.,beta.2. 36500 3.148009e-01 #> 367 beta.1.,beta.2. 36600 3.126353e-01 #> 368 beta.1.,beta.2. 36700 3.105210e-01 #> 369 beta.1.,beta.2. 36800 3.084279e-01 #> 370 beta.1.,beta.2. 36900 3.063603e-01 #> 371 beta.1.,beta.2. 37000 3.043336e-01 #> 372 beta.1.,beta.2. 37100 3.023203e-01 #> 373 beta.1.,beta.2. 37200 3.003220e-01 #> 374 beta.1.,beta.2. 37300 2.983323e-01 #> 375 beta.1.,beta.2. 37400 2.963581e-01 #> 376 beta.1.,beta.2. 37500 2.944046e-01 #> 377 beta.1.,beta.2. 37600 2.924985e-01 #> 378 beta.1.,beta.2. 37700 2.906183e-01 #> 379 beta.1.,beta.2. 37800 2.887512e-01 #> 380 beta.1.,beta.2. 37900 2.869099e-01 #> 381 beta.1.,beta.2. 38000 2.850765e-01 #> 382 beta.1.,beta.2. 38100 2.832430e-01 #> 383 beta.1.,beta.2. 38200 2.814097e-01 #> 384 beta.1.,beta.2. 38300 2.796030e-01 #> 385 beta.1.,beta.2. 38400 2.778181e-01 #> 386 beta.1.,beta.2. 38500 2.760596e-01 #> 387 beta.1.,beta.2. 38600 2.743042e-01 #> 388 beta.1.,beta.2. 38700 2.725626e-01 #> 389 beta.1.,beta.2. 38800 2.708210e-01 #> 390 beta.1.,beta.2. 38900 2.690877e-01 #> 391 beta.1.,beta.2. 39000 2.673614e-01 #> 392 beta.1.,beta.2. 39100 2.656351e-01 #> 393 beta.1.,beta.2. 39200 2.639213e-01 #> 394 beta.1.,beta.2. 39300 2.622214e-01 #> 395 beta.1.,beta.2. 39400 2.605248e-01 #> 396 beta.1.,beta.2. 39500 2.588283e-01 #> 397 beta.1.,beta.2. 39600 2.571434e-01 #> 398 beta.1.,beta.2. 39700 2.554615e-01 #> 399 beta.1.,beta.2. 39800 2.537810e-01 #> 400 beta.1.,beta.2. 39900 2.521252e-01 #> 401 beta.1.,beta.2. 40000 2.504935e-01 #> 402 beta.1.,beta.2. 40100 2.488892e-01 #> 403 beta.1.,beta.2. 40200 2.472959e-01 #> 404 beta.1.,beta.2. 40300 2.457275e-01 #> 405 beta.1.,beta.2. 40400 2.441923e-01 #> 406 beta.1.,beta.2. 40500 2.426613e-01 #> 407 beta.1.,beta.2. 40600 2.411526e-01 #> 408 beta.1.,beta.2. 40700 2.396684e-01 #> 409 beta.1.,beta.2. 40800 2.382291e-01 #> 410 beta.1.,beta.2. 40900 2.367951e-01 #> 411 beta.1.,beta.2. 41000 2.353736e-01 #> 412 beta.1.,beta.2. 41100 2.339666e-01 #> 413 beta.1.,beta.2. 41200 2.325611e-01 #> 414 beta.1.,beta.2. 41300 2.311557e-01 #> 415 beta.1.,beta.2. 41400 2.297563e-01 #> 416 beta.1.,beta.2. 41500 2.283653e-01 #> 417 beta.1.,beta.2. 41600 2.269863e-01 #> 418 beta.1.,beta.2. 41700 2.256259e-01 #> 419 beta.1.,beta.2. 41800 2.242938e-01 #> 420 beta.1.,beta.2. 41900 2.229744e-01 #> 421 beta.1.,beta.2. 42000 2.216551e-01 #> 422 beta.1.,beta.2. 42100 2.203358e-01 #> 423 beta.1.,beta.2. 42200 2.190165e-01 #> 424 beta.1.,beta.2. 42300 2.176971e-01 #> 425 beta.1.,beta.2. 42400 2.163804e-01 #> 426 beta.1.,beta.2. 42500 2.150968e-01 #> 427 beta.1.,beta.2. 42600 2.138344e-01 #> 428 beta.1.,beta.2. 42700 2.125824e-01 #> 429 beta.1.,beta.2. 42800 2.113339e-01 #> 430 beta.1.,beta.2. 42900 2.100859e-01 #> 431 beta.1.,beta.2. 43000 2.088515e-01 #> 432 beta.1.,beta.2. 43100 2.076339e-01 #> 433 beta.1.,beta.2. 43200 2.064492e-01 #> 434 beta.1.,beta.2. 43300 2.052776e-01 #> 435 beta.1.,beta.2. 43400 2.041179e-01 #> 436 beta.1.,beta.2. 43500 2.029666e-01 #> 437 beta.1.,beta.2. 43600 2.018338e-01 #> 438 beta.1.,beta.2. 43700 2.007256e-01 #> 439 beta.1.,beta.2. 43800 1.996347e-01 #> 440 beta.1.,beta.2. 43900 1.985587e-01 #> 441 beta.1.,beta.2. 44000 1.974892e-01 #> 442 beta.1.,beta.2. 44100 1.964197e-01 #> 443 beta.1.,beta.2. 44200 1.953502e-01 #> 444 beta.1.,beta.2. 44300 1.942885e-01 #> 445 beta.1.,beta.2. 44400 1.932456e-01 #> 446 beta.1.,beta.2. 44500 1.922157e-01 #> 447 beta.1.,beta.2. 44600 1.911862e-01 #> 448 beta.1.,beta.2. 44700 1.901613e-01 #> 449 beta.1.,beta.2. 44800 1.891454e-01 #> 450 beta.1.,beta.2. 44900 1.881421e-01 #> 451 beta.1.,beta.2. 45000 1.871387e-01 #> 452 beta.1.,beta.2. 45100 1.861354e-01 #> 453 beta.1.,beta.2. 45200 1.851410e-01 #> 454 beta.1.,beta.2. 45300 1.841523e-01 #> 455 beta.1.,beta.2. 45400 1.831884e-01 #> 456 beta.1.,beta.2. 45500 1.822613e-01 #> 457 beta.1.,beta.2. 45600 1.813469e-01 #> 458 beta.1.,beta.2. 45700 1.804353e-01 #> 459 beta.1.,beta.2. 45800 1.795236e-01 #> 460 beta.1.,beta.2. 45900 1.786235e-01 #> 461 beta.1.,beta.2. 46000 1.777250e-01 #> 462 beta.1.,beta.2. 46100 1.768265e-01 #> 463 beta.1.,beta.2. 46200 1.759280e-01 #> 464 beta.1.,beta.2. 46300 1.750408e-01 #> 465 beta.1.,beta.2. 46400 1.741682e-01 #> 466 beta.1.,beta.2. 46500 1.733006e-01 #> 467 beta.1.,beta.2. 46600 1.724417e-01 #> 468 beta.1.,beta.2. 46700 1.715828e-01 #> 469 beta.1.,beta.2. 46800 1.707240e-01 #> 470 beta.1.,beta.2. 46900 1.698655e-01 #> 471 beta.1.,beta.2. 47000 1.690192e-01 #> 472 beta.1.,beta.2. 47100 1.681822e-01 #> 473 beta.1.,beta.2. 47200 1.673487e-01 #> 474 beta.1.,beta.2. 47300 1.665152e-01 #> 475 beta.1.,beta.2. 47400 1.656817e-01 #> 476 beta.1.,beta.2. 47500 1.648482e-01 #> 477 beta.1.,beta.2. 47600 1.640322e-01 #> 478 beta.1.,beta.2. 47700 1.632500e-01 #> 479 beta.1.,beta.2. 47800 1.624679e-01 #> 480 beta.1.,beta.2. 47900 1.616857e-01 #> 481 beta.1.,beta.2. 48000 1.609060e-01 #> 482 beta.1.,beta.2. 48100 1.601366e-01 #> 483 beta.1.,beta.2. 48200 1.593672e-01 #> 484 beta.1.,beta.2. 48300 1.585978e-01 #> 485 beta.1.,beta.2. 48400 1.578284e-01 #> 486 beta.1.,beta.2. 48500 1.570590e-01 #> 487 beta.1.,beta.2. 48600 1.562896e-01 #> 488 beta.1.,beta.2. 48700 1.555302e-01 #> 489 beta.1.,beta.2. 48800 1.547736e-01 #> 490 beta.1.,beta.2. 48900 1.540170e-01 #> 491 beta.1.,beta.2. 49000 1.532604e-01 #> 492 beta.1.,beta.2. 49100 1.525038e-01 #> 493 beta.1.,beta.2. 49200 1.517472e-01 #> 494 beta.1.,beta.2. 49300 1.509906e-01 #> 495 beta.1.,beta.2. 49400 1.502340e-01 #> 496 beta.1.,beta.2. 49500 1.494882e-01 #> 497 beta.1.,beta.2. 49600 1.487427e-01 #> 498 beta.1.,beta.2. 49700 1.479973e-01 #> 499 beta.1.,beta.2. 49800 1.472519e-01 #> 500 beta.1.,beta.2. 49900 1.465064e-01 #> 501 beta.1.,beta.2. 50000 1.457650e-01 #>  #> attr(,\"class\") #> [1] \"evppi\" \"list\""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî evppi_plot_graph","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî evppi_plot_graph","text":"Base R ggplot2 versions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî evppi_plot_graph","text":"","code":"evppi_plot_base(evppi_obj, pos_legend, col = NULL, annot = FALSE)  evppi_plot_ggplot(evppi_obj, pos_legend = c(0, 0.8), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî evppi_plot_graph","text":"evppi_obj Object class evppi pos_legend Position legend col Colour annot Annotate EVPPI curve parameter names ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Q-Q Plot ‚Äî evppi_qq_plot","title":"Q-Q Plot ‚Äî evppi_qq_plot","text":"Q-Q Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_qq_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Q-Q Plot ‚Äî evppi_qq_plot","text":"","code":"evppi_qq_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual Plot ‚Äî evppi_residual_plot","title":"Residual Plot ‚Äî evppi_residual_plot","text":"Residual Plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/evppi_residual_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual Plot ‚Äî evppi_residual_plot","text":"","code":"evppi_residual_plot(evppi, he, interv)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Additive Model Fitting ‚Äî fit.gam","title":"Gaussian Additive Model Fitting ‚Äî fit.gam","text":"Gaussian Additive Model Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Additive Model Fitting ‚Äî fit.gam","text":"","code":"fit.gam(parameter, inputs, x, form)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Additive Model Fitting ‚Äî fit.gam","text":"parameter Parameter inputs Inputs x Response variable form Formula","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gam.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Gaussian Additive Model Fitting ‚Äî fit.gam","text":"List","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit Gaussian Process ‚Äî fit.gp","title":"Fit Gaussian Process ‚Äî fit.gp","text":"Fit Gaussian Process","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit Gaussian Process ‚Äî fit.gp","text":"","code":"fit.gp(parameter, inputs, x, n.sim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit Gaussian Process ‚Äî fit.gp","text":"parameter Parameters inputs Inputs x Response variable n.sim Number simulations","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.gp.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit Gaussian Process ‚Äî fit.gp","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit INLA ‚Äî fit.inla","title":"Fit INLA ‚Äî fit.inla","text":"Fit INLA","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit INLA ‚Äî fit.inla","text":"","code":"fit.inla(   parameter,   inputs,   x,   mesh,   data.scale,   int.ord,   convex.inner,   convex.outer,   cutoff,   max.edge,   h.value,   family )"},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit INLA ‚Äî fit.inla","text":"parameter Parameters inputs Inputs x Response variable mesh Mesh data.scale data.scale int.ord int.ord convex.inner convex.inner convex.outer convex.outer cutoff Cut-max.edge Maximum edge h.value h.value family family","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/fit.inla.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit INLA ‚Äî fit.inla","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Credible interval ggplot geom ‚Äî geom_cri","title":"Credible interval ggplot geom ‚Äî geom_cri","text":"Credible interval ggplot geom","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Credible interval ggplot geom ‚Äî geom_cri","text":"","code":"geom_cri(plot.cri = TRUE, params = NA)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Credible interval ggplot geom ‚Äî geom_cri","text":"plot.cri plot CrI? Logical params Plot parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":null,"dir":"Reference","previous_headings":"","what":"Geom Quadrant Text ‚Äî geom_quad_txt","title":"Geom Quadrant Text ‚Äî geom_quad_txt","text":"Geom Quadrant Text","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Geom Quadrant Text ‚Äî geom_quad_txt","text":"","code":"geom_quad_txt(he, graph_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/geom_quad_txt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Geom Quadrant Text ‚Äî geom_quad_txt","text":"bcea object containing results Bayesian modelling economic evaluation. graph_params Plot parameters; list","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":null,"dir":"Reference","previous_headings":"","what":"Get fitted values from evppi object ‚Äî get_fitted_","title":"Get fitted values from evppi object ‚Äî get_fitted_","text":"Get fitted values evppi object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get fitted values from evppi object ‚Äî get_fitted_","text":"","code":"get_fitted_(val, voi_methods, voi_models)"},{"path":"https://n8thangreen.github.io/BCEA/reference/get_fitted_.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get fitted values from evppi object ‚Äî get_fitted_","text":"matrix","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":null,"dir":"Reference","previous_headings":"","what":"GrassmannOptim ‚Äî GrassmannOptim","title":"GrassmannOptim ‚Äî GrassmannOptim","text":"function taken GrassmannOptim package Kofi Placid Adragni Seongho Wu https://cran.r-project.org/web/packages/GrassmannOptim/index.html","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"GrassmannOptim ‚Äî GrassmannOptim","text":"","code":"GrassmannOptim(   objfun,   W,   sim_anneal = FALSE,   temp_init = 20,   cooling_rate = 2,   max_iter_sa = 100,   eps_conv = 1e-05,   max_iter = 100,   eps_grad = 1e-05,   eps_f = .Machine$double.eps,   verbose = FALSE )"},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"GrassmannOptim ‚Äî GrassmannOptim","text":"objfun objfun W W sim_anneal sim_anneal temp_init temp_init cooling_rate cooling_rate max_iter_sa max_iter_sa eps_conv eps_conv max_iter max_iter eps_grad eps_grad eps_f eps_f verbose verbose","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/GrassmannOptim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"GrassmannOptim ‚Äî GrassmannOptim","text":"List","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"Plots distribution Incremental Benefit (IB) given value willingness pay threshold.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"","code":"# S3 method for bcea ib.plot(   he,   comparison = NULL,   wtp = 25000,   bw = \"bcv\",   n = 512,   xlim = NULL,   graph = c(\"base\", \"ggplot2\"),   ... )  ib.plot(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. comparison case multiple interventions, specifies one used comparison reference. Default value NULL forces R consider first non-reference intervention comparator. Controls comparator used 2 interventions present wtp value willingness pay threshold. Default value 25000. bw Identifies smoothing bandwidth used construct kernel estimation IB density. n number equally spaced points density estimated. xlim limits plot x-axis. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"ib ggplot object containing requested plot. Returned graph=\"ggplot2\". function produces plot distribution Incremental Benefit given value willingness pay parameter. dashed area indicates positive part distribution (.e. reference cost-effective comparator).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Incremental Benefit (IB) Distribution Plot ‚Äî ib.plot.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. ib.plot(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"IB plot base R version ‚Äî ib_plot_base","title":"IB plot base R version ‚Äî ib_plot_base","text":"Choice base R, ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"IB plot base R version ‚Äî ib_plot_base","text":"","code":"ib_plot_base(he, comparison, wtp, bw, n, xlim)  ib_plot_ggplot(he, comparison, wtp, bw, n, xlim)"},{"path":"https://n8thangreen.github.io/BCEA/reference/ib_plot_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"IB plot base R version ‚Äî ib_plot_base","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Comparison intervention wtp Willingness pay bw band width n Number xlim x-axis limits","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"Produces plot similar tornado plot, based analysis EVPPI. parameter value willingness--pay threshold, barchart plotted describe ratio EVPPI (specific parameter) EVPI. represents relative `importance' parameter terms expected value information.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"","code":"# S3 method for bcea info.rank(   he,   inp,   wtp = NULL,   howManyPars = NA,   graph = c(\"base\", \"ggplot2\", \"plotly\"),   rel = TRUE,   ... )  info.rank(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"bcea object containing results Bayesian modelling economic evaluation. inp Named list running createInputs() containing: parameter = vector parameters individual EVPPI calculated. can given string (vector strings) names numeric vector, corresponding column numbers important parameters. mat = matrix containing simulations parameters monitored call JAGS BUGS. matrix column names matching names parameters values vector parameter match least one values. wtp value wtp analysis performed. specified break-even point current model used. howManyPars Optional maximum number parameters included bar plot. Includes parameters default. graph string used select graphical engine use plotting. (partial-)match one two options \"base\" \"plotly\". Default value \"base\" rel Logical argument specifies whether ratio EVPPI EVPI (rel = TRUE, default) absolute value EVPPI used analysis. ... Additional options. include graphical parameters user can specify: xlim = limits x-axis; ca = font size axis label (default = 0.7 full size). cn = font size parameter names vector (default = 0.7 full size) - base graphics . mai = margins graph (default = c(1.36, 1.5, 1,1)) - base graphics .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"base graphics: data.frame containing ranking parameters value selected summary, chosen wtp; plotly: plotly object, incorporating $rank element data.frame . function produces 'Info-rank' plot. extension standard 'Tornado plots' presents ranking model parameters terms impact expected value information. parameter, specific individual EVPPI computed used measure impact uncertainty parameter decision-making process, terms large expected value gaining information .","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"Anna Heath, Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info.rank.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Information-Rank Plot for bcea Class ‚Äî info.rank.bcea","text":"","code":"if (FALSE) { # Load the post-processed results of the MCMC simulation model # original JAGS output is can be downloaded from here # https://gianluca.statistica.it/book/bcea/code/vaccine.RData  data(\"Vaccine\") m <- bcea(eff, cost) inp <- createInputs(vaccine_mat) info.rank(m, inp)  info.rank(m, inp, graph = \"base\") info.rank(m, inp, graph = \"plotly\") info.rank(m, inp, graph = \"ggplot2\") }"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Info Rank plot parameters ‚Äî inforank_params","title":"Prepare Info Rank plot parameters ‚Äî inforank_params","text":"Prepare Info Rank plot parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Info Rank plot parameters ‚Äî inforank_params","text":"","code":"inforank_params(he, inp, wtp = NULL, rel, howManyPars, extra_args)"},{"path":"https://n8thangreen.github.io/BCEA/reference/inforank_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Info Rank plot parameters ‚Äî inforank_params","text":"bcea object containing results Bayesian modelling economic evaluation. inp Inputs wtp Willingness pay rel Relative size howManyPars mnay parameters use? extra_args Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Info Rank Plot By Graph Device ‚Äî info_rank_graph","title":"Info Rank Plot By Graph Device ‚Äî info_rank_graph","text":"Choice base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Info Rank Plot By Graph Device ‚Äî info_rank_graph","text":"","code":"info_rank_base(he, params)  info_rank_ggplot(he, params)  info_rank_plotly(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/info_rank_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Info Rank Plot By Graph Device ‚Äî info_rank_graph","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph Parameters including data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Check bcea Class ‚Äî is.bcea","title":"Check bcea Class ‚Äî is.bcea","text":"Check bcea Class","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check bcea Class ‚Äî is.bcea","text":"","code":"is.bcea(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check bcea Class ‚Äî is.bcea","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/is.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check bcea Class ‚Äî is.bcea","text":".bcea returns TRUE FALSE depending whether argument bcea class object.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare K-star vertical lines ‚Äî kstar_vlines","title":"Prepare K-star vertical lines ‚Äî kstar_vlines","text":"Prepare K-star vertical lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare K-star vertical lines ‚Äî kstar_vlines","text":"","code":"kstar_vlines(he, plot_params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/kstar_vlines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare K-star vertical lines ‚Äî kstar_vlines","text":"bcea object containing results Bayesian modelling economic evaluation. plot_params Plots parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":null,"dir":"Reference","previous_headings":"","what":"Create Labels for Plot ‚Äî line_labels","title":"Create Labels for Plot ‚Äî line_labels","text":"Create Labels Plot Swapped labels reference second","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create Labels for Plot ‚Äî line_labels","text":"","code":"line_labels(he, ...)  # S3 method for default line_labels(he, ref_first = TRUE, ...)  # S3 method for pairwise line_labels(he, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/line_labels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create Labels for Plot ‚Äî line_labels","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":null,"dir":"Reference","previous_headings":"","what":"Leave-one-out ranking ‚Äî loo_rank","title":"Leave-one-out ranking ‚Äî loo_rank","text":"Leave-one-ranking","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Leave-one-out ranking ‚Äî loo_rank","text":"","code":"loo_rank(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/loo_rank.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Leave-one-out ranking ‚Äî loo_rank","text":"params Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Mesh ‚Äî make.mesh","title":"Make Mesh ‚Äî make.mesh","text":"Fit using INLA methods.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Mesh ‚Äî make.mesh","text":"","code":"make.mesh(data, convex.inner, convex.outer, cutoff, max.edge)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Mesh ‚Äî make.mesh","text":"data Data convex.inner convex.inner convex.outer convex.outer cutoff Cut-value max.edge Maximum edge","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.mesh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make Mesh ‚Äî make.mesh","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":null,"dir":"Reference","previous_headings":"","what":"INLA Fitting ‚Äî make.proj","title":"INLA Fitting ‚Äî make.proj","text":"INLA Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"INLA Fitting ‚Äî make.proj","text":"","code":"make.proj(parameter, inputs, x, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"INLA Fitting ‚Äî make.proj","text":"parameter Parameter inputs Inputs x Response variable k k l l","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.proj.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"INLA Fitting ‚Äî make.proj","text":"list","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":null,"dir":"Reference","previous_headings":"","what":"Make Report ‚Äî make.report","title":"Make Report ‚Äî make.report","text":"Constructs automated report output BCEA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make Report ‚Äî make.report","text":"","code":"make.report(he, evppi = NULL, ext = \"pdf\", echo = FALSE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make Report ‚Äî make.report","text":"bcea object containing results Bayesian modelling economic evaluation. evppi object obtained output call evppi (default NULL, essential producing report). ext string text indicate extension resulting output file. Possible options \"pdf\", \"docx\". requires use pandoc, knitr rmarkdown. echo string (default FALSE) instruct whether report also include BCEA commands used produce analyses. optional argument echo set TRUE (default = FALSE), commands also printed. ... Additional parameters. example, user can specify value willingness pay wtp, used resulting analyses (default break even point). Another additional parameter user can specify name file report written. can done simply passing optional argument filename=\"NAME\". user can also specify object including PSA simulations relevant model parameters. passed function (object psa_sims), make.report automatically construct \"Info-rank plot\", probabilistic form tornado plot, based Expected Value Partial Information.  user can also specify optional argument show.tab (default=FALSE); set TRUE, table values Info-rank also shown.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Make Report ‚Äî make.report","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Make Report ‚Äî make.report","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make.report.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make Report ‚Äî make.report","text":"","code":"if (FALSE) {   data(Vaccine, package = \"BCEA\")   m <- bcea(eff, cost, ref = 2)   make.report(m) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":null,"dir":"Reference","previous_headings":"","what":"Legend Positioning ‚Äî make_legend_plotly","title":"Legend Positioning ‚Äî make_legend_plotly","text":"Legend Positioning","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Legend Positioning ‚Äî make_legend_plotly","text":"","code":"make_legend_plotly(pos_legend)"},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Legend Positioning ‚Äî make_legend_plotly","text":"pos_legend Position legend","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/make_legend_plotly.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Legend Positioning ‚Äî make_legend_plotly","text":"String","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"function deprecated. Use ceac.plot() instead. Plots probability n_int interventions analysed cost-effective.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"","code":"mce.plot(mce, pos = c(1, 0.5), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"mce output call function multi.ce(). pos Parameter set position legend. Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, TRUE indicating use first standard FALSE use second one. Default value c(1,0.5), right inside plot area. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Optional arguments. example, possible specify colours used plot. done vector color=c(...). length vector colors needs number comparators included analysis, otherwise BCEA fall back default values (black, shades grey)","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"mceplot ggplot object containing plot. Returned graph=\"ggplot2\".","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mce.plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots the probability that each intervention is the most cost-effective ‚Äî mce.plot","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  if (FALSE) { # Load the processed results of the MCMC simulation model data(Vaccine) #  # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax)       plot=FALSE            # inhibits graphical output ) # mce <- multi.ce(m)          # uses the results of the economic analysis  # mce.plot(mce,               # plots the probability of being most cost-effective       graph=\"base\")         #  using base graphics # if(require(ggplot2)){ mce.plot(mce,               # the same plot       graph=\"ggplot2\")      #  using ggplot2 instead } }"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"Runs cost-effectiveness analysis, accounts fact one intervention present market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"","code":"mixedAn(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"bcea object containing results Bayesian modelling economic evaluation. value vector market shares associated interventions. size number possible comparators. default, assumes uniform distribution intervention.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"Creates object class mixedAn, subclass bcea contains results health economic evaluation mixed analysis case: Ubar array simulations ''known-distribution'' mixed utilities, value discrete grid approximation willingness pay parameter OL.star array simulations distribution Opportunity Loss mixed strategy, value discrete grid approximation willingness pay parameter evi.star Expected Value Information mixed strategy, value discrete grid approximation willingness pay parameter mkt.shares vector market shares associated available intervention","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"Baio G, Russo P (2009). ‚Äúdecision-theoretic framework application cost-effectiveness analysis regulatory processes.‚Äù Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/mixedAn-set.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-Effectiveness Analysis When Multiple (Possibly Non-Cost-Effective)\r\nInterventions are Present on the Market ‚Äî mixedAn<-","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e, c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0, Kmax)       plot=FALSE)           # inhibits graphical output  mixedAn(m) <- NULL      # uses the results of the mixed strategy                          #  analysis (a \"mixedAn\" object)                         # the vector of market shares can be defined                          #  externally. If NULL, then each of the T                          #  interventions will have 1/T market share                         # produces the plots evi.plot(m)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":null,"dir":"Reference","previous_headings":"","what":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"Computes plots probability n_int interventions analysed cost-effective cost-effectiveness acceptability frontier.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"","code":"# S3 method for bcea multi.ce(he)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"bcea object containing results Bayesian modelling economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"Original bcea object (list) class \"pairwise\" additional: p_best_interv matrix including probability intervention cost-effective values willingness pay parameter ceaf vector containing cost-effectiveness acceptability frontier","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multi.ce.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cost-effectiveness Analysis With Multiple Comparison ‚Äî multi.ce","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)   # Runs the health economic evaluation using BCEA  m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax)       plot=FALSE            # inhibits graphical output )  mce <- multi.ce(m)          # uses the results of the economic analysis  ceac.plot(mce)  ceaf.plot(mce)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Multiple bcea Graphs ‚Äî multiplot","title":"Plot Multiple bcea Graphs ‚Äî multiplot","text":"Arrange plots grid. Sourced R graphics cookbook.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Multiple bcea Graphs ‚Äî multiplot","text":"","code":"multiplot(plotlist = NULL, cols = 1, layout_config = NULL)"},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Multiple bcea Graphs ‚Äî multiplot","text":"plotlist List ggplot objects cols Number columns layout_config Matrix plot configuration","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/multiplot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Multiple bcea Graphs ‚Äî multiplot","text":"ggplot TableGrob object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for bcea ‚Äî new_bcea","title":"Constructor for bcea ‚Äî new_bcea","text":"Constructor bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for bcea ‚Äî new_bcea","text":"","code":"new_bcea(df_ce, k)"},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for bcea ‚Äî new_bcea","text":"df_ce Dataframe simulation eff cost k Vector willingness pay values","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/new_bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for bcea ‚Äî new_bcea","text":"List object class bcea.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"Get number of lines ‚Äî num_lines","title":"Get number of lines ‚Äî num_lines","text":"Get number lines","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get number of lines ‚Äî num_lines","text":"","code":"num_lines(dat)  # S3 method for pairwise num_lines(dat)  # S3 method for bcea num_lines(dat)  # S3 method for evppi num_lines(dat)  # S3 method for default num_lines(dat)"},{"path":"https://n8thangreen.github.io/BCEA/reference/num_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get number of lines ‚Äî num_lines","text":"dat Data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":null,"dir":"Reference","previous_headings":"","what":"Automatically open pdf output using default pdf viewer ‚Äî openPDF","title":"Automatically open pdf output using default pdf viewer ‚Äî openPDF","text":"Automatically open pdf output using default pdf viewer","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Automatically open pdf output using default pdf viewer ‚Äî openPDF","text":"","code":"openPDF(file_name)"},{"path":"https://n8thangreen.github.io/BCEA/reference/openPDF.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Automatically open pdf output using default pdf viewer ‚Äî openPDF","text":"file_name String file names pdf","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"Plots single graph Cost-Effectiveness plane, Expected Incremental Benefit, CEAC EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"","code":"# S3 method for bcea plot(   x,   comparison = NULL,   wtp = 25000,   pos = FALSE,   graph = c(\"base\", \"ggplot2\"),   ... )"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2). wtp value willingness pay parameter. passed ceplane.plot(). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods ceplane.plot() eib.plot(). Please see manual pages individual functions.  Arguments like size, ICER.size plot.cri can supplied functions way. addition graph=\"ggplot2\" arguments named theme objects added plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"plot four graphical summaries health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"default position legend cost-effectiveness plane (produced ceplane.plot()) set c(1, 1.025) overriding default pos=FALSE, since multiple ggplot2 plots rendered slightly different way single plots.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Plot of the Health Economic Analysis ‚Äî plot.bcea","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA he <- bcea(        e=eff, c=cost,        # defines the variables of                               #  effectiveness and cost        ref=2,                # selects the 2nd row of (e,c)                               #  as containing the reference intervention        interventions=treats, # defines the labels to be associated                               #  with each intervention        Kmax=50000,           # maximum value possible for the willingness                               #  to pay threshold; implies that k is chosen                               #  in a grid from the interval (0,Kmax)        plot=FALSE            # does not produce graphical outputs       )  # Plots the summary plots for the \"bcea\" object m using base graphics plot(he, graph = \"base\")   # Plots the same summary plots using ggplot2 if(require(ggplot2)){ plot(he, graph = \"ggplot2\")  ##### Example of a customized plot.bcea with ggplot2 plot(he,   graph = \"ggplot2\",                                      # use ggplot2   theme = theme(plot.title=element_text(size=rel(1.25))), # theme elements must have a name   ICER_size = 1.5,                                        # hidden option in ceplane.plot   size = rel(2.5)                                         # modifies the size of k = labels   )                                                       # in ceplane.plot and eib.plot }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":null,"dir":"Reference","previous_headings":"","what":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"Summary plot health economic analysis risk aversion included.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"","code":"# S3 method for CEriskav plot(x, pos = c(0, 1), graph = c(\"base\", \"ggplot2\"), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"x object class CEriskav, subclass bcea, containing results economic analysis performed accounting risk aversion parameter (obtained output function CEriskav()). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-)match two options \"base\" \"ggplot2\". Default value \"base\". ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"list(eib,evi) two-elements named list ggplot objects containing requested plots. Returned graph=\"ggplot2\". function produces two plots risk aversion analysis. first one EIB function discrete grid approximation willingness parameter possible values risk aversion parameter, r. second one similar plot EVPI.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"Plots Expected Incremental Benefit Expected Value Perfect Information risk aversion included utility function.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.CEriskav.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plots EIB and EVPI for the Risk Aversion Case ‚Äî plot.CEriskav","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem # # Load the processed results of the MCMC simulation model data(Vaccine) #  # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000,           # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax)       plot=FALSE            # inhibits graphical output ) # # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035)  # # Run the cost-effectiveness analysis accounting for risk aversion # \\donttest{    CEriskav(m) <- r # } # # produce the plots # \\donttest{    plot(m)   # } ## Alternative options, using ggplot2 # \\donttest{    plot(m, graph = \"ggplot2\")   # }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"Plot Expected Value Partial Information Respect Set Parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"","code":"# S3 method for evppi plot(x, pos = c(0, 0.8), graph = c(\"base\", \"ggplot2\"), col = c(1, 1), ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"x object class evppi, obtained call function evppi(). pos Parameter set position legend (relevant multiple interventions, ie 2 interventions compared). Can given form string (bottom|top)(right|left) base graphics bottom|top|left|right ggplot2. can two-elements vector, specifies relative position x y axis respectively, alternatively can form logical variable, FALSE indicating use default position TRUE place bottom plot. graph string used select graphical engine use plotting. (partial-) match two options \"base\" \"ggplot2\". Default value \"base\". col Sets colour lines depicted graph. ... Arguments passed methods, graphical parameters (see par()).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"Plot base R ggplot2.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"Gianluca Baio, Andrea Berardi","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.evppi.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot Expected Value of Partial Information With Respect to a\r\nSet of Parameters ‚Äî plot.evppi","text":"","code":"if (FALSE) { data(Vaccine, package = \"BCEA\") treats <- c(\"Status quo\", \"Vaccination\")  # Run the health economic evaluation using BCEA m <- bcea(e.pts, c.pts, ref = 2, interventions = treats)  # Compute the EVPPI for a bunch of parameters inp <- createInputs(vaccine_mat)  # Compute the EVPPI using INLA/SPDE if (require(\"INLA\")) {   x0 <- evppi(m, c(\"beta.1.\" , \"beta.2.\"), input = inp$mat)      plot(x0, pos = c(0,1))    x1 <- evppi(m, c(32,48,49), input = inp$mat)   plot(x1, pos = \"topright\")    plot(x0, col = c(\"black\", \"red\"), pos = \"topright\")   plot(x0, col = c(2,3), pos = \"bottomright\")    plot(x0, pos = c(0,1), graph = \"ggplot2\")   plot(x1, pos = \"top\", graph = \"ggplot2\")    plot(x0, col = c(\"black\", \"red\"), pos = \"right\", graph = \"ggplot2\")   plot(x0, col = c(2,3), size = c(1,2), pos = \"bottom\", graph = \"ggplot2\")    plot(x0, graph = \"ggplot2\", theme = ggplot2::theme_linedraw()) }  if (FALSE)  plot(x0, col = 3, pos = \"topright\") # The vector 'col' must have the number of elements for an EVPI # colour and each of the EVPPI parameters. Forced to black }"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot ‚Äî plot.mesh","title":"Mesh Plot ‚Äî plot.mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot ‚Äî plot.mesh","text":"","code":"# S3 method for mesh plot(mesh, data, plot)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot.mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot ‚Äî plot.mesh","text":"mesh Mesh data Data plot Create plot? logical","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot Credible Intervals ‚Äî plot_eib_cri","title":"Plot Credible Intervals ‚Äî plot_eib_cri","text":"Bayesian posterior credible intervals willingness pay.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot Credible Intervals ‚Äî plot_eib_cri","text":"","code":"plot_eib_cri(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_eib_cri.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot Credible Intervals ‚Äî plot_eib_cri","text":"bcea object containing results Bayesian modelling economic evaluation. params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":null,"dir":"Reference","previous_headings":"","what":"Mesh Plot ‚Äî plot_mesh","title":"Mesh Plot ‚Äî plot_mesh","text":"Option interactively saving plot.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Mesh Plot ‚Äî plot_mesh","text":"","code":"plot_mesh(mesh, data, plot, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/plot_mesh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Mesh Plot ‚Äî plot_mesh","text":"mesh Mesh data Data plot Create plot? logical ... Additional parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":null,"dir":"Reference","previous_headings":"","what":"Gaussian Process Fitting ‚Äî post.density","title":"Gaussian Process Fitting ‚Äî post.density","text":"Gaussian Process Fitting","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Gaussian Process Fitting ‚Äî post.density","text":"","code":"post.density(hyperparams, parameter, x, input.matrix)"},{"path":"https://n8thangreen.github.io/BCEA/reference/post.density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Gaussian Process Fitting ‚Äî post.density","text":"hyperparams Hyperparameters parameter Parameters x Response variable input.matrix Input data matrix","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare Delta arrays ‚Äî prep.x","title":"Prepare Delta arrays ‚Äî prep.x","text":"Prepare Delta arrays","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare Delta arrays ‚Äî prep.x","text":"","code":"prep.x(he, seq_rows, k, l)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep.x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare Delta arrays ‚Äî prep.x","text":"bcea object containing results Bayesian modelling economic evaluation. seq_rows Rows (e,c) keep k e c? 1 2. l Columns (e,c) keep","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare output ‚Äî prepare.output","title":"Prepare output ‚Äî prepare.output","text":"Prepare output","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare output ‚Äî prepare.output","text":"","code":"prepare.output(parameters, inputs)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare output ‚Äî prepare.output","text":"parameters Parameters inputs Inputs","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prepare.output.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare output ‚Äî prepare.output","text":"name","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare CE-plane Parameters ‚Äî prep_ceplane_params","title":"Prepare CE-plane Parameters ‚Äî prep_ceplane_params","text":"ggplot format, combine user-supplied parameters defaults.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare CE-plane Parameters ‚Äî prep_ceplane_params","text":"","code":"prep_ceplane_params(he, wtp, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare CE-plane Parameters ‚Äî prep_ceplane_params","text":"bcea object containing results Bayesian modelling economic evaluation. wtp Willingness--pay ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_ceplane_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare CE-plane Parameters ‚Äî prep_ceplane_params","text":"List pf graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare EIB plot parameters ‚Äî prep_eib_params","title":"Prepare EIB plot parameters ‚Äî prep_eib_params","text":"Parameters general plotting devices.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare EIB plot parameters ‚Äî prep_eib_params","text":"","code":"prep_eib_params(he, plot.cri, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare EIB plot parameters ‚Äî prep_eib_params","text":"bcea object containing results Bayesian modelling economic evaluation. plot.cri Make title including credible interval? Logical ... Additional parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare EIB plot parameters ‚Äî prep_eib_params","text":"List graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare frontier data ‚Äî prep_frontier_data","title":"Prepare frontier data ‚Äî prep_frontier_data","text":"Prepare frontier data","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare frontier data ‚Äî prep_frontier_data","text":"","code":"prep_frontier_data(he, threshold = NULL, start.origin = TRUE)"},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare frontier data ‚Äî prep_frontier_data","text":"bcea object containing results Bayesian modelling economic evaluation. threshold Cost-effectiveness threshold .e angle line. Must >=0 NULL. start.origin frontier start ?","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/prep_frontier_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare frontier data ‚Äî prep_frontier_data","text":"List scatter.data, ceef.points, orig.avg","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea Print Method ‚Äî print.bcea","title":"bcea Print Method ‚Äî print.bcea","text":"bcea Print Method","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea Print Method ‚Äî print.bcea","text":"","code":"# S3 method for bcea print(x, digits = getOption(\"digits\"), give.attr = FALSE, no.list = TRUE, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"bcea Print Method ‚Äî print.bcea","text":"x bcea object containing results Bayesian modelling economic evaluation. digits Minimal number significant digits, see print.default(). give.attr Logical; TRUE (default), show attributes sub structures. .list Logical; TRUE, ‚Äòlist ...‚Äô class printed. ... Potential arguments.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/print.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"bcea Print Method ‚Äî print.bcea","text":"","code":"data(\"Vaccine\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention."},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Quadrant Parameters\r\nrequires just a single comparison group ‚Äî quadrant_params","title":"Quadrant Parameters\r\nrequires just a single comparison group ‚Äî quadrant_params","text":"Quadrant Parameters requires just single comparison group","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quadrant_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Quadrant Parameters\r\nrequires just a single comparison group ‚Äî quadrant_params","text":"","code":"quadrant_params(he, params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":null,"dir":"Reference","previous_headings":"","what":"Allow disabling of the cat messages ‚Äî quiet","title":"Allow disabling of the cat messages ‚Äî quiet","text":"Allow disabling cat messages","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Allow disabling of the cat messages ‚Äî quiet","text":"","code":"quiet(x)"},{"path":"https://n8thangreen.github.io/BCEA/reference/quiet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Allow disabling of the cat messages ‚Äî quiet","text":"x Object quietly return","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Choose Graphical Engine ‚Äî select_plot_type","title":"Choose Graphical Engine ‚Äî select_plot_type","text":"base R, ggplot2 plotly.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Choose Graphical Engine ‚Äî select_plot_type","text":"","code":"select_plot_type(graph)"},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Choose Graphical Engine ‚Äî select_plot_type","text":"graph Type names; string","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/select_plot_type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Choose Graphical Engine ‚Äî select_plot_type","text":"Plot ID integer 1:base R; 2:ggplot2; 3:plotly","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparisons Group ‚Äî setComparisons","title":"Set Comparisons Group ‚Äî setComparisons","text":"One alternative way set (e,c) comparison group. Simply recompute comparisons drop unwanted.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparisons Group ‚Äî setComparisons","text":"","code":"setComparisons(he, comparison)"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparisons Group ‚Äî setComparisons","text":"bcea object containing results Bayesian modelling economic evaluation. comparison Selects comparator, case two interventions analysed. Default NULL plots comparisons together. subset possible comparisons can selected (e.g., comparison=c(1,3) comparison=2).","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Comparison Group ‚Äî setComparisons_assign","title":"Set Comparison Group ‚Äî setComparisons_assign","text":"One alternative way set (e,c) comparison group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Comparison Group ‚Äî setComparisons_assign","text":"","code":"setComparisons(he) <- value  # S3 method for bcea setComparisons(he) <- value  # S3 method for default setComparisons(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Comparison Group ‚Äî setComparisons_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Comparison","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setComparisons_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Comparison Group ‚Äî setComparisons_assign","text":"bcea-type object","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Maximum Willingness to Pay ‚Äî setKmax_assign","title":"Set Maximum Willingness to Pay ‚Äî setKmax_assign","text":"Alternative way define K statistic.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Maximum Willingness to Pay ‚Äî setKmax_assign","text":"","code":"setKmax(he) <- value  # S3 method for bcea setKmax(he) <- value  # S3 method for default setKmax(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Maximum Willingness to Pay ‚Äî setKmax_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Maximum willingness pay","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setKmax_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Maximum Willingness to Pay ‚Äî setKmax_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Reference Group ‚Äî setReferenceGroup_assign","title":"Set Reference Group ‚Äî setReferenceGroup_assign","text":"Alternative way define (e,c) reference group.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Reference Group ‚Äî setReferenceGroup_assign","text":"","code":"setReferenceGroup(he) <- value  # S3 method for bcea setReferenceGroup(he) <- value  # S3 method for default setReferenceGroup(he) <- value"},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Reference Group ‚Äî setReferenceGroup_assign","text":"bcea object containing results Bayesian modelling economic evaluation. value Reference group number","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/setReferenceGroup_assign.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set Reference Group ‚Äî setReferenceGroup_assign","text":"bcea-type object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":null,"dir":"Reference","previous_headings":"","what":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"Using input form MCMC simulations run health economic model, produces summary table simulations cost-effectiveness analysis.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"","code":"sim_table(he, ...)  # S3 method for bcea sim_table(he, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"bcea object containing results Bayesian modelling economic evaluation. ... Additional arguments wtp value willingness pay threshold used summary table.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"Produces following elements: table table simulation statistics economic model names.cols vector labels associated column table wtp selected value willingness pay idx_wtp index associated selected value willingness pay threshold grid used run analysis","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/sim_table.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Table of Simulation Statistics for the Health Economic Model ‚Äî sim_table","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(e=eff,                # defines the variables of            c=cost,               # effectiveness and cost           ref=2,                # selects the 2nd row of (e, c)                                  # as containing the reference intervention           interventions=treats, # defines the labels to be associated                                  # with each intervention           Kmax=50000)           # maximum value possible for the willingness                                  # to pay threshold; implies that k is chosen                                  # in a grid from the interval (0, Kmax)  # Now can save the simulation exercise in an object using sim_table() sim_table(m,         # uses the results of the economic evaluation            wtp=25000) # selects the particular value for k #> $Table #>                  U1        U2         U*         IB2_1          OL           VI #> 1        -36.575816 -38.71760 -36.575816  -2.141786568  2.14178657  -1.75012057 #> 2        -27.925136 -27.67448 -27.674479   0.250657304  0.00000000   7.15121672 #> 3        -28.030244 -33.37394 -28.030244  -5.343696338  5.34369634   6.79545099 #> 4        -53.284080 -47.13734 -47.137342   6.146738369  0.00000000 -12.31164646 #> 5        -43.583889 -40.40469 -40.404691   3.179197609  0.00000000  -5.57899569 #> 6        -42.374564 -33.08547 -33.085465   9.289098731  0.00000000   1.74023011 #> 7        -32.442356 -37.50684 -32.442356  -5.064481574  5.06448157   2.38333915 #> 8        -51.938478 -44.82658 -44.826583   7.111894853  0.00000000 -10.00088760 #> 9        -24.636412 -23.98616 -23.986156   0.650256008  0.00000000  10.83953966 #> 10       -46.181241 -37.34967 -37.349674   8.831567129  0.00000000  -2.52397818 #> 11       -58.095859 -45.57886 -45.578863  12.516995944  0.00000000 -10.75316756 #> 12       -32.026167 -30.02358 -30.023579   2.002587855  0.00000000   4.80211644 #> 13       -24.890578 -26.54063 -24.890578  -1.650056731  1.65005673   9.93511782 #> 14       -51.083390 -34.23447 -34.234473  16.848916771  0.00000000   0.59122249 #> 15       -70.975370 -50.83694 -50.836939  20.138431719  0.00000000 -16.01124327 #> 16       -32.343576 -35.74223 -32.343576  -3.398653900  3.39865390   2.48211897 #> 17       -11.337758 -16.12295 -11.337758  -4.785191763  4.78519176  23.48793764 #> 18       -39.751995 -29.86185 -29.861855   9.890139925  0.00000000   4.96384036 #> 19       -22.209179 -20.04740 -20.047398   2.161781794  0.00000000  14.77829781 #> 20       -45.634345 -62.35286 -45.634345 -16.718519479 16.71851948 -10.80864921 #> 21       -57.546460 -58.46125 -57.546460  -0.914786605  0.91478660 -22.72076498 #> 22       -55.815147 -47.81379 -47.813791   8.001355368  0.00000000 -12.98809588 #> 23       -65.979134 -66.06333 -65.979134  -0.084192580  0.08419258 -31.15343861 #> 24       -15.835575 -26.56841 -15.835575 -10.732836457 10.73283646  18.99012037 #> 25       -21.058746 -35.30491 -21.058746 -14.246165005 14.24616500  13.76694955 #> 26       -60.520260 -42.14622 -42.146217  18.374042590  0.00000000  -7.32052195 #> 27       -28.205540 -35.51466 -28.205540  -7.309120295  7.30912030   6.62015527 #> 28       -32.546793 -35.80048 -32.546793  -3.253683844  3.25368384   2.27890260 #> 29       -16.380093 -21.60875 -16.380093  -5.228658088  5.22865809  18.44560236 #> 30       -13.457847 -14.79877 -13.457847  -1.340925281  1.34092528  21.36784849 #> 31       -65.583149 -52.36537 -52.365370  13.217778975  0.00000000 -17.53967468 #> 32       -17.028951 -19.81625 -17.028951  -2.787299396  2.78729940  17.79674468 #> 33       -26.907486 -24.45174 -24.451744   2.455741729  0.00000000  10.37395142 #> 34       -31.516522 -28.31237 -28.312370   3.204152450  0.00000000   6.51332535 #> 35       -30.040100 -30.98166 -30.040100  -0.941562516  0.94156252   4.78559531 #> 36       -41.829563 -34.43487 -34.434870   7.394693338  0.00000000   0.39082575 #> 37       -47.162775 -40.30695 -40.306946   6.855828925  0.00000000  -5.48125071 #> 38       -23.397917 -36.38016 -23.397917 -12.982237974 12.98223797  11.42777809 #> 39       -45.966264 -42.40697 -42.406965   3.559298695  0.00000000  -7.58127000 #> 40       -30.728312 -33.35439 -30.728312  -2.626079843  2.62607984   4.09738320 #> 41       -50.290582 -46.21712 -46.217119   4.073462811  0.00000000 -11.39142371 #> 42       -21.747744 -29.78883 -21.747744  -8.041085884  8.04108588  13.07795164 #> 43       -36.740928 -30.35715 -30.357146   6.383781601  0.00000000   4.46854936 #> 44       -24.905508 -39.28508 -24.905508 -14.379574532 14.37957453   9.92018707 #> 45       -53.629450 -39.94290 -39.942903  13.686546690  0.00000000  -5.11720771 #> 46       -32.731200 -28.45269 -28.452689   4.278510704  0.00000000   6.37300616 #> 47       -38.813444 -37.71039 -37.710393   1.103050871  0.00000000  -2.88469761 #> 48       -60.413052 -46.71309 -46.713085  13.699966967  0.00000000 -11.88738988 #> 49       -21.078197 -19.70921 -19.709211   1.368985396  0.00000000  15.11648388 #> 50       -31.283131 -32.32021 -31.283131  -1.037080196  1.03708020   3.54256394 #> 51       -32.450047 -41.29502 -32.450047  -8.844978195  8.84497820   2.37564867 #> 52       -53.773562 -50.06956 -50.069563   3.703998418  0.00000000 -15.24386791 #> 53       -39.256315 -31.63766 -31.637663   7.618651922  0.00000000   3.18803237 #> 54       -32.095946 -28.83281 -28.832811   3.263135708  0.00000000   5.99288467 #> 55       -41.251946 -44.26889 -41.251946  -3.016945048  3.01694505  -6.42625028 #> 56       -56.130175 -46.32287 -46.322871   9.807304205  0.00000000 -11.49717582 #> 57       -23.751435 -41.63394 -23.751435 -17.882509187 17.88250919  11.07426052 #> 58       -36.168239 -29.83677 -29.836770   6.331468540  0.00000000   4.98892489 #> 59       -31.311610 -31.06406 -31.064060   0.247550071  0.00000000   3.76163553 #> 60       -57.964741 -44.05080 -44.050797  13.913943492  0.00000000  -9.22510211 #> 61       -42.581161 -36.25501 -36.255012   6.326148865  0.00000000  -1.42931660 #> 62       -39.898754 -29.85403 -29.854033  10.044721225  0.00000000   4.97166211 #> 63       -53.781040 -44.48163 -44.481631   9.299409087  0.00000000  -9.65593570 #> 64       -69.614271 -48.36707 -48.367068  21.247202691  0.00000000 -13.54137270 #> 65       -16.468131 -25.18588 -16.468131  -8.717744390  8.71774439  18.35756408 #> 66       -19.604551 -26.60639 -19.604551  -7.001836291  7.00183629  15.22114415 #> 67       -45.405402 -43.17445 -43.174446   2.230955572  0.00000000  -8.34875062 #> 68       -26.864505 -27.54852 -26.864505  -0.684019271  0.68401927   7.96119020 #> 69       -37.607518 -36.44240 -36.442401   1.165116878  0.00000000  -1.61670555 #> 70       -38.487461 -41.94032 -38.487461  -3.452862170  3.45286217  -3.66176588 #> 71       -23.027365 -25.09422 -23.027365  -2.066858536  2.06685854  11.79833017 #> 72       -14.336558 -18.17653 -14.336558  -3.839968809  3.83996881  20.48913714 #> 73       -45.763256 -37.42407 -37.424070   8.339185994  0.00000000  -2.59837477 #> 74       -38.293169 -38.54554 -38.293169  -0.252373112  0.25237311  -3.46747371 #> 75       -50.172148 -47.85474 -47.854736   2.317412007  0.00000000 -13.02904027 #> 76       -23.809870 -22.01309 -22.013093   1.796777165  0.00000000  12.81260243 #> 77       -37.748501 -30.80923 -30.809229   6.939271892  0.00000000   4.01646583 #> 78       -23.524954 -27.07055 -23.524954  -3.545599930  3.54559993  11.30074119 #> 79       -47.140215 -32.83611 -32.836111  14.304104346  0.00000000   1.98958439 #> 80       -25.063105 -24.82709 -24.827085   0.236019793  0.00000000   9.99860992 #> 81       -37.676658 -35.71838 -35.718380   1.958278380  0.00000000  -0.89268464 #> 82       -27.961527 -37.20587 -27.961527  -9.244344164  9.24434416   6.86416789 #> 83       -26.488039 -32.34358 -26.488039  -5.855537156  5.85553716   8.33765678 #> 84       -17.818883 -28.87161 -17.818883 -11.052727716 11.05272772  17.00681255 #> 85       -39.009508 -35.86738 -35.867378   3.142130329  0.00000000  -1.04168258 #> 86       -38.533784 -31.32709 -31.327093   7.206691469  0.00000000   3.49860255 #> 87       -36.486330 -27.86485 -27.864848   8.621482106  0.00000000   6.96084695 #> 88       -59.789430 -57.11228 -57.112285   2.677145466  0.00000000 -22.28658919 #> 89       -24.151853 -26.45561 -24.151853  -2.303754714  2.30375471  10.67384233 #> 90       -16.370847 -19.69877 -16.370847  -3.327921974  3.32792197  18.45484816 #> 91       -29.632758 -29.51358 -29.513584   0.119174302  0.00000000   5.31211156 #> 92       -39.893569 -39.49942 -39.499421   0.394148010  0.00000000  -4.67372527 #> 93       -21.272298 -33.24018 -21.272298 -11.967880869 11.96788087  13.55339705 #> 94       -30.387725 -37.05681 -30.387725  -6.669085502  6.66908550   4.43797030 #> 95       -33.350816 -31.41187 -31.411871   1.938944615  0.00000000   3.41382385 #> 96       -40.029505 -35.15516 -35.155161   4.874343446  0.00000000  -0.32946605 #> 97       -17.702393 -26.73252 -17.702393  -9.030124646  9.03012465  17.12330254 #> 98       -16.127988 -24.72537 -16.127988  -8.597384356  8.59738436  18.69770758 #> 99       -50.581388 -43.91244 -43.912443   6.668944785  0.00000000  -9.08674742 #> 100      -64.221127 -53.02862 -53.028617  11.192510294  0.00000000 -18.20292172 #> 101      -36.130258 -35.79804 -35.798040   0.332217564  0.00000000  -0.97234507 #> 102      -35.362101 -34.25108 -34.251078   1.111022605  0.00000000   0.57461703 #> 103      -35.294245 -30.87344 -30.873443   4.420801341  0.00000000   3.95225210 #> 104      -57.272375 -38.28796 -38.287956  18.984419500  0.00000000  -3.46226020 #> 105      -32.479352 -25.94254 -25.942536   6.536816830  0.00000000   8.88315967 #> 106      -39.643964 -32.67332 -32.673319   6.970644816  0.00000000   2.15237657 #> 107      -31.195435 -30.59790 -30.597905   0.597530351  0.00000000   4.22779068 #> 108      -75.368749 -68.87114 -68.871141   6.497608011  0.00000000 -34.04544564 #> 109      -61.750837 -40.70930 -40.709297  21.041539633  0.00000000  -5.88360202 #> 110      -20.317452 -29.21184 -20.317452  -8.894387577  8.89438758  14.50824370 #> 111      -27.512964 -33.35347 -27.512964  -5.840509056  5.84050906   7.31273132 #> 112      -36.481115 -33.36829 -33.368290   3.112824813  0.00000000   1.45740490 #> 113      -32.820145 -38.43081 -32.820145  -5.610661383  5.61066138   2.00555042 #> 114      -36.545720 -41.35659 -36.545720  -4.810874470  4.81087447  -1.72002482 #> 115      -32.686484 -32.78388 -32.686484  -0.097400906  0.09740091   2.13921135 #> 116      -23.515710 -25.64790 -23.515710  -2.132185386  2.13218539  11.30998493 #> 117      -25.267368 -26.91231 -25.267368  -1.644946781  1.64494678   9.55832750 #> 118      -37.768277 -36.61777 -36.617767   1.150510161  0.00000000  -1.79207194 #> 119      -26.373342 -25.00879 -25.008792   1.364550375  0.00000000   9.81690373 #> 120      -29.410483 -27.68329 -27.683286   1.727196759  0.00000000   7.14240912 #> 121      -18.101748 -21.30367 -18.101748  -3.201923502  3.20192350  16.72394732 #> 122      -38.641283 -32.76287 -32.762875   5.878408065  0.00000000   2.06282036 #> 123      -33.765743 -44.97225 -33.765743 -11.206504323 11.20650432   1.05995245 #> 124      -23.770044 -32.16060 -23.770044  -8.390560525  8.39056052  11.05565098 #> 125      -30.701205 -40.14649 -30.701205  -9.445283451  9.44528345   4.12449063 #> 126      -50.582718 -46.21238 -46.212379   4.370339514  0.00000000 -11.38668364 #> 127      -20.976377 -22.33520 -20.976377  -1.358827239  1.35882724  13.84931785 #> 128      -22.305728 -22.21149 -22.211487   0.094241483  0.00000000  12.61420841 #> 129      -21.095577 -29.42030 -21.095577  -8.324721461  8.32472146  13.73011804 #> 130      -73.165434 -60.35362 -60.353618  12.811816080  0.00000000 -25.52792240 #> 131      -14.167808 -17.44756 -14.167808  -3.279747387  3.27974739  20.65788730 #> 132      -35.898002 -27.55970 -27.559703   8.338299594  0.00000000   7.26599255 #> 133      -40.270884 -39.93264 -39.932643   0.338240439  0.00000000  -5.10694782 #> 134      -38.734717 -41.17318 -38.734717  -2.438466452  2.43846645  -3.90902136 #> 135      -29.156116 -30.85489 -29.156116  -1.698769769  1.69876977   5.66957895 #> 136      -22.955637 -21.33305 -21.333052   1.622584124  0.00000000  13.49264284 #> 137      -62.252900 -42.70410 -42.704101  19.548799049  0.00000000  -7.87840533 #> 138     -102.177758 -98.97074 -98.970738   3.207020713  0.00000000 -64.14504225 #> 139      -32.796649 -32.64367 -32.643667   0.152981896  0.00000000   2.18202826 #> 140      -47.678110 -33.08435 -33.084353  14.593756554  0.00000000   1.74134216 #> 141      -21.151776 -25.22621 -21.151776  -4.074438158  4.07443816  13.67391883 #> 142      -32.122050 -30.08375 -30.083752   2.038298186  0.00000000   4.74194333 #> 143      -26.296902 -21.54334 -21.543335   4.753566466  0.00000000  13.28235983 #> 144      -43.025423 -36.88249 -36.882494   6.142929215  0.00000000  -2.05679820 #> 145      -42.776739 -36.51205 -36.512047   6.264691852  0.00000000  -1.68635164 #> 146      -37.569702 -36.68284 -36.682845   0.886856848  0.00000000  -1.85714960 #> 147      -24.209486 -27.40378 -24.209486  -3.194292535  3.19429253  10.61620946 #> 148      -47.982241 -48.38364 -47.982241  -0.401395621  0.40139562 -13.15654598 #> 149      -77.713228 -40.99638 -40.996379  36.716849222  0.00000000  -6.17068381 #> 150      -43.769330 -42.28314 -42.283136   1.486194422  0.00000000  -7.45744060 #> 151      -34.694712 -34.31585 -34.315852   0.378859966  0.00000000   0.50984346 #> 152      -41.453318 -36.63997 -36.639973   4.813345061  0.00000000  -1.81427779 #> 153      -16.005288 -31.02543 -16.005288 -15.020139478 15.02013948  18.82040685 #> 154      -25.713444 -28.85638 -25.713444  -3.142937387  3.14293739   9.11225157 #> 155      -37.226397 -35.77600 -35.776001   1.450396149  0.00000000  -0.95030597 #> 156      -55.145835 -48.32931 -48.329315   6.816520307  0.00000000 -13.50361959 #> 157      -32.063611 -34.97150 -32.063611  -2.907888768  2.90788877   2.76208425 #> 158      -26.914447 -27.30613 -26.914447  -0.391679010  0.39167901   7.91124801 #> 159      -30.465354 -34.34856 -30.465354  -3.883208099  3.88320810   4.36034114 #> 160      -17.128446 -22.01007 -17.128446  -4.881623036  4.88162304  17.69724973 #> 161      -18.982632 -21.73387 -18.982632  -2.751241073  2.75124107  15.84306340 #> 162      -29.627395 -31.66658 -29.627395  -2.039187271  2.03918727   5.19829996 #> 163      -48.904341 -46.09079 -46.090787   2.813553833  0.00000000 -11.26509137 #> 164      -18.961366 -37.81434 -18.961366 -18.852976457 18.85297646  15.86432938 #> 165      -54.053516 -40.70611 -40.706107  13.347409462  0.00000000  -5.88041128 #> 166      -23.368485 -27.95576 -23.368485  -4.587274212  4.58727421  11.45721065 #> 167      -68.408452 -55.10636 -55.106361  13.302090755  0.00000000 -20.28066590 #> 168      -14.396777 -19.08608 -14.396777  -4.689303738  4.68930374  20.42891833 #> 169      -21.877446 -22.32783 -21.877446  -0.450385931  0.45038593  12.94824965 #> 170      -52.088647 -39.23704 -39.237038  12.851609740  0.00000000  -4.41134223 #> 171      -40.602559 -38.00148 -38.001479   2.601079055  0.00000000  -3.17578416 #> 172      -90.117895 -64.17205 -64.172054  25.945841723  0.00000000 -29.34635819 #> 173      -42.967861 -41.29128 -41.291278   1.676582647  0.00000000  -6.46558312 #> 174      -18.919770 -24.94893 -18.919770  -6.029161507  6.02916151  15.90592528 #> 175      -27.905601 -24.48260 -24.482599   3.423001841  0.00000000  10.34309617 #> 176      -21.678387 -28.27715 -21.678387  -6.598765388  6.59876539  13.14730854 #> 177      -50.563328 -41.19976 -41.199759   9.363569435  0.00000000  -6.37406371 #> 178      -41.250746 -31.43356 -31.433562   9.817184236  0.00000000   3.39213366 #> 179      -26.031496 -22.41968 -22.419679   3.611816799  0.00000000  12.40601593 #> 180      -38.494198 -29.46339 -29.463386   9.030811584  0.00000000   5.36230906 #> 181      -48.839379 -48.48224 -48.482242   0.357137333  0.00000000 -13.65654643 #> 182      -56.157495 -47.01324 -47.013237   9.144257614  0.00000000 -12.18754196 #> 183      -33.728531 -28.12095 -28.120955   5.607576517  0.00000000   6.70474063 #> 184      -39.189497 -27.14658 -27.146580  12.042916152  0.00000000   7.67911492 #> 185      -31.813690 -25.48912 -25.489124   6.324565778  0.00000000   9.33657110 #> 186      -31.362853 -28.05437 -28.054367   3.308485529  0.00000000   6.77132829 #> 187      -43.777680 -38.65037 -38.650366   5.127314321  0.00000000  -3.82467024 #> 188      -57.779115 -37.64703 -37.647026  20.132089505  0.00000000  -2.82133030 #> 189      -30.089107 -27.57487 -27.574865   2.514241541  0.00000000   7.25083024 #> 190      -39.009576 -36.26101 -36.261014   2.748562204  0.00000000  -1.43531850 #> 191      -61.747320 -57.25016 -57.250158   4.497161323  0.00000000 -22.42446314 #> 192      -18.906172 -18.93203 -18.906172  -0.025862055  0.02586206  15.91952330 #> 193      -56.192418 -54.60127 -54.601272   1.591146473  0.00000000 -19.77557663 #> 194      -19.913740 -21.94147 -19.913740  -2.027729431  2.02772943  14.91195549 #> 195      -58.460399 -46.11007 -46.110069  12.350329984  0.00000000 -11.28437403 #> 196      -34.404608 -38.74589 -34.404608  -4.341280845  4.34128084   0.42108742 #> 197      -23.395499 -27.07562 -23.395499  -3.680120680  3.68012068  11.43019646 #> 198      -34.999742 -27.90102 -27.901016   7.098726119  0.00000000   6.92467946 #> 199      -37.690703 -39.77269 -37.690703  -2.081983179  2.08198318  -2.86500719 #> 200      -33.017476 -31.09734 -31.097345   1.920130630  0.00000000   3.72835039 #> 201      -16.911293 -23.02700 -16.911293  -6.115703832  6.11570383  17.91440223 #> 202      -19.342889 -23.85679 -19.342889  -4.513896800  4.51389680  15.48280598 #> 203      -27.260617 -29.87794 -27.260617  -2.617319288  2.61731929   7.56507813 #> 204      -22.972979 -21.96643 -21.966434   1.006544777  0.00000000  12.85926137 #> 205      -41.258759 -34.07300 -34.073004   7.185755468  0.00000000   0.75269171 #> 206      -30.176787 -36.90721 -30.176787  -6.730420547  6.73042055   4.64890881 #> 207      -31.819854 -36.28683 -31.819854  -4.466981046  4.46698105   3.00584182 #> 208      -15.929191 -22.93669 -15.929191  -7.007494219  7.00749422  18.89650396 #> 209      -38.234151 -38.41219 -38.234151  -0.178038501  0.17803850  -3.40845615 #> 210      -62.016744 -61.40633 -61.406325   0.610418345  0.00000000 -26.58062991 #> 211      -30.920267 -37.56909 -30.920267  -6.648825085  6.64882509   3.90542868 #> 212      -25.915982 -24.11569 -24.115694   1.800287779  0.00000000  10.71000132 #> 213      -23.821314 -33.97603 -23.821314 -10.154715820 10.15471582  11.00438179 #> 214      -13.685559 -17.78091 -13.685559  -4.095351514  4.09535151  21.14013606 #> 215      -61.026306 -55.22339 -55.223392   5.802913499  0.00000000 -20.39769707 #> 216      -27.915241 -35.54518 -27.915241  -7.629944317  7.62994432   6.91045482 #> 217      -27.912598 -24.57577 -24.575766   3.336831905  0.00000000  10.24992953 #> 218      -29.361519 -25.46925 -25.469246   3.892273301  0.00000000   9.35644952 #> 219      -12.601455 -22.99872 -12.601455 -10.397261736 10.39726174  22.22424077 #> 220      -16.385197 -24.37039 -16.385197  -7.985191325  7.98519132  18.44049820 #> 221      -16.073785 -18.56594 -16.073785  -2.492153481  2.49215348  18.75190986 #> 222      -10.511486 -15.03723 -10.511486  -4.525748435  4.52574844  24.31420906 #> 223      -36.407683 -27.70955 -27.709553   8.698129977  0.00000000   7.11614220 #> 224      -41.151571 -35.44398 -35.443981   5.707590081  0.00000000  -0.61828606 #> 225      -29.700500 -28.73529 -28.735287   0.965212388  0.00000000   6.09040790 #> 226      -19.742931 -29.96756 -19.742931 -10.224624550 10.22462455  15.08276386 #> 227      -46.338858 -49.28764 -46.338858  -2.948786348  2.94878635 -11.51316245 #> 228      -32.776804 -29.36455 -29.364546   3.412258791  0.00000000   5.46114964 #> 229      -45.075520 -42.97215 -42.972147   2.103373621  0.00000000  -8.14645149 #> 230      -34.046472 -33.24967 -33.249667   0.796805211  0.00000000   1.57602816 #> 231      -34.146439 -24.05113 -24.051131  10.095307203  0.00000000  10.77456399 #> 232      -43.595901 -42.34848 -42.348483   1.247417781  0.00000000  -7.52278809 #> 233      -35.920060 -35.71861 -35.718614   0.201446577  0.00000000  -0.89291839 #> 234      -47.450643 -47.17662 -47.176621   0.274022030  0.00000000 -12.35092529 #> 235      -50.188006 -51.37649 -50.188006  -1.188482307  1.18848231 -15.36231072 #> 236      -23.931307 -26.88720 -23.931307  -2.955896243  2.95589624  10.89438826 #> 237      -52.131349 -42.23503 -42.235033   9.896315871  0.00000000  -7.40933801 #> 238      -56.067014 -54.62011 -54.620112   1.446901481  0.00000000 -19.79441696 #> 239      -34.180301 -31.95235 -31.952347   2.227953995  0.00000000   2.87334799 #> 240      -25.680040 -26.74639 -25.680040  -1.066353927  1.06635393   9.14565488 #> 241      -51.606925 -42.52557 -42.525569   9.081355645  0.00000000  -7.69987417 #> 242      -61.374696 -51.85838 -51.858384   9.516311985  0.00000000 -17.03268867 #> 243      -39.649164 -39.61088 -39.610881   0.038283277  0.00000000  -4.78518541 #> 244      -36.829876 -32.59939 -32.599386   4.230490439  0.00000000   2.22630929 #> 245      -19.435898 -25.15988 -19.435898  -5.723984879  5.72398488  15.38979749 #> 246      -39.286065 -35.83978 -35.839778   3.446287062  0.00000000  -1.01408268 #> 247      -47.771150 -34.11548 -34.115483  13.655666492  0.00000000   0.71021227 #> 248      -65.590027 -56.88299 -56.882988   8.707038476  0.00000000 -22.05729271 #> 249      -30.152605 -21.74218 -21.742181   8.410423951  0.00000000  13.08351424 #> 250      -35.675897 -43.89898 -35.675897  -8.223085552  8.22308555  -0.85020199 #> 251      -36.380570 -39.16491 -36.380570  -2.784339887  2.78433989  -1.55487424 #> 252      -54.790452 -61.76705 -54.790452  -6.976597383  6.97659738 -19.96475647 #> 253      -43.974843 -40.20818 -40.208177   3.766665475  0.00000000  -5.38248200 #> 254      -28.054282 -29.91975 -28.054282  -1.865465062  1.86546506   6.77141372 #> 255      -41.701393 -37.42450 -37.424497   4.276895637  0.00000000  -2.59880216 #> 256      -42.688927 -34.12267 -34.122671   8.566255956  0.00000000   0.70302429 #> 257      -50.069982 -34.39023 -34.390228  15.679753865  0.00000000   0.43546707 #> 258      -12.441513 -16.35756 -12.441513  -3.916049188  3.91604919  22.38418275 #> 259      -36.842453 -27.35969 -27.359686   9.482767193  0.00000000   7.46600928 #> 260      -41.597584 -47.92649 -41.597584  -6.328903327  6.32890333  -6.77188870 #> 261      -20.124542 -25.20399 -20.124542  -5.079444699  5.07944470  14.70115348 #> 262      -20.024566 -25.57924 -20.024566  -5.554677596  5.55467760  14.80112898 #> 263      -41.074834 -44.20127 -41.074834  -3.126438694  3.12643869  -6.24913866 #> 264      -32.008027 -30.98090 -30.980899   1.027128054  0.00000000   3.84479635 #> 265      -74.782290 -51.80566 -51.805658  22.976631768  0.00000000 -16.97996260 #> 266      -29.614411 -29.25661 -29.256607   0.357803879  0.00000000   5.56908790 #> 267      -18.239860 -24.96996 -18.239860  -6.730099974  6.73009997  16.58583539 #> 268      -39.447020 -37.86202 -37.862022   1.584997404  0.00000000  -3.03632712 #> 269      -32.222699 -33.34726 -32.222699  -1.124558449  1.12455845   2.60299646 #> 270      -49.926383 -65.12687 -49.926383 -15.200488058 15.20048806 -15.10068795 #> 271      -20.049289 -23.54031 -20.049289  -3.491016558  3.49101656  14.77640605 #> 272      -21.018344 -28.16049 -21.018344  -7.142147881  7.14214788  13.80735176 #> 273      -23.063988 -24.53408 -23.063988  -1.470093400  1.47009340  11.76170692 #> 274      -45.332469 -36.85232 -36.852319   8.480149641  0.00000000  -2.02662382 #> 275      -22.501973 -25.81458 -22.501973  -3.312611612  3.31261161  12.32372271 #> 276      -17.259137 -21.34312 -17.259137  -4.083985427  4.08398543  17.56655831 #> 277      -64.258591 -53.43504 -53.435038  10.823552492  0.00000000 -18.60934300 #> 278      -31.883318 -33.88484 -31.883318  -2.001526607  2.00152661   2.94237747 #> 279      -40.748800 -43.27253 -40.748800  -2.523732025  2.52373202  -5.92310437 #> 280      -25.009024 -35.51786 -25.009024 -10.508837099 10.50883710   9.81667094 #> 281      -22.546125 -31.35496 -22.546125  -8.808830889  8.80883089  12.27957070 #> 282      -31.998409 -39.03063 -31.998409  -7.032219381  7.03221938   2.82728644 #> 283      -44.260399 -44.20954 -44.209535   0.050864295  0.00000000  -9.38383985 #> 284      -26.199287 -25.56317 -25.563171   0.636116743  0.00000000   9.26252477 #> 285      -59.461220 -58.10035 -58.100354   1.360865735  0.00000000 -23.27465887 #> 286      -14.259087 -17.21384 -14.259087  -2.954756122  2.95475612  20.56660792 #> 287      -33.132436 -32.33251 -32.332505   0.799930148  0.00000000   2.49318990 #> 288      -42.442961 -40.94860 -40.948604   1.494357113  0.00000000  -6.12290901 #> 289      -42.438076 -34.12373 -34.123731   8.314344832  0.00000000   0.70196443 #> 290      -46.859489 -52.73898 -46.859489  -5.879495688  5.87949569 -12.03379334 #> 291      -19.478622 -16.80279 -16.802794   2.675827412  0.00000000  18.02290103 #> 292      -18.614943 -27.32995 -18.614943  -8.715003836  8.71500384  16.21075223 #> 293      -20.389878 -24.82569 -20.389878  -4.435814335  4.43581434  14.43581704 #> 294      -29.967894 -24.74372 -24.743717   5.224177058  0.00000000  10.08197814 #> 295      -34.107373 -37.55141 -34.107373  -3.444035849  3.44403585   0.71832261 #> 296      -35.955811 -38.87673 -35.955811  -2.920915654  2.92091565  -1.13011518 #> 297      -34.015321 -29.74825 -29.748250   4.267070870  0.00000000   5.07744504 #> 298      -19.207198 -25.80459 -19.207198  -6.597394081  6.59739408  15.61849720 #> 299      -31.550028 -28.64972 -28.649724   2.900304696  0.00000000   6.17597181 #> 300      -41.950825 -35.79362 -35.793622   6.157202273  0.00000000  -0.96792716 #> 301      -52.712579 -40.65718 -40.657179  12.055400063  0.00000000  -5.83148406 #> 302      -20.811115 -23.16218 -20.811115  -2.351063402  2.35106340  14.01457994 #> 303      -28.638310 -28.22114 -28.221139   0.417171118  0.00000000   6.60455598 #> 304      -22.340743 -24.78799 -22.340743  -2.447245561  2.44724556  12.48495232 #> 305      -41.128228 -34.89209 -34.892092   6.236136116  0.00000000  -0.06639652 #> 306      -42.468376 -45.30761 -42.468376  -2.839235666  2.83923567  -7.64268018 #> 307      -22.202042 -26.91001 -22.202042  -4.707966491  4.70796649  12.62365333 #> 308      -26.107776 -27.16343 -26.107776  -1.055651991  1.05565199   8.71791941 #> 309      -32.469032 -44.21170 -32.469032 -11.742664401 11.74266440   2.35666293 #> 310      -36.250363 -27.93476 -27.934761   8.315602547  0.00000000   6.89093467 #> 311      -26.763658 -21.58826 -21.588260   5.175398431  0.00000000  13.23743556 #> 312      -24.996491 -26.19501 -24.996491  -1.198523091  1.19852309   9.82920469 #> 313      -27.690160 -25.57636 -25.576358   2.113801910  0.00000000   9.24933684 #> 314      -38.923543 -41.43007 -38.923543  -2.506523568  2.50652357  -4.09784762 #> 315      -28.741809 -31.57840 -28.741809  -2.836589206  2.83658921   6.08388590 #> 316      -29.244280 -32.08937 -29.244280  -2.845092102  2.84509210   5.58141565 #> 317      -19.496219 -23.40420 -19.496219  -3.907984137  3.90798414  15.32947677 #> 318      -22.919748 -24.63573 -22.919748  -1.715978985  1.71597898  11.90594771 #> 319      -28.403514 -39.82735 -28.403514 -11.423839350 11.42383935   6.42218151 #> 320      -43.053489 -43.17806 -43.053489  -0.124574167  0.12457417  -8.22779408 #> 321      -43.058924 -38.03332 -38.033318   5.025605587  0.00000000  -3.20762281 #> 322      -31.296193 -29.49555 -29.495549   1.800643580  0.00000000   5.33014640 #> 323      -57.015967 -56.05437 -56.054371   0.961595531  0.00000000 -21.22867606 #> 324      -50.463378 -40.83999 -40.839991   9.623386840  0.00000000  -6.01429566 #> 325      -32.891971 -27.40295 -27.402946   5.489024629  0.00000000   7.42274918 #> 326      -44.872848 -45.85009 -44.872848  -0.977243615  0.97724361 -10.04715306 #> 327      -31.603711 -37.08808 -31.603711  -5.484371642  5.48437164   3.22198460 #> 328      -50.686113 -37.78835 -37.788346  12.897766751  0.00000000  -2.96265048 #> 329      -70.509803 -59.14535 -59.145348  11.364455443  0.00000000 -24.31965248 #> 330      -31.584861 -27.75038 -27.750382   3.834479006  0.00000000   7.07531325 #> 331      -32.064159 -23.37741 -23.377405   8.686753892  0.00000000  11.44829023 #> 332      -43.120802 -35.82440 -35.824396   7.296406733  0.00000000  -0.99870040 #> 333      -24.313646 -31.25519 -24.313646  -6.941544782  6.94154478  10.51204920 #> 334      -32.396823 -29.20847 -29.208473   3.188349511  0.00000000   5.61722225 #> 335      -56.096359 -39.74006 -39.740064  16.356295123  0.00000000  -4.91436876 #> 336      -32.651208 -29.49728 -29.497283   3.153925334  0.00000000   5.32841272 #> 337      -24.059658 -25.91636 -24.059658  -1.856704225  1.85670423  10.76603722 #> 338      -33.530768 -30.65437 -30.654375   2.876392962  0.00000000   4.17132057 #> 339      -31.755923 -34.45332 -31.755923  -2.697401099  2.69740110   3.06977256 #> 340      -47.225676 -34.98134 -34.981336  12.244339439  0.00000000  -0.15564111 #> 341      -27.860413 -32.97466 -27.860413  -5.114249763  5.11424976   6.96528195 #> 342      -38.499073 -37.57375 -37.573754   0.925319176  0.00000000  -2.74805827 #> 343      -47.954119 -40.59726 -40.597258   7.356860262  0.00000000  -5.77156298 #> 344      -35.944491 -33.87800 -33.878001   2.066490141  0.00000000   0.94769468 #> 345      -33.596701 -33.91103 -33.596701  -0.314332735  0.31433273   1.22899402 #> 346      -22.280314 -26.56911 -22.280314  -4.288799504  4.28879950  12.54538156 #> 347      -39.555872 -41.54945 -39.555872  -1.993574945  1.99357495  -4.73017701 #> 348      -55.034614 -48.50193 -48.501935   6.532678848  0.00000000 -13.67623952 #> 349      -49.230797 -36.65947 -36.659466  12.571331161  0.00000000  -1.83377034 #> 350      -12.644273 -21.04204 -12.644273  -8.397770304  8.39777030  22.18142266 #> 351      -30.990696 -29.61684 -29.616842   1.373854235  0.00000000   5.20885308 #> 352      -38.940899 -39.97423 -38.940899  -1.033329411  1.03332941  -4.11520320 #> 353      -31.382038 -35.37906 -31.382038  -3.997017403  3.99701740   3.44365726 #> 354      -41.208441 -40.70267 -40.702675   0.505766361  0.00000000  -5.87697955 #> 355      -19.350505 -32.09608 -19.350505 -12.745570485 12.74557049  15.47519080 #> 356      -25.740305 -35.40820 -25.740305  -9.667899262  9.66789926   9.08539054 #> 357       -9.246346 -22.48854  -9.246346 -13.242193568 13.24219357  25.57934912 #> 358      -16.938204 -25.20375 -16.938204  -8.265544251  8.26554425  17.88749154 #> 359      -35.260784 -39.01318 -35.260784  -3.752396471  3.75239647  -0.43508874 #> 360      -62.746500 -57.59926 -57.599258   5.147242088  0.00000000 -22.77356263 #> 361      -49.778597 -33.67946 -33.679464  16.099133318  0.00000000   1.14623158 #> 362      -45.650314 -37.02326 -37.023262   8.627052008  0.00000000  -2.19756679 #> 363      -35.782719 -33.61093 -33.610927   2.171792259  0.00000000   1.21476828 #> 364      -32.287150 -29.66346 -29.663461   2.623688445  0.00000000   5.16223403 #> 365      -39.312632 -35.53679 -35.536787   3.775844792  0.00000000  -0.71109140 #> 366      -14.198326 -22.24242 -14.198326  -8.044090081  8.04409008  20.62736974 #> 367      -49.789702 -50.36333 -49.789702  -0.573627731  0.57362773 -14.96400622 #> 368      -81.765618 -58.65676 -58.656758  23.108860391  0.00000000 -23.83106243 #> 369      -55.325002 -55.97285 -55.325002  -0.647843726  0.64784373 -20.49930643 #> 370      -47.976465 -39.06683 -39.066827   8.909638469  0.00000000  -4.24113159 #> 371      -54.059432 -48.77017 -48.770172   5.289259595  0.00000000 -13.94447687 #> 372      -24.814477 -28.90409 -24.814477  -4.089611144  4.08961114  10.01121862 #> 373      -33.987011 -29.93844 -29.938443   4.048568401  0.00000000   4.88725247 #> 374      -31.932440 -27.82212 -27.822119   4.110320991  0.00000000   7.00357662 #> 375      -27.347021 -24.66487 -24.664867   2.682154158  0.00000000  10.16082877 #> 376      -50.360806 -25.64666 -25.646664  24.714142166  0.00000000   9.17903129 #> 377      -45.970278 -38.66360 -38.663601   7.306676348  0.00000000  -3.83790597 #> 378      -25.344551 -21.10959 -21.109585   4.234966186  0.00000000  13.71611002 #> 379      -28.540140 -25.40397 -25.403971   3.136169461  0.00000000   9.42172473 #> 380      -37.732509 -34.34299 -34.342989   3.389519784  0.00000000   0.48270653 #> 381      -29.206918 -37.81110 -29.206918  -8.604185443  8.60418544   5.61877702 #> 382      -23.418369 -25.85965 -23.418369  -2.441277695  2.44127770  11.40732651 #> 383      -55.028860 -47.11273 -47.112727   7.916133012  0.00000000 -12.28703158 #> 384      -23.246556 -21.37414 -21.374141   1.872415454  0.00000000  13.45155436 #> 385      -31.636229 -24.90962 -24.909618   6.726611124  0.00000000   9.91607769 #> 386      -50.167913 -29.78429 -29.784293  20.383619849  0.00000000   5.04140219 #> 387      -39.692559 -44.20035 -39.692559  -4.507787153  4.50778715  -4.86686338 #> 388      -22.346325 -30.05385 -22.346325  -7.707529047  7.70752905  12.47937068 #> 389      -16.676861 -24.41739 -16.676861  -7.740526097  7.74052610  18.14883472 #> 390      -42.717065 -43.16157 -42.717065  -0.444504585  0.44450459  -7.89136925 #> 391      -29.530537 -27.16250 -27.162499   2.368037859  0.00000000   7.66319639 #> 392      -79.031761 -57.22100 -57.220996  21.810765338  0.00000000 -22.39530035 #> 393      -19.685308 -18.21925 -18.219248   1.466059847  0.00000000  16.60644709 #> 394      -48.051896 -45.03667 -45.036672   3.015223736  0.00000000 -10.21097702 #> 395      -37.208094 -39.48890 -37.208094  -2.280803070  2.28080307  -2.38239862 #> 396      -21.430354 -26.14106 -21.430354  -4.710710084  4.71071008  13.39534092 #> 397      -25.892068 -25.14809 -25.148092   0.743976140  0.00000000   9.67760302 #> 398      -30.339236 -36.43538 -30.339236  -6.096139196  6.09613920   4.48645934 #> 399      -49.472745 -43.28537 -43.285370   6.187375057  0.00000000  -8.45967511 #> 400      -81.406187 -66.37244 -66.372443  15.033743883  0.00000000 -31.54674796 #> 401      -49.845547 -37.83498 -37.834984  12.010562979  0.00000000  -3.00928863 #> 402      -42.432483 -37.88792 -37.887921   4.544561884  0.00000000  -3.06222591 #> 403      -31.889823 -26.26984 -26.269839   5.619983608  0.00000000   8.55585627 #> 404      -36.572721 -32.39904 -32.399040   4.173681238  0.00000000   2.42665507 #> 405      -40.007454 -35.28086 -35.280859   4.726595642  0.00000000  -0.45516322 #> 406      -54.700371 -46.84058 -46.840582   7.859789199  0.00000000 -12.01488650 #> 407      -78.640668 -55.53126 -55.531261  23.109407132  0.00000000 -20.70556561 #> 408      -28.299021 -28.93950 -28.299021  -0.640483025  0.64048303   6.52667474 #> 409      -16.418955 -26.99987 -16.418955 -10.580915682 10.58091568  18.40674055 #> 410      -51.471282 -48.44679 -48.446791   3.024490666  0.00000000 -13.62109591 #> 411      -28.012699 -23.46549 -23.465491   4.547207532  0.00000000  11.36020422 #> 412      -16.616223 -18.02326 -16.616223  -1.407036011  1.40703601  18.20947269 #> 413      -29.901845 -33.98655 -29.901845  -4.084705725  4.08470572   4.92385010 #> 414      -55.402874 -38.26707 -38.267068  17.135806209  0.00000000  -3.44137225 #> 415      -57.093138 -43.13065 -43.130650  13.962488052  0.00000000  -8.30495492 #> 416      -17.373659 -20.58657 -17.373659  -3.212913826  3.21291383  17.45203646 #> 417      -27.521073 -47.86510 -27.521073 -20.344022608 20.34402261   7.30462258 #> 418      -49.127966 -46.01121 -46.011206   3.116760562  0.00000000 -11.18551046 #> 419      -52.097467 -43.33990 -43.339900   8.757566748  0.00000000  -8.51420482 #> 420      -27.902277 -25.21048 -25.210480   2.691796580  0.00000000   9.61521515 #> 421      -24.650320 -25.77015 -24.650320  -1.119827590  1.11982759  10.17537529 #> 422      -55.579027 -54.14658 -54.146575   1.432451835  0.00000000 -19.32087990 #> 423      -23.096051 -25.36241 -23.096051  -2.266360506  2.26636051  11.72964401 #> 424      -31.821541 -29.13669 -29.136692   2.684849374  0.00000000   5.68900342 #> 425      -25.450778 -26.74546 -25.450778  -1.294682921  1.29468292   9.37491726 #> 426      -25.613111 -35.20871 -25.613111  -9.595596818  9.59559682   9.21258406 #> 427      -41.511321 -39.71511 -39.715109   1.796211778  0.00000000  -4.88941393 #> 428      -33.201303 -29.11487 -29.114865   4.086438074  0.00000000   5.71083010 #> 429      -32.869316 -34.06579 -32.869316  -1.196477566  1.19647757   1.95637910 #> 430      -57.469810 -58.10366 -57.469810  -0.633854714  0.63385471 -22.64411479 #> 431      -26.058407 -27.81994 -26.058407  -1.761536995  1.76153699   8.76728819 #> 432      -40.502840 -34.95145 -34.951446   5.551394071  0.00000000  -0.12575042 #> 433      -31.553229 -26.06939 -26.069391   5.483838019  0.00000000   8.75630441 #> 434      -19.950067 -21.08426 -19.950067  -1.134196530  1.13419653  14.87562789 #> 435      -14.710720 -26.01829 -14.710720 -11.307565839 11.30756584  20.11497557 #> 436      -32.903108 -32.23274 -32.232740   0.670367887  0.00000000   2.59295535 #> 437      -20.954125 -22.47452 -20.954125  -1.520399179  1.52039918  13.87157009 #> 438      -11.149409 -16.39406 -11.149409  -5.244653723  5.24465372  23.67628672 #> 439      -17.252369 -20.04964 -17.252369  -2.797267692  2.79726769  17.57332641 #> 440      -62.760848 -46.73360 -46.733598  16.027250661  0.00000000 -11.90790239 #> 441      -21.800120 -30.13001 -21.800120  -8.329888857  8.32988886  13.02557577 #> 442      -95.389259 -76.24788 -76.247879  19.141379825  0.00000000 -41.42218380 #> 443      -55.712193 -52.20574 -52.205744   3.506448211  0.00000000 -17.38004905 #> 444      -55.178720 -45.73175 -45.731750   9.446970087  0.00000000 -10.90605468 #> 445      -32.839359 -28.89622 -28.896221   3.943137637  0.00000000   5.92947386 #> 446      -24.649549 -25.26327 -24.649549  -0.613723443  0.61372344  10.17614653 #> 447      -36.971162 -23.88754 -23.887538  13.083624109  0.00000000  10.93815724 #> 448      -29.037690 -28.67895 -28.678947   0.358742647  0.00000000   6.14674814 #> 449      -40.832560 -27.71767 -27.717672  13.114887551  0.00000000   7.10802291 #> 450      -29.636855 -25.87993 -25.879925   3.756929283  0.00000000   8.94576984 #> 451      -36.305597 -44.01786 -36.305597  -7.712263059  7.71226306  -1.47990135 #> 452      -30.596616 -24.14990 -24.149899   6.446717220  0.00000000  10.67579625 #> 453      -19.958204 -19.83291 -19.832907   0.125297098  0.00000000  14.99278807 #> 454      -26.404944 -24.17396 -24.173964   2.230980087  0.00000000  10.65173122 #> 455      -52.539123 -39.97742 -39.977419  12.561704666  0.00000000  -5.15172326 #> 456      -12.726405 -14.70937 -12.726405  -1.982960923  1.98296092  22.09928988 #> 457      -41.966986 -36.12576 -36.125760   5.841225917  0.00000000  -1.30006435 #> 458      -49.221621 -43.93022 -43.930220   5.291400380  0.00000000  -9.10452504 #> 459      -51.010860 -41.85978 -41.859779   9.151080634  0.00000000  -7.03408355 #> 460      -18.244587 -25.82940 -18.244587  -7.584817069  7.58481707  16.58110850 #> 461      -34.542478 -31.56323 -31.563234   2.979243557  0.00000000   3.26246087 #> 462      -21.038178 -30.14352 -21.038178  -9.105341782  9.10534178  13.78751778 #> 463      -30.912730 -31.20278 -30.912730  -0.290050790  0.29005079   3.91296558 #> 464      -31.983339 -23.85948 -23.859477   8.123862392  0.00000000  10.96621865 #> 465      -35.953317 -37.97291 -35.953317  -2.019590528  2.01959053  -1.12762189 #> 466      -48.813443 -41.14937 -41.149374   7.664068800  0.00000000  -6.32367901 #> 467      -44.051664 -38.05154 -38.051544   6.000120553  0.00000000  -3.22584827 #> 468      -28.167921 -29.27722 -28.167921  -1.109302186  1.10930219   6.65777471 #> 469      -19.199365 -22.68428 -19.199365  -3.484912937  3.48491294  15.62633040 #> 470      -24.635656 -27.00883 -24.635656  -2.373173192  2.37317319  10.19003896 #> 471      -38.373566 -46.28222 -38.373566  -7.908654651  7.90865465  -3.54787049 #> 472      -53.326629 -36.00996 -36.009959  17.316669848  0.00000000  -1.18426370 #> 473      -23.741179 -31.99491 -23.741179  -8.253726252  8.25372625  11.08451643 #> 474      -36.829587 -38.02244 -36.829587  -1.192854218  1.19285422  -2.00389135 #> 475      -27.079921 -30.00773 -27.079921  -2.927805002  2.92780500   7.74577423 #> 476      -58.604785 -50.27755 -50.277550   8.327235040  0.00000000 -15.45185480 #> 477      -33.875914 -35.24758 -33.875914  -1.371664054  1.37166405   0.94978140 #> 478      -48.180093 -45.77968 -45.779680   2.400413477  0.00000000 -10.95398433 #> 479      -58.055256 -57.79063 -57.790631   0.264624996  0.00000000 -22.96493602 #> 480      -20.745155 -22.03950 -20.745155  -1.294341905  1.29434190  14.08054021 #> 481      -29.394351 -33.04709 -29.394351  -3.652734751  3.65273475   5.43134450 #> 482      -50.458030 -39.35587 -39.355874  11.102156756  0.00000000  -4.53017832 #> 483      -29.186569 -29.40917 -29.186569  -0.222597892  0.22259789   5.63912585 #> 484      -26.636414 -35.83730 -26.636414  -9.200887691  9.20088769   8.18928094 #> 485      -49.251887 -36.05800 -36.058001  13.193885882  0.00000000  -1.23230553 #> 486      -30.373973 -32.66693 -30.373973  -2.292957958  2.29295796   4.45172280 #> 487      -19.365675 -27.50661 -19.365675  -8.140930215  8.14093022  15.46002007 #> 488      -79.678864 -65.98274 -65.982739  13.696125048  0.00000000 -31.15704342 #> 489      -27.824948 -32.00825 -27.824948  -4.183297937  4.18329794   7.00074737 #> 490      -67.928752 -49.55983 -49.559834  18.368918604  0.00000000 -14.73413846 #> 491      -34.965478 -30.34624 -30.346244   4.619233456  0.00000000   4.47945084 #> 492      -59.071931 -31.72459 -31.724587  27.347344885  0.00000000   3.10110882 #> 493      -38.043872 -39.89268 -38.043872  -1.848808145  1.84880814  -3.21817653 #> 494      -41.815217 -51.60690 -41.815217  -9.791682772  9.79168277  -6.98952132 #> 495     -120.680862 -78.58210 -78.582101  42.098761734  0.00000000 -43.75640532 #> 496      -24.284295 -26.02300 -24.284295  -1.738704608  1.73870461  10.54140080 #> 497      -24.482993 -31.73269 -24.482993  -7.249693601  7.24969360  10.34270189 #> 498      -56.936619 -47.09342 -47.093419   9.843200012  0.00000000 -12.26772320 #> 499      -38.781073 -28.19426 -28.194261  10.586812386  0.00000000   6.63143442 #> 500      -43.044038 -48.45335 -43.044038  -5.409310461  5.40931046  -8.21834295 #> 501      -47.118025 -38.70137 -38.701368   8.416656399  0.00000000  -3.87567282 #> 502      -30.433807 -26.01824 -26.018236   4.415570891  0.00000000   8.80745959 #> 503      -39.637049 -36.39772 -36.397716   3.239332499  0.00000000  -1.57202109 #> 504      -23.398597 -24.03067 -23.398597  -0.632077148  0.63207715  11.42709806 #> 505      -31.950985 -36.18565 -31.950985  -4.234668662  4.23466866   2.87471040 #> 506      -22.899612 -25.75646 -22.899612  -2.856843470  2.85684347  11.92608294 #> 507      -23.184337 -27.45873 -23.184337  -4.274390473  4.27439047  11.64135790 #> 508      -41.264892 -27.34487 -27.344871  13.920021132  0.00000000   7.48082417 #> 509      -45.728247 -44.07674 -44.076736   1.651511089  0.00000000  -9.25104052 #> 510      -35.348664 -38.89359 -35.348664  -3.544929621  3.54492962  -0.52296864 #> 511      -53.422793 -33.20665 -33.206652  20.216140951  0.00000000   1.61904334 #> 512      -30.229810 -33.53853 -30.229810  -3.308720551  3.30872055   4.59588559 #> 513      -17.233300 -29.85435 -17.233300 -12.621052348 12.62105235  17.59239533 #> 514      -48.343930 -56.03856 -48.343930  -7.694627015  7.69462701 -13.51823466 #> 515      -11.692667 -26.45538 -11.692667 -14.762714168 14.76271417  23.13302788 #> 516      -35.266323 -30.39890 -30.398896   4.867427193  0.00000000   4.42679976 #> 517      -24.193989 -25.91659 -24.193989  -1.722598965  1.72259896  10.63170664 #> 518      -30.529550 -24.99442 -24.994425   5.535125631  0.00000000   9.83127047 #> 519      -32.097426 -28.27028 -28.270282   3.827143794  0.00000000   6.55541339 #> 520      -23.895784 -27.53167 -23.895784  -3.635890978  3.63589098  10.92991137 #> 521      -50.181270 -47.26890 -47.268895   2.912374830  0.00000000 -12.44319983 #> 522      -28.210152 -28.65683 -28.210152  -0.446682444  0.44668244   6.61554371 #> 523      -25.025716 -31.46432 -25.025716  -6.438601917  6.43860192   9.79997960 #> 524      -17.317966 -19.47380 -17.317966  -2.155830210  2.15583021  17.50772956 #> 525      -40.595936 -35.89790 -35.897898   4.698038545  0.00000000  -1.07220253 #> 526      -32.815013 -41.01822 -32.815013  -8.203203816  8.20320382   2.01068211 #> 527      -25.756220 -25.44908 -25.449085   0.307135174  0.00000000   9.37661044 #> 528      -32.843404 -35.77822 -32.843404  -2.934817445  2.93481744   1.98229142 #> 529      -29.495385 -28.35029 -28.350286   1.145098050  0.00000000   6.47540887 #> 530      -34.054072 -36.33171 -34.054072  -2.277641152  2.27764115   0.77162346 #> 531      -14.904893 -23.73310 -14.904893  -8.828210720  8.82821072  19.92080213 #> 532      -37.696372 -36.20627 -36.206269   1.490103016  0.00000000  -1.38057354 #> 533      -20.261330 -17.97789 -17.977894   2.283435258  0.00000000  16.84780103 #> 534      -36.123215 -34.71312 -34.713124   1.410091032  0.00000000   0.11257152 #> 535      -34.169734 -27.43623 -27.436230   6.733503642  0.00000000   7.38946542 #> 536      -29.920431 -24.72467 -24.724668   5.195762399  0.00000000  10.10102711 #> 537      -18.960910 -26.91727 -18.960910  -7.956363094  7.95636309  15.86478577 #> 538      -31.468259 -35.28228 -31.468259  -3.814020774  3.81402077   3.35743675 #> 539      -40.019154 -40.08706 -40.019154  -0.067909089  0.06790909  -5.19345893 #> 540      -20.420177 -20.73350 -20.420177  -0.313321811  0.31332181  14.40551872 #> 541      -38.425118 -33.81820 -33.818195   4.606923159  0.00000000   1.00750023 #> 542      -22.726644 -34.96936 -22.726644 -12.242711856 12.24271186  12.09905167 #> 543      -43.650991 -57.94800 -43.650991 -14.297004944 14.29700494  -8.82529531 #> 544      -33.524695 -25.90479 -25.904795   7.619900716  0.00000000   8.92090057 #> 545      -29.273676 -32.55336 -29.273676  -3.279682206  3.27968221   5.55201952 #> 546      -29.749748 -29.43461 -29.434609   0.315139614  0.00000000   5.39108654 #> 547      -42.449118 -35.57179 -35.571793   6.877324604  0.00000000  -0.74609796 #> 548      -29.374824 -33.96608 -29.374824  -4.591257048  4.59125705   5.45087179 #> 549      -62.199825 -49.38354 -49.383541  12.816283206  0.00000000 -14.55784612 #> 550      -25.731060 -29.90379 -25.731060  -4.172726886  4.17272689   9.09463502 #> 551      -32.200386 -29.07722 -29.077218   3.123167161  0.00000000   5.74847694 #> 552      -29.533295 -34.01888 -29.533295  -4.485583952  4.48558395   5.29240057 #> 553      -20.618117 -29.72542 -20.618117  -9.107298607  9.10729861  14.20757784 #> 554      -31.613999 -44.34537 -31.613999 -12.731371587 12.73137159   3.21169634 #> 555      -34.124607 -21.34105 -21.341046  12.783560443  0.00000000  13.48464887 #> 556      -41.083825 -37.24803 -37.248029   3.835795764  0.00000000  -2.42233394 #> 557      -33.369162 -27.33603 -27.336030   6.033132212  0.00000000   7.48966567 #> 558      -31.057345 -35.79678 -31.057345  -4.739433177  4.73943318   3.76834989 #> 559      -29.123738 -27.66119 -27.661192   1.462545826  0.00000000   7.16450347 #> 560      -14.947314 -22.38510 -14.947314  -7.437786460  7.43778646  19.87838174 #> 561      -33.283995 -31.43934 -31.439336   1.844659717  0.00000000   3.38635974 #> 562      -51.845794 -37.46412 -37.464121  14.381673305  0.00000000  -2.63842524 #> 563      -32.516771 -37.84996 -32.516771  -5.333192666  5.33319267   2.30892417 #> 564      -53.277806 -45.74146 -45.741455   7.536350624  0.00000000 -10.91575973 #> 565      -28.557342 -40.49259 -28.557342 -11.935250065 11.93525007   6.26835347 #> 566      -57.916324 -42.93395 -42.933955  14.982369209  0.00000000  -8.10825921 #> 567      -57.255882 -36.22085 -36.220855  21.035027268  0.00000000  -1.39515961 #> 568      -19.822973 -21.86792 -19.822973  -2.044944378  2.04494438  15.00272185 #> 569      -30.084329 -32.28934 -30.084329  -2.205015788  2.20501579   4.74136622 #> 570      -54.050152 -50.64909 -50.649087   3.401064691  0.00000000 -15.82339212 #> 571      -19.141970 -25.18434 -19.141970  -6.042370802  6.04237080  15.68372496 #> 572      -48.721133 -43.34580 -43.345804   5.375329569  0.00000000  -8.52010834 #> 573      -30.780764 -36.05100 -30.780764  -5.270234925  5.27023492   4.04493168 #> 574      -42.170589 -40.87077 -40.870765   1.299823515  0.00000000  -6.04506989 #> 575      -21.545939 -23.69349 -21.545939  -2.147546556  2.14754656  13.27975599 #> 576      -74.661176 -71.36924 -71.369245   3.291931286  0.00000000 -36.54354945 #> 577      -49.700792 -41.70621 -41.706210   7.994581502  0.00000000  -6.88051507 #> 578      -19.651713 -24.61340 -19.651713  -4.961687066  4.96168707  15.17398225 #> 579      -34.061458 -35.02832 -34.061458  -0.966860914  0.96686091   0.76423734 #> 580      -83.974274 -56.84799 -56.847989  27.126285008  0.00000000 -22.02229321 #> 581      -30.483444 -30.63742 -30.483444  -0.153977186  0.15397719   4.34225104 #> 582      -30.258658 -33.03482 -30.258658  -2.776160827  2.77616083   4.56703775 #> 583      -24.760829 -29.27274 -24.760829  -4.511908177  4.51190818  10.06486656 #> 584      -42.664267 -46.78795 -42.664267  -4.123681582  4.12368158  -7.83857145 #> 585      -28.346076 -34.07851 -28.346076  -5.732436302  5.73243630   6.47961965 #> 586      -32.765553 -22.52607 -22.526072  10.239480544  0.00000000  12.29962300 #> 587      -56.913061 -38.97007 -38.970065  17.942995789  0.00000000  -4.14436992 #> 588      -33.910981 -32.74086 -32.740857   1.170123335  0.00000000   2.08483804 #> 589      -44.907666 -41.21730 -41.217298   3.690367321  0.00000000  -6.39160312 #> 590      -49.050170 -50.49458 -49.050170  -1.444408955  1.44440895 -14.22447420 #> 591      -31.419126 -26.27038 -26.270379   5.148746933  0.00000000   8.55531590 #> 592      -16.566955 -20.09725 -16.566955  -3.530299423  3.53029942  18.25874010 #> 593      -20.743652 -27.11818 -20.743652  -6.374525598  6.37452560  14.08204372 #> 594      -35.965556 -32.75033 -32.750328   3.215228349  0.00000000   2.07536722 #> 595      -39.012811 -50.97758 -39.012811 -11.964772560 11.96477256  -4.18711558 #> 596      -31.774414 -33.06533 -31.774414  -1.290920507  1.29092051   3.05128158 #> 597      -36.623598 -26.41388 -26.413884  10.209713865  0.00000000   8.41181097 #> 598      -37.623354 -40.49778 -37.623354  -2.874420878  2.87442088  -2.79765911 #> 599      -40.803563 -32.01117 -32.011170   8.792393039  0.00000000   2.81452515 #> 600      -18.538313 -23.53675 -18.538313  -4.998437657  4.99843766  16.28738195 #> 601      -27.217819 -34.24666 -27.217819  -7.028842100  7.02884210   7.60787593 #> 602      -32.396334 -32.02075 -32.020750   0.375583738  0.00000000   2.80494544 #> 603      -28.812184 -31.26342 -28.812184  -2.451237807  2.45123781   6.01351161 #> 604      -21.641477 -25.46970 -21.641477  -3.828222002  3.82822200  13.18421832 #> 605      -24.335938 -22.86293 -22.862934   1.473004000  0.00000000  11.96276107 #> 606      -28.565620 -37.81985 -28.565620  -9.254229998  9.25423000   6.26007501 #> 607      -34.686362 -33.62957 -33.629570   1.056792247  0.00000000   1.19612514 #> 608      -37.593407 -29.24701 -29.247010   8.346396833  0.00000000   5.57868556 #> 609      -21.723745 -22.83200 -21.723745  -1.108257352  1.10825735  13.10195001 #> 610      -30.844217 -43.38884 -30.844217 -12.544621174 12.54462117   3.98147850 #> 611      -31.453692 -32.24849 -31.453692  -0.794799048  0.79479905   3.37200357 #> 612      -18.280651 -18.95907 -18.280651  -0.678418631  0.67841863  16.54504473 #> 613      -27.559451 -27.72912 -27.559451  -0.169667908  0.16966791   7.26624431 #> 614      -38.878418 -39.11318 -38.878418  -0.234761683  0.23476168  -4.05272316 #> 615      -45.026930 -35.36524 -35.365235   9.661694063  0.00000000  -0.53954011 #> 616      -31.674043 -29.74781 -29.747808   1.926234730  0.00000000   5.07788686 #> 617      -21.262957 -18.57031 -18.570306   2.692650886  0.00000000  16.25538922 #> 618      -16.240462 -24.70101 -16.240462  -8.460549020  8.46054902  18.58523317 #> 619      -36.988440 -38.17125 -36.988440  -1.182808117  1.18280812  -2.16274473 #> 620      -29.996054 -39.98557 -29.996054  -9.989520149  9.98952015   4.82964117 #> 621      -38.256518 -38.68127 -38.256518  -0.424750509  0.42475051  -3.43082266 #> 622      -45.171302 -44.43136 -44.431358   0.739944094  0.00000000  -9.60566262 #> 623      -17.295390 -19.43233 -17.295390  -2.136938190  2.13693819  17.53030539 #> 624      -22.786569 -33.13922 -22.786569 -10.352649238 10.35264924  12.03912585 #> 625      -26.297341 -22.37879 -22.378791   3.918549440  0.00000000  12.44690419 #> 626      -33.884220 -34.71640 -33.884220  -0.832182948  0.83218295   0.94147490 #> 627      -36.179015 -33.64639 -33.646388   2.532627027  0.00000000   1.17930747 #> 628      -55.569875 -48.86676 -48.866757   6.703117495  0.00000000 -14.04106213 #> 629      -23.678868 -24.23941 -23.678868  -0.560546383  0.56054638  11.14682708 #> 630     -103.000028 -82.64507 -82.645070  20.354957993  0.00000000 -47.81937423 #> 631      -27.673873 -30.00550 -27.673873  -2.331622525  2.33162253   7.15182198 #> 632      -38.244451 -36.31110 -36.311098   1.933352554  0.00000000  -1.48540305 #> 633      -24.206030 -33.00672 -24.206030  -8.800689391  8.80068939  10.61966537 #> 634      -26.502719 -27.36077 -26.502719  -0.858052667  0.85805267   8.32297645 #> 635      -27.612222 -24.78012 -24.780122   2.832100092  0.00000000  10.04557372 #> 636      -28.307704 -28.40625 -28.307704  -0.098543786  0.09854379   6.51799175 #> 637      -24.300246 -25.88779 -24.300246  -1.587540959  1.58754096  10.52544960 #> 638      -27.650521 -27.17288 -27.172876   0.477644926  0.00000000   7.65281945 #> 639      -23.034904 -32.84395 -23.034904  -9.809051081  9.80905108  11.79079171 #> 640      -31.007796 -36.02797 -31.007796  -5.020172748  5.02017275   3.81789981 #> 641      -36.923811 -32.64128 -32.641276   4.282535227  0.00000000   2.18441909 #> 642      -28.739272 -33.46871 -28.739272  -4.729435601  4.72943560   6.08642346 #> 643      -27.207530 -24.67129 -24.671295   2.536234927  0.00000000  10.15440053 #> 644      -46.155008 -48.42937 -46.155008  -2.274366155  2.27436616 -11.32931262 #> 645      -32.877177 -34.59436 -32.877177  -1.717187169  1.71718717   1.94851849 #> 646      -21.039416 -30.64949 -21.039416  -9.610071218  9.61007122  13.78627893 #> 647      -12.535232 -17.02552 -12.535232  -4.490291396  4.49029140  22.29046372 #> 648      -71.752316 -55.70931 -55.709307  16.043009436  0.00000000 -20.88361148 #> 649      -57.564649 -50.01446 -50.014464   7.550185449  0.00000000 -15.18876870 #> 650      -14.534147 -19.02088 -14.534147  -4.486732998  4.48673300  20.29154801 #> 651      -43.861010 -30.92012 -30.920120  12.940889257  0.00000000   3.90557490 #> 652     -100.581078 -68.63295 -68.632955  31.948122885  0.00000000 -33.80725961 #> 653      -46.932119 -32.47424 -32.474241  14.457878080  0.00000000   2.35145399 #> 654      -23.483257 -32.60558 -23.483257  -9.122321408  9.12232141  11.34243827 #> 655      -41.619349 -35.22786 -35.227860   6.391488589  0.00000000  -0.40216482 #> 656      -37.565839 -26.93428 -26.934279  10.631560084  0.00000000   7.89141657 #> 657      -36.978212 -26.44392 -26.443918  10.534294263  0.00000000   8.38177729 #> 658      -18.762839 -19.07872 -18.762839  -0.315882319  0.31588232  16.06285617 #> 659      -23.881180 -30.69811 -23.881180  -6.816931202  6.81693120  10.94451519 #> 660      -20.212419 -24.23479 -20.212419  -4.022370194  4.02237019  14.61327653 #> 661      -27.386252 -29.60026 -27.386252  -2.214011508  2.21401151   7.43944358 #> 662      -33.760830 -29.95261 -29.952607   3.808222942  0.00000000   4.87308810 #> 663      -25.424113 -25.84590 -25.424113  -0.421787206  0.42178721   9.40158240 #> 664      -25.917525 -20.65974 -20.659738   5.257786994  0.00000000  14.16595695 #> 665      -37.985305 -35.18610 -35.186096   2.799209004  0.00000000  -0.36040095 #> 666      -54.452142 -41.55452 -41.554517  12.897625241  0.00000000  -6.72882132 #> 667      -40.430195 -35.31724 -35.317237   5.112957935  0.00000000  -0.49154164 #> 668      -43.338399 -40.45948 -40.459481   2.878917871  0.00000000  -5.63378563 #> 669      -25.503570 -36.38378 -25.503570 -10.880205803 10.88020580   9.32212577 #> 670      -43.904426 -36.63928 -36.639281   7.265144596  0.00000000  -1.81358591 #> 671      -57.185793 -53.11213 -53.112133   4.073660377  0.00000000 -18.28643749 #> 672      -47.848734 -42.76211 -42.762109   5.086625315  0.00000000  -7.93641350 #> 673      -34.882318 -34.38561 -34.385609   0.496709613  0.00000000   0.44008646 #> 674      -29.147228 -29.66610 -29.147228  -0.518872150  0.51887215   5.67846778 #> 675      -48.482399 -38.37061 -38.370610  10.111788557  0.00000000  -3.54491498 #> 676      -16.981128 -20.64239 -16.981128  -3.661265346  3.66126535  17.84456724 #> 677      -36.127876 -26.46127 -26.461274   9.666602109  0.00000000   8.36442115 #> 678      -16.293340 -22.00544 -16.293340  -5.712095424  5.71209542  18.53235503 #> 679      -20.779916 -21.38226 -20.779916  -0.602342595  0.60234259  14.04577922 #> 680      -27.156718 -33.60660 -27.156718  -6.449882809  6.44988281   7.66897712 #> 681      -42.291883 -35.52098 -35.520982   6.770901353  0.00000000  -0.69528644 #> 682      -29.271517 -32.05807 -29.271517  -2.786554577  2.78655458   5.55417813 #> 683      -27.628463 -26.40190 -26.401900   1.226562461  0.00000000   8.42379511 #> 684      -22.226143 -23.06578 -22.226143  -0.839638579  0.83963858  12.59955211 #> 685      -54.185047 -35.93904 -35.939040  18.246007318  0.00000000  -1.11334448 #> 686      -28.152602 -26.69086 -26.690856   1.461745440  0.00000000   8.13483902 #> 687      -27.376054 -26.38231 -26.382306   0.993748465  0.00000000   8.44338982 #> 688      -24.931914 -26.86728 -24.931914  -1.935362703  1.93536270   9.89378157 #> 689      -54.719323 -46.91228 -46.912283   7.807040141  0.00000000 -12.08658745 #> 690      -19.800244 -25.10816 -19.800244  -5.307920373  5.30792037  15.02545097 #> 691      -14.804353 -17.44094 -14.804353  -2.636588352  2.63658835  20.02134198 #> 692      -29.794403 -33.23988 -29.794403  -3.445475370  3.44547537   5.03129277 #> 693      -35.599124 -35.62242 -35.599124  -0.023292316  0.02329232  -0.77342897 #> 694      -24.134607 -29.07750 -24.134607  -4.942895030  4.94289503  10.69108869 #> 695      -34.809486 -53.75713 -34.809486 -18.947641436 18.94764144   0.01620974 #> 696      -41.974096 -29.98304 -29.983039  11.991056841  0.00000000   4.84265633 #> 697      -53.755659 -42.30166 -42.301660  11.453999651  0.00000000  -7.47596448 #> 698      -29.691431 -35.15264 -29.691431  -5.461207457  5.46120746   5.13426402 #> 699      -48.493545 -36.62196 -36.621965  11.871580555  0.00000000  -1.79626930 #> 700      -17.584084 -20.56742 -17.584084  -2.983336704  2.98333670  17.24161154 #> 701      -17.518956 -23.63513 -17.518956  -6.116176247  6.11617625  17.30673940 #> 702      -26.261052 -28.89561 -26.261052  -2.634560389  2.63456039   8.56464313 #> 703      -41.187338 -48.70107 -41.187338  -7.513732812  7.51373281  -6.36164238 #> 704      -48.483854 -55.23514 -48.483854  -6.751284858  6.75128486 -13.65815887 #> 705      -32.240835 -26.00339 -26.003389   6.237445276  0.00000000   8.82230608 #> 706      -44.028389 -39.78822 -39.788225   4.240164306  0.00000000  -4.96252942 #> 707      -69.791359 -67.62816 -67.628157   2.163202160  0.00000000 -32.80246194 #> 708      -45.958216 -36.00022 -36.000224   9.957992246  0.00000000  -1.17452847 #> 709      -16.686028 -24.08880 -16.686028  -7.402771656  7.40277166  18.13966776 #> 710      -34.524775 -32.03828 -32.038283   2.486491689  0.00000000   2.78741203 #> 711      -33.074563 -31.98969 -31.989693   1.084869648  0.00000000   2.83600198 #> 712      -24.072121 -33.79518 -24.072121  -9.723059976  9.72305998  10.75357461 #> 713      -50.905861 -45.65743 -45.657432   5.248429227  0.00000000 -10.83173693 #> 714      -22.458418 -22.80009 -22.458418  -0.341667464  0.34166746  12.36727777 #> 715      -29.877399 -32.09471 -29.877399  -2.217311640  2.21731164   4.94829652 #> 716      -57.616640 -43.88616 -43.886160  13.730480109  0.00000000  -9.06046462 #> 717      -70.737329 -64.36822 -64.368219   6.369110676  0.00000000 -29.54252343 #> 718      -35.589352 -33.37425 -33.374247   2.215104127  0.00000000   1.45144790 #> 719      -44.101915 -33.58816 -33.588163  10.513752693  0.00000000   1.23753266 #> 720      -47.065828 -45.67680 -45.676804   1.389023826  0.00000000 -10.85110859 #> 721      -17.378111 -20.60717 -17.378111  -3.229064179  3.22906418  17.44758462 #> 722      -48.154047 -47.62427 -47.624274   0.529772627  0.00000000 -12.79857893 #> 723      -20.051748 -25.59710 -20.051748  -5.545352971  5.54535297  14.77394716 #> 724      -24.296848 -34.31322 -24.296848 -10.016367825 10.01636782  10.52884740 #> 725      -19.983276 -30.92242 -19.983276 -10.939148988 10.93914899  14.84241965 #> 726      -36.220091 -34.70039 -34.700393   1.519697989  0.00000000   0.12530189 #> 727      -35.139692 -38.20258 -35.139692  -3.062891970  3.06289197  -0.31399643 #> 728      -36.661787 -38.14670 -36.661787  -1.484916533  1.48491653  -1.83609190 #> 729      -35.460999 -41.62391 -35.460999  -6.162907593  6.16290759  -0.63530353 #> 730      -36.384223 -29.42664 -29.426638   6.957585217  0.00000000   5.39905749 #> 731      -55.255368 -66.24716 -55.255368 -10.991787221 10.99178722 -20.42967256 #> 732      -45.784339 -46.34354 -45.784339  -0.559205458  0.55920546 -10.95864395 #> 733      -55.467076 -41.48269 -41.482694  13.984382145  0.00000000  -6.65699843 #> 734      -37.840622 -26.24507 -26.245075  11.595547610  0.00000000   8.58062056 #> 735      -64.659836 -49.28398 -49.283979  15.375856400  0.00000000 -14.45828397 #> 736      -39.613551 -37.61779 -37.617789   1.995762069  0.00000000  -2.79209393 #> 737      -42.686475 -51.68149 -42.686475  -8.995017727  8.99501773  -7.86077972 #> 738      -41.977859 -41.84302 -41.843021   0.134838210  0.00000000  -7.01732583 #> 739      -39.777808 -41.03040 -39.777808  -1.252589406  1.25258941  -4.95211292 #> 740      -40.140699 -35.14091 -35.140910   4.999789465  0.00000000  -0.31521423 #> 741      -34.482068 -35.60986 -34.482068  -1.127794217  1.12779422   0.34362758 #> 742      -21.319758 -30.23062 -21.319758  -8.910857166  8.91085717  13.50593749 #> 743      -27.065400 -45.94810 -27.065400 -18.882704101 18.88270410   7.76029535 #> 744      -53.464070 -50.19521 -50.195211   3.268858234  0.00000000 -15.36951612 #> 745      -29.694765 -52.32428 -29.694765 -22.629518392 22.62951839   5.13092994 #> 746      -22.246166 -21.68257 -21.682570   0.563596276  0.00000000  13.14312517 #> 747      -30.606060 -43.34910 -30.606060 -12.743036247 12.74303625   4.21963563 #> 748      -31.643657 -52.38442 -31.643657 -20.740767803 20.74076780   3.18203855 #> 749      -29.768552 -29.36097 -29.360967   0.407584692  0.00000000   5.46472803 #> 750      -27.278161 -30.56588 -27.278161  -3.287718486  3.28771849   7.54753431 #> 751      -41.608154 -43.13469 -41.608154  -1.526532433  1.52653243  -6.78245896 #> 752      -23.877312 -28.61065 -23.877312  -4.733342413  4.73334241  10.94838341 #> 753      -38.314867 -37.97133 -37.971325   0.343541754  0.00000000  -3.14563015 #> 754      -22.302942 -25.37243 -22.302942  -3.069484780  3.06948478  12.52275283 #> 755      -21.061007 -25.69744 -21.061007  -4.636435243  4.63643524  13.76468853 #> 756      -60.362083 -43.48413 -43.484126  16.877956762  0.00000000  -8.65843107 #> 757      -19.005495 -32.59850 -19.005495 -13.593000732 13.59300073  15.82020060 #> 758      -45.601031 -50.15290 -45.601031  -4.551865603  4.55186560 -10.77533614 #> 759      -38.358107 -36.12408 -36.124075   2.234031789  0.00000000  -1.29837991 #> 760      -24.441087 -28.49494 -24.441087  -4.053857230  4.05385723  10.38460819 #> 761      -36.354903 -52.25131 -36.354903 -15.896404190 15.89640419  -1.52920786 #> 762      -69.971236 -50.91848 -50.918483  19.052753389  0.00000000 -16.09278718 #> 763      -38.165425 -22.57157 -22.571574  15.593850287  0.00000000  12.25412099 #> 764      -22.920200 -21.48503 -21.485030   1.435169768  0.00000000  13.34066491 #> 765      -44.052940 -41.28027 -41.280275   2.772665256  0.00000000  -6.45457922 #> 766      -36.630057 -34.30833 -34.308333   2.321723398  0.00000000   0.51736221 #> 767      -46.067538 -37.22689 -37.226892   8.840645753  0.00000000  -2.40119710 #> 768      -75.262071 -60.16425 -60.164250  15.097820462  0.00000000 -25.33855494 #> 769      -19.301502 -26.31245 -19.301502  -7.010950100  7.01095010  15.52419363 #> 770      -37.798463 -35.18268 -35.182678   2.615785316  0.00000000  -0.35698266 #> 771      -56.605978 -43.78634 -43.786337  12.819641510  0.00000000  -8.96064125 #> 772      -30.158137 -29.60119 -29.601194   0.556942745  0.00000000   5.22450147 #> 773      -70.581256 -57.22425 -57.224248  13.357008360  0.00000000 -22.39855225 #> 774      -83.328383 -63.49388 -63.493885  19.834498709  0.00000000 -28.66818923 #> 775      -27.383194 -24.83538 -24.835380   2.547813896  0.00000000   9.99031555 #> 776      -24.687533 -26.68595 -24.687533  -1.998413049  1.99841305  10.13816211 #> 777      -38.168919 -38.65385 -38.168919  -0.484930753  0.48493075  -3.34322371 #> 778      -23.361377 -24.63014 -23.361377  -1.268765722  1.26876572  11.46431800 #> 779      -36.144074 -40.11087 -36.144074  -3.966796929  3.96679693  -1.31837865 #> 780      -83.367959 -52.68565 -52.685647  30.682311674  0.00000000 -17.85995157 #> 781      -17.280820 -27.71350 -17.280820 -10.432678822 10.43267882  17.54487483 #> 782      -19.374350 -27.84479 -19.374350  -8.470438349  8.47043835  15.45134506 #> 783      -26.036520 -26.12037 -26.036520  -0.083853378  0.08385338   8.78917537 #> 784      -32.273956 -35.94733 -32.273956  -3.673369539  3.67336954   2.55173917 #> 785      -36.317834 -40.42136 -36.317834  -4.103523354  4.10352335  -1.49213892 #> 786      -66.972223 -63.85969 -63.859693   3.112530368  0.00000000 -29.03399750 #> 787      -33.496078 -33.55759 -33.496078  -0.061508774  0.06150877   1.32961689 #> 788      -14.726783 -23.49375 -14.726783  -8.766962093  8.76696209  20.09891197 #> 789      -18.314830 -27.87102 -18.314830  -9.556194688  9.55619469  16.51086504 #> 790      -11.859303 -24.05484 -11.859303 -12.195538672 12.19553867  22.96639242 #> 791      -38.302503 -38.12831 -38.128314   0.174188914  0.00000000  -3.30261837 #> 792      -16.518990 -23.74759 -16.518990  -7.228596022  7.22859602  18.30670565 #> 793      -49.990470 -36.03803 -36.038029  13.952440859  0.00000000  -1.21233369 #> 794      -35.058645 -42.18463 -35.058645  -7.125987807  7.12598781  -0.23294988 #> 795      -24.735892 -26.26556 -24.735892  -1.529672750  1.52967275  10.08980315 #> 796      -21.528604 -22.66836 -21.528604  -1.139751978  1.13975198  13.29709159 #> 797      -50.285771 -33.74901 -33.749010  16.536761000  0.00000000   1.07668502 #> 798      -27.240957 -30.82440 -27.240957  -3.583444467  3.58344447   7.58473852 #> 799      -35.039390 -39.07693 -35.039390  -4.037536872  4.03753687  -0.21369448 #> 800      -26.815881 -26.17295 -26.172952   0.642929622  0.00000000   8.65274350 #> 801      -30.018330 -38.08396 -30.018330  -8.065627475  8.06562747   4.80736495 #> 802      -32.817912 -31.90774 -31.907744   0.910167574  0.00000000   2.91795118 #> 803      -21.702739 -30.03743 -21.702739  -8.334694815  8.33469482  13.12295669 #> 804      -28.186748 -31.66415 -28.186748  -3.477398952  3.47739895   6.63894696 #> 805      -51.255412 -38.88664 -38.886635  12.368777151  0.00000000  -4.06093977 #> 806      -37.227154 -38.04851 -37.227154  -0.821352542  0.82135254  -2.40145868 #> 807      -47.406666 -42.46186 -42.461860   4.944806001  0.00000000  -7.63616498 #> 808      -28.084142 -36.80170 -28.084142  -8.717560006  8.71756001   6.74155355 #> 809      -42.511320 -34.42660 -34.426604   8.084715201  0.00000000   0.39909101 #> 810      -71.634236 -53.05923 -53.059228  18.575008001  0.00000000 -18.23353235 #> 811      -46.286735 -33.87333 -33.873330  12.413404394  0.00000000   0.95236520 #> 812      -43.450818 -39.07254 -39.072542   4.378275336  0.00000000  -4.24684694 #> 813      -27.699432 -31.35557 -27.699432  -3.656141293  3.65614129   7.12626319 #> 814      -22.207304 -18.10226 -18.102257   4.105046650  0.00000000  16.72343791 #> 815      -39.149576 -36.57620 -36.576197   2.573379432  0.00000000  -1.75050139 #> 816      -58.390305 -56.08863 -56.088629   2.301675396  0.00000000 -21.26293413 #> 817      -29.843440 -30.95104 -29.843440  -1.107596782  1.10759678   4.98225555 #> 818      -37.990049 -41.07907 -37.990049  -3.089024448  3.08902445  -3.16435410 #> 819      -29.681971 -27.09089 -27.090888   2.591082579  0.00000000   7.73480721 #> 820      -62.115162 -43.55024 -43.550238  18.564924096  0.00000000  -8.72454248 #> 821      -12.021851 -18.15487 -12.021851  -6.133018843  6.13301884  22.80384441 #> 822      -32.292306 -42.71255 -32.292306 -10.420244916 10.42024492   2.53338895 #> 823      -51.445604 -54.64531 -51.445604  -3.199708976  3.19970898 -16.61990889 #> 824      -27.138755 -26.94209 -26.942089   0.196666211  0.00000000   7.88360677 #> 825      -42.891233 -29.17545 -29.175451  13.715782184  0.00000000   5.65024460 #> 826      -26.652664 -33.80428 -26.652664  -7.151616652  7.15161665   8.17303116 #> 827      -58.106004 -33.70801 -33.708009  24.397995404  0.00000000   1.11768671 #> 828      -75.913976 -55.40180 -55.401802  20.512173741  0.00000000 -20.57610717 #> 829      -22.164701 -24.85288 -22.164701  -2.688176870  2.68817687  12.66099392 #> 830      -24.296165 -25.47363 -24.296165  -1.177462714  1.17746271  10.52953055 #> 831      -41.505855 -36.35584 -36.355844   5.150011296  0.00000000  -1.53014886 #> 832      -25.184077 -25.17551 -25.175506   0.008571006  0.00000000   9.65018921 #> 833      -34.960619 -31.77049 -31.770488   3.190130825  0.00000000   3.05520706 #> 834      -28.337895 -42.63458 -28.337895 -14.296685386 14.29668539   6.48780042 #> 835      -37.132266 -50.89076 -37.132266 -13.758492362 13.75849236  -2.30657094 #> 836      -46.812858 -39.05814 -39.058139   7.754718957  0.00000000  -4.23244363 #> 837      -25.360168 -21.67096 -21.670965   3.689203044  0.00000000  13.15473038 #> 838      -80.307314 -72.27332 -72.273320   8.033993579  0.00000000 -37.44762474 #> 839      -23.865747 -21.64710 -21.647097   2.218650298  0.00000000  13.17859863 #> 840      -28.692002 -24.52981 -24.529809   4.162193259  0.00000000  10.29588663 #> 841      -38.864104 -35.69677 -35.696774   3.167330129  0.00000000  -0.87107827 #> 842      -28.830840 -27.45771 -27.457715   1.373125695  0.00000000   7.36798069 #> 843      -52.338228 -38.43021 -38.430210  13.908017925  0.00000000  -3.60451506 #> 844      -38.194302 -42.21644 -38.194302  -4.022135571  4.02213557  -3.36860621 #> 845      -51.267761 -56.58512 -51.267761  -5.317357640  5.31735764 -16.44206553 #> 846      -27.425589 -26.91016 -26.910158   0.515430892  0.00000000   7.91553716 #> 847      -46.964820 -34.14595 -34.145950  12.818869799  0.00000000   0.67974487 #> 848      -69.948361 -58.21453 -58.214535  11.733826502  0.00000000 -23.38883926 #> 849      -22.565197 -31.35577 -22.565197  -8.790577480  8.79057748  12.26049838 #> 850      -52.752755 -46.71495 -46.714950   6.037805442  0.00000000 -11.88925455 #> 851      -46.358195 -41.23692 -41.236922   5.121273542  0.00000000  -6.41122656 #> 852      -35.892906 -39.78711 -35.892906  -3.894200965  3.89420096  -1.06721063 #> 853      -40.202667 -40.72536 -40.202667  -0.522689015  0.52268901  -5.37697145 #> 854      -41.625298 -43.61490 -41.625298  -1.989598279  1.98959828  -6.79960307 #> 855      -27.528954 -21.91579 -21.915789   5.613165354  0.00000000  12.90990675 #> 856      -55.571381 -46.35836 -46.358360   9.213021711  0.00000000 -11.53266437 #> 857      -34.402609 -34.30217 -34.302174   0.100434950  0.00000000   0.52352154 #> 858      -22.756093 -24.37069 -22.756093  -1.614595722  1.61459572  12.06960235 #> 859      -38.680828 -38.17853 -38.178528   0.502300410  0.00000000  -3.35283236 #> 860      -49.234869 -41.83267 -41.832666   7.402202945  0.00000000  -7.00697117 #> 861      -38.960149 -41.15109 -38.960149  -2.190941338  2.19094134  -4.13445397 #> 862      -63.857382 -54.10906 -54.109059   9.748323302  0.00000000 -19.28336341 #> 863      -31.177706 -29.93048 -29.930485   1.247220738  0.00000000   4.89521048 #> 864      -40.363029 -35.32875 -35.328749   5.034279633  0.00000000  -0.50305356 #> 865      -32.514886 -34.11995 -32.514886  -1.605066510  1.60506651   2.31080930 #> 866      -46.579931 -39.91749 -39.917493   6.662437860  0.00000000  -5.09179770 #> 867      -25.594053 -25.73878 -25.594053  -0.144731614  0.14473161   9.23164228 #> 868      -26.280421 -24.54068 -24.540677   1.739743976  0.00000000  10.28501862 #> 869      -31.059032 -25.87988 -25.879884   5.179147124  0.00000000   8.94581090 #> 870      -26.925822 -27.31467 -26.925822  -0.388847335  0.38884733   7.89987313 #> 871      -48.302524 -61.13593 -48.302524 -12.833404131 12.83340413 -13.47682876 #> 872      -52.343015 -39.08331 -39.083312  13.259703467  0.00000000  -4.25761632 #> 873      -28.070898 -28.10876 -28.070898  -0.037858308  0.03785831   6.75479710 #> 874      -30.596681 -35.01496 -30.596681  -4.418280693  4.41828069   4.22901480 #> 875      -17.113713 -19.86929 -17.113713  -2.755576082  2.75557608  17.71198194 #> 876      -30.988352 -25.60191 -25.601910   5.386442115  0.00000000   9.22378577 #> 877      -24.302616 -22.38162 -22.381616   1.921000034  0.00000000  12.44407907 #> 878      -36.927773 -22.15007 -22.150067  14.777705876  0.00000000  12.67562812 #> 879      -42.768976 -41.20309 -41.203093   1.565883026  0.00000000  -6.37739780 #> 880      -27.810045 -23.77593 -23.775933   4.034111950  0.00000000  11.04976206 #> 881      -27.035196 -27.53486 -27.035196  -0.499663718  0.49966372   7.79049899 #> 882      -26.633828 -23.72083 -23.720833   2.912995124  0.00000000  11.10486214 #> 883      -11.603238 -19.34979 -11.603238  -7.746551719  7.74655172  23.22245695 #> 884      -37.350648 -25.21385 -25.213846  12.136802115  0.00000000   9.61184955 #> 885      -35.764025 -51.28410 -35.764025 -15.520077634 15.52007763  -0.93832968 #> 886      -30.528418 -29.19541 -29.195413   1.333005028  0.00000000   5.63028225 #> 887      -19.664684 -33.00711 -19.664684 -13.342427453 13.34242745  15.16101105 #> 888      -26.945836 -30.73729 -26.945836  -3.791452506  3.79145251   7.87985913 #> 889      -31.795032 -32.25140 -31.795032  -0.456369018  0.45636902   3.03066355 #> 890      -19.864072 -25.46546 -19.864072  -5.601384196  5.60138420  14.96162379 #> 891      -43.449781 -34.84675 -34.846752   8.603028898  0.00000000  -0.02105685 #> 892      -48.431579 -36.92244 -36.922436  11.509142934  0.00000000  -2.09674061 #> 893      -58.545939 -51.24628 -51.246277   7.299662749  0.00000000 -16.42058125 #> 894      -44.473891 -37.53303 -37.533026   6.940864523  0.00000000  -2.70733077 #> 895      -27.464096 -25.18566 -25.185664   2.278432407  0.00000000   9.64003177 #> 896      -31.985743 -27.28566 -27.285655   4.700088091  0.00000000   7.54004019 #> 897      -28.629047 -28.67101 -28.629047  -0.041960933  0.04196093   6.19664824 #> 898      -24.022227 -22.64191 -22.641909   1.380317867  0.00000000  12.18378611 #> 899      -39.767679 -40.65619 -39.767679  -0.888515191  0.88851519  -4.94198417 #> 900      -27.058792 -29.70067 -27.058792  -2.641882610  2.64188261   7.76690335 #> 901      -50.714242 -35.89239 -35.892392  14.821849899  0.00000000  -1.06669708 #> 902      -41.618441 -27.90616 -27.906156  13.712284769  0.00000000   6.91953917 #> 903      -23.858741 -28.49932 -23.858741  -4.640583769  4.64058377  10.96695416 #> 904      -29.713827 -33.99794 -29.713827  -4.284116260  4.28411626   5.11186853 #> 905      -41.751568 -50.67227 -41.751568  -8.920703846  8.92070385  -6.92587272 #> 906      -41.354902 -35.59825 -35.598254   5.756648325  0.00000000  -0.77255869 #> 907      -22.441692 -24.59212 -22.441692  -2.150427818  2.15042782  12.38400289 #> 908      -23.711423 -30.97018 -23.711423  -7.258754464  7.25875446  11.11427272 #> 909      -22.952597 -27.77520 -22.952597  -4.822602851  4.82260285  11.87309822 #> 910      -34.623674 -29.30186 -29.301858   5.321815711  0.00000000   5.52383747 #> 911      -38.242953 -36.63384 -36.633844   1.609108360  0.00000000  -1.80814882 #> 912      -36.578542 -32.44066 -32.440657   4.137884817  0.00000000   2.38503810 #> 913      -19.409410 -28.23941 -19.409410  -8.830003423  8.83000342  15.41628559 #> 914      -30.291435 -32.47680 -30.291435  -2.185368378  2.18536838   4.53426021 #> 915      -41.488242 -34.55997 -34.559969   6.928273201  0.00000000   0.26572671 #> 916      -27.670235 -28.94721 -27.670235  -1.276976270  1.27697627   7.15545988 #> 917      -19.293019 -25.32584 -19.293019  -6.032821327  6.03282133  15.53267584 #> 918      -40.170858 -40.29967 -40.170858  -0.128816294  0.12881629  -5.34516256 #> 919      -30.619475 -32.53209 -30.619475  -1.912613364  1.91261336   4.20622021 #> 920      -32.831904 -33.44078 -32.831904  -0.608876050  0.60887605   1.99379145 #> 921      -46.948254 -33.40729 -33.407286  13.540968028  0.00000000   1.41840927 #> 922      -13.170228 -31.26175 -13.170228 -18.091521986 18.09152199  21.65546707 #> 923      -31.089490 -25.32508 -25.325084   5.764406748  0.00000000   9.50061179 #> 924      -29.535986 -40.37809 -29.535986 -10.842102776 10.84210278   5.28970973 #> 925      -46.770086 -56.78714 -46.770086 -10.017058723 10.01705872 -11.94439034 #> 926      -37.877645 -43.84422 -37.877645  -5.966577482  5.96657748  -3.05194927 #> 927      -21.609433 -28.17352 -21.609433  -6.564089933  6.56408993  13.21626195 #> 928      -29.350343 -22.34239 -22.342385   7.007957804  0.00000000  12.48331008 #> 929      -22.581503 -26.80641 -22.581503  -4.224904307  4.22490431  12.24419193 #> 930      -19.929154 -20.89432 -19.929154  -0.965165211  0.96516521  14.89654095 #> 931      -35.853191 -34.15562 -34.155619   1.697572041  0.00000000   0.67007669 #> 932      -29.280579 -35.66376 -29.280579  -6.383183587  6.38318359   5.54511583 #> 933      -42.782708 -37.12598 -37.125975   5.656732830  0.00000000  -2.30028011 #> 934      -64.398227 -52.68949 -52.689491  11.708735710  0.00000000 -17.86379610 #> 935      -54.375214 -43.86415 -43.864145  10.511068979  0.00000000  -9.03845001 #> 936      -31.105217 -38.24942 -31.105217  -7.144200109  7.14420011   3.72047868 #> 937      -57.280995 -45.58849 -45.588489  11.692505324  0.00000000 -10.76279393 #> 938      -20.314048 -30.83581 -20.314048 -10.521760053 10.52176005  14.51164695 #> 939      -47.717675 -40.38705 -40.387050   7.330624702  0.00000000  -5.56135490 #> 940      -35.017103 -31.77475 -31.774748   3.242355139  0.00000000   3.05094754 #> 941      -28.972912 -37.92054 -28.972912  -8.947624724  8.94762472   5.85278370 #> 942      -61.709263 -41.83632 -41.836323  19.872940260  0.00000000  -7.01062734 #> 943      -28.498268 -26.42468 -26.424679   2.073589096  0.00000000   8.40101680 #> 944      -23.476430 -32.77582 -23.476430  -9.299391160  9.29939116  11.34926577 #> 945      -24.839448 -33.38054 -24.839448  -8.541094674  8.54109467   9.98624705 #> 946      -71.360195 -53.39980 -53.399797  17.960397900  0.00000000 -18.57410164 #> 947      -31.257565 -30.87163 -30.871630   0.385935137  0.00000000   3.95406509 #> 948      -46.666844 -35.06900 -35.069004  11.597839166  0.00000000  -0.24330904 #> 949      -33.773768 -35.18319 -33.773768  -1.409423485  1.40942349   1.05192776 #> 950      -49.664049 -41.50245 -41.502447   8.161601836  0.00000000  -6.67675168 #> 951      -25.020204 -24.96256 -24.962563   0.057640900  0.00000000   9.86313265 #> 952      -50.784434 -35.57488 -35.574881  15.209553481  0.00000000  -0.74918558 #> 953      -47.133325 -44.79773 -44.797729   2.335595875  0.00000000  -9.97203366 #> 954      -15.195139 -18.65556 -15.195139  -3.460424906  3.46042491  19.63055652 #> 955      -58.959383 -50.64467 -50.644665   8.314717988  0.00000000 -15.81896982 #> 956      -37.131031 -39.17107 -37.131031  -2.040035839  2.04003584  -2.30533537 #> 957      -24.064513 -22.93854 -22.938544   1.125969273  0.00000000  11.88715117 #> 958      -15.834385 -23.42056 -15.834385  -7.586177171  7.58617717  18.99130994 #> 959      -30.307759 -29.21873 -29.218734   1.089024986  0.00000000   5.60696113 #> 960      -31.958113 -29.84855 -29.848549   2.109564410  0.00000000   4.97714682 #> 961      -28.570159 -36.18767 -28.570159  -7.617513223  7.61751322   6.25553619 #> 962      -42.529496 -37.83789 -37.837891   4.691604961  0.00000000  -3.01219544 #> 963      -51.352502 -41.09164 -41.091641  10.260860948  0.00000000  -6.26594538 #> 964      -35.772453 -26.09408 -26.094077   9.678376352  0.00000000   8.73161873 #> 965      -21.182119 -24.31967 -21.182119  -3.137546837  3.13754684  13.64357621 #> 966      -41.536898 -36.11154 -36.111537   5.425361506  0.00000000  -1.28584137 #> 967      -45.802594 -38.16879 -38.168789   7.633805431  0.00000000  -3.34309351 #> 968      -31.060313 -28.26441 -28.264414   2.795898975  0.00000000   6.56128170 #> 969      -31.543288 -28.71105 -28.711052   2.832236136  0.00000000   6.11464341 #> 970      -47.005711 -47.06168 -47.005711  -0.055971604  0.05597160 -12.18001586 #> 971      -43.348800 -50.27717 -43.348800  -6.928372094  6.92837209  -8.52310477 #> 972      -18.095025 -21.23151 -18.095025  -3.136485082  3.13648508  16.73067066 #> 973      -37.093331 -36.21202 -36.212021   0.881310055  0.00000000  -1.38632569 #> 974      -20.436049 -22.96945 -20.436049  -2.533403030  2.53340303  14.38964663 #> 975      -51.468477 -38.33349 -38.333485  13.134992288  0.00000000  -3.50778971 #> 976      -29.045457 -30.49799 -29.045457  -1.452534256  1.45253426   5.78023794 #> 977      -28.464792 -40.71263 -28.464792 -12.247837324 12.24783732   6.36090300 #> 978      -40.064048 -31.25327 -31.253271   8.810776482  0.00000000   3.57242398 #> 979      -23.098403 -26.82179 -23.098403  -3.723391350  3.72339135  11.72729237 #> 980      -24.156203 -24.90490 -24.156203  -0.748701646  0.74870165  10.66949266 #> 981      -18.176479 -22.35662 -18.176479  -4.180139211  4.18013921  16.64921596 #> 982      -27.210469 -31.21309 -27.210469  -4.002620995  4.00262100   7.61522651 #> 983      -56.970749 -54.47260 -54.472596   2.498153118  0.00000000 -19.64690070 #> 984      -32.078136 -35.10138 -32.078136  -3.023245302  3.02324530   2.74755926 #> 985      -42.039296 -36.08732 -36.087318   5.951977416  0.00000000  -1.26162316 #> 986      -38.579652 -36.05915 -36.059150   2.520502260  0.00000000  -1.23345433 #> 987      -24.653947 -35.08838 -24.653947 -10.434429882 10.43442988  10.17174875 #> 988      -30.105686 -38.72078 -30.105686  -8.615091202  8.61509120   4.72000927 #> 989      -55.837030 -44.00264 -44.002638  11.834392744  0.00000000  -9.17694241 #> 990      -58.380768 -51.32742 -51.327420   7.053348118  0.00000000 -16.50172487 #> 991      -36.079700 -29.23238 -29.232384   6.847316399  0.00000000   5.59331151 #> 992      -30.576011 -28.39595 -28.395946   2.180064907  0.00000000   6.42974933 #> 993      -16.767220 -23.58544 -16.767220  -6.818216019  6.81821602  18.05847571 #> 994      -89.942217 -60.93182 -60.931819  29.010397336  0.00000000 -26.10612395 #> 995      -46.337516 -47.47181 -46.337516  -1.134293175  1.13429317 -11.51182060 #> 996      -63.170017 -49.02473 -49.024726  14.145290909  0.00000000 -14.19903034 #> 997      -23.653379 -44.83142 -23.653379 -21.178036470 21.17803647  11.17231593 #> 998      -21.476949 -31.85681 -21.476949 -10.379865068 10.37986507  13.34874585 #> 999      -26.663848 -24.42500 -24.425002   2.238845425  0.00000000  10.40069284 #> 1000     -35.234486 -32.59782 -32.597825   2.636661457  0.00000000   2.22787079 #> Average  -36.054123 -34.82570 -32.411228   1.228428062  2.41446695   2.41446695 #>  #> $names.cols #> [1] \"U1\"    \"U2\"    \"U*\"    \"IB2_1\" \"OL\"    \"VI\"    #>  #> $wtp #> [1] 25000 #>  #> $ind.table #> [1] 251 #>"},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions ‚Äî Smoking","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions ‚Äî Smoking","text":"data set contains results Bayesian analysis used model clinical output costs associated health economic evaluation four different smoking cessation interventions.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions ‚Äî Smoking","text":"data list including variables needed smoking cessation cost-effectiveness analysis. variables follows: list(\"cost\") matrix 500 simulations posterior distribution overall costs associated four strategies list(\"data\") dataset containing characteristics smokers UK population list(\"eff\") matrix 500 simulations posterior distribution clinical benefits associated four strategies list(\"life.years\") matrix 500 simulations posterior distribution life years gained strategy list(\"pi_post\") matrix 500 simulations posterior distribution event smoking cessation strategy list(\"smoking\") data frame containing inputs needed network meta-analysis model. data.frame object contains: nobs: record ID number, s: study ID number, : intervention ID number, r_i: number patients quit smoking, n_i: total number patients row-specific arm b_i: reference intervention study list(\"smoking_mat\") matrix obtained running network meta-analysis model based data contained smoking object list(\"treats\") vector labels associated four strategies","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions ‚Äî Smoking","text":"Effectiveness data adapted Hasselblad V. (1998). Meta-analysis Multitreatment Studies. Medical Decision Making 1998;18:37-43. Cost population characteristics data adapted various sources: Taylor, D.H. Jr, et al. (2002). Benefits smoking cessation longevity. American Journal Public Health 2002;92(6) ASH: Action Smoking Health (2013). ASH fact sheet smoking statistics, https://ash.org.uk/files/documents/ASH_106.pdf Flack, S., et al. (2007). Cost-effectiveness interventions smoking cessation. York Health Economics Consortium, January 2007 McGhan, W.F.D., Smith, M. (1996). Pharmacoeconomic analysis smoking-cessation interventions. American Journal Health-System Pharmacy 1996;53:45-52","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Smoking.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of smoking\r\ncessation interventions ‚Äî Smoking","text":"Baio G. (2012). Bayesian Methods Health Economics. CRC/Chapman Hall, London","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":null,"dir":"Reference","previous_headings":"","what":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"Computes weights associated set competing models order perform structural PSA.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"","code":"struct.psa(   models,   effect,   cost,   ref = NULL,   interventions = NULL,   Kmax = 50000,   plot = FALSE,   w = NULL )"},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"models list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n.sim x n.ints simulations model) cost list containing measure costs computed various models (one matrix n.sim x n.ints simulations model) ref intervention considered reference strategy. default value ref=1 means intervention appearing first reference (s) () comparator(s) interventions Defines labels associated intervention. default NULL, assigns labels form \"Intervention1\", ... , \"InterventionT\" Kmax Maximum value willingness pay considered. Default value 50000. willingness pay approximated discrete grid interval [0, Kmax]. grid equal k parameter given, composed 501 elements k=NULL (default) plot logical value indicating whether function produce summary plot w vector weights. default NULL indicate function calculate model weights based DIC individual model fit. behaviour can overridden passing vector w, instance based expert opinion","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"List object bcea object, model weights DIC","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"model list containing output either R2jags R2WinBUGS models need combined model average effect list containing measure effectiveness computed various models (one matrix n_sim x n_ints simulations model) cost list containing measure costs computed various models (one matrix n_sim x n_ints simulations model).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/struct.psa.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Structural Probability Sensitivity Analysis ‚Äî struct.psa","text":"","code":"if (FALSE) { # load sample jags output load(system.file(\"extdata\", \"statins_base.RData\", package = \"BCEA\")) load(system.file(\"extdata\", \"statins_HC.RData\", package = \"BCEA\"))  interventions <- c(\"Atorvastatin\", \"Fluvastatin\",                    \"Lovastatin\", \"Pravastatin\",                    \"Rosuvastatin\", \"Simvastatin\")  m1 <- bcea(eff = statins_base$sims.list$effect,            cost = statins_base$sims.list$cost.tot,            ref = 1, interventions = interventions)  m2 <- bcea(eff = statins_HC$sims.list$effect,            cost = statins_HC$sims.list$cost.tot,            ref = 1, interventions = interventions)  models <- list(statins_base, statins_HC)  effects <- list(statins_base$sims.list$effect,                 statins_HC$sims.list$effect) costs <- list(statins_base$sims.list$cost.tot,               statins_HC$sims.list$cost.tot)  m3 <- struct.psa(models, effects, costs,                  ref = 1, interventions = interventions) }"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"","code":"# S3 method for bcea summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"object bcea object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.bcea.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class bcea ‚Äî summary.bcea","text":"","code":"data(Vaccine)  he <- bcea(eff, cost, interventions = treats, ref = 2) summary(he) #>  #> Cost-effectiveness analysis summary  #>  #> Reference intervention:  Vaccination #> Comparator intervention: Status Quo #>  #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #>  #>  #> Analysis for willingness to pay parameter k = 25000 #>  #>             Expected net benefit #> Status Quo               -36.054 #> Vaccination              -34.826 #>  #>                              EIB  CEAC  ICER #> Vaccination vs Status Quo 1.2284 0.529 20098 #>  #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #>             #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"Prints summary table results mixed analysis economic evaluation given model.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"","code":"# S3 method for mixedAn summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"object object class mixedAn, results function mixedAn(), generating economic evaluation set interventions, considering given market shares option. wtp value willingness pay chosen present analysis. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"Produces table summary information loss expected value information generated inclusion non cost-effective interventions market.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"Baio G, Russo P (2009). ‚Äúdecision-theoretic framework application cost-effectiveness analysis regulatory processes.‚Äù Pharmacoeconomics, 27(8), 5--16. ISSN 20356137, doi:10.1007/bf03320526 . Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.mixedAn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Methods For Objects in the Class mixedAn (Mixed Analysis) ‚Äî summary.mixedAn","text":"","code":"# See Baio G., Dawid A.P. (2011) for a detailed description of the  # Bayesian model and economic problem  # Load the processed results of the MCMC simulation model data(Vaccine)  # Runs the health economic evaluation using BCEA m <- bcea(e=eff, c=cost,    # defines the variables of                              #  effectiveness and cost       ref=2,                # selects the 2nd row of (e,c)                              #  as containing the reference intervention       interventions=treats, # defines the labels to be associated                              #  with each intervention       Kmax=50000            # maximum value possible for the willingness                              #  to pay threshold; implies that k is chosen                              #  in a grid from the interval (0,Kmax) )  mixedAn(m) <- NULL      # uses the results of the mixed strategy                          #  analysis (a \"mixedAn\" object)                         # the vector of market shares can be defined                          #  externally. If NULL, then each of the T                          #  interventions will have 1/T market share  # Prints a summary of the results summary(m,         # uses the results of the mixed strategy analysis          wtp=25000) #  (a \"mixedAn\" object) #>  #> Analysis of mixed strategy for willingness to pay parameter k = 25000 #>  #> Reference intervention: Vaccination (50.00% market share) #> Comparator intervention: Status Quo (50.00% market share) #>  #> Loss in the expected value of information = 0.61 #>                     # selects the relevant willingness to pay                     #  (default: 25,000)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":null,"dir":"Reference","previous_headings":"","what":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"Produces table printout summary results health economic evaluation.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"","code":"# S3 method for pairwise summary(object, wtp = 25000, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"object pairwise object containing results Bayesian modelling economic evaluation. wtp value willingness pay threshold used summary table. ... Additional arguments affecting summary produced.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"Prints summary table information health economic output synthetic information economic measures (EIB, CEAC, EVPI).","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"Baio G, Dawid aP (2011). ‚ÄúProbabilistic sensitivity analysis health economics.‚Äù Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/. Baio G (2013). Bayesian Methods Health Economics. CRC.","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"Gianluca Baio","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/summary.pairwise.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Summary Method for Objects of Class pairwise ‚Äî summary.pairwise","text":"","code":"data(Vaccine) he <- bcea(eff, cost, interventions = treats, ref = 2) he_multi <- multi.ce(he) summary(he_multi) #>  #> Cost-effectiveness analysis summary  #>  #> Intervention(s): Status Quo #>                : Vaccination #>  #> Optimal decision: choose Status Quo for k < 20100 and Vaccination for k >= 20100 #>  #>  #> Analysis for willingness to pay parameter k = 25000 #>  #>             Expected net benefit EIB  CEAC ICER #> Status Quo               -36.054  NA 0.471   NA #> Vaccination              -34.826  NA 0.529   NA #>  #> Optimal intervention (max expected net benefit) for k = 25000: Vaccination #>             #> EVPI 2.4145"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","title":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","text":"Calculate Dataset ICERs bcea Object","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","text":"","code":"tabulate_means(he, comp_label = NULL, ...)"},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","text":"bcea object containing results Bayesian modelling economic evaluation. comp_label Optional vector strings comparison labels ... Additional arguments","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","text":"data.frame object including mean outcomes, comparison identifier, comparison label associated ICER","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/tabulate_means.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate Dataset For ICERs From bcea Object ‚Äî tabulate_means","text":"","code":"data(\"Smoking\") he <- BCEA::bcea(eff, cost) #> No reference selected. Defaulting to first intervention. tabulate_means(he) #>                  lambda.e   lambda.c comparison label     ICER #> intervention 2 -0.2882396  -45.73316          1     1 158.6637 #> intervention 3 -0.4848577  -94.91904          2     2 195.7668 #> intervention 4 -0.7225198 -143.30076          3     3 198.3347"},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"bcea theme ggplot2 ‚Äî theme_bcea","title":"bcea theme ggplot2 ‚Äî theme_bcea","text":"bcea theme ggplot2","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/theme_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"bcea theme ggplot2 ‚Äî theme_bcea","text":"","code":"theme_default()  theme_ceac()  theme_ceplane()  theme_eib()  theme_contour()"},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":null,"dir":"Reference","previous_headings":"","what":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination ‚Äî Vaccine","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination ‚Äî Vaccine","text":"data set contains results Bayesian analysis used model clinical output costs associated influenza vaccination.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination ‚Äî Vaccine","text":"data list including variables needed influenza vaccination. variables follows: list(\"cost\") matrix simulations posterior distribution overall costs associated two treatments list(\"c.pts\")  list(\"cost.GP\") matrix simulations posterior distribution costs GP visits associated two treatments list(\"cost.hosp\") matrix simulations posterior distribution costs hospitalisations associated two treatments list(\"cost.otc\") matrix simulations posterior distribution costs --counter medications associated two treatments list(\"cost.time.\") matrix simulations posterior distribution costs time work associated two treatments list(\"cost.time.vac\") matrix simulations posterior distribution costs time needed get vaccination associated two treatments list(\"cost.travel\") matrix simulations posterior distribution costs travel get vaccination associated two treatments list(\"cost.trt1\") matrix simulations posterior distribution overall costs first line treatment associated two interventions list(\"cost.trt2\") matrix simulations posterior distribution overall costs second line treatment associated two interventions list(\"cost.vac\") matrix simulations posterior distribution costs vaccination list(\"eff\") matrix simulations posterior distribution clinical benefits associated two treatments list(\"e.pts\")  list(\"N\") number subjects reference population list(\"N.outcomes\") number clinical outcomes analysed list(\"N.resources\") number health-care resources study list(\"QALYs.adv\") vector posterior distribution QALYs associated advert events list(\"QALYs.death\") vector posterior distribution QALYs associated death list(\"QALYs.hosp\") vector posterior distribution QALYs associated hospitalisation list(\"QALYs.inf\") vector posterior distribution QALYs associated influenza infection list(\"QALYs.pne\") vector posterior distribution QALYs associated pneumonia list(\"treats\") vector labels associated two treatments list(\"vaccine_mat\") matrix containing simulations parameters used original model","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination ‚Äî Vaccine","text":"Adapted Turner D, Wailoo , Cooper N, Sutton , Abrams K, Nicholson K. cost-effectiveness influenza vaccination healthy adults 50-64 years age. Vaccine. 2006;24:1035-1043.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/Vaccine.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Data set for the Bayesian model for the cost-effectiveness of influenza\r\nvaccination ‚Äî Vaccine","text":"Baio, G., Dawid, . P. (2011). Probabilistic Sensitivity Analysis Health Economics. Statistical Methods Medical Research doi:10.1177/0962280211419832.","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate bcea ‚Äî validate_bcea","title":"Validate bcea ‚Äî validate_bcea","text":"Validate bcea","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate bcea ‚Äî validate_bcea","text":"","code":"validate_bcea(eff, cost, ref, interventions)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_bcea.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate bcea ‚Äî validate_bcea","text":"eff Effectiveness matrix cost Cost matrix ref Reference intervention interventions interventions","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate EIB parameters ‚Äî validate_eib_params","title":"Validate EIB parameters ‚Äî validate_eib_params","text":"Validate EIB parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate EIB parameters ‚Äî validate_eib_params","text":"","code":"validate_eib_params(params)"},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate EIB parameters ‚Äî validate_eib_params","text":"params Graph parameters","code":""},{"path":"https://n8thangreen.github.io/BCEA/reference/validate_eib_params.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate EIB parameters ‚Äî validate_eib_params","text":"List graph parameters","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-246","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.6","title":"BCEA 2.4.6","text":"February 2024 Patch fixing small bugs last CRAN release. Converted help documentation man-roxygen folder md (cf858b1) bugfix: line width CEAC plot. ggplot2 changed version 3 linewidth size argument changed code. Updated scale_linewidth_manual(). (60bea9c) Using testdata folder testthat unit tests. (cbce0fa)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-245","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.5","title":"BCEA 2.4.5","text":"November 2023 Moved internal EVPPI calculation BCEA now uses voi package instead. Refactoring retaining interface functionality. Latest version voi needed use check = TRUE voi::evppi() order access fitting data (6e436b5, 94f5fc5) evppi() tested use cases BCEA book (1c1457d2) Select parameters position (well name) new evppi() (f2e4d005) Use single parameter case like voi package methods sal (#140) New evppi() matching output old evppi() (1e2c5e7) Latest development version voi needed use check = TRUE voi::evppi() order access fitting data (6e436b5, 94f5fc5) longer require INLA package available inside BCEA can remove direct dependency. helps passing CRAN checks GitHub Actions ()","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-244","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.4","title":"BCEA 2.4.4","text":"June 2023 Patch fix CRAN checks error. Suggested package MCMCvis wasn‚Äôt used conditionally unit test. Moved Required packages DESCRIPTION.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-243","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.3","title":"BCEA 2.4.3","text":"May 2023","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-3","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.3","text":"Consistent colours across plots intervention grid plots plot.bcea() (cf1ee43) make.report() change variable name (f940f2e) Fixed issue summary table names interventions wrong order (6a006e3) summary.bcea() now prints results chosen comparisons always . kstar best bcea() object updated subset interventions (#125)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-3","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.3","text":"withr::with_par() used plotting function plot.bcea() temporarily change graphics parameters. (725c536) Using @md markdown syntax function documentation Update psa.struct() add absolute value formula compute weights (1cea278) Use dplyr piping new syntax .data$* simply using speech marks \"*\" (2b280ad)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-3","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.3","text":"Template added GitHub Issues (0ea59fa)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-242","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.2","title":"BCEA 2.4.2","text":"August 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.2","text":"summary.bcea() wasn‚Äôt passing wtp argument sim_table() internally (5440eb3) summary() basic bcea multi.ce objects. Now summary.pairwise() method. (88ade51) struct.psa() output now works summary() plots still work without use $ get bcea object . (b014c83) Changed wtp argument bcea() k wtp plotting functions refers wtp line scalar whereas k grid points. Added error message use new argument. (b014c83) bcea() still allows scalar k added warning give empty plots. Updated GitHub Actions checking package use r-lib/Actions version 2. error finding INLA solved Gabor RStudio (see thread https://community.rstudio.com/t/-finding-inla-package---cran--actions/141398) GrassmannOptim package r-release-macos-x86_64 isn‚Äôt available resulting CRAN check error doesn‚Äôt appear maintained. Tried emailing author bounced. Removed dependency copied GrassmannOptim() function inside package acknowledgement.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"refactoring-2-4-2","dir":"Changelog","previous_headings":"","what":"Refactoring","title":"BCEA 2.4.2","text":"Now uses Rdpack bibliography documentation (229c96d) cost health values Smoking Vaccine data sets renamed c e cost eff. avoid conflict c() function. Changed axes labels cost-effectiveness planes ‚Äúdifferential‚Äù ‚Äúincremental‚Äù. (688d98b)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-2","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.2","text":"Can now specify order interventions labels legend ce plane (contour plots) base R ggplot2 .e.¬†reference first second optional ref_first argument (cc38f07) Can specify currency axes ceplane.plot() ceac.plot() ggplot2 versions (6808aa6) Argument added ceplane.plot() icer_annot annotate ICER points text label intervention name. ggplot2 moment. (a7b4beb) Added pos argument contour2() consistent contour() ceplane.plot(). (50f8f8b) Allow passing ref argument name well index bcea(). (9eab459)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2412","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.2","title":"BCEA 2.4.1.2","text":"April 2022","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bug-fixes-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"BCEA 2.4.1.2","text":"ceplane_ggplot() missing legend Legend bug evppi() Arguments consistent order ceplane.plot() ceplane_plot_base() wasn‚Äôt showing grey area. Fixed removing alpha transparency ceac.plot() wasn‚Äôt showing confidence interval default one comparison Typo fixed dropping dimension compute_vi() setReferenceGroup() CEAC plot legend error; doesn‚Äôt use supplied names generic intervention 1, intervention 2, ‚Ä¶ (#82) Missing multi.ce() line reference group (#80)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-2","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.2","text":"Use cli package warning messages Removed internal helper functions Manual using @keyword internal Refactor contour plots Extend function (ceac.plot()) take style arguments e.g.¬†colour lines, types points line thickness. Resuse ceplane.plot() code contour() line length, seq_len(), remove ; Contributor guidelines (#93) Deprecated functions document contour2() changed xlim, ylim arguments optional; ceplane.plot() since passes contour() ceplane.plot() vignettes written","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-2411","dir":"Changelog","previous_headings":"","what":"BCEA 2.4.1.1","title":"BCEA 2.4.1.1","text":"Oct 2021","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"major-refactoring-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Major refactoring","title":"BCEA 2.4.1.1","text":"Code base improved robustness extensibility. bcea() now helper function calls constructor new_bcea(), separating concerns. new_bcea() composed smaller HEE statistics functions names starting compute_* e.g.¬†compute_CEAC(), compute_EIB(),‚Ä¶. allows us call test individually. also allows flexibility changing adding functionality new_bcea(). Plotting functions rewritten. functions now simply dispatch base R, ggplot2 plotly versions (think strategy pattern). Internally, functions, e.g.ceplane_plot_ggplot(), also split parameter data setting plotting components. modulisation allows us add new layers plots modify existing parameter sets defaults. also return data without plotting step e.g.¬†ggplot2::autoplot(). also means can reuse functionality across plots axes legend setting e.g.¬†BCEA:::where_legend(). Deprecated mce.plot(). Now dispatched ceac.plot() multi.ce() bcea() outputs. multiple comparison plot pairwise comparison interventions returned default. alternative version comparison reference group still available. Plots tables using S3 methods bcea type object. Tables updated. Duplication summary() sim_table() removed. createInputs() used EVPI calculation now dispatches S3 methods JAGS, BUGS, Stan R data types. make.report() rewritten separate section files.","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"new-features-2-4-1-1","dir":"Changelog","previous_headings":"","what":"New features","title":"BCEA 2.4.1.1","text":"Extend ways set comparison interventions. Subsets comparison can still set call plotting function . Now subsets can set original bcea() construction separately using setter functions setComparisons(). Similarly, maximum willingness pay reference group can set setKmax() setReferenceGroup(), respectively. multi.ce() CEriskAv() also now work similarly. operate modifying bcea object, rather creating new one (think decorator pattern). bcea() methods JAGS, WinBUGS, Stan (#76)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"miscellaneous-2-4-1-1","dir":"Changelog","previous_headings":"","what":"Miscellaneous","title":"BCEA 2.4.1.1","text":"Additional help documentation examples. New vignettes plotting comparison intervention setting. Testing suite started. comprehensive yet. Added NEWS.md file track changes package. Details previous releases, dates, versions, fixes enhancements obtained CRAN code comments little patchy. pkgdown GitHub site made. Cheatsheet written published RStudio site (#22). Dependency package ldr removed BCEA removed CRAN (#74)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-11","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1.1","title":"BCEA 2.3-1.1","text":"26 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-23-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.3-1","title":"BCEA 2.3-1","text":"5 Aug 2019","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-6","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-6","title":"BCEA 2.2-6","text":"11 July 2018 Fix evppi allow N selected methods Fix diag.evppi","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-5","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-5","title":"BCEA 2.2-5","text":"18 Nov 2016 changes EVPPI","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-224","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.4","title":"BCEA 2.2.4","text":"Nov 2016 Fixes new ggplot2 version (legend.spacing() plot.title hjust argument)","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-3","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-3","title":"BCEA 2.2-3","text":"22 May 2016 Major update EVPPI include PFC Fixed issues info.rank","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.2-2","title":"BCEA 2.2-2","text":"25 Jan 2016 Minor change ceef.plot align ggplot2 v2.0.0","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-221","dir":"Changelog","previous_headings":"","what":"BCEA 2.2.1","title":"BCEA 2.2.1","text":"Oct 2015 Adds info-rank plot","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-22","dir":"Changelog","previous_headings":"","what":"BCEA 2.2","title":"BCEA 2.2","text":"Oct 2015 Cleaned aligned R‚Äôs settings EVPPI function polished ","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-1","title":"BCEA 2.1-1","text":"6 May 2015 2015 New function EVPPI using SPDE-INLA Modifications EVPPI functions Documentation updated Allows xlim & ylim ceplane.plot(), contour() contour2() functions now possible run bcea scalar wtp Old evppi() function method renamed evppi0, means ‚Äôs also new plot.evppi0 method","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0","title":"BCEA 2.1-0","text":"13 Jan 2015 Migrated (require()) (requireNamespace(,quietly=TRUE)) Documentation updated Added threshold argument ceef.plot function","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-210-pre2","dir":"Changelog","previous_headings":"","what":"BCEA 2.1.0-pre2","title":"BCEA 2.1.0-pre2","text":"Oct 2014 modifications ceef.plot, createInputs, struct.psa","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-21-0-pre1","dir":"Changelog","previous_headings":"","what":"BCEA 2.1-0-pre1","title":"BCEA 2.1-0-pre1","text":"13 Jan 2015 Documentation updated Smoking dataset ceef.plot function included, additional modifications","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2c","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2c","title":"BCEA 2.0-2c","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-v20-2b","dir":"Changelog","previous_headings":"","what":"BCEA v2.0-2b","title":"BCEA v2.0-2b","text":"2 Dec 2013 ceac.plot eib.plot: option comparison included base graphics","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-2","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-2","title":"BCEA 2.0-2","text":"2 Dec 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20-1","dir":"Changelog","previous_headings":"","what":"BCEA 2.0-1","title":"BCEA 2.0-1","text":"31 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-20","dir":"Changelog","previous_headings":"","what":"BCEA 2.0","title":"BCEA 2.0","text":"30 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"feature-updates-2-0","dir":"Changelog","previous_headings":"","what":"Feature updates","title":"BCEA 2.0","text":"Implements two quick general methods compute EVPPI Function CreateInputs(), takes input object class rjags bugs Compute EVPPI one parameters calling function evppi() Results can visualised using specific method plot class evppi show overall EVPI EVPPI selected parameter(s)","code":""},{"path":[]},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-13-0","dir":"Changelog","previous_headings":"","what":"BCEA 1.3-0","title":"BCEA 1.3-0","text":"3 July 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-12","dir":"Changelog","previous_headings":"","what":"BCEA 1.2","title":"BCEA 1.2","text":"17 September 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-111","dir":"Changelog","previous_headings":"","what":"BCEA 1.1.1","title":"BCEA 1.1.1","text":"22 Feb 2013","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-11","dir":"Changelog","previous_headings":"","what":"BCEA 1.1","title":"BCEA 1.1","text":"15 Sept 2012","code":""},{"path":"https://n8thangreen.github.io/BCEA/news/index.html","id":"bcea-10","dir":"Changelog","previous_headings":"","what":"BCEA 1.0","title":"BCEA 1.0","text":"13 May 2012","code":""}]
