% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/TaylorDiagram.R
\name{TaylorDiagram}
\alias{TaylorDiagram}
\title{Taylor Diagram for model evaluation with conditioning}
\usage{
TaylorDiagram(
  mydata,
  obs = "obs",
  mod = "mod",
  group = NULL,
  type = "default",
  normalise = FALSE,
  cols = "brewer1",
  rms.col = "darkgoldenrod",
  cor.col = "black",
  arrow.lwd = 3,
  annotate = "centred\\nRMS error",
  text.obs = "observed",
  key = TRUE,
  key.title = group,
  key.columns = 1,
  key.pos = "right",
  strip = TRUE,
  auto.text = TRUE,
  ...
)
}
\arguments{
\item{mydata}{A data frame minimally containing a column of observations and
a column of predictions.}

\item{obs}{A column of observations with which the predictions (\code{mod})
will be compared.}

\item{mod}{A column of model predictions. Note, \code{mod} can be of length 2
i.e. two lots of model predictions. If two sets of predictions are are
present e.g. \code{mod = c("base", "revised")}, then arrows are shown on
the Taylor Diagram which show the change in model performance in going from
the first to the second. This is useful where, for example, there is
interest in comparing how one model run compares with another using
different assumptions e.g. input data or model set up. See examples below.}

\item{group}{The \code{group} column is used to differentiate between
different models and can be a factor or character. The total number of
models compared will be equal to the number of unique values of
\code{group}.

\code{group} can also be of length two e.g. \code{group = c("model",
  "site")}. In this case all model-site combinations will be shown but they
will only be differentiated by colour/symbol by the first grouping variable
("model" in this case). In essence the plot removes the differentiation by
the second grouping variable. Because there will be different values of
\code{obs} for each group, \code{normalise = TRUE} should be used.}

\item{type}{\code{type} determines how the data are split i.e. conditioned,
and then plotted. The default is will produce a single plot using the
entire data. Type can be one of the built-in types as detailed in
\code{cutData} e.g. \dQuote{season}, \dQuote{year}, \dQuote{weekday} and so
on. For example, \code{type = "season"} will produce four plots --- one for
each season.

It is also possible to choose \code{type} as another variable in the data
frame. If that variable is numeric, then the data will be split into four
quantiles (if possible) and labelled accordingly. If type is an existing
character or factor variable, then those categories/levels will be used
directly. This offers great flexibility for understanding the variation of
different variables and how they depend on one another.

Type can be up length two e.g. \code{type = c("season", "weekday")} will
produce a 2x2 plot split by season and day of the week. Note, when two
types are provided the first forms the columns and the second the rows.

Note that often it will make sense to use \code{type = "site"} when
multiple sites are available. This will ensure that each panel contains
data specific to an individual site.}

\item{normalise}{Should the data be normalised by dividing the standard
deviation of the observations? The statistics can be normalised (and
non-dimensionalised) by dividing both the RMS difference and the standard
deviation of the \code{mod} values by the standard deviation of the
observations (\code{obs}). In this case the \dQuote{observed} point is
plotted on the x-axis at unit distance from the origin. This makes it
possible to plot statistics for different species (maybe with different
units) on the same plot. The normalisation is done by each
\code{group}/\code{type} combination.}

\item{cols}{Colours to be used for plotting. Useful options for categorical
data are available from \code{RColorBrewer} colours --- see the
\code{openair} \code{openColours} function for more details. Useful schemes
include \dQuote{Accent}, \dQuote{Dark2}, \dQuote{Paired}, \dQuote{Pastel1},
\dQuote{Pastel2}, \dQuote{Set1}, \dQuote{Set2}, \dQuote{Set3} --- but see
?\code{brewer.pal} for the maximum useful colours in each. For user defined
the user can supply a list of colour names recognised by R (type
\code{colours()} to see the full list). An example would be \code{cols =
  c("yellow", "green", "blue")}.}

\item{rms.col}{Colour for centred-RMS lines and text.}

\item{cor.col}{Colour for correlation coefficient lines and text.}

\item{arrow.lwd}{Width of arrow used when used for comparing two model
outputs.}

\item{annotate}{Annotation shown for RMS error.}

\item{text.obs}{The plot annotation for observed values; default is
"observed".}

\item{key}{Should the key be shown?}

\item{key.title}{Title for the key.}

\item{key.columns}{Number of columns to be used in the key. With many
pollutants a single column can make to key too wide. The user can thus
choose to use several columns by setting \code{columns} to be less than the
number of pollutants.}

\item{key.pos}{Position of the key e.g. \dQuote{top}, \dQuote{bottom},
\dQuote{left} and \dQuote{right}. See details in \code{lattice:xyplot} for
more details about finer control.}

\item{strip}{Should a strip be shown?}

\item{auto.text}{Either \code{TRUE} (default) or \code{FALSE}. If \code{TRUE}
titles and axis labels will automatically try and format pollutant names
and units properly e.g.  by subscripting the `2' in NO2.}

\item{...}{Other graphical parameters are passed onto \code{cutData} and
\code{lattice:xyplot}. For example, \code{TaylorDiagram} passes the option
\code{hemisphere = "southern"} on to \code{cutData} to provide southern
(rather than default northern) hemisphere handling of \code{type =
  "season"}. Similarly, common graphical parameters, such as \code{layout}
for panel arrangement and \code{pch} and \code{cex} for plot symbol type
and size, are passed on to \code{xyplot}. Most are passed unmodified,
although there are some special cases where \code{openair} may locally
manage this process. For example, common axis and title labelling options
(such as \code{xlab}, \code{ylab}, \code{main}) are passed via
\code{quickText} to handle routine formatting.}
}
\value{
an \link[=openair-package]{openair} object. If retained, e.g., using
\code{output <- TaylorDiagram(thedata, obs = "nox", mod = "mod")}, this
output can be used to recover the data, reproduce or rework the original
plot or undertake further analysis. For example, \code{output$data} will be
a data frame consisting of the group, type, correlation coefficient (R),
the standard deviation of the observations and measurements.
}
\description{
Function to draw Taylor Diagrams for model evaluation. The function allows
conditioning by any categorical or numeric variables, which makes the
function very flexible.
}
\details{
The Taylor Diagram is a very useful model evaluation tool. The diagram
provides a way of showing how three complementary model performance
statistics vary simultaneously. These statistics are the correlation
coefficient R, the standard deviation (sigma) and the (centred)
root-mean-square error. These three statistics can be plotted on one (2D)
graph because of the way they are related to one another which can be
represented through the Law of Cosines.

The \code{openair} version of the Taylor Diagram has several enhancements
that increase its flexibility. In particular, the straightforward way of
producing conditioning plots should prove valuable under many circumstances
(using the \code{type} option). Many examples of Taylor Diagrams focus on
model-observation comparisons for several models using all the available
data. However, more insight can be gained into model performance by
partitioning the data in various ways e.g. by season, daylight/nighttime, day
of the week, by levels of a numeric variable e.g. wind speed or by land-use
type etc.

To consider several pollutants on one plot, a column identifying the
pollutant name can be used e.g. \code{pollutant}. Then the Taylor Diagram can
be plotted as (assuming a data frame \code{thedata}):

\code{TaylorDiagram(thedata, obs = "obs", mod = "mod", group = "model", type
= "pollutant")}

which will give the model performance by pollutant in each panel.

Note that it is important that each panel represents data with the same mean
observed data across different groups. Therefore \code{TaylorDiagram(mydata,
group = "model", type = "season")} is OK, whereas \code{TaylorDiagram(mydata,
group = "season", type = "model")} is not because each panel (representing a
model) will have four different mean values --- one for each season.
Generally, the option \code{group} is either missing (one model being
evaluated) or represents a column giving the model name. However, the data
can be normalised using the \code{normalise} option. Normalisation is carried
out on a per \code{group}/\code{type} basis making it possible to compare
data on different scales e.g. \code{TaylorDiagram(mydata, group = "season",
type = "model", normalise = TRUE)}. In this way it is possible to compare
different pollutants, sites etc. in the same panel.

Also note that if multiple sites are present it makes sense to use \code{type
= "site"} to ensure that each panel represents an individual site with its
own specific standard deviation etc. If this is not the case then select a
single site from the data first e.g. \code{subset(mydata, site ==
"Harwell")}.
}
\examples{
## in the examples below, most effort goes into making some artificial data
## the function itself can be run very simply
\dontrun{
## dummy model data for 2003
dat <- selectByDate(mydata, year = 2003)
dat <- data.frame(date = mydata$date, obs = mydata$nox, mod = mydata$nox)

## now make mod worse by adding bias and noise according to the month
## do this for 3 different models
dat <- transform(dat, month = as.numeric(format(date, "\%m")))
mod1 <- transform(dat, mod = mod + 10 * month + 10 * month * rnorm(nrow(dat)),
model = "model 1")
## lag the results for mod1 to make the correlation coefficient worse
## without affecting the sd
mod1 <- transform(mod1, mod = c(mod[5:length(mod)], mod[(length(mod) - 3) :
length(mod)]))

## model 2
mod2 <- transform(dat, mod = mod + 7 * month + 7 * month * rnorm(nrow(dat)),
model = "model 2")
## model 3
mod3 <- transform(dat, mod = mod + 3 * month + 3 * month * rnorm(nrow(dat)),
model = "model 3")

mod.dat <- rbind(mod1, mod2, mod3)

## basic Taylor plot

TaylorDiagram(mod.dat, obs = "obs", mod = "mod", group = "model")

## Taylor plot by season
TaylorDiagram(mod.dat, obs = "obs", mod = "mod", group = "model", type = "season")

## now show how to evaluate model improvement (or otherwise)
mod1a <- transform(dat, mod = mod + 2 * month + 2 * month * rnorm(nrow(dat)),
model = "model 1")
mod2a <- transform(mod2, mod = mod * 1.3)
mod3a <- transform(dat, mod = mod + 10 * month + 10 * month * rnorm(nrow(dat)),
model = "model 3")
mod.dat2 <- rbind(mod1a, mod2a, mod3a)
mod.dat$mod2 <- mod.dat2$mod

## now we have a data frame with 3 models, 1 set of observations
## and TWO sets of model predictions (mod and mod2)

## do for all models
TaylorDiagram(mod.dat, obs = "obs", mod = c("mod", "mod2"), group = "model")
}
\dontrun{
## all models, by season
TaylorDiagram(mod.dat, obs = "obs", mod = c("mod", "mod2"), group = "model",
type = "season")

## consider two groups (model/month). In this case all months are shown by model
## but are only differentiated by model.

TaylorDiagram(mod.dat, obs = "obs", mod = "mod", group = c("model", "month"))
}
}
\references{
Taylor, K.E.: Summarizing multiple aspects of model performance in a single
diagram. J.  Geophys. Res., 106, 7183-7192, 2001 (also see PCMDI Report 55).
}
\seealso{
\code{taylor.diagram} from the \code{plotrix} package from which
some of the annotation code was used.

Other model evaluation functions: 
\code{\link{conditionalEval}()},
\code{\link{conditionalQuantile}()},
\code{\link{modStats}()}
}
\author{
David Carslaw
}
\concept{model evaluation functions}
