---
title: "How to Use the rstanarm Package"
author: "Jonah Gabry and Ben Goodrich"
date: "`r Sys.Date()`"
output: 
  html_vignette: 
    toc: yes
---
<!--
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{How to Use the rstanarm Package}
-->
```{r, child="children/SETTINGS-knitr.txt"}
```
```{r, child="children/SETTINGS-gg.txt"}
```

# Introduction

This vignette provides an _overview_ of how to use the functions in the 
__rstanarm__ package that focuses on commonalities. The other __rstanarm__ 
vignettes go into the particularities of each of the individual model-estimating
functions.

The goal of the __rstanarm__ package is to make Bayesian estimation _routine_
for the most common regression models that applied researchers use. This will
enable researchers to avoid the counter-intuitiveness of the frequentist
approach to probability and statistics with only minimal changes to their
existing R scripts.

```{r, child="children/four_steps.txt"}
```

Step 1 is necessarily model-specific and is covered in more detail in the other 
vignettes that cover specific forms of the marginal prior distribution and 
likelihood of the outcome. It is somewhat more involved than the corresponding 
first step of a frequentist analysis, which only requires that the likelihood of
the outcome be specified. However, the default priors in the __rstanarm__ 
package should work well in the majority of cases. Steps 2, 3, and 4 are the 
focus of this vignette because they are largely not specific to how the joint 
distribution in Step 1 is specified.

The key concept in Step 3 and Step 4 is the posterior predictive distribution, 
which is the distribution of the outcome implied by the model after having used
the observed data to update our beliefs about the unknown parameters. 
Frequentists, by definition, have no posterior predictive distribution and 
frequentist predictions are subtly different from what applied researchers seek.
Maximum likelihood estimates do _not_ condition on the observed outcome data and
so the uncertainty in the estimates pertains to the variation in the sampling 
distribution of the estimator, i.e. the distribution of estimates that would 
occur if we could repeat the process of drawing a random sample from a 
well-defined population and apply the estimator to each sample. It is possible 
to construct a distribution of predictions under the frequentist paradigm but it
evokes the hypothetical of repeating the process of drawing a random sample, 
applying the estimator each time, and generating point predictions of the 
outcome. In contrast, the posterior predictive distribution conditions on the 
observed outcome data in hand to update beliefs about the unknowns and the 
variation in the resulting distribution of predictions reflects the remaining 
uncertainty in our beliefs about the unknowns.

# Step 1: Specify a posterior distribution

For the sake of discussion, we need some posterior distribution to draw from. 
We will utilize an example from the __HSAUR3__ package by Brian S. Everitt and
Torsten Hothorn, which is used in their 2014 book 
_A Handbook of Statistical Analyses Using R (3rd Edition)_ (Chapman & Hall /
CRC). This book is frequentist in nature and we will show how to obtain the 
corresponding Bayesian results.

The model in section 6.3.2 pertains to whether a survey respondent agrees or 
disagrees with a conservative statement about the role of women in society, 
which is modeled as a function of the gender and education of the respondents. 
The posterior distribution --- with independent priors --- can be written as
$$f\left(\alpha,\beta_1,\beta_2|\mathbf{y},\mathbf{X}\right) \propto
  f\left(\alpha\right) f\left(\beta_1\right) f\left(\beta_2\right) \times
  \prod_{i=1}^J {
  g^{-1}\left(\eta_i\right)^{y_i} 
  \left(1 - g^{-1}\left(\eta_i\right)\right)^{n_i-y_i}},$$
where $\eta_i = \alpha + \beta_1 \mbox{education}_i + \beta_2 \mbox{Female}_i$ 
is the linear predictor and a function of an intercept $\left(\alpha\right)$, a
coefficient on the years of education $\left(\beta_1\right)$, and an 
intercept-shift $\left(\beta_2\right)$ for the case where the respondent is 
female. These data are organized such that $y_i$ is the number of respondents 
who agree with the statement that have the same level of education and the same 
gender, and $n_i - y_i$ is the number of such people who disagree with the 
statement. The inverse link function, $p = g^{-1}\left(\eta_i \right)$, for a 
binomial likelihood can be one of several Cumulative Distribution Functions 
(CDFs) but in this case is the standard logistic CDF,
$g^{-1}\left(\eta_i \right)=\frac{1}{1 + e^{-\eta_i}}$.

Suppose we believe --- prior to seeing the data --- that $\alpha$, $\beta_1$,
and $\beta_2$ are probably close to zero, are as likely to be positive as they
are to be negative, but have a small chance of being quite far from zero. These
beliefs can be represented by Student t distributions with a few degrees of
freedom in order to produce moderately heavy tails. In particular, we will
specify seven degrees of freedom. Note that these purported beliefs may well be
more skeptical than your actual beliefs, which are probably that women and
people with more education have less conservative societal views.

### Note on "prior beliefs" and default priors

In this vignette we use the term "prior beliefs" to refer in generality to the 
information content of the prior distribution (conditional on the model). 
Sometimes previous research on the topic of interest motivates beliefs about 
model parameters, but other times such work may not exist or several studies may
make contradictory claims. Regardless, we nearly always have _some_ knowledge 
that should be reflected in our choice of prior distributions. For example, no 
one believes a logistic regression coefficient will be greater than five in 
absolute value if the predictors are scaled reasonably. You may also have seen
examples of so-called "non-informative" (or "vague", "diffuse", etc.) priors 
like a normal distribution with a variance of 1000. When data are reasonably 
scaled, these priors are almost always a bad idea for various reasons (they give
non-trivial weight to extreme values, reduce computational efficiency, etc). The
default priors in __rstanarm__ are designed to be _weakly informative_, by which
we mean that they avoid placing unwarranted prior weight on nonsensical 
parameter values and provide some regularization to avoid overfitting, but also 
do allow for extreme values if warranted by the data. If additional information 
is available, the weakly informative defaults can be replaced with more 
informative priors.

# Step 2: Draw from the posterior distribution

The likelihood for the sample is just the product over the $J$ groups of
$$g^{-1}\left(\eta_i \right)^{y_i} 
  \left(1 - g^{-1}\left(\eta_i \right)\right)^{n_i-y_i},$$
which can be maximized over $\alpha$, $\beta_1$, and $\beta_2$ to obtain 
frequentist estimates by calling
```{r rstanarm-mle, eval = TRUE}
data("womensrole", package = "HSAUR3")
womensrole$total <- womensrole$agree + womensrole$disagree
womensrole_glm_1 <- glm(cbind(agree, disagree) ~ education + gender,
                        data = womensrole, family = binomial(link = "logit"))
round(coef(summary(womensrole_glm_1)), 3)
```
The p-value for the null hypothesis that $\beta_1 = 0$ is very small, while the
p-value for the null hypothesis that $\beta_2 = 0$ is very large. However, 
frequentist p-values are awkward because they do not pertain to the probability 
that a scientific hypothesis is true but rather to the probability of observing 
a $z$-statistic that is so large (in magnitude) if the null hypothesis were
true. The desire to make probabilistic statements about a scientific hypothesis
is one reason why many people are drawn to the Bayesian approach.

A model with the same likelihood but Student t priors with seven degrees of 
freedom can be specified using the __rstanarm__ package in a similar way by 
prepending `stan_` to the `glm` call and specifying priors (and optionally the
number of cores on your computer to utilize):
```{r rstanarm-mcmc, results="hide", eval = TRUE}
library(rstanarm)
womensrole_bglm_1 <- stan_glm(cbind(agree, disagree) ~ education + gender,
                              data = womensrole,
                              family = binomial(link = "logit"), 
                              prior = student_t(df = 7, 0, 5), 
                              prior_intercept = student_t(df = 7, 0, 5),
                              cores = 2, seed = 12345)
womensrole_bglm_1
```
```{r, echo=FALSE, eval = TRUE}
print(womensrole_bglm_1)
```
As can be seen, the "Bayesian point estimates" --- which are represented by the 
posterior medians --- are very similar to the maximum likelihood estimates. 
Frequentists would ask whether the point estimate is greater in magnitude than 
double the estimated standard deviation of the sampling distribution. But here 
we simply have estimates of the standard deviation of the marginal posterior 
distributions, which are based on a scaling of the Median Absolute Deviation 
(MAD) from the posterior medians to obtain a robust estimator of the posterior 
standard deviation. In addition, we can use the `posterior_interval` function to
obtain a Bayesian uncertainty interval for $\beta_1$:
```{r rstanarm-ci, eval = TRUE}
ci95 <- posterior_interval(womensrole_bglm_1, prob = 0.95, pars = "education")
round(ci95, 2)
```
Unlike frequentist confidence intervals --- which are _not_ interpretable in 
terms of post-data probabilities --- the Bayesian uncertainty interval indicates
we believe after seeing the data that there is a $0.95$ probability that 
$\beta_2$ is between `ci95[1,1]` and `ci95[1,2]`. Alternatively, we could say
that there is essentially zero probability that $\beta_2 > 0$, although
frequentists cannot make such a claim coherently.

Many of the post-estimation methods that are available for a model that is
estimated by `glm` are also available for a model that is estimated by 
`stan_glm`. For example,
```{r rstanarm-methods, eval = TRUE}
cbind(Median = coef(womensrole_bglm_1), MAD_SD = se(womensrole_bglm_1))
summary(residuals(womensrole_bglm_1)) # not deviance residuals
cov2cor(vcov(womensrole_bglm_1))
```

__rstanarm__ does provide a `confint` method, although it is reserved for 
computing confidence intervals in the case that the user elects to estimate a 
model by (penalized) maximum likelihood. When using full Bayesian inference (the
__rstanarm__ default) or approximate Bayesian inference the `posterior_interval`
function should be used to obtain Bayesian uncertainty intervals.

# Step 3: Criticize the model

The `launch_shinystan` function in the __shinystan__ package provides almost all
the tools you need to visualize the posterior distribution and diagnose any
problems with the Markov chains. In this case, the results are fine and to
verify that, you can call
```{r rstanarm-shinystan, eval = FALSE}
launch_shinystan(womensrole_bglm_1, ppd = FALSE)
```
which will open a web browser that drives the visualizations. 

For the rest of this subsection, we focus on what users can do programmatically
to evaluate whether a model is adequate. A minimal requirement for Bayesian
estimates is that the model should fit the data that the estimates conditioned 
on. The key function here is `posterior_predict`, which can be passed a new 
`data.frame` to predict out-of-sample, but in this case is omitted to obtain 
in-sample posterior predictions:
```{r rstanarm-posterior_predict, eval = TRUE}
y_rep <- posterior_predict(womensrole_bglm_1)
dim(y_rep)
```
The resulting matrix has rows equal to the number of posterior simulations, 
which in this case is $2000$ and columns equal to the number of observations in
the original dataset, which is $42$ combinations of education and gender. Each
element of this matrix is a predicted number of respondents with that value of
education and gender who agreed with the survey question and thus should be
reasonably close to the observed proportion of agreements in the data. We can
create a plot to check this:
```{r rstanarm-criticism-plot, fig.width=8, out.width="90%", fig.cap="Posterior predictive boxplots vs. observed datapoints", eval = TRUE}
par(mfrow = 1:2, mar = c(5,3.7,1,0) + 0.1, las = 3)
boxplot(sweep(y_rep[,womensrole$gender == "Male"], 2, STATS = 
               womensrole$total[womensrole$gender == "Male"], FUN = "/"), 
        axes = FALSE, main = "Male", pch = NA,
        xlab = "Years of Education", ylab = "Proportion of Agrees")
with(womensrole, axis(1, at = education[gender == "Male"] + 1, 
                      labels = 0:20))
axis(2, las = 1)
with(womensrole[womensrole$gender == "Male",], 
     points(education + 1,  agree / (agree + disagree), 
            pch = 16, col = "red"))
boxplot(sweep(y_rep[,womensrole$gender == "Female"], 2, STATS = 
          womensrole$total[womensrole$gender == "Female"], FUN = "/"), 
          axes = FALSE, main = "Female", pch = NA,
        xlab = "Years of Education", ylab = "")
with(womensrole, axis(1, at = education[gender == "Female"] + 1,
     labels = 0:20))
with(womensrole[womensrole$gender == "Female",], 
     points(education + 1,  agree / (agree + disagree), 
            pch = 16, col = "red"))
```
Here the boxplots provide the median, interquartile range, and hinges of the 
posterior predictive distribution for a given gender and level of education, 
while the red points represent the corresponding observed data. As can be seen, 
the model predicts the observed data fairly well for six to sixteen years of 
education but predicts less well for very low or very high levels of education 
where there are less data.

Consequently, we might consider a model where education has a quadratic effect 
on agreement, which is easy to specify using R's formula-based syntax.
```{r, rstanarm-update, results="hide", eval = TRUE}
(womensrole_bglm_2 <- update(womensrole_bglm_1, formula. = . ~ . + I(education^2)))
```
```{r, echo=FALSE}
print(womensrole_bglm_2)
```
Frequentists would test the null hypothesis that the coefficient on the squared
level of education is zero. Bayesians might ask whether such a model is expected
to produce better out-of-sample predictions than a model with only the level of
education. The latter question can be answered using leave-one-out
cross-validation or the approximation thereof provided by the `loo` function in
the __loo__ package, for which a method is provided by the __rstanarm__ package.
```{r rstanarm-loo, eval = TRUE}
loo_bglm_1 <- loo(womensrole_bglm_1)
loo_bglm_2 <- loo(womensrole_bglm_2)
```
First, we verify that the posterior is not too sensitive to any particular 
observation in the dataset.

```{r rstanarm-loo-plot, fig.width=7, out.width="70%", eval = TRUE}
par(mfrow = 1:2, mar = c(5,3.8,1,0) + 0.1, las = 3)
plot(loo_bglm_1, label_points = TRUE)
plot(loo_bglm_2, label_points = TRUE)
```

There are only one or two moderate outliers (whose statistics are greater than
$0.5$), which should not have too much of an effect on the resulting model 
comparison:
```{r, rstanarm-loo-compare, eval = TRUE}
loo_compare(loo_bglm_1, loo_bglm_2)
```
In this case, there is little difference in the expected log pointwise deviance
between the two models, so we are essentially indifferent between them after
taking into account that the second model estimates an additional parameter. The
"LOO Information Criterion (LOOIC)"
```{r, rstanarm-loo-print, eval = TRUE}
loo_bglm_1
```
has the same purpose as the Akaike Information Criterion (AIC) that is used by
frequentists. Both are intended to estimate the expected log predicted density 
(ELPD) for a new dataset. However, the AIC ignores priors and assumes that the 
posterior distribution is multivariate normal, whereas the functions from the 
loo package used here do not assume that the posterior distribution is 
multivariate normal and integrate over uncertainty in the parameters. This only 
assumes that any one observation can be omitted without having a major effect on
the posterior distribution, which can be judged using the plots above.

# Step 4: Analyze manipulations of predictors

Frequentists attempt to interpret the estimates of the model, which is difficult
except when the model is linear, has no inverse link function, and contains no
interaction terms. Bayesians can avoid this difficulty simply by inspecting the
posterior predictive distribution at different levels of the predictors. For
example,
```{r, rstanarm-posterior_predict-manipulate, eval = TRUE}
# note: in newdata we want agree and disagree to sum to the number of people we
# want to predict for. the values of agree and disagree don't matter so long as
# their sum is the desired number of trials. we need to explicitly imply the
# number of trials like this because our original data are aggregate. if we had
# bernoulli data then it would be a given we wanted to predict for single
# individuals.
newdata <- data.frame(agree = c(0,0), disagree = c(100,100), education = c(12,16),
                      gender = factor("Female", levels = c("Male", "Female")))
y_rep <- posterior_predict(womensrole_bglm_2, newdata)
summary(apply(y_rep, 1, diff))
```
As can be seen, out of $100$ women who have a college degree versus $100$ women 
with only a high school degree, we would expect about $20$ fewer
college-educated women to agree with the question. There is an even chance that
the difference is between $24$ and $16$, a one-in-four chance that it is
greater, and one-in-four chance that it is less.

# Troubleshooting

This section provides suggestions for how to proceed when you encounter warning 
messages generated by the modeling functions in the __rstanarm__ package. The 
example models below are used just for the purposes of concisely demonstrating 
certain difficulties and possible remedies (we won't worry about the merit of 
the models themselves). The references at the end provide more information on
the relevant issues.

### Markov chains did not converge

__Recommendation:__ run the chains for more iterations (in certain cases, see qualification below). <br>

By default, all __rstanarm__ modeling functions will run four randomly 
initialized Markov chains, each for 2000 iterations (including a warmup period 
of 1000 iterations that is discarded). All chains must converge to the target 
distribution for inferences to be valid. For most models, the default settings 
are sufficient, but if you see a warning message about Markov chains not 
converging, the first thing to try is increasing the number of iterations. This 
can be done by specifying the `iter` argument. However, if all parameters have
proper priors (no priors were set to `NULL`), and you used the default values
for iterations (2000) and chains (4), and Rhats (explained below) are greater
than 2, then increasing the number of iterations alone is unlikely to solve to
the problem.

One way to monitor whether a chain has converged to the equilibrium distribution
is to compare its behavior to other randomly initialized chains. This is the 
motivation for the Gelman and Rubin potential scale reduction statistic Rhat. 
The Rhat statistic measures the ratio of the average variance of the draws
within each chain to the variance of the pooled draws across chains; if all
chains are at equilibrium, these will be the same and Rhat will be one. If the
chains have not converged to a common distribution, the Rhat statistic will tend
to be greater than one.

Gelman and Rubin's recommendation is that the independent Markov chains be 
initialized with diffuse starting values for the parameters and sampled until 
all values for Rhat are below 1.1. When any Rhat values are above 1.1
__rstanarm__ will print a warning message like this:

    Markov chains did not converge! Do not analyze results! 

To illustrate how to check the Rhat values after fitting a model using 
__rstanarm__ we'll fit two models and run them for different numbers of 
iterations.

```{r, rstanarm-rhat-fit, results='hide', warning=TRUE, eval = TRUE}
bad_rhat <- stan_glm(mpg ~ ., data = mtcars, iter = 20, chains = 2, seed = 12345)
good_rhat <- update(bad_rhat, iter = 1000, chains = 2, seed = 12345)
```

Here the first model leads to the warning message about convergence but the 
second model does not. Indeed, we can see that many Rhat values are much bigger
than 1 for the first model:

```{r, rstasnarm-rhat-bad, eval = TRUE}
rhat <- summary(bad_rhat)[, "Rhat"]
rhat[rhat > 1.1]
```

Since we didn't get a warning for the second model we shouldn't find any 
parameters with an Rhat far from 1:

```{r, rstasnarm-rhat-good, eval = TRUE}
any(summary(good_rhat)[, "Rhat"] > 1.1)
```

Details on the computation of Rhat and some of its limitations can be found in
[Stan Modeling Language User's Guide and Reference Manual](https://mc-stan.org/users/documentation/).


### Divergent transitions

__Recommendation:__ increase the target acceptance rate `adapt_delta`. <br>

Hamiltonian Monte Carlo (HMC), the MCMC algorithm used by 
[Stan](https://mc-stan.org), works by simulating the evolution of a Hamiltonian 
system. Stan uses a [symplectic 
integrator](https://en.wikipedia.org/wiki/Symplectic_integrator) to approximate 
the exact solution of the Hamiltonian dynamics. When the step size parameter is 
too large relative to the curvature of the log posterior this approximation can 
diverge and threaten the validity of the sampler. __rstanarm__ will print a 
warning if there are any divergent transitions after the warmup period, in which
case the posterior sample may be biased. The recommended method is to increase 
the `adapt_delta` parameter -- target average proposal acceptance probability in
the adaptation -- which will in turn reduce the step size. Each of the modeling 
functions accepts an `adapt_delta` argument, so to increase `adapt_delta` you 
can simply change the value from the default value to a value closer to $1$. To 
reduce the frequency with which users need to manually set `adapt_delta`, the 
default value depends on the prior distribution used (see `help("adapt_delta", 
package = "rstanarm")` for details).

The downside to increasing the target acceptance rate -- and, as a consequence, 
decreasing the step size -- is that sampling will tend to be slower. 
Intuitively, this is because a smaller step size means that more steps are 
required to explore the posterior distribution. Since the validity of the 
estimates is not guaranteed if there are any post-warmup divergent transitions, 
the slower sampling is a minor cost.

### Maximum treedepth exceeded

__Recommendation:__ increase the maximum allowed treedepth `max_treedepth` unless
all other convergence diagnostics are ok. <br>

Configuring the No-U-Turn-Sampler (the variant of HMC used by Stan) involves 
putting a cap on the depth of the trees that it evaluates during each iteration.
This is controlled through a maximum depth parameter `max_treedepth`. When the 
maximum allowed tree depth is reached it indicates that NUTS is terminating 
prematurely to avoid excessively long execution time. If __rstanarm__ prints a
warning about transitions exceeding the maximum treedepth you should try 
increasing the `max_treedepth` parameter using the optional `control` argument. 
For example, to increase `max_treedepth` to 16 (the default used __rstanarm__ is
15) you can provide the argument `control = list(max_treedepth = 16)` to any of 
the __rstanarm__ modeling functions. If you do not see a warning about hitting 
the maximum treedepth (which is rare), then you do not need to worry.

With the models __rstanarm__ is capable of fitting, when you get a warning about 
max treedepth you will typically also get warnings about other diagnostics. However,
if you see a max treedepth warning but all other convergence diagnostics
are fine, you can typically ignore the warning. In that case the warning
likely indicates efficiency issues but not that the results are invalid to analyze.

# Conclusion

In this vignette, we have gone through the four steps of a Bayesian analysis. 
The first step --- specifying the posterior distribution --- varies considerably
from one analysis to the next because the likelihood function employed differs
depending on the nature of the outcome variable and our prior beliefs about the
parameters in the model varies not only from situation to situation but from
researcher to researcher. However, given a posterior distribution and given that
this posterior distribution can be drawn from using the __rstanarm__ package,
the remaining steps are conceptually similar across analyses. The key is to draw
from the posterior predictive distribution of the outcome, which is the what the
model predicts the outcome to be after having updated our beliefs about the
unknown parameters with the observed data. Posterior predictive distributions
can be used for model checking and for making inferences about how manipulations
of the predictors would affect the outcome.

Of course, all of this assumes that you have obtained draws from the posterior 
distribution faithfully. The functions in the __rstanarm__ package will throw 
warnings if there is evidence that the draws are tainted, and we have discussed 
some steps to remedy these problems. For the most part, the model-fitting 
functions in the __rstanarm__ package are unlikely to produce many such 
warnings, but they may appear in more complicated models.

If the posterior distribution that you specify in the first step cannot be 
sampled from using the __rstanarm__ package, then it is often possible to create
a hand-written program in the the Stan language so that the posterior 
distribution can be drawn from using the __rstan__ package. See the 
documentation for the __rstan__ package or https://mc-stan.org for more details
about this more advanced usage of Stan. However, many relatively simple models
can be fit using the __rstanarm__ package without writing any code in the Stan
language, which is illustrated for each estimating function in the __rstanarm__
package in the other [vignettes](index.html).

# References

Betancourt, M. J., & Girolami, M. (2013). Hamiltonian Monte Carlo for 
hierarchical models. [arXiv preprint](https://arxiv.org/abs/1312.0906).

Stan Development Team. (2015). _Stan modeling language user's guide and
reference manual, Version 2.9.0_. https://mc-stan.org/documentation/. See the 
'Hamiltonian Monte Carlo Sampling' chapter.

Gelman, A., & Rubin, D. B. (1992). Inference from iterative simulation using 
multiple sequences. _Statistical Science_, 7(4), 457 -- 472.

Gelman, A., & Shirley, K. (2011). Inference from simulations and monitoring 
convergence. In S. Brooks, A. Gelman, G. Jones, & X. Meng (Eds.), _Handbook of 
Markov chain Monte Carlo_. Boca Raton: Chapman & Hall/CRC.
