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RStan Getting Started
RStan is the R interface to Stan. For more information on Stan and its modeling language visit the Stan website at http://mc-stan.org/
To install the latest development version of RStan use
# run the next line if you already have rstan installed
# remove.packages(c("StanHeaders", "rstan"))
install.packages("rstan", repos = c('https://stan-dev.r-universe.dev', getOption("repos")))
This version is not yet officially released, but it provides access to a more recent version of the Stan language than the latest official RStan release. See the Configuring C++ Toolchain and Verifying Installation sections below to make sure you have the necessary setup for compiling Stan programs (you can ignore the Installing RStan section below).
Using R version 4.2.0 or later is strongly recommended. If necessary, you can install the latest version of R from here.
In addition, we strongly recommend that you use RStudio version 1.4.x or later because it has great support for Stan.
Prior to installing RStan, you need to configure your R installation to be able to compile C++ code. Follow the link below for your respective operating system for more instructions:
- Windows - Configuring C++ Toolchain
- Mac - Configuring C++ Toolchain
- Linux - Configuring C++ Toolchain
To be on the safe side, it is sometimes necessary to remove any existing RStan via
remove.packages("rstan")
if (file.exists(".RData")) file.remove(".RData")
Then, restart R.
If you are using R version 3.x on a Mac, go to here. Otherwise, you can usually simply type (exactly like this)
Sys.setenv(DOWNLOAD_STATIC_LIBV8 = 1) # only necessary for Linux without the nodejs library / headers
install.packages("rstan", repos = "https://cloud.r-project.org/", dependencies = TRUE)
To verify your installation, you can run the RStan example/test model:
example(stan_model, package = "rstan", run.dontrun = TRUE)
The model should then compile and sample. You may also see the warning:
The rest of this document assumes that you have already installed RStan by following the instructions above.
The package name is rstan (all lowercase), so we start by executing
library("rstan") # observe startup messages
As the startup message says, if you are using rstan locally on a multicore machine and have plenty of RAM to estimate your model in parallel, at this point execute
options(mc.cores = parallel::detectCores())
In addition, you should follow the second startup message that says to execute
rstan_options(auto_write = TRUE)
which allows you to automatically save a bare version of a compiled Stan program to the hard disk so that it does not need to be recompiled (unless you change it). You will need to run these commands each time you load the rstan library.
Finally, if you use Windows, there will be a third startup message saying not to use --march=native
compiler flag. You can ignore this warning if you followed the steps above and your Makevars.win
file does not contain this flag.
This is an example in Section 5.5 of Gelman et al (2003), which studied coaching effects from eight schools. For simplicity, we call this example "eight schools."
We start by writing a Stan program for the model in a text file. If you are using RStudio version 1.2.x or greater, click on File -> New File -> Stan File . Otherwise, open your favorite text editor. Either way, paste in the following and save your work to a file called schools.stan
in R's working directory (which can be seen by executing getwd()
)
// saved as schools.stan
data {
int<lower=0> J; // number of schools
array[J] real y; // estimated treatment effects
array[J] real<lower=0> sigma; // standard error of effect estimates
}
parameters {
real mu; // population treatment effect
real<lower=0> tau; // standard deviation in treatment effects
vector[J] eta; // unscaled deviation from mu by school
}
transformed parameters {
vector[J] theta = mu + tau * eta; // school treatment effects
}
model {
target += normal_lpdf(eta | 0, 1); // prior log-density
target += normal_lpdf(y | theta, sigma); // log-likelihood
}
Be sure that your Stan programs ends in a blank line without any characters including spaces and comments.
In this Stan program, we let theta
be a transformation of mu
, eta
, and tau
instead of declaring theta
in the parameters
block, which allows the sampler will run more efficiently (see detailed explanation). We can prepare the data (which typically is a named list) in R with:
schools_dat <- list(J = 8,
y = c(28, 8, -3, 7, -1, 1, 18, 12),
sigma = c(15, 10, 16, 11, 9, 11, 10, 18))
And we can get a fit with the following R command. Note that the argument to file =
should point to where the file is on your file system unless you have put it in the working directory of R in which case the below will work.
fit <- stan(file = 'schools.stan', data = schools_dat)
The object fit
, returned from function stan
is an S4 object of class
stanfit
. Methods such as print
, plot
, and pairs
are associated with the
fitted result so we can use the following code to check out the results in fit
.
print
provides a summary for the parameter of the model as well
as the log-posterior with name lp__
(see the following example output).
For more methods and details of class stanfit
, see the help of class stanfit
.
In particular, we can use the extract
function on stanfit
objects to
obtain the samples. extract
extracts samples from the stanfit
object as a list of arrays for parameters of interest, or just an array.
In addition, S3 functions as.array
, as.matrix
, and as.data.frame
are defined
for stanfit
objects (using help("as.array.stanfit")
to check
out the help document in R).
print(fit)
plot(fit)
pairs(fit, pars = c("mu", "tau", "lp__"))
la <- extract(fit, permuted = TRUE) # return a list of arrays
mu <- la$mu
### return an array of three dimensions: iterations, chains, parameters
a <- extract(fit, permuted = FALSE)
### use S3 functions on stanfit objects
a2 <- as.array(fit)
m <- as.matrix(fit)
d <- as.data.frame(fit)
The Rats example is also a popular example. For example, we can find the
OpenBUGS version from
here, which originally is from
Gelfand et al (1990).
The data are about the growth of 30 rats weekly for five weeks.
In the following table, we list the data, in which we use x
to denote the dates
the data were collected. We can try this example using the linked data
rats.txt
and model code rats.stan.
Rat | x=8 | x=15 | x=22 | x=29 | x=36 | Rat | x=8 | x=15 | x=22 | x=29 | x=36 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 151 | 199 | 246 | 283 | 320 | 16 | 160 | 207 | 248 | 288 | 324 | |
2 | 145 | 199 | 249 | 293 | 354 | 17 | 142 | 187 | 234 | 280 | 316 | |
3 | 147 | 214 | 263 | 312 | 328 | 18 | 156 | 203 | 243 | 283 | 317 | |
4 | 155 | 200 | 237 | 272 | 297 | 19 | 157 | 212 | 259 | 307 | 336 | |
5 | 135 | 188 | 230 | 280 | 323 | 20 | 152 | 203 | 246 | 286 | 321 | |
6 | 159 | 210 | 252 | 298 | 331 | 21 | 154 | 205 | 253 | 298 | 334 | |
7 | 141 | 189 | 231 | 275 | 305 | 22 | 139 | 190 | 225 | 267 | 302 | |
8 | 159 | 201 | 248 | 297 | 338 | 23 | 146 | 191 | 229 | 272 | 302 | |
9 | 177 | 236 | 285 | 350 | 376 | 24 | 157 | 211 | 250 | 285 | 323 | |
10 | 134 | 182 | 220 | 260 | 296 | 25 | 132 | 185 | 237 | 286 | 331 | |
11 | 160 | 208 | 261 | 313 | 352 | 26 | 160 | 207 | 257 | 303 | 345 | |
12 | 143 | 188 | 220 | 273 | 314 | 27 | 169 | 216 | 261 | 295 | 333 | |
13 | 154 | 200 | 244 | 289 | 325 | 28 | 157 | 205 | 248 | 289 | 316 | |
14 | 171 | 221 | 270 | 326 | 358 | 29 | 137 | 180 | 219 | 258 | 291 | |
15 | 163 | 216 | 242 | 281 | 312 | 30 | 153 | 200 | 244 | 286 | 324 |
y <- as.matrix(read.table('https://raw.github.com/wiki/stan-dev/rstan/rats.txt', header = TRUE))
x <- c(8, 15, 22, 29, 36)
xbar <- mean(x)
N <- nrow(y)
T <- ncol(y)
rats_fit <- stan(file='https://raw.githubusercontent.com/stan-dev/example-models/master/bugs_examples/vol1/rats/rats.stan', data = list(N=N, T=T, y=y, x=x, xbar=xbar))
You can run many of the BUGS examples and some others that we have created in Stan by executing
model <- stan_demo()
and choosing an example model from the list that pops up. The first time you call stan_demo()
, it will ask you if you want to download these examples. You should choose option 1 to put them in the directory where rstan was installed so that they can be used in the future without redownloading them. The model
object above is an instance of class stanfit
, so you can call print
, plot
, pairs
, extract
, etc. on it afterward.
More details about RStan can be found in the documentation including the vignette of package rstan.
For example, using help(stan)
and help("stanfit-class")
to check out the help for function stan
and S4 class stanfit
.
And see Stan's modeling language manual for details about Stan's samplers, optimizers, and the Stan modeling language.
In addition, the Stan User's Mailing list can be used to discuss the use of Stan, post examples or ask questions about (R)Stan. When help is needed, it is important to provide enough information such as the following:
- properly formatted syntax in the Stan modeling language
- data
- necessary R code
- dump of error message using
verbose=TRUE
andcores=1
when calling thestan
orsampling
functions - information about R by using function
sessionInfo()
in R
- Gelman, A., Carlin, J. B., Stern, H. S., and Rubin, D. B. (2003). Bayesian Data Analysis, CRC Press, London, 2nd Edition.
- Stan Development Team. Stan Modeling Language User's Guide and Reference Manual.
- Gelfand, A. E., Hills S. E., Racine-Poon, A., and Smith A. F. M. (1990). "Illustration of Bayesian Inference in Normal Data Models Using Gibbs Sampling", Journal of the American Statistical Association, 85, 972-985.
- Stan
- R
- BUGS
- OpenBUGS
- JAGS
- Rcpp