The bayesnec package makes use of the R package
brms (Paul Christian Bürkner 2017;
Paul-Christian Bürkner 2018) (https://cran.r-project.org/package=brms) which relies on
stan (https://mc-stan.org/). You will need to have either
RStan (https://mc-stan.org/users/interfaces/rstan.html) or
cmdstanr(https://mc-stan.org/cmdstanr/) installed and configured
on your computer to run bayesnec.
Quick start guides can be found for both RStan https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started
and cmdstanr https://mc-stan.org/cmdstanr/articles/cmdstanr.html.
However, in our experience getting either package to work can be a bit
fiddly, particularly on Windows machines.
We have prepared an installation workflow for cmdstanr
specifically for Windows 10 that may resolve issues if the above Quick
start links fail to result in a working version of brms.
Note that this workflow has also been known to resolve some issues with
RStan, although it was not developed with that
intention.
cmdstanr installation workflow
These instructions are derived from the instructions at https://mc-stan.org/docs/2_24/cmdstan-guide/cmdstan-installation.html
The high level steps are:
Rtools, this is what cmdstan will
use to make the executablesgit, this is will be used to get the
cmdstan codecmdstan
cmdstan and run a modelThese instructions assume you have R and Rstudio installed already.
Rtools
Install Rtools from https://cran.r-project.org/bin/windows/Rtools/
Go to the install location and check that the following usr\bin and mingw64\bin directories exist:
Check that a mingw32-make.exe file is in one of those directories.
RTools may not always install
mingw32-make.exe but it can be installed manually if
needed by the following instructions:
Open RTools Bash, which comes with RTools
(hit Windows Key, type rtools bash, and hit enter). In the
RTools Bash console window, type:
pacman -Sy mingw-w64-x86_64-make
Check that the mingw32-make.exe file is in one of the
RTools folders listed in 1b.
C:\cmdstan\stan\lib\stan_math\lib\tbb to save having to do
it later (in the later install cmdstan step below)Test the paths are set correctly
echo %PATH%
\c\RTools\RTools40\usr\binFinal check to see if it installed properly.
In the terminal type:
g++ --version
and
mingw32-make --version
Check that it both return a version number. If they produce an error there is a problem with the installation.
git
If git is not already on your system, install it here: https://git-scm.com/download/win
To check that git is installed. In RStudio:
git --version
cmdstan
In R studio
a. Navigate to the terminal
b. change directory to c:\ drive using the code:
cd \c
c. download latest version of cmdstan from githup - this may take a
few minutes:
git clone https://github.com/stan-dev/cmdstan.git --recursive
d. change directory to where cmdstan is downloaded:
cd cmdstan
e. clean up the space (just to be sure):
mingw32-make clean-all
f. compile the code: mingw32-make build
This will take a few minutes and should end with similar phrase as “““— CmdStan v2.23.0 built —”“”
g. Add cmdstan library to system environment path by
adding C:\cmdstan\stan\lib\stan_math\lib\tbb to the path
(using the same instructions as 1.c.)
h. Reboot your computer
i. cmdstan is missing a file that must be manually added
to the C:\cmdstan\make folder. Open notepad and copy paste
the following two lines of text:
CXXFLAGS += -Wno-nonnull
TBB_CXXFLAGS= -U__MSVCRT_VERSION__ -D__MSVCRT_VERSION__=0x0E00
local and ensure that it
has no file extension. For example, if you used notepad the default file
extension is .txt which can be deleted by right clicking the file and
selecting rename. If you can’t see the file extensions, click the view
tab in your folder ribbon and make sure the
file name extension box is checked. Instructions for how to
remove a file extension can be found at: https://www.computerhope.com/issues/ch002089.htm
cmdstanr following the
instructions at: https://mc-stan.org/cmdstanr/articles/cmdstanr.html
# we recommend running this is a fresh R session or restarting your current session
install.packages("cmdstanr", repos = c("https://mc-stan.org/r-packages/", getOption("repos")))
library(cmdstanr)## This is cmdstanr version 0.5.3## - CmdStanR documentation and vignettes: mc-stan.org/cmdstanr## - CmdStan path: /Users/dbarneche/.cmdstan/cmdstan-2.33.1## - CmdStan version: 2.33.1## This is posterior version 1.4.1##
## Attaching package: 'posterior'## The following objects are masked from 'package:stats':
##
## mad, sd, var## The following objects are masked from 'package:base':
##
## %in%, match## This is bayesplot version 1.10.0## - Online documentation and vignettes at mc-stan.org/bayesplot## - bayesplot theme set to bayesplot::theme_default()## * Does _not_ affect other ggplot2 plots## * See ?bayesplot_theme_set for details on theme setting##
## Attaching package: 'bayesplot'## The following object is masked from 'package:posterior':
##
## rhat## The following object is masked from 'package:brms':
##
## rhat
color_scheme_set("brightblue")Make sure the path points to the cmdstan
installation
cmdstan_path()## [1] "/Users/dbarneche/.cmdstan/cmdstan-2.33.1"If not, manually set it
set_cmdstan_path("C:/cmdstan")## Warning: Path not set. Can't find directory: C:/cmdstanTo use cmdstan as a backend for brms call
the relevant options.
options(brms.backend = "cmdstanr")Setting the path and backend may be required each time you use
cmdstan
check_cmdstan_toolchain()## The C++ toolchain required for CmdStan is setup properly!This should return the message
The C++ toolchain required for CmdStan is setup properly!
If cmdstan is installed, the following example model
should work.
Set up data:
file <- file.path(cmdstan_path(), "examples", "bernoulli", "bernoulli.stan")
mod <- cmdstan_model(file)## Model executable is up to date!
mod$print()## data {
## int<lower=0> N;
## array[N] int<lower=0,upper=1> y;
## }
## parameters {
## real<lower=0,upper=1> theta;
## }
## model {
## theta ~ beta(1,1); // uniform prior on interval 0,1
## y ~ bernoulli(theta);
## }Run a Monte Carlo Markov Chain:
# names correspond to the data block in the Stan program
data_list <- list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
fit <- mod$sample(
data = data_list,
seed = 123,
chains = 4,
parallel_chains = 4,
refresh = 500
)## Running MCMC with 4 parallel chains...
##
## Chain 1 Iteration: 1 / 2000 [ 0%] (Warmup)
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##
## All 4 chains finished successfully.
## Mean chain execution time: 0.0 seconds.
## Total execution time: 0.2 seconds.Check that the model has successfully fitted by examining the model parameters
fit$summary()## # A tibble: 2 × 10
## variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
## <chr> <num> <num> <num> <num> <num> <num> <num> <num> <num>
## 1 lp__ -7.26 -6.99 0.695 0.331 -8.70 -6.75 1.00 1661. 1619.
## 2 theta 0.249 0.233 0.119 0.122 0.0816 0.468 1.00 1494. 1630.
require(cmdstanr)
set_cmdstan_path("C:/cmdstan")## Warning: Path not set. Can't find directory: C:/cmdstan
options(brms.backend = "cmdstanr")
require(brms)
fit <- brm(count ~ zAge + zBase * Trt + (1|patient),
data = epilepsy, family = poisson(), silent = 2, refresh = 0)## Compiling Stan program...##
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## boost::hash<T> hasher;
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Running MCMC with 4 chains, at most 10 in parallel...
##
## Chain 4 finished in 1.7 seconds.
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##
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## Mean chain execution time: 1.7 seconds.
## Total execution time: 1.9 seconds.
summary(fit)## Family: poisson
## Links: mu = log
## Formula: count ~ zAge + zBase * Trt + (1 | patient)
## Data: epilepsy (Number of observations: 236)
## Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
## total post-warmup draws = 4000
##
## Group-Level Effects:
## ~patient (Number of levels: 59)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.58 0.07 0.46 0.73 1.01 793 1694
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## Intercept 1.77 0.12 1.54 2.00 1.00 941 1752
## zAge 0.09 0.09 -0.08 0.26 1.01 825 1493
## zBase 0.70 0.12 0.46 0.93 1.01 723 1202
## Trt1 -0.27 0.17 -0.58 0.06 1.00 847 1774
## zBase:Trt1 0.05 0.17 -0.27 0.39 1.01 759 1316
##
## Draws were sampled using sample(hmc). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).