bayesnec
The bayesnec
package makes use of the R package
brms
(Paul Christian Bürkner 2017;
PaulChristian Bürkner 2018) (https://cran.rproject.org/package=brms) which relies on
stan
(https://mcstan.org/). You will need to have either
RStan
(https://mcstan.org/users/interfaces/rstan.html) or
cmdstanr
(https://mcstan.org/cmdstanr/) installed and configured
on your computer to run bayesnec
.
Quick start guides can be found for both RStan
https://github.com/standev/rstan/wiki/RStanGettingStarted
and cmdstanr
https://mcstan.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 workflowThese instructions are derived from the instructions at https://mcstan.org/docs/2_24/cmdstanguide/cmdstaninstallation.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.rproject.org/bin/windows/Rtools/
Go to the install location and check that the following usr\bin and mingw64\bin directories exist:
Check that a mingw32make.exe file is in one of those directories.
RTools
may not always install
mingw32make.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 mingww64x86_64make
Check that the mingw32make.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\bin
Final check to see if it installed properly.
In the terminal type:
g++ version
and
mingw32make 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://gitscm.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/standev/cmdstan.git recursive
d. change directory to where cmdstan
is downloaded:
cd cmdstan
e. clean up the space (just to be sure):
mingw32make cleanall
f. compile the code: mingw32make 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 += Wnononnull
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.htmcmdstanr
following the
instructions at: https://mcstan.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://mcstan.org/rpackages/", getOption("repos")))
library(cmdstanr)
## This is cmdstanr version 0.5.3
##  CmdStanR documentation and vignettes: mcstan.org/cmdstanr
##  CmdStan path: /Users/dbarneche/.cmdstan/cmdstan2.33.1
##  CmdStan version: 2.33.1
library(posterior)
## 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
library(bayesplot)
## This is bayesplot version 1.10.0
##  Online documentation and vignettes at mcstan.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/cmdstan2.33.1"
If not, manually set it
set_cmdstan_path("C:/cmdstan")
## Warning: Path not set. Can't find directory: C:/cmdstan
To 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.path(cmdstan_path(), "examples", "bernoulli", "bernoulli.stan")
file < cmdstan_model(file) mod
## Model executable is up to date!
$print() mod
## data {
## int<lower=0> N;
## array[N] int<lower=0,upper=1> y;
## }
## parameters {
## real<lower=0,upper=1> theta;
## }
## model {
## theta ~ beta(1,1); // uniform prior on interval 0,1
## y ~ bernoulli(theta);
## }
Run a Monte Carlo Markov Chain:
# names correspond to the data block in the Stan program
< list(N = 10, y = c(0,1,0,0,0,0,0,0,0,1))
data_list
< mod$sample(
fit data = data_list,
seed = 123,
chains = 4,
parallel_chains = 4,
refresh = 500
)
## Running MCMC with 4 parallel chains...
##
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## Chain 1 finished in 0.0 seconds.
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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
$summary() fit
## # 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)
< brm(count ~ zAge + zBase * Trt + (1patient),
fit data = epilepsy, family = poisson(), silent = 2, refresh = 0)
## Compiling Stan program...
##

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## In file included from /var/folders/80/x2f5bfts2_n9g_2h7w8rx3jm0000gn/T/RtmpGymuJf/model5a0b7979b3af.hpp:1:
## In file included from /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/src/stan/model/model_header.hpp:4:
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## In file included from /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/stan/math/prim/functor/ode_rk45.hpp:9:
## In file included from /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/numeric/odeint.hpp:76:
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## In file included from /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/function/function_base.hpp:21:
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## In file included from /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/type_index/stl_type_index.hpp:47:
## /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/container_hash/hash.hpp:132:33: warning: 'unary_function<const std::error_category
##
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## struct hash_base : std::unary_function<T, std::size_t> {};
## ^
## /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/container_hash/hash.hpp:692:18: note: in instantiation of template class 'boost::hash_detail::hash_base<const std::error_category *>' requested here
## : public boost::hash_detail::hash_base<T*>
## ^
## /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/container_hash/hash.hpp:420:24: note: in instantiation of template class 'boost::hash<const std::error_category *>' requested here
## boost::hash<T> hasher;
## ^
## /Users/dbarneche/.cmdstan/cmdstan2.33.1/stan/lib/stan_math/lib/boost_1.78.0/boost/container_hash/hash.hpp:551:9: note: in instantiation of function template specialization 'boost::hash_combine<const std::error_category *>' requested here
## hash_combine(seed, &v.category());
## ^
## /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__functional/unary_function.h:23:29: note: 'unary_function<const std::error_category *, unsigned long>' has been explicitly marked deprecated here
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## ^
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## ^
## /Library/Developer/CommandLineTools/SDKs/MacOSX.sdk/usr/include/c++/v1/__config:835:49: note: expanded from macro '_LIBCPP_DEPRECATED'
## # define _LIBCPP_DEPRECATED __attribute__((deprecated))
## ^
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Running MCMC with 4 chains, at most 10 in parallel...
##
## Chain 4 finished in 1.7 seconds.
## Chain 1 finished in 1.8 seconds.
## Chain 2 finished in 1.7 seconds.
## Chain 3 finished in 1.7 seconds.
##
## All 4 chains finished successfully.
## 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 postwarmup draws = 4000
##
## GroupLevel Effects:
## ~patient (Number of levels: 59)
## Estimate Est.Error l95% CI u95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.58 0.07 0.46 0.73 1.01 793 1694
##
## PopulationLevel Effects:
## Estimate Est.Error l95% CI u95% 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).