The goal of mcunit is to provide unit tests for MCMC and Monte Carlo methods. It extends the package testthat.
You can install the current version of mcunit from bitbucket with:
This shows how to test if a sampler has a specific mean:
sampler <- function(n) rnorm(n,mean=3.2,1)
expect_mc_iid_mean(sampler,mean=3.2)
expect_mc_iid_mean(sampler,mean=3.5)
#> Error: Test failed with p-value=2.734643e-20 in iteration 1
This shows check of a simple MCMC sampler
object <- list(genprior=function() rnorm(1),
gendata=function(theta) rnorm(5,theta),
stepMCMC=function(theta,data,thinning){
f <- function(x) prod(dnorm(data,x))*dnorm(x)
for (i in 1:thinning){
thetanew = rnorm(1,mean=theta,sd=1)
if (runif(1)<f(thetanew)/f(theta))
theta <- thetanew
}
theta
}
)
expect_mcmc_reversible(object)
## And now with an error in the sampler:
## sampling until sample is accepted.
object$stepMCMC <- function(theta,data,thinning){
f <- function(x) prod(dnorm(data,x))*dnorm(x)
for (i in 1:thinning){
repeat{
thetanew = rnorm(1,mean=theta,sd=1)
if (runif(1)<f(thetanew)/f(theta)) break;
}
theta <- thetanew
}
theta
}
expect_mcmc_reversible(object,control=list(n=1e4))
#> Error: Test failed with p-value=3.586124e-11 in iteration 1