Running multiple chains is a good way of checking that our MCMC is working, in the sense that it is converging on the same stationary distribution. Each chain is completely independent of all others, and so this qualifies as an embarrassingly parallel problem.

This vignette will demonstrate how to run drjacoby with multiple chains, first in serial and then in parallel over cores.

## Setup

As always, we require some data, some parameters, and some functions to work with (see earlier examples). The underlying model is not our focus here, so we will use a very basic setup

# define data
x <- rnorm(10)

# define parameters dataframe
df_params <- data.frame(name = "mu", min = -10, max = 10, init = 0)

# define cpp loglike function
loglike <- "SEXP loglike(std::vector<double> params, std::vector<double> x) {
double ret = 0.0;
for (int i = 0; i < int(x.size()); ++i) {
ret += -0.5*log(2*M_PI) - (x[i] - params[0])*(x[i] - params[0])/2.0;
}
return Rcpp::wrap(ret);
}"

# define cpp logprior function
logprior <- "SEXP logprior(std::vector<double> params){
double ret = -log(20.0);
return Rcpp::wrap(ret);
}"

## Running multiple chains

Whenever the input argument cluster is NULL, chains will run in serial. This is true by default, so running multiple chains in serial is simply a case of specifying the chains argument:

# run MCMC in serial
mcmc_out <- run_mcmc(data = x,
df_params = df_params,
loglike = loglike,
logprior = logprior,
burnin = 1e3,
samples = 1e3,
chains = 2,
pb_markdown = TRUE)
#> MCMC chain 1
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 22.8%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.4%
#>
#> completed in 0.001910 seconds
#> MCMC chain 2
#> burn-in
#>
|
|======================================================================| 100%
#> acceptance rate: 23.1%
#> sampling phase
#>
|
|======================================================================| 100%
#> acceptance rate: 23.5%
#>
#> completed in 0.001633 seconds

When we look at our MCMC output (using the plot_par() function) we can see that there are 2 chains, each of which contains independent draws from the posterior. If we used multiple temperature rungs then these would also be duplicated over chains.

# summarise output
mcmc_out
#> drjacoby output:
#> 2 chains
#> 1 rungs
#> 1000 burn-in iterations
#> 1000 sampling iterations
#> 1 parameters

# compare mu over both chains
plot_par(mcmc_out, "mu")

Running in parallel is only slightly more complex. Before running anything we need to know how many cores our machine has. You may know this number already, but if you don’t then the parallel package has a handy function for detecting the number of cores for you:

cores <- parallel::detectCores()

Next we make a cluster object, which creates multiple copies of R running in parallel over different cores. Here we are using all available cores, but if you want to hold some back for other intensive tasks then simply use a smaller number of cores when specifying this cluster.

cl <- parallel::makeCluster(cores)

We then run the usual run_mcmc() function, this time passing in the cluster as an argument. This causes drjacoby to use a clusterApplyLB() call rather than an ordinary lapply() call over different chains. Each chain is added to a queue over the specified number of cores - when the first job completes, the next job is placed on the node that has become free and this continues until all jobs are complete.

Note that output is supressed when running in parallel to avoid sending print commands to multiple cores, so you will not see the usual progress bars.

# run MCMC in parallel
mcmc_out <- run_mcmc(data = x,
df_params = df_params,
loglike = loglike,
logprior = logprior,
burnin = 1e3,
samples = 1e3,
chains = 2,
cluster = cl,
pb_markdown = TRUE)

Finally, it is good practice to shut down the workers once we are finished:

parallel::stopCluster(cl)

Running chains in parallel tends to be faster than running in serial, although remember that you will see a delay the first time you run a new set of functions as the likelihood and prior must compile on every chain.

## Comparing chains

One method of evaluating convergence is the rhat statistic, initially proposed by Gelman & Rubin (1992). This method involves comparing the within-chain with the between-chain variance. Similar estimates for these two values (rhat -> 1) can be used as an indicator of convergence. An approximate rhat estimate is automatically generated in drjacoby:

# Rhat statistic
mcmc_out$chain1$diagnostics$rhat #> NULL Chains from the MCMC will be autocorrelated. To assess the extent of autocorrelation we can view the correlation betwen values at a range of lags usin the plot_autocorrelation() function, specifying the chain and parameter: plot_autocorrelation(mcmc_out, par = "mu", chain = 1) Chain autocorrealtion can be incorporated into estimate of effective sample size, which are automatically calculated by drjacoby: # Effective sample size (from 1000 sampling iterations) mcmc_out$chain1$diagnostics$ess
#> NULL