plot.hmclearn function

Plot Histograms of the Posterior Distribution

Calls mcmc_hist from the bayesplot package to display histograms of the posterior

## S3 method for class 'hmclearn'
plot(x, burnin = NULL, ...)

Arguments

• x: an object of class hmclearn, usually a result of a call to mh or hmc
• burnin: optional numeric parameter for the number of initial MCMC samples to omit from the summary
• ...: optional additional arguments to pass to the bayesplot functions

Returns

Calls mcmc_hist from the bayesplot package, which returns a list including a ggplot2 object.

Examples

# poisson regression example
set.seed(7363)
X <- cbind(1, matrix(rnorm(40), ncol=2))
betavals <- c(0.8, -0.5, 1.1)
lmu <- X %*% betavals
y <- sapply(exp(lmu), FUN = rpois, n=1)

f <- hmc(N = 1000,
          theta.init = rep(0, 3),
          epsilon = c(0.03, 0.02, 0.015),
          L = 10,
          logPOSTERIOR = poisson_posterior,
          glogPOSTERIOR = g_poisson_posterior,
          varnames = paste0("beta", 0:2),
          param = list(y=y, X=X),
          parallel=FALSE, chains=2)

plot(f, burnin=100)

References

Gabry, Jonah and Mahr, Tristan (2019). bayesplot: Plotting for Bayesian Models. https://mc-stan.org/bayesplot/