Plot Histograms of the Posterior Distribution

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

# S3 method for 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

Value

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

References

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

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)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.