plot.hmclearn: Plot Histograms of the Posterior Distribution

In hmclearn: Fit Statistical Models Using Hamiltonian Monte Carlo

Description

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

Usage

## 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

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)

hmclearn documentation built on Oct. 23, 2020, 8:04 p.m.