Plot marginal distributions for a fitted model

plot_marginals(
  fit,
  pars = NULL,
  mfrow = NULL,
  add.mle = TRUE,
  add.monitor = TRUE,
  breaks = 30
)

Arguments

fit	A fitted object returned by `sample_admb`.
pars	A numeric or character vector of parameters which to plot, for plotting a subset of the total (defaults to all)
mfrow	A custom grid size (vector of two) to be called as `par(mfrow)`, overriding the defaults.
add.mle	Whether to add marginal normal distributions determined from the inverse Hessian file
add.monitor	Whether to add ESS and Rhat information
breaks	The number of breaks to use in `hist()`, defaulting to 30

Details

This function plots grid cells of all parameters in a model, comparing the marginal posterior histogram vs the asymptotic normal (red lines) from the inverse Hessian. Its intended use is to quickly gauge differences between frequentist and Bayesian inference on the same model.

If fit$monitor exists the effective sample size (ESS) and R-hat estimates are printed in the top right corner. See https://mc-stan.org/rstan/reference/Rhat.html for more information. Generally Rhat>1.05 or ESS<100 (per chain) suggest inference may be unreliable.

This function is customized to work with multipage PDFs, specifically: pdf('marginals.pdf', onefile=TRUE, width=7,height=5) produces a nice readable file.

Examples

fit <- readRDS(system.file('examples', 'fit.RDS', package='adnuts'))
plot_marginals(fit, pars=1:2)