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Visualize posterior distributions with density and trace plots

Source: R/monetPlot.R
monetPlot.Rd

Creates a combined diagnostic plot showing both the posterior density and MCMC trace plot for a specified parameter. Helps assess convergence and visualize posterior uncertainty. The plot displays the median and 90% highest posterior density (HPD) interval.

Usage

monetPlot(bml, parameter, label = NULL, r = 2, yaxis = TRUE)

Arguments

bml

A fitted model object of class "bml" returned by bml. Must be fitted with monitor = TRUE to store MCMC chains.

parameter

Character string specifying the parameter to plot. Must use the internal parameter name (i.e., row names from bml$reg.table). Examples: "b[1]" (intercept), "b[2]" (first covariate), "b.mm.1" (first mm block coefficient), "sigma.mm" (mm random effect SD).

label

Optional character string for the parameter label displayed on the plot. If NULL (default), uses the internal parameter name.

r

Number of decimal places for displayed quantiles and statistics. Default: 2.

yaxis

Logical; if TRUE (default), display axis titles ("Density" and "Scans"). If FALSE, omit axis titles for cleaner appearance when combining multiple plots.

Value

A ggplot object (using patchwork) combining two panels:

  • Top panel: Posterior density with shaded 90% HPD interval. Solid vertical line at zero, dashed line at posterior median.

  • Bottom panel: Trace plot showing MCMC iterations across chains. Same reference lines as top panel. Helps diagnose convergence and mixing.

Details

Interpreting the Plot:

  • Density panel: Shows the posterior distribution. The dashed line marks the median (central estimate). Shading indicates the 90% credible region.

  • Trace panel: Shows parameter values across MCMC iterations for each chain. Good mixing looks like "fuzzy caterpillars" with chains overlapping. Poor mixing shows trends, stickiness, or separation between chains.

Convergence Checks:

  • Chains should overlap and explore the same space

  • No sustained trends or drift

  • Rapid mixing (no long autocorrelation)

Use mcmcDiag for formal convergence statistics (Gelman-Rubin, Geweke, etc.).

See also

bml, mcmcDiag, summary.bml

Author

Benjamin Rosche <benrosche@nyu.edu>

Examples

if (FALSE) { # \dontrun{
data(coalgov)

# Fit model with monitoring enabled
m1 <- bml(
  Surv(dur_wkb, event_wkb) ~ 1 + majority +
    mm(id = id(pid, gid), vars = vars(cohesion), fn = fn(w ~ 1/n), RE = TRUE) +
    hm(id = id(cid), type = "RE"),
  family = "Weibull",
  monitor = TRUE,  # Required for monetPlot
  data = coalgov
)

# Plot intercept
monetPlot(m1, parameter = "b[1]", label = "Intercept")

# Plot majority coefficient with custom label
monetPlot(m1, parameter = "b[2]", label = "Majority Government Effect")

# Plot mm coefficient
monetPlot(m1, parameter = "b.mm.1", label = "Party Fragmentation")

# Plot random effect SD
monetPlot(m1, parameter = "sigma.mm.1")

# List available parameters
rownames(m1$reg.table)
} # }