RoBMA (version 4.0.0)

plot_diagnostic: Plot MCMC Diagnostics

Description

plot_diagnostic creates visual MCMC diagnostics for a fitted brma object. Convenience wrappers are available for trace, density, and autocorrelation plots.

Usage

plot_diagnostic(x, ...)
# S3 method for brma
plot_diagnostic(
  x,
  parameter = NULL,
  parameter_mods = NULL,
  parameter_scale = NULL,
  type,
  plot_type = "base",
  lags = 30,
  ...
)
plot_diagnostic_autocorrelation(x, ...)
# S3 method for brma
plot_diagnostic_autocorrelation(
  x,
  parameter = NULL,
  parameter_mods = NULL,
  parameter_scale = NULL,
  type = "autocorrelation",
  plot_type = "base",
  lags = 30,
  ...
)
plot_diagnostic_trace(x, ...)
# S3 method for brma
plot_diagnostic_trace(
  x,
  parameter = NULL,
  parameter_mods = NULL,
  parameter_scale = NULL,
  type = "trace",
  plot_type = "base",
  lags = 30,
  ...
)
plot_diagnostic_density(x, ...)
# S3 method for brma
plot_diagnostic_density(
  x,
  parameter = NULL,
  parameter_mods = NULL,
  parameter_scale = NULL,
  type = "density",
  plot_type = "base",
  lags = 30,
  ...
)

Value

plot_diagnostic returns the object returned by BayesTools::JAGS_diagnostics(), invisibly for base graphics.

Arguments

x

a fitted brma object

...

additional graphical arguments passed through RoBMA's diagnostic setup to BayesTools::JAGS_diagnostics().

parameter

base parameter to plot. Defaults to NULL, which uses "mu" or the meta-regression intercept. Valid values include "mu", "tau", "rho", "PET", "PEESE", and "omega" or "weightfunction" when present.

parameter_mods

moderator term for location regression.

parameter_scale

term for scale regression.

type

diagnostic plot type. Convenience wrappers set a type-specific default but still forward this argument to plot_diagnostic.brma().

plot_type

whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".

lags

number of lags for autocorrelation plots. Defaults to 30.

See Also

summary.brma()