plot_prior: Plot Prior Distributions

In RoBMA: Robust Bayesian Meta-Analyses

Plot Prior Distributions

Description

plot_prior visualizes prior distributions stored in brma, BMA, and RoBMA objects. This is especially useful for objects created with only_priors = TRUE.

Usage

plot_prior(x, ...)

## S3 method for class 'prior'
plot_prior(x, plot_type = "base", ...)

## S3 method for class 'brma'
plot_prior(
  x,
  parameter = "mu",
  parameter_mods,
  parameter_scale,
  standardized_coefficients = TRUE,
  output_measure = NULL,
  transform = NULL,
  plot_type = "base",
  ...
)

Arguments

x	a brma, BMA, or RoBMA object, or a prior distribution object.
...	additional arguments passed to the prior plotting method.
plot_type	whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".
parameter	character. Base parameter to plot. Defaults to "mu". Common options are "mu", "tau", "rho", "PET", "PEESE", "omega", and "bias", with aliases "effect" = "mu", "heterogeneity" = "tau", and "weightfunction" = "omega". "bias" plots only non-mixed or homogeneous bias priors; for mixed weightfunction and PET/PEESE mixtures use "omega", "PET", or "PEESE". Moderator and scale terms can also be selected by name when unambiguous. A character vector requests multiple base parameters.
parameter_mods	character. Moderator term to plot. Use "intercept" for the adjusted effect in meta-regression models.
parameter_scale	character. Scale-regression term to plot. Use "intercept" for the heterogeneity intercept in location-scale models.
standardized_coefficients	whether to plot moderator and scale-regression priors on the standardized predictor scale. Defaults to TRUE, which shows the priors as specified. Set to FALSE to transform them to the original predictor scale when continuous predictors were standardized.
output_measure	effect-size measure for location/effect predictions. Defaults to the fitted measure. Supported conversions are among "SMD", "COR", "ZCOR", and "OR"; "RR", "HR", "IRR", "RD", and "GEN" can only be returned on their fitted measure. Use transform = "EXP" for ratio-scale output from log-scale measures.
transform	optional display transformation. Currently "EXP" exponentiates log-scale measures "OR", "RR", "HR", and "IRR".

Details

output_measure and transform transform the prior plotting scale only for effect-size location priors ("mu" or the meta-regression intercept).

Value

plot_prior returns either NULL invisibly if plot_type = "base" or a ggplot2 object if plot_type = "ggplot". If multiple parameters are requested, a named list is returned, invisibly for base plots.

See Also

BMA() RoBMA() brma() prior()

Examples

## Not run: 
if (requireNamespace("metadat", quietly = TRUE)) {
  data(dat.lehmann2018, package = "metadat")

  priors <- BMA(
    yi           = yi,
    vi           = vi,
    mods         = ~ Preregistered,
    data         = dat.lehmann2018,
    measure      = "SMD",
    only_priors  = TRUE
  )

  plot_prior(priors, parameter = "mu")
  plot_prior(priors, parameter = "tau")
  plot_prior(priors, parameter_mods = "Preregistered")
}

## End(Not run)

RoBMA documentation built on May 7, 2026, 5:08 p.m.