marginal_plot: Plots marginal estimates of a fitted RoBMA regression object

In RoBMA: Robust Bayesian Meta-Analyses

Plots marginal estimates of a fitted RoBMA regression object

Description

marginal_plot allows to visualize prior and posterior distributions of marginal estimates of a RoBMA regression model.

Usage

marginal_plot(
  x,
  parameter,
  conditional = FALSE,
  plot_type = "base",
  prior = FALSE,
  output_scale = NULL,
  dots_prior = NULL,
  ...
)

Arguments

x	a fitted RoBMA regression object
parameter	regression parameter to be plotted
conditional	whether conditional marginal estimates should be plotted. Defaults to FALSE which plots the model-averaged estimates.
plot_type	whether to use a base plot "base" or ggplot2 "ggplot" for plotting. Defaults to "base".
prior	whether prior distribution should be added to figure. Defaults to FALSE.
output_scale	transform the effect sizes and the meta-analytic effect size estimate to a different scale. Defaults to NULL which returns the same scale as the model was estimated on.
dots_prior	list of additional graphical arguments to be passed to the plotting function of the prior distribution. Supported arguments are lwd, lty, col, and col.fill, to adjust the line thickness, line type, line color, and fill color of the prior distribution respectively.
...	list of additional graphical arguments to be passed to the plotting function. Supported arguments are lwd, lty, col, col.fill, xlab, ylab, main, xlim, ylim to adjust the line thickness, line type, line color, fill color, x-label, y-label, title, x-axis range, and y-axis range respectively.

Value

plot.RoBMA returns either NULL if plot_type = "base" or an object object of class 'ggplot2' if plot_type = "ggplot2".

See Also

RoBMA()

RoBMA documentation built on July 26, 2023, 5:13 p.m.