plot_marginal: Plot samples from the marginal posterior distributions

In BayesTools: Tools for Bayesian Analyses

Plot samples from the marginal posterior distributions

Description

Plot samples from the marginal posterior distributions

Usage

plot_marginal(
  samples,
  parameter,
  plot_type = "base",
  prior = FALSE,
  n_points = 1000,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  rescale_x = FALSE,
  par_name = NULL,
  dots_prior = list(),
  ...
)

Arguments

samples	samples from a posterior distribution for a parameter generated by marginal_inference.
parameter	parameter name to be plotted.
plot_type	whether to use a base plot "base" or ggplot2 "ggplot" for plotting.
prior	whether prior distribution should be added to the figure
n_points	number of equally spaced points in the x_range if x_seq is unspecified
transformation	transformation to be applied to the prior distribution. Either a character specifying one of the prepared transformations: lin linear transformation in form of a + b*x tanh also known as Fisher's z transformation exp exponential transformation , or a list containing the transformation function fun, inverse transformation function inv, and the Jacobian of the transformation jac. See examples for details.
transformation_arguments	a list with named arguments for the transformation
transformation_settings	boolean indicating whether the settings the x_seq or x_range was specified on the transformed support
rescale_x	allows to rescale x-axis in case a weightfunction is plotted.
par_name	a type of parameter for which the prior is specified. Only relevant if the prior corresponds to a mu parameter that needs to be transformed.
dots_prior	additional arguments for the prior distribution plot
...	additional arguments

Value

plot_marginal returns either NULL or an object of class 'ggplot' if plot_type is plot_type = "ggplot".

See Also

prior() marginal_inference() plot_posterior()

BayesTools documentation built on July 26, 2023, 5:37 p.m.