Plot samples from the mixed posterior distributions

Source: R/model-averaging-plots.R

Plot samples from the mixed posterior distributions

plot_posterior(
  samples,
  parameter,
  plot_type = "base",
  prior = FALSE,
  n_points = 1000,
  n_samples = 10000,
  force_samples = FALSE,
  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 mix_posteriors.

parameter

parameter name to be plotted. Use "PETPEESE" for PET-PEESE plot with parameters "PET" and "PEESE", and "weightfunction" for plotting a weightfunction with parameters "omega".

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

n_samples

number of samples from the prior distribution if the density cannot be obtained analytically (or if samples are forced with force_samples = TRUE)

force_samples

should prior be sampled instead of obtaining analytic solution whenever possible

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