marginal_posterior: Model-average marginal posterior distributions

In BayesTools: Tools for Bayesian Analyses

Model-average marginal posterior distributions

Description

Creates marginal model-averages posterior distributions for a given parameter based on model-averaged posterior samples and parameter name (and formula with at specification).

Usage

marginal_posterior(
  samples,
  parameter,
  formula = NULL,
  at = NULL,
  prior_samples = FALSE,
  use_formula = TRUE,
  transformation = NULL,
  transformation_arguments = NULL,
  transformation_settings = FALSE,
  n_samples = 10000,
  ...
)

Arguments

samples	model-averaged posterior samples created by mix_posteriors()
parameter	parameter of interest
formula	model formula (needs to be specified if parameter was part of a formula)
at	named list with predictor levels of the formula for which marginalization should be performed. If a predictor level is missing, 0 is used for continuous predictors, the baseline factor level is used for factors with contrast = "treatment" prior distributions, and the parameter is completely omitted for for factors with contrast = "meandif",
prior_samples	whether marginal prior distributions should be generated contrast = "orthonormal", and contrast = "independent" levels
use_formula	whether the parameter should be evaluated as a part of supplied formula
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
n_samples	number of samples to be drawn for the model-averaged posterior distribution
...	additional arguments

Value

marginal_posterior returns a named list of mixed marginal posterior distributions (either a vector of matrix). #'

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