marginal_inference {BayesTools}

Model-average marginal posterior distributions and marginal Bayes factors

Description

Creates marginal model-averaged and conditional posterior distributions based on a list of models, vector of parameters, formula, and a list of indicators of the null or alternative hypothesis models for each parameter. Computes inclusion Bayes factors for each marginal estimate via a Savage-Dickey density approximation.

Usage

marginal_inference(
  model_list,
  marginal_parameters,
  parameters,
  is_null_list,
  formula,
  null_hypothesis = 0,
  normal_approximation = FALSE,
  n_samples = 10000,
  seed = NULL,
  silent = FALSE
)

Arguments

model_list

list of models, each of which contains marginal likelihood estimated with bridge sampling marglik and prior model odds prior_weights

marginal_parameters

parameters for which the the marginal summary should be created

parameters

all parameters included in the model_list that are relevant for the formula (all of which need to have specification of is_null_list)

is_null_list

list with entries for each parameter carrying either logical vector of indicators specifying whether the model corresponds to the null or alternative hypothesis (or an integer vector indexing models corresponding to the null hypothesis)

formula

model formula (needs to be specified if parameter was part of a formula)

null_hypothesis

point null hypothesis to test. Defaults to 0

normal_approximation

whether the height of prior and posterior density should be approximated via a normal distribution (rather than kernel density). Defaults to FALSE.

n_samples

number of samples to be drawn for the model-averaged posterior distribution

seed

seed for random number generation

silent

whether warnings should be returned silently. Defaults to FALSE

Value

mix_posteriors returns a named list of mixed posterior distributions (either a vector of matrix).

See Also

ensemble_inference mix_posteriors BayesTools_ensemble_tables