Compute posterior probabilities and inclusion Bayes factors

Source: R/model-averaging.R

Computes prior probabilities, posterior probabilities, and inclusion Bayes factors based either on (1) a list of models, vector of parameters, and a list of indicators the models represent the null or alternative hypothesis for each parameter, (2) on prior model odds, marginal likelihoods, and indicator whether the models represent the null or alternative hypothesis, or (3) list of models for each model.

compute_inference(prior_weights, margliks, is_null = NULL, conditional = FALSE)

ensemble_inference(model_list, parameters, is_null_list, conditional = FALSE)

models_inference(model_list)

Arguments

prior_weights

vector of prior model odds

margliks

vector of marginal likelihoods

is_null

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)

conditional

whether prior and posterior model probabilities should be returned only for the conditional model. Defaults to FALSE

model_list

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

parameters

vector of parameters names for which inference should be drawn

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)

Value

compute_inference returns a named list of prior probabilities, posterior probabilities, and Bayes factors, ppoint gives the distribution function, ensemble_inference gives a list of named lists of inferences for each parameter, and models_inference returns a list of models, each expanded by the inference list.