model_fit_loop {BayesGP}R Documentation

Repeated fitting Bayesian Hierarchical Models for a sequence of values of the looping variable.

Description

Performs repeated model fitting over a sequence of values for a specified variable within a hierarchical model. This function repeatedly fits a model for each value of the looping variable, compiles the log marginal likelihoods, and calculates the posterior probabilities for the variable's values.

Usage

model_fit_loop(
  loop_holder = "LOOP",
  loop_values,
  prior_func = function(x) {
     1
 },
  parallel = FALSE,
  cores = (parallel::detectCores() - 1),
  ...
)

Arguments

loop_holder

A string specifying the name of the variable to loop over. The default value is 'LOOP'.

loop_values

A numeric vector containing the values to loop over for the specified variable.

prior_func

A function that takes the specified loop_values and returns the values of the prior for the loop variable. By default, it is a uniform prior which returns a constant value, indicating equal probability for all values.

parallel

Logical, indicating whether or not to run the model fitting in parallel (default is FALSE).

cores

The number of cores to use for parallel execution (default is detected cores - 1).

...

Additional arguments passed to the model fitting function 'model_fit'.

Value

A data frame containing the values of the looping variable, their corresponding log marginal likelihoods, and posterior probabilities.


[Package BayesGP version 0.1.3 Index]