Fit a Bayesian nodesplitting model — bayes

Fit a Bayesian nodesplitting model with gemtc::mtc.nodesplit(). This is not possible for all networks and the function will return an error if the nodes cannot be split.

Usage

bayes_nodesplit(configured_data, n_adapt = 5000, n_iter = 20000, async = FALSE)

Arguments

configured_data: list. Input dataset created by setup_configure() or setup_exclude()
n_adapt: numeric. Number of adaptation iterations. Defaults to 5000 and can normally be left unchanged
n_iter: numeric. Number of simulation iterations. Defaults to 20000 and can normally be left unchanged
async: Whether or not the function is being used asynchronously. Default FALSE

Value

mtc.nodesplit object containing an mtc.result object for each node

Examples

# \donttest{
nodesplit_path <- system.file("extdata", "continuous_nodesplit.csv", package = "metainsight")
loaded_data <- setup_load(data_path = nodesplit_path,
                          outcome = "continuous")

configured_data <- setup_configure(loaded_data = loaded_data,
                                   reference_treatment = "Placebo",
                                   effects = "random",
                                   outcome_measure = "MD",
                                   ranking_option = "good",
                                   seed = 123)

# n_adapt and n_iter are set low to run quickly, but should be left as the
# default values in real use

nodesplit_model <- bayes_nodesplit(configured_data,
                                   n_adapt = 100,
                                   n_iter = 100)
# }