run_MIBench_all_dgps: Run all experiments for one multiple imputation algorithm

In mneunhoe/MIBench: What the Package Does (One Line, Title Case)

Run all experiments for one multiple imputation algorithm

Description

Run all experiments for one multiple imputation algorithm

Usage

run_MIBench_all_dgps(
  MIalgorithm,
  m = 10,
  store_runs = FALSE,
  store_results = FALSE,
  n_repetitions = 1000,
  n_cores = 4,
  seed = NULL
)

Arguments

MIalgorithm	A multiple imputation algorithm in the format of MIBench
m	The number of imputations
store_runs	Set to TRUE if you want to store the imputations and all data of the experiments on disk. Default is FALSE.
store_results	Set to TRUE if you want to store the summary of the results of the experiments on disk. Default is FALSE.
n_repetitions	The number of repetitions (default is 1000) with fresh draws from the dgp function.
n_cores	The number of cores for parallel processing of the experiments.
seed	A random seed for the experiments. Note that a exact replication depends on the seed and the number of cores n_cores.

Value

A list of class "MIbench_imputations"

Examples

df <- cars
regression <- lm(log(dist)~speed, data = df)
# Specifiying no scenario to simulate at the mean of speed.
simloglm(regression)
# Explicitily specifying a scenario.
simloglm(regression, scenario = list(speed = c(5, 10, 20)))

mneunhoe/MIBench documentation built on May 22, 2023, 11:07 p.m.