gen_mc: Generate Monte Carlo Estimates

In power4mome: Power Analysis for Moderation and Mediation

Generate Monte Carlo Estimates

Description

Get a list of the output of lavaan::sem() and generate Monte Carlo estimates of model parameters.

Usage

gen_mc(
  fit_all,
  R = 100,
  ...,
  iseed = NULL,
  parallel = FALSE,
  progress = FALSE,
  ncores = max(1, parallel::detectCores(logical = FALSE) - 1),
  compute_implied_stats = FALSE
)

Arguments

fit_all	The output of fit_model() or an object of the class fit_out.
R	The number of replications to generate the Monte Carlo estimates for each fit output.
...	Optional arguments to be passed to manymome::do_mc() when generating the Monte Carlo estimates.
iseed	The seed for the random number generator. Default is NULL and the seed is not changed.
parallel	If TRUE, parallel processing will be used to generate Monte Carlo estimates for the fit outputs. Default is FALSE.
progress	If TRUE, the progress will be displayed. Default is 'FALSE.
ncores	The number of CPU cores to use if parallel processing is used.
compute_implied_stats	Whether implied statistics are computed in each Monte Carlo replication. Usually not needed and so default to FALSE.

Details

The function gen_mc() simply calls manymome::do_mc() on each output of lavaan::sem() in fit_all. The simulated estimates can then be used to test effects such as indirect effects, usually by functions from the manymome package, such as manymome::indirect_effect().

Value

An mc_list object, which is a list of the output of manymome::do_mc().

The role of gen_mc()

This function is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to customize the workflow of the power analysis.

See Also

See power4test() for the all-in-one function that uses this function.

Examples

# Specify the population model

mod <-
"m ~ x
 y ~ m + x"

# Specify the effect sizes (population parameter values)

es <-
"
y ~ m: m
m ~ x: m
y ~ x: n
"

# Generate several simulated datasets

data_all <- sim_data(nrep = 5,
                     model = mod,
                     pop_es = es,
                     n = 100,
                     iseed = 1234)

# Fit the population model to each datasets

fit_all <- fit_model(data_all)

# Generate Monte Carlo estimates for each replication

mc_all <- gen_mc(fit_all,
                 R = 100,
                 iseed = 4567)

mc_all

power4mome documentation built on Sept. 9, 2025, 5:35 p.m.