Generate Monte Carlo Estimates

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.

Value

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

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().

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.

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
#> [[1]]
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> [[2]]
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> [[3]]
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> [[4]]
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> [[5]]
#> 
#> == A 'mc_out' class object ==
#> 
#> Number of Monte Carlo replications: 100 
#> 
#> 
#> attr(,"class")
#> [1] "mc_list" "list"