sim_out: Create a 'sim_out' Object

In power4mome: Power Analysis for Moderation and Mediation

Create a 'sim_out' Object

Description

Combine the outputs of sim_data(), fit_model(), and optionally gen_mc() and/or gen_boot() to one single object.

Usage

sim_out(data_all, ...)

## S3 method for class 'sim_out'
print(x, digits = 3, digits_descriptive = 2, fit_to_all_args = list(), ...)

Arguments

data_all	The output of sim_data().
...	Named arguments of objects to be added to each replication under the element extra. For example, if set to fit = fit_all, where fit_all is the output of fit_model(), then data_all[[1]]$extra$fit will be set to the first output in fit_all.
x	The sim_out object to be printed.
digits	The numbers of digits displayed after the decimal.
digits_descriptive	The number of digits displayed after the decimal for the descriptive statistics table.
fit_to_all_args	A named list of arguments to be passed to lavaan::sem() when the model is fitted to a sample combined from

Details

It merges into one object the output of sim_data(), which is a list of nrep simulated datasets, fit_model(), which is a list of the lavaan::sem() output for the nrep datasets, and optionally the output of gen_mc() or gen_boot(), which is a list of the R sets of Monte Carlo or bootstrap estimates based on the results of fit_model(). The list has nrep elements, each element with the data, the model fit results, and optionally the Monte Carlo estimates matched.

This object can then be used for testing effects of interests, which are further processed to estimate the power of this test.

The function sim_out() is used by the all-in-one function power4test(). Users usually do not call this function directly, though developers can use this function to develop other functions for power analysis, or to build their own workflows to do the power analysis.

Value

The function sim_out() returns a sim_out object, which is a list of length equal to the length of data_all. Each element of the list is a sim_data object with the element extra added to it. Other named elements will be added under this name. For example. the output of fit_model() for this replication can be added to fit, under extra. See the description of the argument ... for details.

The print method of sim_out returns x invisibly. Called for its side effect.

See Also

power4test()

Examples

# Specify the model

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

# Specify the population values

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

# Generate the simulated datasets

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

# Fit the population model to each dataset

fits <- fit_model(dats)

# Combine the results to one object

sim_out_all <- sim_out(data_all = dats,
                       fit = fits)
sim_out_all

# Verify that the elements of fits are set to extra$fit

library(lavaan)
parameterEstimates(fits[[1]])
parameterEstimates(sim_out_all[[1]]$extra$fit)
parameterEstimates(fits[[2]])
parameterEstimates(sim_out_all[[2]]$extra$fit)

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