fit2mc_out: Monte Carlo Estimates for a 'lavaan' Output

In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

Monte Carlo Estimates for a lavaan Output

Description

Generate Monte Carlo estimates from the output of lavaan::sem().

Usage

fit2mc_out(fit, progress = TRUE)

Arguments

fit	The fit object. This function only supports a lavaan::lavaan object. It can also be a lavaan.mi object returned by semTools::runMI() or its wrapper, such as semTools::sem.mi().
progress	Logical. Display progress or not. Default is TRUE.

Details

This function is for advanced users. do_mc() is a function users should try first because do_mc() has a general interface for input-specific functions like this one.

fit2mc_out() can be used to extract the stored Monte Carlo estimates so that they can be reused by indirect_effect(), cond_indirect_effects() and related functions to form Monte Carlo confidence intervals for effects such as indirect effects and conditional indirect effects.

This approach removes the need to repeat Monte Carlo simulation in each call to indirect_effect(), cond_indirect_effects(), and related functions. It also ensures that the same set of Monte Carlo estimates is used in all subsequent analyses.

Value

A mc_out-class object that can be used for the mc_out argument of indirect_effect(), cond_indirect_effects(), and related functions for forming Monte Carlo confidence intervals.

The object is a list with the number of elements equal to the number of Monte Carlo replications. Each element is a list of the parameter estimates and sample variances and covariances of the variables in each Monte Carlo replication.

See Also

do_mc(), the general purpose function that users should try first before using this function.

Examples

library(lavaan)
data(data_med_mod_ab1)
dat <- data_med_mod_ab1
dat$"x:w" <- dat$x * dat$w
dat$"m:w" <- dat$m * dat$w
mod <-
"
m ~ x + w + x:w + c1 + c2
y ~ m + w + m:w + x + c1 + c2
"

fit <- sem(model = mod, data = dat, fixed.x = FALSE,
           baseline = FALSE)
# In real research, R should be 5000 or even 10000.
fit <- gen_mc_est(fit, R = 100, seed = 453253)
fit_mc_out <- fit2mc_out(fit)
out <- cond_indirect_effects(wlevels = "w",
                             x = "x",
                             y = "y",
                             m = "m",
                             fit = fit,
                             mc_ci = TRUE,
                             mc_out = fit_mc_out)
out

manymome documentation built on Oct. 4, 2024, 5:10 p.m.