R: Bootstrap Estimates for a 'lavaan' Output

fit2boot_out {manymome}

R Documentation

Bootstrap Estimates for a `lavaan` Output

Description

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

Usage

fit2boot_out(fit)

fit2boot_out_do_boot(
  fit,
  R = 100,
  seed = NULL,
  parallel = FALSE,
  ncores = max(parallel::detectCores(logical = FALSE) - 1, 1),
  make_cluster_args = list(),
  progress = TRUE,
  internal = list()
)

Arguments

`fit`	The fit object. This function only supports a lavaan::lavaan object.
`R`	The number of bootstrap samples. Default is 100.
`seed`	The seed for the random resampling. Default is `NULL`.
`parallel`	Logical. Whether parallel processing will be used. Default is `NULL`.
`ncores`	Integer. The number of CPU cores to use when `parallel` is `TRUE`. Default is the number of non-logical cores minus one (one minimum). Will raise an error if greater than the number of cores detected by `parallel::detectCores()`. If `ncores` is set, it will override `make_cluster_args`.
`make_cluster_args`	A named list of additional arguments to be passed to `parallel::makeCluster()`. For advanced users. See `parallel::makeCluster()` for details. Default is `list()`.
`progress`	Logical. Display progress or not. Default is `TRUE`.
`internal`	A list of arguments to be used internally for debugging. Default is `list()`.

Details

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

If bootstrapping confidence intervals was requested when calling lavaan::sem() by setting se = "boot", fit2boot_out() can be used to extract the stored bootstrap estimates so that they can be reused by indirect_effect(), cond_indirect_effects() and related functions to form bootstrapping confidence intervals for effects such as indirect effects and conditional indirect effects.

If bootstrapping confidence was not requested when fitting the model by lavaan::sem(), fit2boot_out_do_boot() can be used to generate nonparametric bootstrap estimates from the output of lavaan::sem() and store them for use by indirect_effect(), cond_indirect_effects(), and related functions.

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

Value

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

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

Functions

fit2boot_out(): Process stored bootstrap estimates for functions such as cond_indirect_effects().
fit2boot_out_do_boot(): Do bootstrapping and store information to be used by cond_indirect_effects() and related functions. Support parallel processing.

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
"

# Bootstrapping not requested in calling lavaan::sem()
fit <- sem(model = mod, data = dat, fixed.x = FALSE,
           se = "none", baseline = FALSE)
fit_boot_out <- fit2boot_out_do_boot(fit = fit,
                                     R = 40,
                                     seed = 1234,
                                     progress = FALSE)
out <- cond_indirect_effects(wlevels = "w",
                             x = "x",
                             y = "y",
                             m = "m",
                             fit = fit,
                             boot_ci = TRUE,
                             boot_out = fit_boot_out)
out

[Package manymome version 0.2.2 Index]

Bootstrap Estimates for a lavaan Output