do_boot: Bootstrap Estimates for 'indirect_effects' and...
In manymome: Mediation, Moderation and Moderated-Mediation After Model Fitting

do_boot

R Documentation

Bootstrap Estimates for 'indirect_effects' and 'cond_indirect_effects'

Description

Generate bootstrap estimates to be used by cond_indirect_effects(), indirect_effect(), and cond_indirect(),

Usage

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

Arguments

`fit`	It can be (a) a list of `lm` class objects, or the output of `lm2list()` (i.e., an `lm_list`-class object), or (b) the output of `lavaan::sem()`. If it is a single model fitted by `lm()`, it will be automatically converted to a list by `lm2list()`.
`R`	The number of bootstrap samples. Default is 100.
`seed`	The seed for the bootstrapping. Default is `NULL` and seed is not set.
`parallel`	Logical. Whether parallel processing will be used. Default is `TRUE`.
`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()`, no additional arguments.
`progress`	Logical. Display progress or not. Default is `TRUE`.

Details

It does nonparametric bootstrapping to generate bootstrap estimates of the parameter estimates in a model fitted either by lavaan::sem() or by a sequence of calls to lm(). The stored estimates can then be used by cond_indirect_effects(), indirect_effect(), and cond_indirect() to form bootstrapping confidence intervals.

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

It determines the type of the fit object automatically and then calls lm2boot_out(), fit2boot_out(), or fit2boot_out_do_boot().

Multigroup Models

Since Version 0.1.14.2, support for multigroup models has been added for models fitted by lavaan. The implementation of bootstrapping is identical to that used by lavaan, with resampling done within each group.

Value

A boot_out-class object that can be used for the boot_out argument of cond_indirect_effects(), indirect_effect(), and cond_indirect() for forming bootstrap 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.

Examples

data(data_med_mod_ab1)
dat <- data_med_mod_ab1
lm_m <- lm(m ~ x*w + c1 + c2, dat)
lm_y <- lm(y ~ m*w + x + c1 + c2, dat)
lm_out <- lm2list(lm_m, lm_y)
# In real research, R should be 2000 or even 5000
# In real research, no need to set parallel and progress to FALSE
# Parallel processing is enabled by default and
# progress is displayed by default.
lm_boot_out <- do_boot(lm_out, R = 50, seed = 1234,
                       parallel = FALSE,
                       progress = FALSE)
wlevels <- mod_levels(w = "w", fit = lm_out)
wlevels
out <- cond_indirect_effects(wlevels = wlevels,
                             x = "x",
                             y = "y",
                             m = "m",
                             fit = lm_out,
                             boot_ci = TRUE,
                             boot_out = lm_boot_out)
out

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