R: Bootstrap Estimates for 'lm' Outputs

lm2boot_out {manymome}

R Documentation

Bootstrap Estimates for `lm` Outputs

Description

Generate bootstrap estimates for models in a list of 'lm' outputs.

Usage

lm2boot_out(outputs, R = 100, seed = NULL, progress = TRUE)

lm2boot_out_parallel(
  outputs,
  R = 100,
  seed = NULL,
  parallel = FALSE,
  ncores = max(parallel::detectCores(logical = FALSE) - 1, 1),
  make_cluster_args = list(),
  progress = TRUE
)

Arguments

`outputs`	A list of `lm` class objects, or the output of `lm2list()` (i.e., an `lm_list`-class object).
`R`	The number of bootstrap samples. Default is 100.
`seed`	The seed for the random resampling. Default is `NULL`.
`progress`	Logical. Display progress or not. Default is `TRUE`.
`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()`.

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.

It does nonparametric bootstrapping to generate bootstrap estimates of the regression coefficients in the regression models of a list of lm() outputs, or an lm_list-class object created by lm2list(). The stored estimates can be used by indirect_effect(), cond_indirect_effects(), and related functions in forming bootstrapping confidence intervals for effects such as indirect effect and conditional indirect effects.

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

lm2boot_out(): Generate bootstrap estimates using one process (serial, without parallelization).
lm2boot_out_parallel(): Generate bootstrap estimates using parallel processing.

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 progress to FALSE
# Progress is displayed by default.
lm_boot_out <- lm2boot_out(lm_out, R = 100, seed = 1234,
                           progress = FALSE)
out <- cond_indirect_effects(wlevels = "w",
                             x = "x",
                             y = "y",
                             m = "m",
                             fit = lm_out,
                             boot_ci = TRUE,
                             boot_out = lm_boot_out)
out

[Package manymome version 0.2.1 Index]

Bootstrap Estimates for lm Outputs