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Bootstrap Estimates for 'indirect_effects' and 'cond_indirect_effects'

Source: R/do_boot.R
do_boot.Rd

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

Either (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().

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.

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.

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.

See also

lm2boot_out(), fit2boot_out(), and fit2boot_out_do_boot(), which implements the bootstrapping.

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
#>                w
#> M+1.0SD 6.046455
#> Mean    4.990179
#> M-1.0SD 3.933902
out <- cond_indirect_effects(wlevels = wlevels,
                             x = "x",
                             y = "y",
                             m = "m",
                             fit = lm_out,
                             boot_ci = TRUE,
                             boot_out = lm_boot_out)
out
#> 
#> == Conditional indirect effects ==
#> 
#>  Path: x -> m -> y
#>  Conditional on moderator(s): w
#>  Moderator(s) represented by: w
#> 
#>       [w]   (w)    ind  CI.lo CI.hi Sig    m~x   y~m
#> 1 M+1.0SD 6.046  0.248  0.029 0.452 Sig  0.342 0.725
#> 2 Mean    4.990  0.024 -0.084 0.219      0.063 0.375
#> 3 M-1.0SD 3.934 -0.006 -0.066 0.108     -0.216 0.026
#> 
#>  - [CI.lo to CI.hi] are 95.0% percentile confidence intervals by
#>    nonparametric bootstrapping with 50 samples.
#>  - The 'ind' column shows the indirect effects.
#>  - ‘m~x’,‘y~m’ is/are the path coefficient(s) along the path conditional
#>    on the moderator(s).
#>