Compute and Store Bootstrap Estimates — store

This function computes bootstrap estimates of a fitted structural equation model and stores the estimates for further processing.

Usage

store_boot(
  object,
  type = "std.all",
  do_bootstrapping = TRUE,
  R = 1000,
  boot_type = "ordinary",
  parallel = c("no", "multicore", "snow"),
  ncpus = parallel::detectCores(logical = FALSE) - 1,
  iseed = NULL,
  keep.idx = FALSE,
  bootstrapLavaan_args = list()
)

Arguments

object: A 'lavaan'-class object, fitted with 'se = "boot"'.
type: The type of standard estimates. The same argument of lavaan::standardizedSolution(), and support all values supported by lavaan::standardizedSolution(). Default is "std.all".
do_bootstrapping: If TRUE and bootstrapping was not requested when fitting the model, bootstrapping will be done using lavaan::bootstrapLavaan(). Default is TRUE.
R: If lavaan::bootstrapLavaan() is called (see do_bootstrapping), this is the number of bootstrap samples, to be used by lavaan::bootstrapLavaan().
boot_type: If lavaan::bootstrapLavaan() is called (see do_bootstrapping), this is type of bootstrapping, to be passed to the argument type of lavaan::bootstrapLavaan(). Default is "ordinary". See the help page of lavaan::bootstrapLavaan() for details.
parallel: If lavaan::bootstrapLavaan() is called (see do_bootstrapping), whether parallel processing will be used. to be passed to the argument of the same name in lavaan::bootstrapLavaan(). Default is "no". Can be "snow" or "multicore". See the help page of lavaan::bootstrapLavaan() for details.
ncpus: If lavaan::bootstrapLavaan() is called (see do_bootstrapping), and parallel processing is to be used, this is the number of CPU cores to use, to be passed to the argument of the same name in lavaan::bootstrapLavaan(). Default is parallel::detectCores(logical = FALSE) - 1, the number of physical cores minus 1, different from the default of lavaan::bootstrapLavaan() but identical to the default of lavaan::sem() and lavaan::cfa().
iseed: If lavaan::bootstrapLavaan() is called (see do_bootstrapping), this should be an integer used to generate reproducible bootstrap results, to be passed to the argument of the same name in lavaan::bootstrapLavaan(). Default is NULL but it should nearly always be set to an arbitrary integer. See the help page of lavaan::bootstrapLavaan() for details.
keep.idx: Whether the indices of cases selected in each bootstrap sample is to be stored. To be passed to the argument of the same name in lavaan::bootstrapLavaan(). Default is FALSE.
bootstrapLavaan_args: A named list of additional arguments to be passed to lavaan::bootstrapLavaan(). Note that the other arguments in store_boot() takes precedence, overriding arguments of the same names in this list, if any.

Value

The original lavaan object is returned with the following objects stored in the external slot:

sbt_boot_std: The matrix of bootstrap estimates in the standardized solution.
sbt_boot_def: The matrix of bootstrap estimates of user-defined parameters, if any.
sbt_boot_ustd: The matrix of bootstrap estimates of free parameters, if bootstrapping is not requested when fitting the model (i.e., se is not set to "boot" or "bootstrap" when fitting the model in lavaan).

Details

The function store_boot() receives a lavaan::lavaan object, optionally fitted with bootstrapping standard errors requested, and compute and store the bootstrap estimates of user-defined parameters and estimates in the standardized solution.

If bootstrapping was not requested when fitting the model (i.e., se not set to "boot" or "bootstrap"), then bootstrapping will be conducted using lavaan::bootstrapLavaan() to compute bootstrap estimates of free parameters. Otherwise, the stored bootstrap estimates will be used in subsequent steps.

For standardized solution bootstrap estimates, it works by calling lavaan::standardizedSolution() with the bootstrap estimates of free parameters in each bootstrap sample to compute the standardized estimates in each sample.

For user-defined parameters, it works by calling the function used to compute user-defined parameters with the bootstrap estimates of free parameters in each bootstrap samples to compute the user-defined parameters.

The bootstrap estimates are then stored in the external slot of the fit object for further processing.

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448. Based on semhelpinghands::standardizedSolution_boot_ci(), which was originally proposed in an issue at GitHub https://github.com/simsem/semTools/issues/101#issue-1021974657, inspired by a discussion at the Google group for lavaan https://groups.google.com/g/lavaan/c/qQBXSz5cd0o/m/R8YT5HxNAgAJ. Unlike semhelpinghands::standardizedSolution_boot_ci(), this function only computes and stores the bootstrap estimates.

Examples


library(lavaan)
set.seed(5478374)
n <- 50
x <- runif(n) - .5
m <- .40 * x + rnorm(n, 0, sqrt(1 - .40))
y <- .30 * m + rnorm(n, 0, sqrt(1 - .30))
dat <- data.frame(x = x, y = y, m = m)
model <-
'
m ~ a*x
y ~ b*m
ab := a*b
'

# Should set bootstrap to at least 2000 in real studies
fit <- sem(model, data = dat, fixed.x = FALSE,
           se = "boot",
           bootstrap = 100)
summary(fit)
#> lavaan 0.6-19 ended normally after 1 iteration
#> 
#>   Estimator                                         ML
#>   Optimization method                           NLMINB
#>   Number of model parameters                         5
#> 
#>   Number of observations                            50
#> 
#> Model Test User Model:
#>                                                       
#>   Test statistic                                 0.020
#>   Degrees of freedom                                 1
#>   P-value (Chi-square)                           0.887
#> 
#> Parameter Estimates:
#> 
#>   Standard errors                            Bootstrap
#>   Number of requested bootstrap draws              100
#>   Number of successful bootstrap draws             100
#> 
#> Regressions:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>   m ~                                                 
#>     x          (a)    0.569    0.325    1.749    0.080
#>   y ~                                                 
#>     m          (b)    0.219    0.146    1.495    0.135
#> 
#> Variances:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>    .m                 0.460    0.086    5.381    0.000
#>    .y                 0.570    0.110    5.178    0.000
#>     x                 0.078    0.012    6.782    0.000
#> 
#> Defined Parameters:
#>                    Estimate  Std.Err  z-value  P(>|z|)
#>     ab                0.125    0.126    0.992    0.321
#> 

fit <- store_boot(fit)