parameterEstimates_boot: Bootstrap CIs for Parameter Estimates

Description

Functions for forming bootstrap confidence intervals for the parameter estimates.

Usage

parameterEstimates_boot(
  object,
  level = 0.95,
  boot_org_ratio = FALSE,
  boot_ci_type = c("perc", "bc", "bca.simple"),
  save_boot_est = TRUE,
  boot_pvalue = TRUE,
  boot_pvalue_min_size = 1000,
  standardized = FALSE,
  ...
)

Value

The output of lavaan::parameterEstimates(), with bootstrap confidence intervals appended to the right, with class set to sbt_ustd_boot. It has a print method (print.sbt_ustd_boot()) that can be used to print the parameter estimates in a format similar to that of the printout of the summary() of a lavaan::lavaan object.

Arguments

object: A 'lavaan'-class object, fitted with 'se = "boot"'.
level: The level of confidence of the confidence intervals. Default is .95.
boot_org_ratio: The ratio of (a) the distance of the bootstrap confidence limit from the point estimate to (b) the distance of the original confidence limit in object from the point estimate. Default is FALSE.
boot_ci_type: The type of the bootstrapping confidence intervals. Support percentile confidence intervals ("perc", the default) and bias-corrected confidence intervals ("bc" or "bca.simple").
save_boot_est: Whether the bootstrap estimates of the parameter estimates are saved. If saved, the bootstrap estimates of the free parameters will be stored in the attribute boot_est_ustd, while the bootstrap estimates of user-defined parameters, if any, will be stored in the attribute boot_def. Default is TRUE.
boot_pvalue: Whether asymmetric bootstrap p-values are computed. Default is TRUE.
boot_pvalue_min_size: Integer. The asymmetric bootstrap p-values will be computed only if the number of valid bootstrap estimates is at least this value. Otherwise, NA will be returned. If the number of valid bootstrap samples is less than this value, then boot_pvalue will be set to FALSE.
standardized: The type of standardized estimates. The same argument of lavaan::parameterEstimates(), and support all values supported by lavaan::parameterEstimates(). It is recommended to use standardizedSolution_boot() or lavaan::standardizedSolution() because this function only report the point estimates of the standardized solution, without standard errors or confidence intervals.
...: Other arguments to be passed to lavaan::parameterEstimates().

Author

Shu Fai Cheung https://orcid.org/0000-0002-9871-9448.

Details

parameterEstimates_boot() receives a lavaan::lavaan object and form bootstrap confidence intervals for the parameter estimates.

The function store_boot() should be called first to compute and store bootstrap estimates. This function will then retrieve them.

Bootstrap Confidence Intervals

It supports percentile and bias-corrected bootstrap confidence intervals.

Bootstrap Standard Errors

The standard errors are the standard deviation of the bootstrap estimates.

Bootstrap Asymmetric p-Values

If percentile bootstrap confidence interval is requested, asymmetric bootstrap p-values are also computed, using the method presented in Asparouhov and Muthén (2021).

References

Asparouhov, A., & Muthén, B. (2021). Bootstrap p-value computation. Retrieved from https://www.statmodel.com/download/FAQ-Bootstrap%20-%20Pvalue.pdf

Examples

Run this code


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)
summary(fit)
fit <- store_boot(fit,
                  do_bootstrapping = TRUE,
                  R = 100,
                  iseed = 1234)
est <- parameterEstimates_boot(fit)
est

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