parameterEstimates_boot"
In semboottools: Bootstrapping Helpers for Structural Equation Modelling

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(semboottools)
library(lavaan)

Function Syntax

parameterEstimates_boot(object,
                        level = .95,
                        standardized = FALSE,
                        boot_org_ratio = FALSE,
                        boot_ci_type = c("perc", "bc", "bca.simple"),
                        save_boot_est = TRUE,
                        boot_pvalue = TRUE,
                        boot_pvalue_min_size = 1000,
                        ...)

Arguments

| Argument | Description | |-----------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | object | A model fitted by lavaan. | | level | Confidence level for the confidence intervals. For example, .95 gives 95% confidence intervals. | | standardized | Whether to return standardized estimates. Same as in lavaan::parameterEstimates(). You can use "std.all", "std.lv", etc. For detailed standardized results with CIs, use standardizedSolution_boot() instead. | | boot_org_ratio | Whether to calculate how wide the bootstrap confidence interval is compared to the original confidence interval (from delta method). Useful to compare the two methods. | | boot_ci_type | Method for forming bootstrap confidence intervals. "perc" gives percentile intervals; "bc" and "bca.simple" give bias-corrected intervals. | | save_boot_est | Whether to save the bootstrap estimates in the result. Saved in attributes boot_est_ustd (free parameters) and boot_def (user-defined parameters) if TRUE. | | boot_pvalue | Whether to compute asymmetric p-values based on bootstrap results. Only available when percentile confidence intervals are used. | | boot_pvalue_min_size | Minimum number of valid bootstrap samples needed to compute asymmetric p-values. If fewer samples are available, p-values will not be computed and will be shown as NA. | | ... | Additional arguments passed to lavaan::parameterEstimates(). |

Example

Data and Model

# Set seed for reproducibility
set.seed(1234)

# Generate data
n <- 1000
x <- runif(n) - 0.5
m <- 0.20 * x + rnorm(n)
y <- 0.17 * m + rnorm(n)
dat <- data.frame(x, y, m)

# Specify mediation model in lavaan syntax
mod <- '
  m ~ a * x
  y ~ b * m + cp * x
  ab := a * b
  total := a * b + cp
'

Basic usage: default settings

# Ensure bootstrap estimates are stored
fit <- sem(mod, data = dat, fixed.x = FALSE)
fit <- store_boot(fit) 
est_boot <- parameterEstimates_boot(fit)
print(est_boot)

parameterEstimates_boot(): Different Options

# Change confidence level to 99%
est_boot <- parameterEstimates_boot(fit, level = 0.99)
# Use bias-corrected (BC) bootstrap confidence intervals
est_boot <- parameterEstimates_boot(fit, boot_ci_type = "bc")
# Turn off asymmetric bootstrap p-values
est_boot <- parameterEstimates_boot(fit, boot_pvalue = FALSE)
# Do not save bootstrap estimates (for memory saving)
est_boot <- parameterEstimates_boot(fit, save_boot_est = FALSE)
# Compute and display bootstrap-to-original CI ratio
est_boot <- parameterEstimates_boot(fit, boot_org_ratio = TRUE)
# Combine options: BC CI, 99% level, no p-values
est_boot <- parameterEstimates_boot(fit,
                                    level = 0.99,
                                    boot_ci_type = "bc",
                                    boot_pvalue = FALSE)

print(): Different Options

# Print with more decimal places (e.g., 5 digits)
print(est_boot, nd = 5)
# Print in lavaan-style text format (similar to summary())
print(est_boot, output = "text")
# Print as a clean data frame table
print(est_boot, output = "table")
# Drop specific columns (e.g., "Z") in lavaan.printer format
print(est_boot, drop_cols = "Z")
# Combine options: 5 decimal digits, text format
print(est_boot, nd = 5, output = "text")