semmcci (version 1.1.5)

Func: Monte Carlo Confidence Intervals (List)

Description

Calculates Monte Carlo confidence intervals for defined parameters.

Usage

Func(coef, func, ..., est, alpha = c(0.001, 0.01, 0.05), ncores = NULL)

Value

Returns an object of class semmcci which is a list with the following elements:

call

Function call.

args

List of function arguments.

thetahat

Parameter estimates \(\hat{\theta}\).

thetahatstar

Sampling distribution of parameter estimates \(\hat{\theta}^{\ast}\).

fun

Function used ("Func").

Arguments

coef

List. A list of parameters.

func

R function.

1. The first argument x is the argument coef.

2. The function algebraically manipulates coef to return at a new numeric vector. It is best to have a named vector as an output.

3. The function can take additional named arguments passed using ....

...

Additional arguments to pass to func.

est

Numeric vector. Vector of original parameter estimates.

alpha

Numeric vector. Significance level \(\alpha\).

ncores

Positive integer. Number of cores to use. If ncores = NULL, use single core.

Author

Ivan Jacob Agaloos Pesigan

Details

The distribution of parameters is provided as a list (params) and the definition of the function of paremeters is provided by a function (func). Confidence intervals for defined parameters are generated using the generated sampling distribution.

References

MacKinnon, D. P., Lockwood, C. M., & Williams, J. (2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99-128. tools:::Rd_expr_doi("10.1207/s15327906mbr3901_4")

Pesigan, I. J. A., & Cheung, S. F. (2024). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. tools:::Rd_expr_doi("10.3758/s13428-023-02114-4")

Preacher, K. J., & Selig, J. P. (2012). Advantages of Monte Carlo confidence intervals for indirect effects. Communication Methods and Measures, 6(2), 77–98. tools:::Rd_expr_doi("10.1080/19312458.2012.679848")

See Also

Other Monte Carlo in Structural Equation Modeling Functions: MC(), MCFunc(), MCGeneric(), MCMI(), MCStd()

Examples

library(semmcci)

## Generate Parameters -----------------------------------------------------
coef <- lapply(
  X = 1:5,
  FUN = function(i) {
    rnorm(n = 1)
  }
)

## Func() ------------------------------------------------------------------
### Define func ------------------------------------------------------------
func <- function(x) {
  out <- exp(x)
  names(out) <- "exp"
  out
}
### Generate Confidence Intervals ------------------------------------------
Func(
  coef,
  func = func,
  est = 1,
  alpha = 0.05
)