MCStd {semmcci}

Standardized Monte Carlo Confidence Intervals

Description

Calculates standardized Monte Carlo confidence intervals for free and defined parameters.

Usage

MCStd(mc, alpha = c(0.001, 0.01, 0.05))

Arguments

mc

Output of the MC() or MCMI() function.

alpha

Numeric vector. Significance level \alpha.

Details

The empirical sampling distribution of parameter estimates from the argument mc is standardized, that is, each randomly generated vector of parameters is standardized. Defined parameters are computed from the standardized component parameters. Confidence intervals are generated using the standardized empirical sampling distribution.

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 ("MCStd").

Author(s)

Ivan Jacob Agaloos Pesigan

References

Pesigan, I. J. A., & Cheung, S. F. (2023). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. doi:10.3758/s13428-023-02114-4

See Also

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

Examples

library(semmcci)
library(lavaan)

# Data ---------------------------------------------------------------------
data("Tal.Or", package = "psych")
df <- mice::ampute(Tal.Or)$amp

# Monte Carlo --------------------------------------------------------------
## Fit Model in lavaan -----------------------------------------------------
model <- "
  reaction ~ cp * cond + b * pmi
  pmi ~ a * cond
  cond ~~ cond
  indirect := a * b
  direct := cp
  total := cp + (a * b)
"
fit <- sem(data = df, model = model, missing = "fiml")

## MC() --------------------------------------------------------------------
unstd <- MC(
  fit,
  R = 5L, # use a large value e.g., 20000L for actual research
  alpha = 0.05
)

## Standardized Monte Carlo ------------------------------------------------
MCStd(unstd, alpha = 0.05)

# Monte Carlo (Multiple Imputation) ----------------------------------------
## Multiple Imputation -----------------------------------------------------
mi <- mice::mice(
  data = df,
  print = FALSE,
  m = 5L, # use a large value e.g., 100L for actual research,
  seed = 42
)

## Fit Model in lavaan -----------------------------------------------------
fit <- sem(data = df, model = model) # use default listwise deletion

## MCMI() ------------------------------------------------------------------
unstd <- MCMI(
  fit,
  mi = mi,
  R = 5L, # use a large value e.g., 20000L for actual research
  alpha = 0.05
)

## Standardized Monte Carlo ------------------------------------------------
MCStd(unstd, alpha = 0.05)