MCMI: Monte Carlo Confidence Intervals (Multiple Imputation)

In semmcci: Monte Carlo Confidence Intervals in Structural Equation Modeling

Monte Carlo Confidence Intervals (Multiple Imputation)

Description

Calculates Monte Carlo confidence intervals for free and defined parameters. Missing values are handled using multilple imputation.

Usage

MCMI(
  lav,
  mi,
  R = 20000L,
  alpha = c(0.001, 0.01, 0.05),
  decomposition = "eigen",
  pd = TRUE,
  tol = 1e-06,
  seed = NULL
)

Arguments

lav	Object of class lavaan.
mi	Object of class mids (output of mice::mice()), object of class amelia (output of Amelia::amelia()), or a list of multiply imputed data sets.
R	Positive integer. Number of Monte Carlo replications.
alpha	Numeric vector. Significance level \alpha.
decomposition	Character string. Matrix decomposition of the sampling variance-covariance matrix for the data generation. If decomposition = "chol", use Cholesky decomposition. If decomposition = "eigen", use eigenvalue decomposition. If decomposition = "svd", use singular value decomposition.
pd	Logical. If pd = TRUE, check if the sampling variance-covariance matrix is positive definite using tol.
tol	Numeric. Tolerance used for pd.
seed	Integer. Random seed for reproducibility.

Details

A sampling distribution of parameter estimates is generated from the multivariate normal distribution using the parameter estimates and the sampling variance-covariance matrix obtained using multiple imputation. Confidence intervals for free and defined parameters are generated using the simulated sampling distribution. Parameters can be defined using the ⁠:=⁠ operator in the lavaan model syntax.

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 ("MCMI").

References

Pesigan, I. J. A., & Cheung, S. F. (2023). Monte Carlo confidence intervals for the indirect effect with missing data. Behavior Research Methods. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3758/s13428-023-02114-4")}

Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. John Wiley & Sons, Inc.

See Also

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

Examples

library(semmcci)
library(lavaan)

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

# 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 -----------------------------------------------------
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) # use default listwise deletion

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

semmcci documentation built on June 22, 2024, 7:20 p.m.