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semmcci (version 1.1.5)

print.semmcci: Print Method for Object of Class semmcci

Description

Print Method for Object of Class semmcci

Usage

# S3 method for semmcci
print(x, alpha = NULL, digits = 4, ...)

Value

Prints a matrix of estimates, standard errors, number of Monte Carlo replications, and confidence intervals.

Arguments

x

an object of class semmcci.

alpha

Numeric vector. Significance level \(\alpha\). If alpha = NULL, use the argument alpha used in x.

digits

Integer indicating the number of decimal places to display.

...

further arguments.

Author

Ivan Jacob Agaloos Pesigan

Examples

Run this code
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
)

## Standardized Monte Carlo ------------------------------------------------
std <- MCStd(unstd)
print(unstd)
print(std)

# 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
)

## Standardized Monte Carlo ------------------------------------------------
std <- MCStd(unstd)
print(unstd)
print(std)

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