Parameter Estimates
Usage
# S3 method for class 'semmcci'
coef(object, ...)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
)
## Standardized Monte Carlo ------------------------------------------------
std <- MCStd(unstd)
coef(unstd)
#> cp b a cond~~cond
#> 0.2658766 0.4704099 0.3888388 0.2466416
#> reaction~~reaction pmi~~pmi reaction~1 pmi~1
#> 1.9396195 1.7688235 0.6922417 5.3945599
#> cond~1 indirect direct total
#> 0.4485313 0.1829136 0.2658766 0.4487902
coef(std)
#> cp b a cond~~cond
#> 0.08557756 0.40972808 0.14369120 1.00000000
#> reaction~~reaction pmi~~pmi reaction~1 pmi~1
#> 0.81472274 0.97935284 0.44864665 4.01405539
#> cond~1 indirect direct total
#> 0.90314927 0.05887432 0.08557756 0.14445188
# 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)
coef(unstd)
#> cp b a cond~~cond
#> 0.3183181 0.4912705 0.4057651 0.2465993
#> reaction~~reaction pmi~~pmi indirect direct
#> 1.9103791 1.7655638 0.2004329 0.3183181
#> total
#> 0.5187510
coef(std)
#> cp b a cond~~cond
#> 0.08872727 0.43502714 0.15172185 1.00000000
#> reaction~~reaction pmi~~pmi indirect direct
#> 0.79116630 0.97698048 0.06600312 0.08872727
#> total
#> 0.15473040