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
#> 0.2387180 0.4812565 0.5722387 0.2491255
#> reaction~~reaction pmi~~pmi reaction~1 pmi~1
#> 1.9301333 1.7263351 0.7027880 5.2689670
#> cond~1 indirect direct total
#> 0.4852165 0.2753936 0.2387180 0.5141116
coef(std)
#> cp b a cond~~cond
#> 0.07697822 0.41806030 0.21242103 1.00000000
#> reaction~~reaction pmi~~pmi reaction~1 pmi~1
#> 0.80562787 0.95487731 0.45404397 3.91865219
#> cond~1 indirect direct total
#> 0.97213491 0.08880480 0.07697822 0.16578301
# 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
#> 0.1685092 0.4589709 0.5011024 0.2492432
#> reaction~~reaction pmi~~pmi indirect direct
#> 1.9960264 1.7489519 0.2325101 0.1685092
#> total
#> 0.4010193
coef(std)
#> cp b a cond~~cond
#> 0.03267557 0.42341103 0.24847576 1.00000000
#> reaction~~reaction pmi~~pmi indirect direct
#> 0.81277998 0.93825980 0.10520738 0.03267557
#> total
#> 0.13788295