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##Example from semTools in Exploring the lavaan ecosystem
##Symposiom presented at Modern Modeling Methods, 2014
##Alexander Schoemann
library(lavaan)
library(semTools)
##Example
#Specify model (based on lavaan example)
HS.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
#Create missing data in the HS data. Use a MCAR process
HSMiss <- HolzingerSwineford1939[,paste("x", 1:9, sep="")]
randomMiss <- rbinom(prod(dim(HSMiss)), 1, 0.1)
randomMiss <- matrix(as.logical(randomMiss), nrow=nrow(HSMiss))
HSMiss[randomMiss] <- NA
#Fit model with runMI
#HSMIss is data set with 10% missing MCAR
out <- runMI(HS.model, data=HSMiss, m = 20, chi="all", fun = "cfa")
summary(out)
inspect(out, "fit")
#Provides fraction of missing information and fit for each chi square type
inspect(out, "impute")
##measurementInvariance example
HW.model <- ' visual =~ x1 + x2 + x3
textual =~ x4 + x5 + x6
speed =~ x7 + x8 + x9 '
measurementInvariance(HW.model, data=HolzingerSwineford1939, group="school")
Measurement invariance tests:
Model 1: configural invariance:
chisq df pvalue cfi rmsea bic
115.851 48.000 0.000 0.923 0.097 7706.822
Model 2: weak invariance (equal loadings):
chisq df pvalue cfi rmsea bic
124.044 54.000 0.000 0.921 0.093 7680.771
[Model 1 versus model 2]
delta.chisq delta.df delta.p.value delta.cfi
8.192 6.000 0.224 0.002
Model 3: strong invariance (equal loadings + intercepts):
chisq df pvalue cfi rmsea bic
164.103 60.000 0.000 0.882 0.107 7686.588
[Model 1 versus model 3]
delta.chisq delta.df delta.p.value delta.cfi
48.251 12.000 0.000 0.041
[Model 2 versus model 3]
delta.chisq delta.df delta.p.value delta.cfi
40.059 6.000 0.000 0.038
Model 4: equal loadings + intercepts + means:
chisq df pvalue cfi rmsea bic
204.605 63.000 0.000 0.840 0.122 7709.969
[Model 1 versus model 4]
delta.chisq delta.df delta.p.value delta.cfi
88.754 15.000 0.000 0.083
[Model 3 versus model 4]
delta.chisq delta.df delta.p.value delta.cfi
40.502 3.000 0.000 0.042