Extracting Item Parameters from a Fitted cfa Object in lavaan

This function extract item parameters from a fitted lavaan::cfa object in lavaan. It extract item loadings, item intercepts and the mean and covariance matrix of latent variables in a confirmatory factor analysis model.

cfa.extract.itempars(object)

Arguments

object	Fitted cfa object

Value

List with following entries

L

Matrix of item loadings

nu

Vector of item intercepts

psi

Residual covariance matrix

Sigma

Covariance matrix of latent variables

nu

Vector of means of latent variables

...

Further values

See also

See IRTLikelihood.cfa for extracting the individual likelihood from fitted confirmatory factor analyses.

lavaan::cfa

Examples

#############################################################################
# EXAMPLE 1: CFA data.Students
#############################################################################

library(lavaan)
library(CDM)

data(data.Students, package="CDM")
dat <- data.Students

dat1 <- dat[, paste0( "mj", 1:4 ) ]

#*** Model 1: Unidimensional model scale mj
lavmodel <- "
   mj=~ mj1 + mj2 + mj3 + mj4
   mj ~~ mj
     "
mod1 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
summary(mod1, standardized=TRUE, rsquare=TRUE )
# extract parameters
res1 <- TAM::cfa.extract.itempars( mod1 )

if (FALSE) {
#*** Model 2: Scale mj - explicit modelling of item intercepts
lavmodel <- "
   mj=~ mj1 + mj2 + mj3 + mj4
   mj ~~ mj
   mj1 ~ 1
     "
mod2 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
summary(mod2, standardized=TRUE, rsquare=TRUE )
res2 <- TAM::cfa.extract.itempars( mod2 )

#*** Model 3: Tau-parallel measurements scale mj
lavmodel <- "
   mj=~ a*mj1 + a*mj2 + a*mj3 + a*mj4
   mj ~~ 1*mj
   mj1 ~ b*1
   mj2 ~ b*1
   mj3 ~ b*1
   mj4 ~ b*1
     "
mod3 <- lavaan::cfa( lavmodel, data=dat1, std.lv=TRUE )
summary(mod3, standardized=TRUE, rsquare=TRUE )
res3 <- TAM::cfa.extract.itempars( mod3 )

#*** Model 4: Two-dimensional CFA with scales mj and sc
dat2 <- dat[, c(paste0("mj",1:4), paste0("sc",1:4)) ]
# lavaan model with shortage "__" operator
lavmodel <- "
   mj=~ mj1__mj4
   sc=~ sc1__sc4
   mj ~~ sc
   mj ~~ 1*mj
   sc ~~ 1*sc
     "
lavmodel <- TAM::lavaanify.IRT( lavmodel, data=dat2 )$lavaan.syntax
cat(lavmodel)
mod4 <- lavaan::cfa( lavmodel, data=dat2, std.lv=TRUE )
summary(mod4, standardized=TRUE, rsquare=TRUE )
res4 <- TAM::cfa.extract.itempars( mod4 )
}