vignettes/lecture_secondcfa.R

In doomlab/learnSEM: Learning Tutorials for Structural Equation Modeling

## ---- include = FALSE-----------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----echo = F, message = F, warning = F-----------------
knitr::opts_chunk$set(echo = TRUE)
library(lavaan)
library(semPlot)

## ----echo=FALSE, out.width = "75%", fig.align="center"----
knitr::include_graphics("pictures/second_order.png")

## ----echo=FALSE, out.width = "75%", fig.align="center"----
knitr::include_graphics("pictures/bi_factor.png")

## -------------------------------------------------------
library(lavaan)
library(semPlot)

##import the data
wisc4.cov <- lav_matrix_lower2full(c(8.29,
                                    5.37,9.06,
                                    2.83,4.44,8.35,
                                    2.83,3.32,3.36,8.88,
                                    5.50,6.66,4.20,3.43,9.18,
                                    6.18,6.73,4.01,3.33,6.77,9.12,
                                    3.52,3.77,3.19,2.75,3.88,4.05,8.88,
                                    3.79,4.50,3.72,3.39,4.53,4.70,4.54,8.94,
                                    2.30,2.67,2.40,2.38,2.06,2.59,2.65,2.83,8.76,
                                    3.06,4.04,3.70,2.79,3.59,3.67,3.44,4.20,4.53,9.73))

wisc4.sd <- c(2.88,3.01,2.89,2.98,3.03,3.02,2.98,2.99,2.96,3.12) 

names(wisc4.sd) <- 
  colnames(wisc4.cov) <- 
  rownames(wisc4.cov) <- c("Comprehension", "Information", 
                         "Matrix.Reasoning", "Picture.Concepts", 
                         "Similarities", "Vocabulary",  "Digit.Span", 
                         "Letter.Number",  "Coding", "Symbol.Search") 

## -------------------------------------------------------
##first order model
wisc4.fourFactor.model <- '
gc =~ Comprehension + Information +  Similarities + Vocabulary 
gf =~ Matrix.Reasoning + Picture.Concepts
gsm =~  Digit.Span + Letter.Number
gs =~ Coding + Symbol.Search
'   

## -------------------------------------------------------
wisc4.fourFactor.fit <- cfa(model = wisc4.fourFactor.model, 
                            sample.cov = wisc4.cov, 
                            sample.nobs = 550)

## -------------------------------------------------------
summary(wisc4.fourFactor.fit, 
        fit.measure = TRUE, 
        standardized = TRUE,
        rsquare = TRUE)

## -------------------------------------------------------
semPaths(wisc4.fourFactor.fit, 
         whatLabels="std", 
         edge.label.cex = 1,
         edge.color = "black",
         what = "std",
         layout="tree")

## -------------------------------------------------------
wisc4.higherOrder.model <- '
gc =~ Comprehension + Information + Similarities + Vocabulary 
gf =~ Matrix.Reasoning + Picture.Concepts
gsm =~  Digit.Span + Letter.Number
gs =~ Coding + Symbol.Search

g =~ gf + gc  + gsm + gs 
'

## -------------------------------------------------------
wisc4.higherOrder.fit <- cfa(model = wisc4.higherOrder.model, 
                             sample.cov = wisc4.cov, 
                             sample.nobs = 550)

## -------------------------------------------------------
summary(wisc4.higherOrder.fit, 
        fit.measure=TRUE, 
        standardized=TRUE, 
        rsquare = TRUE)

## -------------------------------------------------------
semPaths(wisc4.higherOrder.fit, 
         whatLabels="std", 
         edge.label.cex = 1,
         edge.color = "black",
         what = "std",
         layout="tree")

## -------------------------------------------------------
wisc4.bifactor.model <- '
gc =~ Comprehension + Information +  Similarities + Vocabulary 
gf =~ a*Matrix.Reasoning + a*Picture.Concepts  
gsm =~  b*Digit.Span + b*Letter.Number
gs =~ c*Coding + c*Symbol.Search 
g =~ Information + Comprehension + Matrix.Reasoning + Picture.Concepts + Similarities + Vocabulary +  Digit.Span + Letter.Number + Coding + Symbol.Search
'

## -------------------------------------------------------
wisc4.bifactor.fit <- cfa(model = wisc4.bifactor.model, 
                          sample.cov = wisc4.cov,
                          sample.nobs = 550, 
                          orthogonal = TRUE)

## -------------------------------------------------------
summary(wisc4.bifactor.fit, 
        fit.measure = TRUE, 
        rsquare = TRUE, 
        standardized = TRUE)

## -------------------------------------------------------
semPaths(wisc4.bifactor.fit, 
         whatLabels="std", 
         edge.label.cex = 1,
         edge.color = "black",
         what = "std",
         layout="tree")