
efa
An maximum likelihood exploratory factor analysis, provided by the standard R exploratory factor analysis factanal
, which requires the specified number of factors as an input to the analysis. Then constructs the corresponding multiple indicator measurement model (MIMM) suggested by the exploratory factor analysis loadings, and the lessR
code needed to run the confirmatory analysis of the model.
corEFA(x=mycor, n.factors, rotate=c("promax", "varimax"),
min.loading=.2, show.initial=FALSE, sort=TRUE, ...)efa(...)
TRUE
, then display initial factor extraction
before rotation.factanal
directly.Also provides the associated multiple indicator measurement model suggested by the exploratory factor analysis. Each MIMM factor is defined by the items that have the highest loading on the corresponding exploratory factor.
Correlation
.# input correlation matrix of perfect two-factor model
# Factor Pattern for each Factor: 0.8, 0.6, 0.4
# Factor-Factor correlation: 0.3
mycor <- matrix(nrow=6, ncol=6, byrow=TRUE,
c(1.000,0.480,0.320,0.192,0.144,0.096,
0.480,1.000,0.240,0.144,0.108,0.072,
0.320,0.240,1.000,0.096,0.072,0.048,
0.192,0.144,0.096,1.000,0.480,0.320,
0.144,0.108,0.072,0.480,1.000,0.240,
0.096,0.072,0.048,0.320,0.240,1.000))
colnames(mycor) <- c("X1", "X2", "X3", "X4", "X5", "X6")
rownames(mycor) <- colnames(mycor)
# default factor analysis of default correlation matrix mycor
# with two factors extracted
corEFA(n.factors=2)
# abbreviated form
# use all items to construct the MIMM, regardless of their loadings
# show the initial factor extraction
efa(n.factors=2, min.loading=NA, show.initial=TRUE)
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