lessR (version 2.6)

corEFA: Exploratory Factor Analysis and Multiple Indicator Measurement Model

Description

Abbreviation: 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 to run the confirmatory analysis of the model.

Usage

corEFA(x=mycor, n.factors, rotate=c("promax", "varimax"), 
          min.loading=.2, show.initial=FALSE, sort=TRUE, ...)
efa(...)

Arguments

x

Correlation matrix.

n.factors

Number of factors.

rotate

Rotation method.

min.loading

Minimum loading to include in suggested factor for confirmatory analysis and for the display of the loadings for the exploratory analysis. To ignore, set to NA.

show.initial

If TRUE, then display initial factor extraction before rotation.

sort

Sort the input variables by their highest factor loadings (but only first just list those items with loadings larger than 0.5).

...

Parameter values.

Details

Only the loadings from the exploratory factor analysis are provided, with either an oblique (promax), by default, or an orthogonal (varimax) rotation. If more information is desired, run 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.

See Also

cr.

Examples

# 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)