faEKC {fungible}

Calculate Reference Eigenvalues for the Empirical Kaiser Criterion

Description

Calculate Reference Eigenvalues for the Empirical Kaiser Criterion

Usage

faEKC(R = NULL, NSubj = NULL, Plot = FALSE)

Arguments

R

Input correlation matrix.

NSubj

Number of subjects (observations) used to create R.

Plot

(logical). If Plot = TRUE the function will plot the observed and reference eigenvalues of R.

Value

• ljEKC,

• ljEKC1,

• dimensions The estimated number of common factors.

Author(s)

Niels Waller

References

Braeken, J. & Van Assen, M. A. (2017). An empirical Kaiser criterion. Psychological Methods, 22(3), 450-466.

See Also

Other Factor Analysis Routines: BiFAD(), Box26, GenerateBoxData(), Ledermann(), SLi(), SchmidLeiman(), faAlign(), faIB(), faLocalMin(), faMB(), faMain(), faScores(), faSort(), faStandardize(), faX(), fals(), fapa(), fareg(), fsIndeterminacy(), orderFactors(), print.faMB(), print.faMain(), promaxQ(), summary.faMB(), summary.faMain()

Examples

data(AmzBoxes)
AmzBox20<- GenerateBoxData(XYZ = AmzBoxes[,2:4], 
                           BoxStudy = 20)$BoxData
RAmzBox20 <- cor(AmzBox20)
EKCout  <- faEKC(R = RAmzBox20, 
                NSubj = 98,
                Plot = TRUE)