This function standardizes the unrotated factor loadings using two methods: Kaiser's normalization and Cureton-Mulaik standardization.
faStandardize(method, lambda)The resulting output can be used to standardize the factor loadings as well as providing the inverse matrix used to unstandardize the factor loadings after rotating the factor solution.
Dv: (Matrix) A diagonal weight matrix used to standardize the unrotated factor loadings. Pre-multiplying the loadings matrix by the diagonal weight matrix (i.e., Dv
DvInv: (Matrix) The inverse of the diagonal weight matrix used to standardize. To unstandardize the ultimate rotated solution, pre-multiply the rotated factor loadings by the inverse of Dv (i.e., DvInv
lambda: (Matrix) The standardized, unrotated factor loadings matrix.
unstndLambda: (Matrix) The original, unstandardized, unrotated factor loadings matrix. (DvInv
(Character) The method used for standardization. There are three option: "none", "Kaiser", and "CM".
"none": No standardization is conducted on the unrotated factor loadings matrix
"Kaiser": The rows of the unrotated factor loadings matrix are rescaled to have unit-lengths.
"CM": Apply the Cureton-Mulaik standardization to the unrotated factor loadings matrix.
(Matrix) The unrotated factor loadings matrix (or data frame).
Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111-150.
Cureton, E. E., & Mulaik, S. A. (1975). The weighted varimax rotation and the promax rotation. Psychometrika, 40(2), 183-195.
Other Factor Analysis Routines:
BiFAD(),
Box26,
GenerateBoxData(),
Ledermann(),
SLi(),
SchmidLeiman(),
faAlign(),
faEKC(),
faIB(),
faLocalMin(),
faMB(),
faMain(),
faScores(),
faSort(),
faX(),
fals(),
fapa(),
fareg(),
fsIndeterminacy(),
orderFactors(),
print.faMB(),
print.faMain(),
promaxQ(),
summary.faMB(),
summary.faMain()