faStandardize {fungible} | R Documentation |
Standardize the Unrotated Factor Loadings
Description
This function standardizes the unrotated factor loadings using two methods: Kaiser's normalization and Cureton-Mulaik standardization.
Usage
faStandardize(method, lambda)
Arguments
method |
(Character) The method used for standardization. There are three option: "none", "Kaiser", and "CM".
|
lambda |
(Matrix) The unrotated factor loadings matrix (or data frame). |
Value
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
References
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.
See Also
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()