| varimax {stats} | R Documentation |
Rotation Methods for Factor Analysis
Description
These functions ‘rotate’ loading matrices in factor analysis.
Usage
varimax(x, normalize = TRUE, eps = 1e-5)
promax(x, m = 4)
Arguments
x |
A loadings matrix, with |
m |
The power used the target for |
normalize |
logical. Should Kaiser normalization be performed?
If so the rows of |
eps |
The tolerance for stopping: the relative change in the sum of singular values. |
Details
These seek a ‘rotation’ of the factors x %*% T that
aims to clarify the structure of the loadings matrix. The matrix
T is a rotation (possibly with reflection) for varimax,
but a general linear transformation for promax, with the
variance of the factors being preserved.
Value
A list with components
loadings |
The ‘rotated’ loadings matrix,
|
rotmat |
The ‘rotation’ matrix. |
References
Hendrickson AE, White PO (1964). “PROMAX: A Quick Method for Rotation to Oblique Simple Structure.” British Journal of Statistical Psychology, 17(1), 65–70. doi:10.1111/j.2044-8317.1964.tb00244.x.
Horst P (1965). Factor Analysis of Data Matrices. Holt, Rinehart and Winston.
Kaiser HF (1958). “The Varimax Criterion for Analytic Rotation in Factor Analysis.” Psychometrika, 23(3), 187–200. doi:10.1007/BF02289233.
Lawley DN, Maxwell AE (1971). Factor Analysis as a Statistical Method, 2nd edition. Butterworth & Co Publishers Ltd. ISBN 978-0408701525.
See Also
Examples
## varimax with normalize = TRUE is the default
fa <- factanal( ~., 2, data = swiss)
varimax(loadings(fa), normalize = FALSE)
promax(loadings(fa))