| varimax {stats} | R Documentation |
These functions ‘rotate’ loading matrices in factor analysis.
varimax(x, normalize = TRUE, eps = 1e-5) promax(x, m = 4)
x |
A loadings matrix, with p rows and k < p columns |
m |
The power used the target for promax. Values of 2 to
4 are recommended. |
normalize |
logical. Should Kaiser normalization be performed?
If so the rows of x are re-scaled to unit length before
rotation, and scaled back afterwards. |
eps |
The tolerance for stopping: the relative change in the sum of singular values. |
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.
A list with components
loadings |
The ‘rotated’ loadings matrix,
x %*% rotmat, of class "loadings". |
rotmat |
The ‘rotation’ matrix. |
Hendrickson, A. E. and White, P. O. (1964) Promax: a quick method for rotation to orthogonal oblique structure. British Journal of Statistical Psychology, 17, 65–70.
Horst, P. (1965) Factor Analysis of Data Matrices. Holt, Rinehart and Winston. Chapter 10.
Kaiser, H. F. (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200.
Lawley, D. N. and Maxwell, A. E. (1971) Factor Analysis as a Statistical Method. Second edition. Butterworths.
## varimax with normalize = TRUE is the default fa <- factanal( ~., 2, data = swiss) varimax(loadings(fa), normalize = FALSE) promax(loadings(fa))