varimax {stats}  R Documentation 
These functions ‘rotate’ loading matrices in factor analysis.
varimax(x, normalize = TRUE, eps = 1e5) promax(x, m = 4)
x 
A loadings matrix, with p rows and k < p columns 
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. 
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,

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. doi: 10.1111/j.20448317.1964.tb00244.x.
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. doi: 10.1007/BF02289233.
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))