varimax {mva}R Documentation

Rotation Methods for Factor Analysis


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.
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.

See Also

factanal, Harman74.cor.


## varimax with normalize = T is the default
fa <- factanal( ~., 2, data = swiss)
varimax(fa$loadings, normalize = FALSE)

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