sla.varimax {RScaLAPACK} | R Documentation |
These functions ‘rotate’ loading matrices in factor analysis using ScaLAPACK.
sla.varimax(x, normalize = TRUE, eps = 1e-5, NPROWS=0, NPCOLS=0, MB=16) sla.promax(x, m = 4, NPROWS=0, NPCOLS=0, MB=16)
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. |
NPROWS |
Number of Process Rows in the Process Grid. |
NPCOLS |
Number of Process Cols in the Process Grid. |
MB |
Block Size. |
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. |
Nagiza Samatova (samatovan@ornl.gov), Guruprasad Kora (koragh@ornl.gov), Srikanth Yoginath (yoginathsb@ornl.gov), David Bauer (bauerda@ornl.gov)
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
for the normal varmax,
promax
for the normal promax,
sla.factanal
,
factanal
, Harman74.cor
.
## varimax with normalize = T is the default v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) v2 <- c(1,2,1,1,1,1,2,1,2,1,3,4,3,3,3,4,6,5) v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) v4 <- c(3,3,4,3,3,1,1,2,1,1,1,1,2,1,1,5,6,4) v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) m1 <- cbind(v1,v2,v3,v4,v5,v6) fa <- sla.factanal(m1, factors=3, rotation="sla.promax", NPROWS=2, NPCOLS=2, MB=2 ) sla.varimax(fa$loadings, normalize = FALSE, NPROWS=2, NPCOLS=2, MB=2) sla.promax(fa$loadings, NPROWS=2, NPCOLS=2, MB=2)