Comments (4)
You can do the same with $li
instead of $l1
. The second contains the same information than $li
but we a variance = 1 (variance equals the eigenvalue for $li
). If you aims to draw biplot (superposition of row and columns), you should use $co
and $l1
(correlation biplot) or $c1
and $li
(distance biplot). If you use separate plots for row and columns, you can draw $li
and $co
.
from ade4.
Hi,
there is no function to do that in ade4
. Varimax is a procedure of rotation allowing to perform such task. It is mainly used in psychometry and available in package psych
or function varimax
in package stats
. If you want, you can apply on the outputs of ade4
:
library(ade4)
irisX <- iris[,1:4]
pca1 <- dudi.pca(irisX, scannf = FALSE)
vv < - varimax(as.matrix(pca1$co))
vv$loadings # rotated variables
as.matrix(pca1$l1)%*%vv$rotmat # rotated individuals
This is equivalent to
psych::principal(irisX, rotate="varimax", nfactors=2, scores=TRUE)
Cheers
from ade4.
Thanks a lot Stephane! So, that is pretty cool - I wasn't aware of that stats::varimax
function!
One last thing though: I usually only use the $li
components of the dudi
objects, which I believe are the coordinates of the individuals on the principal axes (I always use that with s.label
... I hope I've got that right!) - what does the $l1
component refer to and why do we use that one specifically in combination with the rotation matrix?
Thanks a lot
Erwan
from ade4.
Thanks Stephane! That shed some light on the subject indeed
from ade4.
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