Thanks Juan. I thought that was what I was looking for, but really, I
want to know which of the original covariates could best be used to take
advantage of their colinearity without creating new variables. I think
PCA creates new variables. SAS and SPSS can do what I'm talking about,
but I would like to use R for this.
Thanks,
Ian
Martínez Ovando Juan Carlos wrote:
Hello Ian,
?princomp
If your covariates are scalars, and the following documents:
http://www.jstatsoft.org/v07/i01/drdoc.pdf
http://www.bioconductor.org/workshops/Milan/PDF/Lab12.pdf
Best wishes.
Saludos,
Juan Carlos Martínez Ovando
Banco de México
Av. 5 de Mayo No. 18
Piso 5 Sección D
Col. Centro
06059 México, D. F.
Tel. +52 55 52.37.20.00 ext. 3594
Fax. +52 55 52.37.27.03
e-mail: [EMAIL PROTECTED]
-----Mensaje original-----
De: Ian Fiske [mailto:[EMAIL PROTECTED]
Enviado el: Martes, 12 de Octubre de 2004 04:08 PM
Para: [EMAIL PROTECTED]
Asunto: [R] covariate selection?
Hello,
I am hoping someone can help me with the following multivariate issue:
I have a model consisting of about 50 covariates. I would like to
reduce this to about 5 covariate for the reduced model by combining
cofactors that are strongly correlated. Is there a package or function
that would help me with this in R? I appreciate any suggestions.
Thanks,
Ian
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