Date:         Fri, 9 Nov 2007 12:16:06 -0800
Reply-To:     Paige Miller <paige.miller@KODAK.COM>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Paige Miller <paige.miller@KODAK.COM>
Organization: http://groups.google.com
Subject:      Re: Univariate tests before multivariate modeling in logistic
Comments: To: sas-l@uga.edu
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"Sometime back, don't remember now how, you wrote that the covariates
must appropriately be transformed before they can be used with PLS.

"Can you please direct me to a paper that discusses this issue? I
don'y know whta kinds of transformations I must perfom on the
predictor varuiables before they can be used with PLS.

"Also, once done with PLS, do we have to un-transform the inputs back
to their original form. (I hope not!)"
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Reply:

The only transformation I can ever remember recommending is to center
and scale your predictor (covariate) variables so that they have mean
zero and variance 1. Even this is optional in the proper setting. Of
course, in specific instances, you might want to take the logarithm or
square root or other transform of your predictors, but this is done on
an individual variable and individual dataset basis.

You shouldn't have to un-transform your predictor variables. Good
software should make this transparent. Of course, bad software
exists...

Reference: Rasmus Bro, Age K. Smilde (2003), "Centering and scaling in
component analysis", J. of Chemometrics, Vol 17, No. 1, pp 16-33

--
Paige Miller
paige\dot\miller \at\ kodak\dot\com