Date: Fri, 9 Nov 2007 12:16:06 -0800 Reply-To: Paige Miller Sender: "SAS(r) Discussion" From: Paige Miller Organization: http://groups.google.com Subject: Re: Univariate tests before multivariate modeling in logistic Comments: To: sas-l@uga.edu In-Reply-To: <1194614148.822042.240880@i38g2000prf.googlegroups.com> Content-Type: text/plain; charset="us-ascii" Received via e-mail: -------------------------------------------------------------------------------- "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!)" -------------------------------------------------------------------------------- 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 

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