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Date:         Wed, 24 May 2006 10:25:02 -0400
Reply-To:     Peter Flom <flom@NDRI.ORG>
Sender:       "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From:         Peter Flom <flom@NDRI.ORG>
Subject:      Re: Problems using PROC GLM to fit a covariate model
Comments: To: Chris.Howden@CBR.COM.AU
Content-Type: text/plain; charset=US-ASCII

Peter L. Flom, PhD Assistant Director, Statistics and Data Analysis Core Center for Drug Use and HIV Research National Development and Research Institutes 71 W. 23rd St New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax)

>>> Chris Howden <Chris.Howden@CBR.COM.AU> 05/24/06 3:43 AM >>> wrote <<< SAS is having some computational problems when fitting a covariate model and I'm a little unsure as to wether it's a algorthmic problem or a statistical problem.

It's a simple covariate model with 1 covariate and 1 factor with 7 levels (2 reps in each level).

The covariate varies between levels but NOT within levels...and this appears to be the problem. SAS cannot fit this model. However if I introduce within level variation of the covariate then SAS can fit the model.

This confuses me since I can think of no theoretical reason why a covariate needs to have within level variation for the model to be valid....hence I'm concluding its an algorithimic problem with SAS. >>>

No problem with SAS, big problem with your model

Your two independent variables are completely colinear. There is no way to evaluate this model, you must either:

1) Eliminate either the covariate or the factor - this won't hurt your model, since the factor is equivalent to the covariate

2) Get more data, where the covariate does vary within the factor. Even then, a simple GLM is unlikely to be right, since, unless you get a HUGE amount of new data, there will still be substantial colinearity.



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