```Date: Wed, 24 May 2006 10:25:02 -0400 Reply-To: Peter Flom Sender: "SAS(r) Discussion" From: Peter Flom 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 http://cduhr.ndri.org www.peterflom.com New York, NY 10010 (212) 845-4485 (voice) (917) 438-0894 (fax) >>> Chris Howden 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. HTH Peter ```

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