```Date: Tue, 27 Oct 2009 10:25:15 -0700 Reply-To: Dale McLerran Sender: "SAS(r) Discussion" From: Dale McLerran Subject: Re: PROC GLM parameterization XXXX In-Reply-To: Content-Type: text/plain; charset=us-ascii Dan, The model which you want to estimate is Y = mu + D1*(X=1) + D2*(X=2) + ... + Dk*(X=k) where mu is the grand mean, which in the observed sample is simply Ybar. We can rewrite this equation as: Y - mu = D1*(X=1) + D2*(X=2) + ... + Dk*(X=k) or y - Ybar = D1*(X=1) + D2*(X=2) + ... + Dk*(X=k) Note that this latter equation does not have any intercept. By rewriting the equation as shown above we are able to obtain the model which you wish to estimate, but only by constructing a new response variable Ynew = Y - Ybar and modeling Ynew without an intercept term. Hence, the steps required to fit this model are as follows: 1) Compute the grand mean of the response variable among observation that will be used for fitting the regression model - i.e., observations that do not have missing values for any of the predictor variables. 2) Construct a new response variable as Ynew = Y - Ybar 3) Employ Ynew as your response in PROC GLM and specify the NOINT option. Of course, the model which we estimate with the GLM procedure does not have mu on the right hand side. But that is a trivial problem since you already know the sample estimate of mu. HTH, Dale --------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: dmclerra@NO_SPAMfhcrc.org Ph: (206) 667-2926 Fax: (206) 667-5977 --------------------------------------- --- On Tue, 10/27/09, Dan Abner wrote: > From: Dan Abner > Subject: PROC GLM parameterization XXXX > To: SAS-L@LISTSERV.UGA.EDU > Date: Tuesday, October 27, 2009, 5:26 AM > Hello, > > How can I obtain an effect or deviation parameterization of > a model > estimated in PROC GLM as opposed to the default reference > group > parameterization? > > In other words, I want a parameterization of the model > where the intercept > is the grand mean and cofficients for discrete groups are > deviations from > the grand mean. > > Thank you, > > Dan > ```

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