Date: Fri, 22 Feb 2002 09:08:54 -0800
Reply-To: Dale McLerran <stringplayer_2@YAHOO.COM>
Sender: "SAS(r) Discussion" <SAS-L@LISTSERV.UGA.EDU>
From: Dale McLerran <stringplayer_2@YAHOO.COM>
Subject: Re: proc GLM?
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--- Jeremy Fox <jerfox@STANFORD.EDU> wrote:
> Dale McLerran <firstname.lastname@example.org> wrote:
> : For an ordered response coded (1,2,3,4), you may have a tough time
> : fitting an appropriate joint model. What is your joint model and
> : how do you fit it? (I can think of a couple of different methods,
> : but want to assess what you would do.)
> I would fit a seemingly unrelated regression (SUR) model where all of
> the dependent variables are the 90 different responses. This is just
> estimated by stacking the 90 equations and running GLS.
> Now the problem becomes is that there is a very surprising
> mathematical result that if all the regressors in each equation are
> the same, then SUR is equivalent (I think numerically!) to equation
> equation OLS. This leads me to believe that accounting for dependence
> across equations is not helpful in a linear regression setting where
> the covariates are going to be the same in each equation.
> The original poster just seems to have two covariates, time and
> treatment. Running proc glm 90 times (one for each response) seems
> good to me. This would account for any differences in the mean
> response for each category that is consistent across time and
> treatment groups.
So, you would fit a model which assumes normality even though you
know that to be false, you would ignore the covariances among the
responses even though you expect that the covariances would be high
among at least some of the responses, you would report statistics
for 90 different regressions and attempt to interpret the parameters
for all 90, and you would not make any effort to adjust for having
performed multiple experiments. I can't think of a worse scenario!
Fred Hutchinson Cancer Research Center
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