Re: st: GLM and ANOVA complaints

[David Airey <david.airey@vanderbilt.edu>]

> David Airey referred to a book by R. B. Darlington in which SAS's PROC 
> GLM was
> described as being able to provide different specifications of 
> indicator
> variables.  I am not familiar with R. B. Darlington's reference or 
> with the
> phrase "ANOVA effect parameters," but after posting a response 
> earlier, it
> dawned on me that David might be referring to specifying the contrasts 
> in terms
> of sum-to-zero dummy variables in lieu of 0/1 dummy variables.  If so, 
> then
> this is what -desmat- and other sources refer to as deviance contrasts.
> There's a description of this in S. Rabe-Hesketh & B. Everitt, _A 
> Handbook of
> Statistical Analyses Using Stata, 2nd. Edition. (Boca Raton: Chapman &
> Hall/CRC, 2000), pp. 72-75, among other places.
Joseph kindly suggested a user created program _desmat_ as a solution 
to my first complaint, which was that glm seems to require the user to 
specify the underlying indicator variables in multicategorical 
variables. No, it's not that hard to do this on one's own, but that is 
tedious and prone to error. ANOVA/MANOVA in Stata creates the 
underlying variables as needed. Why does GLM not in Stata? It should.

What Darlington said (p. 225, Regression and Linear Models, 1990) was,

"The ability to create a whole set of coded variables with a single 
command is the fundamental distinction between an ordinary regression 
program and a general linear model program. We will use the term GLM to 
refer to these programs as a class. Although we think of a 
multicategorical variable as a single variable, the GLM program treats 
it as a set of variables."

My complaint was that glm in Stata (it seems to me) is not smart enough 
on it's own to generate the needed underlying indicator variables to 
represent multicategorical variables. It is too much like regression in 
it's syntax requirements for variables. And xi just handles up to two 
way interactions, though xi can be used with other commands.

Regarding the other complaint, my work computer has spent 8 hours so 
far on an ANOVA (384 observations, 2 between subject factors, 4 within 
subject factors, matsize 6000). A statistician at work ran an 
equivalent data set (so he said) in 30 seconds using SAS Proc Mixed. 
Once my machine finishes, I'm handing him the same data set and asking 
he run it with an equivalent model in SAS GLM and SAS Mixed. Should be 
interesting to compare. I just cannot imagine the approaches were the 
same with such different timings.

-Dave

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