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Re: Next step in covariance matrix


From: John Darrington
Subject: Re: Next step in covariance matrix
Date: Sat, 31 Oct 2009 08:58:58 +0000
User-agent: Mutt/1.5.18 (2008-05-17)

I've pushed a set of changes which drops the first category of each categorical
variable from the covariance matrix.

Implementation of GLM etc may require some extra accessor functions, but I've
refrained from trying to predict what these might be.  Let me know what you 
require.

J'

On Sun, Oct 25, 2009 at 11:54:16PM -0400, Jason Stover wrote:
     I haven't tested it yet, but it looks like the computation of the
     dimension might be wrong when categorical variables are involved.
     If a categorical variable has k categories, its contribution to the
     dimension should be k-1. But this line in covariance.c:
     
     cov->dim = cov->n_vars + categoricals_total (cov->categoricals);
     
     ...suggests the contribution to the dimension would be k.
     
     The contribution to the dimension is k-1 because the range of possible
     values of k categories is spanned by k-1 basis vectors.  The kth
     vector is the origin, which corresponds to exactly one of the
     categories.  Which is chosen as the origin is arbitrary (some software
     chooses the first category seen, some the last).
     
     > So what procedures would be best ? ANOVA, MANOVA, UNIANOVA or a subset 
     > of GLM? And are there any good texts on how to perform anova from a 
covariance
     > matrix?  Most seem to assume that the sums of squares have been 
seperately 
     > calculated.
     
     glm.q now uses the new routines, so that might be a good place to start.
     
     There are plenty of pertinent texts that cover computation of least
     squares estimates from a covariance matrix, but the ones I have seen
     are aimed at interpreting results and establishing theory rather than
     computations. I'll look up a few and send them along.
     
     Golub and Van Loan has all of the necessary computations, but doesn't
     mention "covariance matrices" by name. They just mention least squares.
     
     
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