So if it takes a long time to converge and the G matrix is not positive definite, can one trust the fixed effects part of the model?
Dr. Paul R. Swank,
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center-Houston
From: SAS(r) Discussion [mailto:SAS-L@LISTSERV.UGA.EDU] On Behalf Of Dale McLerran
Sent: Thursday, April 30, 2009 7:40 PM
Subject: Re: complex var-cov structure in mixed
If you fit the models separately for the two groups, then you
would be able to estimate different covariance structures for
each group. But if group has to affect parameters of the fixed
effects part of the design, then you can't use such an approach.
However, you can allow the components of variance (as well as
residual error variances) to differ across groups. Refer to
the GROUP= option on both the RANDOM and REPEATED statements.
When a variable is named to the GROUP= option, then different
levels of that variable produce different estimates in the
variance/covariance structure. Effectively, if the variance
of one of the effects named on the random statement goes to
zero for some group level, then you do estimate different
covariance structures across groups.
Fred Hutchinson Cancer Research Center
Ph: (206) 667-2926
Fax: (206) 667-5977
--- On Thu, 4/30/09, Swank, Paul R <Paul.R.Swank@UTH.TMC.EDU> wrote:
> From: Swank, Paul R <Paul.R.Swank@UTH.TMC.EDU>
> Subject: complex var-cov structure in mixed
> To: SAS-L@LISTSERV.UGA.EDU
> Date: Thursday, April 30, 2009, 1:20 PM
> A colleague has approached me with
> the following problem. In a two group design with a growth
> curve model, the structure of the var-cov matrix is
> different between groups. Unfortunately, in one group, the
> variance on the slope and the covariance between the
> intercept and slope is close to zero, causing great problems
> with convergence and NPD messages about the matrix, using
> SAS Proc Mixed. The question is, while one can allow the
> parameters to vary between groups, is it possible to allow
> the structure itself to vary between groups so that you have
> intercept and slope variance, plus covariance, in one group,
> but only intercept variance in the other.
> (Apologies for cross-listing).
> Dr. Paul R. Swank,
> Professor and Director of Research
> Children's Learning Institute
> University of Texas Health Science Center-Houston