```Date: Wed, 22 Mar 2006 14:16:03 -0800 Reply-To: Dale McLerran Sender: "SAS(r) Discussion" From: Dale McLerran Subject: Re: Specifying a residual correlation matrix In-Reply-To: <1143054180.259751.124190@t31g2000cwb.googlegroups.com> Content-Type: text/plain; charset=iso-8859-1 --- Eson wrote: > Hi all, > > I wonder if it is possible to specify a tailor-made residual > correlation matrix (R) to be used in a mixed model analysis (PROC > MIXED > or PROC GLIMMIX)? > > The reason for the question is that we have observations on a number > of > individuals (one obs per individual) and we are interested in > estimating the effects of some (fixed) explanatory variables on the > outcome. However, we know that these individuals are relatives and > would like to account for this similarity. The idea was then to force > SAS to use an R-matrix that we have defined and that show the > relationship between them, since we believe that e is not > ND(0,I*sigma_e), but rather ND(0,R*sigma_e). I believe this is pretty > straight forward MME, but I am not sure that SAS can handle it. > > All and any ideas are welcome! > > Ulf > Ulf, You don't say whether there are constraints on the covariance structure indicated by R. The MIXED procedure can estimate many covariance structures and chances are that the covariance structure that you would assume is among those that are available in the MIXED procedure. If you were to state a little bit more about your assumptions regarding R, then we can offer a more definitive statement. Occassionally one encounters a covariance structure which cannot be effectively estimated employing the MIXED procedure. In such cases, one can often estimate the model employing the procedure NLMIXED. Whether the model can be estimated employing NLMIXED may depend on the number of random effects one must assume. When you have R-side (residual) random effects, then if you are to employ NLMIXED to estimate your model, you must specify a separate random effect for each individual within a family group. If a family group is quite large (say, 15 or more), then you may run into problems using NLMIXED. I would note that NLMIXED can become quite inefficient as the number of random effects gets large. Bottom line: we can't really give you any definitive statement unless you tell us more about your assumptions about R. Dale --------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: dmclerra@NO_SPAMfhcrc.org Ph: (206) 667-2926 Fax: (206) 667-5977 --------------------------------------- __________________________________________________ Do You Yahoo!? Tired of spam? Yahoo! Mail has the best spam protection around http://mail.yahoo.com ```

Back to: Top of message | Previous page | Main SAS-L page