```Date: Wed, 31 Oct 2001 15:34:59 -0500 Reply-To: Paige Miller Sender: "SAS(r) Discussion" From: Paige Miller Organization: Eastman Kodak Company Subject: Re: Multivariate Variance Components in SAS Content-Type: text/plain; charset=us-ascii Perhaps there are a multiplicity of meanings of "multivariate variance components", and so the options you mention are not what I was looking for (sigh). From the PROC MIXED documentation: <--quoting from the PROC MIXED Documentation--> The standard linear model, as used by the GLM procedure, is one of the most common statistical models: y = X*beta + epsilon In this expression, y represents a vector of observed data, beta is an unknown vector of fixed-effects parameters with known design matrix X, and epsilon is an unknown random error vector modeling the statistical noise around X*beta. The mixed model generalizes the standard linear model as follows: y = X*beta + Z*gamma + epsilon Here, gamma is an unknown vector of random-effects parameters with known design matrix Z, and epsilon is an unknown random error vector whose elements are no longer required to be independent and homogeneous. ... The variance of y is thus V = ZGZ' + R <--End of quote from PROC MIXED manual--> The options in SAS that you point to allow you to specify the structure of R, and thus the model estimation does not have to assume that the errors are independent and identical. The multivariate variance components I was hoping to estimate come from the situation where y is a matrix and not a vector. Each row of y represent multiple measurements made on each observation (object) in the study. -- Paige Miller Eastman Kodak Company paige.miller@kodak.com "It's nothing until I call it!" -- Bill Klem, NL Umpire "When you get the choice to sit it out or dance, I hope you dance" -- Lee Ann Womack Dale McLerran wrote: > > Paige, > > PROC MIXED is able to handle multivariate problems. Take a look > at the Kroneker product variance structures available when you > use the repeated statement. These structures have type specifications > UN@CS, UN@AR(1), or UN@UN. If you must use a random statement, > then I am sure that there would be some method of model specification > which could handle multivariate models. However, without knowing > anything more about the problem other than that you have a > multivariate response, it is really next to impossible to give > anything more concrete. > > Dale > > >Date: Wed, 31 Oct 2001 12:53:11 -0500 > >Reply-To: Paige Miller > > Paige Miller Multivariate Variance Components in SAS SAS-L@LISTSERV.UGA.EDU > >Anyone know how one can compute multivariate variance components in SAS > >8? > >Obviously, the univariate case is handled via PROC MIXED or PROC > >VARCOMP, but neither produce multivariate variance-covariance component > >matrices. ```

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