```Date: Tue, 15 Oct 2002 14:25:18 -0700 Reply-To: Dale McLerran Sender: "SAS(r) Discussion" From: Dale McLerran Subject: Re: repeated cov structure question... Comments: To: Kimberly Austin In-Reply-To: <20021015185711.81035.qmail@web40707.mail.yahoo.com> Content-Type: text/plain; charset=us-ascii --- Kimberly Austin wrote: > Dale and other interested SAS-Lers, > Thanks (again) for your input on my cov structure question. Of > course, I have a few follow-up questions that I am currently mulling > over: > 1. How should I approach selection of a spatial cov structure? > Initially I thought a linear structure was most intuitive given that > the spatial (time) effect I am using is Julian date. However, after > doing some snooping, I found that SP(LIN) would not run because the > convergence criteria for REML wasn't met. I have tried a few other > structures and found the exponential SP(EXP) structure does run. > Before I do any further snooping I need to get a better grasp of how > to "properly" approach selection of a cov structure. Should this be > a concern, and if so, how should I approach it? Did you try the SP(POW) covariance structure. If you had equally spaced observations (though with some observations possibly missing), the SP(POW) structure would be identical to an AR(1) covariance structure. AR(1) is a common structure employed for time series data. That is why I employed SP(POW) in the template which I suggested yesterday. The structure SP(LIN) = V(1-pDij)1(pDij<=1) is actually a nonlinear covariance structure. The covariance decreases linearly with increased distance as long as pDij<=1. When pDij>1, then the covariance is 0. So, this structure is linear in two segments, but that makes the overall structure nonlinear. I would think that you might run into estimation problems with such a model. > 2. I am still wrestling with the idea that I need to include my > experimental unit as a random effect to account for between-subject > variation with in my treatments (repeated observations were taken on > experimental units within treatments only, i.e., units belonged to > only one treatment group through the course of the study). However, > when including a random statement [random unit(treatment)] in > addition to a repeated statement, the number of subjects listed under > "model information" in the output is given as 1 (where as without the > random statement the correct number of subjects is given). You probably had your code structured something like proc mixed data=mydata; class ID ...; model response = ...; random ID; repeated / subject=ID type=sp(exp)(time); run; as opposed to proc mixed data=mydata; class ID ...; model response = ...; random intercept / subject=ID; repeated / subject=ID type=sp(exp)(time); run; >Also, > the maximum number of observations on a subject is given as my total > sample size as opposed to the actual maximum number of observations > made of a subject. So I guess my question is, when inluding subjects > (within treatment) as a random effect to account for between-subject > variation, in addition to using a repeated statement to account for > correlation among repeated observations on a subject, does the > repeated statement still "work" proplerly given these differences? The repeated statement will still work. Note, though, that a simple random effects model such as proc mixed data=mydata; class ID ...; model response = ...; random intercept / subject=ID; run; is identical in covariance structure to a repeated measures design with compound symmetric error structure modeled by proc mixed data=mydata; class ID ...; model response = ...; repeated / subject=ID type=cs; run; The spatial covariance structures model a decay in the covariance with increased distance. If there is little decay over time, then the random and repeated effects may not be separable. However, to the extent that you do have a person effect, and within the person the covariance between observation close in time is larger than the covariance between observations distant in time, then you should be able to estimate both random and repeated effects. > I hope that made sense. > Any input, or relevant references to dig into that you know of, are > welcomed. > Thanks, again, for your time, > Kimberly Dale ===== --------------------------------------- Dale McLerran Fred Hutchinson Cancer Research Center mailto: dmclerra@fhcrc.org Ph: (206) 667-2926 Fax: (206) 667-5977 --------------------------------------- __________________________________________________ Do you Yahoo!? Faith Hill - Exclusive Performances, Videos & More http://faith.yahoo.com ```

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