thanks Tianlin, of course that's possible, but how do you get the Satterthwaite adjusted degrees of freedom is the other question. ... View more

Better to stay with empirical Bayes estimates from MIXED for a problem like this, since the posteriors will give results based on the Jeffreys priors. ... View more

For 3 variances, with 2 correlated, that might be a heuristic approach that gets to an approximate df. But right now, we're struggling with finding a way to get it for 2. I think we're all agreed now that SAS doesn't provide such a calculation in MIXED or GLIMMIX. At this point, we have to resort to writing our own code to carry this out. I wonder if it's worth it given that it's straightforward to just use the PRIOR option in MIXED and generate it from the posteriors. ... View more

Thank you for that reference. I am familiar with the Francq paper, he lays out the algebra in a clear way. Unfortunately he doesn't address the df question. ... View more

Yes that could be done and in fact is what i would do. The problem is getting a degrees of freedom for that to put an uncertainty limit on it. This is one of the reasons why a frequentist approach to this kind of problem is not good. You have to deal with these kinds of complications. ... View more

Here's some sample data and proc mixed code that describes the model and a provisional ESTIMATE statement.    /* read in raw data for 9 batches */ data a0 ; input Month B01 B02 B05 B06 B08 B09 B10 B12 B20 ; datalines ; 0 103.7 103.7 103.7 100.8 101.2 100.1 99.9 102.7 101.5 3 99.4 103.1 100.9 100.8 98.1 101.3 101.6 101.7 104.7 6 102.4 102.4 102.4 93.4 99.9 100.1 95.1 99.4 100.8 9 101.0 98.9 99.7 101.2 98.2 98.4 99.5 98.7 99.9 12 100.0 101.2 99.3 96.5 92.7 92.3 98.1 98.7 97.8 18 99.2 91.7 98.7 94.3 95.7 96.1 95.9 100.1 95.1 24 95.5 96.2 94.3 93.4 94.4 92.8 96.1 94.8 95.9 ; run; proc print data=a0 ; title1 "Raw Data 9 Batches Page 8/22" ; run ; /* Create vector of responses by batch */ data a1 ; set a0 ; array batx (9) B01 B02 B05 B06 B08 B09 B10 B12 B20 ; array baty (9) $3 ('B01' 'B02' 'B05' 'B06' 'B08' 'B09' 'B10' 'B12' 'B20') ; do i = 1 to 9 ; y = batx(i) ; batch =baty(i) ; output; end ; keep batch month y; run ; proc print data=a1 ; title1 "Data for analysis" ; run; proc mixed data=a1 nobound covtest ranks mmeq mmeqsol ; class batch ; model y = month / solution covb outpm = out1 alphap=0.10 solution ddfm=kr ; random intercept / subject=batch; estimate "A2 - Process Model 12 months Including batch component" intercept 1 month 12 | Intercept 1 / cl alpha=0.10 e ; ods output mmeqsol=invmmeq coef=e; title1 "Mixed regression model with random batch intercept only"; title2 "Batch mean and Product calculations for 0.50 OOS, 95% Coverage" ; run;   the point estimate is correct in the estimate card. But what I want is        sqrt[Var(InterceptF + 12*Month) + Var(InterceptR)]  where Var=Variance,  InterceptF is the fixed intercept estimate, Month is the fixed time covariate and InterceptR is the estimate of the random component on intercept due to Batch.     This is not really a standard error as such, it's the standard deviation of the population of batches at time 12 months. The challenge here is to get the Satterthwaite adjusted degrees of freedom for this sum of variances. Actually this is the simple case. If we add the random component due to batch on the time covariate, Month, and assume an unstructured covariance matrix, then we have the sum of 3 variances where 2 are correlated. In addition, note that Var(InterceptF + 12*Month) is not a chi-square.     ... View more

The problem is that I'm trying to describe an inference space that is not covered by SAS. SAS has broad, intermediate and narrow. But the space I am trying to describe does not fit into this trichotomization. So for example, if I have mixed model, and to make it simple, say just a single variance component, say v_b, and then residual error, v_r. I have say a linear combination of the fixed effects, say L'B0, then I want an interval estimate based on Var(L'B0_hat) + v_b_hat. It's not a confidence interval, it's a population interval of the population of units belonging to random factor b. I don't know how to describe this in words very clearly I know, but I cannot copy notation to this page.   ... View more

Rick I did discuss this with Jill Tao and she mentioned that the calculation i want to make is not available with the ESTIMATE statement. So I was wondering if someone had looked at this. I can send you the code, but it doesn't show the linear combination of fixed and variance components that I want to carry out. If you send me your email, I can send you the expression on a powerpoint slide. I can't copy the equations here. My email is saltan@its,jnj.com. ... View more