As pointed out by @PaigeMiller , missing data cell is the most common cause of a non-estimable lsmeans. You either need to take out the interaction term(s) with missing data cell, or maybe combine your data categories to get rid of missing data cells. Thanks, Jill ... View more

There might be some confounding in your data and that is why in addition to age 50, you get a zero estimate for age 64. To see if that is the case, you might run the following program and examine the E option output - proc glm data=sample; class PN age (ref='50') ; model Score=year_mixed age year_mixed*age / e; weight wt; run; quit; Thanks, Jill ... View more

Yes, specifying subject=subjid or subject=subjid(clinic) would make no statistical difference in your case, but numerically, specifying subject=subjid(clinic) would make the model to be processed by subjects and therefore is more efficient. You must have a common subject in your RANDOM and REPEATED statements in order for the model to be processed by subjects, and that is why nesting here helps -- clicnic is the common subject syntactically. Thanks, Jill ... View more

Then I am not sure why you wanted to "mirror" the PROC MIXED model. PROC MIXED assumes the response variable being normal. You might use PROC GLIMMIX to fit a model, use DIST= to specify a distribution that might work better than normal for your data. ... View more

You would need to use the same transformed dependent variable in PROC GLIMMIX as you did in PROC MIXED in order to get the same result in PROC GLIMMIX. For example,   proc glimmix data= nobullestrusC plots = all; class HeiferID Breed FSGrp Year DRE; model sqrtTmin = Year Date Breed|DRE FSGrp|DRE / ddfm=kr; random DRE / subject=HeiferID type=csh residual; run; ... View more

The MIXED approach is using the square root of y as the response variable; the PROC GLIMMIX approach is modeling the square root of mean (mu), not y. The two models are different.   Thanks, Jill ... View more

You might use the PARMS statement to specify a starting value for your covariance parameter to see if that helps. This might take several rounds of trial and error. Jill ... View more

repeated age / type=cs; just estimates the residual variances and break it into two pieces. So your R matrix is a constant variance (sum of the two estimates) and zero covariance. repeated age / type=cs subject=person; estimates a block-diagonal covariance matrix R, with each block corresponds to each person. Within each block, the structure is a compound symmetry . You can add the R option in the REPEATED statement to see the R matrix in the output. If you want to model the correlations in your data, you might need to use the second specification (with the SUBJECT= option). And go with whatever this model produces for your fixed effects. ... View more

Are you comparing the models with --   repeated age / type=cs ; and repeated age / type=cs subject=Person; Or something else? ... View more

I am not sure about your question -- no subject= option or no repeated statement? What is the model now? And is your data a repeated measures?   Thanks, Jill ... View more

I used the data and program below for your questions:   data pr; input Person Gender $ y1 y2 y3 y4; y=y1; Age=8; output; y=y2; Age=10; output; y=y3; Age=12; output; y=y4; Age=14; output; drop y1-y4; datalines; 1 F 21.0 20.0 21.5 23.0 2 F 21.0 21.5 24.0 25.5 3 F 20.5 24.0 24.5 26.0 4 F 23.5 24.5 25.0 26.5 5 F 21.5 23.0 22.5 23.5 6 F 20.0 21.0 21.0 22.5 7 F 21.5 22.5 23.0 25.0 8 F 23.0 23.0 23.5 24.0 9 F 20.0 21.0 22.0 21.5 10 F 16.5 19.0 19.0 19.5 11 F 24.5 25.0 28.0 28.0 12 M 26.0 25.0 29.0 31.0 13 M 21.5 22.5 23.0 26.5 14 M 23.0 22.5 24.0 27.5 15 M 25.5 27.5 26.5 27.0 16 M 20.0 23.5 22.5 26.0 17 M 24.5 25.5 27.0 28.5 18 M 22.0 22.0 24.5 26.5 19 M 24.0 21.5 24.5 25.5 20 M 23.0 20.5 31.0 26.0 21 M 27.5 28.0 31.0 31.5 22 M 23.0 23.0 23.5 25.0 23 M 21.5 23.5 24.0 28.0 24 M 17.0 24.5 26.0 29.5 25 M 22.5 25.5 25.5 26.0 26 M 23.0 24.5 26.0 30.0 27 M 22.0 21.5 23.5 25.0 ; proc mixed data=pr ; class Person Gender age; model y = Gender Age Gender*Age / s; repeated age / type=cs subject=Person r; run;   proc mixed data=pr ; class Person Gender age; model y = Gender Age Gender*Age / s; repeated age / type=cs subject=Person*age r; run;   You can see that the above two programs produced the same results. Generally speaking, the second program have a repeated statement that is too complex than necessary. It seems to be okay for this model, but if you use a different covariance structure, such as AR(1) or UN, you might see something like the following- NOTE: Convergence criteria met but final Hessian is not positive definite. So I would use the first model specification.     ... View more

1. AR(1) is often used in the REPEATED statement in PROC MIXED. There is no REPEATED statement in PROC NLMIXED. The RANDOM statement in PROC NLMIXED is like fitting a random coefficients model, and AR(1) is almost never used to model the covariance between random intercept and slopes. 2. If for a reason beyond my understanding, you have to specify an AR(1) structure in PROC NLMIXED, then you would need to have at least 3 random effects (u0, u1, and u2) in order for AR(1) to make sense. For example, random u0 u1 u2 ~ normal([0, 0, 0],[s2, s2*rho, s2, s2*rho**2, s2*rho, s2]) subject=subject; where s2 and rho are your model parameters.   Hope this helps, Jill  ... View more

Yes I think PROC GENMOD should work too. ... View more