Just to add to Steve's comments....  You have two random statements, but only the second one (for residual) is written with explicit subject= syntax. Thus, for the overall model fit, observations are not processed by subject, because there is no unique (single) subject designation (there are, implicitly, different levels of subjects). In the output table under these circumstances, you get the number of subjects you mentioned. But your results should be correct. ... View more

I doubt if this is something for a future release, since it is not part of model fitting of post-model-fitting testing or estimation. This will have to be a judgement call of the researcher. ... View more

The statement estimate 's2: 1'  int 1 | int 1 / subject 0 1 cl ilink e ; only is of direct use if there are no fixed effects in the model (only the intercept). The statement can be used with factors or covariates, but the meaning is different in each case. In fact, this statement is likely not giving you what you want with fixed effects. By the way, it is useful to add an E option to these statements to see what GLIMMIX is doing, because the procedure fills in the blanks when the fixed effects are not explicitly given. For instance, with a continuous variable, the procedure is using 0 for X; this is meaningless for you, since the smallest value of your covariable is 36.  I know you want to get a correction for fixed effects, but if there are fixed effects, then your EBLUPs really should be for specific values of the fixed effects.  See my comments below on different variations of your possible model.   This estimate statement makes sense here without fixed effects. You get a logit of 0.564 and SE of 0.532). PROC glimmix data=school NOCLPRINT MAXLMMUPDATE=100 ;    title 'no fixed effects';    class school school_type;    model final_pass = / s cl dist=bin link=logit ;    random intercept/ subject=school solution cl ;    estimate 's2: 1'  int 1 | int 1 / subject 0 1 cl ilink e ; run;   For all the runs from now on, I give the simple version of the BLUP statement (allowing for defaults in GLIMMIX), listed as "1", and then I give the exact same statement where the missing terms are explicitly given (listed as "1b"). That is the first two estimate statements give the exact same answer for each run. As you can see, the first estimate statement really is doing the calculation for entry_score=0 (an impossible value for this data set). You average value of the covariable is about 70, so I added an estimate statement with this value. You can see that one is now getting a similar EBLUP as found with no fixed effects. The SE is different. PROC glimmix data=school NOCLPRINT MAXLMMUPDATE=100 ;    title 'only continuous fixed';    class school school_type;    model final_pass = entry_score  / s cl dist=bin link=logit ;    random intercept/ subject=school solution cl ;    estimate 's2: 1'  int 1 | int 1 / subject 0 1 cl ilink e ;    estimate 's2: 1b' int 1 entry_score 0 | int 1 / subject 0 1 cl ilink e;    estimate 's2: X=70' int 1 entry_score 70 | int 1 / subject 0 1 cl ilink e; run;   Things are trickier with a factor (class variable). Your data set is not balanced -- there are about twice as many public as private schools. Note that here your first estimate statement really is giving the EBLUP for the LSMEAN of the two types of schools (1b)  (this is not the marginal means because of the imbalance). You can kind of recover an EBLUP similar to the no-fixed-effect run by using the 0.16 and 0.84 coefficients for school type (1c estimate) (not same ratio as actual data because this model fitting is not for a linear model -- there is skewness that affects results). I actually think you should be looking at EBLUPs for each school type, not trying to get a global shool type. I give those estimate statements. PROC glimmix data=school NOCLPRINT MAXLMMUPDATE=100 ;    title 'only categorical fixed';    class school school_type;    model final_pass = school_type / s cl dist=bin link=logit ;    random intercept / subject=school solution cl ;    estimate 's2: 1'  int 1 | int 1 / subject 0 1 cl ilink e ; *identical to next one;    estimate 's2: 1b' int 1 school_type 0.5 0.5  | int 1 / subject 0 1 cl ilink e ;    estimate 's2: 1c' int 1 school_type 0.16 0.84  | int 1 / subject 0 1 cl ilink e ;    *^because of imbalance in numbers in two school types, coefficients are not equal above;    estimate 's2: sch type 1' int 1 school_type 1 0  | int 1 / subject 0 1 cl ilink e;    estimate 's2: sch type 2' int 1 school_type 0 1  | int 1 / subject 0 1 cl ilink e; run;   Thinks get even trickier with continuous and factor both in the model. As mentioned above, there is imbalance in observations in the two school types. Plus, the distribution of the continuous variable is not the same for the two school types. Thus, it is very hard to define a global type of central fixed effect. As you can see below, if you use the first estimate statement, you are getting the EBLUP for X=0 and the LSMEAN of the two school types (not the marginal means) (compare 1 and 1b results). Not a useful result. By trial and error, one can get a kind of central-fixed-effect EBLUP with the third estimate statement (1d). This is a bit strange because it is for X=36 (about the minimum observed X), and for the LSMEANS of the categorical variable. It is likely due to the combination of imbalance and different distributions for X. This is giving you about the same as the first run with no fixed effects. I would prefer if you got EBLUPs for each school type at the mean of the X (about 70). See these estimate statements. PROC glimmix data=school NOCLPRINT MAXLMMUPDATE=100 ;    title 'categorical and continuous fixed';    class school school_type;    model final_pass = entry_score school_type / s cl dist=bin link=logit ;    random intercept/ subject=school solution cl ;    estimate 's2: 1'  int 1 | int 1 / subject 0 1 cl ilink e ;    estimate 's2: 1b' int 1   school_type 0.5 0.5 entry_score 0 | int 1 / subject 0 1 cl ilink e;    *^X=0 is meaningless, but that is what you get with first estimate statement;    estimate 's2: X=36 (1d)' int 1 school_type 0.5 0.5 entry_score 36 | int 1 / subject 0 1 cl ilink e;    estimate 's2: sch typ 1 X=70' int 1 school_type 1 0 entry_score 70 | int 1 / subject 0 1 cl ilink e ;    estimate 's2: sch typ 2 X=70' int 1 school_type 0 1 entry_score 70 | int 1 / subject 0 1 cl ilink e ; run;   Overall, as I stated in other posts, I don't think there is a good way of doing what you originally wanted. That is, I feel you need to be explicit for the fixed effects if they are in the model. ... View more

Estimate statements are tricky, and these two do not mean the same thing. I will try to send you a more detailed response soon. ... View more

I was looking at your table for school A. If you used the code: estimate 's2'  | int 1 / subject 0 1 cl ilink; for the situation with fixed effects (you give a categorical and a continuous predictor in your code), then you are getting the EBLUP for the last level of the categorical variable (always a coefficient of 0) and for the continuous predictor with a value of 0 (which may be impossible). You are not getting any kind of 'global mean'. If you want the EBLUP for the second level of the categorical predictor (C) and when the continuous predictor (say X) has a value of 3.5, then you use: estimate 'example' int 1 X 3.5 C 0 1 | int 1 / subject 1 cl ilink;   In general, you will get different EBLUPS with and without fixed effect predictors, and the SEs will be smaller with the fixed effects.     ... View more

I am not sure what else you are looking for. One needs to see your full model code to give any more specific advice. You have the general syntax to use. Based on what you showed, we can't tell if you are doing things appropriately. By the way, chapters 6 and 8 in SAS for Mixed Models, 2nd edition (Littell et al. 2006) gives plenty of examples and explanations of this. ... View more

You are right that NLMIXED is the procedure to use for nonlinear models with clustering (subjects). But NLMIXED is essentially a programming language, and there is no simple way to give you advice at this point. The forum participants usually want to provide help with existing code (corrections, changes, etc.), and typically will not write out code from scratch. Plus, you are not sure about a model to use. I think you will have to first do some homework and learn a bit about NLMIXED, and then figure out some models that you think are appropriate. People on this forum can then give advice on ways to fix problems, and so on.   The SAS User's Guide can describe NLMIXED, but the book "SAS for Mixed Models, 2nd edition" is even better to learn about this procedure. In terms of models, I am sure that many have been proposed in the literature for this type of economic data. ... View more

You seem to have several observations where your response is larger than 1 or smaller than 0. That is why these are "not proportions". Probably you made an error in calculating the proprotion. And more seriously: the beta conditional distribution is defined between 0 and 1 (0 < prop < 1). That means that all 0s and 1s are converted to missing values. You cannot use the beta distribution if you have 0 and 1 unless you want to throw away data. Many references fail to make this clear. To get around this, you only have ad hoc solutions. For instance: Convert all 0s to a very small number (smaller than the smallest nonzero real value that you could observe). Same idea for the 1s. Of course, this is creating artificial data. Would be OK for occassional 0s and 1s, but it appears that you have many. ... View more

I respectfully disagree with Jacob. Getting empirical BLUPs is straightforward with GLMMs. All covered in Stroup's book "Generalized Linear Mixed Models" (2013). There can be a small bias in parameter estimates, but not enough to prevent the calculation.   Syntax will depend on the exact form of your model and random statements. If you have one covariable (X), then the first "int 1" portion of your estimate statement would be changed to: "int 1 X 5.5" if you wanted to get the BLUP for X=5.5.   ... View more