Hi,   I have a 4-level data structure that looks like the following:   Level 1: Waves Level 2: Individuals  Level 3: Family Level 4: Neighbourhood   Level 1 is nested in level 2, level 2 is nested in level 3, and level 3 is nested in level 4.  I would like to have fixed effects at the neighbourhood level (using neighbourhood IDs) with random effects at the family and individual level. I tried both GLIMMIX (random intercept / subject statements) and GENMOD (repeated / subject statements) but it seems that because I have many neighbourhood IDs and a large number of observations, I kept running into errors and memory issues. I was suggested that, alternatively, I could have fixed effects at the neighbourhood level with a SAS option that correct the standard errors for the fixed parameters for clustering (i.e., family and individual level). But I am not sure how to do this in SAS. In Stata, there is a "vce cluster" option in simple logistic regression procedure. I wonder if there is anything similar in SAS?  I also saw from previous posts/comments to use the empirical statement in GLIMMIX. I tried but I think in my case it is not really useful (you still need the subject statements which seemed to be too complex to handle together with the FE in this case).  Any suggestions on how to model this? Thank you very much!     ... View more

Hi,   I am trying to model a three level logistic regression using PROC GLIMMIX. When I first added only my level 3 clustering variable into the model, the estimates and odds ratio I got look fine and reasonable. However, when I added both the level 3 and level 2 clustering variable to the model, the estimates and odds ratio I got became really big that are probably unreal (e.g., odds ratio of 40 to odds ratio of 300). I wonder if anyone knows the reasons behind and how I can fix it? Thanks for the help!    The code I used for having both level 3 and level 2 clusters, which gave me really large parameters:   proc glimmix data=new noclprint method=laplace; class clustervar_L3 clustervar_L2; model event(event='1')=dur dur2 income incouple rent houseprice age sex /solution cl link=logit dist=binary  ddfm=bw oddsratio(DIFF=LAST LABEL); random intercept / solution type = un subject= clustervar_L3; random intercept / solution type = un subject= clustervar_L2(clustervar_L3); covtest / wald; run;             ... View more

Hi,   I am working on a study in which I have to match the respondents based on their best friend's age and sex. An example looks like the following. I would like to use the respondent's age and sex and his/her best friend's age and sex to predict the number of hours a respondent goes to a party a week. However, to examine whether the effect is really from the best friend or from the sex and age of the best friend, I want to do random matching based on the best friend's age and sex. That is, I would like assign each respondents to a person who has the same age and sex as his/her best friend but is in fact not his/her best friend. For example, for the first respondent, he should be match with the best friend of the fourth respondent, and the second respondent should be match with the 8th, etc.    ID       friend's sex     friend's age       sex         age          party hour/week 1               F                     20                 M           30                       5h 2               M                    21                 F            29                       0h 3               F                     23                 F            25                       3h 4               F                     20                 M           24                       1h 5               M                    22                 M           22                       3h 6               F                     20                 F            21                       5h 7               F                     23                 F            22                       4h 8               M                    21                 F            20                       5h 9               F                     24                 M           20                      10h 10             F                     24                 F            27                       8h   I would be really appreciated if anyone can help me regarding how to program this and to make a random matching using age and sex possible. Any advice or question are also welcome.   Best,         ... View more

Hello,    I have encountered a problem with PROC GLIMMIX. I have 3 dummies as for a variable with 4 categories. The dummies are used as IVs in PROC GLIMMIX with binary outcome. However, one of the coefficients of the dummies is always zero in the output. I have checked the coding of the dummies and didn't find any issues. Here is my code:   proc glimmix data = LONG Method=quad; class cluster; model event(event='1') = dur child_1f_2m child_1m_2f child_bothmales /solution link=logit dist=binary ddfm=bw; random intercept /subject=cluster; run;   child_1f_2m child_1m_2f child_bothmales are the dummies and the reference category is child_bothfemales.   Thank you for any input! Cheers!     ... View more