The goal of this exercise is to find out what happens if I intentionally use a level2 variable at level 1 in HLM. I found that the coefficients and standard errors remain about the same. The parameter that differed was just the degree of freedom, which was consistent with my expectation.
Using my old […]
proc glimmix data=kaz2.Year2AnalysisSample; class SCHOOL_NAME grade_level race_cate; model &z_post = treat grade_level /solution ddfm=kr dist=normal link=identity s ; random SCHOOL_NAME; covtest /wald; lsmeans grade_level / diff; run;
I encountered a situation where somehow PROC GLIMMIX can't produce results while PROC MIXED can. In this WORD document, I showed you how I edited the PROC GLIMMIX syntax to be a PROC MIXED syntax. Please ignore my macro %w.
While running PROC GLIMMIX with a large dataset with categorical variables explicitly treated as CLASS variables AND with weight, I got the following error message:
WARNING: Obtaining minimum variance quadratic unbiased estimates as starting values for the covariance parameters failed.
The weight and the use of categorical variables were the cause of the problem as […]
HLM (multilevel models) and econometric analyses (e.g., time series analysis, ARIMA, etc.) are treated as different approaches (the goal of which is to deal with data dependency problem), they can be implemented in the same model via. SAS PROC GLIMMIX. However, I believe doing this is computationally demanding and models may not converge.
I will edit this essay later.
Imagine I have an HLM model where level-1 are students and level-2 are schools. I can enter teacher-level variables, but people who are used to HLM software by SSI will wonder how the use of teacher-level variables is possible without making the model 3-level models (level1 students, level2 teachers, […]
PROC MIXED and PROC GLIMMIX produce identical results for the following linear model settings.
proc glimmix data=kaz.asdf; where IMPACT_ANALYSIS=1; class school; model y = x1 x2 x3 /solution ddfm=kr dist=normal link=identity s ; random school; run;
proc mixed data=kaz.asdf; where IMPACT_ANALYSIS=1; class school; model y = x1 x2 x3 /solution s ddfm=kr; random school; run;
When you run a statistical interaction model (e.g., Y=TREATMENT + GENDER + TREATMENT*GENDER), you also want to run a mathematically equivalent model: Y= T_MALE + C_MALE + T_FEMALE + C_FEMALE.
SUBGROUP defines the four groups below.
This is for linear model (HLM because I have the random statement).
proc glimmix data=asdf3 namelen=32; class CAMPUSID […]
When running PROC GLIMMIX (SAS) in a macro-driven way (e.g., running similar models 100 times), what gets annoying is some HLM models do not converge and you have to comb through output and decide which models to convert to fixed effect models, which is simpler and is easier to converge. The following allows the execution […]