Gllamm Syntax - Specifying Level 1 & Level 2 Variables?

[Camille Smith HigherEdRes]

Hi, I'm looking for a way to do weighted multilevel ordinal logit models in an older version of Stata, and I found gllamm when researching this online. My only question is, does this package somehow "know" which variables are at level 1 vs. level 2? I don't see a way to specify in the syntax which belong on which level.

[Andrew Musau]

gllamm is from SSC (FAQ Advice #12). Here is a replication of a three-level mixed-effects ordered logit regression estimated using meologit.

webuse tvsfpors, clear
meologit thk prethk cc##tv || school: || class:
gen cctv= c.cc#c.tv
gllamm thk prethk cc tv cctv,  link(ologit) fam(binom) i(class school)

Res.:

. meologit thk prethk cc##tv || school: || class:

Fitting fixed-effects model:

Iteration 0:   log likelihood =  -2212.775  
Iteration 1:   log likelihood =  -2125.509  
Iteration 2:   log likelihood = -2125.1034  
Iteration 3:   log likelihood = -2125.1032  

Refining starting values:

Grid node 0:   log likelihood = -2152.1514

Fitting full model:

Iteration 0:   log likelihood = -2152.1514  (not concave)
Iteration 1:   log likelihood = -2125.9213  (not concave)
Iteration 2:   log likelihood = -2120.1861  
Iteration 3:   log likelihood = -2115.6177  
Iteration 4:   log likelihood = -2114.5896  
Iteration 5:   log likelihood = -2114.5881  
Iteration 6:   log likelihood = -2114.5881  

Mixed-effects ologit regression                 Number of obs     =      1,600

-------------------------------------------------------------
                |     No. of       Observations per Group
 Group Variable |     Groups    Minimum    Average    Maximum
----------------+--------------------------------------------
         school |         28         18       57.1        137
          class |        135          1       11.9         28
-------------------------------------------------------------

Integration method: mvaghermite                 Integration pts.  =          7

                                                Wald chi2(4)      =     124.39
Log likelihood = -2114.5881                     Prob > chi2       =     0.0000
------------------------------------------------------------------------------
         thk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
      prethk |   .4085273    .039616    10.31   0.000     .3308814    .4861731
        1.cc |   .8844369   .2099124     4.21   0.000     .4730161    1.295858
        1.tv |    .236448   .2049065     1.15   0.249    -.1651614    .6380575
             |
       cc#tv |
        1 1  |  -.3717699   .2958887    -1.26   0.209     -.951701    .2081612
-------------+----------------------------------------------------------------
       /cut1 |  -.0959459   .1688988                     -.4269815    .2350896
       /cut2 |   1.177478   .1704946                      .8433151    1.511642
       /cut3 |   2.383672   .1786736                      2.033478    2.733865
-------------+----------------------------------------------------------------
school       |
   var(_cons)|   .0448735   .0425387                      .0069997    .2876749
-------------+----------------------------------------------------------------
school>class |
   var(_cons)|   .1482157   .0637521                       .063792    .3443674
------------------------------------------------------------------------------
LR test vs. ologit model: chi2(2) = 21.03                 Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.

. 
. gen cctv= c.cc#c.tv

. 
. gllamm thk prethk cc tv cctv,  link(ologit) fam(binom) i(class school)

Iteration 0:   log likelihood = -2121.6111  
Iteration 1:   log likelihood = -2120.8482  (not concave)
Iteration 2:   log likelihood = -2114.8798  
Iteration 3:   log likelihood = -2114.5888  
Iteration 4:   log likelihood = -2114.5882  
Iteration 5:   log likelihood = -2114.5882  
 
number of level 1 units = 1600
number of level 2 units = 135
number of level 3 units = 28
 
Condition Number = 16.629801
 
gllamm model 
 
log likelihood = -2114.5882
 
------------------------------------------------------------------------------
         thk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
thk          |
      prethk |   .4085236   .0396155    10.31   0.000     .3308787    .4861686
          cc |   .8842381   .2097764     4.22   0.000     .4730839    1.295392
          tv |    .236287    .204821     1.15   0.249    -.1651549    .6377288
        cctv |  -.3716533    .295649    -1.26   0.209    -.9511147    .2078081
-------------+----------------------------------------------------------------
_cut11       |
       _cons |  -.0961664   .1690369    -0.57   0.569    -.4274727    .2351399
-------------+----------------------------------------------------------------
_cut12       |
       _cons |   1.177255   .1706313     6.90   0.000     .8428241    1.511686
-------------+----------------------------------------------------------------
_cut13       |
       _cons |   2.383446   .1787996    13.33   0.000     2.033005    2.733887
------------------------------------------------------------------------------
 
 
Variances and covariances of random effects
------------------------------------------------------------------------------

 
***level 2 (class)
 
    var(1): .14819423 (.0637178)
 
***level 3 (school)
 
    var(1): .04489426 (.04254136)
------------------------------------------------------------------------------

 

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