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# st: Interpreting gllamm

 From Malin Lundberg Rasmussen To "'statalist@hsphsun2.harvard.edu'" Subject st: Interpreting gllamm Date Tue, 13 Nov 2012 15:41:08 +0100

```Dear statalist

I hope you can help me and my lack of statisticcally/STATA knowledge. And have patience; there are questions in the end ;o)

I have a group of patients and have studies their eyes (dependent variables). I want to see if a finding in 1995 can predict an outcome in 2011.
The covariate (ma1995) I am investigating is consisting of continuus data (1-10), the outcome (ret_2011) is a grading level/categorical (level 10-85 (12 levels in all)). And since I am dealing with eyes my data is not independent.
And just a view of my variates:
tab ma1995

ma1995 |      Freq.     Percent        Cum.
------------+-----------------------------------
0 |          1        0.95        0.95
1 |         49       46.67       47.62
2 |         20       19.05       66.67
3 |         12       11.43       78.10
4 |          9        8.57       86.67
5 |          5        4.76       91.43
6 |          5        4.76       96.19
7 |          2        1.90       98.10
10 |          1        0.95       99.05
21 |          1        0.95      100.00
------------+-----------------------------------
Total |        105      100.00

. tab ret_2011

ret_2011 |      Freq.     Percent        Cum.
------------+-----------------------------------
20 |         17       16.19       16.19
35 |         34       32.38       48.57
43 |         15       14.29       62.86
47 |          3        2.86       65.71
61 |         22       20.95       86.67
65 |          9        8.57       95.24
71 |          1        0.95       96.19
75 |          3        2.86       99.05
85 |          1        0.95      100.00
------------+-----------------------------------
Total |        105      100.00

I was told I could use the STATA command gllamm, but I have a problem interpreting the outcome...
I write

gllamm ret_2011 ma1995, i(inr_)

And get the following results:

Iteration 0:   log likelihood = -440.63147  (not concave)
Iteration 1:   log likelihood = -440.45372
Iteration 2:   log likelihood = -416.91371  (not concave)
Iteration 3:   log likelihood = -414.37278
Iteration 4:   log likelihood = -410.47618
Iteration 5:   log likelihood = -410.22201
Iteration 6:   log likelihood = -410.22191
Iteration 7:   log likelihood = -410.22191

number of level 1 units = 105
number of level 2 units = 73

Condition Number = 8.5127997

gllamm model

log likelihood = -410.22191

------------------------------------------------------------------------------
ret_2011 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
ma1995 |   .5467041    .171913     3.18   0.001     .2097609    .8836473
_cons |    40.5618   .6920948    58.61   0.000     39.20532    41.91828
------------------------------------------------------------------------------

Variance at level 1
------------------------------------------------------------------------------

16.905984 (2.8159187)

Variances and covariances of random effects
------------------------------------------------------------------------------

***level 2 (inr_)

var(1): 156.99649 (9.1764146)
------------------------------------------------------------------------------

My questions are:
1) What does 'not concave' mean?
2) What is the "Condition Number"?
3) I have read that the Coef. is the average score - but this doesn't seem to fit (see tab ma1995 above)
4) What is statistically significant (p=0.001)?? - that there is a connection, between ma1995 and the outcome? And if so, can I see what the connection is (e.g. the higher ma1995, the higher ret_2011)
5) and last; what does the last outcome say (variance at level 1 and level 2)??

I am very sorry for my ignorance!

Hoping for some help

Best regards
Malin

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```