'stata: esttab adding rows for mixed effects analysis
I am using melogit to run a mixed effects. The output after the model looks fine, but when I try to report it with esttab it adds a bunch of other rows that just have _skip(10) as the value. Can someone explain why these come up, if it's bad that they are skipped, and if I can suppress them? Thanks!
melogit outcome i.treatment##i.time [pweight=pweight] || survey_id:
Fitting fixed-effects model:
Iteration 0: log likelihood = -166.88948
Iteration 1: log likelihood = -166.2013
Iteration 2: log likelihood = -166.19374
Iteration 3: log likelihood = -166.19374
Refining starting values:
Grid node 0: log likelihood = -158.29412
Fitting full model:
Iteration 0: log pseudolikelihood = -158.29412
Iteration 1: log pseudolikelihood = -155.0005
Iteration 2: log pseudolikelihood = -154.52065
Iteration 3: log pseudolikelihood = -154.49826
Iteration 4: log pseudolikelihood = -154.49821
Iteration 5: log pseudolikelihood = -154.49821
Mixed-effects logistic regression Number of obs = 293
Group variable: survey_id Number of groups = 223
Obs per group:
min = 1
avg = 1.3
max = 2
Integration method: mvaghermite Integration pts. = 7
Wald chi2(5) = 14.15
Log pseudolikelihood = -154.49821 Prob > chi2 = 0.0147
(Std. Err. adjusted for 223 clusters in survey_id)
--------------------------------------------------------------------------------
| Robust
outcome | Coef. Std. Err. z P>|z| [95% Conf. Interval]
---------------+----------------------------------------------------------------
treatment |
1 | -.8677773 .88814 -0.98 0.329 -2.6085 .872945
2 | 2.186636 .9361024 2.34 0.019 .3519094 4.021363
|
2.time | -3.055517 1.049473 -2.91 0.004 -5.112445 -.9985883
|
treatment#time |
1 2 | 2.977814 1.427803 2.09 0.037 .1793714 5.776256
2 2 | 1.633108 1.320658 1.24 0.216 -.955334 4.221551
|
_cons | -.7764148 .3043046 -2.55 0.011 -1.372841 -.1799886
---------------+----------------------------------------------------------------
survey_id |
var(_cons)| 4.327927 1.877214 1.849592 10.12707
--------------------------------------------------------------------------------
. estat ic
Akaike's information criterion and Bayesian information criterion
-----------------------------------------------------------------------------
Model | Obs ll(null) ll(model) df AIC BIC
-------------+---------------------------------------------------------------
. | 293 . -154.4982 7 322.9964 348.7576
-----------------------------------------------------------------------------
Note: N=Obs used in calculating BIC; see [R] BIC note.
. estimates store test
. esttab test, star(* 0.1 ** 0.05 *** 0.01) constant b(a2) compress ci aic bic noabbrev eform
-----------------------
(1)
outcome
-----------------------
outcome
0b.treatment .
_skip(10)
1.treatment 0.42
[0.074,2.39]
2.treatment 8.91**
[1.42,55.8]
1b.time .
_skip(10)
2.time 0.047***
[0.0060,0.37]
0b.treatment#1b.time .
_skip(10)
0b.treatment#2o.time .
_skip(10)
1o.treatment#1b.time .
_skip(10)
1.treatment#2.time 19.6**
[1.20,322.5]
2o.treatment#1b.time .
_skip(10)
2.treatment#2.time 5.12
[0.38,68.1]
_cons 0.46**
[0.25,0.84]
-----------------------
/
var(_cons[survey_id]) 75.8**
[1.91,3002.5]
-----------------------
N 293
AIC 323.0
BIC 348.8
-----------------------
Exponentiated coefficients; 95% confidence intervals in brackets
* p<0.1, ** p<0.05, *** p<0.01
Sources
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