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Re: st: meglm
From
Stas Kolenikov <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: meglm
Date
Wed, 16 Oct 2013 10:39:48 -0500
If this is a fractional response, then -meglm- is probably using the
convention of -probit- that a zero is a zero, and everything else is a
one. The likelihood of 0 indicates exactly that. -probit- gets around
it with a complicated heuristics of perfect prediction; -meglm- may or
may not be doing that, and numeric integration just blurs the things
by less-than-perfect calculation of the likelihood.
-- Stas Kolenikov, PhD, PStat (ASA, SSC)
-- Senior Survey Statistician, Abt SRBI
-- Opinions stated in this email are mine only, and do not reflect the
position of my employer
-- http://stas.kolenikov.name
On Wed, Oct 16, 2013 at 7:17 AM, Alfonso Sanchez-Penalver
<[email protected]> wrote:
> I forgot to mention that ysm is a fractional response variable, which is why I'm trying -meglm- instead of -meprobit-.
>
> Alfonso Sánchez-Peñalver
>
>> On Oct 16, 2013, at 8:00 AM, Alfonso S <[email protected]> wrote:
>>
>> Hi,
>>
>> I am trying to get my head around using the -meglm- command in Stata 13 for the mac, and I must be doing something wrong. The following are the results I get when running it:
>>
>> ----------------------------------------------
>> . meglm ysm ldis_totcurexpppa sch_enrlunsh lsch_enrtotal y_dum2 y_dum3 y_dum4 y_dum5 y_dum6 y_dum7 y_dum8 mldis_totcurexppp mlunch mlenrol || sprp_sch:, covariance(exchangeable) family(binomial) link(probit)
>>
>> Fitting fixed-effects model:
>>
>> Iteration 0: log likelihood = -2347.0075
>> Iteration 1: log likelihood = 0
>> Iteration 2: log likelihood = 0
>>
>> Refining starting values:
>>
>> Grid node 0: log likelihood = -8.835e-06
>>
>> Fitting full model:
>>
>> Iteration 0: log likelihood = -8.835e-06 (not concave)
>> Iteration 1: log likelihood = -1.147e-12
>> Iteration 2: log likelihood = -1.142e-12
>>
>> Mixed-effects GLM Number of obs = 6856
>> Family: binomial
>> Link: probit
>> Group variable: sprp_sch Number of groups = 857
>>
>> Obs per group: min = 8
>> avg = 8.0
>> max = 8
>>
>> Integration method: mvaghermite Integration points = 7
>>
>> Wald chi2(0) = .
>> Log likelihood = -1.142e-12 Prob > chi2 = .
>> -----------------------------------------------------------------------------------
>> ysm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
>> ------------------+----------------------------------------------------------------
>> ldis_totcurexpppa | -.6370373 . . . . .
>> sch_enrlunsh | 3.177517 . . . . .
>> lsch_enrtotal | .0229953 . . . . .
>> y_dum2 | .699251 . . . . .
>> y_dum3 | .8369304 . . . . .
>> y_dum4 | .7515347 . . . . .
>> y_dum5 | .6936224 . . . . .
>> y_dum6 | .6023263 . . . . .
>> y_dum7 | .6499135 . . . . .
>> y_dum8 | .6696426 . . . . .
>> mldis_totcurexppp | .3410811 . . . . .
>> mlunch | 6.249509 . . . . .
>> mlenrol | .0017366 . . . . .
>> _cons | 12.76935 . . . . .
>> ------------------+----------------------------------------------------------------
>> sprp_sch |
>> var(_cons)| .5950633 . . .
>> -----------------------------------------------------------------------------------
>> LR test vs. probit regression: chi2(0) = 0.00 Prob > chi2 = .
>>
>> Note: LR test is conservative and provided only for reference.
>> -----------------------------------------------
>>
>> To see that the variables were doing fine I ran the -xtgee- estimation and got normal results
>> -----------------------------------------------
>>
>> . xtgee ysm ldis_totcurexpppa sch_enrlunsh lsch_enrtotal y_dum2 y_dum3 y_dum4 y_dum5 y_dum6 y_dum7 y_dum8 mldis_totcurexppp mlunch mlenrol, family(binomial) link(probit) corr(exch)
>>
>> Iteration 1: tolerance = .21314375
>> Iteration 2: tolerance = .00124994
>> Iteration 3: tolerance = .00001481
>> Iteration 4: tolerance = 3.957e-07
>>
>> GEE population-averaged model Number of obs = 6856
>> Group variable: sprp_sch Number of groups = 857
>> Link: probit Obs per group: min = 8
>> Family: binomial avg = 8.0
>> Correlation: exchangeable max = 8
>> Wald chi2(13) = 113.63
>> Scale parameter: 1 Prob > chi2 = 0.0000
>>
>> -----------------------------------------------------------------------------------
>> ysm | Coef. Std. Err. z P>|z| [95% Conf. Interval]
>> ------------------+----------------------------------------------------------------
>> ldis_totcurexpppa | .2161359 .3463714 0.62 0.533 -.4627396 .8950114
>> sch_enrlunsh | -.0725626 .2464216 -0.29 0.768 -.5555401 .4104148
>> lsch_enrtotal | -.0349141 .1043114 -0.33 0.738 -.2393606 .1695323
>> y_dum2 | -.1418307 .0822838 -1.72 0.085 -.3031039 .0194425
>> y_dum3 | -.2154219 .0750023 -2.87 0.004 -.3624236 -.0684201
>> y_dum4 | -.1581733 .0651644 -2.43 0.015 -.2858932 -.0304533
>> y_dum5 | -.1202846 .0596719 -2.02 0.044 -.2372394 -.0033298
>> y_dum6 | -.0590736 .0576424 -1.02 0.305 -.1720506 .0539034
>> y_dum7 | -.0919327 .0537733 -1.71 0.087 -.1973266 .0134611
>> y_dum8 | -.1063621 .0504099 -2.11 0.035 -.2051636 -.0075606
>> mldis_totcurexppp | -.0413327 .4116867 -0.10 0.920 -.8482237 .7655583
>> mlunch | -1.001581 .2810579 -3.56 0.000 -1.552444 -.4507177
>> mlenrol | .0000284 .0003227 0.09 0.930 -.0006039 .0006608
>> _cons | -.3508665 2.187324 -0.16 0.873 -4.637943 3.93621
>> -----------------------------------------------------------------------------------
>>
>> ---------------------------------------------------------------------------------------
>>
>> Can someone tell me if I am specifying the meglm command wrong? If not, why does it not reproduce the results from xtgee?
>>
>> Thanks,
>>
>> Alfonso.
>>
>>
>> *
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>
> *
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