Approved:exelstata
Date: Sun, 15 Apr 2007 15:27:02 +0200
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From: martina brandt <[email protected]>
To: [email protected]
Subject: st: GLLAMM versus xtmixed
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dear richard, thanks for the hint!
the code that seems to work is:
xi: xtmixed lhstdwoe ce_gesunde_ ce_altere_ both_ ce_spheu endsmeetex i.edukat religion ce_n_kidsk ce_ngeschwk muto vato vaso ce_nentfe_ fineep_ ce_erbez ce_isicn if e(sample) ||land: ||hhnr: ||beobnr: , ml variance
i.edukat _Iedukat_1-3 (naturally coded; _Iedukat_1 omitted)
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -1938.2228
Iteration 1: log likelihood = -1934.4078
Iteration 2: log likelihood = -1934.305
Iteration 3: log likelihood = -1934.3013
Iteration 4: log likelihood = -1934.3013
Computing standard errors:
Mixed-effects ML regression Number of obs = 1134
-----------------------------------------------------------
| No. of Observations per Group
Group Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
land | 10 20 113.4 278
hhnr | 1039 1 1.1 3
beobnr | 1083 1 1.0 2
-----------------------------------------------------------
Wald chi2(17) = 176.35
Log likelihood = -1934.3013 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
lhstdwoe | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
ce_gesunde_ | .1135772 .0402843 2.82 0.005 .0346214 .1925329
ce_altere_ | .033269 .0075909 4.38 0.000 .0183911 .048147
both_ | -.0755805 .1062736 -0.71 0.477 -.2838728 .1327119
ce_spheu | .2184644 .0555883 3.93 0.000 .1095134 .3274154
endsmeetex | -.1923056 .1115012 -1.72 0.085 -.410844 .0262327
_Iedukat_2 | -.1145846 .1080963 -1.06 0.289 -.3264494 .0972803
_Iedukat_3 | -.1320016 .1165883 -1.13 0.258 -.3605105 .0965073
religion | -.1790786 .1399235 -1.28 0.201 -.4533235 .0951664
ce_n_kidsk | -.0440056 .0362299 -1.21 0.225 -.1150149 .0270036
ce_ngeschwk | -.0629339 .0253449 -2.48 0.013 -.1126089 -.0132589
muto | .3857507 .0897365 4.30 0.000 .2098705 .561631
vato | .1961351 .1258618 1.56 0.119 -.0505496 .4428197
vaso | -.4357497 .1249964 -3.49 0.000 -.6807381 -.1907612
ce_nentfe_ | -.2243865 .0381961 -5.87 0.000 -.2992495 -.1495235
fineep_ | -.0709821 .1534461 -0.46 0.644 -.3717309 .2297666
ce_erbez | -.0105379 .0124366 -0.85 0.397 -.0349131 .0138373
ce_isicn | -.0562341 .0170115 -3.31 0.001 -.0895761 -.0228922
_cons | .497216 .1540194 3.23 0.001 .1953435 .7990885
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
land: Identity |
var(_cons) | .0377874 .0315882 .0073416 .1944934
-----------------------------+------------------------------------------------
hhnr: Identity |
var(_cons) | .4514975 .285685 .1306345 1.560461
-----------------------------+------------------------------------------------
beobnr: Identity |
var(_cons) | 1.116612 .290321 .6707928 1.858729
-----------------------------+------------------------------------------------
var(Residual) | .2948126 .0578313 .2007113 .4330322
------------------------------------------------------------------------------
LR test vs. linear regression: chi2(3) = 77.25 Prob > chi2 = 0.0000
Note: LR test is conservative and provided only for reference
and with gllamm (default link/family) i get:
xi: gllamm lhstdwoe ce_gesunde_ ce_altere_ both_ ce_spheu endsmeetex i.edukat religion ce_n_kidsk ce_ngeschwk muto vato vaso ce_nentfe_ fineep_ ce_erbez ce_isicn if e(sample), i(beobnr hhnr land) nip(10) adapt robust
i.edukat _Iedukat_1-3 (naturally coded; _Iedukat_1 omitted)
insufficient observations
r(2001);
any ideas???
THANKS! martina
-----Ursp:
r�ngliche Nachricht-----
Von: [email protected]
Gesendet: 14.04.07 20:44:24
An: [email protected]
Betreff: Re: st: GLLAMM versus xtmixed
At 01:10 PM 4/14/2007, martina brandt wrote:
can anyone imagine why gllamm (adapt) should not be able to estimate
a linear multilevel model with 4 levels and 1100 1.level, 700 2.
level, 500 3. level and 10 4.level observations , error:
"insufficient observations" - whereas with xtmixed (ml) it works
without problems?
thanks for helping! martina
It is always possible your code is wrong. Why don't you post the
xtmixed code that works, and the gllamm code (with error message)
that does not?
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
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