<>
They seem to be in perfect agreement in this example. Does she say anything about -loneway- in the pages cited by you?
*************
webuse nlswork, clear
gen age2 = age*age
gen ttl_exp2 = ttl_exp*ttl_exp
gen tenure2 = tenure*tenure
gen byte black = race==2
xtreg ln_w, mle
xtmixed ln_w || idcode:, mle
/*
have to take the square root
of the results for variance
to see equivalence
*/
gllamm ln_w, i(idcode)/*
*/ nip(12) adapt
*************
HTH
Martin
-----Ursprüngliche Nachricht-----
Von: [email protected] [mailto:[email protected]] Im Auftrag von José Maria Pacheco de Souza
Gesendet: Montag, 29. Juni 2009 17:57
An: [email protected]
Betreff: st: disagreement between xtreg and xtmixed outputs
Dear statalisters:
I ran xtreg and xtmixed (and 1oneway) with the same dataset but got
different results, as I show bellow:
xtreg mensuração , i(id) mle
Iteration 0: log likelihood = -38.39068
Random-effects ML regression Number of obs =
60
Group variable: id Number of groups
= 30
Random effects u_i ~ Gaussian Obs per group: min =
2
avg =
2.0
max =
2
Wald
chi2(0) = 0.00
Log likelihood = -38.39068 Prob > chi2 =
.
mensuração Coef. Std. Err. z P>z [95% Conf.
Interval]
_cons 2.132833 .1942075 10.98 0.000 1.752194 2.513473
/sigma_u 1.052955 .1387291 .8133212
1.363193
/sigma_e .2134579 0
.2134579 .2134579
rho .9605257 . . .
Likelihood-ratio test of sigma_u=0: chibar2(01)= 101.09 Prob>=chibar2 =
0.000
xtmixed mensuração || id:, mle
Performing EM optimization:
Performing gradient-based optimization:
Iteration 0: log likelihood = -23.147063
Iteration 1: log likelihood = -23.147063
Computing standard errors:
Mixed-effects ML regression Number of obs =
60
Group variable: id Number of groups =
30
Obs per
group: min = 2
avg =
2.0
max =
2
Wald
chi2(0) = .
Log likelihood = -23.147063 Prob > chi2 =
.
mensuração Coef. Std. Err. z P>z [95% Conf.
Interval]
_cons .132833 .1942075 10.98 0.000 1.752194
2.513473
Random-effects Parameters Estimate Std. Err. [95% Conf. Interval]
id: Identity
sd(_cons) 1.062051 .1375417 .823967 1.368929
sd(Residual) .0841923 .0108692 .0653706 .1084331
LR test vs. linear regression: chibar2(01) = 131.58 Prob >= chibar2 =
0.0000
. loneway mensuração id
One-way Analysis of Variance for mensuração:
Number of obs = 60
R-squared = 0.9969
Source SS df MS F
Prob > F
Between id 67.889766 29 2.3410264 330.26 0.0000
Within id .21265011 30 .00708834
Total 68.102416 59 1.1542782
Intraclass Asy.
correlation S.E. [95% Conf. Interval]
------------------------------------------------
0.99396 0.00222 0.98962 0.99831
Estimated SD of id effect 1.080263
Estimated SD within id .0841923
Est. reliability of a id mean 0.99697
(evaluated at n=2.00)
. dis sqrt(2.3410264)
1.5300413
. dis sqrt(.00708834)
.08419228
. dis 1.062051^2/((1.062051^2)+(.0841923^2))
.99375499
. dis 1.052955^2/(( 1.052955^2)+(.2134579^2))
.96052575
I am using version 10 updated, intercooled. The data is in the long format.
I followed the models presented in pages 64 and 65 of Sophia
Rabe-Hesketh´s Multilevel and Longitudinal Modeling Using Stata, second
edition and really am puzlled with the no concordance of results. Could it
be some problem with my data set (which is not that presented by Sophia)?
Thank you for any advice.
José Maria
Jose Maria Pacheco de Souza, Professor Titular (aposentado)
Departamento de Epidemiologia/Faculdade de Saude Publica, USP
Av. Dr. Arnaldo, 715
01246-904 - S. Paulo/SP - Brasil
fones (11)3061-7747; (11)3768-8612;(11)3714-2403
www.fsp.usp.br/~jmpsouza
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