On On, 2005-08-17, 08:52, Shige Song skrev:> Hi,
>
> I compare results of a simple two-level poisson estimated using lmer
> and those estimated using MLwiN and Stata (v.9).
>
> In R, I trype:
>
-------------------------------------------------------------------------------------------
> m2 <- lmer(.D ~ offset(log(.Y)) + (1|pcid2) + educy + agri, male,
poisson)
>
-------------------------------------------------------------------------------------------
>
> In Stata, I type:
>
-------------------------------------------------------------------------------------------
> xtpois _D educy agri, e(_Y) i(pcid2)
You're not fitting the same model! `lmer' uses Gaussian random effects
and
the default for `xtpois' is gamma random effects.
Also, note that even if you'd specified a Gaussian random effect (through
a `normal' to the right of the `,' in your `xtpois' call) the same
fitting
criterion is not used since `xtpois' uses adaptive Gauss-Hermite
quadrature and `lmer' defaults to PQL.
For comparable results, try the following:
m2 <- lmer(.D ~ offset(log(.Y)) + (1|pcid2) + educy + agri, male, poisson,
method = "AGQ")
xtpois _D educy agri, e(_Y) i(pcid2) re normal
You may also want to try G??ran Brostr??m's `glmmML' package.
HTH,
Henric
>
> Results using R look like:
>
-------------------------------------------------------------------------------------------
> ..
> Random effects:
> Groups Name Variance Std.Dev.
> pcid2 (Intercept) 5e-10 2.2361e-05
> # of obs: 25360, groups: pcid2, 174
>
> Estimated scale (compare to 1) 7.190793
>
> Fixed effects:
> Estimate Std. Error z value Pr(>|z|)
> (Intercept) -3.28548086 0.00408771 -803.75 < 2.2e-16 ***
> educy 0.00507475 0.00039616 12.81 < 2.2e-16 ***
> agri 0.01346887 0.00334837 4.02 5.758e-05 ***
> ..
>
------------------------------------------------------------------------------------------
>
> Results using Stata look like:
>
>
------------------------------------------------------------------------------
> _D | Coef. Std. Err. z P>|z| [95% Conf.
> Interval]
>
-------------+----------------------------------------------------------------
> educy | .0120431 .0004441 27.12 0.000 .0111725
> .0129136
> agri | .0293177 .0035586 8.24 0.000 .022343
> .0362924
> _cons | -3.325073 .0076275 -435.93 0.000 -3.340023
> -3.310123
> _Y | (exposure)
>
-------------+----------------------------------------------------------------
> /lnalpha | -4.982977 .1156474 -5.209641
> -4.756312
>
-------------+----------------------------------------------------------------
> alpha | .0068536 .0007926 .0054636
> .0085973
>
------------------------------------------------------------------------------
>
>
> As you can see, the discrepency is significant! And results using
> MLwiN agree with Stata. Any help will be greately appreciated!
>
> Shige
>
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