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Re: st: Deriving Bayes estimates from xtmelogit
From
Jamie Fagg <[email protected]>
To
[email protected]
Subject
Re: st: Deriving Bayes estimates from xtmelogit
Date
Fri, 10 Sep 2010 14:25:41 +0100
Dear Bobby and Stas,
Thanks for your replies. They were both very helpful. I'll take a look
at the estimates using gllamm to see the extent of the differences
between the mean and modal estimates.
Apologies if this message is received twice - I sent it some hours ago
and I think I may have used rich formatting instead of plain text.
Jamie
On 9 September 2010 17:11, Roberto G. Gutierrez, StataCorp
<[email protected]> wrote:
> Jamie Fagg <[email protected]> asks:
>
>> I am trying to derive Bayes estimates from a 3-level logistic regression
>> model in Stata version 10.1.
>
>> The model has the following structure, where psychometric items are nested
>> in individuals, nested in geographic areas.
>
>> Level 1: psychometric items
>> Level 2: individuals
>> Level 3: geographic areas
>
>> I read in Rabe-Hesketh and Skrondal (Multilevel and Longitudinal modelling
>> using Stata, 2008, p.162) that "Empirical Bayes predictions of the random
>> intercepts ... can be obtained" from xtmixed using -predict- with the
>> reffects option
>
>> I couldn't find any analogous advice in the section about multilevel
>> logistic regression models so I ran the model and ran the predict eb,
>> reffects (see code below).
>
>> Is this the correct way to derive Bayes predictions from xtmelogit?
>
> Yes this would be correct, with the one caveat that what you obtain are
> empirical Bayes _modal_ predictions, rather than empirical Bayes mean
> predictions. In a logistic regression setting, the posterior distribution of
> the random effects is no longer symmetric, and thus the posterior modes and
> the posterior means, while very similar for most data, are not strictly equal.
>
> Posterior mean predictions after multilevel logistic regression are not
> available in current official Stata, but you could obtain these by
> alternatively fitting your model using -gllamm-; see -ssc describe gllamm- for
> details.
>
> --Bobby
> [email protected]
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