Re: st: -predict , reffects- after -xtmelogit-

[Jeph Herrin <stata@spandrel.net>]

Thanks much for the thorough explanation. Very helpful.

cheers!
Jeph

On 12/21/2010 3:49 PM, Roberto G. Gutierrez, StataCorp wrote:
> Jeph Herrin<stata@spandrel.net>  asks about obtaining MLE estimates of random
> effects after estimation with -xtmelogit-:
>
> > Thanks, the typo was that I had -yrwe- and -ywre- and mixed them up. But
> > this still doesn't tell me how to get the mle random effects, if that is
> > possible.
> The posterior modes obtained from
>
>      . predict r, reffects
>
> after -xtmelogit- are actually MLE's in and of themselves, because a mode is
> the point where the distribution peaks and thus represents the maximum of the
> posterior distribution of the random effects.  These posterior MLE's condition
> on three things:
>
>     1. The regression coefficients are as estimated
>     2. The random effects are normally distributed
>     3. The standard deviation of the random effects is as estimated
>
> My guess is that Jeph would like estimates that only condition on 1. and not
> on 2. and 3., as is discussed in Section 2.9.1 of Rabe-Hesketh and Skrondal
> (2008).  That discussion uses -xtmixed- and linear models, and thus the MLEs
> can be obtained post-estimation by linear regression of the partial residuals
> on dummy variables identifying the clusters.
>
> Such an approach is not generalizable to -xtmelogit- because of the
> non-linearity of the model.  However, you can achieve the same effect by using
> -logit- with a linear offset calculated from the regression coefficients as
> obtained from -xtmelogit-.  To illustrate,
>
>      . webuse bangladesh, clear
>      . xtmelogit c_use age || district:		// age introduced to make
>      						// things interesting
> 					
>      . predict r_mode, reffects			// Posterior modes
>      . predict xbeta, xb				// Obtain linear predictor
>
>      . forvalues i = 1/61 {			// Generate dummy variables
>      .     gen dd`i' = district == `i'
>      . }
>
> Note above that -district- has 60 unique values, but is coded as 1 through
> 61 with 54 missing.  We next obtain the MLEs of the random effects using
> -logit- with dummy variables and an offset
>
>      . logit c_use dd*, nocons offset(xbeta)	// MLEs of random effects
>
> Some dummies are omitted because of perfect prediction, but such is life with
> binary data sometimes.
>
> The key here is that by including -xbeta- as an offset we are conditioning on
> the regression coefficients as estimated by -xtmelogit-.  Not conditioning on
> these would be akin to unrestricted logistic regression with covariates and
> dummy variables for clusters, in other words, fixed-effects logistic
> regression.
>
> The only remaining task is to take these logit coefficients and place them in
> the data according to the values of -district-.  One way is
>
>      . gen r_mle = .
>      . forvalues i = 1/61 {
>      .         replace r_mle = _b[dd`i'] if district == `i'
>      . }
>      . replace r_mle = . if r_mle == 0
>
> The last line is necessary because cases coefficients on dropped variables are
> stored as 0 in e(b) rather than missing.
>
> We can now compare with the standard random-effects predictions
>
>      . bysort district: gen tolist = _n==1
>      . list district r_mode r_mle if tolist
>
> Finally, if you compare the standard deviation of the MLEs of the random
> effects to that obtained as sd(_cons) from -xtmelogit-
>
>      . xtmelogit c_use age || district:
>      . sum r_mle if tolist
>
> you will still not get matching numbers.  The main reason for this is that by
> estimating random-effects directly, you are no longer imposing that they are
> normally distributed but instead allowing them to "be everything they can be".
> I'm not sure how you would estimate individual random effects while at the
> same time imposing normality.
>
> --Bobby
> rgutierrez@stata.com
>
> Reference:
>
> Rabe-Hesketh, S. and A. Skrondal. 2008. Multilevel and Longitudinal Modeling
> 	Using Stata.  College Station, TX: Stata Press.
>
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