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Re: st: Very low sensitivity of fixed-effects logit


From   "Leda Inga" <ledainga@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Very low sensitivity of fixed-effects logit
Date   Thu, 5 Jun 2008 18:22:16 -0500

Thank you very much. I've used the following commands:

Fixed-effects models:

# delimit;
xi: xtlogit Y X1 X2 X3 i.X4  if [rural==1] [iw=pwi], fe i(V001) nolog or
;
delimit cr

Random-effects models.

# delimit;
xi: xtlogit Y X1 X2 X3 i.X4  region3 region5 if [rural==1] [iw=pwi],
re i(V001) nolog or
;
delimit cr

I didn't use svy: because it didn't allow me to use xtlogit (I'm
interested in excluding the effect of certain unobserved cluster
variables, as geographical access, in X4 and get a consistent
estimator of its impact on Y). I know that when using survey data it's
not correct to use the "if", but svy, subpop( www). Nevertheless,  I
couldn't think of another way.

To calcute the predictions I used:

predict Yhat if e(sample), pu0

I didn't use lsens because it can't be used after xtlogit.

I hope this information helps to clarify my problem.



2008/6/5, Steven Samuels <sjhsamuels@earthlink.net>:
> Leda,
>
> You need to tell us exactly which logistic commands you ran.  -svy: logit-
> would be appropriate for fixed-effects logistic regression with stratified,
> weighted, and clustered, data. -logistic- itself will not take stratum
> variables, but will take weights and clustering.
>
> -xtmelogit- cannot handle survey weights, but would otherwise be the proper
> program for random effects logistic regression. The user-written package
> -gllamm- can do multi-level logistic regression with weights.  With random
> effects models, several types of predictions are possible, depending on
> whether random-effects are permitted in the prediction.
>
> The user-written command -rocss- was written for Stata Version 8. It does
> not accept  weights and shouldn't be used with weighted survey data.  After
> -logistic-, -logit-, or -svy: logistic-, you should try Stata's built-in
> commands:  -lsens- and -lroc- .
>
> After -gllamm- , you could compute your own tables of specificity and
> sensitivity by printing out the weighted tables of probabilities.  If there
> are too many distinct values, round the probabilities to the nearest 0.01
> before tabling.
>
> Steve
>
>
>
> On Jun 5, 2008, at 5:40 PM, Leda Inga wrote:
>
> > Hi,
> >
> > I'm runnig a fixed-effects and random-effects logit with DHS
> > (Demographic Health Survey) data. The groups are the clusters within
> > which each female belongs to. Given the recomendation in a previous
> > statalist mail
> (http://www.stata.com/statalist/archive/2007-06/msg00818.html
> > ), I calculated the percentege correctly predicted for both models.
> > Nevertheless, I'm very surprised because the sensitivity (% correctly
> > predicted of positive outcomes) is very low for the fixed-effects
> > logit while the specificity (% correctly predicted of negative
> > outcomes: zeros) very high. On the other hand, both measures are more
> > acceptable for the random effects models. Besides, the pvalue of the
> > hausman test is zero.
> > Are this measures (sensititvity and specificity) the most appropiate
> > for measuring the quality of the results of this kind of models? And
> > are the results I've gotten frequent when comparing a fixed effects
> > versus a random effects model?
> >
> > Here are the results given by rocss, a command the calculates the
> > sensitivity (sens) and specificity (spec) for different cutoffs:
> >
> > Fixed-effects logit:
> >
> >    cutoff     sens     spec   omspec    cclass    carea
> >    ------------------------------------------------------
> > 1.    0.000   1.0000   0.0000   1.0000   47.5149   0.0000
> > 2.    0.100   0.7250   0.6656   0.3344   69.3837   0.5741
> > 3.    0.200   0.4250   0.9026   0.0974   67.5660   0.7104
> > 4.    0.300   0.2313   0.9637   0.0363   61.5734   0.7304
> > 5.    0.400   0.1219   0.9908   0.0092   57.7961   0.7352
> > 6.    0.500   0.0580   0.9951   0.0049   54.9844   0.7356
> > 7.    0.600   0.0209   0.9995   0.0005   53.4507   0.7358
> > 8.    0.700   0.0078   1.0000   0.0000   52.8543   0.7358
> > 9.    0.800   0.0036   1.0000   0.0000   52.6555   0.7358
> > 10.    0.900   0.0006   1.0000   0.0000   52.5135   0.7358
> > 11.    1.000   0.0000   1.0000   0.0000   52.4851   0.7358
> >
> +------------------------------------------------------+
> >
> > Random-effects logit:
> >
> >    cutoff     sens     spec   omspec    cclass    carea
> >    ------------------------------------------------------
> > 1.    0.000   1.0000   0.0000   1.0000   48.9190   0.0000
> > 2.    0.100   0.9791   0.1930   0.8070   57.7572   0.1910
> > 3.    0.200   0.9291   0.3791   0.6209   64.8135   0.3685
> > 4.    0.300   0.8490   0.5321   0.4679   68.7099   0.5046
> > 5.    0.400   0.7528   0.6726   0.3274   71.1808   0.6171
> > 6.    0.500   0.6353   0.7809   0.2191   70.9670   0.6923
> > 7.    0.600   0.5036   0.8721   0.1279   69.1851   0.7442
> > 8.    0.700   0.3871   0.9293   0.0707   66.4053   0.7697
> > 9.    0.800   0.2448   0.9674   0.0326   61.3923   0.7817
> > 10.    0.900   0.0971   0.9930   0.0070   55.4764   0.7861
> > 11.    1.000   0.0000   1.0000   0.0000   51.0810   0.7864
> >
> >
> > Any help would be very appreciated.
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/support/faqs/res/findit.html
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> >
>
> *
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>
*
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