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 <[email protected]>:
> 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/
> >
>
> *
> * 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/
>
*
* 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/
