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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/ > > > > * > * 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/

**Follow-Ups**:**Re: st: Very low sensitivity of fixed-effects logit***From:*Steven Samuels <sjhsamuels@earthlink.net>

**References**:**st: Very low sensitivity of fixed-effects logit***From:*"Leda Inga" <ledainga@gmail.com>

**Re: st: Very low sensitivity of fixed-effects logit***From:*Steven Samuels <sjhsamuels@earthlink.net>

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