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


From   Steven Samuels <sjhsamuels@earthlink.net>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Very low sensitivity of fixed-effects logit
Date   Thu, 5 Jun 2008 18:46:29 -0400

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.
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