Dear stata listers:
In version 9, When I use predict after " svy: logit" the predicted probabilities range from roughly 0.8 to greater than 2.0. I don't understand this. When predicting in Version 8.0 (under version control) I get predictions that are credible. The survey logistic regression give the same coefficients for both version 8.0 and version 9.0 , the problem is only the prediction.
Below an example with partial output. Any suggestions ?
Thanks,
Matt
---------------------------------------------------------
* these lines just generate some data
clear
set seed 1
set obs 100
gen x=uniform()
gen logit = 3 * x +uniform() -.5
gen pred = exp(logit)/ (1+ exp(logit))
gen y= uniform() < pred
gen psum = floor((_n-1)/10)
gen stratm2 = floor((_n-1)/50)
gen patwt=uniform()+0.3
version 8.0
svyset [pweight=patwt], psu(psum) strata(stratm2)
svylogit y x
predict temp
sum temp
version 9.0
svyset psum [pweight=patwt], strata(stratm2)
svy : logit y x
predict temp2
sum temp2 , detail
list temp temp2
===================================
Partial : Output from example:
. svy : logit y x
(running logit on estimation sample)
Survey: Logistic regression
Number of strata = 2 Number of obs = 100
Number of PSUs = 10 Population size = 78.583418
Design df = 8
F( 1, 8) = 3.96
Prob > F = 0.0817
------------------------------------------------------------------------------
| Linearized
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x | 1.827507 .9179305 1.99 0.082 -.2892447 3.944258
_cons | .8259731 .6490185 1.27 0.239 -.6706663 2.322613
------------------------------------------------------------------------------
. predict temp2
(option p assumed; Pr(y))
. sum temp2 , detail
Pr(y)
-------------------------------------------------------------
Percentiles Smallest
1% .8489111 .8265356
5% .9179934 .8712866
10% 1.011611 .8794591 Obs 100
25% 1.270084 .8906985 Sum of Wgt. 100
50% 1.845705 Mean 1.807649
Largest Std. Dev. .5650812
75% 2.330985 2.634669
90% 2.535905 2.639847 Variance .3193168
95% 2.624683 2.642325 Skewness -.1069881
99% 2.643911 2.645498 Kurtosis 1.704952
.
. list temp temp2
+---------------------+
| temp temp2 |
|---------------------|
1. | .8856528 2.047086 |
2. | .925658 2.521828 |
3. | .8470134 1.711366 |
4. | .8434895 1.684424 |
5. | .9310861 2.603494 |
|---------------------|
6. | .8632787 1.842793 |
7. | .9102473 2.316658 |
8. | .9132985 2.354591 |
9. | .8684405 1.88724 |
10. | .8197377 1.514571 |
|---------------------|
11. | .7393818 1.042758 |
12. | .8519451 1.749939 |
13. | .865721 1.863644 |
14. | .7067101 .8794591 |
15. | .9132301 2.353728 |
|---------------------|
16. | .8504521 1.738151 |
17. | .8946925 2.139596 |
18. | .8698905 1.899991 |
19. | .7050133 .8712866 |
20. | .6956219 .8265356 |
....
Matthias Schonlau
Statistician, RAND
412-683-2300 x4917
http://www.rand.org/statistics/bios/stat_schonlau_matthias.html
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