|
|
|
||||
|
|
|||||
Occasionally, there might be a need for generating the predicted probabilities manually from a multinomial logistic regression. The code generates the predicted probabilities using a little bit of matrix calculation.
use http://www.ats.ucla.edu/stat/data/stata/mlogit_demo_data, clear mlogit y write female math, nolog Multinomial logistic regression Number of obs = 200 LR chi2(9) = 31.76 Prob > chi2 = 0.0002 Log likelihood = -159.59108 Pseudo R2 = 0.0905 ------------------------------------------------------------------------------ y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- 1 | write | -.0592852 .0313425 -1.89 0.059 -.1207155 .002145 female | .0253633 .5020617 0.05 0.960 -.9586595 1.009386 math | -.0525018 .0349184 -1.50 0.133 -.1209407 .0159371 _cons | 3.818145 1.534455 2.49 0.013 .8106693 6.825621 -------------+---------------------------------------------------------------- 2 | write | .0339394 .0554246 0.61 0.540 -.0746909 .1425697 female | .7612508 .7302687 1.04 0.297 -.6700495 2.192551 math | .0238891 .0448113 0.53 0.594 -.0639394 .1117177 _cons | -6.297374 2.5963 -2.43 0.015 -11.38603 -1.20872 -------------+---------------------------------------------------------------- 3 | write | -.0414901 .0347647 -1.19 0.233 -.1096276 .0266475 female | .6969475 .5588378 1.25 0.212 -.3983546 1.79225 math | -.0748394 .0388773 -1.93 0.054 -.1510375 .0013586 _cons | 3.471735 1.698489 2.04 0.041 .1427571 6.800713 ------------------------------------------------------------------------------ (y==4 is the base outcome) predict p1 p2 p3 p4, p list in 1/4 +-----------------------------------------------------------------------+ | y female math write p1 p2 p3 p4 | |-----------------------------------------------------------------------| 1. | 4 male 41 52 .1678438 .0198305 .1198363 .6924894 | 2. | 4 female 53 59 .0674177 .0798579 .081622 .7711025 | 3. | 4 male 54 33 .2449829 .0132924 .0932966 .6484281 | 4. | 4 male 47 44 .1942302 .017215 .105188 .6833668 | +-----------------------------------------------------------------------+ mat a = e(b) mat list a a[1,12] 1: 1: 1: 1: 2: 2: 2: 2: write female math _cons write female math _cons y1 -.05928522 .02536326 -.05250177 3.8181451 .03393942 .76125081 .02388913 -6.2973737 3: 3: 3: 3: write female math _cons y1 -.04149009 .69694747 -.07483944 3.471735 preserve clear input y female math cons 52 0 41 1 59 1 53 1 end* converting everything to matrices mkmat y female math cons, mat(X) mat a1 = a[1, "1:"] mat a2 = a[1, "2:"] mat a3 = a[1, "3:"] mat biga = (a1 \ a2 \a3)' mat list biga biga[4,3] y1 y1 y1 1:write -.05928522 .03393942 -.04149009 1:female .02536326 .76125081 .69694747 1:math -.05250177 .02388913 -.07483944 1:_cons 3.8181451 -6.2973737 3.471735 mat xb = X*biga mat list xb xb[2,3] y1 y1 y1 r1 -1.4172591 -3.5530693 -1.7541665 r2 -2.4369136 -2.267573 -2.2457229* converting back to data svmat xb gen denom = 1+exp(xb1) + exp(xb2) + exp(xb3) gen p1 = exp(xb1)/denom gen p2 = exp(xb2)/denom gen p3 = exp(xb3)/denom list p1 p2 p3 +--------------------------------+ | p1 p2 p3 | |--------------------------------| 1. | .1678438 .0198305 .1198363 | 2. | .0674177 .0798579 .081622 | +--------------------------------+ restore
UCLA Researchers are invited to our Statistical Consulting Services
We recommend others to our list of Other Resources for Statistical Computing Help
These pages are Copyrighted (c) by UCLA Academic Technology Services