------------------------------------------------------------------------------- name: log: Q:\C-modelling\runmlwin\website\logfiles\2020-03-27\16\7.4.smcl log type: smcl opened on: 27 Mar 2020, 18:22:07 . **************************************************************************** . * Module 7: Multilevel Models for Binary Responses Stata Practicals . * . * P7.4: Predicted Probabilities from a Multilevel Model . * . * George Leckie . * Centre for Multilevel Modelling, 2010 . **************************************************************************** . * Stata do-file to replicate all analyses using runmlwin . * . * George Leckie . * Centre for Multilevel Modelling, 2013 . * http://www.bristol.ac.uk/cmm/software/runmlwin/ . **************************************************************************** . . use "http://www.bristol.ac.uk/cmm/media/runmlwin/7.4.dta", clear . . runmlwin antemed cons magec magecsq meduc2 meduc3 wealthc, /// > level2(comm: cons) /// > level1(womid:) /// > discrete(distribution(binomial) link(logit) denominator(cons) pql2) / > // > nopause MLwiN 3.05 multilevel model Number of obs = 5366 Binomial logit response model (hierarchical) Estimation algorithm: IGLS, PQL2 ----------------------------------------------------------- | No. of Observations per Group Level Variable | Groups Minimum Average Maximum ----------------+------------------------------------------ comm | 361 3 14.9 25 ----------------------------------------------------------- Run time (seconds) = 0.80 Number of iterations = 5 ------------------------------------------------------------------------------ antemed | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- cons | -.4546834 .0827282 -5.50 0.000 -.6168277 -.2925392 magec | -.0003881 .006554 -0.06 0.953 -.0132337 .0124574 magecsq | -.0010068 .0006828 -1.47 0.140 -.002345 .0003314 meduc2 | .5478987 .0845976 6.48 0.000 .3820905 .7137069 meduc3 | 1.309241 .0977836 13.39 0.000 1.117589 1.500893 wealthc | .3977514 .029586 13.44 0.000 .3397638 .4557389 ------------------------------------------------------------------------------ ------------------------------------------------------------------------------ Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval] -----------------------------+------------------------------------------------ Level 2: comm | var(cons) | .8538518 .0935791 .6704402 1.037263 ------------------------------------------------------------------------------ . . replace magec = 0 (5,366 real changes made) . . replace magecsq = 0 (5,366 real changes made) . . predict predxb . . generate medianpredprob = invlogit(predxb) . . egen pickone = tag(wealth meduc) . . sort wealth meduc . . list wealth meduc medianpredprob if pickone==1, abbreviate(14) sepby(wealth) +---------------------------------+ | wealth meduc medianpredprob | |---------------------------------| 331. | 1 1 .2221039 | 1015. | 1 2 .3305831 | 1137. | 1 3 .5139447 | |---------------------------------| 1375. | 2 1 .2982393 | 1741. | 2 2 .4236524 | 2110. | 2 3 .6114817 | |---------------------------------| 2310. | 3 1 .3874735 | 2668. | 3 2 .5224733 | 3090. | 3 3 .700839 | |---------------------------------| 3276. | 4 1 .4849561 | 3576. | 4 2 .6195657 | 3904. | 4 3 .777135 | |---------------------------------| 4209. | 5 1 .5835972 | 4412. | 5 2 .7079517 | 5185. | 5 3 .8384579 | +---------------------------------+ . . generate medianpredlogit = logit(medianpredprob) . . keep if pickone==1 (5,351 observations deleted) . . keep wealth meduc medianpredprob medianpredlogit . . list, abbreviate(15) sepby(wealth) +---------------------------------------------------+ | meduc wealth medianpredprob medianpredlogit | |---------------------------------------------------| 1. | 1 1 .2221039 -1.253448 | 2. | 2 1 .3305831 -.7055489 | 3. | 3 1 .5139447 .0557934 | |---------------------------------------------------| 4. | 1 2 .2982393 -.8556963 | 5. | 2 2 .4236524 -.3077976 | 6. | 3 2 .6114817 .4535449 | |---------------------------------------------------| 7. | 1 3 .3874735 -.4579449 | 8. | 2 3 .5224733 .0899537 | 9. | 3 3 .700839 .8512962 | |---------------------------------------------------| 10. | 1 4 .4849561 -.0601936 | 11. | 2 4 .6195657 .4877051 | 12. | 3 4 .777135 1.249048 | |---------------------------------------------------| 13. | 1 5 .5835972 .3375579 | 14. | 2 5 .7079517 .8854565 | 15. | 3 5 .8384579 1.646799 | +---------------------------------------------------+ . . expand 1000 (14,985 observations created) . . generate u = rnormal(0,sqrt(0.889)) . . generate meanpredprob = invlogit(medianpredlogit + u) . . collapse (mean) meanpredprob, by(wealth meduc medianpredprob) . . list wealth meduc medianpredprob meanpredprob, abbreviate(14) sepby(wealth) +------------------------------------------------+ | wealth meduc medianpredprob meanpredprob | |------------------------------------------------| 1. | 1 1 .2221039 .2607134 | 2. | 1 2 .3305831 .351727 | 3. | 1 3 .5139447 .5015319 | |------------------------------------------------| 4. | 2 1 .2982393 .3314112 | 5. | 2 2 .4236524 .4415864 | 6. | 2 3 .6114817 .5943345 | |------------------------------------------------| 7. | 3 1 .3874735 .4119753 | 8. | 3 2 .5224733 .5235564 | 9. | 3 3 .700839 .6627779 | |------------------------------------------------| 10. | 4 1 .4849561 .497875 | 11. | 4 2 .6195657 .59526 | 12. | 4 3 .777135 .7437027 | |------------------------------------------------| 13. | 5 1 .5835972 .5741999 | 14. | 5 2 .7079517 .6912331 | 15. | 5 3 .8384579 .8028967 | +------------------------------------------------+ . end of do-file