. * do-file for lecture 4a of VHM 802/812, Winter 2023 . version 17 /* works also with versions 14-16, except for table command */ . set more off . cd "r:\" r:\ . . use nocardia.dta, clear . tabulate casecont dclox, chi2 row exact lrchi2 +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ | Cloxacillin used on Case - | farm Control | no yes | Total -----------+----------------------+---------- no | 35 19 | 54 | 64.81 35.19 | 100.00 -----------+----------------------+---------- yes | 46 8 | 54 | 85.19 14.81 | 100.00 -----------+----------------------+---------- Total | 81 27 | 108 | 75.00 25.00 | 100.00 Pearson chi2(1) = 5.9753 Pr = 0.015 Likelihood-ratio chi2(1) = 6.1130 Pr = 0.013 Fisher's exact = 0.025 1-sided Fisher's exact = 0.013 . cc casecont dclox Proportion | Exposed Unexposed | Total exposed -----------------+------------------------+------------------------ Cases | 8 46 | 54 0.1481 Controls | 19 35 | 54 0.3519 -----------------+------------------------+------------------------ Total | 27 81 | 108 0.2500 | | | Point estimate | [95% conf. interval] |------------------------+------------------------ Odds ratio | .3203661 | .1091298 .8852277 (exact) Prev. frac. ex. | .6796339 | .1147723 .8908702 (exact) Prev. frac. pop | .2391304 | +------------------------------------------------- chi2(1) = 5.98 Pr>chi2 = 0.0145 . * logistic regression for case-control design . logit casecont dclox Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -71.810582 Iteration 2: log likelihood = -71.803418 Iteration 3: log likelihood = -71.803418 Logistic regression Number of obs = 108 LR chi2(1) = 6.11 Prob > chi2 = 0.0134 Log likelihood = -71.803418 Pseudo R2 = 0.0408 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dclox | -1.138291 .4774329 -2.38 0.017 -2.074042 -.2025395 _cons | .2732933 .2243002 1.22 0.223 -.1663269 .7129136 ------------------------------------------------------------------------------ . logit casecont dclox, or /* displays ORs */ Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -71.810582 Iteration 2: log likelihood = -71.803418 Iteration 3: log likelihood = -71.803418 Logistic regression Number of obs = 108 LR chi2(1) = 6.11 Prob > chi2 = 0.0134 Log likelihood = -71.803418 Pseudo R2 = 0.0408 ------------------------------------------------------------------------------ casecont | Odds ratio Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dclox | .3203661 .1529533 -2.38 0.017 .1256768 .8166542 _cons | 1.314286 .2947945 1.22 0.223 .8467694 2.039926 ------------------------------------------------------------------------------ Note: _cons estimates baseline odds. . logistic casecont dclox /* same using logistic command */ Logistic regression Number of obs = 108 LR chi2(1) = 6.11 Prob > chi2 = 0.0134 Log likelihood = -71.803418 Pseudo R2 = 0.0408 ------------------------------------------------------------------------------ casecont | Odds ratio Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dclox | .3203661 .1529533 -2.38 0.017 .1256768 .8166542 _cons | 1.314286 .2947945 1.22 0.223 .8467694 2.039926 ------------------------------------------------------------------------------ Note: _cons estimates baseline odds. . logit dclox casecont /* to demonstrate same log(OR) */ Iteration 0: log likelihood = -60.732196 Iteration 1: log likelihood = -57.735076 Iteration 2: log likelihood = -57.675769 Iteration 3: log likelihood = -57.675718 Iteration 4: log likelihood = -57.675718 Logistic regression Number of obs = 108 LR chi2(1) = 6.11 Prob > chi2 = 0.0134 Log likelihood = -57.675718 Pseudo R2 = 0.0503 ------------------------------------------------------------------------------ dclox | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- casecont | -1.138291 .4774329 -2.38 0.017 -2.074042 -.2025396 _cons | -.6109091 .2849614 -2.14 0.032 -1.169423 -.052395 ------------------------------------------------------------------------------ . . * logistic regression for categorical predictor . tabulate casecont dbarn, chi2 row exact lrchi2 +----------------+ | Key | |----------------| | frequency | | row percentage | +----------------+ Enumerating sample-space combinations: stage 3: enumerations = 1 stage 2: enumerations = 3 stage 1: enumerations = 0 Case - | dry cow housing Control | freestal tiestall other | Total -----------+---------------------------------+---------- no | 13 38 3 | 54 | 24.07 70.37 5.56 | 100.00 -----------+---------------------------------+---------- yes | 22 29 3 | 54 | 40.74 53.70 5.56 | 100.00 -----------+---------------------------------+---------- Total | 35 67 6 | 108 | 32.41 62.04 5.56 | 100.00 Pearson chi2(2) = 3.5232 Pr = 0.172 Likelihood-ratio chi2(2) = 3.5531 Pr = 0.169 Fisher's exact = 0.157 . logit casecont i.dbarn Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -73.083934 Iteration 2: log likelihood = -73.083342 Iteration 3: log likelihood = -73.083342 Logistic regression Number of obs = 108 LR chi2(2) = 3.55 Prob > chi2 = 0.1692 Log likelihood = -73.083342 Pseudo R2 = 0.0237 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dbarn | tiestall | -.7963834 .4279909 -1.86 0.063 -1.63523 .0424632 other | -.5260931 .8882816 -0.59 0.554 -2.267093 1.214907 | _cons | .5260931 .3498251 1.50 0.133 -.1595516 1.211738 ------------------------------------------------------------------------------ . logistic casecont i.dbarn Logistic regression Number of obs = 108 LR chi2(2) = 3.55 Prob > chi2 = 0.1692 Log likelihood = -73.083342 Pseudo R2 = 0.0237 ------------------------------------------------------------------------------ casecont | Odds ratio Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dbarn | tiestall | .4509569 .1930054 -1.86 0.063 .1949075 1.043378 other | .5909091 .5248937 -0.59 0.554 .1036129 3.36998 | _cons | 1.692308 .5920118 1.50 0.133 .852526 3.359317 ------------------------------------------------------------------------------ Note: _cons estimates baseline odds. . testparm i.dbarn /* multiple Wald test for dbarn */ ( 1) [casecont]2.dbarn = 0 ( 2) [casecont]3.dbarn = 0 chi2( 2) = 3.46 Prob > chi2 = 0.1771 . . * multiple logistic regression . logit casecont dneo dclox dcpct Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -54.156164 Iteration 2: log likelihood = -53.993934 Iteration 3: log likelihood = -53.993656 Iteration 4: log likelihood = -53.993656 Logistic regression Number of obs = 108 LR chi2(3) = 41.73 Prob > chi2 = 0.0000 Log likelihood = -53.993656 Pseudo R2 = 0.2787 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | 2.212564 .5780423 3.83 0.000 1.079622 3.345506 dclox | -1.412516 .5572076 -2.53 0.011 -2.504623 -.3204093 dcpct | .0226682 .0071871 3.15 0.002 .0085817 .0367547 _cons | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 ------------------------------------------------------------------------------ . logistic casecont dneo dclox dcpct /* shows ORs */ Logistic regression Number of obs = 108 LR chi2(3) = 41.73 Prob > chi2 = 0.0000 Log likelihood = -53.993656 Pseudo R2 = 0.2787 ------------------------------------------------------------------------------ casecont | Odds ratio Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | 9.139118 5.282797 3.83 0.000 2.943566 28.37493 dclox | .2435298 .1356966 -2.53 0.011 .0817064 .7258519 dcpct | 1.022927 .0073519 3.15 0.002 1.008619 1.037439 _cons | .0505763 .0390574 -3.86 0.000 .0111329 .2297654 ------------------------------------------------------------------------------ Note: _cons estimates baseline odds. . testparm dclox /* Wald test as chi^2-test, same P as z-test */ ( 1) [casecont]dclox = 0 chi2( 1) = 6.43 Prob > chi2 = 0.0112 . testparm dneo dclox /* Wald test for dneo+dclox */ ( 1) [casecont]dneo = 0 ( 2) [casecont]dclox = 0 chi2( 2) = 21.40 Prob > chi2 = 0.0000 . * profile likelihood CI for dclox, using logprof add-on command . logprof dclox Maximum Profile Log-Likelihood = -53.993656 ------------------------------------------------------------------------------- casecont | Coef. [95% Conf. Interval] ---------------+--------------------------------------------------------------- dclox | -1.412516 -2.5473765 -.34321502 ------------------------------------------------------------------------------- . . * display of iterations of ML estimation . logit casecont dneo dclox dcpct, trace -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 0: Parameter vector: casecont: casecont: casecont: casecont: dneo dclox dcpct _cons r1 0 0 0 0 log likelihood = -74.859896 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 1: Parameter vector: casecont: casecont: casecont: casecont: dneo dclox dcpct _cons r1 2.235522 -1.60015 .0228446 -2.857956 log likelihood = -54.156164 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 2: Parameter vector: casecont: casecont: casecont: casecont: dneo dclox dcpct _cons r1 2.204562 -1.406852 .0225647 -2.974098 log likelihood = -53.993934 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 3: Parameter vector: casecont: casecont: casecont: casecont: dneo dclox dcpct _cons r1 2.212543 -1.4125 .022668 -2.984243 log likelihood = -53.993656 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 4: Parameter vector: casecont: casecont: casecont: casecont: dneo dclox dcpct _cons r1 2.212564 -1.412516 .0226682 -2.984272 log likelihood = -53.993656 -------------------------------------------------------------------------------------------------------------------------------------------------------- Logistic regression Number of obs = 108 LR chi2(3) = 41.73 Prob > chi2 = 0.0000 Log likelihood = -53.993656 Pseudo R2 = 0.2787 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | 2.212564 .5780423 3.83 0.000 1.079622 3.345506 dclox | -1.412516 .5572076 -2.53 0.011 -2.504623 -.3204093 dcpct | .0226682 .0071871 3.15 0.002 .0085817 .0367547 _cons | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 ------------------------------------------------------------------------------ . . * likelihood-ratio tests . logit casecont dneo dclox dcpct Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -54.156164 Iteration 2: log likelihood = -53.993934 Iteration 3: log likelihood = -53.993656 Iteration 4: log likelihood = -53.993656 Logistic regression Number of obs = 108 LR chi2(3) = 41.73 Prob > chi2 = 0.0000 Log likelihood = -53.993656 Pseudo R2 = 0.2787 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | 2.212564 .5780423 3.83 0.000 1.079622 3.345506 dclox | -1.412516 .5572076 -2.53 0.011 -2.504623 -.3204093 dcpct | .0226682 .0071871 3.15 0.002 .0085817 .0367547 _cons | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 ------------------------------------------------------------------------------ . estimates store full . logit casecont dneo dcpct Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -57.649097 Iteration 2: log likelihood = -57.364934 Iteration 3: log likelihood = -57.364653 Iteration 4: log likelihood = -57.364653 Logistic regression Number of obs = 108 LR chi2(2) = 34.99 Prob > chi2 = 0.0000 Log likelihood = -57.364653 Pseudo R2 = 0.2337 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | 2.400661 .5634153 4.26 0.000 1.296388 3.504935 dcpct | .0190068 .0069935 2.72 0.007 .0052998 .0327138 _cons | -3.218885 .7853907 -4.10 0.000 -4.758222 -1.679547 ------------------------------------------------------------------------------ . lrtest full /* likelihood-ratio test for dclox */ Likelihood-ratio test Assumption: . nested within full LR chi2(1) = 6.74 Prob > chi2 = 0.0094 . logit casecont dcpct Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -69.088754 Iteration 2: log likelihood = -69.074669 Iteration 3: log likelihood = -69.074669 Logistic regression Number of obs = 108 LR chi2(1) = 11.57 Prob > chi2 = 0.0007 Log likelihood = -69.074669 Pseudo R2 = 0.0773 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dcpct | .0189543 .0060179 3.15 0.002 .0071595 .0307492 _cons | -1.462424 .5230518 -2.80 0.005 -2.487586 -.4372608 ------------------------------------------------------------------------------ . lrtest full /* likelihood-ratio test for dneo+dclox */ Likelihood-ratio test Assumption: . nested within full LR chi2(2) = 30.16 Prob > chi2 = 0.0000 . lrtest full, stats /* same with all fit statistics */ Likelihood-ratio test Assumption: . nested within full LR chi2(2) = 30.16 Prob > chi2 = 0.0000 Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | N ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- . | 108 -74.8599 -69.07467 2 142.1493 147.5136 full | 108 -74.8599 -53.99366 4 115.9873 126.7158 ----------------------------------------------------------------------------- Note: BIC uses N = number of observations. See [R] BIC note. . estimates store red . logit casecont Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -74.859896 Logistic regression Number of obs = 108 LR chi2(0) = 0.00 Prob > chi2 = . Log likelihood = -74.859896 Pseudo R2 = 0.0000 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _cons | 0 .1924501 0.00 1.000 -.3771952 .3771952 ------------------------------------------------------------------------------ . estimates store null . estimates stats * Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | N ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- full | 108 -74.8599 -53.99366 4 115.9873 126.7158 red | 108 -74.8599 -69.07467 2 142.1493 147.5136 null | 108 -74.8599 -74.8599 1 151.7198 154.4019 ----------------------------------------------------------------------------- Note: BIC uses N = number of observations. See [R] BIC note. . estimates table * ----------------------------------------------------- Variable | full red null -------------+--------------------------------------- dneo | 2.2125639 dclox | -1.412516 dcpct | .02266825 .01895434 _cons | -2.9842724 -1.4624236 0 ----------------------------------------------------- . . use mice.dta, clear . logit dead dose Iteration 0: log likelihood = -75.669723 Iteration 1: log likelihood = -64.201632 Iteration 2: log likelihood = -63.944944 Iteration 3: log likelihood = -63.944713 Iteration 4: log likelihood = -63.944713 Logistic regression Number of obs = 120 LR chi2(1) = 23.45 Prob > chi2 = 0.0000 Log likelihood = -63.944713 Pseudo R2 = 0.1549 ------------------------------------------------------------------------------ dead | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dose | 14.6369 3.332533 4.39 0.000 8.10526 21.16855 _cons | -3.569738 .7053432 -5.06 0.000 -4.952185 -2.187291 ------------------------------------------------------------------------------ . estimates store red . * does not work with non-integer dose values: logit dead i.dose . egen Dose=group(dose), label /* create dose groups (in new variable Dose) */ . logit dead i.Dose /* dose as categorical predictor */ note: 4.Dose != 0 predicts failure perfectly; 4.Dose omitted and 10 obs not used. Iteration 0: log likelihood = -71.523665 Iteration 1: log likelihood = -59.113208 Iteration 2: log likelihood = -58.82041 Iteration 3: log likelihood = -58.817856 Iteration 4: log likelihood = -58.817856 Logistic regression Number of obs = 110 LR chi2(10) = 25.41 Prob > chi2 = 0.0046 Log likelihood = -58.817856 Pseudo R2 = 0.1776 ------------------------------------------------------------------------------ dead | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- Dose | .1 | .81093 1.317616 0.62 0.538 -1.771549 3.393409 .1259 | -9.56e-17 1.490712 -0.00 1.000 -2.921742 2.921742 .1413 | 0 (empty) .15 | -9.56e-17 1.490712 -0.00 1.000 -2.921742 2.921742 .1588 | .81093 1.317616 0.62 0.538 -1.771549 3.393409 .1778 | 1.791759 1.236033 1.45 0.147 -.6308209 4.214339 .1995 | 2.60269 1.236033 2.11 0.035 .1801093 5.02527 .2239 | 1.791759 1.236033 1.45 0.147 -.6308209 4.214339 .2512 | 2.197224 1.229273 1.79 0.074 -.2121055 4.606554 .2818 | 2.197224 1.229273 1.79 0.074 -.2121055 4.606554 .3162 | 3.583519 1.317616 2.72 0.007 1.00104 6.165998 | _cons | -2.197224 1.054092 -2.08 0.037 -4.263208 -.1312411 ------------------------------------------------------------------------------ . logit dead i.Dose, asis /* avoids exclusion of dose=0.1413 */ Iteration 0: log likelihood = -75.669723 Iteration 1: log likelihood = -60.073875 Iteration 2: log likelihood = -59.076968 Iteration 3: log likelihood = -58.874743 Iteration 4: log likelihood = -58.831027 Iteration 5: log likelihood = -58.820395 Iteration 6: log likelihood = -58.81828 Iteration 7: log likelihood = -58.817905 Iteration 8: log likelihood = -58.817866 Iteration 9: log likelihood = -58.817858 Iteration 10: log likelihood = -58.817856 Logistic regression Number of obs = 120 LR chi2(11) = 33.70 Prob > chi2 = 0.0004 Log likelihood = -58.817856 Pseudo R2 = 0.2227 ------------------------------------------------------------------------------ dead | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- Dose | .1 | .8110233 1.31765 0.62 0.538 -1.771523 3.39357 .1259 | -7.83e-16 1.490771 -0.00 1.000 -2.921858 2.921858 .1413 | -14.73911 1505.568 -0.01 0.992 -2965.599 2936.12 .15 | 2.90e-16 1.490771 0.00 1.000 -2.921858 2.921858 .1588 | .8110233 1.31765 0.62 0.538 -1.771523 3.39357 .1778 | 1.791833 1.23607 1.45 0.147 -.6308193 4.214485 .1995 | 2.602659 1.236064 2.11 0.035 .1800167 5.0253 .2239 | 1.791833 1.23607 1.45 0.147 -.6308193 4.214485 .2512 | 2.197249 1.229309 1.79 0.074 -.2121518 4.606649 .2818 | 2.197249 1.229309 1.79 0.074 -.2121518 4.606649 .3162 | 3.583471 1.317628 2.72 0.007 1.000967 6.165976 | _cons | -2.197324 1.054134 -2.08 0.037 -4.26339 -.1312584 ------------------------------------------------------------------------------ . logit dead i.Dose, asis trace /* showing convergence process */ -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 0: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 0 0 0 0 0 0 0 0 0 0 dead: dead: 12. Dose _cons r1 0 -.7308875 log likelihood = -75.669723 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 1: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .5128205 1.14e-14 -.5128205 1.14e-14 .5128205 1.538462 2.564103 1.538462 2.051282 2.051282 dead: dead: 12. Dose _cons r1 3.589744 -1.884734 log likelihood = -60.073875 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 2: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8756869 -2.01e-15 -1.630307 -2.01e-15 .8756869 1.840062 2.554934 1.840062 2.203774 2.203774 dead: dead: 12. Dose _cons r1 3.484351 -2.26729 log likelihood = -59.076968 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 3: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .7838782 2.26e-15 -3.132326 2.26e-15 .7838782 1.770934 2.616282 1.770934 2.192047 2.192047 dead: dead: 12. Dose _cons r1 3.607197 -2.16817 log likelihood = -58.874743 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 4: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8339775 -4.60e-15 -4.962829 -4.60e-15 .8339775 1.809369 2.589805 1.809369 2.201125 2.201125 dead: dead: 12. Dose _cons r1 3.560565 -2.222022 log likelihood = -58.831027 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 5: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8077708 -4.61e-17 -6.116858 -1.71e-16 .8077708 1.789285 2.603871 1.789285 2.196459 2.196459 dead: dead: 12. Dose _cons r1 3.585359 -2.193845 log likelihood = -58.820395 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 6: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8168411 -1.36e-15 -8.982863 -1.49e-15 .8168411 1.796387 2.600456 1.796387 2.198647 2.198647 dead: dead: 12. Dose _cons r1 3.580031 -2.203547 log likelihood = -58.81828 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 7: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8101734 -3.32e-16 -10.11501 -4.42e-16 .8101734 1.791163 2.602943 1.791163 2.197029 2.197029 dead: dead: 12. Dose _cons r1 3.583905 -2.196416 log likelihood = -58.817905 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 8: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .811592 -1.16e-15 -11.9885 -1.27e-15 .811592 1.792281 2.602468 1.792281 2.197395 2.197395 dead: dead: 12. Dose _cons r1 3.58318 -2.197931 log likelihood = -58.817866 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 9: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8106818 7.71e-16 -13.36448 6.59e-16 .8106818 1.791564 2.602773 1.791564 2.19716 2.19716 dead: dead: 12. Dose _cons r1 3.583645 -2.196959 log likelihood = -58.817858 -------------------------------------------------------------------------------------------------------------------------------------------------------- Iteration 10: Parameter vector: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: dead: 1b. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose Dose r1 0 .8110233 -7.83e-16 -14.73911 2.90e-16 .8110233 1.791833 2.602659 1.791833 2.197249 2.197249 dead: dead: 12. Dose _cons r1 3.583471 -2.197324 log likelihood = -58.817856 -------------------------------------------------------------------------------------------------------------------------------------------------------- Logistic regression Number of obs = 120 LR chi2(11) = 33.70 Prob > chi2 = 0.0004 Log likelihood = -58.817856 Pseudo R2 = 0.2227 ------------------------------------------------------------------------------ dead | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- Dose | .1 | .8110233 1.31765 0.62 0.538 -1.771523 3.39357 .1259 | -7.83e-16 1.490771 -0.00 1.000 -2.921858 2.921858 .1413 | -14.73911 1505.568 -0.01 0.992 -2965.599 2936.12 .15 | 2.90e-16 1.490771 0.00 1.000 -2.921858 2.921858 .1588 | .8110233 1.31765 0.62 0.538 -1.771523 3.39357 .1778 | 1.791833 1.23607 1.45 0.147 -.6308193 4.214485 .1995 | 2.602659 1.236064 2.11 0.035 .1800167 5.0253 .2239 | 1.791833 1.23607 1.45 0.147 -.6308193 4.214485 .2512 | 2.197249 1.229309 1.79 0.074 -.2121518 4.606649 .2818 | 2.197249 1.229309 1.79 0.074 -.2121518 4.606649 .3162 | 3.583471 1.317628 2.72 0.007 1.000967 6.165976 | _cons | -2.197324 1.054134 -2.08 0.037 -4.26339 -.1312584 ------------------------------------------------------------------------------ . testparm i.Dose /* note: multiple Wald test for dose fails here, LR-test = 33.70 */ ( 1) [dead]2.Dose = 0 ( 2) [dead]3.Dose = 0 ( 3) [dead]4.Dose = 0 ( 4) [dead]5.Dose = 0 ( 5) [dead]6.Dose = 0 ( 6) [dead]7.Dose = 0 ( 7) [dead]8.Dose = 0 ( 8) [dead]9.Dose = 0 ( 9) [dead]10.Dose = 0 (10) [dead]11.Dose = 0 (11) [dead]12.Dose = 0 chi2( 11) = 19.22 Prob > chi2 = 0.0573 . lrtest red, stats /* likelihood-ratio (goodness-of-fit) test ok */ Likelihood-ratio test Assumption: red nested within . LR chi2(10) = 10.25 Prob > chi2 = 0.4185 Akaike's information criterion and Bayesian information criterion ----------------------------------------------------------------------------- Model | N ll(null) ll(model) df AIC BIC -------------+--------------------------------------------------------------- red | 120 -75.66972 -63.94471 2 131.8894 137.4644 . | 120 -75.66972 -58.81786 12 141.6357 175.0856 ----------------------------------------------------------------------------- Note: BIC uses N = number of observations. See [R] BIC note. . . * prediction after logistic regression for Nocardia data . use nocardia.dta, clear . * effects on probability scale . logit casecont i.dneo i.dclox dcpct Iteration 0: log likelihood = -74.859896 Iteration 1: log likelihood = -54.156164 Iteration 2: log likelihood = -53.993934 Iteration 3: log likelihood = -53.993656 Iteration 4: log likelihood = -53.993656 Logistic regression Number of obs = 108 LR chi2(3) = 41.73 Prob > chi2 = 0.0000 Log likelihood = -53.993656 Pseudo R2 = 0.2787 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- dneo | yes | 2.212564 .5780423 3.83 0.000 1.079622 3.345506 | dclox | yes | -1.412516 .5572076 -2.53 0.011 -2.504623 -.3204093 dcpct | .0226682 .0071871 3.15 0.002 .0085817 .0367547 _cons | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 ------------------------------------------------------------------------------ . predict phat (option pr assumed; Pr(casecont)) . predict logitphat, xb . * plots of predicted values - for demonstration only (not valid in case-control study) . twoway (connected logitphat dcpct if dneo==1 & dclox==0, sort) (connected logitphat dcpct if dneo==0 & dclox==0, sort) . twoway (connected phat dcpct if dneo==1 & dclox==0, sort) (connected phat dcpct if dneo==0 & dclox==0, sort) . * manually created prediction values and plots . set obs 119 Number of observations (_N) was 108, now 119. . replace dneo=0 in 109/119 (11 real changes made) . replace dclox=0 in 109/119 (11 real changes made) . replace dcpct=0+(_n-109)*10 in 109/119 (11 real changes made) . capture drop phat logitphat . predict phat (option pr assumed; Pr(casecont)) . predict logitphat, xb . twoway (connected logitphat dcpct in 109/119, sort) . twoway (connected phat dcpct in 109/119, sort) . . * estimation and plots by margins and marginsplot commands . * logit scale . margins, at(dcpct=(0(10)100) dneo=1 dclox=0) at(dcpct=(0(10)100) dneo=0 dclox=0) predict(xb) plot Adjusted predictions Number of obs = 108 Model VCE: OIM Expression: Linear prediction (log odds), predict(xb) 1._at: dneo = 1 dclox = 0 dcpct = 0 2._at: dneo = 1 dclox = 0 dcpct = 10 3._at: dneo = 1 dclox = 0 dcpct = 20 4._at: dneo = 1 dclox = 0 dcpct = 30 5._at: dneo = 1 dclox = 0 dcpct = 40 6._at: dneo = 1 dclox = 0 dcpct = 50 7._at: dneo = 1 dclox = 0 dcpct = 60 8._at: dneo = 1 dclox = 0 dcpct = 70 9._at: dneo = 1 dclox = 0 dcpct = 80 10._at: dneo = 1 dclox = 0 dcpct = 90 11._at: dneo = 1 dclox = 0 dcpct = 100 12._at: dneo = 0 dclox = 0 dcpct = 0 13._at: dneo = 0 dclox = 0 dcpct = 10 14._at: dneo = 0 dclox = 0 dcpct = 20 15._at: dneo = 0 dclox = 0 dcpct = 30 16._at: dneo = 0 dclox = 0 dcpct = 40 17._at: dneo = 0 dclox = 0 dcpct = 50 18._at: dneo = 0 dclox = 0 dcpct = 60 19._at: dneo = 0 dclox = 0 dcpct = 70 20._at: dneo = 0 dclox = 0 dcpct = 80 21._at: dneo = 0 dclox = 0 dcpct = 90 22._at: dneo = 0 dclox = 0 dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | -.7717085 .5987154 -1.29 0.197 -1.945169 .4017521 2 | -.545026 .5378343 -1.01 0.311 -1.599162 .5091099 3 | -.3183436 .4800024 -0.66 0.507 -1.259131 .6224439 4 | -.0916611 .426462 -0.21 0.830 -.9275113 .744189 5 | .1350213 .379036 0.36 0.722 -.6078756 .8779183 6 | .3617038 .3402906 1.06 0.288 -.3052536 1.028661 7 | .5883862 .3134615 1.88 0.061 -.0259869 1.202759 8 | .8150687 .3017439 2.70 0.007 .2236615 1.406476 9 | 1.041751 .306874 3.39 0.001 .4402891 1.643213 10 | 1.268434 .3280623 3.87 0.000 .6254433 1.911424 11 | 1.495116 .3625038 4.12 0.000 .7846216 2.205611 12 | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 13 | -2.75759 .7219825 -3.82 0.000 -4.17265 -1.34253 14 | -2.530907 .6756357 -3.75 0.000 -3.855129 -1.206686 15 | -2.304225 .6340656 -3.63 0.000 -3.546971 -1.061479 16 | -2.077543 .5982691 -3.47 0.001 -3.250128 -.9049568 17 | -1.85086 .569336 -3.25 0.001 -2.966738 -.7349821 18 | -1.624178 .5483539 -2.96 0.003 -2.698932 -.5494237 19 | -1.397495 .5362569 -2.61 0.009 -2.448539 -.3464509 20 | -1.170813 .5336496 -2.19 0.028 -2.216747 -.1248787 21 | -.9441303 .5406693 -1.75 0.081 -2.003823 .1155621 22 | -.7174478 .5569521 -1.29 0.198 -1.809054 .3741583 ------------------------------------------------------------------------------ Variables that uniquely identify margins: dcpct _atopt Multiple at() options specified: _atoption=1: dcpct=(0(10)100) dneo=1 dclox=0 _atoption=2: dcpct=(0(10)100) dneo=0 dclox=0 . * probability scale, the default . margins, at(dcpct=(0(10)100) dneo=1 dclox=0) at(dcpct=(0(10)100) dneo=0 dclox=0) Adjusted predictions Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() 1._at: dneo = 1 dclox = 0 dcpct = 0 2._at: dneo = 1 dclox = 0 dcpct = 10 3._at: dneo = 1 dclox = 0 dcpct = 20 4._at: dneo = 1 dclox = 0 dcpct = 30 5._at: dneo = 1 dclox = 0 dcpct = 40 6._at: dneo = 1 dclox = 0 dcpct = 50 7._at: dneo = 1 dclox = 0 dcpct = 60 8._at: dneo = 1 dclox = 0 dcpct = 70 9._at: dneo = 1 dclox = 0 dcpct = 80 10._at: dneo = 1 dclox = 0 dcpct = 90 11._at: dneo = 1 dclox = 0 dcpct = 100 12._at: dneo = 0 dclox = 0 dcpct = 0 13._at: dneo = 0 dclox = 0 dcpct = 10 14._at: dneo = 0 dclox = 0 dcpct = 20 15._at: dneo = 0 dclox = 0 dcpct = 30 16._at: dneo = 0 dclox = 0 dcpct = 40 17._at: dneo = 0 dclox = 0 dcpct = 50 18._at: dneo = 0 dclox = 0 dcpct = 60 19._at: dneo = 0 dclox = 0 dcpct = 70 20._at: dneo = 0 dclox = 0 dcpct = 80 21._at: dneo = 0 dclox = 0 dcpct = 90 22._at: dneo = 0 dclox = 0 dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | .3161096 .1294329 2.44 0.015 .0624258 .5697934 2 | .3670192 .1249476 2.94 0.003 .1221264 .6119119 3 | .4210795 .1170109 3.60 0.000 .1917423 .6504167 4 | .4771007 .1063919 4.48 0.000 .2685765 .685625 5 | .5337041 .0943284 5.66 0.000 .3488238 .7185845 6 | .5894528 .0823497 7.16 0.000 .4280503 .7508553 7 | .6429948 .0719559 8.94 0.000 .5019639 .7840257 8 | .6931886 .0641743 10.80 0.000 .5674091 .818968 9 | .7391878 .059162 12.49 0.000 .6232324 .8551431 10 | .7804745 .0562082 13.89 0.000 .6703084 .8906406 11 | .8168449 .0542339 15.06 0.000 .7105483 .9231415 12 | .0481415 .0353873 1.36 0.174 -.0212164 .1174994 13 | .0596594 .0405034 1.47 0.141 -.0197257 .1390445 14 | .0737197 .0461358 1.60 0.110 -.0167049 .1641442 15 | .0907736 .0523318 1.73 0.083 -.0117949 .1933422 16 | .1112988 .0591756 1.88 0.060 -.0046833 .2272809 17 | .1357719 .0668047 2.03 0.042 .0048371 .2667067 18 | .1646295 .0754133 2.18 0.029 .0168222 .3124368 19 | .1982139 .0852247 2.33 0.020 .0311765 .3652512 20 | .2367081 .0964184 2.46 0.014 .0477315 .4256847 21 | .2800668 .1090148 2.57 0.010 .0664017 .4937319 22 | .3279552 .1227526 2.67 0.008 .0873646 .5685459 ------------------------------------------------------------------------------ . marginsplot, noci /* CIs not quite correct anyway */ Variables that uniquely identify margins: dcpct _atopt Multiple at() options specified: _atoption=1: dcpct=(0(10)100) dneo=1 dclox=0 _atoption=2: dcpct=(0(10)100) dneo=0 dclox=0 . margins, over(dcpct) at(dneo=1 dclox=0) at(dneo=0 dclox=0) Predictive margins Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() Over: dcpct 1._at: 0.dcpct dneo = 1 dclox = 0 1.dcpct dneo = 1 dclox = 0 3.dcpct dneo = 1 dclox = 0 5.dcpct dneo = 1 dclox = 0 7.dcpct dneo = 1 dclox = 0 10.dcpct dneo = 1 dclox = 0 14.dcpct dneo = 1 dclox = 0 20.dcpct dneo = 1 dclox = 0 25.dcpct dneo = 1 dclox = 0 30.dcpct dneo = 1 dclox = 0 40.dcpct dneo = 1 dclox = 0 50.dcpct dneo = 1 dclox = 0 75.dcpct dneo = 1 dclox = 0 80.dcpct dneo = 1 dclox = 0 83.dcpct dneo = 1 dclox = 0 90.dcpct dneo = 1 dclox = 0 95.dcpct dneo = 1 dclox = 0 99.dcpct dneo = 1 dclox = 0 100.dcpct dneo = 1 dclox = 0 2._at: 0.dcpct dneo = 0 dclox = 0 1.dcpct dneo = 0 dclox = 0 3.dcpct dneo = 0 dclox = 0 5.dcpct dneo = 0 dclox = 0 7.dcpct dneo = 0 dclox = 0 10.dcpct dneo = 0 dclox = 0 14.dcpct dneo = 0 dclox = 0 20.dcpct dneo = 0 dclox = 0 25.dcpct dneo = 0 dclox = 0 30.dcpct dneo = 0 dclox = 0 40.dcpct dneo = 0 dclox = 0 50.dcpct dneo = 0 dclox = 0 75.dcpct dneo = 0 dclox = 0 80.dcpct dneo = 0 dclox = 0 83.dcpct dneo = 0 dclox = 0 90.dcpct dneo = 0 dclox = 0 95.dcpct dneo = 0 dclox = 0 99.dcpct dneo = 0 dclox = 0 100.dcpct dneo = 0 dclox = 0 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at#dcpct | 1 0 | .3161096 .1294329 2.44 0.015 .0624258 .5697934 1 1 | .3210305 .1291508 2.49 0.013 .0678996 .5741613 1 3 | .3309916 .1284744 2.58 0.010 .0791864 .5827968 1 5 | .3411065 .1276492 2.67 0.008 .0909186 .5912944 1 7 | .3513681 .1266768 2.77 0.006 .1030861 .5996502 1 10 | .3670192 .1249476 2.94 0.003 .1221264 .6119119 1 14 | .3883263 .1221557 3.18 0.001 .1489056 .627747 1 20 | .4210795 .1170109 3.60 0.000 .1917423 .6504167 1 25 | .4489281 .1119689 4.01 0.000 .2294731 .6683832 1 30 | .4771007 .1063919 4.48 0.000 .2685765 .685625 1 40 | .5337041 .0943284 5.66 0.000 .3488238 .7185845 1 50 | .5894528 .0823497 7.16 0.000 .4280503 .7508553 1 75 | .7167526 .0613502 11.68 0.000 .5965083 .8369968 1 80 | .7391878 .059162 12.49 0.000 .6232324 .8551431 1 83 | .7520836 .0581075 12.94 0.000 .638195 .8659722 1 90 | .7804745 .0562082 13.89 0.000 .6703084 .8906406 1 95 | .7992759 .0551635 14.49 0.000 .6911574 .9073944 1 99 | .8134292 .0544159 14.95 0.000 .7067759 .9200824 1 100 | .8168449 .0542339 15.06 0.000 .7105483 .9231415 2 0 | .0481415 .0353873 1.36 0.174 -.0212164 .1174994 2 1 | .0491909 .0358766 1.37 0.170 -.0211258 .1195077 2 3 | .0513552 .0368698 1.39 0.164 -.0209082 .1236186 2 5 | .0536094 .0378828 1.42 0.157 -.0206395 .1278583 2 7 | .0559567 .0389158 1.44 0.150 -.0203169 .1322302 2 10 | .0596594 .0405034 1.47 0.141 -.0197257 .1390445 2 14 | .064954 .0426923 1.52 0.128 -.0187215 .1486294 2 20 | .0737197 .0461358 1.60 0.110 -.0167049 .1641442 2 25 | .081843 .0491592 1.66 0.096 -.0145073 .1781932 2 30 | .0907736 .0523318 1.73 0.083 -.0117949 .1933422 2 40 | .1112988 .0591756 1.88 0.060 -.0046833 .2272809 2 50 | .1357719 .0668047 2.03 0.042 .0048371 .2667067 2 75 | .216844 .0906423 2.39 0.017 .0391884 .3944995 2 80 | .2367081 .0964184 2.46 0.014 .0477315 .4256847 2 83 | .2492141 .1000555 2.49 0.013 .0531089 .4453194 2 90 | .2800668 .1090148 2.57 0.010 .0664017 .4937319 2 95 | .3034783 .115771 2.62 0.009 .0765712 .5303853 2 99 | .3229788 .1213422 2.66 0.008 .0851524 .5608051 2 100 | .3279552 .1227526 2.67 0.008 .0873646 .5685459 ------------------------------------------------------------------------------ . marginsplot, noci /* CIs not quite correct anyway */ Variables that uniquely identify margins: dcpct _atopt Multiple at() options specified: _atoption=1: dneo=1 dclox=0 _atoption=2: dneo=0 dclox=0 . * figure in lecture . margins, over(dcpct) at(dneo=0 dclox=0) at(dneo=1 dclox=0) at(dneo=0 dclox=1) at(dneo=1 dclox=1) Predictive margins Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() Over: dcpct 1._at: 0.dcpct dneo = 0 dclox = 0 1.dcpct dneo = 0 dclox = 0 3.dcpct dneo = 0 dclox = 0 5.dcpct dneo = 0 dclox = 0 7.dcpct dneo = 0 dclox = 0 10.dcpct dneo = 0 dclox = 0 14.dcpct dneo = 0 dclox = 0 20.dcpct dneo = 0 dclox = 0 25.dcpct dneo = 0 dclox = 0 30.dcpct dneo = 0 dclox = 0 40.dcpct dneo = 0 dclox = 0 50.dcpct dneo = 0 dclox = 0 75.dcpct dneo = 0 dclox = 0 80.dcpct dneo = 0 dclox = 0 83.dcpct dneo = 0 dclox = 0 90.dcpct dneo = 0 dclox = 0 95.dcpct dneo = 0 dclox = 0 99.dcpct dneo = 0 dclox = 0 100.dcpct dneo = 0 dclox = 0 2._at: 0.dcpct dneo = 1 dclox = 0 1.dcpct dneo = 1 dclox = 0 3.dcpct dneo = 1 dclox = 0 5.dcpct dneo = 1 dclox = 0 7.dcpct dneo = 1 dclox = 0 10.dcpct dneo = 1 dclox = 0 14.dcpct dneo = 1 dclox = 0 20.dcpct dneo = 1 dclox = 0 25.dcpct dneo = 1 dclox = 0 30.dcpct dneo = 1 dclox = 0 40.dcpct dneo = 1 dclox = 0 50.dcpct dneo = 1 dclox = 0 75.dcpct dneo = 1 dclox = 0 80.dcpct dneo = 1 dclox = 0 83.dcpct dneo = 1 dclox = 0 90.dcpct dneo = 1 dclox = 0 95.dcpct dneo = 1 dclox = 0 99.dcpct dneo = 1 dclox = 0 100.dcpct dneo = 1 dclox = 0 3._at: 0.dcpct dneo = 0 dclox = 1 1.dcpct dneo = 0 dclox = 1 3.dcpct dneo = 0 dclox = 1 5.dcpct dneo = 0 dclox = 1 7.dcpct dneo = 0 dclox = 1 10.dcpct dneo = 0 dclox = 1 14.dcpct dneo = 0 dclox = 1 20.dcpct dneo = 0 dclox = 1 25.dcpct dneo = 0 dclox = 1 30.dcpct dneo = 0 dclox = 1 40.dcpct dneo = 0 dclox = 1 50.dcpct dneo = 0 dclox = 1 75.dcpct dneo = 0 dclox = 1 80.dcpct dneo = 0 dclox = 1 83.dcpct dneo = 0 dclox = 1 90.dcpct dneo = 0 dclox = 1 95.dcpct dneo = 0 dclox = 1 99.dcpct dneo = 0 dclox = 1 100.dcpct dneo = 0 dclox = 1 4._at: 0.dcpct dneo = 1 dclox = 1 1.dcpct dneo = 1 dclox = 1 3.dcpct dneo = 1 dclox = 1 5.dcpct dneo = 1 dclox = 1 7.dcpct dneo = 1 dclox = 1 10.dcpct dneo = 1 dclox = 1 14.dcpct dneo = 1 dclox = 1 20.dcpct dneo = 1 dclox = 1 25.dcpct dneo = 1 dclox = 1 30.dcpct dneo = 1 dclox = 1 40.dcpct dneo = 1 dclox = 1 50.dcpct dneo = 1 dclox = 1 75.dcpct dneo = 1 dclox = 1 80.dcpct dneo = 1 dclox = 1 83.dcpct dneo = 1 dclox = 1 90.dcpct dneo = 1 dclox = 1 95.dcpct dneo = 1 dclox = 1 99.dcpct dneo = 1 dclox = 1 100.dcpct dneo = 1 dclox = 1 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at#dcpct | 1 0 | .0481415 .0353873 1.36 0.174 -.0212164 .1174994 1 1 | .0491909 .0358766 1.37 0.170 -.0211258 .1195077 1 3 | .0513552 .0368698 1.39 0.164 -.0209082 .1236186 1 5 | .0536094 .0378828 1.42 0.157 -.0206395 .1278583 1 7 | .0559567 .0389158 1.44 0.150 -.0203169 .1322302 1 10 | .0596594 .0405034 1.47 0.141 -.0197257 .1390445 1 14 | .064954 .0426923 1.52 0.128 -.0187215 .1486294 1 20 | .0737197 .0461358 1.60 0.110 -.0167049 .1641442 1 25 | .081843 .0491592 1.66 0.096 -.0145073 .1781932 1 30 | .0907736 .0523318 1.73 0.083 -.0117949 .1933422 1 40 | .1112988 .0591756 1.88 0.060 -.0046833 .2272809 1 50 | .1357719 .0668047 2.03 0.042 .0048371 .2667067 1 75 | .216844 .0906423 2.39 0.017 .0391884 .3944995 1 80 | .2367081 .0964184 2.46 0.014 .0477315 .4256847 1 83 | .2492141 .1000555 2.49 0.013 .0531089 .4453194 1 90 | .2800668 .1090148 2.57 0.010 .0664017 .4937319 1 95 | .3034783 .115771 2.62 0.009 .0765712 .5303853 1 99 | .3229788 .1213422 2.66 0.008 .0851524 .5608051 1 100 | .3279552 .1227526 2.67 0.008 .0873646 .5685459 2 0 | .3161096 .1294329 2.44 0.015 .0624258 .5697934 2 1 | .3210305 .1291508 2.49 0.013 .0678996 .5741613 2 3 | .3309916 .1284744 2.58 0.010 .0791864 .5827968 2 5 | .3411065 .1276492 2.67 0.008 .0909186 .5912944 2 7 | .3513681 .1266768 2.77 0.006 .1030861 .5996502 2 10 | .3670192 .1249476 2.94 0.003 .1221264 .6119119 2 14 | .3883263 .1221557 3.18 0.001 .1489056 .627747 2 20 | .4210795 .1170109 3.60 0.000 .1917423 .6504167 2 25 | .4489281 .1119689 4.01 0.000 .2294731 .6683832 2 30 | .4771007 .1063919 4.48 0.000 .2685765 .685625 2 40 | .5337041 .0943284 5.66 0.000 .3488238 .7185845 2 50 | .5894528 .0823497 7.16 0.000 .4280503 .7508553 2 75 | .7167526 .0613502 11.68 0.000 .5965083 .8369968 2 80 | .7391878 .059162 12.49 0.000 .6232324 .8551431 2 83 | .7520836 .0581075 12.94 0.000 .638195 .8659722 2 90 | .7804745 .0562082 13.89 0.000 .6703084 .8906406 2 95 | .7992759 .0551635 14.49 0.000 .6911574 .9073944 2 99 | .8134292 .0544159 14.95 0.000 .7067759 .9200824 2 100 | .8168449 .0542339 15.06 0.000 .7105483 .9231415 3 0 | .012167 .0113038 1.08 0.282 -.009988 .0343219 3 1 | .0124425 .0114918 1.08 0.279 -.0100811 .034966 3 3 | .013012 .0118767 1.10 0.273 -.010266 .03629 3 5 | .0136073 .0122738 1.11 0.268 -.0104489 .0376635 3 7 | .0142294 .0126833 1.12 0.262 -.0106294 .0390883 3 10 | .0152155 .013322 1.14 0.253 -.0108951 .0413261 3 14 | .0166356 .0142212 1.17 0.242 -.0112374 .0445086 3 20 | .0190132 .0156799 1.21 0.225 -.0117188 .0497453 3 25 | .0212466 .0170054 1.25 0.212 -.0120834 .0545766 3 30 | .023736 .018441 1.29 0.198 -.0124077 .0598796 3 40 | .0295964 .021688 1.36 0.172 -.0129112 .0721041 3 50 | .0368492 .0255313 1.44 0.149 -.0131913 .0868897 3 75 | .0631701 .0388438 1.63 0.104 -.0129622 .1393025 3 80 | .0702191 .0423739 1.66 0.097 -.0128323 .1532704 3 83 | .0747909 .0446697 1.67 0.094 -.0127601 .1623419 3 90 | .0865389 .0506051 1.71 0.087 -.0126453 .1857232 3 95 | .0959285 .0553947 1.73 0.083 -.012643 .2045001 3 99 | .1040855 .0595911 1.75 0.081 -.012711 .220882 3 100 | .1062184 .0606939 1.75 0.080 -.0127395 .2251763 4 0 | .1011761 .0737216 1.37 0.170 -.0433155 .2456678 4 1 | .1032563 .0745313 1.39 0.166 -.0428223 .2493348 4 3 | .1075303 .0761506 1.41 0.158 -.041722 .2567827 4 5 | .1119592 .077768 1.44 0.150 -.0404632 .2643816 4 7 | .1165467 .0793811 1.47 0.142 -.0390374 .2721307 4 10 | .1237333 .0817875 1.51 0.130 -.0365673 .2840339 4 14 | .1339044 .0849584 1.58 0.115 -.0326111 .3004199 4 20 | .1504777 .0895928 1.68 0.093 -.0251211 .3260764 4 25 | .1655474 .0932996 1.77 0.076 -.0173165 .3484113 4 30 | .1818033 .096825 1.88 0.060 -.0079701 .3715768 4 40 | .217977 .1032015 2.11 0.035 .0157058 .4202482 4 50 | .2590692 .1085332 2.39 0.017 .0463479 .4717904 4 75 | .381283 .117435 3.25 0.001 .1511146 .6114514 4 80 | .4083562 .1186504 3.44 0.001 .1758057 .6409068 4 83 | .4248829 .1193208 3.56 0.000 .1910185 .6587473 4 90 | .4640416 .1207365 3.84 0.000 .2274024 .7006808 4 95 | .4923153 .1216232 4.05 0.000 .2539383 .7306923 4 99 | .5149785 .1222476 4.21 0.000 .2753775 .7545794 4 100 | .5206383 .1223902 4.25 0.000 .280758 .7605186 ------------------------------------------------------------------------------ . marginsplot, noci /* CIs not quite correct anyway */ Variables that uniquely identify margins: dcpct _atopt Multiple at() options specified: _atoption=1: dneo=0 dclox=0 _atoption=2: dneo=1 dclox=0 _atoption=3: dneo=0 dclox=1 _atoption=4: dneo=1 dclox=1 . * illustration of weighting . margins, at(dcpct=(0(10)100)) predict(xb) Predictive margins Number of obs = 108 Model VCE: OIM Expression: Linear prediction (log odds), predict(xb) 1._at: dcpct = 0 2._at: dcpct = 10 3._at: dcpct = 20 4._at: dcpct = 30 5._at: dcpct = 40 6._at: dcpct = 50 7._at: dcpct = 60 8._at: dcpct = 70 9._at: dcpct = 80 10._at: dcpct = 90 11._at: dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | -1.821385 .6139347 -2.97 0.003 -3.024675 -.6180956 2 | -1.594703 .5487433 -2.91 0.004 -2.67022 -.5191858 3 | -1.36802 .4854417 -2.82 0.005 -2.319469 -.4165722 4 | -1.141338 .4248753 -2.69 0.007 -1.974078 -.3085977 5 | -.9146556 .3683957 -2.48 0.013 -1.636698 -.1926133 6 | -.6879731 .3181865 -2.16 0.031 -1.311607 -.064339 7 | -.4612907 .2776705 -1.66 0.097 -1.005515 .0829335 8 | -.2346082 .2515752 -0.93 0.351 -.7276865 .2584701 9 | -.0079257 .2445612 -0.03 0.974 -.487257 .4714055 10 | .2187567 .2581885 0.85 0.397 -.2872834 .7247968 11 | .4454392 .2895571 1.54 0.124 -.1220823 1.012961 ------------------------------------------------------------------------------ . table dneo dclox /* Stata 16: table dneo dclox, row col */ ----------------------------------------------------- | Cloxacillin used on farm | no yes Total ----------------------+------------------------------ Neomycin used on farm | no | 33 12 45 yes | 59 15 74 Total | 92 27 119 ----------------------------------------------------- . lincom _cons+0*dcpct+1.dclox*27/108+1.dneo*74/108 /* dcpct=0 */ ( 1) .6851852*[casecont]1.dneo + .25*[casecont]1.dclox + [casecont]_cons = 0 ------------------------------------------------------------------------------ casecont | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | -1.821385 .6139347 -2.97 0.003 -3.024675 -.6180956 ------------------------------------------------------------------------------ . margins, at(dcpct=(0(10)100)) predict(xb) atmeans /* same as above */ Adjusted predictions Number of obs = 108 Model VCE: OIM Expression: Linear prediction (log odds), predict(xb) 1._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 0 2._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 10 3._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 20 4._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 30 5._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 40 6._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 50 7._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 60 8._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 70 9._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 80 10._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 90 11._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | -1.821385 .6139347 -2.97 0.003 -3.024675 -.6180956 2 | -1.594703 .5487433 -2.91 0.004 -2.67022 -.5191858 3 | -1.36802 .4854417 -2.82 0.005 -2.319469 -.4165722 4 | -1.141338 .4248753 -2.69 0.007 -1.974078 -.3085976 5 | -.9146556 .3683957 -2.48 0.013 -1.636698 -.1926133 6 | -.6879731 .3181865 -2.16 0.031 -1.311607 -.064339 7 | -.4612906 .2776705 -1.66 0.097 -1.005515 .0829335 8 | -.2346082 .2515752 -0.93 0.351 -.7276865 .2584701 9 | -.0079257 .2445612 -0.03 0.974 -.487257 .4714055 10 | .2187567 .2581885 0.85 0.397 -.2872834 .7247968 11 | .4454392 .2895571 1.54 0.124 -.1220823 1.012961 ------------------------------------------------------------------------------ . margins, at(dcpct=(0(10)100)) predict(xb) over(dneo dclox) Predictive margins Number of obs = 108 Model VCE: OIM Expression: Linear prediction (log odds), predict(xb) Over: dneo dclox 1._at: 0.dneo#0.dclox dcpct = 0 0.dneo#1.dclox dcpct = 0 1.dneo#0.dclox dcpct = 0 1.dneo#1.dclox dcpct = 0 2._at: 0.dneo#0.dclox dcpct = 10 0.dneo#1.dclox dcpct = 10 1.dneo#0.dclox dcpct = 10 1.dneo#1.dclox dcpct = 10 3._at: 0.dneo#0.dclox dcpct = 20 0.dneo#1.dclox dcpct = 20 1.dneo#0.dclox dcpct = 20 1.dneo#1.dclox dcpct = 20 4._at: 0.dneo#0.dclox dcpct = 30 0.dneo#1.dclox dcpct = 30 1.dneo#0.dclox dcpct = 30 1.dneo#1.dclox dcpct = 30 5._at: 0.dneo#0.dclox dcpct = 40 0.dneo#1.dclox dcpct = 40 1.dneo#0.dclox dcpct = 40 1.dneo#1.dclox dcpct = 40 6._at: 0.dneo#0.dclox dcpct = 50 0.dneo#1.dclox dcpct = 50 1.dneo#0.dclox dcpct = 50 1.dneo#1.dclox dcpct = 50 7._at: 0.dneo#0.dclox dcpct = 60 0.dneo#1.dclox dcpct = 60 1.dneo#0.dclox dcpct = 60 1.dneo#1.dclox dcpct = 60 8._at: 0.dneo#0.dclox dcpct = 70 0.dneo#1.dclox dcpct = 70 1.dneo#0.dclox dcpct = 70 1.dneo#1.dclox dcpct = 70 9._at: 0.dneo#0.dclox dcpct = 80 0.dneo#1.dclox dcpct = 80 1.dneo#0.dclox dcpct = 80 1.dneo#1.dclox dcpct = 80 10._at: 0.dneo#0.dclox dcpct = 90 0.dneo#1.dclox dcpct = 90 1.dneo#0.dclox dcpct = 90 1.dneo#1.dclox dcpct = 90 11._at: 0.dneo#0.dclox dcpct = 100 0.dneo#1.dclox dcpct = 100 1.dneo#0.dclox dcpct = 100 1.dneo#1.dclox dcpct = 100 -------------------------------------------------------------------------------- | Delta-method | Margin std. err. z P>|z| [95% conf. interval] ---------------+---------------------------------------------------------------- _at#dneo#dclox | 1#no#no | -2.984272 .7722467 -3.86 0.000 -4.497848 -1.470697 1#no#yes | -4.396788 .9404953 -4.67 0.000 -6.240125 -2.553452 1#yes#no | -.7717085 .5987154 -1.29 0.197 -1.945169 .4017521 1#yes#yes | -2.184225 .8106662 -2.69 0.007 -3.773101 -.5953479 2#no#no | -2.75759 .7219825 -3.82 0.000 -4.17265 -1.34253 2#no#yes | -4.170106 .8890788 -4.69 0.000 -5.912668 -2.427544 2#yes#no | -.545026 .5378343 -1.01 0.311 -1.599162 .5091099 2#yes#yes | -1.957542 .754335 -2.60 0.009 -3.436012 -.4790726 3#no#no | -2.530907 .6756357 -3.75 0.000 -3.855129 -1.206686 3#no#yes | -3.943424 .8406675 -4.69 0.000 -5.591102 -2.295745 3#yes#no | -.3183436 .4800024 -0.66 0.507 -1.259131 .6224439 3#yes#yes | -1.73086 .7008523 -2.47 0.014 -3.104505 -.3572144 4#no#no | -2.304225 .6340656 -3.63 0.000 -3.546971 -1.061479 4#no#yes | -3.716741 .79581 -4.67 0.000 -5.2765 -2.156982 4#yes#no | -.0916611 .426462 -0.21 0.830 -.9275113 .744189 4#yes#yes | -1.504177 .6509204 -2.31 0.021 -2.779958 -.2283966 5#no#no | -2.077543 .5982691 -3.47 0.001 -3.250128 -.9049568 5#no#yes | -3.490059 .7551398 -4.62 0.000 -4.970105 -2.010012 5#yes#no | .1350213 .379036 0.36 0.722 -.6078756 .8779183 5#yes#yes | -1.277495 .6054187 -2.11 0.035 -2.464094 -.0908958 6#no#no | -1.85086 .569336 -3.25 0.001 -2.966738 -.7349821 6#no#yes | -3.263376 .7193676 -4.54 0.000 -4.673311 -1.853442 6#yes#no | .3617038 .3402906 1.06 0.288 -.3052536 1.028661 6#yes#yes | -1.050812 .5654177 -1.86 0.063 -2.159011 .0573861 7#no#no | -1.624178 .5483539 -2.96 0.003 -2.698932 -.5494237 7#no#yes | -3.036694 .6892563 -4.41 0.000 -4.387611 -1.685776 7#yes#no | .5883862 .3134615 1.88 0.061 -.0259869 1.202759 7#yes#yes | -.8241298 .5321593 -1.55 0.121 -1.867143 .2188833 8#no#no | -1.397495 .5362569 -2.61 0.009 -2.448539 -.3464509 8#no#yes | -2.810011 .6655747 -4.22 0.000 -4.114514 -1.505509 8#yes#no | .8150687 .3017439 2.70 0.007 .2236615 1.406476 8#yes#yes | -.5974473 .5069722 -1.18 0.239 -1.591095 .3962 9#no#no | -1.170813 .5336496 -2.19 0.028 -2.216747 -.1248787 9#no#yes | -2.583329 .649027 -3.98 0.000 -3.855398 -1.311259 9#yes#no | 1.041751 .306874 3.39 0.001 .4402891 1.643213 9#yes#yes | -.3707649 .4910999 -0.75 0.450 -1.333303 .5917732 10#no#no | -.9441303 .5406693 -1.75 0.081 -2.003823 .1155621 10#no#yes | -2.356646 .6401668 -3.68 0.000 -3.61135 -1.101943 10#yes#no | 1.268434 .3280623 3.87 0.000 .6254433 1.911424 10#yes#yes | -.1440824 .4854568 -0.30 0.767 -1.09556 .8073953 11#no#no | -.7174478 .5569521 -1.29 0.198 -1.809054 .3741583 11#no#yes | -2.129964 .6393135 -3.33 0.001 -3.382995 -.8769324 11#yes#no | 1.495116 .3625038 4.12 0.000 .7846216 2.205611 11#yes#yes | .0826 .4903962 0.17 0.866 -.8785588 1.043759 -------------------------------------------------------------------------------- . di ((-2.984272)*22+(-4.396788)*12+(-.7717085)*59+(-2.184225)*15)/108 /* dcpct=0 again */ -1.8213853 . margins, at(dcpct=(0(10)100)) atmeans /* backtransformed */ Adjusted predictions Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() 1._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 0 2._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 10 3._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 20 4._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 30 5._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 40 6._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 50 7._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 60 8._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 70 9._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 80 10._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 90 11._at: 0.dneo = .3148148 (mean) 1.dneo = .6851852 (mean) 0.dclox = .75 (mean) 1.dclox = .25 (mean) dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | .1392677 .0735937 1.89 0.058 -.0049733 .2835087 2 | .1687233 .0769644 2.19 0.028 .0178758 .3195707 3 | .2029399 .0785228 2.58 0.010 .0490381 .3568416 4 | .2420748 .0779538 3.11 0.002 .0892881 .3948615 5 | .2860481 .0752355 3.80 0.000 .1385893 .4335069 6 | .3344841 .0708297 4.72 0.000 .1956604 .4733079 7 | .3866797 .0658519 5.87 0.000 .2576123 .5157471 8 | .4416155 .0620362 7.12 0.000 .3200267 .5632043 9 | .4980186 .0611394 8.15 0.000 .3781877 .6178495 10 | .5544721 .063781 8.69 0.000 .4294636 .6794806 11 | .6095543 .068914 8.85 0.000 .4744854 .7446232 ------------------------------------------------------------------------------ . di invlogit(-1.821385) /* dcpct=0, transformed to probability scale */ .13926777 . * weighting on probability scale . margins, at(dcpct=(0(10)100)) /* not the same! */ Predictive margins Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() 1._at: dcpct = 0 2._at: dcpct = 10 3._at: dcpct = 20 4._at: dcpct = 30 5._at: dcpct = 40 6._at: dcpct = 50 7._at: dcpct = 60 8._at: dcpct = 70 9._at: dcpct = 80 10._at: dcpct = 90 11._at: dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | .1979002 .0842364 2.35 0.019 .0328 .3630005 2 | .2315299 .0825239 2.81 0.005 .069786 .3932737 3 | .2680634 .0785496 3.41 0.001 .114109 .4220178 4 | .3070171 .0726128 4.23 0.000 .1646987 .4493355 5 | .3477956 .0653188 5.32 0.000 .2197731 .4758182 6 | .3897493 .0575728 6.77 0.000 .2769088 .5025898 7 | .4322352 .050553 8.55 0.000 .3331532 .5313172 8 | .4746683 .045601 10.41 0.000 .385292 .5640446 9 | .5165521 .0438292 11.79 0.000 .4306484 .6024558 10 | .5574866 .0454685 12.26 0.000 .4683699 .6466033 11 | .597158 .0497083 12.01 0.000 .4997316 .6945844 ------------------------------------------------------------------------------ . margins, at(dcpct=(0(10)100)) expression(invlogit(predict(xb))) /* same as preceding line */ Predictive margins Number of obs = 108 Model VCE: OIM Expression: invlogit(predict(xb)) 1._at: dcpct = 0 2._at: dcpct = 10 3._at: dcpct = 20 4._at: dcpct = 30 5._at: dcpct = 40 6._at: dcpct = 50 7._at: dcpct = 60 8._at: dcpct = 70 9._at: dcpct = 80 10._at: dcpct = 90 11._at: dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | .1979002 .0842364 2.35 0.019 .0328 .3630005 2 | .2315299 .0825239 2.81 0.005 .069786 .3932737 3 | .2680634 .0785496 3.41 0.001 .114109 .4220178 4 | .3070171 .0726128 4.23 0.000 .1646987 .4493355 5 | .3477956 .0653188 5.32 0.000 .2197731 .4758182 6 | .3897493 .0575728 6.77 0.000 .2769088 .5025898 7 | .4322352 .050553 8.55 0.000 .3331532 .5313172 8 | .4746683 .045601 10.41 0.000 .385292 .5640446 9 | .5165521 .0438292 11.79 0.000 .4306484 .6024558 10 | .5574866 .0454685 12.26 0.000 .4683699 .6466033 11 | .597158 .0497083 12.01 0.000 .4997316 .6945844 ------------------------------------------------------------------------------ . margins, at(dcpct=(0(10)100)) predict(pr) /* same as preceding line */ Predictive margins Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict(pr) 1._at: dcpct = 0 2._at: dcpct = 10 3._at: dcpct = 20 4._at: dcpct = 30 5._at: dcpct = 40 6._at: dcpct = 50 7._at: dcpct = 60 8._at: dcpct = 70 9._at: dcpct = 80 10._at: dcpct = 90 11._at: dcpct = 100 ------------------------------------------------------------------------------ | Delta-method | Margin std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- _at | 1 | .1979002 .0842364 2.35 0.019 .0328 .3630005 2 | .2315299 .0825239 2.81 0.005 .069786 .3932737 3 | .2680634 .0785496 3.41 0.001 .114109 .4220178 4 | .3070171 .0726128 4.23 0.000 .1646987 .4493355 5 | .3477956 .0653188 5.32 0.000 .2197731 .4758182 6 | .3897493 .0575728 6.77 0.000 .2769088 .5025898 7 | .4322352 .050553 8.55 0.000 .3331532 .5313172 8 | .4746683 .045601 10.41 0.000 .385292 .5640446 9 | .5165521 .0438292 11.79 0.000 .4306484 .6024558 10 | .5574866 .0454685 12.26 0.000 .4683699 .6466033 11 | .597158 .0497083 12.01 0.000 .4997316 .6945844 ------------------------------------------------------------------------------ . margins, at(dcpct=(0(10)100)) over(dneo dclox) Predictive margins Number of obs = 108 Model VCE: OIM Expression: Pr(casecont), predict() Over: dneo dclox 1._at: 0.dneo#0.dclox dcpct = 0 0.dneo#1.dclox dcpct = 0 1.dneo#0.dclox dcpct = 0 1.dneo#1.dclox dcpct = 0 2._at: 0.dneo#0.dclox dcpct = 10 0.dneo#1.dclox dcpct = 10 1.dneo#0.dclox dcpct = 10 1.dneo#1.dclox dcpct = 10 3._at: 0.dneo#0.dclox dcpct = 20 0.dneo#1.dclox dcpct = 20 1.dneo#0.dclox dcpct = 20 1.dneo#1.dclox dcpct = 20 4._at: 0.dneo#0.dclox dcpct = 30 0.dneo#1.dclox dcpct = 30 1.dneo#0.dclox dcpct = 30 1.dneo#1.dclox dcpct = 30 5._at: 0.dneo#0.dclox dcpct = 40 0.dneo#1.dclox dcpct = 40 1.dneo#0.dclox dcpct = 40 1.dneo#1.dclox dcpct = 40 6._at: 0.dneo#0.dclox dcpct = 50 0.dneo#1.dclox dcpct = 50 1.dneo#0.dclox dcpct = 50 1.dneo#1.dclox dcpct = 50 7._at: 0.dneo#0.dclox dcpct = 60 0.dneo#1.dclox dcpct = 60 1.dneo#0.dclox dcpct = 60 1.dneo#1.dclox dcpct = 60 8._at: 0.dneo#0.dclox dcpct = 70 0.dneo#1.dclox dcpct = 70 1.dneo#0.dclox dcpct = 70 1.dneo#1.dclox dcpct = 70 9._at: 0.dneo#0.dclox dcpct = 80 0.dneo#1.dclox dcpct = 80 1.dneo#0.dclox dcpct = 80 1.dneo#1.dclox dcpct = 80 10._at: 0.dneo#0.dclox dcpct = 90 0.dneo#1.dclox dcpct = 90 1.dneo#0.dclox dcpct = 90 1.dneo#1.dclox dcpct = 90 11._at: 0.dneo#0.dclox dcpct = 100 0.dneo#1.dclox dcpct = 100 1.dneo#0.dclox dcpct = 100 1.dneo#1.dclox dcpct = 100 -------------------------------------------------------------------------------- | Delta-method | Margin std. err. z P>|z| [95% conf. interval] ---------------+---------------------------------------------------------------- _at#dneo#dclox | 1#no#no | .0481415 .0353873 1.36 0.174 -.0212164 .1174994 1#no#yes | .012167 .0113038 1.08 0.282 -.009988 .0343219 1#yes#no | .3161096 .1294329 2.44 0.015 .0624258 .5697934 1#yes#yes | .1011761 .0737216 1.37 0.170 -.0433155 .2456678 2#no#no | .0596594 .0405034 1.47 0.141 -.0197257 .1390445 2#no#yes | .0152155 .013322 1.14 0.253 -.0108951 .0413261 2#yes#no | .3670192 .1249476 2.94 0.003 .1221264 .6119119 2#yes#yes | .1237333 .0817875 1.51 0.130 -.0365673 .2840339 3#no#no | .0737197 .0461358 1.60 0.110 -.0167049 .1641442 3#no#yes | .0190132 .0156799 1.21 0.225 -.0117188 .0497453 3#yes#no | .4210795 .1170109 3.60 0.000 .1917423 .6504167 3#yes#yes | .1504777 .0895928 1.68 0.093 -.0251211 .3260764 4#no#no | .0907736 .0523318 1.73 0.083 -.0117949 .1933422 4#no#yes | .023736 .018441 1.29 0.198 -.0124077 .0598796 4#yes#no | .4771007 .1063919 4.48 0.000 .2685765 .685625 4#yes#yes | .1818033 .096825 1.88 0.060 -.0079701 .3715768 5#no#no | .1112988 .0591756 1.88 0.060 -.0046833 .2272809 5#no#yes | .0295964 .021688 1.36 0.172 -.0129112 .0721041 5#yes#no | .5337041 .0943284 5.66 0.000 .3488238 .7185845 5#yes#yes | .217977 .1032015 2.11 0.035 .0157058 .4202482 6#no#no | .1357719 .0668047 2.03 0.042 .0048371 .2667067 6#no#yes | .0368492 .0255313 1.44 0.149 -.0131913 .0868897 6#yes#no | .5894528 .0823497 7.16 0.000 .4280503 .7508553 6#yes#yes | .2590692 .1085332 2.39 0.017 .0463479 .4717904 7#no#no | .1646295 .0754133 2.18 0.029 .0168222 .3124368 7#no#yes | .0457954 .0301193 1.52 0.128 -.0132372 .1048281 7#yes#no | .6429948 .0719559 8.94 0.000 .5019639 .7840257 7#yes#yes | .3048877 .1127812 2.70 0.007 .0838407 .5259347 8#no#no | .1982139 .0852247 2.33 0.020 .0311765 .3652512 8#no#yes | .0567856 .0356488 1.59 0.111 -.0130848 .126656 8#yes#no | .6931886 .0641743 10.80 0.000 .5674091 .818968 8#yes#yes | .3549279 .1160734 3.06 0.002 .1274283 .5824275 9#no#no | .2367081 .0964184 2.46 0.014 .0477315 .4256847 9#no#yes | .0702191 .0423739 1.66 0.097 -.0128323 .1532704 9#yes#no | .7391878 .059162 12.49 0.000 .6232324 .8551431 9#yes#yes | .4083562 .1186504 3.44 0.001 .1758057 .6409068 10#no#no | .2800668 .1090148 2.57 0.010 .0664017 .4937319 10#no#yes | .0865389 .0506051 1.71 0.087 -.0126453 .1857232 10#yes#no | .7804745 .0562082 13.89 0.000 .6703084 .8906406 10#yes#yes | .4640416 .1207365 3.84 0.000 .2274024 .7006808 11#no#no | .3279552 .1227526 2.67 0.008 .0873646 .5685459 11#no#yes | .1062184 .0606939 1.75 0.080 -.0127395 .2251763 11#yes#no | .8168449 .0542339 15.06 0.000 .7105483 .9231415 11#yes#yes | .5206383 .1223902 4.25 0.000 .280758 .7605186 -------------------------------------------------------------------------------- . di (.0481415*22+.012167*12+.3161096*59+.1011761*15)/108 /* dcpct=0, weighted at probability scale */ .19790023 . end of do-file