= Stata mlogit = The '''`mlogit`''' command fits a multinomial logit model. <> ---- == Usage == In terms of syntax and reading output, `mlogit` is very similar to [[Stata/Logit|logit]]. Naturally, when the dependent variable is categorical rather than binary, the `mlogit` command should be used instead. The key is to recognize whether `mlogit` or [[Stata/Ologit|ologit]] is more appropriate. Even when there is a natural ordering to the categories, `ologit` may not be a superior model. As an example, adapted from [[https://www.statalist.org/forums/forum/general-stata-discussion/general/1653984-ordinal-or-multinomial-regression?p=1654012#post1654012]]: {{{ . use https://www3.nd.edu/~rwilliam/statafiles/mroz.dta . gen lfstatus = cond(hours==0, 0, cond(inrange(hours,1,1249), 1, 2)) . label define lfstatus 0 "non-participation" 1 "part-time work" 2 "full-time work" . label values lfstatus lfstatus . mlogit lfstatus kidslt6 kidsge6 age educ exper nwifeinc Iteration 0: log likelihood = -809.85106 Iteration 1: log likelihood = -682.09452 Iteration 2: log likelihood = -676.45369 Iteration 3: log likelihood = -676.35678 Iteration 4: log likelihood = -676.35676 Multinomial logistic regression Number of obs = 753 LR chi2(12) = 266.99 Prob > chi2 = 0.0000 Log likelihood = -676.35676 Pseudo R2 = 0.1648 ----------------------------------------------------------------------------------- lfstatus | Coefficient Std. err. z P>|z| [95% conf. interval] ------------------+---------------------------------------------------------------- non_participation | (base outcome) ------------------+---------------------------------------------------------------- part_time_work | kidslt6 | -1.029752 .2192135 -4.70 0.000 -1.459402 -.6001012 kidsge6 | .1452962 .0810486 1.79 0.073 -.0135561 .3041485 age | -.061935 .0161806 -3.83 0.000 -.0936485 -.0302215 educ | .2352844 .0489108 4.81 0.000 .139421 .3311478 exper | .0836159 .0155026 5.39 0.000 .0532314 .1140004 nwifeinc | -.0191471 .0093588 -2.05 0.041 -.0374899 -.0008043 _cons | -1.051627 .9599877 -1.10 0.273 -2.933168 .8299147 ------------------+---------------------------------------------------------------- full_time_work | kidslt6 | -2.04806 .2883306 -7.10 0.000 -2.613177 -1.482942 kidsge6 | -.0562924 .089552 -0.63 0.530 -.2318111 .1192262 age | -.1267562 .0173295 -7.31 0.000 -.1607214 -.0927911 educ | .2225451 .0508362 4.38 0.000 .1229081 .3221822 exper | .1554865 .0162169 9.59 0.000 .123702 .187271 nwifeinc | -.0218055 .0102905 -2.12 0.034 -.0419746 -.0016364 _cons | 1.552764 .9850566 1.58 0.115 -.3779109 3.48344 ----------------------------------------------------------------------------------- . estimates store mlogit . ologit lfstatus kidslt6 kidsge6 age educ exper nwifeinc Iteration 0: log likelihood = -809.85106 Iteration 1: log likelihood = -686.68524 Iteration 2: log likelihood = -685.50088 Iteration 3: log likelihood = -685.49686 Iteration 4: log likelihood = -685.49686 Ordered logistic regression Number of obs = 753 LR chi2(6) = 248.71 Prob > chi2 = 0.0000 Log likelihood = -685.49686 Pseudo R2 = 0.1536 ------------------------------------------------------------------------------ lfstatus | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- kidslt6 | -1.390614 .1813509 -7.67 0.000 -1.746055 -1.035172 kidsge6 | -.0341089 .0623172 -0.55 0.584 -.1562484 .0880307 age | -.0916151 .0121459 -7.54 0.000 -.1154207 -.0678096 educ | .158401 .0356408 4.44 0.000 .0885464 .2282557 exper | .1159833 .0112204 10.34 0.000 .0939917 .137975 nwifeinc | -.0153582 .0073408 -2.09 0.036 -.0297459 -.0009705 -------------+---------------------------------------------------------------- /cut1 | -1.75244 .7084357 -3.140949 -.3639319 /cut2 | -.3338748 .7054785 -1.716587 1.048838 ------------------------------------------------------------------------------ . estimates store ologit . lrtest mlogit ologit, force Likelihood-ratio test Assumption: ologit nested within mlogit LR chi2(6) = 18.28 Prob > chi2 = 0.0056 }}} The model with fewer constraints, more free parameters, fewer degrees of freedom is the '''simplified''' and '''nested''' model. The model with more constraints, more estimated parameters, greater degrees of freedom is the '''full''' model. If there is not a significant difference between two such models, then the simplified model is preferred. The null hypothesis is formulated such that the simplified (i.e, `ologit`) model is true, and the [[Statistics/LikelihoodRatioTest|likelihood ratio]] chi-squared [[Statistics/TestStatistic|test statistic]] is calculated. If the null hypothesis is rejected, as it is above, then simplification of the model is not justified and the more complex (i.e., `mlogit`) model is preferred. ---- == See also == [[https://www.stata.com/manuals/rmlogit.pdf|Stata manual for mlogit]] [[https://www.stata.com/manuals/rmlogitpostestimation.pdf|Stata manual for mlogit post-estimation]] ---- CategoryRicottone