Stata 15 help for mlogit

[R] mlogit -- Multinomial (polytomous) logistic regression

Syntax

mlogit depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Main noconstant suppress constant term baseoutcome(#) value of depvar that will be the base outcome constraints(clist) apply specified linear constraints; clist has the form #[-#][,#[-#] ... ] collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) rrr report relative-risk ratios nocnsreport do not display constraints display_options control columns and column formats, row spacing, line width, display of omitted variables and base and empty cells, and factor-variable labeling

Maximization maximize_options control the maximization process; seldom used

coeflegend display legend instead of statistics ------------------------------------------------------------------------- indepvars may contain factor variables; see fvvarlist. indepvars may contain time-series operators; see tsvarlist. bayes, bootstrap, by, fmm, fp, jackknife, mfp, mi estimate, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: mlogit and [FMM] fmm: mlogit. vce(bootstrap) and vce(jackknife) are not allowed with the mi estimate prefix. Weights are not allowed with the bootstrap prefix. vce() and weights are not allowed with the svy prefix. fweights, iweights, and pweights are allowed; see weight. coeflegend does not appear in the dialog box. See [R] mlogit postestimation for features available after estimation.

Menu

Statistics > Categorical outcomes > Multinomial logistic regression

Description

mlogit fits a multinomial logit model for a categorical dependent variable with outcomes that have no natural ordering. The actual values taken by the dependent variable are irrelevant. The multinomial logit model is also known as the polytomous logistic regression model. Some people refer to conditional logistic regression as multinomial logistic regression. If you are one of them, see [R] clogit.

Options

+-------+ ----+ Model +------------------------------------------------------------

noconstant; see [R] estimation options.

baseoutcome(#) specifies the value of depvar to be treated as the base outcome. The default is to choose the most frequent outcome.

constraints(clist), collinear; see [R] estimation options.

+-----------+ ----+ SE/Robust +--------------------------------------------------------

vce(vcetype) specifies the type of standard error reported, which includes types that are derived from asymptotic theory (oim), that are robust to some kinds of misspecification (robust), that allow for intragroup correlation (cluster clustvar), and that use bootstrap or jackknife methods (bootstrap, jackknife); see [R] vce_option.

If specifying vce(bootstrap) or vce(jackknife), you must also specify baseoutcome().

+-----------+ ----+ Reporting +--------------------------------------------------------

level(#); see [R] estimation options.

rrr reports the estimated coefficients transformed to relative-risk ratios, that is, exp(b) rather than b; see Description of the model in [R] mlogit. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated. rrr may be specified at estimation or when replaying previously estimated results.

nocnsreport; see [R] estimation options.

display_options: noci, nopvalues, noomitted, vsquish, noemptycells, baselevels, allbaselevels, nofvlabel, fvwrap(#), fvwrapon(style), cformat(%fmt), pformat(%fmt), sformat(%fmt), and nolstretch; see [R] estimation options.

+--------------+ ----+ Maximization +-----------------------------------------------------

maximize_options: difficult, technique(algorithm_spec), iterate(#), [no]log, trace, gradient, showstep, hessian, showtolerance, tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance, and from(init_specs); see [R] maximize. These options are seldom used.

The following option is available with mlogit but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Examples

Setup . webuse sysdsn1

Fit multinomial logistic regression model . mlogit insure age male nonwhite i.site

Same as above, but use 2 as the base outcome . mlogit insure age male nonwhite i.site, base(2)

Replay, reporting relative-risk ratios . mlogit, rrr

Setup . constraint 1 [Uninsure] . constraint 2 [Prepaid]: 2.site 3.site

Fit multinomial logistic regression model with constraints . mlogit insure age male nonwhite i.site, constraint(1) . mlogit insure age male nonwhite i.site, constraint(2) . mlogit insure age male nonwhite i.site, constraint(1/2)

Stored results

mlogit stores the following in e():

Scalars e(N) number of observations e(N_cd) number of completely determined observations e(k_out) number of outcomes e(k) number of parameters e(k_eq) number of equations in e(b) e(k_eq_model) number of equations in overall model test e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(r2_p) pseudo-R-squared e(ll) log likelihood e(ll_0) log likelihood, constant-only model e(N_clust) number of clusters e(chi2) chi-squared e(p) p-value for model test e(k_eq_base) equation number of the base outcome e(baseout) the value of depvar to be treated as the base outcome e(ibaseout) index of the base outcome e(rank) rank of e(V) e(ic) number of iterations e(rc) return code e(converged) 1 if converged, 0 otherwise

Macros e(cmd) mlogit e(cmdline) command as typed e(depvar) name of dependent variable e(wtype) weight type e(wexp) weight expression e(title) title in estimation output e(clustvar) name of cluster variable e(chi2type) Wald or LR; type of model chi-squared test e(vce) vcetype specified in vce() e(vcetype) title used to label Std. Err. e(eqnames) names of equations e(baselab) value label corresponding to base outcome e(opt) type of optimization e(which) max or min; whether optimizer is to perform maximization or minimization e(ml_method) type of ml method e(user) name of likelihood-evaluator program e(technique) maximization technique e(properties) b V e(predict) program used to implement predict e(marginsnotok) predictions disallowed by margins e(marginsdefault) default predict() specification for margins e(asbalanced) factor variables fvset as asbalanced e(asobserved) factor variables fvset as asobserved

Matrices e(b) coefficient vector e(out) outcome values e(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample


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