help mlogit dialogs: mlogit svy: mlogit
also see: mlogit postestimation
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Title
[R] mlogit -- Multinomial (polytomous) logistic regression
Syntax
mlogit depvar [indepvars] [if] [in] [weight] [, options]
options description
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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 spacing and display of omitted variables
and base and empty cells
Maximization
maximize_options control the maximization process; seldom used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, fracpoly, jackknife, mfp, mi estimate, rolling, statsby,
and svy are allowed; see prefix.
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.
See [R] mlogit postestimation for features available after estimation.
Menu
Statistics > Categorical outcomes > Multinomial logistic regression
Description
mlogit fits maximum-likelihood multinomial logit models, also known as
polytomous logistic regression. You can define constraints to perform
constrained estimation. Some people refer to conditional logistic
regression as multinomial logit. If you are one of them, see [R] clogit.
See logistic estimation commands for a list of related estimation
commands.
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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; 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, i.e., exp(b) rather than b. 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: noomitted, vsquish, noemptycells, baselevels,
allbaselevels; see [R] estimation options.
+--------------+
----+ Maximization +-----------------------------------------------------
maximize_options: difficult, technique(algorithm_spec), iterate(#),
[no]log, trace, gradient, showstep, hessian, showtolerance,
tolerance(#), ltolerance(#), nrtolerance(#), nonrtolerance,
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)
Saved results
mlogit saves the following in e():
Scalars
e(N) number of 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 model Wald test
e(k_dv) number of dependent variables
e(k_autoCns) number of base, empty, and omitted constraints
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) significance
e(k_eform) number of leading equations appropriate for eform
output
e(k_eq_base) equation number of the base outcome
e(baseout) the value of depvar to be treated as 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(k_eq_model_skip) equations to skip in most reports
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(predict) program used to implement predict
e(marginsnotok) predictions disallowed by 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
Also see
Manual: [R] mlogit
Help: [R] mlogit postestimation;
[R] clogit, [R] logistic, [R] logit, [R] mprobit, [R] nlogit,
[R] ologit, [R] rologit, [R] slogit [SVY] svy estimation
