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

__Syntax__

__mlog__**it** *depvar* [*indepvars*] [*if*] [*in*] [*weight*] [**,** *options*]

*options* Description
-------------------------------------------------------------------------
Main
__nocons__**tant** suppress constant term
__b__**aseoutcome(***#***)** value of *depvar* that will be the base outcome
__c__**onstraints(***clist***)** apply specified linear constraints; *clist* has the
form *#*[**-***#*][**,***#*[**-***#*] *...* ]
__col__**linear** keep collinear variables

SE/Robust
**vce(***vcetype***)** *vcetype* may be **oim**, __r__**obust**, __cl__**uster** *clustvar*,
__boot__**strap**, or __jack__**knife**

Reporting
__l__**evel(***#***)** set confidence level; default is **level(95)**
__rr__**r** report relative-risk ratios
__nocnsr__**eport** 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

__coefl__**egend** 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.
**fweight**s, **iweight**s, and **pweight**s 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**, __nopv__**alues**, __noomit__**ted**, **vsquish**, __noempty__**cells**,
__base__**levels**, __allbase__**levels**, __nofvlab__**el**, **fvwrap(***#***)**, **fvwrapon(***style***)**,
**cformat(***%fmt***)**, **pformat(%***fmt***)**, **sformat(%***fmt***)**, and **nolstretch**; see **[R]**
**estimation options**.

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

*maximize_options*: __dif__**ficult**, __tech__**nique(***algorithm_spec***)**, __iter__**ate(***#***)**,
[__no__]__lo__**g**, __tr__**ace**, __grad__**ient**, **showstep**, __hess__**ian**, __showtol__**erance**,
__tol__**erance(***#***)**, __ltol__**erance(***#***)**, __nrtol__**erance(***#***)**, __nonrtol__**erance**, 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