**[R] logit** -- Logistic regression, reporting coefficients

__Syntax__

__logi__**t** *depvar* [*indepvars*] [*if*] [*in*] [*weight*] [**,** *options*]

*options* Description
-------------------------------------------------------------------------
Model
__nocons__**tant** suppress constant term
__off__**set(***varname***)** include *varname* in model with coefficient
constrained to 1
**asis** retain perfect predictor variables
__const__**raints(***constraints***)** apply specified linear constraints
__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)**
**or** report odds 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

__nocoe__**f** do not display coefficient table; seldom
used
__coefl__**egend** display legend instead of statistics
-------------------------------------------------------------------------
*indepvars* may contain factor variables; see fvvarlist.
*depvar* and *indepvars* may contain time-series operators; see tsvarlist.
**bayes**, **bootstrap**, **by**, **fmm**, **fp**, **jackknife**, **mfp**, **mi estimate**, **nestreg**,
**rolling**, **statsby**, **stepwise**, and **svy** are allowed; see prefix. For more
details, see **[BAYES] bayes: logit** and **[FMM] fmm: logit**.
**vce(bootstrap)** and **vce(jackknife)** are not allowed with the **mi estimate**
prefix.
Weights are not allowed with the **bootstrap** prefix.
**vce()**, **nocoef**, and weights are not allowed with the **svy** prefix.
**fweight**s, **iweight**s, and **pweight**s are allowed; see weight.
**nocoef** and **coeflegend** do not appear in the dialog box.
See **[R] logit postestimation** for features available after estimation.

__Menu__

**Statistics > Binary outcomes > Logistic regression**

__Description__

**logit** fits a logit model for a binary response by maximum likelihood; it
models the probability of a positive outcome given a set of regressors.
*depvar* equal to nonzero and nonmissing (typically *depvar* equal to one)
indicates a positive outcome, whereas *depvar* equal to zero indicates a
negative outcome.

Also see **[R] logistic**; **logistic** displays estimates as odds ratios. Many
users prefer the **logistic** command to **logit**. Results are the same
regardless of which you use -- both are the maximum-likelihood estimator.
Several auxiliary commands that can be run after **logit**, **probit**, or
**logistic** estimation are described in **[R] logistic postestimation**. A list
of related estimation commands is given in logistic estimation commands.

__Options__

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

**noconstant**, **offset(***varname***)**, **constraints(***constraints***)**, **collinear**; see **[R]**
**estimation options**.

**asis** forces retention of perfect predictor variables and their associated
perfectly predicted observations and may produce instabilities in
maximization; see **[R] probit**.

+-----------+
----+ 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*.

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

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

**or** reports the estimated coefficients transformed to odds ratios, that
is, 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. **or** 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 options are available with **logit** but are not shown in the
dialog box:

**nocoef** specifies that the coefficient table not be displayed. This
option is sometimes used by program writers but is of no use
interactively.

**coeflegend**; see **[R] estimation options**.

__Examples__

---------------------------------------------------------------------------
Setup
**. webuse lbw**

Logistic regression
**. logit low age lwt i.race smoke ptl ht ui**
**. logit, level(99)**

---------------------------------------------------------------------------
Setup
**. webuse nhanes2d**
**. svyset**

Logistic regression using survey data
**. svy: logit highbp height weight age female**
---------------------------------------------------------------------------

__Stored results__

**logit** stores the following in **e()**:

Scalars
**e(N)** number of observations
**e(N_cds)** number of completely determined successes
**e(N_cdf)** number of completely determined failures
**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(rank)** rank of **e(V)**
**e(ic)** number of iterations
**e(rc)** return code
**e(converged)** **1** if converged, **0** otherwise

Macros
**e(cmd)** **logit**
**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(offset)** linear offset 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(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(estat_cmd)** program used to implement **estat**
**e(predict)** program used to implement **predict**
**e(marginsok)** predictions allowed by **margins**
**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(Cns)** constraints matrix
**e(ilog)** iteration log (up to 20 iterations)
**e(gradient)** gradient vector
**e(mns)** vector of means of the independent variables
**e(rules)** information about perfect predictors
**e(V)** variance-covariance matrix of the estimators
**e(V_modelbased)** model-based variance

Functions
**e(sample)** marks estimation sample