help logit dialogs: logit svy: logit
also see: logit postestimation
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Title
[R] logit -- Logistic regression, reporting coefficients
Syntax
logit depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
offset(varname) include varname in model with coefficient
constrained to 1
asis retain perfect predictor variables
constraints(constraints) apply specified linear constraints
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)
or report odds 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
+ nocoef do not display coefficient table; seldom
used
+ coeflegend display coefficients' legend instead of
coefficient table
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+ nocoef and coeflegend do not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, fracpoly, jackknife, mfp, mi estimate, nestreg, rolling,
statsby, stepwise, 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(), nocoef, and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] logit postestimation for features available after estimation.
Menu
Statistics > Binary outcomes > Logistic regression
Description
logit fits a maximum-likelihood logit model. depvar=0 indicates a
negative outcome; depvar!=0 & depvar!=. (typically depvar=1) indicate a
positive 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.
If estimating on grouped data, see [R] glogit.
See http://www.stata.com/support/faqs/stat/rasch.html if interested in
the Rasch model.
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, that are
robust to some kinds of misspecification, that allow for intragroup
correlation, and that use bootstrap or jackknife methods; see [R]
vce_option.
+-----------+
----+ Reporting +--------------------------------------------------------
level(#); see [R] estimation options.
or reports the estimated coefficients transformed to odds 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. or 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 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
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Setup
. webuse lbw
Logistic regression
. logit low age lwt i.race smoke ptl ht ui
. logit, level(99)
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Setup
. webuse nhanes2d
. svyset
Logistic regression using survey data
. svy: logit highbp height weight age female
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Saved results
logit saves 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 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, contant-only model
e(N_clust) number of clusters
e(chi2) chi-squared
e(p) significance
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) offset
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(singularHmethod) m-marquardt or hybrid; method used when Hessian is
singular
e(crittype) optimization criterion
e(properties) b V
e(estat_cmd) program used to implement estat
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(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
Also see
Manual: [R] logit
Help: [R] logit postestimation;
[R] brier, [R] exlogistic, [R] glogit, [R] logistic, [R] probit,
> [R] roc, [SVY] svy estimation, [XT] xtlogit
