Stata 15 help for logit

[R] logit -- Logistic regression, reporting coefficients


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

options Description ------------------------------------------------------------------------- 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 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

nocoef do not display coefficient table; seldom used coeflegend 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. fweights, iweights, and pweights are allowed; see weight. nocoef and coeflegend do not appear in the dialog box. See [R] logit postestimation for features available after estimation.


Statistics > Binary outcomes > Logistic regression


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.


+-------+ ----+ 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, 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 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.


--------------------------------------------------------------------------- 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

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