Stata 15 help for logistic

[R] logistic -- Logistic regression, reporting odds ratios


logistic 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) coef report estimated coefficients 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

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, fp, jackknife, mfp, mi estimate, nestreg, rolling, statsby, stepwise, and svy are allowed; see prefix. For more details, see [BAYES] bayes: logistic. 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. coeflegend does not appear in the dialog box. See [R] logistic postestimation for features available after estimation.


Statistics > Binary outcomes > Logistic regression


logistic fits a logistic regression model of depvar on indepvars, where depvar is a 0/1 variable (or, more precisely, a 0/non-0 variable). Without arguments, logistic redisplays the last logistic estimates. logistic displays estimates as odds ratios; to view coefficients, type logit after running logistic. To obtain odds ratios for any covariate pattern relative to another, see [R] lincom.

See logistic estimation commands for a list of related 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.

coef causes logistic to report the estimated coefficients rather than the odds ratios (exponentiated coefficients). coef may be specified when the model is fit or may be used later to redisplay results. coef affects only how results are displayed and not how they are estimated.

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 option is available with logistic but is not shown in the dialog box:

coeflegend; see [R] estimation options.


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

Logistic regression . logistic low age lwt i.race smoke ptl ht ui

Same as above, but use a robust estimate of variance . logistic low age lwt i.race smoke ptl ht ui, vce(robust)

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

Logistic regression using survey data . svy: logistic highbp height weight age female

--------------------------------------------------------------------------- Setup . webuse mheart5 . mi set mlong . mi register imputed age bmi . mi impute mvn age bmi = attack smokes hsgrad female, add(10)

Fit the logistic model separately on each of the 10 imputed datasets and combine the results . mi estimate, or: logistic attack smokes age bmi hsgrad female


Video examples

Logistic regression, part 1: Binary predictors

Logistic regression, part 2: Continuous predictors

Logistic regression, part 3: Factor variables

Stored results

logistic 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) logistic 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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