Stata 15 help for zioprobit

[R] zioprobit -- Zero-inflated ordered probit regression


zioprobit depvar [indepvars] [if] [in] [weight], inflate(varlist[, noconstant offset(varname)]|_cons) [options]

options Description ------------------------------------------------------------------------- Model * inflate() equation that determines excess zero values offset(varname) include varname in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables

SE/Robust vce(vcetype) vcetype may be oim, robust, cluster clustvar, opg, bootstrap, or jackknife

Reporting level(#) set confidence level; default is level(95) 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 ------------------------------------------------------------------------- * inflate(varlist[, noconstant offset(varname)]|_cons) is required. indepvars and varlist may contain factor variables; see fvvarlist. bayes, bootstrap, by, fp, jackknife, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: zioprobit. 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] zioprobit postestimation for features available after estimation.


Statistics > Ordinal outcomes > Zero-inflated ordered probit regression


zioprobit fits a model for a discrete ordered outcome with a high fraction of zeros, called zero inflation. This model is known as a zero-inflated ordered probit (ZIOP) model. In the context of ZIOP models, zero is an actual 0 value or the lowest outcome category. The ZIOP model accounts for the zero inflation by assuming that the zero-valued outcomes come from both a probit model and an ordered probit model, allowing potentially different sets of covariates for each model.


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

inflate(varlist[, noconstant offset(varname)]|_cons) specifies the equation that determines the excess zero values; this option is required. Conceptually, omitting inflate() would be equivalent to fitting the model with oprobit; see [R] oprobit.

inflate(varlist[, noconstant offset(varname)]) specifies the variables in the equation that determines the excess zeros. To suppress the constant in this equation, specify the noconstant suboption. You may optionally include an offset for this varlist.

inflate(_cons) specifies that the equation determining the excess zero values contains only an intercept. To run a zero-inflated model of depvar with only an intercept in both equations, type zioprobit depvar, inflate(_cons).

offset(varname), constraints(constraints), 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, opg), 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.

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.

Setting the optimization type to technique(bhhh) resets the default vcetype to vce(opg).

The following option is available with zioprobit but is not shown in the dialog box:

coeflegend; see [R] estimation options.


Setup . webuse tobacco

Zero-inflated ordered probit regression . zioprobit tobacco education income i.female age, inflate(education income i.parent age i.female i.religion)

Stored results

zioprobit stores the following in e():

Scalars e(N) number of observations e(N_zero) number of zeros or lowest-category observations e(k_cat) number of categories 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_aux) number of auxiliary parameters e(k_dv) number of dependent variables e(df_m) model degrees of freedom e(ll) log likelihood 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) zioprobit 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(offset1) offset e(offset2) offset for inflate() 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(predict) program used to implement predict e(marginsok) predictions allowed by margins 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(Cns) constraints matrix e(ilog) iteration log (up to 20 iterations) e(gradient) gradient vector e(cat) category values e(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

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