Stata 15 help for zip

[R] zip -- Zero-inflated Poisson regression


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

options Description ------------------------------------------------------------------------- Model * inflate() equation that determines whether the count is zero noconstant suppress constant term exposure(varname_e) include ln(varname_e) in model with coefficient constrained to 1 offset(varname_o) include varname_o in model with coefficient constrained to 1 constraints(constraints) apply specified linear constraints collinear keep collinear variables probit use probit model to characterize excess zeros; default is logit

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

Reporting level(#) set confidence level; default is level(95) irr report incidence-rate 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

coeflegend display legend instead of statistics ------------------------------------------------------------------------- * inflate(varlist[, 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: zip. 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] zip postestimation for features available after estimation.


Statistics > Count outcomes > Zero-inflated Poisson regression


zip fits a zero-inflated Poisson (ZIP) model to count data with excess zero counts. The ZIP model assumes that the excess zero counts come from a logit or probit model and the remaining counts come from a Poisson model.


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

inflate(varlist[, offset(varname)]|_cons) specifies the equation that determines whether the observed count is zero. Conceptually, omitting inflate() would be equivalent to fitting the model with poisson.

inflate(varlist[, offset(varname)]) specifies the variables in the equation. You may optionally include an offset for this varlist.

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

noconstant, exposure(varname_e), offset(varname_o), constraints(constraints), collinear; see [R] estimation options.

probit requests that a probit, instead of logit, model be used to characterize the excess zeros in the data.

+-----------+ ----+ 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.

irr reports estimated coefficients transformed to incidence-rate ratios. Standard errors and confidence intervals are similarly transformed. This option affects how results are displayed, not how they are estimated or stored. irr 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.

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

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

coeflegend; see [R] estimation options.


Setup . webuse fish

Fit zero-inflated Poisson model . zip count persons livebait, inflate(child camper)

Replay results, displaying incidence-rate ratios . zip, irr

Stored results

zip stores the following in e():

Scalars e(N) number of observations e(N_zero) number of zero observations 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(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) zip e(cmdline) command as typed e(depvar) name of dependent variable e(inflate) logit or probit 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(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(V) variance-covariance matrix of the estimators e(V_modelbased) model-based variance

Functions e(sample) marks estimation sample

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