help zinb dialogs: zinb svy: zinb
also see: zinb postestimation
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Title
[R] zinb -- Zero-inflated negative binomial regression
Syntax
zinb depvar [indepvars] [if] [in] [weight], inflate(varlist[, offset(
varname)]|_cons) [options]
options description
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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
vuong perform Vuong test
zip perform ZIP likelihood-ratio test
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
+ coeflegend display coefficients' legend instead of
coefficient table
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* inflate(varlist[, offset(varname)]|_cons) is required.
+ coeflegend does not appear in the dialog box.
indepvars and varlist may contain factor variables; see fvvarlist.
bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see
prefix.
Weights are not allowed with the bootstrap prefix.
vce(), vuong, zip, and weights are not allowed with the svy prefix.
fweights, iweights, and pweights are allowed; see weight.
See [R] zinb postestimation for features available after estimation.
Menu
Statistics > Count outcomes > Zero-inflated negative binomial regression
Description
zinb estimates a zero-inflated negative binomial (ZINB) regression of
depvar on indepvars, where depvar is a nonnegative count variable.
Options
+-------+
----+ 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
nbreg.
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
zinb depvar, inflate(_cons).
noconstant, exposure(varname_e), offset(varname_o),
constraints(constraints), collinear; see [R] estimation options.
probit specifies 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, 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.
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.
vuong specifies that the Vuong (1989) test of ZINB versus negative
binomial be reported. This test statistic has a standard normal
distribution with large positive values favoring the ZINB model and
large negative values favoring the negative binomial model.
zip requests that a likelihood-ratio test comparing the ZINB model with
the zero-inflated Poisson model be included in the output.
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.
Setting the optimization type to technique(bhhh) resets the default
vcetype to vce(opg).
The following option is available with zinb but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Example
. webuse fish
. zinb count persons livebait, inflate(child camper)
. zinb count persons livebait, inflate(child camper) vuong
Saved results
zinb saves 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
e(k_eq_model) number of equations in model Wald test
e(k_aux) number of auxiliary parameters
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(ll) log likelihood
e(ll_0) log likelihood, constant-only model
e(df_c) degrees of freedom for comparison test
e(N_clust) number of clusters
e(chi2) chi-squared
e(p) significance of model test
e(chi2_cp) chi-squared for test of alpha = 0
e(vuong) Vuong test statistic
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) zinb
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(chi2_cpt) Wald or LR; type of model chi-squared test
corresponding to e(chi2_cp)
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(diparm#) display transformed parameter #
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(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
Reference
Vuong, Q. 1989. Likelihood ratio tests for model selection and
non-nested hypotheses. Econometrica 57: 307-333.
Also see
Manual: [R] zinb
Help: [R] zinb postestimation;
[R] nbreg, [R] poisson, [XT] xtnbreg, [R] zip, [R] ztnb
