help ztnb dialogs: ztnb svy: ztnb
also see: ztnb postestimation
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Title
[R] ztnb -- Zero-truncated negative binomial regression
Syntax
ztnb depvar [indepvars] [if] [in] [weight] [, options]
options description
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Model
noconstant suppress constant term
dispersion(mean) parameterization of dispersion;
dispersion(mean) is the default
dispersion(constant) constant dispersion for all observations
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
SE/Robust
vce(vcetype) vcetype may be oim, robust, cluster clustvar,
opg, bootstrap, or jackknife
Reporting
level(#) set confidence level; default is level(95)
nolrtest suppress likelihood-ratio test
irr report incidence-rate ratios
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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+ coeflegend does not appear in the dialog box.
indepvars may contain factor variables; see fvvarlist.
depvar and indepvars may contain time-series operators; see tsvarlist.
bootstrap, by, jackknife, rolling, statsby, and svy are allowed; see
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.
See [R] ztnb postestimation for features available after estimation.
Menu
Statistics > Count outcomes > Zero-truncated negative binomial regression
Description
ztnb fits a zero-truncated negative binomial (ZTNB) regression model of
depvar on indepvars, where depvar is a positive count variable.
Options
+-------+
----+ Model +------------------------------------------------------------
noconstant; see [R] estimation options.
dispersion(mean|constant) specifies the parameterization of the model.
dispersion(mean), the default, yields a model with dispersion equal
to 1 + alpha*exp(xb + offset); that is, the dispersion is a function
of the expected mean: exp(xb + offset). dispersion(constant) has
dispersion equal to 1 + delta; that is, it is a constant for all
observations.
exposure(varname_e), offset(varname_o), 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, 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.
nolrtest suppresses fitting the Poisson model. Without this option, a
comparison Poisson model is fit, and the likelihood is used in a
likelihood-ratio test of the null hypothesis that the dispersion
parameter is zero.
irr reports estimated coefficients transformed to incidence-rate ratios,
i.e., 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 stored. You can specify irr
at estimation or when you replay previously estimated results.
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 ztnb but is not shown in the
dialog box:
coeflegend; see [R] estimation options.
Remarks
ztnb fits two different parameterizations of ZTNB models, namely the
mean-dispersion and constant-dispersion models. They are equivalent to
those modeled by nbreg; see [R] nbreg.
Examples
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. webuse rod93
. ztnb deaths i.cohort
. ztnb deaths i.cohort, exp(exposure)
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. webuse medpar, clear
. ztnb los died hmo type2-type3, vce(cluster provnum)
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Saved results
ztnb saves the following in e():
Scalars
e(N) number of 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(r2_p) pseudo-R-squared
e(ll) log likelihood
e(ll_0) log likelihood, constant-only model
e(ll_c) log likelihood, comparison model
e(alpha) the value of alpha
e(N_clust) number of clusters
e(chi2) chi-squared
e(chi2_c) chi-squared for comparison test
e(p) significance
e(rank) rank of e(V)
e(rank0) rank of e(V) for constant-only model
e(ic) number of iterations
e(rc) return code
e(converged) 1 if converged, 0 otherwise
Macros
e(cmd) ztnb
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) offset
e(chi2type) Wald or LR; type of model chi-squared test
e(dispers) mean or constant
e(vce) vcetype specified in vce()
e(vcetype) title used to label Std. Err.
e(chi2_ct) Wald or LR; type of model chi-squared test
corresponding to e(chi2_c)
e(diparm#) display transformed parameter #
e(diparm_opt2) options for displaying transformed parameters
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
Also see
Manual: [R] ztnb
Help: [R] ztnb postestimation;
[R] nbreg, [R] poisson, [SVY] svy estimation, [XT] xtnbreg, [R]
zip, [R] ztp
