Stata 15 help for tnbreg

[R] tnbreg -- Truncated negative binomial regression

Syntax

tnbreg depvar [indepvars] [if] [in] [weight] [, options]

options Description ------------------------------------------------------------------------- Model noconstant suppress constant term ll(#|varname) truncation point; default value is ll(0), zero truncation dispersion(mean) parameterization of dispersion; 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 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, rolling, statsby, and svy are allowed; see prefix. For more details, see [BAYES] bayes: tnbreg. 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] tnbreg postestimation for features available after estimation.

Menu

Statistics > Count outcomes > Truncated negative binomial regression

Description

tnbreg estimates the parameters of a truncated negative binomial model by maximum likelihood. The dependent variable depvar is regressed on indepvars, where depvar is a positive count variable whose values are all above the truncation point.

Options

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

noconstant; see [R] estimation options.

ll(#|varname) specifies the truncation point, which is a nonnegative integer. The default is zero truncation, ll(0).

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 (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.

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, that is, 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. 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 tnbreg but is not shown in the dialog box:

coeflegend; see [R] estimation options.

Remarks

tnbreg fits the mean-dispersion and the constant-dispersion parameterizations of truncated negative binomial models. These parameterizations extend those implemented in nbreg to the truncated-data case; see [R] nbreg.

Examples

--------------------------------------------------------------------------- Setup . webuse medpar

Truncated negative binomial regression with default truncation point of 0 . tnbreg los died hmo type2-type3

Same as above, but cluster on provnum . tnbreg los died hmo type2-type3, vce(cluster provnum)

---------------------------------------------------------------------------

Setup . webuse rod93

Truncated negative binomial regression with truncation point of 9 . tnbreg deaths i.cohort, ll(9)

Same as above, but specify exposure variable . tnbreg deaths i.cohort, ll(9) exp(exposure)

---------------------------------------------------------------------------

Stored results

tnbreg stores the following in e():

Scalars e(N) number of observations e(k) number of parameters e(k_aux) number of auxiliary 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(ll_c) log likelihood, comparison model e(alpha) value of alpha e(delta) value of delta e(N_clust) number of clusters e(chi2) chi-squared e(chi2_c) chi-squared for comparison test e(p) p-value for model test 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) tnbreg e(cmdline) command as typed e(depvar) name of dependent variable e(llopt) contents of ll(), or 0 if not specified 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(chi2_ct) Wald or LR; type of model chi-squared test corresponding to e(chi2_c) e(dispers) mean or constant 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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