help for cnbreg                                                  (Joseph Hilbe)

Negative binomial regression - canonical parameterization

cnbreg depvar [varlist] [if exp] [in range] [, offset(varname) exposure(varname) cluster(varname) level(#) from(asis) irr robust nolog maximize_options survey_options]

aweights, fweights, iweights, and pweights are allowed; see help weights.

cnbreg provides access to all maximize options; see help maximize.

cnbreg provides access to all survey options; see help svy.


cnbreg fits a maximum-likelihood negative binomial regression model, with canonical parameterization, of depvar on indepvars, where depvar is a non-negative count variable.

cnbreg acccepts all of the help maximize options, the constraint() option, and all survey options and capabilities documented in [SVY]; including multi-level surveys; poststratification; and BRR, jackknife, and linearization VCE estimators.

This program uses ml lf method.


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

offset(varname) specifies a varname in model with coefficient constrained to 1.

exposure(varname) specifies a ln(varname) in model with coefficient constrained to 1.

constraints(constraints) apply specified linear constraints.

+-----------+ ----+ SE/Robust +--------------------------------------------------------


robust specifies that the Huber/White/sandwich estimator of variance is to be used in place of the traditional calculation. robust combined with cluster() allows observations which are not independent within cluster (although they must be independent between clusters). If you specify pweights, robust is implied.

vce(options) allowed. vce() supports robust, opg, and native. vce does not support options bootstrap or jacknife, However, cnbreg does support the bootstrap and jacknife commands, so these modeling capabilities are allowed.

+-----------+ ----+ Reporting +-------------------------------------------------------- level(#) specifies the confidence level, in percent, for confidence intervals of the coefficients; see help level.

nolog suppresses the iteration log.

+-------------+ ----+ max options +------------------------------------------------------

maximize_options: technique(algorithm_spec), [no]log, trace, hessian, gradient, showstep, shownrtolerance, difficult, iterate(#), tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#), nonrtolerance, from(init_specs); see maximize.

+-------------+ ----+ svy options +------------------------------------------------------

survey_options are all available. See help svy

Author and support

Joseph Hilbe, Arizona State University: jhilbe@aol.com


cnbreg is a user authored program. Support is by author. NOTE: The canonnical parameterization views the negative binomial as a GLM-type probability function; not a Poisson-gamma mixture model. It includes the negative binomial ancillary parameter in the variance function.

cnbreg requires a nonnegative response.


. cnbreg los hmo white type2 type3, nolog irr

. cnbreg los hmo white type2 type3, nolog exposure(pop) cluster(state)

. bootstrap: cnbreg los hmo white type2 type3, nolog irr

Also see

Reference: Hardin, J. & Hilbe J. (2001), Generalized Linear Models and Extensions, Stata Press.

Online: help glm nbreg