help for gnpoisson                                               (Joseph Hilbe)

Generalized Poisson regression

gnpoisson 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.

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

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


gnpoisson fits a maximum-likelihood generalized Poisson regression model of depvar on indepvars, where depvar is a non-negative count variable.

gnpoisson 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, gnpoisson 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: hilbe@asu.edu


gnpoisson is a user authored program. Support is by author. NOTE: Positive values of phi adjust for Poisson overdispersion; negative values adjust for Poisson under-dispersion.

gnpoisson requires a nonnegative response.


. gnpoisson los hmo white type2 type3, nolog irr

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

. bootstrap: gnpoisson los hmo white type2 type3, nolog

Also see

Reference: Hardin, J. & Hilbe J. (2007), Generalized Linear Models and Extensions, 2nd edition Stata Press.

Hilbe, J.M. (2011), Negative Binomial Regression, 2nd edition, Cambridge University Press

Online: help glm nbreg