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help for gnpoisson                                               (Joseph Hilbe)
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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.


Description

    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.


Options

        +-------+
    ----+ 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 +--------------------------------------------------------

    cluster(varname)

    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


Remarks

    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.


Examples

    . 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