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help for glgamma2                                                (Joseph Hilbe)
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Generalized 2-parameter log-gamma regression

        glgamma2  depvar [varlist] [if exp] [in range] [, offset(varname)
             exposure(varname) cluster(varname) level(#) from(asis) eform
             robust nolog maximize_options survey_options]



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

    glgamma2 provides access to all maximize options; see help maximize.

    glgamma2 provides access to all survey options; see help svy.


Description

    glgamma2 fits a maximum-likelihood generalized 2-parameter log-gamma
    regression model of depvar on indepvars, where depvar is a non-negative
    count variable. The program may be used to model under-dispersed Poisson
    count data. Under-dispersion is indicated if the scale parameter, phi, is
    greater than 1; values under 1 indicate over-dispersion. Other methods
    are available for modeling overdispersed Poisson data, including the
    negative binomial, but few methods are commonly available to deal with
    under-dispersion. glgamma2 can also be used with models having a positive
    continuous response.

    glgamma2 allows the parameterization of the natural log of the scale
    parameter, lnphi.  Without parameterization of lnphi, the model reduces
    to the non-generalized form, but without the additional transformed value
    of phi. Run lgamma2 if phi is not to be parameterized.

    glgamma2 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, glgamma2 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


Remarks

    glgamma2 is a user authored program. Support is by author.


    glgamma2 requires a response with any positive real number. A response of
    0 will result in an error.


Examples

    . glgamma2 los hmo white type2 type3, nolog lnphi(hmo white type2 type3)

    . glgamma2 los hmo white type2 type3, nolog exposure(pop) cluster(state)
        lnphi(hmo white)

    . bootstrap: glgamma2 los hmo white type2 type3, nolog lnphi(hmo white
        type2 type3)


Also see

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


    Online:  help streg