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help for givgauss2                                               (Joseph Hilbe)
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Generalized 2-parameter log-inverse Gaussian regression

        givgauss2  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.

    givgauss2 provides access to all maximize options; see help maximize.

    givgauss2 provides access to all survey options; see help svy.


Description

    givgauss2 fits a maximum-likelihood generalized 2-parameter log-inverse
    Gaussian 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. givgauss2 can also be
    used with models having a positive continuous response.

    givgauss2 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 ivgauss2 if phi is not to be parameterized.

    givgauss2 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, givgauss2 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

    givgauss2 is a user authored program. Support is by author.


    givgauss2 requires a response with any positive real number. A response
    of 0 will result in an error.


Examples

    . givgauss2 los hmo white type2 type3, nolog lnphi(hmo white type2 type3)

    . givgauss2 los hmo white type2 type3, nolog exposure(pop) cluster(state)
        lnphi(hmo white)

    . bootstrap: givgauss2 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 glgamma2