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help for lgamma2                                                 (Joseph Hilbe)
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2-parameter log-gamma regression

        lgamma2  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.

    lgamma2 provides access to all maximize options; see help maximize.

    lgamma2 provides access to all survey options; see help svy.


Description

    lgamma2 fits a maximum-likelihood 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. lgamma2 can also be used with models having a positive
    continuous response.


    lgamma2 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, lgamma2 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

    lgamma2 is a user authored program. Support is by author.


    lgamma2 requires a response with any positive real number. A response of
    0 will result in an error.


Examples

    . lgamma2 los hmo white type2 type3, nolog

    . lgamma2 los hmo white type2 type3, nolog exposure(pop) cluster(state)

    . bootstrap: lgamma2 los hmo white type2 type3, nolog ef


Also see

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


    Online:  help streg