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help for hnblogit                                                (Joseph Hilbe)
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Negative binomial-logit hurdle regression

        hnblogit  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.

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

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


Description

    hnblogit fits a negative binomial-logit maximum-likelihood hurdle model
    of depvar on indepvars, where depvar is a non-negative count variable.

    hnblogit 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, hnblogit 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

    hnblogit is a user authored program. Support is by author. hnblogit is
    one of a suite of hurdle programs created by the author. 2Oct2005

    hnblogit requires a response with counts including a number of 0's. irr
    for binary model.


Examples

    . hnblogit accident co_70_74 co_75_79 op_75_79, nolog exposure(service)

    . hnblogit accident co_70_74 co_75_79 op_75_79, nolog exposure(service)
        cluster(ship)

    . bootstrap: hnblogit accident co_70_74 co_75_79 op_75_79, nolog
        exposure(service)