ztnb.hlp

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{smcl}
{* 24mar2005}{...}
{cmd:help ztnb} {right:dialog:  {bf:{dialog ztnb}} {space 14}}
{right:also see:  {help ztnb postestimation}}
{hline}

{title:Title}

{p2colset 5 17 19 2}{...}
{p2col :{hi:[R] ztnb} {hline 2}}Zero-truncated negative binomial regression
{p_end}
{p2colreset}{...}


{title:Syntax}

{p 8 13 2}
{cmd:ztnb} {depvar} [{indepvars}] {ifin} {weight} 
   [{cmd:,} {it:options}]

{synoptset 26 tabbed}{...}
{synopthdr}
{synoptline}
{syntab:Model}
{synopt :{opt nocon:stant}}suppress constant term{p_end}
{synopt :{opt d:ispersion}{cmd:(}{opt m:ean}{cmd:)}}parameterization of 
dispersion; {cmd:dispersion(mean)} is the default{p_end}
{synopt :{opt d:ispersion}{cmd:(}{opt c:onstant}{cmd:)}}constant dispersion 
for all observations{p_end}
{synopt :{opth e:xposure(varname:varname_e)}}include ln({it:varname_e}) in model with 
coefficient constrained to 1{p_end}
{synopt :{opth off:set(varname:varname_o)}}include {it:varname_o} in model with coefficient 
constrained to 1{p_end}
{synopt :{opt const:raints}{cmd:(}{it:{help estimation options##constraints():constraints}}{cmd:)}}apply specified linear constraints{p_end}

{syntab:SE/Robust}
{synopt :{opth vce(vcetype)}}{it:vcetype} may be {opt oim}, {opt r:obust}, 
{opt opg}, {opt boot:strap}, or {opt jack:knife}{p_end}
{synopt :{opt r:obust}}synonym for {cmd:vce(robust)}{p_end}
{synopt :{opth cl:uster(varname)}}adjust standard errors for intragroup 
correlation{p_end}

{syntab:Reporting}
{synopt :{opt l:evel(#)}}set confidence level; default is {cmd:level(95)}
{p_end}
{synopt :{opt nolr:test}}suppress likelihood-ratio test{p_end}
{synopt :{opt ir:r}}report incidence-rate rations{p_end}

{syntab:Max options}
{synopt :{it:{help ztnb##ztnb_maximize:maximize_options}}}control the 
maximization process; seldom used{p_end}
{synoptline}
{p2colreset}{...}
{p 4 6 2} {cmd:bootstrap}, {cmd:by}, {cmd:jackknife}, {cmd:rolling}, 
{cmd:statsby}, and {cmd:xi} are allowed; see {help prefix}.{p_end}
{p 4 6 2} {cmd:fweight}s, {cmd:iweight}s, and {cmd:pweight}s are allowed; see
{help weight}.{p_end}
{p 4 6 2} See {help ztnb postestimation} for additional capabilities of
estimation commands. 


{title:Description}

{pstd}
{cmd:ztnb} fits a zero-truncated negative binomial regression model of
{depvar} on {indepvars}, where {it:depvar} is a positive count variable.


{title:Options}

{dlgtab:Model}

{phang}
{opt noconstant}; see {help estimation options##noconstant:estimation options}.

{phang}
{cmd:dispersion(mean}{c |}{cmd:constant)} specifies the parameterization of
the model.  {cmd:dispersion(mean)}, the default, yields a model with
dispersion equal to 1 + alpha*exp(xb + offset); that is, the dispersion
is a function of the expected mean: exp(xb + offset).
{cmd:dispersion(constant)} has dispersion equal to 1 + delta; that is, it is a
constant for all observations.

{phang}
{opt exposure(varname_e)}, {opt offset(varname_o)}, and 
{opt constraints(constraints)}; see {help estimation options}.

{dlgtab:SE/Robust}

{phang}
{opt vce(vcetype)}; see {it:{help vce_option}}.

{phang}
{opt robust}, {opt cluster(varname)}; see {help estimation options##robust:estimation options}.

{dlgtab:Reporting}

{phang}
{opt level(#)}; see {help estimation options##level():estimation options}.

{phang}
{opt nolrtest} suppresses fitting the Poisson model.  Without this option, a
comparison Poisson model is fitted, and the likelihood is used in a
likelihood-ratio test of the null hypothesis that the dispersion parameter is
zero.

{phang}
{opt irr} reports estimated coefficients transformed to incidence-rate
ratios, i.e., exp(b) rather than b.  Standard errors and confidence
intervals are similarly transformed.  This option affects how results are
displayed, not how they are estimated or stored.  You can specify {opt irr}
at estimation or when you replay previously estimated results.

{marker ztnb_maximize}{...}
{dlgtab:Max options}

{phang}
{it:maximize_options}: {opt dif:ficult}, {opt tech:nique(algorithm_spec)},
{opt iter:ate(#)}, [{cmd:{ul:no}}]{opt lo:g}, {opt tr:ace},
{opt grad:ient},
{opt showstep},
{opt hess:ian},
{opt shownr:tolerance},
{opt tol:erance(#)}, {opt ltol:erance(#)},
{opt gtol:erance(#)}, {opt nrtol:erance(#)}, {opt nonrtol:erance},
{opt from(init_specs)}; see {help maximize}.  These options are seldom used.


{title:Remarks}

{pstd}
{cmd:ztnb} will fit two different parameterizations of the zero-truncated
negative binomial model.  Assume truncated zeros are included in the data, the
default, also given by the option {cmd:dispersion(mean)}, has dispersion for
the i-th observation equal to 1 + alpha*exp(xb + offset); i.e., the dispersion
is a function of the expected mean of the counts for the i-th observation:
exp(xb + offset); the alternative parameterization, given by the option
{cmd:dispersion(constant)}, has dispersion equal to 1 + delta; i.e., it is a
constant for all observations.


{title:Examples}

{phang}{cmd:. ztnb deaths coh2 coh3, exposure(obstime)}{p_end}

{phang}{cmd:. ztnb deaths coh2 coh3, exposure(obstime) dispersion(constant)}{p_end}


{title:Also see}

{psee}
Manual: {bf:[R] ztnb}{p_end}

{psee}
Online:  {help ztnb postestimation};{break}
{helpb constraint}, {helpb glm}, {helpb nbreg}, {helpb poisson}, 
{helpb "svy: nbreg"}, {helpb xtnbreg}, {helpb zip}, {helpb ztp}{p_end}