{smcl}
{hline}
help for {hi:lgamma2} {right:(Joseph Hilbe)}
{hline}
{title:2-parameter log-gamma regression}
{p 8 13 2}{cmd:lgamma2}{space 2}{it:depvar} [{it:varlist}]
[{cmd:if} {it:exp}] [{cmd:in} {it:range}] [, {cmdab:off:set(}{it:varname}{cmd:)}
{cmdab:exp:osure(}{it:varname}{cmd:)} {cmdab:cl:uster(}{it:varname}{cmd:)}
{cmdab:l:evel(}{it:#}{cmd:)} {cmdab:from:(}{it:asis}{cmd:)} {cmdab:ef:orm} {cmdab:rob:ust}
{cmd:nolog} {it:maximize_options} {it:survey_options}]
{p 4 4 2}
{cmd:aweight}s, {cmd:fweight}s, {cmd:iweight}s, and {cmd:pweight}s are
allowed; see help {help weights}.
{p 4 4 2}
{cmd:lgamma2} provides access to all {it:maximize} options; see help {help maximize}.
{p 4 4 2}
{cmd:lgamma2} provides access to all {it:survey} options; see help {help svy}.
{title:Description}
{p 4 4 2}{cmd:lgamma2} fits a maximum-likelihood 2-parameter log-gamma regression model of {it:depvar}
on {it:indepvars}, where {it: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. {cmd:lgamma2} can also be used with models having a positive continuous
response.
{p 4 4 2}{cmd:lgamma2} acccepts all of the {it:help maximize} options, the {it:constraint()}
option, and all survey options and capabilities documented in {cmd:[SVY]}; including
multi-level surveys; poststratification; and BRR, jackknife, and linearization VCE estimators.
{p 4 4 2}This program uses {cmd:ml lf} method.
{title:Options}
{dlgtab:Model}
{phang}
{opth offset(varname)} specifies a {it:varname} in model with coefficient constrained to 1.
{phang}
{opth exposure(varname)} specifies a {it:ln(varname)} in model with coefficient constrained to 1.
{phang}
{opth constraints(constraints)} apply specified linear constraints.
{dlgtab:SE/Robust}
{phang}
{opth cluster(varname)}
{p 4 8 2}
{cmd:robust} specifies that the Huber/White/sandwich estimator of
variance is to be used in place of the traditional calculation. {cmd:robust}
combined with {cmd:cluster}{cmd:(}{cmd:)} allows observations which are not
independent within cluster (although they must be independent between
clusters). If you specify {cmd:pweight}s, {cmd:robust} is implied.
{phang}
{opth vce(options)} allowed. {cmd:vce}{cmd:(}{cmd:)} supports {it:robust}, {it:opg}, and {it:native}.
{cmd:vce} does not support options {it:bootstrap} or {it:jacknife}, However, {cmd:lgamma2} does support
the {cmd:bootstrap} and {cmd:jacknife} commands, so these modeling capabilities are allowed.
{dlgtab:Reporting}
{p 4 8 2}{cmd:level(}{it:#}{cmd:)} specifies the confidence level, in percent,
for confidence intervals of the coefficients; see help {help level}.
{p 4 8 2}
{cmd:nolog} suppresses the iteration log.
{dlgtab:max options}
{phang}
{p 4 8 2}
{it:maximize_options}: technique(algorithm_spec), [no]log, trace, hessian, gradient, showstep,
shownrtolerance, difficult, iterate(#), tolerance(#), ltolerance(#), gtolerance(#), nrtolerance(#),
nonrtolerance, from(init_specs); see {help maximize}.
{dlgtab:svy options}
{phang}
{it:survey_options} are all available. See help {help svy}
{title:Author and support}
{phang}
{cmd: Joseph Hilbe},
{cmd: Arizona State University}:
{cmd: jhilbe@aol.com}
{title:Remarks}
{pstd}
{cmd:lgamma2} is a user authored program. Support is by author.
{pstd}
{cmd:lgamma2} requires a response with any positive real number. A response of 0 will
result in an error.
{title:Examples}
{phang}{cmd:. lgamma2 los hmo white type2 type3, nolog}
{phang}{cmd:. lgamma2 los hmo white type2 type3, nolog exposure(pop) cluster(state)}
{phang}{cmd:. bootstrap: lgamma2 los hmo white type2 type3, nolog ef}
{title:Also see}
{psee}
Reference: {bf: Hardin, J. and Hilbe, J., (2001), Generalized Linear Models and Extensions, Stata Press.}
{psee}
Online: {helpb help} {helpb streg}