{smcl} {hline} help for {hi:hgclg} {right:(Joseph Hilbe)} {hline} {title:Geometric-complementary log log hurdle regression} {p 8 13 2}{cmd:hgclg}{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:ir:r} {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:hgclg} provides access to all {it:maximize} options; see help {help maximize}. {p 4 4 2} {cmd:hgclg} provides access to all {it:survey} options; see help {help svy}. {title:Description} {p 4 4 2}{cmd:hgclg} fits a geometric-cloglog maximum-likelihood hurdle model of {it:depvar} on {it:indepvars}, where {it:depvar} is a non-negative count variable. {p 4 4 2}{cmd:hgclg} 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:hgclg} 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:hgclg} is a user authored program. Support is by author. {cmd:hgclg} is one of a suite of hurdle programs created by the author. 2Oct2005 {pstd} {cmd:hgclg} requires a response with counts including a number of 0's. {cmd:irr} for binary model. {title:Examples} {phang}{cmd:. hgclg accident co_70_74 co_75_79 op_75_79, nolog exposure(service)} {phang}{cmd:. hgclg accident co_70_74 co_75_79 op_75_79, nolog exposure(service) cluster(ship)} {phang}{cmd:. bootstrap: hgclg accident co_70_74 co_75_79 op_75_79, nolog exposure(service)}