% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/HeckmantS.R
\name{HeckmantS}
\alias{HeckmantS}
\title{Heckman-t Model fit Function}
\usage{
HeckmantS(selection, outcome, data = sys.frame(sys.parent()), df, start = NULL)
}
\arguments{
\item{selection}{Selection equation.}

\item{outcome}{Primary regression equation.}

\item{data}{Database.}

\item{df}{Initial value for the degrees of freedom. Values greater than 2 are
recommended because the model uses a finite-variance Student-t error.}

\item{start}{Optional starting values on the natural scale
(selection, outcome, sigma, rho, df).}
}
\value{
A list with estimated coefficients, log-likelihood and diagnostics.
}
\description{
Estimates the parameters of the Heckman-t model. The scale, correlation and
degrees of freedom are optimized on unconstrained scales to improve numerical
stability: sigma = exp(eta_sigma), rho = tanh(eta_rho), and df = 2 + exp(eta_df).
}
\examples{
data(MEPS2001)
dados <- MEPS2001[seq_len(500), ]
selectEq <- dambexp ~ age + female + educ + blhisp + totchr + ins + income
outcomeEq <- lnambx ~ age + female + educ + blhisp + totchr + ins
HeckmantS(selectEq, outcomeEq, data = dados, df = 12)

}
\references{
Marchenko, Y. V. and Genton, M. G. (2012). A Heckman selection-t
model. \emph{Journal of the American Statistical Association}, 107(497),
304-317.
}
