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\name{marginal}
\alias{marginal}
\alias{marginal.ategel}
\title{Marginal effects Summary}
\description{
It produces the summary table of marginal effects for GLM estimation with
GEL. Only implemented for ATEgel.
}
\usage{
\method{marginal}{ategel}(object, ...)
}
\arguments{
\item{object}{An object of class \code{ategel} returned by the function
\code{\link{ATEgel}}}
\item{...}{Other arguments for other methods}
}
\value{
It returns a matrix with the marginal effects, the standard errors based on the Delta
method when the link is nonlinear, the t-ratios, and the pvalues.}
\references{
Owen, A.B. (2001),
Empirical Likelihood.
\emph{Monographs on Statistics and Applied Probability 92, Chapman and
Hall/CRC}
}
\examples{
## We create some artificial data with unbalanced groups and binary outcome
genDat <- function(n)
{
eta=c(-1, .5, -.25, -.1)
Z <- matrix(rnorm(n*4),ncol=4)
b <- c(27.4, 13.7, 13.7, 13.7)
bZ <- c(Z\%*\%b)
Y1 <- as.numeric(rnorm(n, mean=210+bZ)>220)
Y0 <- as.numeric(rnorm(n, mean=200-.5*bZ)>220)
etaZ <- c(Z\%*\%eta)
pZ <- exp(etaZ)/(1+exp(etaZ))
T <- rbinom(n, 1, pZ)
Y <- T*Y1+(1-T)*Y0
X1 <- exp(Z[,1]/2)
X2 <- Z[,2]/(1+exp(Z[,1]))
X3 <- (Z[,1]*Z[,3]/25+0.6)^3
X4 <- (Z[,2]+Z[,4]+20)^2
data.frame(Y=Y, cbind(X1,X2,X3,X4), T=T)
}
dat <- genDat(200)
res <- ATEgel(Y~T, ~X1+X2+X3+X4, data=dat, type="ET", family="logit")
summary(res)
marginal(res)
}
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