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\name{glmRob.cubif}
\alias{glmRob.cubif}
\title{Robust GLM CUBIF Fitter}
\description{
Robustly fit a generalized linear model using a
\bold{c}onditionally \bold{u}nbiased \bold{b}ounded
\bold{i}n\bold{f}luence (\dQuote{cubif}) estimator. This function is
called by the high-level function \code{\link{glmRob}} when
\code{method = "cubif"} (the default) is specified.
}
\usage{
glmRob.cubif(x, y, intercept = FALSE, offset = 0,
family = binomial(), null.dev = TRUE, control)
}
\arguments{
\item{x}{a numeric model matrix.}
\item{y}{either a numeric vector containing the response or, in the case of the binomial family, a two-column numeric matrix containing the number of successes and failures.}
\item{intercept}{a logical value. If \code{TRUE} a column of ones is added to the design matrix.}
\item{offset}{a numeric vector containing the offset.}
\item{family}{a family object.}
\item{null.dev}{a logical value. If \code{TRUE} the null deviance is computed.}
\item{control}{a list of control parameters. See \code{\link{glmRob.cubif.control}}.}
}
\value{
See \code{\link{glmRob.object}}.
}
\references{
Kunsch, L., Stefanski L. and Carroll, R. (1989).
Conditionally Unbiased Bounded-Influence Estimation in General
Regression Models, with Applications to Generalized Linear Models.
JASA \bold{84}, 460--466.
Marazzi, A. (1993).
Algorithms, routines and S functions for robust statistics.
Wadsworth & Brooks/Cole, Pacific Grove, CA.
}
\seealso{
\code{\link{glmRob}},
\code{\link{glmRob.cubif.control}}.
}
\keyword{robust}
\keyword{regression}
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