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\name{predict.lmrob}
\alias{predict.lmrob}
\title{Predict method for Robust Linear Model ("lmrob") Fits}
\description{
Predicted values based on robust linear model object.
}
\usage{
\method{predict}{lmrob}(object, newdata, se.fit = FALSE,
scale = NULL, df = NULL,
interval = c("none", "confidence", "prediction"), level = 0.95,
type = c("response", "terms"), terms = NULL,
na.action = na.pass, pred.var = res.var/weights, weights = 1, ...)
}
\arguments{
%% the following is +- copy-pasted from predict.lm.Rd:
\item{object}{object of class inheriting from \code{"lmrob"}}
\item{newdata}{an optional data frame in which to look for variables with
which to predict. If omitted, the fitted values are used.}
\item{se.fit}{a switch indicating if standard errors are required.}
\item{scale}{scale parameter for std.err. calculation}
\item{df}{degrees of freedom for scale}
\item{interval}{type of interval calculation.}
\item{level}{tolerance/confidence level}
\item{type}{Type of prediction (response or model term).}
\item{terms}{if \code{type="terms"}, which terms (default is all terms)}
\item{na.action}{function determining what should be done with missing
values in \code{newdata}. The default is to predict \code{NA}.}
\item{pred.var}{the variance(s) for future observations to be assumed
for prediction intervals. See \sQuote{Details}.}
\item{weights}{variance weights for prediction. This can be a numeric
vector or a one-sided model formula. In the latter case, it is
interpreted as an expression evaluated in \code{newdata}}
\item{\dots}{further arguments passed to or from other methods.}
}
% \details{
% }
\value{
%% the following is +- copy-pasted from predict.lm.Rd:
\code{predict.lmrob} produces a vector of predictions or a matrix of
predictions and bounds with column names \code{fit}, \code{lwr}, and
\code{upr} if \code{interval} is set. If \code{se.fit} is
\code{TRUE}, a list with the following components is returned:
\item{fit}{vector or matrix as above}
\item{se.fit}{standard error of predicted means}
\item{residual.scale}{residual standard deviations}
\item{df}{degrees of freedom for residual}
}
% \references{
% }
\author{Andreas Ruckstuhl}
\seealso{
\code{\link{lmrob}} and the (non-robust) traditional
\code{\link{predict.lm}} method.
}
% \examples{
% }
\keyword{robust}
\keyword{regression}
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