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% Origin: src/library/stats/man/glm.summaries.Rd (as of 2011-10-23)
\name{residuals.glmrob}
\alias{residuals.glmrob}
\title{Residuals of Robust Generalized Linear Model Fits}
\usage{
\method{residuals}{glmrob}(object,
type = c("deviance", "pearson", "working",
"response", "partial"),
\dots)
}
\arguments{
\item{object}{an object of class \code{glmrob}, typically the result of
a call to \code{\link{glmrob}}.}
\item{type}{the type of residuals which should be returned.
The alternatives are: \code{"deviance"} (default), \code{"pearson"},
\code{"working"}, \code{"response"}, and \code{"partial"}.}
\item{\dots}{further arguments passed to or from other methods.}
}
\description{
Compute residuals of a fitted \code{\link{glmrob}} model, i.e., robust
generalized linear model fit.
}
\details{
The references in \code{\link{glm}} define the types of residuals:
Davison & Snell is a good reference for the usages of each.
The partial residuals are a matrix of working residuals, with each
column formed by omitting a term from the model.
The \code{residuals} (S3) method (see \code{\link{methods}}) for
\code{\link{glmrob}} models has been modeled to follow closely the
method for classical (non-robust) \code{\link{glm}} fitted models.
Possibly, see its documentation, i.e., \link{residuals.glm}, for
further details.
}
\seealso{
\code{\link{glmrob}} for computing \code{object}, \code{\link{anova.glmrob}};
the corresponding \emph{generic} functions, \code{\link{summary.glmrob}},
\code{\link{coef}},
% \code{\link{deviance}}, \code{\link{effects}},
\code{\link{fitted}},
\code{\link{residuals}}.
}
\references{
See those for the classical GLM's, \code{\link{glm}}.
}
\examples{
### -------- Gamma family -- data from example(glm) ---
clotting <- data.frame(
u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
summary(cl <- glm (lot1 ~ log(u), data=clotting, family=Gamma))
summary(ro <- glmrob(lot1 ~ log(u), data=clotting, family=Gamma))
clotM5.high <- within(clotting, { lot1[5] <- 60 })
cl5.high <- glm (lot1 ~ log(u), data=clotM5.high, family=Gamma)
ro5.high <- glmrob(lot1 ~ log(u), data=clotM5.high, family=Gamma)
rr <- range(residuals(ro), residuals(cl), residuals(ro5.high))
plot(residuals(ro5.high) ~ residuals(cl5.high), xlim = rr, ylim = rr, asp = 1)
abline(0,1, col=2, lty=3)
points(residuals(ro) ~ residuals(cl), col = "gray", pch=3)
## Show all kinds of residuals:
r.types <- c("deviance", "pearson", "working", "response")
sapply(r.types, residuals, object = ro5.high)
}
\keyword{models}
\keyword{regression}
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