\HeaderA{glm.summaries}{Accessing Generalized Linear Model Fits}{glm.summaries}
\aliasA{family.glm}{glm.summaries}{family.glm}
\aliasA{residuals.glm}{glm.summaries}{residuals.glm}
\keyword{models}{glm.summaries}
\keyword{regression}{glm.summaries}
\begin{Description}\relax
These functions are all \code{\LinkA{methods}{methods}} for class \code{glm} or
\code{summary.glm} objects.
\end{Description}
\begin{Usage}
\begin{verbatim}
## S3 method for class 'glm':
family(object, ...)

## S3 method for class 'glm':
residuals(object, type = c("deviance", "pearson", "working",
                           "response", "partial"), ...)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] an object of class \code{glm}, typically the result of
a call to \code{\LinkA{glm}{glm}}.
\item[\code{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[\code{...}] further arguments passed to or from other methods.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
The references 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.

How \code{residuals} treats cases with missing values in the original
fit is determined by the \code{na.action} argument of that fit.
If \code{na.action = na.omit} omitted cases will not appear in the
residuals, whereas if \code{na.action = na.exclude} they will appear,
with residual value \code{NA}.  See also \code{\LinkA{naresid}{naresid}}.

For fits done with \code{y = FALSE} the response values are computed
from other components.
\end{Details}
\begin{References}\relax
Davison, A. C. and Snell, E. J. (1991)
\emph{Residuals and diagnostics.}  In: Statistical Theory
and Modelling. In Honour of Sir David Cox, FRS, eds.
Hinkley, D. V., Reid, N. and Snell, E. J., Chapman \& Hall.

Hastie, T. J. and Pregibon, D. (1992)
\emph{Generalized linear models.}
Chapter 6 of \emph{Statistical Models in S}
eds J. M. Chambers and T. J. Hastie, Wadsworth \& Brooks/Cole.

McCullagh P. and Nelder, J. A. (1989)
\emph{Generalized Linear Models.}
London: Chapman and Hall.
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{glm}{glm}} for computing \code{glm.obj}, \code{\LinkA{anova.glm}{anova.glm}};
the corresponding \emph{generic} functions, \code{\LinkA{summary.glm}{summary.glm}},
\code{\LinkA{coef}{coef}}, \code{\LinkA{deviance}{deviance}},
\code{\LinkA{df.residual}{df.residual}},
\code{\LinkA{effects}{effects}}, \code{\LinkA{fitted}{fitted}},
\code{\LinkA{residuals}{residuals}}.

\LinkA{influence.measures}{influence.measures} for deletion diagnostics, including
standardized (\code{\LinkA{rstandard}{rstandard}})
and studentized (\code{\LinkA{rstudent}{rstudent}}) residuals.
\end{SeeAlso}

