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% File src/library/stats/man/glm.summaries.Rd% Part of the R package, https://www.R-project.org% Copyright 1995-2007 R Core Team% Distributed under GPL 2 or later\name{glm.summaries}\alias{family.glm}\alias{residuals.glm}\title{Accessing Generalized Linear Model Fits}\usage{\method{family}{glm}(object, \dots)\method{residuals}{glm}(object, type = c("deviance", "pearson", "working","response", "partial"), \dots)}\arguments{\item{object}{an object of class \code{glm}, typically the result ofa call to \code{\link{glm}}.}\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"}.Can be abbreviated.}\item{\dots}{further arguments passed to or from other methods.}}\description{These functions are all \code{\link{methods}} for class \code{glm} or\code{summary.glm} objects.}\details{The references define the types of residuals: Davison & Snell is agood reference for the usages of each.The partial residuals are a matrix of working residuals, with eachcolumn formed by omitting a term from the model.How \code{residuals} treats cases with missing values in the originalfit is determined by the \code{na.action} argument of that fit.If \code{na.action = na.omit} omitted cases will not appear in theresiduals, whereas if \code{na.action = na.exclude} they will appear,with residual value \code{NA}. See also \code{\link{naresid}}.For fits done with \code{y = FALSE} the response values are computedfrom other components.}\seealso{\code{\link{glm}} for computing \code{glm.obj}, \code{\link{anova.glm}};the corresponding \emph{generic} functions, \code{\link{summary.glm}},\code{\link{coef}}, \code{\link{deviance}},\code{\link{df.residual}},\code{\link{effects}}, \code{\link{fitted}},\code{\link{residuals}}.\link{influence.measures} for deletion diagnostics, includingstandardized (\code{\link{rstandard}})and studentized (\code{\link{rstudent}}) residuals.}\references{Davison, A. C. and Snell, E. J. (1991)\emph{Residuals and diagnostics.} In: Statistical Theoryand 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.}\keyword{models}\keyword{regression}