| % File src/library/stats/man/anova.glm.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2013 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{anova.glm} |
| \title{Analysis of Deviance for Generalized Linear Model Fits} |
| \usage{ |
| \method{anova}{glm}(object, \dots, dispersion = NULL, test = NULL) |
| } |
| \alias{anova.glm} |
| \description{ |
| Compute an analysis of deviance table for one or more generalized |
| linear model fits. |
| } |
| \arguments{ |
| \item{object, \dots}{objects of class \code{glm}, typically |
| the result of a call to \code{\link{glm}}, or a list of |
| \code{objects} for the \code{"glmlist"} method.} |
| \item{dispersion}{the dispersion parameter for the fitting family. |
| By default it is obtained from the object(s).} |
| \item{test}{a character string, (partially) matching one of \code{"Chisq"}, |
| \code{"LRT"}, \code{"Rao"}, |
| \code{"F"} or \code{"Cp"}. See \code{\link{stat.anova}}.} |
| } |
| \details{ |
| Specifying a single object gives a sequential analysis of deviance |
| table for that fit. That is, the reductions in the residual deviance |
| as each term of the formula is added in turn are given in as |
| the rows of a table, plus the residual deviances themselves. |
| |
| If more than one object is specified, the table has a row for the |
| residual degrees of freedom and deviance for each model. |
| For all but the first model, the change in degrees of freedom and |
| deviance is also given. (This only makes statistical sense if the |
| models are nested.) It is conventional to list the models from |
| smallest to largest, but this is up to the user. |
| |
| The table will optionally contain test statistics (and P values) |
| comparing the reduction in deviance for the row to the residuals. |
| For models with known dispersion (e.g., binomial and Poisson fits) |
| the chi-squared test is most appropriate, and for those with |
| dispersion estimated by moments (e.g., \code{gaussian}, |
| \code{quasibinomial} and \code{quasipoisson} fits) the F test is |
| most appropriate. Mallows' \eqn{C_p}{Cp} statistic is the residual |
| deviance plus twice the estimate of \eqn{\sigma^2} times |
| the residual degrees of freedom, which is closely related to AIC (and |
| a multiple of it if the dispersion is known). |
| You can also choose \code{"LRT"} and |
| \code{"Rao"} for likelihood ratio tests and Rao's efficient score test. |
| The former is synonymous with \code{"Chisq"} (although both have |
| an asymptotic chi-square distribution). |
| |
| The dispersion estimate will be taken from the largest model, using |
| the value returned by \code{\link{summary.glm}}. As this will in most |
| cases use a Chisquared-based estimate, the F tests are not based on |
| the residual deviance in the analysis of deviance table shown. |
| } |
| \value{ |
| An object of class \code{"anova"} inheriting from class \code{"data.frame"}. |
| } |
| \section{Warning}{ |
| The comparison between two or more models will only be valid if they |
| are fitted to the same dataset. This may be a problem if there are |
| missing values and \R's default of \code{na.action = na.omit} is used, |
| and \code{anova} will detect this with an error. |
| } |
| \references{ |
| 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. |
| } |
| \seealso{ |
| \code{\link{glm}}, \code{\link{anova}}. |
| |
| \code{\link{drop1}} for |
| so-called \sQuote{type II} anova where each term is dropped one at a |
| time respecting their hierarchy. |
| } |
| \examples{ |
| ## --- Continuing the Example from '?glm': |
| \dontshow{require(utils) |
| example("glm", echo = FALSE)} |
| anova(glm.D93) |
| anova(glm.D93, test = "Cp") |
| anova(glm.D93, test = "Chisq") |
| glm.D93a <- |
| update(glm.D93, ~treatment*outcome) # equivalent to Pearson Chi-square |
| anova(glm.D93, glm.D93a, test = "Rao") |
| } |
| \keyword{models} |
| \keyword{regression} |
| |