| % File src/library/stats/man/summary.aov.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2007 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{summary.aov} |
| \alias{summary.aov} |
| \alias{summary.aovlist} |
| \alias{print.summary.aov} |
| \alias{print.summary.aovlist} |
| \title{Summarize an Analysis of Variance Model} |
| \usage{ |
| \method{summary}{aov}(object, intercept = FALSE, split, |
| expand.split = TRUE, keep.zero.df = TRUE, \dots) |
| |
| \method{summary}{aovlist}(object, \dots) |
| } |
| \arguments{ |
| \item{object}{An object of class \code{"aov"} or \code{"aovlist"}.} |
| \item{intercept}{logical: should intercept terms be included?} |
| \item{split}{an optional named list, with names corresponding to terms |
| in the model. Each component is itself a list with integer |
| components giving contrasts whose contributions are to be summed.} |
| \item{expand.split}{logical: should the split apply also to |
| interactions involving the factor?} |
| \item{keep.zero.df}{logical: should terms with no degrees of freedom |
| be included?} |
| \item{\dots}{Arguments to be passed to or from other methods, |
| for \code{summary.aovlist} including those for \code{summary.aov}.} |
| } |
| \description{ |
| Summarize an analysis of variance model. |
| } |
| \value{ |
| An object of class \code{c("summary.aov", "listof")} or |
| \code{"summary.aovlist"} respectively. |
| |
| For fits with a single stratum the result will be a list of |
| ANOVA tables, one for each response (even if there is only one response): |
| the tables are of class \code{"anova"} inheriting from class |
| \code{"data.frame"}. They have columns \code{"Df"}, \code{"Sum Sq"}, |
| \code{"Mean Sq"}, as well as \code{"F value"} and \code{"Pr(>F)"} if |
| there are non-zero residual degrees of freedom. There is a row for |
| each term in the model, plus one for \code{"Residuals"} if there |
| are any. |
| |
| For multistratum fits the return value is a list of such summaries, |
| one for each stratum. |
| } |
| \note{ |
| The use of \code{expand.split = TRUE} is little tested: it is always |
| possible to set it to \code{FALSE} and specify exactly all |
| the splits required. |
| } |
| \seealso{ |
| \code{\link{aov}}, \code{\link{summary}}, \code{\link{model.tables}}, |
| \code{\link{TukeyHSD}} |
| } |
| \examples{ |
| ## For a simple example see example(aov) |
| |
| # Cochran and Cox (1957, p.164) |
| # 3x3 factorial with ordered factors, each is average of 12. |
| CC <- data.frame( |
| y = c(449, 413, 326, 409, 358, 291, 341, 278, 312)/12, |
| P = ordered(gl(3, 3)), N = ordered(gl(3, 1, 9)) |
| ) |
| CC.aov <- aov(y ~ N * P, data = CC , weights = rep(12, 9)) |
| summary(CC.aov) |
| |
| # Split both main effects into linear and quadratic parts. |
| summary(CC.aov, split = list(N = list(L = 1, Q = 2), |
| P = list(L = 1, Q = 2))) |
| |
| # Split only the interaction |
| summary(CC.aov, split = list("N:P" = list(L.L = 1, Q = 2:4))) |
| |
| # split on just one var |
| summary(CC.aov, split = list(P = list(lin = 1, quad = 2))) |
| summary(CC.aov, split = list(P = list(lin = 1, quad = 2)), |
| expand.split = FALSE)} |
| \keyword{models} |
| \keyword{regression} |