% File src/library/stats/man/model.tables.Rd % Part of the R package, https://www.R-project.org % Copyright 1995-2015 R Core Team % Distributed under GPL 2 or later \name{model.tables} \alias{model.tables} \alias{model.tables.aov} \alias{model.tables.aovlist} \title{Compute Tables of Results from an \code{aov} Model Fit} \description{ Computes summary tables for model fits, especially complex \code{aov} fits. } \usage{ model.tables(x, \dots) \method{model.tables}{aov}(x, type = "effects", se = FALSE, cterms, \dots) \method{model.tables}{aovlist}(x, type = "effects", se = FALSE, \dots) } \arguments{ \item{x}{a model object, usually produced by \code{aov}} \item{type}{type of table: currently only \code{"effects"} and \code{"means"} are implemented. Can be abbreviated.} \item{se}{should standard errors be computed?} \item{cterms}{A character vector giving the names of the terms for which tables should be computed. The default is all tables.} \item{\dots}{further arguments passed to or from other methods.} } \details{ For \code{type = "effects"} give tables of the coefficients for each term, optionally with standard errors. For \code{type = "means"} give tables of the mean response for each combinations of levels of the factors in a term. The \code{"aov"} method cannot be applied to components of a \code{"aovlist"} fit. } \value{ An object of class \code{"tables.aov"}, as list which may contain components \item{tables}{A list of tables for each requested term.} \item{n}{The replication information for each term.} \item{se}{Standard error information.} } \section{Warning}{ The implementation is incomplete, and only the simpler cases have been tested thoroughly. Weighted \code{aov} fits are not supported. } \seealso{ \code{\link{aov}}, \code{\link{proj}}, \code{\link{replications}}, \code{\link{TukeyHSD}}, \code{\link{se.contrast}} } \examples{\donttest{ options(contrasts = c("contr.helmert", "contr.treatment")) npk.aov <- aov(yield ~ block + N*P*K, npk) model.tables(npk.aov, "means", se = TRUE) ## as a test, not particularly sensible statistically npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) model.tables(npk.aovE, se = TRUE) model.tables(npk.aovE, "means") }} \keyword{models}