% File src/library/stats/man/dummy.coef.Rd % Part of the R package, https://www.R-project.org % Copyright 1995-2013 R Core Team % Distributed under GPL 2 or later \name{dummy.coef} \title{Extract Coefficients in Original Coding} \usage{ dummy.coef(object, \dots) \method{dummy.coef}{lm}(object, use.na = FALSE, \dots) \method{dummy.coef}{aovlist}(object, use.na = FALSE, \dots) } \alias{dummy.coef} \alias{dummy.coef.lm} \alias{dummy.coef.aovlist} \arguments{ \item{object}{a linear model fit.} \item{use.na}{logical flag for coefficients in a singular model. If \code{use.na} is true, undetermined coefficients will be missing; if false they will get one possible value.} \item{\dots}{arguments passed to or from other methods.} } \description{ This extracts coefficients in terms of the original levels of the coefficients rather than the coded variables. } \details{ A fitted linear model has coefficients for the contrasts of the factor terms, usually one less in number than the number of levels. This function re-expresses the coefficients in the original coding; as the coefficients will have been fitted in the reduced basis, any implied constraints (e.g., zero sum for \code{contr.helmert} or \code{contr.sum}) will be respected. There will be little point in using \code{dummy.coef} for \code{contr.treatment} contrasts, as the missing coefficients are by definition zero. The method used has some limitations, and will give incomplete results for terms such as \code{poly(x, 2)}. However, it is adequate for its main purpose, \code{aov} models. } \value{ A list giving for each term the values of the coefficients. For a \I{multistratum} \code{aov} model, such a list for each stratum. } \section{Warning}{ This function is intended for human inspection of the output: it should not be used for calculations. Use coded variables for all calculations. The results differ from S for singular values, where S can be incorrect. } \seealso{\code{\link{aov}}, \code{\link{model.tables}}} \examples{ options(contrasts = c("contr.helmert", "contr.poly")) ## From Venables and Ripley (2002) p.165. npk.aov <- aov(yield ~ block + N*P*K, npk) dummy.coef(npk.aov) npk.aovE <- aov(yield ~ N*P*K + Error(block), npk) dummy.coef(npk.aovE) } \keyword{models}