The R Project SVN R

\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
  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.
data(npk, package="MASS")
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}