\Header{dummy.coef}{Extract Coefficients in Original Coding}
\methalias{dummy.coef.aovlist}{dummy.coef}
\methalias{dummy.coef.lm}{dummy.coef}
\alias{print.dummy.coef}{dummy.coef}
\methalias{print.dummy.coef.list}{dummy.coef}
\keyword{models}{dummy.coef}
\begin{Description}\relax
This extracts coefficients in terms of the original levels of the
coefficients rather than the coded variables.\end{Description}
\begin{Usage}
\begin{verbatim}
dummy.coef(object, ...)
dummy.coef.lm(object, use.na = FALSE)
dummy.coef.aovlist(object, use.na = FALSE)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] a linear model fit
\item[\code{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.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
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.\end{Details}
\begin{Value}
A list giving for each term the values of the coefficients. For a
multistratum \code{aov} model, such a list for each stratum.\end{Value}
\begin{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.\end{Section}
\begin{Author}\relax
B.D. Ripley\end{Author}
\begin{SeeAlso}\relax
\code{\Link{aov}}, \code{\Link{model.tables}}\end{SeeAlso}
\begin{Examples}
\begin{ExampleCode}
options(contrasts=c("contr.helmert", "contr.poly"))
## From Venables and Ripley (1997) p.210.
N <- c(0,1,0,1,1,1,0,0,0,1,1,0,1,1,0,0,1,0,1,0,1,1,0,0)
P <- c(1,1,0,0,0,1,0,1,1,1,0,0,0,1,0,1,1,0,0,1,0,1,1,0)
K <- c(1,0,0,1,0,1,1,0,0,1,0,1,0,1,1,0,0,0,1,1,1,0,1,0)
yield <- c(49.5,62.8,46.8,57.0,59.8,58.5,55.5,56.0,62.8,55.8,69.5,
55.0, 62.0,48.8,45.5,44.2,52.0,51.5,49.8,48.8,57.2,59.0,53.2,56.0)

npk <- data.frame(block=gl(6,4), N=factor(N), P=factor(P),
                  K=factor(K), yield=yield)
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)
\end{ExampleCode}
\end{Examples}

