\HeaderA{smoothCon}{Prediction/Construction wrapper functions for GAM smooth terms}{smoothCon}
\aliasA{PredictMat}{smoothCon}{PredictMat}
\keyword{models}{smoothCon}
\keyword{smooth}{smoothCon}
\keyword{regression}{smoothCon}
\begin{Description}\relax
Wrapper functions for construction of and prediction from smooth
terms in a GAM. The purpose of the wrappers is to allow user-transparant
re-parameterization of smooth terms, in order to allow identifiability
constraints to be absorbed into the parameterization of each term, if required.
\end{Description}
\begin{Usage}
\begin{verbatim}
smoothCon(object,data,knots,absorb.cons=FALSE)
PredictMat(object,data)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{object}] is a smooth specification object or a smooth object.
\item[\code{data}] A data frame containing the values of the (named) covariates at which the smooth term is to be 
evaluated.
\item[\code{knots}] An optional data frame supplying any knot locations to be
supplied for basis construction.
\item[\code{absorb.cons}] Set to \code{TRUE} in order to have identifiability
constraints absorbed into the basis.
\end{ldescription}
\end{Arguments}
\begin{Details}\relax
These wrapper functions exist to allow smooths specified using
\code{\LinkA{smooth.construct}{smooth.construct}} and \code{\LinkA{Predict.matrix}{Predict.matrix}} method
functions to be re-parameterized so that identifiability constraints are no
longer required in fitting. This is done in a user transparent
manner, but is typically of no importance in use of GAMs. 

The parameterization used by \code{\LinkA{gam}{gam}} can be controlled via
\code{\LinkA{gam.control}{gam.control}}.
\end{Details}
\begin{Value}
From \code{smoothCon} a \code{smooth} object returned by the
appropriate \code{\LinkA{smooth.construct}{smooth.construct}} method function. If constraints are
to be absorbed then the object will have an attributes \code{"qrc"} and
\code{"nCons"}, the qr decomposition of the constraint matrix (returned by
\code{\LinkA{qr}{qr}}) and the number of constraints, respectively: these are  used in
the re-parameterization. 

For \code{predictMat} a matrix which will map the parameters associated with
the smooth to the vector of values of the smooth evaluated at the covariate
values given in \code{object}.
\end{Value}
\begin{Author}\relax
Simon N. Wood \email{simon.wood@r-project.org}
\end{Author}
\begin{References}\relax
\url{http://www.stats.gla.ac.uk/~simon/}
\end{References}
\begin{SeeAlso}\relax
\code{\LinkA{gam.control}{gam.control}},
\code{\LinkA{smooth.construct}{smooth.construct}}, \code{\LinkA{Predict.matrix}{Predict.matrix}}
\end{SeeAlso}

