\Header{gam.fit}{Generalized Additive Models fitting using penalized regression splines and 
GCV}
\keyword{models}{gam.fit}
\keyword{smooth}{gam.fit}
\keyword{regression}{gam.fit}
\begin{Description}\relax
This is an internal function of package \code{mgcv}. It is a modification
of the function \code{glm.fit}, designed to be called from \code{gam}. The major
modification is that rather than solving a weighted least squares problem at each IRLS step, 
a weighted, penalized least squares problem
is solved at each IRLS step with smoothing parameters associated with each penalty chosen by GCV or UBRE,
using routine \code{mgcv}. For further information on usage see code for \code{gam}. Some regularization of the 
IRLS weights is also permitted as a way of addressing identifiability related problems (see 
\code{\Link{gam.control}}). Negative binomial parameter estimation is supported.
\end{Description}
\begin{Author}\relax
Simon N. Wood \email{simon@stats.gla.ac.uk}
\end{Author}
\begin{References}\relax
Gu and Wahba (1991) Minimizing GCV/GML scores with multiple smoothing parameters via
the Newton method. SIAM J. Sci. Statist. Comput. 12:383-398

Wood, S.N. (2000)  Modelling and Smoothing Parameter Estimation
with Multiple  Quadratic Penalties. J.R.Statist.Soc.B 62(2):413-428

Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114

Wood, S.N. (in press) Stable and efficient multiple smoothing parameter estimation for
generalized additive models. J. Amer. Statist. Ass.

\url{http://www.stats.gla.ac.uk/~simon/}
\end{References}
\begin{SeeAlso}\relax
\code{\Link{gam}}, \code{\Link{mgcv}}, \code{\Link{magic}}
\end{SeeAlso}

