\HeaderA{mgcv.control}{Setting mgcv defaults}{mgcv.control}
\keyword{models}{mgcv.control}
\keyword{smooth}{mgcv.control}
\keyword{regression}{mgcv.control}
\begin{Description}\relax
This is an internal function of package \code{mgcv} which allows control of the numerical 
options for fitting a generalized ridge regression problem using routine \code{mgcv}.
\end{Description}
\begin{Usage}
\begin{verbatim}
mgcv.control(conv.tol=1e-7,max.half=20,target.edf=NULL,min.edf=-1)
\end{verbatim}
\end{Usage}
\begin{Arguments}
\begin{ldescription}
\item[\code{conv.tol}] The convergence tolerance.
\item[\code{max.half}] successive step halvings are employed if the Newton method and then the steepest descent backup
fail to improve the UBRE/GCV score. This is how many to use before giving up.
\item[\code{target.edf}] If this is non-null it indicates that cautious optimization should be used, which 
opts for the local minimum closest to the target model edf if there are multiple local minima in the 
GCV/UBRE score.
\item[\code{min.edf}] Lower bound on the model edf. Useful for avoiding numerical problems at high smoothing parameter
values. Negative for none.
\end{ldescription}
\end{Arguments}
\begin{Author}\relax
Simon N. Wood \email{simon.wood@r-project.org}
\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

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

