gam.outer.Rd 2.75 KB
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\name{gam.outer}
\alias{gam.outer}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Minimize GCV or UBRE score of a GAM using `outer' iteration}
\description{Estimation of GAM smoothing parameters is most stable if
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optimization of the smoothness selection score (GCV, GACV, UBRE/AIC, REML, ML etc) 
is outer to the penalized iteratively
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re-weighted least squares scheme used to estimate the model given smoothing 
parameters.

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This routine optimizes a smoothness selection score in this way. Basically the 
score is evaluated for each trial set of smoothing parameters by
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estimating the GAM for those smoothing parameters. The score is minimized
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w.r.t. the parameters numerically, using \code{newton} (default), \code{bfgs}, \code{optim} or \code{nlm}. Exact
(first and second) derivatives of the score can be used by fitting with
\code{\link{gam.fit3}}. This
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improves efficiency and reliability relative to relying on finite
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difference derivatives.  

Not normally called directly, but rather a service routine for \code{\link{gam}}.
}
\usage{
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gam.outer(lsp,fscale,family,control,method,optimizer,
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          criterion,scale,gamma,G,start=NULL,...)
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}
%- maybe also `usage' for other objects documented here.
\arguments{ 
\item{lsp}{The log smoothing parameters.}

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\item{fscale}{Typical scale of the GCV or UBRE/AIC score.}
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\item{family}{the model family.}

\item{control}{control argument to pass to \code{\link{gam.fit}} if pure
finite differencing is being used.}

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\item{method}{method argument to \code{\link{gam}} defining the smoothness criterion to use (but depending on whether or not scale known).}

\item{optimizer}{The argument to \code{\link{gam}} defining the numerical optimization method to use. }

\item{criterion}{Which smoothness selction criterion to use. One of \code{"UBRE"},
\code{"GCV"}, \code{"GACV"}, \code{"REML"} or \code{"P-REML"}.   }

\item{scale}{Supplied scale parameter. Positive indicates known.}
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\item{gamma}{ The degree of freedom inflation factor for the GCV/UBRE/AIC score.}
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\item{G}{List produced by \code{mgcv:::gam.setup}, containing most of what's
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needed to actually fit a GAM.}
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\item{start}{starting parameter values.}
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\item{...}{other arguments, typically for passing on to \code{gam.fit3} (ultimately).}
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}
\details{ 
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See Wood (2008) for full details on `outer iteration'.
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}

\references{
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Wood, S.N. (2011) Fast stable restricted maximum likelihood 
and marginal likelihood estimation of semiparametric generalized linear 
models. Journal of the Royal Statistical Society (B) 73(1):3-36
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\url{http://www.maths.bris.ac.uk/~sw15190/}
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}
\author{ Simon N. Wood \email{simon.wood@r-project.org}}


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\seealso{\code{\link{gam.fit3}},  \code{\link{gam}}, \code{\link{magic}}}
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\keyword{models} \keyword{smooth} \keyword{regression}%-- one or more ...