| gam.outer {mgcv} | R Documentation |
Estimation of GAM smoothing parameters is most stable if optimization of the UBRE or GCV score is outer to the penalized iteratively re-weighted least squares scheme used to estimate the model given smoothing parameters.
This routine optimizes a GCV or UBRE score in this way. Basically the GCV or
UBRE score is evaluated for each trial set of smoothing parameters by
estimating the GAM for those smoothing parameters. The score is minimized
w.r.t. the parameters numerically, using optim or nlm. Exact
derivatives of the score can be used by fitting with gam.fit2,
which improves efficiency and reliability relative to relying solely on finite
difference derivatives.
Note that there is a choise between basing GCV/UBRE scores on the deviance or
the Pearson statistic: see gam.method.
Not normally called directly, but rather a service routine for gam.
gam.outer(lsp,fscale,family,control,method,gamma,G)
lsp |
The log smoothing parameters. |
fscale |
Typical scale of the GCV or UBRE score. |
family |
the model family. |
control |
control argument to pass to gam.fit if pure
finite differencing is being used. |
method |
method list reurned from gam.method. This defines
the optimization method to use. |
gamma |
The degree of freedom inflation factor for the GCV/UBRE score. |
G |
List produced by gam.setup, containing most of what's
needed to actually fit GAM. |
Estimation of smoothing parameters by optimizing GCV scores obtained at convergence of the P-IRLS iteration was proposed by O'Sullivan et al. (1986), and is here termed `outer' iteration.
Simon N. Wood simon.wood@r-project.org
O 'Sullivan, Yandall & Raynor (1986) Automatic smoothing of regression functions in generalized linear models. J. Amer. Statist. Assoc. 81:96-103.
http://www.stats.gla.ac.uk/~simon/