mgcv which allows
control of the numerical options for fitting a GAM.
Typically users will want to modify the defaults if model fitting fails to
converge, or if the warnings are generated which suggest a
loss of numerical stability during fitting.gam.control(irls.reg=0.0,epsilon = 1e-04, maxit = 20,globit = 20,
mgcv.tol=1e-6,mgcv.half=15,nb.theta.mult=10000, trace = FALSE,
fit.method="magic",perf.iter=NULL,spIterType="perf",
rank.tol=.Machine$double.eps^0.5)gam.fit.globit IRLS iterations with normal GCV
optimization have been performed. Note that fit method "magic" makes
no distinction between cautioumaxit iterations will be performed
using cautious GCV/UBRE optimization.nb.theta.mult, while the minimum value is set to
the initial value divided by nb.theta.multTRUE to turn on diagnostic output."mgcv" to use the method described in Wood
(2000). Set to "magic" to use a newer numerically more stable method
(Wood, 2004),
which allows regularization and mixtures of fixed and estimated smoothing
parameters. Set to spIterType instead.fit.method="magic"."mgcv",
maxit and globit control the maximum iterations of the IRLS algorithm, as follows:
the algorithm will first execute up to
globit steps in which the GCV/UBRE algorithm performs a global search for the best overall
smoothing parameter at every iteration. If convergence is not achieved within globit iterations, then a further
maxit steps are taken, in which the overall smoothing parameter estimate is taken as the
one locally minimising the GCV/UBRE score and resulting in the lowest EDF change. The difference
between the two phases is only significant if the GCV/UBRE function develops more than one minima.
The reason for this approach is that the GCV/UBRE score for the IRLS problem can develop `phantom'
minimima for some models: these are minima which are not present in the GCV/UBRE score of the IRLS
problem resulting from moving the parameters to the minimum! Such minima can lead to convergence
failures, which are usually fixed by the second phase.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. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass.
gam, gam.fit, glm.control