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. To change the default
choise of fitting method, see gam.method.gam.control(irls.reg=0.0,epsilon = 1e-06, maxit = 100,globit = 20,
mgcv.tol=1e-7,mgcv.half=15,nb.theta.mult=10000, trace = FALSE,
rank.tol=.Machine$double.eps^0.5,absorb.cons=TRUE,
max.tprs.knots=5000,nlm=list(),optim=list(),newton=list(),
outerPIsteps=1)globit IRLS iterations with normal GCV
optimization have been performed. Note that only fitting based on
mgcvmaxit 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).TRUE then the GAM is set up using a
parameterization which requires no further constraint. Usually this means that
all the smooths are automatically centered (i.e. they sum to zero over the
covariate values). If FALSE then thebs="tp"). The set up cost (and
storage) for these smooths scales as the square of the number of initial knots, so if it's too
high you can appear nlm if this is
used for outer estimation of smoothing parameters. See details.optim if this
is used for outer estimation of smoothing parameters. See details.nlm stores control arguments for calls to routine
nlm. The list has the following named elements: (i) ndigit is
the number of significant digits in the GCV/UBRE score - by default this is
worked out from epsilon; (ii) gradtol is the tolerance used to
judge convergence of the gradient of the GCV/UBRE score to zero - by default
set to 100*epsilon; (iii) stepmax is the maximum allowable log
smoothing parameter step - defaults to 2; (iv) steptol is the minimum
allowable step length - defaults to 1e-4; (v) iterlim is the maximum
number of optimization steps allowed - defaults to 200; (vi)
check.analyticals indicates whether the built in exact derivative
calculations should be checked numerically - defaults to FALSE. Any of
these which are not supplied and named in the list are set to their default
values.Outer iteration using newton is controlled by the list newton
with the following elements : conv.tol (default
1e-6) is the relative convergence tolerance; maxNstep is the maximum
length allowed for an element of the Newton search direction (default 5);
maxSstep is the maximum length allowed for an element of the steepest
descent direction (only used if Newton fails - default 2); maxHalf is
the maximum number of step halvings to permit before giving up (default 30);
use.svd determines whether to use an SVD step or QR step for the part
of fitting that determines problem rank, etc. (defaults to FALSE).
Outer iteration using optim is controlled using list
optim, which currently has one element: factr which takes
default value 1e7.
When fitting is been done by calls to routine 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.method gam, gam.fit, glm.control