mgcv (version 0.9-6)

gam.control: Setting GAM fitting defaults

Description

This is an internal function of package 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.

Usage

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=TRUE,rank.tol=.Machine$double.eps^0.5)

Arguments

irls.reg

For most models this should be 0. The iteratively re-weighted least squares method by which GAMs are fitted can fail to converge in some circumstances. For example data with many zeroes can cause problems in a model with a log link, because a mean of ze

epsilon

This is used for judging conversion of the GLM IRLS loop in gam.fit.

maxit

Maximum number of IRLS iterations to perform using cautious GCV/UBRE opttimization, after globit IRLS iterations with normal GCV optimization have been performed. Note that fit method "magic" makes no distinction between cautio

globit

Maximum number of IRLS iterations to perform with normal GCV/UBRE optimization. If convergence is not achieved after these iterations then a further maxit iterations will be performed using cautious GCV/UBRE optimization.

mgcv.tol

The convergence tolerance parameter to use in GCV/UBRE optimization.

mgcv.half

If a step of the GCV/UBRE optimization method leads to a worse GCV/UBRE score, then the step length is halved. This is the number of halvings to try before giving up.

nb.theta.mult

Controls the limits on theta when negative binomial parameter is to be estimated. Maximum theta is set to the initial value multiplied by nb.theta.mult, while the minimum value is set to the initial value divided by nb.theta.mult

trace

Set this to TRUE to turn on diagnostic output.

fit.method

set to "mgcv" to use the method described in Wood (2000). Set to "magic" to use a newer numerically more stable method, which allows regularization and mixtures of fixed and estimated smoothing parameters. Set to "fastest

perf.iter

set to TRUE to use Gu's approach to finding smoothing parameters in which GCV or UBRE is applied to the penalized linear modelling problem produced at each IRLS iteration. This method is very fast, but means that it is often possible to fin

rank.tol

The tolerance used to estimate rank when using fit.method="magic".

Details

With fit method "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.

References

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

Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114

http://www.stats.gla.ac.uk/~simon/

See Also

gam, gam.fit, glm.control