gam.control {mgcv}  R Documentation 
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. To change the default
choise of fitting method, see gam.method
.
gam.control(irls.reg=0.0,epsilon = 1e06, maxit = 100,globit = 20, mgcv.tol=1e7,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(), outerPIsteps=4)
irls.reg 
For most models this should be 0. The iteratively reweighted 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 zero corresponds to an infinite range of linear predictor
values. Such convergence problems are caused by a fundamental lack of identifiability, but do not show up as
lack of identifiability in the penalized linear model problems that have to be solved at each stage of iteration.
In such circumstances it is possible to apply a ridge regression penalty to the model to impose identifiability, and
irls.reg is the size of the penalty. The penalty can not be used if the
underlying fitting method is mgcv (not the default  see
gam.method for details).

epsilon 
This is used for judging conversion of the GLM IRLS loop in
gam.fit or gam.fit2 . 
maxit 
Maximum number of IRLS iterations to perform using cautious
GCV/UBRE optimization, after globit IRLS iterations with normal GCV
optimization have been performed. Note that only fitting based on
mgcv (not default) makes
any distinction between cautious and global optimization. 
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. 
rank.tol 
The tolerance used to estimate the rank of the fitting
problem, for methods which deal with rank deficient cases (basically all
except those based on mgcv ). 
absorb.cons 
If 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 the ordinary parameterizations of the
smooths are used, which require constraints to be imposed during fitting. 
max.tprs.knots 
This is the default initial maximum number of knots to allow
when constructing a t.p.r.s bases (bs="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 to freeze R. Usually one would want to use an alternative
smoothing basis (or te terms), or the approach illustrated in the
examples in gam , rather than simply increasing this default. 
nlm 
list of control parameters to pass to nlm if this is
used for outer estimation of smoothing parameters. See details. 
optim 
list of control parameters to pass to optim if this
is used for outer estimation of smoothin parameters. See details. 
outerPIsteps 
The number of performance interation steps used to initialize outer iteration. Less than 1 means that only one performance iteration step is taken to get the function scale, but the corresponding smoothing parameter estimates are discarded. 
When outer iteration is used for fitting then the control list
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 1e4; (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 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.
Simon N. Wood simon.wood@rproject.org
Gu and Wahba (1991) Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J. Sci. Statist. Comput. 12:383398
Wood, S.N. (2000) Modelling and Smoothing Parameter Estimation with Multiple Quadratic Penalties. J.R.Statist.Soc.B 62(2):413428
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95114
Wood, S.N. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass.
http://www.stats.gla.ac.uk/~simon/
gam.method
gam
, gam.fit
, glm.control