gam.control
Setting GAM fitting defaults
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
arguments method
and optimizer
.
- Keywords
- models, regression, smooth
Usage
gam.control(nthreads=1,irls.reg=0.0,epsilon = 1e-07, maxit = 200,
mgcv.tol=1e-7,mgcv.half=15, trace = FALSE,
rank.tol=.Machine$double.eps^0.5,nlm=list(),
optim=list(),newton=list(),outerPIsteps=0,
idLinksBases=TRUE,scalePenalty=TRUE,efs.lspmax=15,
efs.tol=.1,keepData=FALSE,scale.est="fletcher",
edge.correct=FALSE)
Arguments
- nthreads
Some parts of some smoothing parameter selection methods (e.g. REML) can use some parallelization in the C code if your R installation supports openMP, and
nthreads
is set to more than 1. Note that it is usually better to use the number of physical cores here, rather than the number of hyper-threading cores.- 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 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.- epsilon
This is used for judging conversion of the GLM IRLS loop in
gam.fit
orgam.fit3
.- maxit
Maximum number of IRLS iterations to perform.
- 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.
- trace
Set this to
TRUE
to turn on diagnostic output.- rank.tol
The tolerance used to estimate the rank of the fitting problem.
- nlm
list of control parameters to pass to
nlm
if this is used for outer estimation of smoothing parameters (not default). See details.- optim
list of control parameters to pass to
optim
if this is used for outer estimation of smoothing parameters (not default). See details.- newton
list of control parameters to pass to default Newton optimizer used for outer estimation of log smoothing parameters. See details.
- outerPIsteps
The number of performance interation steps used to initialize outer iteration.
- idLinksBases
If smooth terms have their smoothing parameters linked via the
id
mechanism (sees
), should they also have the same bases. Set this toFALSE
only if you are sure you know what you are doing (you should almost surely setscalePenalty
toFALSE
as well in this case).- scalePenalty
gamm
is somewhat sensitive to the absolute scaling of the penalty matrices of a smooth relative to its model matrix. This option rescales the penalty matrices to accomodate this problem. Probably should be set toFALSE
if you are linking smoothing parameters but have setidLinkBases
toFALSE
.- efs.lspmax
maximum log smoothing parameters to allow under extended Fellner Schall smoothing parameter optimization.
- efs.tol
change in REML to count as negligible when testing for EFS convergence. If the step is small and the last 3 steps led to a REML change smaller than this, then stop.
- keepData
Should a copy of the original
data
argument be kept in thegam
object? Strict compatibility with classglm
would keep it, but it wastes space to do so.- scale.est
How to estimate the scale parameter for exponential family models estimated by outer iteration. See
gam.scale
.- edge.correct
With RE/ML smoothing parameter selection in
gam
using the default Newton RE/ML optimizer, it is possible to improve inference at the `completely smooth' edge of the smoothing parameter space, by decreasing smoothing parameters until there is a small increase in the negative RE/ML (e.g. 0.02). Set toTRUE
or to a number representing the target increase to use. Only changes the corrected smoothing parameter matrix,Vc
.
Details
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).
If outer iteration using nlm
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 10*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 optim
is controlled using list
optim
, which currently has one element: factr
which takes
default value 1e7.
References
Wood, S.N. (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society (B) 73(1):3-36
Wood, S.N. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Amer. Statist. Ass.99:673-686.