# 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`

or`gam.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 (see`s`

), should they also have the same bases. Set this to`FALSE`

only if you are sure you know what you are doing (you should almost surely set`scalePenalty`

to`FALSE`

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 to`FALSE`

if you are linking smoothing parameters but have set`idLinkBases`

to`FALSE`

.- 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 the`gam`

object? Strict compatibility with class`glm`

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 to`TRUE`

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.

##### See Also

*Documentation reproduced from package mgcv, version 1.8-31, License: GPL (>= 2)*