| 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 = 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())
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. 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. |
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 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.
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 ./usr/lib/R/library/mgcv/html/gam.check.html 0000644 0000000 0000000 00000016171 10232723744 021015 0 ustar root root 0000000 0000000
| gam.check {mgcv} | R Documentation |
Takes a fitted gam object produced by gam() and produces some diagnostic information
about the fitting procedure and results.
gam.check(b)
b |
a fitted gam object as produced by gam(). |
This function plots 4 standard diagnostic plots, and some other convergence diagnostics. Output differs depending on whether the underlyin