| gam.convergence {mgcv} | R Documentation |
When fitting GAMs there is a tradeoff between speed of fitting and probability of
fit convergence. The default fitting options specified by
gam.method (as the default for argument method of
gam), always opt for certainty of convergence over speed of
fit. In the additive modelling contexts this means using fitting routine
magic rather than the slightly faster routine
mgcv. In the Generalized Additive Model case it means using
`outer' iteration in preference to `performance iteration': see
gam.outer for details.
It is possible for the default `outer' iteration to fail when finding intial
smoothing parameters using a few steps of outer iteration (if you get a
convergence failure message from magic when outer iterating, then this
is what has happened): lower outerPIsteps in gam.control
to fix this.
There are two things that you can do to speed up GAM fitting. (i) Change the
method argument to gam so that `performance iteration' is
used in place of the default outer iteration. See the perf.magic option
under gam.method, for example. Usually performance iteration
converges well and is quick. (ii) For large datasets it may be worth changing
the smoothing basis to use bs="cr" (see s for details)
for 1-d smooths, and to use te smooths in place of
s smooths for smooths of more than one variable. This is because
the default thin plate regression spline basis "tp" is costly to set up
for large datasets (much over 1000 data, say). Alternatively see the last few
examples for gam.
If the GAM estimation process fails to converge when using performance
iteration, then switch to outer iteration via the method argument of
gam (see gam.method). If it still fails, try
increasing the number of IRLS iterations (see gam.control) or
perhaps experiment with the convergence tolerance.
If you still have problems, it's worth noting that a GAM is just a (penalized)
GLM and the IRLS scheme used to estimate GLMs is not guaranteed to
converge. Hence non convergence of a GAM may relate to a lack of stability in
the basic IRLS scheme. Therefore it is worth trying to establish whether the IRLS iterations
are capable of converging. To do this fit the problematic GAM with all smooth
terms specified with fx=TRUE so that the smoothing parameters are all
fixed at zero. If this `largest' model can converge then, then the maintainer
would quite like to know about your problem! If it doesn't converge, then its
likely that your model is just too flexible for the IRLS process itself. Having tried
increasing maxit in gam.control, there are several other
possibilities for stabilizing the iteration. It is possible to try (i) setting lower bounds on the
smoothing parameters using the min.sp argument of gam: this may
or may not change the model being fitted; (ii)
reducing the flexibility of the model by reducing the basis dimensions
k in the specification of s and te model terms: this
obviously changes the model being fitted somewhat; (iii)
introduce a small regularization term into the fitting via the irls.reg
argument of gam.control: this option obviously changes the nature of
the fit somewhat, since parameter estimates are pulled towards zero by doing
this.
Usually, a major contributer to fitting difficulties is that the model is a very poor description of the data.
Simon N. Wood simon.wood@r-project.org