[R] MGCV: Use of irls.reg option

[Simon Wood]

Hi Mark,

irls.reg is kind of `legacy code'. Does model fitting actually fail for 
your example, or is it just that the
estimated spatial smooth looks unpleasant?

best,
Simon


On 06/21/2012 01:28 AM, r-help.20.trevva at spamgourmet.com wrote:
> Hi,
>
> In the help files in the  mgcv package for the gam.control() function,
> there is an option irls.reg. The help files describe this option as:
>
> 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.
>
> I am trying to fit a poisson GLM model with a log-link function and am
> having problems similar to those described - in particular, the model
> has a spatial s(lon,lat) term and there are lot of zeros around the
> edges of my domain which are making the TPRS do strange thing. It
> sounds like irls.reg might be the answer to my problems. The question
> I have is how to use it? What is an appropriate value? I can't seem to
> find any more information than that provided, and I don't know if I
> really understand what it is doing. Are there any examples or
> references on this that I have overlooked during my googling that
> could help?
>
> Best wishes,
>
> Mark Payne
> DTU Aqua,
> Copenhagen, Denmark
>
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