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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
