[R] gam()

[Henric Nilsson]

Dear all,

I've now spent a couple of days trying to learn R and, in particular, the 
gam() function, and I now have a few questions and reflections regarding 
the latter. Maybe these things are implemented in some way that I'm not yet 
aware of or have perhaps been decided by the R community to not be what's 
wanted. Of course, my lack of complete theoretical understanding of what 
mgcv really does may also show...

1. When fitting models where a factor interacts with a smooth term, say 
y~a+s(x,by=a.1)+s(x,by=a.2), I noticed that the rug in the plot of each of 
the smooth terms is identical. I expected the rug in the plot of e.g. 
s(x,by=a.1) to only include those x for which a.1=1 to be able to judge if 
observations of x where a.1=1 are sparse in any region. Also, it would be 
really if nice the "by=..." was included in the output of the plot.gam() 
and the "Approximate significance of smooth terms:" part of the summary.gam().

2. John Fox has modified anova.glm() into anova.gam() 
(http://www.socsci.mcmaster.ca/jfox/Books/Companion/nonparametric-regression.txt) 
for comparison of two or more fitted models based on the difference between 
residual deviances. Indiscriminate use of such a procedure shouldn't 
perhaps be encouraged, but I think that many users expect it to be part of 
the mgcv package since this model selection idea is covered in several 
texts and also implemented in S-plus (and may be OK for truly nested 
models). And even if it's been decided that this functionality is not 
wanted in mgcv, perhaps another function comparing several models by the 
GCV/UBRE score and other useful statistics can be implemented?

3. Some authors [1, 2] suggests pointwise estimation of odds ratios and 
corresponding confidence intervals based on the smooth terms in a GAM. 
Maybe something for mgcv?
[1] Figueiras, A. & Cadarso-Suárez C. (2001) "Application of Nonparametric 
Models for calculating Odds Ratios and Their Confidence Intervals for 
Continuous Exposures", American Journal of Epidemiology, 154(3), 264-275.
[2] Saez, M., Cadarso-Suárez C. & Figueiras, A. (2003) "np.OR: an S-Plus 
function for pointwise nonparametric estimation of odds-ratios of 
continuous predictors", Computer Methods and Programs in Biomedicine, 71, 
175-179.

4. For each purely parametric covariate a t-test is produced; I'd like to 
have something like S-plus' anova.gam() to get an overall test. (Perhaps 
with the addition of a choice between Type I and Type III tests, but I 
guess that may be controversial). Is it possible?

//Henric

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