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-Su�rez 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-Su�rez 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
--------------------------------------------------------------------------------------- Henric Nilsson, Statistician
Statisticon AB, �stra �gatan 31, SE-753 22 UPPSALA Phone (Direct): +46 (0)18 18 22 37 Mobile: +46 (0)70 211 68 36 Fax: +46 (0)18 18 22 33
<http://www.statisticon.se>
______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
