It doesn't break anything - you can transform the predictors pretty much any way you like, and it is often sensible as a way of tackling very uneven leverage. By transforming predictors, all you are changing in the model is what "smooth" means. e.g. smooth w.r.t. log(x) is somewhat different to sooth w.r.t. x.
best, Simon On 16/12/13 04:54, PETER MITCHELL wrote:
Hi all, I am applying a Presence/absence Generalized additive model to model the distribution of marine algae species in R. I have found that log transforming the environmental variables improves the explained deviance of the model considerably. While log transforming is common practice in GLM, I have been unable to find any papers where this is performed in a GAM. Im wondering whether this breaks any of the rules of GAMs and is statistically acceptable? Thanks all Peter [[alternative HTML version deleted]] ______________________________________________ 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.
-- Simon Wood, Mathematical Science, University of Bath BA2 7AY UK +44 (0)1225 386603 http://people.bath.ac.uk/sw283 ______________________________________________ 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.
