Dear R users, apologies if this has been debated before, but I was unable to find it anywhere (with respect to shrinkage approach).
I am trying to evaluate explained deviance of each model term in a GAM. I am using a the mgcv library for fitting a GAM to binary data. Thin plate regression spline (TPRS) with shrinkage component (bs="ts") was used to effectivelly shrink out any terms that do not contribute to the model, as a way of automated model selection. Once I obtain the result I can easily obtain information about the overall deviance explained, but is there a way I assess the contribution of each model term? I could do a forward (or backward) model selection and simply note the difference in the explained deviance, but I assume that would be very different from the shrinkage-based approach, because the contribution of each term would depend on what is already in the model. Any advice would be greatly appreciated. Sincerely Yours, Tilen Genov ______________________________________________ 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.
