hello, I am looking for a step() function for GAM's. In the book Statistical Computing by Crawley and a removal of predictors has been done "by hand" model <- gam(y ~s(x1) +s(x2) + s(x3)) summary(model) model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable #then comparing these two models if an significant increase occurs. anova(model, model2, test="F") isn't there a way to drop and add variables automatically until the best model is received? like in step(lm(...))? Or as in grasp.step.gam() - but that doesn't work when I tried it outside GRASP-R. thanks for your help, cheers Martin
There isn't a step.gam() in mgcv yet.... it is one of the things that I'd like to do eventually, although it will probably be based on comparison of GCV/UBRE scores rather than H_0 testing. best, Simon> I am looking for a step() function for GAM's. > In the book Statistical Computing by Crawley and a removal of predictors has > been done "by hand" > > model <- gam(y ~s(x1) +s(x2) + s(x3)) > summary(model) > model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable > #then comparing these two models if an significant increase occurs. > anova(model, model2, test="F") > > isn't there a way to drop and add variables automatically until the best model > is received? like in step(lm(...))? > Or as in grasp.step.gam() - but that doesn't work when I tried it outside > GRASP-R. > > thanks for your help, cheers Martin > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > stat.math.ethz.ch/mailman/listinfo/r-help >_____________________________________________________________________> Simon Wood simon at stats.gla.ac.uk stats.gla.ac.uk/~simon >> Department of Statistics, University of Glasgow, Glasgow, G12 8QQ >>> Direct telephone: (0)141 330 4530 Fax: (0)141 330 4814