Hi,everyone, I am studying the generalized additive model and employ the package 'mgcv' developed by professor Wood. However,I can not understand the example listed in check.in function. For example,
library(mgcv) set.seed(1) dat <- gamSim(1,n=400,scale=2) ## fit a GAM with quite low `k' b<-gam(y~s(x0,k=6)+s(x1,k=6)+s(x2,k=6)+s(x3,k=6),data=dat) plot(b,pages=1,residuals=TRUE) ## hint of a problem in s(x2) ## the following suggests a problem with s(x2) gam.check(b) ## Another approach (see below for more obvious method).... ## check for residual pattern, removeable by increasing `k' ## typically `k', below, chould be substantially larger than ## the original, `k' but certainly less than n/2. ## Note use of cheap "cs" shrinkage smoothers, and gamma=1.4 ## to reduce chance of overfitting... rsd <- residuals(b) gam(rsd~s(x0,k=40,bs="cs"),gamma=1.4,data=dat) ## fine gam(rsd~s(x1,k=40,bs="cs"),gamma=1.4,data=dat) ## fine /gam(rsd~s(x2,k=40,bs="cs"),gamma=1.4,data=dat) ## `k' too low/ gam(rsd~s(x3,k=40,bs="cs"),gamma=1.4,data=dat) ## fine why the model is not good for x2? > gam(rsd~s(x2,k=40,bs="cs"),gamma=1.4,data=dat) ## `k' too low Family: gaussian Link function: identity Formula: rsd ~ s(x2, k = 40, bs = "cs") Estimated degrees of freedom: 9.0093 total = 10.00926 GCV score: 4.494652 For the results,we can see that the EDF is much less than K-1,so according to "If the effective degrees of freedom for a model term are estimated to be much less than k-1 then this is unlikely to be very worthwhile",I think the results are reasonable. Why? Thanks in advance wanhai -- View this message in context: http://r.789695.n4.nabble.com/check-k-function-in-mgcv-packages-tp4634050.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
