mgcv I have made 5 models, each one using a subset of my data, defined by quantiles. The response/explanitory variables are the same in each case, but the deviance explained by each in each model differs, along with the P values.
Is there a clever way of comparing the contributions of each explanitory variable in each model? The best I've come up with is using portions of the output of
summary(GAMx) to create a monstrous table with the
Δ Deviance explained,
Δ AIC and
P value for each each model, with the rows being the smoothed and parametric variables.
This is pretty ugly and hard to interpret. Are there any functions or packages in
R that would allow a comparison of models - with enough detail to show the contributions of each variable?
library(mgcv) set.seed(0) n<-200;sig2<-4 x0 <- runif(n, 0, 1);x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) y<-x0^2+x1*x2 +runif(n,-0.3,0.3) g1<-gam(y~s(x0,x1,x2)) g2<-gam(y~s(x0,x1,x2)) g3<-gam(y~s(x0,x1,x2)) g4<-gam(y~s(x0,x1,x2)) g5<-gam(y~s(x0,x1,x2))
All 5 models here will be the same, but that shouldn't actually matter.