# NOT RUN {
data(tecator)
x=tecator$absorp.fdata
x1 <- fdata.deriv(x)
x2 <- fdata.deriv(x,nderiv=2)
y=tecator$y$Fat
xcat0 <- cut(rnorm(length(y)),4)
xcat1 <- cut(tecator$y$Protein,4)
xcat2 <- cut(tecator$y$Water,4)
ind <- 1:129
dat <- data.frame("Fat"=y, x1$data, xcat1, xcat2)
ldat <- list("df"=dat[ind,],"x"=x[ind,],"x1"=x1[ind,],"x2"=x2[ind,])
# 3 functionals (x,x1,x2), 3 factors (xcat0, xcat1, xcat2)
# and 100 scalars (impact poitns of x1)
# Time consuming
res.gam1 <- fregre.gsam.vs(data=ldat,y="Fat") # All the covariates
summary(res.gam1)
res.gam1$ipredictors
covar <- c("xcat0","xcat1","xcat2","x","x1","x2")
res.gam2 <- fregre.gsam.vs(data=ldat, y="Fat", include=covar)
summary(res.gam2)
res.gam2$ipredictors
# Prediction like fregre.gsam()
newldat <- list("df"=dat[-ind,],"x"=x[-ind,],"x1"=x1[-ind,],
"x2"=x2[-ind,])
pred.gam1 <- predict(res.gam1,newldat)
pred.gam2 <- predict(res.gam2,newldat)
plot(dat[-ind,"Fat"],pred.gam1)
points(dat[-ind,"Fat"],pred.gam2,col=2)
# }
# NOT RUN {
# }
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