d = read.table("../data/ozone.data",header=T)

plot(d$temperature,d$ozone)
fit1 = smooth.spline(d$temperature,d$ozone,df=5)
lines(fit1)
d = d[order(d$temperature),]
fit.loess0 = loess(ozone~temperature,data=d,span=0.5,degree=0)
pred0 = predict(fit.loess0,d,lwd=2)
lines(d$temperature,pred0,col=2,lwd=2)
fit.loess1 = loess(ozone~temperature,data=d,span=0.5,degree=1)
pred1 = predict(fit.loess1,d,lwd=2)
lines(d$temperature,pred1,col=3,lwd=2)
fit.loess2 = loess(ozone~temperature,data=d,span=0.5,degree=2)
pred2 = predict(fit.loess2,d,lwd=2)
lines(d$temperature,pred2,col=4,lwd=2)
legend("topleft",c("s-spline","loess0","loess1","loess2"),lwd=2,
       lty=1,col=1:4)


fit = loess(ozone~temperature+wind,data=d,span=0.9)
pred = predict(fit,d)

library(lattice)
Wind = equal.count(d$wind,
                    number = 3,
                    overlap = .1)
Temp = equal.count(d$temperature,
                   number = 3,
                   overlap = .1)
d$pred = pred

d = d[order(d$radiation),]
xyplot(ozone+pred ~ radiation | Temp*Wind, data = d,
       xlab = "Radiation",
       ylab = "Ozone",layout=c(3,3),
       scales=list(x=list(log = 10, equispaced.log=TRUE)),
#       par.settings=list(superpose.symbol=list(type=c("l","p"))),
       type=c("l","p"),
       alpha=1/5,lwd=2,col=1:2,lty="solid",as.table=TRUE)