## ----------------------------------------------------------------------------
##   R scripts for Haerdle/Mueller/Sperlich/Werwatz: "Nonparametric and
##   Semiparametric Modelling", Springer Series in Statistics, 2004
## ----------------------------------------------------------------------------
##   Script      SPMkernelregression-Sliders
## ----------------------------------------------------------------------------
##   Description computes the regression of food on net-income
##               for the UK 1976 expenditure data (with additional
##               slider to choose bandwidth and polynomial degree)
## ----------------------------------------------------------------------------
##   Author      Marlene Mueller, 2010/07/11
## ----------------------------------------------------------------------------

library(KernSmooth)
library(tcltk)
library(TeachingDemos)

x <- read.csv2("fes76.txt", dec=".")
x <- x[,c("NIC","FOO")]
x <- x[order(x$NIC),]
y <- x$FOO
x <- x$NIC

##s <- sample(1:length(x),1000	)
##x <- x[s]; y <- y[s]

xr <- range(x)
xd <- diff(xr)

xr[1] <- min(x) - xd/5
xr[2] <- max(x) + xd/5
xd <- diff(xr)

kreg.refresh <- function(...) {
    degree <- slider(no=2)
    title <- "Engel Curve (Nadaraya-Watson)"
    if (degree>0){
        title <- paste("Engel Curve (local polynomial: p = ",degree,")",sep="")
    }
    plot(x,y,col="lightblue",pch=20,main=title,xlab="netincome",
         ylab="food expenditure")
    mh <- locpoly(x,y,degree=degree,kernel="normal",bandwidth=slider(no=1))
    lines(mh)
}

slider(sl.functions=kreg.refresh,
       sl.names=c("Bandwidth", "Degree"),
       sl.mins=c(xd/10000, 0),
       sl.maxs=c(xd/2, 1),
       sl.deltas=c(xd/1000,1),
       sl.defaults=c(diff(range(x))/10,0),
       title = "Choose Kernel Regression Parameters")