# NOT RUN {
## Example - we compare the nonparametric local linear kernel regression
## method with the regression spline for the cps71 data. Note that there
## are no categorical predictors in this dataset so we are merely
## comparing and contrasting the two nonparametric estimates.
data(cps71)
attach(cps71)
require(np)
model.crs <- crs(logwage~age,complexity="degree-knots")
model.np <- npreg(logwage~age,regtype="ll")
plot(age,logwage,cex=0.25,col="grey",
sub=paste("crs-CV = ", formatC(model.crs$cv.score,format="f",digits=3),
", npreg-CV = ", formatC(model.np$bws$fval,format="f",digits=3),sep=""))
lines(age,fitted(model.crs),lty=1,col=1)
lines(age,fitted(model.np),lty=2,col=2)
crs.txt <- paste("crs (R-squared = ",formatC(model.crs$r.squared,format="f",digits=3),")",sep="")
np.txt <- paste("ll-npreg (R-squared = ",formatC(model.np$R2,format="f",digits=3),")",sep="")
legend(22.5,15,c(crs.txt,np.txt),lty=c(1,2),col=c(1,2),bty="n")
summary(model.crs)
summary(model.np)
detach("package:np")
# }
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