% \VignetteIndexEntry{Duration of Unemployment - Cubic B-Splines}
% \VignetteDepends{mgcv}

\documentclass[a4paper]{article}

\title{Duration of Unemployment - Cubic B-Splines}

\begin{document}

\maketitle


<<echo=FALSE,eval=FALSE>>=
options(width=80)
@

The unemployment data from catdata are loaded.

<<eval=FALSE>>=
library(catdata)
data(unemployment, package="catdata")
attach(unemployment)
@

The GAM is fitted by using the library "mgcv".

<<eval=FALSE>>=
library(mgcv)
@

Now the response "durbin" is transformed and the GAM is fitted.

<<eval=FALSE>>=
durbin[durbin==2] <- 0

gamage <- gam(durbin ~ s(age, bs="ps", m=c(2,1), k=15), family=binomial())
@

To plot the fitted probabilities for the whole range of age probabilities have to be predicted.

<<eval=FALSE>>=
minage <- min(age)
maxage <- max(age)
ageindex <- seq(from=minage, to=maxage, by=0.01)

pred <- predict(gamage, newdata=data.frame("age"=ageindex), type="response")
@

The following function describes the code for B--Splines.

<<eval=FALSE>>=
bspline<-function(x,k,i,m=2)
{if (m==-1)
{res<-as.numeric(x<k[i+1]&x>=k[i])}
else{
z0<-(x-k[i])/(k[i+m+1]-k[i])
z1<-(k[i+m+2]-x)/(k[i+m+2]-k[i+1])
res<- z0*bspline(x,k,i,m-1)+z1*bspline(x,k,i+1,m-1)}
res}
@

Now the knots for the B--Splines are defined, furthermore for each age the corresponding mean of durbin is computed.

<<eval=FALSE>>=
k <- gamage$smooth[[1]]$knots

meanage <- c()

for (i in minage:maxage){
meanage[i] <- sum(durbin[age==i])
if(sum(durbin[age==i])!=0){
meanage[i] <- mean(durbin[age==i])
}
}
@

Now the line for the predicted probabilities, the B--Splines and the corresponding means for each age are plotted.

<<fig=TRUE, width=8,eval=FALSE>>=
par(cex=1.3, lwd=1.5)
plot(ageindex, pred, type="l", ylim=c(0,0.8), col="gray", xlab="age/years", 
     ylab="short-term unemployment")
for(i in 1:15)
{lines(ageindex,(0.5*gamage$coefficients[i+1]+0.7)*(bspline(x=ageindex,k=k,
i=i+1,m=2)),type="l", col="gray")}
points(minage:maxage, meanage[minage:maxage], pch=20)
@

Via the option "fx=TRUE" a unpenalized gam is fitted, afterwards again the probabilities for the whole range of age are computed.

<<eval=FALSE>>=
gamage2 <- gam(durbin ~ s(age, bs="ps", fx=TRUE, m=c(2,1),k=15), family=binomial())

pred2 <- predict(gamage2, newdata=data.frame("age"=ageindex), type="response")
@

Now for the unpenalized GAM the new probabilities, the new B--Splines and again the means are plotted. The fitted line for the probabilities is very wiggly now.

<<fig=TRUE,width=8,eval=FALSE>>=
par(cex=1.3, lwd=1.5)
plot(ageindex, pred2, type="l", ylim=c(0,0.8), col="gray", xlab="age/years", 
     ylab="short-term unemployment")
for(i in 1:15)
{lines(ageindex, (0.1*gamage2$coefficients[i+1]+0.3)*
  bspline(x=ageindex,k=k,i=i+1,m=2),type="l", col="gray")}
points(minage:maxage, meanage[minage:maxage],pch=20)
@

<<echo=FALSE,results=hide,eval=FALSE>>=
detach(unemployment)
rm(unemployment)
@
\end{document}