% \VignetteIndexEntry{Duration of Unemployment - Logit Model}
% \VignetteDepends{}
%\VignetteEngine{knitr::knitr}
%\VignetteEncoding{UTF-8}
\documentclass[a4paper]{article}

\title{Duration of Unemployment - Logit Model}

\begin{document}

\maketitle


<<echo=FALSE,eval=FALSE>>=
options(width=60)
@

At first the "unemployment" data from the "catdata" package are loaded and attached.
<<results='hide',eval=FALSE>>=
library(catdata)
data(unemployment)
attach(unemployment)
@


Now a frequency table is created and used to fit a Logit model based on grouped data.
<<eval=FALSE>>=
durbin <- as.factor(durbin)
table.durbin <- ftable(subset(unemployment, select=c("age", "durbin")), 
col.vars="durbin")
rels<-table.durbin[,1]/rowSums(table.durbin)
age.new <- min(age):max(age)

model1 <- glm(table.durbin ~ age.new, family=binomial)
summary(model1)
@

Here the observed frequencies are plotted against the fitted probabilities.

<<eval=FALSE>>=
plot(age.new, model1$fitted.values, xlab="Age", ylab="Observed/Fitted values", 
type="l", ylim=c(0,1))
points(age.new,table.durbin[,1]/rowSums(table.durbin))
@

The standardized deviance residuals are plotted against the predicted values and a quantile plot is created.

<<eval=FALSE>>=
plot(model1$fitted.values,sqrt(rowSums(table.durbin))*rstandard(model1), 
xlab="Predicted values", ylab="Residuals")
@
<<eval=FALSE>>=
qqnorm(sqrt(rowSums(table.durbin))*rstandard(model1), main="", 
       ylab="Standardized deviance residuals")
qqline(sqrt(rowSums(table.durbin))*rstandard(model1), lwd=2.5, 
       lty="dashed", col="red")
@

\end{document}
