\HeaderA{occupationalStatus}{Occupational Status of Fathers and their Sons}{occupationalStatus}
\keyword{datasets}{occupationalStatus}
\begin{Description}\relax
Cross-classification of a sample of British males according to each
subject's occupational status and his father's occupational status.
\end{Description}
\begin{Usage}
\begin{verbatim}occupationalStatus\end{verbatim}
\end{Usage}
\begin{Format}\relax
A \code{\LinkA{table}{table}} of counts, with classifying factors
\code{origin} (father's occupational status; levels \code{1:8})
and \code{destination} (son's occupational status; levels \code{1:8}).
\end{Format}
\begin{Source}\relax
Goodman, L. A. (1979)
Simple Models for the Analysis of Association in Cross-Classifications
having Ordered Categories.
\emph{J. Am.\ Stat.\ Assoc.}, \bold{74} (367), 537--552.

The data set has been in package \pkg{gnm} and been provided by the
package authors.
\end{Source}
\begin{Examples}
\begin{ExampleCode}
require(stats); require(graphics)

plot(occupationalStatus)

##  Fit a uniform association model separating diagonal effects
Diag <- as.factor(diag(1:8))
Rscore <- scale(as.numeric(row(occupationalStatus)), scale = FALSE)
Cscore <- scale(as.numeric(col(occupationalStatus)), scale = FALSE)
modUnif <- glm(Freq ~ origin + destination + Diag + Rscore:Cscore,
               family = poisson, data = occupationalStatus)

summary(modUnif)
plot(modUnif) # 4 plots, with warning about  h_ii ~= 1
\end{ExampleCode}
\end{Examples}

