\Header{VADeaths}{Death Rates in Virginia (1940)}
\keyword{datasets}{VADeaths}
\begin{Description}\relax
Death rates per 100 in Virginia in 1940.
\end{Description}
\begin{Usage}
\begin{verbatim}data(VADeaths)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A matrix with 5 rows and 5 columns.
\end{Format}
\begin{Details}\relax
The death rates are cross-classified by age group (rows) and
population group (columns).  The age groups are: 50--54, 55--59,
60--64, 65--69, 70--74 and the population groups are Rural/Male,
Rural/Female, Urban/Male and Urban/Female.

This provides a rather nice 3-way analysis of variance example.
\end{Details}
\begin{Source}\relax
Moyneau, L.,  Gilliam, S. K., and  Florant, L. C.(1947)
Differences in Virginia death rates by color, sex, age,
and rural or urban residence.
\emph{American Sociological Review}, \bold{12}, 525--535.
\end{Source}
\begin{References}\relax
McNeil, D. R. (1977)
\emph{Interactive Data Analysis}.
Wiley.
\end{References}
\begin{Examples}
\begin{ExampleCode}
require(stats)
data(VADeaths)
n <- length(dr <- c(VADeaths))
nam <- names(VADeaths)
d.VAD <- data.frame(
 Drate = dr,
 age = rep(ordered(rownames(VADeaths)),length=n),
 gender= gl(2,5,n, labels= c("M", "F")),
 site =  gl(2,10,  labels= c("rural", "urban")))
coplot(Drate ~ as.numeric(age) | gender * site, data = d.VAD,
       panel = panel.smooth, xlab = "VADeaths data - Given: gender")
summary(aov.VAD <- aov(Drate ~ .^2, data = d.VAD))
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(aov.VAD)
par(opar)
\end{ExampleCode}
\end{Examples}

