src/library/datasets/man/VADeaths.Rd

% File src/library/datasets/man/VADeaths.Rd
% Part of the R package, https://www.R-project.org
% Copyright 1995-2007 R Core Team
% Distributed under GPL 2 or later

\name{VADeaths}
\docType{data}
\alias{VADeaths}
\title{Death Rates in Virginia (1940)}
\description{
  Death rates per 1000 in Virginia in 1940.
}
\usage{VADeaths}
\format{A matrix with 5 rows and 4 columns.}
\details{
  The death rates are measured per 1000 population per year.  They
  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.
}
\source{
  Molyneaux, 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.
}
\references{
  McNeil, D. R. (1977)
  \emph{Interactive Data Analysis}.
  Wiley.
}
\examples{
require(stats); require(graphics)
n <- length(dr <- c(VADeaths))
nam <- names(VADeaths)
d.VAD <- data.frame(
 Drate = dr,
 age = rep(ordered(rownames(VADeaths)), length.out = 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)
}
\keyword{datasets}