agridat (version 1.16)

box.cork: Weight of cork samples on four sides of trees

Description

The cork data gives the weights of cork borings of the trunk for 28 trees on the north (N), east (E), south (S) and west (W) directions.

Arguments

Format

Data frame with 28 observations on the following 5 variables.

tree

tree number

dir

direction N,E,S,W

y

weight of cork deposit (centigrams), north direction

References

K.V. Mardia, J.T. Kent and J.M. Bibby (1979) Multivariate Analysis, Academic Press.

Russell D Wolfinger, (1996). Heterogeneous Variance: Covariance Structures for Repeated Measures. Journal of Agricultural, Biological, and Environmental Statistics, 1, 205-230.

Examples

Run this code
# NOT RUN {
data(box.cork)
dat <- box.cork

if(require(lattice) & require(reshape2)){  
  dat2 <- acast(dat, tree ~ dir, value.var='y')
  splom(dat2, pscales=3,
        prepanel.limits = function(x) c(25,100),
        main="box.cork", xlab="Cork yield on side of tree",
        panel=function(x,y,...){
          panel.splom(x,y,...)
          panel.abline(0,1,col="gray80")
        })
}

# ----------------------------------------------------------------------------

# }
# NOT RUN {
  require(plotrix)
  require(reshape2)
  ## Each tree is one line
  dat2 <- acast(dat, tree ~ dir, value.var='y')
  radial.plot(dat2, start=pi/2, rp.type='p', clockwise=TRUE,
              radial.lim=c(0,100), main="box.cork",
              lwd=2, labels=c('North','East','South','West'),
              line.col=rep(c("royalblue","red","#009900","dark orange",
                             "#999999","#a6761d","deep pink"),
                           length=nrow(dat2)))
# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  # asreml3
  require(asreml)

  data(box.cork)
  dat <- box.cork
  
  # Unstructured covariance
  dat$dir <- factor(dat$dir)
  dat$tree <- factor(dat$tree)

  dat <- dat[order(dat$tree, dat$dir), ]
  
  # Unstructured covariance matrix
  m1 <- asreml(y~dir, data=dat,
               rcov = ~ tree:us(dir, init=rep(200,10)))
  ## Note: 'rcor' is a personal function to extract the correlation
  ## round(rcor(m1)$dir, 2)
  ##        E      N      S      W
  ## E 219.93 223.75 229.06 171.37
  ## N 223.75 290.41 288.44 226.27
  ## S 229.06 288.44 350.00 259.54
  ## W 171.37 226.27 259.54 226.00

  # Factor Analytic with different specific variances
  # Note: Wolfinger used a common diagonal variance
  m2 <- update(m1, rcov=~tree:facv(dir,1))
  ## round(rcor(m2)$dir, 2)
  ##        E      N      S      W
  ## E 219.94 209.46 232.85 182.27
  ## N 209.46 290.41 291.82 228.43
  ## S 232.85 291.82 349.99 253.94
  ## W 182.27 228.43 253.94 225.99

# }
# NOT RUN {
# ----------------------------------------------------------------------------

# }
# NOT RUN {
  ## require(asreml4)

  ## data(box.cork)
  ## dat <- box.cork
  
  ## # Unstructured covariance
  ## dat$dir <- factor(dat$dir)
  ## dat$tree <- factor(dat$tree)

  ## dat <- dat[order(dat$tree, dat$dir), ]
  
  ## # Unstructured covariance matrix
  ## m1 <- asreml(y~dir, data=dat,
  ##              resid = ~ tree:us(dir, init=rep(200,10)))

  ## library(lucid)
  ## vc(m1)
  
  ## # Note: 'rcor' is a personal function to extract the correlation
  ## # round(rcor(m1)$dir, 2)
  ## #        E      N      S      W
  ## # E 219.93 223.75 229.06 171.37
  ## # N 223.75 290.41 288.44 226.27
  ## # S 229.06 288.44 350.00 259.54
  ## # W 171.37 226.27 259.54 226.00

  ## # Factor Analytic with different specific variances
  ## # Note: Wolfinger used a common diagonal variance
  ## # FIXME - does not work with asreml4
  ## m2 <- update(m1, resid = ~tree:fa(dir,1))
  ## # round(rcor(m2)$dir, 2)
  ##        E      N      S      W
  ## # E 219.94 209.46 232.85 182.27
  ## # N 209.46 290.41 291.82 228.43
  ## # S 232.85 291.82 349.99 253.94
  ## # W 182.27 228.43 253.94 225.99
# }
# NOT RUN {
# }

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