## Not run: 

  library(agridat)
  data(box.cork)
  dat <- box.cork

  libs(reshape2, lattice)
  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")
        })


  ## Radial star plot, each tree is one line
  libs(plotrix)
  libs(reshape2)
  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)))
  
  # asreml 4
  libs(asreml)

  # 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, residual = ~ tree:us(dir))
    
  libs(lucid)
  vc(m1)
    
  # Note: 'rcor' is a personal function to extract the correlations
  # into a matrix format
  # round(kw::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
    
  # Note: Wolfinger used a common diagonal variance
  
  # Factor Analytic with different specific variances
  # fixme: does not work with asreml4
  # m2 <- update(m1, residual = ~tree:facv(dir,1))
  # round(kw::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
    

## End(Not run)