box.cork {agridat} R Documentation

## 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.

### 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

### Source

C.R. Rao (1948). Tests of significance in multivariate analysis. Biometrika, 35, 58-79. https://doi.org/10.2307/2332629

### 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

```## 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')
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)
```

[Package agridat version 1.18 Index]