# 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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