kiwishade | R Documentation |

The `kiwishade`

data frame has 48 rows and 4 columns.
The data are from a designed experiment that
compared different kiwifruit shading treatments.
There are four vines in each plot, and four plots (one for each of four
treatments: none, Aug2Dec, Dec2Feb, and Feb2May) in each of three blocks
(locations: west, north, east). Each
plot has the same number of vines, each block has the same number of
plots, with each treatment occurring the same number of times.

`kiwishade`

This data frame contains the following columns:

- yield
Total yield (in kg)

- plot
a factor with levels

`east.Aug2Dec`

,`east.Dec2Feb`

,`east.Feb2May`

,`east.none`

,`north.Aug2Dec`

,`north.Dec2Feb`

,`north.Feb2May`

,`north.none`

,`west.Aug2Dec`

,`west.Dec2Feb`

,`west.Feb2May`

,`west.none`

- block
a factor indicating the location of the plot with levels

`east`

,`north`

,`west`

- shade
a factor representing the period for which the experimenter placed shading over the vines; with levels:

`none`

no shading,`Aug2Dec`

August - December,`Dec2Feb`

December - February,`Feb2May`

February - May

The northernmost plots were grouped together because they were similarly affected by shading from the sun in the north. For the remaining two blocks shelter effects, whether from the west or from the east, were thought more important.

Snelgar, W.P., Manson. P.J., Martin, P.J. 1992. Influence of time of shading on flowering and yield of kiwifruit vines. Journal of Horticultural Science 67: 481-487.

Maindonald J H 1992. Statistical design, analysis and presentation issues. New Zealand Journal of Agricultural Research 35: 121-141.

```
print("Data Summary - Example 2.2.1")
attach(kiwishade)
kiwimeans <- aggregate(yield, by=list(block, shade), mean)
names(kiwimeans) <- c("block","shade","meanyield")
kiwimeans[1:4,]
pause()
print("Multilevel Design - Example 9.3")
kiwishade.aov <- aov(yield ~ shade+Error(block/shade),data=kiwishade)
summary(kiwishade.aov)
pause()
sapply(split(yield, shade), mean)
pause()
kiwi.table <- t(sapply(split(yield, plot), as.vector))
kiwi.means <- sapply(split(yield, plot), mean)
kiwi.means.table <- matrix(rep(kiwi.means,4), nrow=12, ncol=4)
kiwi.summary <- data.frame(kiwi.means, kiwi.table-kiwi.means.table)
names(kiwi.summary)<- c("Mean", "Vine 1", "Vine 2", "Vine 3", "Vine 4")
kiwi.summary
mean(kiwi.means) # the grand mean (only for balanced design)
if(require(lme4, quietly=TRUE)) {
kiwishade.lmer <- lmer(yield ~ shade + (1|block) + (1|block:plot),
data=kiwishade)
## block:shade is an alternative to block:plot
kiwishade.lmer
## Residuals and estimated effects
library(lattice)
xyplot(residuals(kiwishade.lmer) ~ fitted(kiwishade.lmer)|block,
data=kiwishade, groups=shade,
layout=c(3,1), par.strip.text=list(cex=1.0),
xlab="Fitted values (Treatment + block + plot effects)",
ylab="Residuals", pch=1:4, grid=TRUE,
scales=list(x=list(alternating=FALSE), tck=0.5),
key=list(space="top", points=list(pch=1:4),
text=list(labels=levels(kiwishade$shade)),columns=4))
ploteff <- ranef(kiwishade.lmer, drop=TRUE)[[1]]
qqmath(ploteff, xlab="Normal quantiles", ylab="Plot effect estimates",
scales=list(tck=0.5))
}
```