R Dataset / Package DAAG / kiwishade

Submitted by pmagunia on March 9, 2018 - 1:06 PM
Dataset License
GNU General Public License v2.0
Attachment Size
dataset-83216.csv 1.78 KB

Kiwi Shading Data


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.




This data frame contains the following columns:


Total yield (in kg)


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


a factor indicating the location of the plot with levels east, north, west


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")
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)
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")
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),
## block:shade is an alternative to block:plotkiwishade.lmer
##                  Residuals and estimated effects
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),
ploteff <- ranef(kiwishade.lmer, drop=TRUE)[[1]]
qqmath(ploteff, xlab="Normal quantiles", ylab="Plot effect estimates",

Dataset imported from https://www.r-project.org.

Documentation License
GNU General Public License v2.0

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