R Dataset / Package DAAG / kiwishade
| Attachment | Size |
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| dataset-83216.csv | 1.78 KB |
| Documentation |
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On this Picostat.com statistics page, you will find information about the kiwishade data set which pertains to Kiwi Shading Data. The kiwishade data set is found in the DAAG R package. Try to load the kiwishade data set in R by issuing the following command at the console data("kiwishade"). This may load the data into a variable called kiwishade. If R says the kiwishade data set is not found, you can try installing the package by issuing this command install.packages("DAAG") and then attempt to reload the data with library("DAAG") followed by data("kiwishade"). Perhaps strangley, if R gives you no output after entering a command, it means the command succeeded. If it succeeded you can see the data by typing kiwishade at the command-line which should display the entire dataset. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the kiwishade R data set. The size of this file is about 1,826 bytes. Kiwi Shading DataDescriptionThe Usagekiwishade FormatThis data frame contains the following columns:
DetailsThe 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. SourceSnelgar, 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. ReferencesMaindonald J H 1992. Statistical design, analysis and presentation issues. New Zealand Journal of Agricultural Research 35: 121-141. Examples
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: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),
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))
}
-- Dataset imported from https://www.r-project.org. |
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