block_designs.R

data <- read.table("Strawberry.txt",header=TRUE)
attach(data)
boxplot(Yield~Treatment)
boxplot(Yield~Block)

res <- lm(Yield~factor(Block)*Treatment)
anova(res)

res <- lm(Yield~factor(Block)+Treatment)
anova(res)

interaction.plot(Treatment,Block,Yield)

#
# Alfalfa, Randomized Complete Block Design
#

data <- read.table("Alfalfa.txt",header=TRUE)
attach(data)
table(Block,Variety)
boxplot(Yield~Block:Variety)
blockf <- factor(Block)

res <- lm(Yield~blockf*Variety)
anova(res)

#  aov command
help(aov)

res <- aov(Yield~blockf*Variety)
summary(res)

#
# Alfalfa, subsetting
#   That is, they took 3 subsamples from each EU
#   Way too many samples
#
# Soultion 1:  Average across the subsamples
#

data
nYield <- tapply(Yield,Variety:blockf,mean)
nYield
nVar <- rep(c("DuPuits","Flamand","Ladak","Narrag"),c(4,4,4,4))
nVar
nBlock <- factor(rep(c(1,2,3,4),4))

resn <- lm(nYield~nVar+nBlock)
anova(resn)

#
# Soultion 2: Manual F-test
#

res <- aov(Yield~blockf*Variety)
summary(res)

(f.value <- 12.4003/.4757)
1-pf(f.value,3,9)

anova(resn)

#
# Solution 3:
# Creative ANOVA
# 

res2 <- aov(Yield~Variety+Error(blockf/Variety))
summary(res2)
anova(res)
# Error term says that Variety should be compared to the blockf/Variery MSE