library(reshape2)
library(gplots)
library(multcomp)

DF = read.csv('../../Data/ASCII_Comma/Chapter_12/dogs.txt', header=FALSE)
colnames(DF) = sprintf('Dog%d', seq(10))
DF$Anesthesia = c('Isofluorane', 'Halothane', 'Cyclopropane')

DF_melt = melt(DF, id.vars=c('Anesthesia'), variable.name='Dog', value.name='Concentration')
DF_melt$Anesthesia = as.factor(DF_melt$Anesthesia)

fit = aov(Concentration ~ Anesthesia + Dog, data=DF_melt) # 2-way ANOVA
print(summary(fit))

fit = aov(Concentration ~ Anesthesia, data=DF_melt) # 1-way ANOVA
print(summary(fit))

#postscript("../../WriteUp/Graphics/Chapter12/prob_26_plotmeans.eps", onefile=FALSE, horizontal=FALSE)
plotmeans(Concentration ~ Anesthesia, data=DF_melt)
#dev.off()

# A Tukey comparison of means test:
#
TukeyHSD(fit)

par(las=2)
par(mar=c(5, 8, 4, 2))
plot(TukeyHSD(fit))

# Use the multcomp package:
#
par(las=2)
par(mar=c(5, 4, 6, 2))
tuk = glht(fit, linfct=mcp(Anesthesia='Tukey'))
plot(cld(tuk, level=0.05), col='lightgrey')

# A nonparametric test for one-way ANOVA:
#
print(kruskal.test(Concentration ~ Anesthesia, data=DF_melt))