library(multcomp)

DF_I = read.csv('../../Data/ASCII_Comma/Chapter_12/watchi.txt', header=FALSE, col.names='Cycles')
DF_I$Type = 'TypeI'

DF_II = read.csv('../../Data/ASCII_Comma/Chapter_12/watchii.txt', header=FALSE, col.names='Cycles')
DF_II$Type = 'TypeII'

DF_III = read.csv('../../Data/ASCII_Comma/Chapter_12/watchiii.txt', header=FALSE, col.names='Cycles')
DF_III$Type = 'TypeIII'

DF = rbind( DF_I, DF_II, DF_III )
DF$Type = as.factor(DF$Type)

fit = aov(Cycles ~ Type, data=DF)
print(summary(fit))

#postscript("../../WriteUp/Graphics/Chapter12/prob_28_plotmeans.eps", onefile=FALSE, horizontal=FALSE)
plotmeans(Cycles ~ Type, data=DF)
#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(Type='Tukey'))
plot(cld(tuk, level=0.05), col='lightgrey')

# A nonparametric test for one-way ANOVA:
#
print(kruskal.test(Cycles ~ Type, data=DF))