# Fisher's Iris Data is used at several points in S books
# Note that our copy of iris data is slightly different!

# read data
iris <- read.table("iris.dat",header=T)

# univariate analyses
for (i in names(iris)[1:4]) {
	tmp <- aov(paste(i,"~","species"),iris)
	print(tmp)
	print(summary(tmp))
	print(lsmean(tmp))
}

# step-down analysis (e.g. ANCOVA)
print(lm(sepwid ~ petlen + species, iris))
print(lm(petwid ~ petlen + sepwid + species, iris))
print(lm(seplen ~ petlen + sepwid + petwid + species, iris))

# multivariate plots
library(pda) # pick up mpoints()
pairs(iris[,1:4], panel=function(x,y) mpoints(x,y,group=iris$species))

# discriminant analysis
library(MASS) # pick up DA routines
iris.da <- lda(iris[,1:4],iris$species)
# eigenvalues
print(iris.da$svd^2)
# canonical variates
iris.canvar <- predict(iris.da, iris[,1:4], dimen=2)$x
# plot of first two canonical variates
mplot(iris.canvar[,1],iris.canvar[,2],group=iris$species,
	xlab="Canonical Variate 1", ylab="Canonical Variate 2")