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##############################################

# R Code for canonical correlation analysis #

##############################################

# We will use the built-in iris data set.

# We will consider the entire data set (all three species)

attach(iris)

# We will standardize the variables first

# by dividing by each column's standard deviation:

# (we will remove column 5, the species labels)

iris.std

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# u1 = 0.92*Sepal.Length - 0.39*Sepal.Width

# v1 = 0.94*Petal.Length - 0.33*Petal.Width

# Plotting the first set of canonical variables:

u1

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colnames(xrels)

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.098,.308,-.27,-.183),

ncol=4, byrow=T)

R21

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P.value

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#### Testing about x2 in the model:

## Full model:

my.n

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pt.est

# A point prediction for this site is:

# predicted CPU time = 151.8 hours, predicted disk input/output capacity

= 349.6.

middle.mat

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my.ys

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# Recall the variables:

# X1 = Sales growth

# X2 = Sales profitability

# X3 = New account sales

# X4 = Creativity Test

# X5 = Mechanical Reasoning Test

# X6 = Abstract Reasoning Test# X7 = Mathematics Test

# X8 = Historical Facts Test

# X9 = Sports Trivia Test

# X10 = Music Trivia Test

# We will try to predict the "performance" variables (x1, x2, x3) using

the

# "test score" variables (x4, x5, ..., x10).

# Fitting the multivariate linear regression model:

sales.mod.1

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X.mat.redu