LDA on Iris

Chapter 7: LDA on Iris

Red shows input, black shows output

data(iris3)

Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]),

+ Sp = rep(c("s","c","v"), rep(50,3)))

train <- sample(1:150, 75) #selects 75 to be training sample

table(Iris$Sp[train])

c s v

27 22 26

## your answer may differ

## c s v

## 22 23 30 (note: total=75)

z <- lda(Sp ~ ., Iris, prior = c(1,1,1)/3, subset = train)

predict(z, Iris[-train, ])$class

[1] s s s s s s s s s s s s s s s s s s s s s s s s s s s s c c c c c c

[35] c c c c v c c c c c c c c c c c c v v v v v v v v v v v v v v v v v

[69] v v v v v v v

Levels: c s v

> ## [1] s s s s s s s s s s s s s s s s s s s s s s s s s s s c c c

> ## [31] c c c c c c c v c c c c v c c c c c c c c c c c c v v v v v

> ## [61] v v v v v v v v v v v v v v v

> (z1 <- update(z, . ~ . - Petal.W.))

Call:

lda(Sp ~ Sepal.L. + Sepal.W. + Petal.L., data = Iris, prior = c(1,

1, 1)/3, subset = train)

Prior probabilities of groups:

c s v

0.3333333 0.3333333 0.3333333

Group means:

Sepal.L. Sepal.W. Petal.L.

c 6.044444 2.751852 4.396296

s 4.954545 3.459091 1.422727

v 6.607692 2.942308 5.557692

Coefficients of linear discriminants:

LD1 LD2

Sepal.L. 1.145946 -1.329531

Sepal.W. 0.823182 3.501668

Petal.L. -3.259627 1.048042

Proportion of trace:

LD1 LD2

0.9913 0.0087

plot(z, dimen=1)

plot(z, type="density", dimen=1)