library(MASS)
table(iris$Species)
##
## setosa versicolor virginica
## 50 50 50
lookup <- c(setosa='blue', versicolor='green', virginica='orange')
col.ind <- lookup[iris$Species]
pairs(iris[-5], pch=21, col="gray", bg=col.ind)
lda.fit <- lda(Species ~ ., data = iris)
lda.fit
## Call:
## lda(Species ~ ., data = iris)
##
## Prior probabilities of groups:
## setosa versicolor virginica
## 0.3333333 0.3333333 0.3333333
##
## Group means:
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## setosa 5.006 3.428 1.462 0.246
## versicolor 5.936 2.770 4.260 1.326
## virginica 6.588 2.974 5.552 2.026
##
## Coefficients of linear discriminants:
## LD1 LD2
## Sepal.Length 0.8293776 0.02410215
## Sepal.Width 1.5344731 2.16452123
## Petal.Length -2.2012117 -0.93192121
## Petal.Width -2.8104603 2.83918785
##
## Proportion of trace:
## LD1 LD2
## 0.9912 0.0088
plot(Sepal.Width ~ Sepal.Length, data = iris, pch=21, col="gray", bg= col.ind)
points(lda.fit$means[,1], lda.fit$means[,2], pch=21, cex=2,
col="black", bg=lookup)
lda.pred <- predict(lda.fit)
head(lda.pred$x)
## LD1 LD2
## 1 8.061800 0.3004206
## 2 7.128688 -0.7866604
## 3 7.489828 -0.2653845
## 4 6.813201 -0.6706311
## 5 8.132309 0.5144625
## 6 7.701947 1.4617210
table(pred=lda.pred$class, true=iris$Species)
## true
## pred setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 48 1
## virginica 0 2 49
1 - mean(lda.pred$class == iris$Species)
## [1] 0.02
lda.cv <- predict(lda.fit, CV=TRUE)