Nguồn Lớp học tại VLU * “R and machine learning” *.

library(datasets)
library(MASS)
library(ggplot2)
data(iris)
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
fit <- lda(Species~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, data=iris)
fit
## Call:
## lda(Species ~ Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, 
##     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
predicted <- predict(fit, iris)
plot.data <- cbind(iris, predict(fit)$x)
head(plot.data)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species      LD1        LD2
## 1          5.1         3.5          1.4         0.2  setosa 8.061800  0.3004206
## 2          4.9         3.0          1.4         0.2  setosa 7.128688 -0.7866604
## 3          4.7         3.2          1.3         0.2  setosa 7.489828 -0.2653845
## 4          4.6         3.1          1.5         0.2  setosa 6.813201 -0.6706311
## 5          5.0         3.6          1.4         0.2  setosa 8.132309  0.5144625
## 6          5.4         3.9          1.7         0.4  setosa 7.701947  1.4617210
ggplot(data=plot.data, aes(x=LD1, y=LD2, col=Species))+geom_point()

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