ohdatamineDISC2

# 4 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 1 1 11 1 1 11 1 2

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Unformatted text preview: y 1.240248e-05 -0.0059924778 . . . insulin -3.895587e-03 0.0005754322 . . . . . . . . . 4 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 0 1 1 11 1 1 11 1 2 1 22 1 1 12 1 2 −2 2 1 2 2 2 2 2 2 22 22 12 2 22 3 2 2 22 2 2 2 2 −4 LD2 1 3 3 3 3 3 33 33 3 3 33 3 3 3 3 3 1 3 3 33 3 3 333 33 3 33 3333 33 3 3 3 3 3 3 333 33 2 3 23 33 33 3 33 33 3 33 2 2 2 33 3 3 3 3 23 2 2 3 3 −6 −4 −2 0 2 . . . . . . Example of Linear Discrimination diab.ld=lda(diab[,1:5],grouping=diab[,6]) names(diab.ld) [1] "prior" "counts" "means" "scaling" "lev" "svd" [8] "call" > table(predict(diab.ld,diab[,1:5])\$class,diab[,6]) 123 1 26 0 0 2 5 31 3 3 1 5 73 . . . . . "N" . Cross-validation To determine an estimate of the misclassiﬁcation rate that is not biased, we use cross-validation. Usually for LDA we use leave-one out cross validation (n fold) X 1 ∪ X2 ∪ X 3 . . . ∪ X n . . . . . . conf <- function(class.predict,class){ confusion=table(class.predict,class) return(confusion) } library(class) train <- rbind(iris3[1:25,,1], iris3[1:25,,2], iris3[1:25,,3]) test <- rbind(iris3[26:50,,1], iris3[26:50,,2], iris3[26:50,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) knn(train, test, cl, k = 3, prob=TRUE) iris.knncv2=knn.cv(train, cl, k = 2, prob = TRUE) iris.knncv4=knn.cv(train, cl, k = 4, prob = TRUE) iris.knncv8=knn.cv(train, cl, k =...
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## This note was uploaded on 07/29/2011 for the course STAT 202 at Stanford.

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