CS计算机代考程序代写 ## ——————————————

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## ST4060 / ST6015 / ST6040
## R script – Wedn 24 Nov 2021 lecture
## Eric Wolsztynski
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## Types of challenges…
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# (1) Data scales…

x = iris[,c(2:4)]
y = iris[,5]
K = 2

par(mfrow=c(2,1), mar=c(3,3,1,1))
#
# clustering without scaling:
ko = kmeans(x, K)
is = c(1,3)
plot(x[,is],col=c(1,2,4)[ko$cluster], pch=20, cex=2)
#
# clustering with scaling:
z = apply(x,2,scale)
koz = kmeans(z, K)
plot(x[,is],col=c(1,2,4)[koz$cluster], pch=20, cex=2)

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# (2) Very large P…

pairs(iris[,1:4], pch=20)
cor(iris[,1:4]) # features 3 and 4 redundant

# See JMI for another example with large P

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# (3) Very large D…

plot(EuStockMarkets, lwd=3)

dev.new()
pca = prcomp(EuStockMarkets)
par(mfcol=c(4,1), mar=c(1,3,0,1))
for(i in 1:4){
plot(pca$x[,i], t=’l’, lwd=2)
}
dev.new()
plot(pca)

plot(prcomp(iris[,1:4]))
plot(prcomp(iris[,1:4], scale.=TRUE))