A<-read.table(file="datiRD.txt",header=TRUE)
A

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## ESEMPIO 1

cor(A)

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## ESEMPIO 2

B <- matrix(nrow=27, ncol=5)
B[,1] <- A[,1]/A[,5]*1000
B[,2] <- A[,2]/A[,5]*1000
B[,3] <- A[,3]/A[,5]*1000
B[,4] <- A[,4]/A[,5]*1000
B[,5] <- A[,6]/A[,5]*1000

B

cor(B)

biplot(princomp(B))

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## ESEMPIO 3

C <- B
C[,1] <- (B[,1]-mean(B[,1]))/sd(B[,1])
C[,2] <- (B[,2]-mean(B[,2]))/sd(B[,2])
C[,3] <- (B[,3]-mean(B[,3]))/sd(B[,3])
C[,4] <- (B[,4]-mean(B[,4]))/sd(B[,4])
C[,5] <- (B[,5]-mean(B[,5]))/sd(B[,5])

C

cov(C)

biplot(princomp(C))

D<-read.table(file="datiRD_stand.txt",header=TRUE)
D
biplot(princomp(D))

plot(princomp(D))

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## CLASSIFICAZIONE

E<-read.table(file="datiRD_stand_HT.txt",header=TRUE)
E
biplot(princomp(E))

princomp(E)$loadings

E.class <- E

E.class[,1] <- 0.276*E[,1]+0.744*E[,2]+0.608*E[,3]

E.class[,2] <- 0
E.class[,3] <- 0

E.class