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23 20 20 21 24 22 23 20 25 23 27 27 22 24 25 22 28 28 27 25 16 28 21 24 27 14 14 27 20 21 22 21 12 20 24 19 28 23 27 22 27 26 22 21 19 24 19 26 22 28 21 23 28 10 24 21 21 24 24 25 25 23 21 16 17 25 24 23 25 23 28 26 22 19 26 18 18 25 27 12 15 21 23 22 21 24 27 22 28 26 10 19 22 21 24 25 21 20 21 24 23 18 24 24 19 20 18 20 27 23 26 23 17 21 25 23 27 24 20 27 21 24 21 15 25 25 22 24 21 22 23 22 20 23 25 23 22 25 26 22 24 24 25 20 26 21 26 21 22 16 26 28 18 25 23 21 20 25 22 21 16 18
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R Code
gp <- function(lambda, p) { (p^lambda-(1-p)^lambda)/lambda } sortx <- sort(x) c <- array(NA,dim=c(201)) for (i in 1:201) { if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx) } bitmap(file='test1.png') plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda') grid() dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Distribution (lambda)',1,TRUE) a<-table.element(a,'Correlation',1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE) a<-table.element(a,c[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE) a<-table.element(a,(c[100]+c[102])/2) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE) a<-table.element(a,c[115]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE) a<-table.element(a,c[151]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE) a<-table.element(a,c[201]) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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1 seconds
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Big Analytics Cloud Computing Center
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