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Data:
166.06 154.50 146.87 145.10 143.32 137.03 132.42 130.71 128.60 130.39 138.43 154.74 184.35 163.39 149.06 147.10 138.06 134.13 139.87 133.68 125.47 137.03 140.50 157.13 159.55 160.36 156.48 153.03 138.03 139.70 138.23 145.68 139.90 142.06 145.77 171.19 171.61 150.21 144.65 140.33 129.61 130.40 128.13 125.35 129.73 136.84 137.80 153.00 165.03 172.25 177.06 142.10 136.16 135.87 119.84 119.84 126.13 133.58 132.27 153.77 161.90 155.11 156.55 138.47 130.16 133.20 152.71 121.87 129.57 127.52 132.90 143.10 154.94 166.86 147.10 142.97 127.77 131.43 126.84 123.10 127.80 133.23 148.90 143.45
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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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