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Data:
87.3 97.6 107.1 96.1 109.5 105.0 83.9 89.2 107.0 113.6 108.1 91.9 104.9 99.2 104.3 104.0 101.5 105.4 88.7 83.6 98.0 108.9 92.8 82.0 101.3 106.3 94.0 102.8 102.0 105.1 92.4 81.4 105.8 120.3 100.7 88.8 94.3 99.9 103.4 103.3 98.8 104.2 91.2 74.7 108.5 114.5 96.9 89.6 97.1 100.3 122.6 115.4 109.0 129.1 102.8 96.2 127.7 128.9 126.5 119.8 113.2 114.1 134.1 130.0 121.8 132.1 105.3 103.0 117.1 126.3 138.1 119.5 138.0 135.5 178.6 162.2 176.9 204.9 132.2 142.5 164.3 174.9 175.4 143.0 158.7 155.4 176.6 163.3 178.9 182.7
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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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