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Data:
92.7 122.8 115.4 128.3 120.2 118.9 114.7 114.6 121.7 129.7 115.2 100.7 89.8 128.7 105.9 111.7 130.3 108 109.6 139 123.6 131 120.7 81.1 88.4 128.9 120.8 95 132 117.9 102.4 117.4 113.5 108 127.6 86.9 76.1 128.8 104.1 121.2 140.2 116 115 112.3 128.9 131.2 128.7 85.8 78.2 128.4 105.5 120.3 135.4 107.1 96.9 95.1 113.1 104.5 106.3 66.6 87.8 117.3 102.1 98.9 130.2 102.4 89.9 95.9 95.4 116.2 115.7 74.1 75.2 107.7 103.7 107.1 113.5 94.7 90.9 97 111 109.6 110.1 77.7 78 113.3 111.4 95.4 141.2 120.4 124.9 106.4 116.5 102.1 94.1 74 87
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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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