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Data:
91.46 90.24 93.90 96.34 93.90 86.59 75.61 70.73 74.39 84.15 89.02 87.80 74.39 70.73 74.39 78.05 82.93 82.93 79.27 75.61 76.83 78.05 80.49 81.71 78.05 82.93 85.37 84.15 86.59 87.80 86.59 85.37 84.15 81.71 80.49 84.15 89.02 96.34 100.00 100.00 100.00 98.78 96.34 93.90 93.90 92.68 91.46 91.46 86.59 91.46 91.46 95.12 95.12 95.12 92.68 91.46 93.90 98.78 97.56 92.68 80.49 79.27 82.93 91.46 97.56 100.00 98.78 96.34 96.34 92.68 91.46 92.68 89.02 91.46 92.68 91.46 92.68 95.12 96.34 95.12 91.46 80.49 76.83 76.83 73.17 76.83 78.05 76.83 76.83 78.05 81.71 81.71 82.93 75.61 70.73 68.29 65.85 69.51 70.73 67.07 65.85 65.85 65.85 67.07 68.29 69.51 70.73 65.85 59.76 63.41 67.07 71.95 76.83 79.27 78.05 78.05 80.49 82.93 87.80
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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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