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Data:
4.8 4.8 4.7 4.7 4.7 4.6 5.0 5.4 5.5 5.6 5.6 5.8 6.0 6.1 6.1 6.0 6.0 6.1 6.5 7.1 7.4 7.4 7.5 7.6 7.8 7.8 7.7 7.6 7.5 7.3 7.6 8.0 8.8 7.9 7.8 7.7 7.8 7.7 7.5 7.3 7.1 7.0 7.3 7.8 7.9 7.9 7.8 7.8 7.9 7.8 7.6 7.4 7.2 6.9 7.1 7.5 7.6 7.4 7.3 7.2 7.3 7.2 7.1 7.0 6.9 6.8 7.2 7.6 7.7 7.6 7.5 7.5 7.6 7.6 7.6 7.5 7.3 7.2 7.4 8.0 8.2 8.0 7.7 7.7 7.8 7.8 7.7 7.5 7.3 7.1 7.1 7.2 6.8 6.6 6.4 6.4 6.5 6.3 5.9 5.5 5.2 4.9 5.4 5.8 5.7 5.6 5.5 5.4 5.4 5.4 5.5 5.8 5.7 5.4 5.6 5.8 6.2 6.8 6.7 6.7 6.4 6.3 6.3 6.4 6.3 6.0 6.3 6.3 6.6 7.5 7.8 7.9 7.8 7.6 7.5 7.6 7.5 7.3 7.6 7.5 7.6 7.9 7.9 8.1 8.2 8.0 7.5 6.8 6.5 6.6 7.6 8.0 8.1 7.7 7.5 7.6 7.8 7.8 7.8 7.5 7.5 7.1 7.5 7.5 7.6 7.7 7.7 7.9 8.1 8.2 8.2 8.2 7.9 7.3 6.9 6.6 6.7 6.9 7.0 7.1
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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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