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Data:
12.9 12.8 7.4 6.7 14.8 13.3 11.1 8.2 11.4 6.4 11.3 10 6.4 10.8 13.8 11.7 13.4 11.7 9 9.7 10.8 12.7 11.8 5.9 11.4 13 11.3 6.7 12.1 13.3 5.7 13.3 7.6 11.1 13 9.9 11.1 4.35 12.7 18.1 12.6 19.1 18.4 14.7 10.6 12.6 16.2 18.9 14.1 16.15 14.75 14.8 12.45 12.65 17.35 18.4 11.6 17.75 15.25 17.65 14.75 9.9 16 13.85 17.1 14.6 15.4 17.6 13.9 16.25 15.65 14.6 11.2 16.35 15.85 7.65 12.35 15.6 13.1 12.85 9.5 11.85 13.6 17.6 16.1 13.35 15.15
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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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