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16.3 17.4 18.1 18.0 17.8 17.5 17.4 17.7 18.1 18.5 18.7 18.9 19.5 19.9 20.2 20.2 19.9 19.6 19.4 19.6 19.8 20.1 20.2 20.2 20.8 21.2 21.3 20.9 20.6 20.5 20.8 21.3 21.9 22.3 22.6 22.7 23.7 24.3 24.6 24.5 24.5 24.2 24.4 24.8 25.2 25.6 25.9 25.8 26.7 27.1 27.0 26.6 26.1 25.6 25.6 25.6 25.8 26.0 25.9 25.4 26.0 26.0 25.8 25.1 24.5 23.8 23.7 23.6 23.7 23.9 23.8 23.5 23.9 23.9 23.6 23.0 22.4 21.7 21.2 21.1 21.2 21.6
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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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