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9.8 10.0 10.2 10.3 10.3 10.4 10.6 11.1 11.3 11.4 11.3 11.5 11.5 11.8 11.8 11.9 11.8 11.9 11.9 12.2 12.2 12.1 12.3 12.4 12.5 12.6 12.7 12.4 12.3 11.9 12.0 12.6 13.2 13.6 14.1 14.7 14.7 15.0 15.3 15.5 15.1 15.0 15.2 15.9 16.2 16.8 17.1 17.6 17.9 17.8 17.6 17.1 16.7 16.1 16.2 15.8 15.7 15.7 15.5 15.4 15.3 15.3 15.0 14.6 14.1 13.8 13.7 13.3 13.3 13.6 13.6 13.9 14.1 13.9 13.4 12.9 12.1 11.9 11.8 12.1 12.4 12.5
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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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