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5 3 0 7 4 1 6 3 12 0 5 6 6 6 2 1 5 7 3 3 3 7 8 6 3 5 5 10 2 6 4 6 8 4 5 10 6 7 4 10 4 3 3 3 3 7 15 0 0 4 5 5 2 3 0 9 2 7 7 0 0 10 2 1 8 6 11 3 8 6 9 9 8 8 7 6 5 4 6 3 2 12 8 5 9 6 5 2 4 7 5 6 7 8 6 0 1 5 5 5 7 7 1 3 4 8 6 6 2 2 3 3 0 2 8 8 0 5 9 6 6 3 9 7 8 0 7 0 5 0 14 5 2 8 4 2 6 3 5 9 3 3 0 10 4 2 3 10 7 0 6 8 0 4 10 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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