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21 15 18 11 8 19 4 20 16 14 10 13 14 8 23 11 9 24 5 15 5 19 6 13 11 17 17 5 9 15 17 17 20 12 7 16 7 14 24 15 15 10 14 18 12 9 9 8 18 10 17 14 16 10 19 10 14 10 4 19 9 12 16 11 18 11 24 17 18 9 19 18 12 23 22 14 14 16 23 7 10 12 12 12 17 21 16 11 14 13 9 19 13 19 13 13 13 14 12 22 11 5 18 19 14 15 12 19 15 17 8 10 12 12 20 12 12 14 6 10 18 18 7 18 9 17 22 11 15 17 15 22 9 13 20 14 14 12 20 20 8 17 9 18 22 10 13 15 18 18 12 12 20 12 16 16 18 16 13 17 13 17
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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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