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21 16 19 16 23 12 19 16 13 20 27 8 13 15 5 14 24 24 9 19 19 18 12 25 19 20 24 12 17 16 11 20 11 20 19 17 18 17 27 19 19 11 22 20 24 16 16 11 20 20 12 8 21 18 16 18 20 20 17 15 17 23 19 17 12 24 18 20 16 20 22 16 17 12 14 23 15 17 28 23 13 19 13 22 20 10 17 18 23 17 19 18 22 16 16 16 16 18 18 17 19 20 17 25 26 19 16 19 25 15 12 21 15 28 26 25 12 15 14 22 21 23 25 22 24 20 22 16 22 24 16 27 20 27 24 19 16 26 22 21 14 12 22 20 18 23 23 12 16 23 18 23 15 17 22 20 20 20 22 18 17 21
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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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