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17 18 20 28 22 16 18 14 11 27 22 23 24 14 23 24 24 8 23 21 24 25 23 21 21 26 18 17 15 13 26 16 21 20 14 20 22 20 18 24 20 19 23 26 22 20 13 22 22 20 6 21 20 23 20 24 22 18 23 21 24 21 20 11 22 27 25 18 20 24 27 21 18 15 24 22 14 28 26 17 18 11 26 23 15 22 26 20 18 19 23 24 23 27 23 18 16 21 19 25 17 20 22 21 17 17 22 25 15 22 21 16 28 22 21 12 25 24 24 25 25 26 22 24 17 20 15 24 26 21 25 23 28 18 21 21 23 15 23 21 10 21 18 19 22 24 15 18 26 21 23 16 22 16 19 20 25 21 21 16 20
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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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