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26 23 25 23 19 29 25 21 22 25 24 18 22 15 22 28 20 12 24 20 21 20 21 23 28 24 24 24 23 23 29 24 18 25 21 26 22 22 22 23 30 23 17 23 23 25 24 24 23 21 24 24 28 16 20 29 27 22 28 16 25 24 28 24 23 30 24 21 25 25 22 23 26 23 25 21 25 24 29 22 27 26 22 24 27 24 24 29 22 21 24 24 23 20 27 26 25 21 21 19 21 21 16 22 29 15 17 15 21 21 19 24 20 17 23 24 14 19 24 13 22 16 19 25 25 23 24 26 26 25 18 21 26 23 23 22 20 13 24 15 14 22 10 24 22 24 19 20 13 20 22 24 29 12 20 21 24 22 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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