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15 13 14 13 12 17 12 13 13 16 12 12 13 16 15 12 13 20 15 12 15 11 13 13 14 14 14 15 16 16 16 13 13 14 13 14 12 17 14 15 13 14 15 19 14 13 12 14 15 15 12 14 11 12 10 13 14 14 15 15 13 15 16 12 17 15 18 12 16 15 15 12 13 10 14 11 12 14 12 14 12 13 13 14 12 15 13 13 11 12 16 11 13 12 17 14 15 8 13 13 15 14 13 14 12 19 15 14 14 15 13 15 14 11 17 13 9 12 13 17 14 13 16 14 14 14 10 12 13 14 18 14 14 13 13 16 15 13 14 8 13 13 16 14 13 14 12 16 18 16 15 18 15 14 14 15 9 17 11 15 15 15 13 14 15 15 14 13
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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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