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14 8 12 7 10 7 16 11 14 6 16 11 16 12 7 13 11 15 7 9 7 14 15 7 15 17 15 14 14 8 8 14 14 8 11 16 10 8 14 16 13 5 8 10 8 13 15 6 12 16 5 15 12 8 13 14 12 16 10 15 8 16 19 14 6 13 15 7 13 4 14 13 11 14 12 15 14 13 8 6 7 13 13 11 5 12 8 11 14 9 10 13 16 16 11 8 4 7 14 11 17 15 17 5 4 10 11 15 10 9 12 15 7 13 12 14 14 8 15 12 12 16 9 15 15 6 14 15 10 6 14 12 8 11 13 9 15 13 15 14 16 14 14 10 10 4 8 15 16 12 12 15 9 12 14 11
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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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