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24 0 10 4 45 2 4 9 3 10 7 40 5 8 6 1 2 0 3 44 5 14 37 3 0 8 11 6 4 26 37 5 4 39 8 3 5 5 45 2 15 5 4 7 7 19 1 3 4 57 4 47 8 42 0 5 5 29 5 10 10 7 79 5 43 3 60 7 79 8 4 9 4 9 2 0 26 40 35 79 5 10 10 2 4 0 44 41 43 6 5 55 11 3 5 76 0 20 0 15 5 18 6 23 0 14 12 3 6 17 47 32 0 0 3 0 2 0 6 1 0 5 5 3 9 45 8 5 4 47 10 32 46 5 5 9 34 0 38 0 30 3 70 5 4 22 54 3 3 50 50 15 9 5 40 5 7 5 6 24 0 3 45 45 6 5 44 4 50 30 30 10 3 7 50 7 7 79 55 45 2 14 17 3 7 30 10 11 7 5 5 4 0 0 6 7 3
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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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