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41 39 30 31 34 35 39 34 36 37 38 36 38 39 33 32 36 38 39 32 32 31 39 37 39 41 36 33 33 34 31 27 37 34 34 32 29 36 29 35 37 34 38 35 38 37 38 33 36 38 32 32 32 34 32 37 39 29 37 35 30 38 34 31 34 35 36 30 39 35 38 31 34 38 34 39 37 34 28 37 33 37 35 37 32 33 38 33 29 33 31 36 35 32 29 39 37 35 37 32 38 37 36 32 33 40 38 41 36 43 30 31 32 32 37 37 33 34 33 38 33 31 38 37 33 31 39 44 33 35 32 28 40 27 37 32 28 34 30 35 31 32 30 30 31 40 32 36 32 35 38 42 34 35 35 33 36 32 33 34 32 34
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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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