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Data:
2.97 3.04 3.12 3.21 3.34 3.45 3.74 4.02 4.24 4.87 5.62 6.02 5.98 5.89 5.76 5.58 5.39 5.19 5.16 5.2 5.25 5.26 5.21 5.18 5.13 5.03 5.01 4.87 4.86 4.82 4.69 4.65 4.61 4.47 4.37 4.29 4.2 4.19 4.09 3.88 3.87 3.74 3.61 3.43 3.29 3.18 3.07 3.02 2.97 2.98 3.01 3.06 3.12 3.16 3.19 3.21 3.27 3.36 3.45 3.52 3.58 3.62 3.5 3.43 3.41 3.48 3.63 3.76 3.8 3.72 3.67 3.58 3.47 3.43 3.55 3.65 3.7 3.7 3.93 4.15 4.24
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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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