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Data:
159.81 147.75 147.13 140.33 142.87 139.67 135.35 136.32 129.27 126.81 137.17 150.94 173.71 156.68 146.84 144.17 134.81 135.13 131.45 133.77 134.87 140.90 136.57 155.52 160.16 158.04 148.42 150.70 135.26 134.63 132.23 132.55 134.13 136.94 141.73 165.68 162.48 145.86 142.19 137.30 131.71 133.67 133.81 127.48 128.10 134.32 135.83 151.87 158.87 163.86 158.58 140.13 136.87 134.20 126.19 122.52 124.20 133.87 136.53 148.90 151.19 151.29 149.06 138.80 134.77 135.43 141.19 126.77 126.43 131.00 134.00 138.13 151.06 158.61 144.03 139.57 128.74 127.20 125.90 122.06 127.23 135.13 144.83 134.94
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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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