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Data:
100.70 97.90 96.50 96.60 96.60 95.50 91.80 89.30 87.00 85.90 88.00 87.90 89.20 90.90 91.60 90.20 89.10 87.50 86.30 86.00 84.40 86.10 91.00 92.70 88.00 84.30 82.20 80.80 79.40 80.20 82.20 82.20 81.20 82.10 88.10 88.50 92.10 98.60 100.90 100.60 101.10 102.10 103.60 102.80 108.30 104.00 106.10 106.30 109.00 111.00 113.70 112.70 110.30 114.50 119.30 121.80 125.40 129.70 129.40 134.50 141.20 141.40 152.20 167.70 173.30 168.70 172.60 169.80 172.00 179.40 174.60 172.50 172.60 176.30 178.90 179.60 179.90 180.30 180.90 177.70
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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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