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16.4 17.8 22.3 22.8 18.3 22.4 23.9 21.3 23.0 21.4 21.2 20.9 17.9 20.7 22.2 19.8 17.7 19.6 20.8 19.8 18.6 21. 18.6 18.9 17.3 20.0 19.9 19.5 16.2 17.6 19.8 19.4 17.2 21.1 17.8 17.5 18.0 19.1 17.7 19.2 15.1 16.3 18.6 17.2 17.8 19.1 16.6 16.0 16.7 17.4 17.9 17.8 13.9 15.9 17.9 15.4 16.4 17.9 15.3 14.6 14.9 15.0 16.7 16.3 11.7 15.1 15.5 15.0 15.4 16.0 14.7 14.8
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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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