| 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')
 
 |