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9 8 10 8 9 10 9 9 9 9 10 9 13 11 10 8 11 6 7 9 9 10 9 10 8 10 10 11 10 9 7 11 6 11 10 9 10 10 9 8 9 9 10 11 7.5 9 10 8 3 10 10 10 4 10 8 9 13 10 8 9 11 10 9 10 7 10 10 11 12 8 10 6 9 11 10 10 8 10 9 9 10 10 11 9 12 7 9 9 11 8 9 9 9 9 11 9 7 15 9 9 12 10 9 10 10 9 10 10 9 9 9 9 11 9 7 11 9 7 12 8 9 9 9 9 11 9 10 10 9 8 10 9 8 10 9 13 8 11 9 8 9 10 10 10 9 11 7 9 10 11 8 8 7 9 9 10 9 11 8 11 9 9 9 7 10 9 9
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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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