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26 20 19 19 20 25 25 22 26 22 17 22 19 24 26 21 13 26 20 22 14 21 7 23 17 25 25 19 20 23 22 22 21 15 20 22 18 20 28 22 18 23 20 25 26 15 17 23 21 13 18 19 22 16 24 18 20 24 14 22 24 18 21 23 17 22 24 21 22 16 21 23 22 24 24 16 16 21 26 15 25 18 23 20 17 25 24 17 19 20 15 27 22 23 16 19 25 19 19 26 21 20 24 22 20 18 18 24 24 22 23 22 20 18 25 18 16 20 19 15 19 19 16 17 28 23 25 20 17 23 16 23 11 18 24 23 21 16 24 23 18 20 9 24 25 20 21 25 22 21 21 22 27 24 24 21 18 16 22 20 18 20
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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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