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Data:
0.83 0.88 0.99 0.78 0.84 0.52 0.29 0.87 0.84 0.74 0.53 0.89 0.83 0.87 0.91 0.89 0.95 0.51 0.99 0.83 0.85 0.58 0.70 0.86 0.82 0.42 0.40 0.48 0.68 0.44 0.57 0.38 0.51 0.54 0.61 0.26 0.33 0.35 0.36 0.24 0.26 0.28 0.35 0.30 0.02 0.02 0.02 0.25 0.32 0.47 0.26 0.25 0.48 0.47 0.38 0.27 0.59 0.49 0.19 0.27 0.47 0.13 0.20 0.47
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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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