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12192.5 11268.8 9097.4 12639.8 13040.1 11687.3 11191.7 11391.9 11793.1 13933.2 12778.1 11810.3 13698.4 11956.6 10723.8 13938.9 13979.8 13807.4 12973.9 12509.8 12934.1 14908.3 13772.1 13012.6 14049.9 11816.5 11593.2 14466.2 13615.9 14733.9 13880.7 13527.5 13584.0 16170.2 13260.6 14741.9 15486.5 13154.5 12621.2 15031.6 15452.4 15428.0 13105.9 14716.8 14180.0 16202.2 14392.4 15140.6 15960.1 14351.3 13230.2 15202.1 17157.3 16159.1 13405.7 17224.7 17338.4 17370.6 18817.8 16593.2 17979.5
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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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