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Data:
15.22 14.28 14.61 14.19 14.02 14.22 14.80 15.05 15.24 15.85 15.43 15.41 15.53 15.95 15.72 15.68 16.06 15.27 16.01 15.44 15.47 15.49 15.38 16.62 17.25 16.37 16.14 15.76 15.54 15.46 15.26 16.02 15.67 15.67 15.48 16.07 16.65 16.18 16.55 16.58 17.73 17.94 18.66 18.73 19.07 19.48 19.52 19.60 20.32 19.84 19.81 20.64 22.12 21.50 21.77 20.29 21.76 22.35 22.15 23.83 24.46 25.13 24.36 24.45 23.66 25.97 25.20 24.41 25.32 26.36 28.03 28.95 27.25 27.47 28.75 29.24 28.03 27.34 26.47 28.30 27.90 26.69 28.31 28.84 28.56 28.25 28.93 28.22 31.77 31.64 30.60 32.34 31.51 31.39 32.19 33.11 33.99 34.30 34.53 33.67 34.72 34.91 36.24 37.47 36.94 38.55 39.88 37.78 40.09 40.17 40.67 44.82 40.89 41.47 44.67
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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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