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Data:
100.00 102.83 109.50 115.91 107.94 110.86 118.89 123.38 113.33 116.38 122.04 125.47 115.62 117.91 122.40 125.05 114.18 114.74 120.63 123.68 112.84 115.64 122.32 124.59 116.33 117.45 125.64 128.38 119.87 121.22 128.98 131.35 121.35 123.72 131.06 134.55 125.93 128.90 136.19 140.34 130.48 134.68 141.05 145.44 136.21 139.85 147.13 151.44 143.62 148.55 153.54 159.79 152.55 155.84 160.38 164.22 156.40 160.05 165.60 171.15 161.90 167.21 171.34 176.83 166.27 172.30 176.71 182.99 172.07 178.17 182.20 188.49 176.88 182.13 185.32 192.86 180.27 184.92 187.82 194.94 184.36 188.80 193.42 199.76 188.78 191.49 194.87 198.28 183.24 204.87
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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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