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395.3 395.1 403.5 403.3 405.7 406.7 407.2 412.4 415.9 414 411.8 409.9 412.4 415.9 416.3 417.2 421.8 421.4 415.1 412.4 411.8 408.8 404.5 402.5 409.4 410.7 413.4 415.2 417.7 417.8 417.9 418.4 418.2 416.6 418.9 421 423.5 432.3 432.3 428.6 426.7 427.3 428.5 437 442 444.9 441.4 440.3 447.1 455.3 478.6 486.5 487.8 485.9 483.8 488.4 494 493.6 487.3 482.1 484.2 496.8 501.1 499.8 495.5 498.1 503.8 516.2 526.1 527.1 525.1 528.9 540.1 549 556 568.9 589.1 590.3 603.3 638.8 643 656.7 656.1 654.1 659.9 662.1 669.2 673.1 678.3 677.4 678.5 672.4 665.3 667.9 672.1 662.5 682.3 692.1 702.7 721.4 733.2 747.7 737.6 729.3 706.1 674.3 659 645.7
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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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