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Data:
798.6 798.4 806.8 806.6 809 810 810.5 815.7 819.2 817.3 815.1 813.2 815.7 819.2 819.6 820.5 825.1 824.7 818.4 815.7 815.1 812.1 807.8 805.8 812.7 814 816.7 818.5 821 821.1 821.2 821.7 821.5 819.9 822.2 824.3 826.8 835.6 835.6 831.9 830 830.6 831.8 840.3 845.3 848.2 844.7 843.6 850.4 858.6 881.9 889.8 891.1 889.2 887.1 891.7 897.3 896.9 890.6 885.4 887.5 900.1 904.4 903.1 898.8 901.4 907.1 919.5 929.4 930.4 928.4 932.2 943.4 952.3 959.3 972.2 992.4 993.6 1006.6 1042.1 1046.3 1060 1059.4 1057.4 1063.2 1065.4 1072.5 1076.4 1081.6 1080.7 1081.8 1075.7 1068.6 1071.2 1075.4 1065.8 1085.6 1095.4 1106 1124.7 1136.5 1151 1140.9 1132.6 1109.4 1077.6 1062.3 1049
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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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1 seconds
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Big Analytics Cloud Computing Center
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