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Data:
10001.60 10411.75 10673.38 10539.51 10723.78 10682.06 10283.19 10377.18 10486.64 10545.38 10554.27 10532.54 10324.31 10695.25 10827.81 10872.48 10971.19 11145.65 11234.68 11333.88 10997.97 11036.89 11257.35 11533.59 11963.12 12185.15 12377.62 12512.89 12631.48 12268.53 12754.80 13407.75 13480.21 13673.28 13239.71 13557.69 13901.28 13200.58 13406.97 12538.12 12419.57 12193.88 12656.63 12812.48 12056.67 11322.38 11530.75 11114.08 9181.73 8614.55 8595.56 8396.20 7690.50 7235.47 7992.12 8398.37 8593.01 8679.75 9374.63 9634.97
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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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