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Data X:
109.20 88.60 94.30 98.30 86.40 80.60 104.10 108.20 93.40 71.90 94.10 94.90 96.40 91.10 84.40 86.40 88.00 75.10 109.70 103.00 82.10 68.00 96.40 94.30 90.00 88.00 76.10 82.50 81.40 66.50 97.20 94.10 80.70 70.50 87.80 89.50 99.60 84.20 75.10 92.00 80.80 73.10 99.80 90.00 83.10 72.40 78.80 87.30 91.00 80.10 73.60 86.40 74.50 71.20 92.40 81.50 85.30 69.90 84.20 90.70 100.30
Data Y:
72.50 59.40 85.70 88.20 62.80 87.00 79.20 112.00 79.20 132.10 40.10 69.00 59.40 73.80 57.40 81.10 46.60 41.40 71.20 67.90 72.00 145.50 39.70 51.90 73.70 70.90 60.80 61.00 54.50 39.10 66.60 58.50 59.80 80.90 37.30 44.60 48.70 54.00 49.50 61.60 35.00 35.70 51.30 49.00 41.50 72.50 42.10 44.10 45.10 50.30 40.90 47.20 36.90 40.90 38.30 46.30 28.40 78.40 36.80 50.70 42.80
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R Code
n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mx <- 0 mxli <- -999 for (i in 1:401) { l[i] <- (i-201)/100 if (l[i] != 0) { x1 <- (x^l[i] - 1) / l[i] } else { x1 <- log(x) } c[i] <- cor(x1,y) if (mx < abs(c[i])) { mx <- abs(c[i]) mxli <- l[i] } } c mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } r<-lm(y~x) se <- sqrt(var(r$residuals)) r1 <- lm(y~x1) se1 <- sqrt(var(r1$residuals)) bitmap(file='test1.png') plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation') grid() dev.off() bitmap(file='test2.png') plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y') abline(r) grid() mtext(paste('Residual Standard Deviation = ',se)) dev.off() bitmap(file='test3.png') plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y') abline(r1) grid() mtext(paste('Residual Standard Deviation = ',se1)) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'# observations x',header=TRUE) a<-table.element(a,n) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'maximum correlation',header=TRUE) a<-table.element(a,mx) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'optimal lambda(x)',header=TRUE) a<-table.element(a,mxli) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (orginial)',header=TRUE) a<-table.element(a,se) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Residual SD (transformed)',header=TRUE) a<-table.element(a,se1) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable.tab')
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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