Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
106.7 110.2 125.9 100.1 106.4 114.8 81.3 87 104.2 108 105 94.5 92 95.9 108.8 103.4 102.1 110.1 83.2 82.7 106.8 113.7 102.5 96.6 92.1 95.6 102.3 98.6 98.2 104.5 84 73.8 103.9 106 97.2 102.6 89 93.8 116.7 106.8 98.5 118.7 90 91.9 113.3 113.1 104.1 108.7 96.7 101 116.9 105.8 99 129.4 83 88.9 115.9 104.2 113.4 112.2 100.8 107.3 126.6 102.9 117.9 128.8 87.5 93.8 122.7 126.2 124.6 116.7 115.2 111.1 129.9 113.3 118.5 133.5 102.1 102.4
Data Y:
124.9 132 151.4 108.9 121.3 123.4 90.3 79.3 117.2 116.9 120.8 96.1 100.8 105.3 116.1 112.8 114.5 117.2 77.1 80.1 120.3 133.4 109.4 93.2 91.2 99.2 108.2 101.5 106.9 104.4 77.9 60 99.5 95 105.6 102.5 93.3 97.3 127 111.7 96.4 133 72.2 95.8 124.1 127.6 110.7 104.6 112.7 115.3 139.4 119 97.4 154 81.5 88.8 127.7 105.1 114.9 106.4 104.5 121.6 141.4 99 126.7 134.1 81.3 88.6 132.7 132.9 134.4 103.7 119.7 115 132.9 108.5 113.9 142.9 95.2 93
Sample Range:
(leave blank to include all observations)
From:
To:
Chart options
Label y-axis:
Label x-axis:
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')
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation