Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
133.39 133.74 129.67 126.70 126.84 128.45 129.84 128.83 129.22 132.14 137.40 141.78 138.74 137.63 139.61 136.82 134.24 128.64 126.43 127.25 126.72 124.18 121.73 122.34 124.74 122.81 123.40 125.68 130.78 129.41 129.49 130.51 129.01 127.33 129.41 132.06 129.35 129.47 130.92 133.39 132.48 130.86 132.75 131.81 133.56 136.38 139.22 137.23 136.16 135.03 140.33 140.85 138.58 137.08 137.75 131.10 125.54 116.36 111.10 117.81
Data Y:
98.86 100.83 117.15 106.96 101.25 115.80 90.85 100.62 118.61 114.66 108.00 105.61 98.34 99.69 108.84 106.86 101.98 118.40 84.10 99.48 117.67 110.08 113.10 106.34 102.91 104.68 120.06 104.68 114.24 119.13 88.77 104.47 119.33 121.10 117.36 106.03 110.19 109.46 123.49 110.29 113.62 121.83 96.15 108.32 116.94 127.23 117.78 103.95 115.07 117.26 114.14 121.93 113.41 120.48 99.79 103.74 121.41 120.27 103.33 98.02
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
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation