Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
150.85 147.79 141.96 148.39 147.71 150.6 151.18 152.24 157.19 154.62 157.22 159.7 160.55 149.66 151.69 154.13 151.48 153.34 155.8 158.87 156.09 156.3 156.4 154.09 161.32 160.12 155.17 154.51 151.38 152.59 153.98 154.91 153.01 155.09 155.53 161.86 166.03 164.54 164.33 163.21 159.95 164.18 167.13 166.8 166.29 168.07 167.1 163.53 168.28 169.07 165.84 163.88 157.33 161 163.54 161.21 158.92 160.18 159.9 164.46
Data Y:
126.51 131.02 136.51 138.04 132.92 129.61 122.96 124.04 121.29 124.56 118.53 113.14 114.15 122.17 129.23 131.19 129.12 128.28 126.83 138.13 140.52 146.83 135.14 131.84 125.7 128.98 133.25 136.76 133.24 128.54 121.08 120.23 119.08 125.75 126.89 126.6 121.89 123.44 126.46 129.49 127.78 125.29 119.02 119.96 122.86 131.89 132.73 135.01 136.71 142.73 144.43 144.93 138.75 130.22 122.19 128.4 140.43 153.5 149.33 142.97
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