Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
100.00 111.86 94.12 67.79 146.04 125.66 142.87 130.03 110.01 132.79 88.24 91.97 96.65 97.15 90.73 52.53 156.91 144.88 169.43 133.38 131.28 116.33 89.15 85.08 89.39 103.09 85.35 45.99 152.82 130.37 150.50 126.97 123.24 126.57 100.26 91.76 100.28 121.65 97.29 62.55 154.99 147.85 147.40 156.80 126.81 131.76 99.21 87.35 100.76 110.57 76.46 56.51 124.95 118.29 136.43 128.62 100.74 111.75 93.43 83.33
Data Y:
100.00 96.67 91.06 89.04 82.91 84.05 91.76 90.10 85.98 99.82 70.90 83.87 99.21 92.81 95.35 89.66 86.77 88.26 101.23 88.26 96.32 100.44 74.85 88.08 100.61 102.10 98.95 89.40 92.90 92.29 104.12 92.99 95.79 102.72 81.07 91.32 98.60 107.27 99.30 87.64 97.02 98.86 96.23 102.80 95.62 101.58 84.14 87.47 102.37 101.40 87.12 82.65 79.75 81.68 90.36 82.47 80.46 90.01 72.39 78.09
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