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Data X:
2756.76 2849.27 2921.44 2981.85 3080.58 3106.22 3119.31 3061.26 3097.31 3161.69 3257.16 3277.01 3295.32 3363.99 3494.17 3667.03 3813.06 3917.96 3895.51 3801.06 3570.12 3701.61 3862.27 3970.1 4138.52 4199.75 4290.89 4443.91 4502.64 4356.98 4591.27 4696.96 4621.4 4562.84 4202.52 4296.49 4435.23 4105.18 4116.68 3844.49 3720.98 3674.4 3857.62 3801.06 3504.37 3032.6 3047.03 2962.34 2197.82 2014.45 1862.83 1905.41 1810.99 1670.07 1864.44 2052.02 2029.6 2070.83 2293.41 2443.27
Data Y:
0.86 0.88 0.88 0.88 0.87 0.88 0.87 0.85 0.84 0.83 0.86 0.87 0.85 0.89 0.98 1.01 1 1.01 1.05 1 0.99 1.02 1.11 1.15 1.18 1.2 1.22 1.2 1.23 1.23 1.21 1.25 1.2 1.2 1.21 1.25 1.23 1.2 1.18 1.16 1.12 1.11 1.1 1.08 1.01 1.01 0.99 1.07 1.13 1.09 0.95 0.79 0.73 0.7 0.65 0.61 0.53 0.51 0.41 0.42
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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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