y <- c(96.8,114.1,110.3,103.9,101.6,94.6,95.9,104.7,102.8,98.1,113.9,80.9,95.7,113.2,105.9,108.8,102.3,99,100.7,115.5,100.7,109.9,114.6,85.4,100.5,114.8,116.5,112.9,102,106,105.3,118.8,106.1,109.3,117.2,92.5,104.2,112.5,122.4,113.3,100,110.7,112.8,109.8,117.3,109.1,115.9,96,99.8,116.8,115.7,99.4,94.3,91,93.2,103.1,94.1,91.8,102.7,82.6) x <- c(92.9,107.7,103.5,91.1,79.8,71.9,82.9,90.1,100.7,90.7,108.8,44.1,93.6,107.4,96.5,93.6,76.5,76.7,84,103.3,88.5,99,105.9,44.7,94,107.1,104.8,102.5,77.7,85.2,91.3,106.5,92.4,97.5,107,51.1,98.6,102.2,114.3,99.4,72.5,92.3,99.4,85.9,109.4,97.6,104.7,56.9,86.7,108.5,103.4,86.2,71,75.9,87.1,102,88.5,87.8,100.8,50.6) #'GNU S' R Code compiled by R2WASP v. 1.0.44 () #Author: Prof. Dr. P. Wessa #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ #Source of accompanying publication: Office for Research, Development, and Education #Technical description: Write here your technical program description (don't use hard returns!) n <- length(x) c <- array(NA,dim=c(401)) l <- array(NA,dim=c(401)) mx <- 0 mxli <- -999 for (i in 1:401) { l[i] <- (i-201)/100 if (l[i] != 0) { x1 <- (x^l[i] - 1) / l[i] } else { x1 <- log(x) } c[i] <- cor(x1,y) if (mx < abs(c[i])) { mx <- abs(c[i]) mxli <- l[i] } } c mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } r<-lm(y~x) se <- sqrt(var(r$residuals)) r1 <- lm(y~x1) se1 <- sqrt(var(r1$residuals)) postscript(file="/var/www/html/rcomp/tmp/1xeva1258362817.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation') grid() dev.off() postscript(file="/var/www/html/rcomp/tmp/20wo01258362817.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y') abline(r) grid() mtext(paste('Residual Standard Deviation = ',se)) dev.off() postscript(file="/var/www/html/rcomp/tmp/3ki2l1258362817.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y') abline(r1) grid() mtext(paste('Residual Standard Deviation = ',se1)) dev.off() #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab load(file="/var/www/html/rcomp/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="/var/www/html/rcomp/tmp/4ky9m1258362817.tab") system("convert tmp/1xeva1258362817.ps tmp/1xeva1258362817.png") system("convert tmp/20wo01258362817.ps tmp/20wo01258362817.png") system("convert tmp/3ki2l1258362817.ps tmp/3ki2l1258362817.png")