R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(7.5,8,7.9,7.8,7.7,7.6,7.4,7.2,7.3,7.4,7.5,7.6,8,8.3,8.3,8.2,7.8,8,8,7.9,7.4,7.5,7.4,7.3,7.5,7.5,7.5,7.1,6.8,6.9,6.6,6.7,6.5,6.6,6.5,6.5,6.7,6.8,6.1,5.8,5.8,6.1,5.6,5.4,5.1,5.2,5,5.2,5.4,5.5,5.1,4.8,5,4.9,4.6,4.3,4.3,4.4,4.4,4.5)
> x <- c(5.7,6.1,6,5.9,5.8,5.7,5.6,5.4,5.4,5.5,5.6,5.7,5.9,6.1,6,5.8,5.8,5.7,5.5,5.3,5.2,5.2,5,5.1,5.1,5.2,4.9,4.8,4.5,4.5,4.4,4.4,4.2,4.1,3.9,3.8,3.9,4.2,4.1,3.8,3.6,3.7,3.5,3.4,3.1,3.1,3.1,3.2,3.3,3.5,3.6,3.5,3.3,3.2,3.1,3.2,3,3,3.1,3.4)
> #'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
  [1] 0.9691524 0.9691973 0.9692416 0.9692852 0.9693282 0.9693706 0.9694123
  [8] 0.9694534 0.9694939 0.9695338 0.9695730 0.9696116 0.9696496 0.9696870
 [15] 0.9697237 0.9697598 0.9697952 0.9698300 0.9698642 0.9698977 0.9699307
 [22] 0.9699629 0.9699946 0.9700255 0.9700559 0.9700856 0.9701147 0.9701431
 [29] 0.9701709 0.9701981 0.9702246 0.9702504 0.9702757 0.9703002 0.9703242
 [36] 0.9703474 0.9703701 0.9703921 0.9704134 0.9704341 0.9704542 0.9704736
 [43] 0.9704923 0.9705104 0.9705278 0.9705446 0.9705608 0.9705763 0.9705911
 [50] 0.9706053 0.9706188 0.9706317 0.9706439 0.9706555 0.9706664 0.9706767
 [57] 0.9706863 0.9706952 0.9707035 0.9707112 0.9707181 0.9707244 0.9707301
 [64] 0.9707351 0.9707394 0.9707431 0.9707461 0.9707485 0.9707502 0.9707512
 [71] 0.9707516 0.9707513 0.9707504 0.9707488 0.9707465 0.9707435 0.9707399
 [78] 0.9707357 0.9707308 0.9707252 0.9707189 0.9707120 0.9707044 0.9706962
 [85] 0.9706872 0.9706777 0.9706674 0.9706565 0.9706449 0.9706327 0.9706198
 [92] 0.9706062 0.9705920 0.9705771 0.9705615 0.9705453 0.9705283 0.9705108
 [99] 0.9704925 0.9704736 0.9704540 0.9704338 0.9704129 0.9703913 0.9703691
[106] 0.9703462 0.9703226 0.9702983 0.9702734 0.9702478 0.9702216 0.9701947
[113] 0.9701671 0.9701388 0.9701099 0.9700803 0.9700501 0.9700191 0.9699875
[120] 0.9699553 0.9699224 0.9698888 0.9698545 0.9698196 0.9697840 0.9697477
[127] 0.9697108 0.9696732 0.9696349 0.9695960 0.9695564 0.9695162 0.9694752
[134] 0.9694336 0.9693914 0.9693485 0.9693049 0.9692606 0.9692157 0.9691701
[141] 0.9691239 0.9690770 0.9690294 0.9689812 0.9689323 0.9688827 0.9688325
[148] 0.9687816 0.9687301 0.9686779 0.9686250 0.9685715 0.9685173 0.9684625
[155] 0.9684070 0.9683508 0.9682940 0.9682365 0.9681783 0.9681195 0.9680601
[162] 0.9680000 0.9679392 0.9678778 0.9678157 0.9677529 0.9676896 0.9676255
[169] 0.9675608 0.9674954 0.9674294 0.9673628 0.9672955 0.9672275 0.9671589
[176] 0.9670896 0.9670197 0.9669491 0.9668779 0.9668061 0.9667335 0.9666604
[183] 0.9665866 0.9665121 0.9664370 0.9663613 0.9662849 0.9662079 0.9661302
[190] 0.9660519 0.9659729 0.9658933 0.9658131 0.9657322 0.9656507 0.9655685
[197] 0.9654857 0.9654023 0.9653182 0.9652335 0.9651482 0.9650622 0.9649756
[204] 0.9648884 0.9648005 0.9647120 0.9646228 0.9645331 0.9644427 0.9643517
[211] 0.9642600 0.9641677 0.9640748 0.9639813 0.9638871 0.9637923 0.9636969
[218] 0.9636009 0.9635043 0.9634070 0.9633091 0.9632106 0.9631115 0.9630117
[225] 0.9629114 0.9628104 0.9627088 0.9626066 0.9625038 0.9624004 0.9622964
[232] 0.9621917 0.9620865 0.9619806 0.9618741 0.9617670 0.9616594 0.9615511
[239] 0.9614422 0.9613327 0.9612226 0.9611119 0.9610006 0.9608887 0.9607762
[246] 0.9606631 0.9605494 0.9604352 0.9603203 0.9602048 0.9600888 0.9599721
[253] 0.9598549 0.9597371 0.9596187 0.9594997 0.9593801 0.9592599 0.9591392
[260] 0.9590178 0.9588959 0.9587734 0.9586504 0.9585267 0.9584025 0.9582777
[267] 0.9581523 0.9580264 0.9578998 0.9577727 0.9576451 0.9575169 0.9573881
[274] 0.9572587 0.9571288 0.9569983 0.9568672 0.9567356 0.9566034 0.9564707
[281] 0.9563374 0.9562035 0.9560691 0.9559342 0.9557987 0.9556626 0.9555260
[288] 0.9553888 0.9552511 0.9551128 0.9549740 0.9548347 0.9546948 0.9545543
[295] 0.9544133 0.9542718 0.9541298 0.9539872 0.9538440 0.9537004 0.9535562
[302] 0.9534114 0.9532662 0.9531204 0.9529741 0.9528272 0.9526799 0.9525320
[309] 0.9523835 0.9522346 0.9520851 0.9519352 0.9517847 0.9516336 0.9514821
[316] 0.9513301 0.9511775 0.9510245 0.9508709 0.9507168 0.9505622 0.9504071
[323] 0.9502515 0.9500954 0.9499388 0.9497817 0.9496241 0.9494660 0.9493074
[330] 0.9491483 0.9489887 0.9488287 0.9486681 0.9485070 0.9483455 0.9481835
[337] 0.9480210 0.9478580 0.9476945 0.9475305 0.9473661 0.9472012 0.9470358
[344] 0.9468699 0.9467036 0.9465368 0.9463695 0.9462017 0.9460335 0.9458648
[351] 0.9456957 0.9455261 0.9453560 0.9451854 0.9450145 0.9448430 0.9446711
[358] 0.9444987 0.9443259 0.9441526 0.9439789 0.9438048 0.9436301 0.9434551
[365] 0.9432796 0.9431036 0.9429272 0.9427504 0.9425732 0.9423954 0.9422173
[372] 0.9420387 0.9418597 0.9416803 0.9415004 0.9413201 0.9411394 0.9409583
[379] 0.9407767 0.9405947 0.9404123 0.9402295 0.9400462 0.9398625 0.9396785
[386] 0.9394940 0.9393091 0.9391238 0.9389380 0.9387519 0.9385654 0.9383784
[393] 0.9381911 0.9380033 0.9378152 0.9376266 0.9374377 0.9372484 0.9370586
[400] 0.9368685 0.9366780
> mx
[1] 0.9707516
> mxli
[1] -1.3
> 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/1ig531257772850.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()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/22w4z1257772850.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()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3m9061257772850.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()
null device 
          1 
> 
> #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/4tp5f1257772850.tab") 
> 
> system("convert tmp/1ig531257772850.ps tmp/1ig531257772850.png")
> system("convert tmp/22w4z1257772850.ps tmp/22w4z1257772850.png")
> system("convert tmp/3m9061257772850.ps tmp/3m9061257772850.png")
> 
> 
> proc.time()
   user  system elapsed 
  0.784   0.509   0.946