R version 2.6.0 (2007-10-03)
Copyright (C) 2007 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(4.8,-4.2,1.6,5.2,9.2,4.6,10.6)
> x <- c(4.2,2.6,3,3.8,4,3.5,4.1)
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: Wessa P., (2007), Box-Cox Linearity Plot (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_boxcoxlin.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description
> 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] 0.9101779 0.9101483 0.9101183 0.9100880 0.9100573 0.9100263 0.9099949
  [8] 0.9099632 0.9099311 0.9098986 0.9098658 0.9098326 0.9097991 0.9097652
 [15] 0.9097310 0.9096964 0.9096615 0.9096262 0.9095905 0.9095545 0.9095181
 [22] 0.9094814 0.9094443 0.9094068 0.9093690 0.9093308 0.9092923 0.9092534
 [29] 0.9092141 0.9091745 0.9091345 0.9090942 0.9090535 0.9090124 0.9089710
 [36] 0.9089292 0.9088871 0.9088446 0.9088017 0.9087585 0.9087149 0.9086710
 [43] 0.9086266 0.9085820 0.9085369 0.9084915 0.9084457 0.9083996 0.9083531
 [50] 0.9083062 0.9082590 0.9082114 0.9081635 0.9081151 0.9080665 0.9080174
 [57] 0.9079680 0.9079182 0.9078681 0.9078175 0.9077667 0.9077154 0.9076638
 [64] 0.9076118 0.9075595 0.9075068 0.9074537 0.9074003 0.9073465 0.9072923
 [71] 0.9072377 0.9071828 0.9071276 0.9070719 0.9070159 0.9069595 0.9069028
 [78] 0.9068457 0.9067882 0.9067303 0.9066721 0.9066135 0.9065546 0.9064953
 [85] 0.9064356 0.9063755 0.9063151 0.9062543 0.9061931 0.9061316 0.9060697
 [92] 0.9060075 0.9059448 0.9058818 0.9058184 0.9057547 0.9056906 0.9056261
 [99] 0.9055613 0.9054961 0.9054305 0.9053645 0.9052982 0.9052315 0.9051644
[106] 0.9050970 0.9050292 0.9049611 0.9048925 0.9048236 0.9047543 0.9046847
[113] 0.9046147 0.9045443 0.9044736 0.9044025 0.9043310 0.9042591 0.9041869
[120] 0.9041143 0.9040413 0.9039680 0.9038943 0.9038203 0.9037458 0.9036710
[127] 0.9035958 0.9035203 0.9034444 0.9033681 0.9032915 0.9032145 0.9031371
[134] 0.9030593 0.9029812 0.9029027 0.9028239 0.9027447 0.9026651 0.9025851
[141] 0.9025048 0.9024241 0.9023431 0.9022616 0.9021799 0.9020977 0.9020152
[148] 0.9019323 0.9018490 0.9017654 0.9016814 0.9015971 0.9015123 0.9014273
[155] 0.9013418 0.9012560 0.9011698 0.9010832 0.9009963 0.9009090 0.9008214
[162] 0.9007334 0.9006450 0.9005563 0.9004672 0.9003777 0.9002879 0.9001977
[169] 0.9001071 0.9000162 0.8999249 0.8998332 0.8997412 0.8996488 0.8995561
[176] 0.8994630 0.8993695 0.8992757 0.8991815 0.8990870 0.8989920 0.8988968
[183] 0.8988011 0.8987051 0.8986088 0.8985120 0.8984150 0.8983175 0.8982197
[190] 0.8981216 0.8980230 0.8979242 0.8978249 0.8977253 0.8976254 0.8975251
[197] 0.8974244 0.8973234 0.8972220 0.8971202 0.8970181 0.8969157 0.8968128
[204] 0.8967097 0.8966061 0.8965023 0.8963980 0.8962934 0.8961885 0.8960832
[211] 0.8959775 0.8958715 0.8957651 0.8956584 0.8955513 0.8954439 0.8953361
[218] 0.8952280 0.8951195 0.8950106 0.8949014 0.8947919 0.8946820 0.8945717
[225] 0.8944611 0.8943502 0.8942389 0.8941273 0.8940153 0.8939029 0.8937902
[232] 0.8936772 0.8935638 0.8934500 0.8933360 0.8932215 0.8931068 0.8929916
[239] 0.8928762 0.8927603 0.8926442 0.8925277 0.8924108 0.8922936 0.8921761
[246] 0.8920582 0.8919400 0.8918214 0.8917025 0.8915832 0.8914636 0.8913437
[253] 0.8912234 0.8911028 0.8909818 0.8908605 0.8907389 0.8906169 0.8904946
[260] 0.8903719 0.8902489 0.8901256 0.8900019 0.8898779 0.8897536 0.8896289
[267] 0.8895039 0.8893785 0.8892528 0.8891268 0.8890005 0.8888738 0.8887468
[274] 0.8886194 0.8884917 0.8883637 0.8882353 0.8881067 0.8879776 0.8878483
[281] 0.8877186 0.8875886 0.8874583 0.8873276 0.8871967 0.8870653 0.8869337
[288] 0.8868017 0.8866694 0.8865368 0.8864039 0.8862706 0.8861370 0.8860031
[295] 0.8858688 0.8857343 0.8855994 0.8854642 0.8853286 0.8851928 0.8850566
[302] 0.8849201 0.8847833 0.8846461 0.8845087 0.8843709 0.8842328 0.8840944
[309] 0.8839557 0.8838166 0.8836773 0.8835376 0.8833976 0.8832573 0.8831167
[316] 0.8829757 0.8828345 0.8826929 0.8825511 0.8824089 0.8822664 0.8821236
[323] 0.8819804 0.8818370 0.8816933 0.8815492 0.8814049 0.8812602 0.8811153
[330] 0.8809700 0.8808244 0.8806785 0.8805323 0.8803858 0.8802390 0.8800919
[337] 0.8799445 0.8797968 0.8796488 0.8795005 0.8793519 0.8792030 0.8790538
[344] 0.8789043 0.8787544 0.8786043 0.8784539 0.8783032 0.8781522 0.8780009
[351] 0.8778493 0.8776974 0.8775453 0.8773928 0.8772400 0.8770870 0.8769336
[358] 0.8767800 0.8766260 0.8764718 0.8763173 0.8761625 0.8760074 0.8758520
[365] 0.8756963 0.8755404 0.8753841 0.8752276 0.8750708 0.8749137 0.8747563
[372] 0.8745986 0.8744407 0.8742824 0.8741239 0.8739651 0.8738061 0.8736467
[379] 0.8734871 0.8733271 0.8731669 0.8730065 0.8728457 0.8726847 0.8725234
[386] 0.8723618 0.8722000 0.8720378 0.8718754 0.8717127 0.8715498 0.8713866
[393] 0.8712231 0.8710593 0.8708953 0.8707310 0.8705664 0.8704016 0.8702365
[400] 0.8700711 0.8699054
> mx
[1] 0.9101779
> mxli
[1] -2
> 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/1i2h41193406854.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/2f6zm1193406854.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/35urv1193406854.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 
> 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/4ltqi1193406854.tab") 
> 
> system("convert tmp/1i2h41193406854.ps tmp/1i2h41193406854.png")
> system("convert tmp/2f6zm1193406854.ps tmp/2f6zm1193406854.png")
> system("convert tmp/35urv1193406854.ps tmp/35urv1193406854.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.875   0.785   1.992