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(174.1,180.4,182.6,207.1,213.7,186.5,179.1,168.3,156.5,144.3,138.9,137.8,136.3,140.3,149.1,149.2,140.4,129,124.7,130.8,130.1,133.2,130.1,126.6,124.8,125.3,126.9,120.1,118.7,117.7,113.4,107.5,107.6,114.3,114.9,111.2,109.9,108.6,109.2,106.4,103.7,103,96.9,104.7,102.2,99,95.8,94.5,102.7,103.2,105.6,103.9,107.2,100.7,92.1,90.3,93.4,98.5,100.8,102.3,104.7,101.1,101.4,99.5,98.4,96.3,100.7,101.2,100.3,97.8,97.4,98.6,99.7,99,98.1,97,98.5,103.8,114.4,124.5,134.2,131.8,125.6,119.9,114.9,115.5,112.5,111.4,115.3,110.8,103.7,111.1,113,111.2,117.6,121.7,127.3,129.8,137.1,141.4,137.4,130.7,117.2,110.8,111.4,108.2,108.8,110.2,109.5,109.5,116,111.2,112.1,114,119.1,114.1,115.1,115.4,110.8,116,119.2,126.5,127.8,131.3,140.3,137.3,143,134.5,139.9,159.3,170.4,175,175.8,180.9,180.3,169.6,172.3,184.8,177.7,184.6,211.4)
> x <- c(145.3,143.6,142.8,155.9,156.2,149.8,152.7,155.5,159.3,143,141.4,142.8,146.4,152.3,164.3,168,171.3,162.7,150.2,142.5,138.2,138,145.1,138.4,131.8,130.8,126.3,123,124,120.8,122.1,106.5,104.3,108.7,113.8,112.5,106.1,98.4,96,99.3,97.5,95.3,88,94.7,99.4,98.9,96.4,95.3,99.5,101.6,103.9,106.6,108.3,102,93.8,91.6,97.7,94.8,98,103.8,97.8,91.2,89.3,87.5,90.4,94.2,102.2,101.3,96,90.8,93.2,90.9,91.1,90.2,94.3,96,99,103.3,113.1,112.8,112.1,107.4,111,110.5,110.8,112.4,111.5,116.2,122.5,121.3,113.9,110.7,120.8,141.1,147.4,148,158.1,165,187,190.3,182.4,168.8,151.2,120.1,112.5,106.2,107.1,108.5,106.5,108.3,125.6,124,127.2,136.9,135.8,124.3,115.4,113.6,114.4,118.4,117,116.5,115.4,113.6,117.4,116.9,116.4,111.1,110.2,118.9,131.8,130.6,138.3,148.4,148.7,144.3,152.5,162.9,167.2,166.5,185.6)
> #'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] 0.7455747 0.7456969 0.7458184 0.7459392 0.7460594 0.7461789 0.7462978
  [8] 0.7464159 0.7465334 0.7466502 0.7467663 0.7468818 0.7469965 0.7471106
 [15] 0.7472240 0.7473367 0.7474487 0.7475600 0.7476707 0.7477806 0.7478898
 [22] 0.7479984 0.7481062 0.7482133 0.7483198 0.7484255 0.7485305 0.7486348
 [29] 0.7487384 0.7488413 0.7489435 0.7490449 0.7491457 0.7492457 0.7493450
 [36] 0.7494436 0.7495415 0.7496386 0.7497350 0.7498307 0.7499257 0.7500199
 [43] 0.7501134 0.7502062 0.7502982 0.7503895 0.7504800 0.7505698 0.7506589
 [50] 0.7507472 0.7508348 0.7509216 0.7510077 0.7510931 0.7511776 0.7512615
 [57] 0.7513445 0.7514269 0.7515084 0.7515892 0.7516693 0.7517486 0.7518271
 [64] 0.7519048 0.7519818 0.7520581 0.7521335 0.7522082 0.7522821 0.7523552
 [71] 0.7524276 0.7524992 0.7525700 0.7526400 0.7527093 0.7527777 0.7528454
 [78] 0.7529123 0.7529784 0.7530437 0.7531083 0.7531720 0.7532350 0.7532971
 [85] 0.7533585 0.7534190 0.7534788 0.7535378 0.7535959 0.7536533 0.7537099
 [92] 0.7537656 0.7538206 0.7538747 0.7539280 0.7539805 0.7540323 0.7540832
 [99] 0.7541332 0.7541825 0.7542310 0.7542786 0.7543254 0.7543714 0.7544166
[106] 0.7544609 0.7545044 0.7545471 0.7545890 0.7546300 0.7546703 0.7547096
[113] 0.7547482 0.7547859 0.7548228 0.7548588 0.7548940 0.7549284 0.7549619
[120] 0.7549946 0.7550265 0.7550575 0.7550876 0.7551170 0.7551454 0.7551730
[127] 0.7551998 0.7552257 0.7552508 0.7552750 0.7552984 0.7553209 0.7553426
[134] 0.7553634 0.7553833 0.7554024 0.7554206 0.7554380 0.7554544 0.7554701
[141] 0.7554849 0.7554988 0.7555118 0.7555240 0.7555353 0.7555457 0.7555552
[148] 0.7555639 0.7555718 0.7555787 0.7555848 0.7555899 0.7555943 0.7555977
[155] 0.7556003 0.7556019 0.7556027 0.7556026 0.7556017 0.7555998 0.7555971
[162] 0.7555935 0.7555890 0.7555836 0.7555773 0.7555701 0.7555621 0.7555531
[169] 0.7555433 0.7555325 0.7555209 0.7555084 0.7554950 0.7554806 0.7554654
[176] 0.7554493 0.7554323 0.7554144 0.7553956 0.7553759 0.7553553 0.7553338
[183] 0.7553114 0.7552881 0.7552638 0.7552387 0.7552127 0.7551857 0.7551579
[190] 0.7551291 0.7550995 0.7550689 0.7550374 0.7550050 0.7549717 0.7549375
[197] 0.7549024 0.7548663 0.7548294 0.7547915 0.7547527 0.7547130 0.7546724
[204] 0.7546308 0.7545884 0.7545450 0.7545007 0.7544555 0.7544094 0.7543623
[211] 0.7543143 0.7542654 0.7542156 0.7541649 0.7541132 0.7540606 0.7540071
[218] 0.7539526 0.7538973 0.7538410 0.7537838 0.7537256 0.7536665 0.7536065
[225] 0.7535456 0.7534837 0.7534210 0.7533572 0.7532926 0.7532270 0.7531605
[232] 0.7530931 0.7530247 0.7529554 0.7528852 0.7528140 0.7527419 0.7526689
[239] 0.7525949 0.7525200 0.7524442 0.7523674 0.7522897 0.7522111 0.7521315
[246] 0.7520510 0.7519696 0.7518872 0.7518039 0.7517197 0.7516345 0.7515484
[253] 0.7514613 0.7513733 0.7512844 0.7511945 0.7511037 0.7510120 0.7509193
[260] 0.7508257 0.7507311 0.7506356 0.7505392 0.7504418 0.7503435 0.7502443
[267] 0.7501441 0.7500430 0.7499409 0.7498379 0.7497340 0.7496291 0.7495233
[274] 0.7494165 0.7493088 0.7492002 0.7490906 0.7489801 0.7488687 0.7487563
[281] 0.7486429 0.7485287 0.7484135 0.7482973 0.7481803 0.7480622 0.7479433
[288] 0.7478234 0.7477026 0.7475808 0.7474581 0.7473345 0.7472099 0.7470844
[295] 0.7469579 0.7468305 0.7467022 0.7465729 0.7464427 0.7463116 0.7461795
[302] 0.7460465 0.7459126 0.7457777 0.7456419 0.7455052 0.7453675 0.7452289
[309] 0.7450893 0.7449489 0.7448074 0.7446651 0.7445218 0.7443776 0.7442325
[316] 0.7440864 0.7439394 0.7437915 0.7436426 0.7434929 0.7433421 0.7431905
[323] 0.7430379 0.7428844 0.7427300 0.7425746 0.7424184 0.7422612 0.7421030
[330] 0.7419440 0.7417840 0.7416231 0.7414613 0.7412985 0.7411349 0.7409703
[337] 0.7408047 0.7406383 0.7404710 0.7403027 0.7401335 0.7399634 0.7397924
[344] 0.7396204 0.7394476 0.7392738 0.7390991 0.7389235 0.7387470 0.7385695
[351] 0.7383912 0.7382119 0.7380318 0.7378507 0.7376687 0.7374858 0.7373020
[358] 0.7371173 0.7369317 0.7367451 0.7365577 0.7363694 0.7361801 0.7359900
[365] 0.7357990 0.7356070 0.7354142 0.7352204 0.7350258 0.7348303 0.7346338
[372] 0.7344365 0.7342383 0.7340392 0.7338391 0.7336382 0.7334364 0.7332337
[379] 0.7330302 0.7328257 0.7326204 0.7324141 0.7322070 0.7319990 0.7317901
[386] 0.7315803 0.7313696 0.7311581 0.7309457 0.7307324 0.7305182 0.7303031
[393] 0.7300872 0.7298704 0.7296527 0.7294342 0.7292147 0.7289944 0.7287733
[400] 0.7285512 0.7283283
> mx
[1] 0.7556027
> mxli
[1] -0.44
> 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/1s5e11197026316.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/27i4g1197026316.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/39ncd1197026316.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/4fg7w1197026316.tab") 
> 
> system("convert tmp/1s5e11197026316.ps tmp/1s5e11197026316.png")
> system("convert tmp/27i4g1197026316.ps tmp/27i4g1197026316.png")
> system("convert tmp/39ncd1197026316.ps tmp/39ncd1197026316.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.053   0.515   1.392