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(373,371,354,357,363,364,363,358,357,357,380,378,376,380,379,384,392,394,392,396,392,396,419,421,420,418,410,418,426,428,430,424,423,427,441,449,452,462,455,461,461,463,462,456,455,456,472,472,471,465,459,465,468,467,463,460,462,461,476,476,471,453,443,442,444,438,427,424,416,406,431,434,418)
> x <- c(140,132,117,114,113,110,107,103,98,98,137,148,147,139,130,128,127,123,118,114,108,111,151,159,158,148,138,137,136,133,126,120,114,116,153,162,161,149,139,135,130,127,122,117,112,113,149,157,157,147,137,132,125,123,117,114,111,112,144,150,149,134,123,116,117,111,105,102,95,93,124,130,124)
> #'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] 0.3553348 0.3553132 0.3552915 0.3552696 0.3552475 0.3552253 0.3552028
  [8] 0.3551801 0.3551572 0.3551341 0.3551108 0.3550874 0.3550637 0.3550398
 [15] 0.3550158 0.3549915 0.3549671 0.3549424 0.3549176 0.3548925 0.3548673
 [22] 0.3548419 0.3548163 0.3547905 0.3547645 0.3547383 0.3547119 0.3546854
 [29] 0.3546586 0.3546317 0.3546045 0.3545772 0.3545497 0.3545219 0.3544940
 [36] 0.3544660 0.3544377 0.3544092 0.3543806 0.3543517 0.3543227 0.3542935
 [43] 0.3542641 0.3542345 0.3542047 0.3541747 0.3541446 0.3541142 0.3540837
 [50] 0.3540530 0.3540221 0.3539911 0.3539598 0.3539284 0.3538967 0.3538649
 [57] 0.3538329 0.3538008 0.3537684 0.3537359 0.3537032 0.3536703 0.3536372
 [64] 0.3536039 0.3535705 0.3535369 0.3535031 0.3534691 0.3534350 0.3534006
 [71] 0.3533661 0.3533315 0.3532966 0.3532616 0.3532263 0.3531909 0.3531554
 [78] 0.3531196 0.3530837 0.3530476 0.3530114 0.3529749 0.3529383 0.3529015
 [85] 0.3528645 0.3528274 0.3527901 0.3527526 0.3527150 0.3526772 0.3526392
 [92] 0.3526010 0.3525627 0.3525242 0.3524855 0.3524467 0.3524076 0.3523685
 [99] 0.3523291 0.3522896 0.3522499 0.3522101 0.3521701 0.3521299 0.3520895
[106] 0.3520490 0.3520083 0.3519675 0.3519265 0.3518853 0.3518440 0.3518025
[113] 0.3517608 0.3517190 0.3516770 0.3516349 0.3515926 0.3515501 0.3515075
[120] 0.3514647 0.3514217 0.3513786 0.3513354 0.3512919 0.3512483 0.3512046
[127] 0.3511607 0.3511166 0.3510724 0.3510281 0.3509835 0.3509388 0.3508940
[134] 0.3508490 0.3508039 0.3507586 0.3507131 0.3506675 0.3506217 0.3505758
[141] 0.3505298 0.3504835 0.3504372 0.3503906 0.3503440 0.3502972 0.3502502
[148] 0.3502031 0.3501558 0.3501084 0.3500608 0.3500131 0.3499652 0.3499172
[155] 0.3498691 0.3498208 0.3497723 0.3497237 0.3496750 0.3496261 0.3495771
[162] 0.3495279 0.3494786 0.3494291 0.3493795 0.3493298 0.3492799 0.3492299
[169] 0.3491797 0.3491294 0.3490790 0.3490284 0.3489777 0.3489268 0.3488758
[176] 0.3488246 0.3487734 0.3487219 0.3486704 0.3486187 0.3485669 0.3485149
[183] 0.3484628 0.3484106 0.3483582 0.3483057 0.3482531 0.3482003 0.3481474
[190] 0.3480944 0.3480412 0.3479879 0.3479345 0.3478809 0.3478272 0.3477734
[197] 0.3477195 0.3476654 0.3476112 0.3475568 0.3475024 0.3474478 0.3473931
[204] 0.3473382 0.3472833 0.3472282 0.3471729 0.3471176 0.3470621 0.3470065
[211] 0.3469508 0.3468950 0.3468390 0.3467829 0.3467267 0.3466704 0.3466139
[218] 0.3465573 0.3465006 0.3464438 0.3463869 0.3463298 0.3462727 0.3462154
[225] 0.3461579 0.3461004 0.3460428 0.3459850 0.3459271 0.3458691 0.3458110
[232] 0.3457528 0.3456945 0.3456360 0.3455774 0.3455188 0.3454600 0.3454011
[239] 0.3453420 0.3452829 0.3452237 0.3451643 0.3451049 0.3450453 0.3449856
[246] 0.3449258 0.3448659 0.3448059 0.3447458 0.3446856 0.3446252 0.3445648
[253] 0.3445042 0.3444436 0.3443828 0.3443220 0.3442610 0.3441999 0.3441387
[260] 0.3440775 0.3440161 0.3439546 0.3438930 0.3438313 0.3437695 0.3437076
[267] 0.3436456 0.3435836 0.3435214 0.3434591 0.3433967 0.3433342 0.3432716
[274] 0.3432089 0.3431461 0.3430832 0.3430203 0.3429572 0.3428940 0.3428308
[281] 0.3427674 0.3427040 0.3426404 0.3425768 0.3425130 0.3424492 0.3423853
[288] 0.3423213 0.3422572 0.3421930 0.3421287 0.3420643 0.3419998 0.3419353
[295] 0.3418706 0.3418059 0.3417411 0.3416762 0.3416112 0.3415461 0.3414809
[302] 0.3414157 0.3413503 0.3412849 0.3412194 0.3411538 0.3410881 0.3410223
[309] 0.3409565 0.3408905 0.3408245 0.3407584 0.3406922 0.3406260 0.3405596
[316] 0.3404932 0.3404267 0.3403601 0.3402934 0.3402267 0.3401599 0.3400930
[323] 0.3400260 0.3399589 0.3398918 0.3398246 0.3397573 0.3396899 0.3396225
[330] 0.3395550 0.3394874 0.3394197 0.3393520 0.3392842 0.3392163 0.3391483
[337] 0.3390803 0.3390122 0.3389440 0.3388757 0.3388074 0.3387390 0.3386706
[344] 0.3386020 0.3385334 0.3384648 0.3383960 0.3383272 0.3382583 0.3381894
[351] 0.3381204 0.3380513 0.3379822 0.3379130 0.3378437 0.3377744 0.3377049
[358] 0.3376355 0.3375659 0.3374963 0.3374267 0.3373570 0.3372872 0.3372173
[365] 0.3371474 0.3370774 0.3370074 0.3369373 0.3368671 0.3367969 0.3367266
[372] 0.3366563 0.3365859 0.3365155 0.3364450 0.3363744 0.3363038 0.3362331
[379] 0.3361623 0.3360915 0.3360207 0.3359498 0.3358788 0.3358078 0.3357367
[386] 0.3356656 0.3355944 0.3355231 0.3354519 0.3353805 0.3353091 0.3352377
[393] 0.3351662 0.3350946 0.3350230 0.3349513 0.3348796 0.3348079 0.3347361
[400] 0.3346642 0.3345923
> mx
[1] 0.3553348
> 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/17fla1194180682.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/21pty1194180682.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/3pr551194180682.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/4vjsd1194180683.tab") 
> 
> system("convert tmp/17fla1194180682.ps tmp/17fla1194180682.png")
> system("convert tmp/21pty1194180682.ps tmp/21pty1194180682.png")
> system("convert tmp/3pr551194180682.ps tmp/3pr551194180682.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.984   0.853   2.100