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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> x <- c(112.6,113.8,107.8,103.2,103.3,101.2,107.7,110.4,101.9,115.9,89.9,88.6,117.2,123.9,100.0,103.6,94.1,98.7,119.5,112.7,104.4,124.7,89.1,97.0,121.6,118.8,114.0,111.5,97.2,102.5,113.4,109.8,104.9,126.1,80.0,96.8,117.2,112.3,117.3,111.1,102.2,104.3,122.9,107.6,121.3,131.5,89.0,104.4,128.9,135.9,133.3,121.3,120.5,120.4,137.9,126.1,133.2,146.6,103.4,117.2)
> #'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 Normality Plot (v1.0.4) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_boxcoxnorm.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(qnorm(ppoints(x), mean=0, sd=1),x1)
+ if (mx < c[i])
+ {
+ mx <- c[i]
+ mxli <- l[i]
+ }
+ }
> c
  [1] 0.3404810 0.3407908 0.3411005 0.3414101 0.3417194 0.3420286 0.3423377
  [8] 0.3426465 0.3429552 0.3432637 0.3435721 0.3438803 0.3441883 0.3444961
 [15] 0.3448038 0.3451113 0.3454186 0.3457258 0.3460327 0.3463395 0.3466461
 [22] 0.3469525 0.3472588 0.3475648 0.3478707 0.3481764 0.3484819 0.3487872
 [29] 0.3490924 0.3493973 0.3497021 0.3500067 0.3503111 0.3506153 0.3509193
 [36] 0.3512231 0.3515267 0.3518301 0.3521334 0.3524364 0.3527393 0.3530419
 [43] 0.3533444 0.3536466 0.3539487 0.3542505 0.3545522 0.3548536 0.3551548
 [50] 0.3554559 0.3557567 0.3560574 0.3563578 0.3566580 0.3569580 0.3572578
 [57] 0.3575574 0.3578568 0.3581560 0.3584549 0.3587537 0.3590522 0.3593505
 [64] 0.3596486 0.3599465 0.3602442 0.3605416 0.3608389 0.3611359 0.3614327
 [71] 0.3617292 0.3620256 0.3623217 0.3626176 0.3629133 0.3632088 0.3635040
 [78] 0.3637990 0.3640938 0.3643883 0.3646826 0.3649767 0.3652706 0.3655642
 [85] 0.3658576 0.3661508 0.3664437 0.3667364 0.3670289 0.3673211 0.3676131
 [92] 0.3679048 0.3681964 0.3684876 0.3687787 0.3690695 0.3693600 0.3696503
 [99] 0.3699404 0.3702302 0.3705198 0.3708091 0.3710982 0.3713871 0.3716757
[106] 0.3719640 0.3722521 0.3725399 0.3728275 0.3731149 0.3734020 0.3736888
[113] 0.3739754 0.3742617 0.3745478 0.3748336 0.3751192 0.3754045 0.3756896
[120] 0.3759744 0.3762589 0.3765432 0.3768272 0.3771109 0.3773944 0.3776776
[127] 0.3779606 0.3782433 0.3785257 0.3788079 0.3790898 0.3793714 0.3796528
[134] 0.3799338 0.3802147 0.3804952 0.3807755 0.3810555 0.3813352 0.3816147
[141] 0.3818939 0.3821728 0.3824515 0.3827298 0.3830079 0.3832857 0.3835632
[148] 0.3838405 0.3841175 0.3843942 0.3846706 0.3849467 0.3852226 0.3854981
[155] 0.3857734 0.3860484 0.3863231 0.3865975 0.3868717 0.3871455 0.3874191
[162] 0.3876924 0.3879654 0.3882381 0.3885105 0.3887826 0.3890544 0.3893259
[169] 0.3895972 0.3898681 0.3901388 0.3904091 0.3906792 0.3909489 0.3912184
[176] 0.3914876 0.3917565 0.3920250 0.3922933 0.3925613 0.3928289 0.3930963
[183] 0.3933634 0.3936301 0.3938966 0.3941628 0.3944286 0.3946942 0.3949594
[190] 0.3952243 0.3954890 0.3957533 0.3960173 0.3962810 0.3965444 0.3968075
[197] 0.3970703 0.3973327 0.3975949 0.3978567 0.3981182 0.3983794 0.3986403
[204] 0.3989009 0.3991612 0.3994211 0.3996807 0.3999400 0.4001990 0.4004577
[211] 0.4007161 0.4009741 0.4012318 0.4014892 0.4017463 0.4020030 0.4022595
[218] 0.4025156 0.4027713 0.4030268 0.4032819 0.4035367 0.4037912 0.4040454
[225] 0.4042992 0.4045527 0.4048058 0.4050587 0.4053112 0.4055634 0.4058152
[232] 0.4060667 0.4063179 0.4065688 0.4068193 0.4070695 0.4073193 0.4075689
[239] 0.4078180 0.4080669 0.4083154 0.4085636 0.4088114 0.4090589 0.4093061
[246] 0.4095529 0.4097994 0.4100455 0.4102913 0.4105368 0.4107819 0.4110267
[253] 0.4112711 0.4115152 0.4117589 0.4120023 0.4122454 0.4124881 0.4127305
[260] 0.4129725 0.4132142 0.4134555 0.4136965 0.4139371 0.4141774 0.4144174
[267] 0.4146569 0.4148962 0.4151351 0.4153736 0.4156118 0.4158496 0.4160871
[274] 0.4163242 0.4165610 0.4167974 0.4170335 0.4172692 0.4175046 0.4177396
[281] 0.4179742 0.4182085 0.4184424 0.4186760 0.4189092 0.4191421 0.4193746
[288] 0.4196067 0.4198385 0.4200699 0.4203010 0.4205317 0.4207620 0.4209920
[295] 0.4212216 0.4214508 0.4216797 0.4219082 0.4221364 0.4223642 0.4225916
[302] 0.4228187 0.4230454 0.4232717 0.4234977 0.4237233 0.4239485 0.4241734
[309] 0.4243979 0.4246220 0.4248458 0.4250691 0.4252922 0.4255148 0.4257371
[316] 0.4259590 0.4261805 0.4264017 0.4266225 0.4268429 0.4270629 0.4272826
[323] 0.4275019 0.4277208 0.4279394 0.4281575 0.4283753 0.4285928 0.4288098
[330] 0.4290265 0.4292428 0.4294587 0.4296742 0.4298894 0.4301042 0.4303186
[337] 0.4305326 0.4307462 0.4309595 0.4311724 0.4313849 0.4315970 0.4318087
[344] 0.4320201 0.4322311 0.4324417 0.4326519 0.4328617 0.4330712 0.4332803
[351] 0.4334889 0.4336972 0.4339051 0.4341127 0.4343198 0.4345266 0.4347329
[358] 0.4349389 0.4351445 0.4353498 0.4355546 0.4357590 0.4359631 0.4361667
[365] 0.4363700 0.4365729 0.4367754 0.4369775 0.4371792 0.4373806 0.4375815
[372] 0.4377820 0.4379822 0.4381820 0.4383814 0.4385803 0.4387789 0.4389771
[379] 0.4391750 0.4393724 0.4395694 0.4397660 0.4399623 0.4401581 0.4403536
[386] 0.4405486 0.4407433 0.4409376 0.4411314 0.4413249 0.4415180 0.4417107
[393] 0.4419030 0.4420949 0.4422864 0.4424775 0.4426682 0.4428585 0.4430484
[400] 0.4432379 0.4434270
> mx
[1] 0.443427
> mxli
[1] 2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/19z301194296445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(l,c,main='Box-Cox Normality Plot',xlab='Lambda',ylab='correlation')
> mtext(paste('Optimal Lambda =',mxli))
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/21e7j1194296445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(x,main='Histogram of Original Data',xlab='X',ylab='frequency')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/38zxl1194296445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(x1,main='Histogram of Transformed Data',xlab='X',ylab='frequency')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/4x7hr1194296445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x)
> grid()
> mtext('Original Data')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/57zqj1194296445.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x1)
> grid()
> mtext('Transformed Data')
> 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 Normality 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',header=TRUE)
> a<-table.element(a,mxli)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/6jael1194296445.tab") 
> 
> system("convert tmp/19z301194296445.ps tmp/19z301194296445.png")
> system("convert tmp/21e7j1194296445.ps tmp/21e7j1194296445.png")
> system("convert tmp/38zxl1194296445.ps tmp/38zxl1194296445.png")
> system("convert tmp/4x7hr1194296445.ps tmp/4x7hr1194296445.png")
> system("convert tmp/57zqj1194296445.ps tmp/57zqj1194296445.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.353   0.797   1.619