R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(112,118,132,129,121,135,148,148,136,119,104,118,115,126,141,135,125,149,170,170,158,133,114,140,145,150,178,163,172,178,199,199,184,162,146,166,171,180,193,181,183,218,230,242,209,191,172,194,196,196,236,235,229,243,264,272,237,211,180,201,204,188,235,227,234,264,302,293,259,229,203,229,242,233,267,269,270,315,364,347,312,274,237,278,284,277,317,313,318,374,413,405,355,306,271,306,315,301,356,348,355,422,465,467,404,347,305,336,340,318,362,348,363,435,491,505,404,359,310,337,360,342,406,396,420,472,548,559,463,407,362,405,417,391,419,461,472,535,622,606,508,461,390,432)
> par3 = '2'
> par2 = '-2'
> par1 = 'Full Box-Cox transform'
> par3 <- '2'
> par2 <- '-2'
> par1 <- 'Full Box-Cox transform'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., (2012), Box-Cox Normality Plot (v1.1.3) 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
> #
> par2 <- abs(as.numeric(par2)*100)
> par3 <- as.numeric(par3)*100
> numlam <- par2 + par3 + 1
> n <- length(x)
> c <- array(NA,dim=c(numlam))
> l <- array(NA,dim=c(numlam))
> mx <- 0
> mxli <- -999
> for (i in 1:numlam)
+ {
+ l[i] <- (i-par2-1)/100
+ if (l[i] != 0)
+ {
+ if (par1 == 'Full Box-Cox transform') x1 <- (x^l[i] - 1) / l[i]
+ if (par1 == 'Simple Box-Cox transform') x1 <- x^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] 0.8571024 0.8577127 0.8583215 0.8589290 0.8595350 0.8601396 0.8607427
  [8] 0.8613444 0.8619445 0.8625431 0.8631401 0.8637356 0.8643296 0.8649219
 [15] 0.8655126 0.8661016 0.8666890 0.8672748 0.8678588 0.8684411 0.8690217
 [22] 0.8696005 0.8701776 0.8707528 0.8713263 0.8718979 0.8724676 0.8730355
 [29] 0.8736015 0.8741656 0.8747277 0.8752879 0.8758462 0.8764024 0.8769566
 [36] 0.8775088 0.8780589 0.8786070 0.8791530 0.8796968 0.8802385 0.8807781
 [43] 0.8813155 0.8818507 0.8823836 0.8829144 0.8834428 0.8839690 0.8844929
 [50] 0.8850145 0.8855338 0.8860507 0.8865652 0.8870773 0.8875869 0.8880942
 [57] 0.8885990 0.8891012 0.8896010 0.8900983 0.8905930 0.8910851 0.8915747
 [64] 0.8920616 0.8925460 0.8930276 0.8935066 0.8939829 0.8944565 0.8949274
 [71] 0.8953955 0.8958609 0.8963234 0.8967831 0.8972400 0.8976941 0.8981452
 [78] 0.8985935 0.8990388 0.8994813 0.8999207 0.9003572 0.9007907 0.9012211
 [85] 0.9016485 0.9020729 0.9024942 0.9029123 0.9033274 0.9037393 0.9041481
 [92] 0.9045537 0.9049561 0.9053552 0.9057512 0.9061438 0.9065332 0.9069193
 [99] 0.9073021 0.9076815 0.9080576 0.9084303 0.9087996 0.9091655 0.9095280
[106] 0.9098870 0.9102426 0.9105947 0.9109432 0.9112883 0.9116297 0.9119677
[113] 0.9123020 0.9126328 0.9129599 0.9132834 0.9136032 0.9139194 0.9142319
[120] 0.9145407 0.9148457 0.9151471 0.9154446 0.9157384 0.9160284 0.9163146
[127] 0.9165969 0.9168754 0.9171501 0.9174209 0.9176878 0.9179508 0.9182098
[134] 0.9184649 0.9187161 0.9189633 0.9192065 0.9194457 0.9196809 0.9199121
[141] 0.9201392 0.9203622 0.9205812 0.9207961 0.9210069 0.9212135 0.9214161
[148] 0.9216144 0.9218087 0.9219987 0.9221846 0.9223663 0.9225437 0.9227169
[155] 0.9228859 0.9230507 0.9232111 0.9233673 0.9235192 0.9236668 0.9238101
[162] 0.9239491 0.9240838 0.9242141 0.9243400 0.9244616 0.9245788 0.9246916
[169] 0.9248000 0.9249040 0.9250036 0.9250988 0.9251895 0.9252758 0.9253576
[176] 0.9254350 0.9255079 0.9255763 0.9256403 0.9256997 0.9257546 0.9258050
[183] 0.9258509 0.9258923 0.9259291 0.9259614 0.9259892 0.9260124 0.9260310
[190] 0.9260451 0.9260546 0.9260595 0.9260598 0.9260556 0.9260467 0.9260333
[197] 0.9260153 0.9259926 0.9259654 0.9259335 0.9258970 0.9258559 0.9258101
[204] 0.9257598 0.9257048 0.9256451 0.9255809 0.9255120 0.9254384 0.9253602
[211] 0.9252774 0.9251899 0.9250978 0.9250010 0.9248996 0.9247936 0.9246829
[218] 0.9245675 0.9244475 0.9243228 0.9241935 0.9240596 0.9239210 0.9237777
[225] 0.9236298 0.9234773 0.9233201 0.9231583 0.9229919 0.9228208 0.9226451
[232] 0.9224648 0.9222798 0.9220903 0.9218961 0.9216973 0.9214939 0.9212858
[239] 0.9210732 0.9208560 0.9206342 0.9204078 0.9201768 0.9199413 0.9197011
[246] 0.9194565 0.9192072 0.9189534 0.9186951 0.9184322 0.9181648 0.9178928
[253] 0.9176164 0.9173354 0.9170499 0.9167600 0.9164655 0.9161666 0.9158632
[260] 0.9155553 0.9152430 0.9149262 0.9146050 0.9142794 0.9139493 0.9136149
[267] 0.9132760 0.9129328 0.9125852 0.9122332 0.9118768 0.9115161 0.9111511
[274] 0.9107818 0.9104081 0.9100302 0.9096479 0.9092614 0.9088706 0.9084756
[281] 0.9080763 0.9076728 0.9072651 0.9068532 0.9064370 0.9060167 0.9055923
[288] 0.9051637 0.9047309 0.9042941 0.9038531 0.9034080 0.9029589 0.9025056
[295] 0.9020484 0.9015871 0.9011217 0.9006524 0.9001790 0.8997017 0.8992205
[302] 0.8987352 0.8982461 0.8977530 0.8972560 0.8967552 0.8962504 0.8957419
[309] 0.8952294 0.8947132 0.8941932 0.8936693 0.8931417 0.8926104 0.8920753
[316] 0.8915365 0.8909940 0.8904478 0.8898979 0.8893444 0.8887873 0.8882265
[323] 0.8876622 0.8870942 0.8865228 0.8859477 0.8853692 0.8847871 0.8842015
[330] 0.8836125 0.8830200 0.8824241 0.8818248 0.8812221 0.8806160 0.8800066
[337] 0.8793938 0.8787777 0.8781583 0.8775356 0.8769097 0.8762805 0.8756481
[344] 0.8750125 0.8743737 0.8737317 0.8730866 0.8724384 0.8717870 0.8711326
[351] 0.8704751 0.8698146 0.8691511 0.8684845 0.8678150 0.8671425 0.8664670
[358] 0.8657886 0.8651074 0.8644232 0.8637362 0.8630463 0.8623536 0.8616581
[365] 0.8609598 0.8602588 0.8595550 0.8588485 0.8581393 0.8574275 0.8567129
[372] 0.8559957 0.8552760 0.8545536 0.8538286 0.8531011 0.8523710 0.8516385
[379] 0.8509034 0.8501659 0.8494259 0.8486835 0.8479386 0.8471914 0.8464418
[386] 0.8456899 0.8449357 0.8441791 0.8434202 0.8426591 0.8418958 0.8411302
[393] 0.8403624 0.8395924 0.8388203 0.8380460 0.8372697 0.8364912 0.8357106
[400] 0.8349280 0.8341433
> mx
[1] 0.9260598
> mxli
[1] -0.08
> if (mxli != 0)
+ {
+ if (par1 == 'Full Box-Cox transform') x1 <- (x^mxli - 1) / mxli
+ if (par1 == 'Simple Box-Cox transform') x1 <- x^mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/wessaorg/rcomp/tmp/1rm4l1355125060.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/2y6my1355125060.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/3zd0f1355125060.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/4gqdp1355125060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x)
> qqline(x)
> grid()
> mtext('Original Data')
> dev.off()
null device 
          1 
> postscript(file="/var/wessaorg/rcomp/tmp/59p801355125060.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x1)
> qqline(x1)
> grid()
> mtext('Transformed Data')
> dev.off()
null device 
          1 
> 
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/6xt421355125060.tab") 
> 
> try(system("convert tmp/1rm4l1355125060.ps tmp/1rm4l1355125060.png",intern=TRUE))
character(0)
> try(system("convert tmp/2y6my1355125060.ps tmp/2y6my1355125060.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zd0f1355125060.ps tmp/3zd0f1355125060.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gqdp1355125060.ps tmp/4gqdp1355125060.png",intern=TRUE))
character(0)
> try(system("convert tmp/59p801355125060.ps tmp/59p801355125060.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.628   0.413   3.034