R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(36,36,36,36,36,37.5,36.5,36,36.5,36.6,36,36.6,36.6,36.5,36.6,36.6,36.5,36.5,36.6,36,36,36,36,36,36,37,36,36.9,36.9,36,33.6,34.6,37.1,36.9,37.4,36.6,37,37.5,37.5,37,36.9,36.9,36,36.5,36,37.1,36,36.6,37,37,36,37.9,39.1,38.1,38.5,38,38,37.9,37.6,37,37,37.6,37,36.5)
> par5 = 'Yes'
> par4 = '0'
> par3 = '2'
> par2 = '-2'
> par1 = 'Full Box-Cox transform'
> par5 <- 'Yes'
> par4 <- '0'
> par3 <- '2'
> par2 <- '-2'
> par1 <- 'Full Box-Cox transform'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Box-Cox Normality Plot (v1.1.5) 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
> if(par4=='') par4 <- 0
> par4 <- as.numeric(par4)
> numlam <- par2 + par3 + 1
> x <- x + par4
> n <- length(x)
> c <- array(NA,dim=c(numlam))
> l <- array(NA,dim=c(numlam))
> mx <- -1
> 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]
+ x1.best <- x1
+ }
+ }
> c
  [1] 0.4415971 0.4416631 0.4417290 0.4417949 0.4418608 0.4419266 0.4419923
  [8] 0.4420580 0.4421237 0.4421893 0.4422548 0.4423203 0.4423858 0.4424511
 [15] 0.4425165 0.4425818 0.4426470 0.4427122 0.4427774 0.4428425 0.4429075
 [22] 0.4429725 0.4430375 0.4431024 0.4431672 0.4432320 0.4432968 0.4433615
 [29] 0.4434261 0.4434907 0.4435553 0.4436198 0.4436842 0.4437486 0.4438130
 [36] 0.4438773 0.4439415 0.4440057 0.4440698 0.4441339 0.4441980 0.4442620
 [43] 0.4443259 0.4443898 0.4444537 0.4445175 0.4445812 0.4446449 0.4447085
 [50] 0.4447721 0.4448357 0.4448992 0.4449626 0.4450260 0.4450894 0.4451527
 [57] 0.4452159 0.4452791 0.4453422 0.4454053 0.4454684 0.4455314 0.4455943
 [64] 0.4456572 0.4457200 0.4457828 0.4458456 0.4459082 0.4459709 0.4460335
 [71] 0.4460960 0.4461585 0.4462209 0.4462833 0.4463457 0.4464080 0.4464702
 [78] 0.4465324 0.4465945 0.4466566 0.4467186 0.4467806 0.4468426 0.4469044
 [85] 0.4469663 0.4470281 0.4470898 0.4471515 0.4472131 0.4472747 0.4473362
 [92] 0.4473977 0.4474591 0.4475205 0.4475819 0.4476431 0.4477044 0.4477655
 [99] 0.4478267 0.4478878 0.4479488 0.4480098 0.4480707 0.4481316 0.4481924
[106] 0.4482532 0.4483139 0.4483746 0.4484352 0.4484958 0.4485563 0.4486168
[113] 0.4486772 0.4487376 0.4487979 0.4488582 0.4489184 0.4489786 0.4490387
[120] 0.4490987 0.4491588 0.4492187 0.4492787 0.4493385 0.4493983 0.4494581
[127] 0.4495178 0.4495775 0.4496371 0.4496967 0.4497562 0.4498157 0.4498751
[134] 0.4499344 0.4499938 0.4500530 0.4501122 0.4501714 0.4502305 0.4502896
[141] 0.4503486 0.4504075 0.4504664 0.4505253 0.4505841 0.4506429 0.4507016
[148] 0.4507602 0.4508188 0.4508774 0.4509359 0.4509944 0.4510528 0.4511111
[155] 0.4511694 0.4512277 0.4512859 0.4513440 0.4514021 0.4514602 0.4515182
[162] 0.4515761 0.4516340 0.4516919 0.4517497 0.4518074 0.4518651 0.4519228
[169] 0.4519804 0.4520379 0.4520954 0.4521529 0.4522103 0.4522676 0.4523249
[176] 0.4523822 0.4524393 0.4524965 0.4525536 0.4526106 0.4526676 0.4527246
[183] 0.4527815 0.4528383 0.4528951 0.4529518 0.4530085 0.4530651 0.4531217
[190] 0.4531783 0.4532348 0.4532912 0.4533476 0.4534039 0.4534602 0.4535164
[197] 0.4535726 0.4536288 0.4536848 0.4537409 0.4537968 0.4538528 0.4539087
[204] 0.4539645 0.4540203 0.4540760 0.4541317 0.4541873 0.4542429 0.4542984
[211] 0.4543539 0.4544093 0.4544647 0.4545200 0.4545753 0.4546305 0.4546857
[218] 0.4547408 0.4547959 0.4548509 0.4549059 0.4549608 0.4550157 0.4550705
[225] 0.4551253 0.4551800 0.4552347 0.4552893 0.4553438 0.4553984 0.4554528
[232] 0.4555072 0.4555616 0.4556159 0.4556702 0.4557244 0.4557786 0.4558327
[239] 0.4558867 0.4559408 0.4559947 0.4560486 0.4561025 0.4561563 0.4562101
[246] 0.4562638 0.4563174 0.4563711 0.4564246 0.4564781 0.4565316 0.4565850
[253] 0.4566384 0.4566917 0.4567449 0.4567981 0.4568513 0.4569044 0.4569574
[260] 0.4570104 0.4570634 0.4571163 0.4571692 0.4572220 0.4572747 0.4573274
[267] 0.4573801 0.4574327 0.4574852 0.4575377 0.4575902 0.4576426 0.4576949
[274] 0.4577472 0.4577995 0.4578517 0.4579038 0.4579559 0.4580080 0.4580600
[281] 0.4581119 0.4581638 0.4582157 0.4582675 0.4583192 0.4583709 0.4584225
[288] 0.4584741 0.4585257 0.4585772 0.4586286 0.4586800 0.4587314 0.4587827
[295] 0.4588339 0.4588851 0.4589362 0.4589873 0.4590384 0.4590894 0.4591403
[302] 0.4591912 0.4592420 0.4592928 0.4593436 0.4593943 0.4594449 0.4594955
[309] 0.4595460 0.4595965 0.4596470 0.4596973 0.4597477 0.4597980 0.4598482
[316] 0.4598984 0.4599485 0.4599986 0.4600487 0.4600986 0.4601486 0.4601985
[323] 0.4602483 0.4602981 0.4603478 0.4603975 0.4604471 0.4604967 0.4605463
[330] 0.4605958 0.4606452 0.4606946 0.4607439 0.4607932 0.4608424 0.4608916
[337] 0.4609407 0.4609898 0.4610389 0.4610879 0.4611368 0.4611857 0.4612345
[344] 0.4612833 0.4613320 0.4613807 0.4614293 0.4614779 0.4615265 0.4615749
[351] 0.4616234 0.4616718 0.4617201 0.4617684 0.4618166 0.4618648 0.4619129
[358] 0.4619610 0.4620090 0.4620570 0.4621049 0.4621528 0.4622007 0.4622484
[365] 0.4622962 0.4623439 0.4623915 0.4624391 0.4624866 0.4625341 0.4625815
[372] 0.4626289 0.4626762 0.4627235 0.4627708 0.4628179 0.4628651 0.4629122
[379] 0.4629592 0.4630062 0.4630531 0.4631000 0.4631468 0.4631936 0.4632404
[386] 0.4632870 0.4633337 0.4633803 0.4634268 0.4634733 0.4635197 0.4635661
[393] 0.4636124 0.4636587 0.4637050 0.4637511 0.4637973 0.4638434 0.4638894
[400] 0.4639354 0.4639813
> mx
[1] 0.4639813
> mxli
[1] 2
> x1.best
 [1] 647.500 647.500 647.500 647.500 647.500 702.625 665.625 647.500 665.625
[10] 669.280 647.500 669.280 669.280 665.625 669.280 669.280 665.625 665.625
[19] 669.280 647.500 647.500 647.500 647.500 647.500 647.500 684.000 647.500
[28] 680.305 680.305 647.500 563.980 598.080 687.705 680.305 698.880 669.280
[37] 684.000 702.625 702.625 684.000 680.305 680.305 647.500 665.625 647.500
[46] 687.705 647.500 669.280 684.000 684.000 647.500 717.705 763.905 725.305
[55] 740.625 721.500 721.500 717.705 706.380 684.000 684.000 706.380 684.000
[64] 665.625
> 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/1qcyk1423603477.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/2uw4v1423603477.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/36e651423603477.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/4fkf61423603477.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/55sqs1423603477.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)
> if(mx<0) {
+ a<-table.row.start(a)
+ a<-table.element(a,'Warning: maximum correlation is negative! The Box-Cox transformation must not be used.',2)
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/6jh3s1423603477.tab") 
> if(par5=='Yes') {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Obs.',header=T)
+ a<-table.element(a,'Original',header=T)
+ a<-table.element(a,'Transformed',header=T)
+ a<-table.row.end(a)
+ for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x1.best[i])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/7cxj41423603477.tab") 
+ }
> 
> try(system("convert tmp/1qcyk1423603477.ps tmp/1qcyk1423603477.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uw4v1423603477.ps tmp/2uw4v1423603477.png",intern=TRUE))
character(0)
> try(system("convert tmp/36e651423603477.ps tmp/36e651423603477.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fkf61423603477.ps tmp/4fkf61423603477.png",intern=TRUE))
character(0)
> try(system("convert tmp/55sqs1423603477.ps tmp/55sqs1423603477.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.019   0.312   2.349