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(467037,460070,447988,442867,436087,431328,484015,509673,512927,502831,470984,471067,476049,474605,470439,461251,454724,455626,516847,525192,522975,518585,509239,512238,519164,517009,509933,509127,500857,506971,569323,579714,577992,565464,547344,554788,562325,560854,555332,543599,536662,542722,593530,610763,612613,611324,594167,595454,590865,589379,584428,573100,567456,569028,620735,628884,628232,612117,595404,597141,593408,590072,579799,574205,572775,572942,619567,625809,619916,587625,565742,557274)
> #'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] 0.8215277 0.8215714 0.8216110 0.8216410 0.8216853 0.8217201 0.8217573
  [8] 0.8217943 0.8218329 0.8218669 0.8219029 0.8219395 0.8219761 0.8220116
 [15] 0.8220483 0.8220818 0.8221169 0.8221526 0.8221871 0.8222214 0.8222548
 [22] 0.8222890 0.8223224 0.8223557 0.8223891 0.8224219 0.8224541 0.8224867
 [29] 0.8225188 0.8225508 0.8225824 0.8226136 0.8226450 0.8226758 0.8227067
 [36] 0.8227370 0.8227672 0.8227972 0.8228270 0.8228564 0.8228858 0.8229148
 [43] 0.8229435 0.8229721 0.8230004 0.8230285 0.8230563 0.8230839 0.8231113
 [50] 0.8231384 0.8231653 0.8231919 0.8232183 0.8232445 0.8232704 0.8232960
 [57] 0.8233215 0.8233467 0.8233716 0.8233964 0.8234208 0.8234451 0.8234691
 [64] 0.8234928 0.8235164 0.8235396 0.8235627 0.8235855 0.8236080 0.8236304
 [71] 0.8236524 0.8236743 0.8236959 0.8237172 0.8237384 0.8237592 0.8237799
 [78] 0.8238003 0.8238204 0.8238403 0.8238600 0.8238794 0.8238986 0.8239176
 [85] 0.8239363 0.8239548 0.8239730 0.8239910 0.8240087 0.8240262 0.8240435
 [92] 0.8240605 0.8240773 0.8240939 0.8241102 0.8241262 0.8241420 0.8241576
 [99] 0.8241730 0.8241881 0.8242029 0.8242175 0.8242319 0.8242460 0.8242599
[106] 0.8242736 0.8242870 0.8243002 0.8243131 0.8243258 0.8243382 0.8243504
[113] 0.8243624 0.8243741 0.8243856 0.8243968 0.8244078 0.8244186 0.8244291
[120] 0.8244394 0.8244494 0.8244592 0.8244687 0.8244780 0.8244871 0.8244959
[127] 0.8245045 0.8245129 0.8245210 0.8245288 0.8245364 0.8245438 0.8245509
[134] 0.8245578 0.8245645 0.8245709 0.8245771 0.8245830 0.8245887 0.8245941
[141] 0.8245993 0.8246043 0.8246090 0.8246135 0.8246177 0.8246217 0.8246255
[148] 0.8246290 0.8246323 0.8246353 0.8246381 0.8246407 0.8246430 0.8246451
[155] 0.8246469 0.8246485 0.8246498 0.8246509 0.8246518 0.8246524 0.8246528
[162] 0.8246529 0.8246528 0.8246525 0.8246519 0.8246511 0.8246500 0.8246487
[169] 0.8246472 0.8246454 0.8246434 0.8246411 0.8246386 0.8246359 0.8246329
[176] 0.8246297 0.8246262 0.8246225 0.8246185 0.8246144 0.8246099 0.8246053
[183] 0.8246003 0.8245952 0.8245898 0.8245842 0.8245783 0.8245722 0.8245659
[190] 0.8245593 0.8245524 0.8245454 0.8245381 0.8245305 0.8245227 0.8245147
[197] 0.8245064 0.8244979 0.8244892 0.8244802 0.8244710 0.8244615 0.8244518
[204] 0.8244419 0.8244317 0.8244213 0.8244106 0.8243997 0.8243886 0.8243772
[211] 0.8243656 0.8243538 0.8243417 0.8243293 0.8243168 0.8243040 0.8242909
[218] 0.8242776 0.8242641 0.8242504 0.8242364 0.8242221 0.8242077 0.8241929
[225] 0.8241780 0.8241628 0.8241474 0.8241317 0.8241158 0.8240997 0.8240833
[232] 0.8240667 0.8240499 0.8240328 0.8240155 0.8239979 0.8239801 0.8239621
[239] 0.8239438 0.8239253 0.8239065 0.8238876 0.8238684 0.8238489 0.8238292
[246] 0.8238093 0.8237891 0.8237687 0.8237481 0.8237272 0.8237061 0.8236848
[253] 0.8236632 0.8236414 0.8236194 0.8235971 0.8235746 0.8235518 0.8235288
[260] 0.8235056 0.8234821 0.8234585 0.8234345 0.8234104 0.8233860 0.8233613
[267] 0.8233365 0.8233114 0.8232861 0.8232605 0.8232347 0.8232087 0.8231824
[274] 0.8231559 0.8231292 0.8231022 0.8230750 0.8230476 0.8230199 0.8229920
[281] 0.8229639 0.8229355 0.8229069 0.8228781 0.8228490 0.8228197 0.8227902
[288] 0.8227604 0.8227305 0.8227002 0.8226698 0.8226391 0.8226082 0.8225770
[295] 0.8225456 0.8225140 0.8224822 0.8224501 0.8224178 0.8223853 0.8223525
[302] 0.8223195 0.8222863 0.8222529 0.8222192 0.8221853 0.8221511 0.8221167
[309] 0.8220821 0.8220473 0.8220122 0.8219770 0.8219414 0.8219057 0.8218697
[316] 0.8218335 0.8217971 0.8217604 0.8217235 0.8216864 0.8216490 0.8216115
[323] 0.8215737 0.8215356 0.8214974 0.8214589 0.8214202 0.8213812 0.8213421
[330] 0.8213027 0.8212631 0.8212232 0.8211831 0.8211428 0.8211023 0.8210616
[337] 0.8210206 0.8209794 0.8209380 0.8208963 0.8208544 0.8208123 0.8207700
[344] 0.8207274 0.8206847 0.8206417 0.8205984 0.8205550 0.8205113 0.8204674
[351] 0.8204233 0.8203790 0.8203344 0.8202896 0.8202446 0.8201993 0.8201539
[358] 0.8201082 0.8200623 0.8200162 0.8199698 0.8199232 0.8198764 0.8198294
[365] 0.8197822 0.8197347 0.8196870 0.8196391 0.8195910 0.8195427 0.8194941
[372] 0.8194453 0.8193963 0.8193471 0.8192976 0.8192480 0.8191981 0.8191480
[379] 0.8190977 0.8190471 0.8189964 0.8189454 0.8188942 0.8188428 0.8187911
[386] 0.8187393 0.8186872 0.8186349 0.8185824 0.8185297 0.8184767 0.8184236
[393] 0.8183702 0.8183166 0.8182628 0.8182087 0.8181545 0.8181000 0.8180454
[400] 0.8179905 0.8179354
> mx
[1] 0.824653
> mxli
[1] -0.39
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1a2ed1197361452.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/2e6w21197361452.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/3a7nm1197361452.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/4wvrz1197361452.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/5cnlq1197361452.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/6lb5w1197361452.tab") 
> 
> system("convert tmp/1a2ed1197361452.ps tmp/1a2ed1197361452.png")
> system("convert tmp/2e6w21197361452.ps tmp/2e6w21197361452.png")
> system("convert tmp/3a7nm1197361452.ps tmp/3a7nm1197361452.png")
> system("convert tmp/4wvrz1197361452.ps tmp/4wvrz1197361452.png")
> system("convert tmp/5cnlq1197361452.ps tmp/5cnlq1197361452.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.421   0.825   1.943