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(281,295,294,302,314,321,313,310,319,316,319,333,356,358,340,328,355,356,351,359,378,378,389.,407,413,404,406,402,383,392,398,400,405,420,439,441,424,423,434,429,421,430,424,437,456,469,476,510,549,554,557,610,675,596,633,632,596,585,627,629)
> #'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] 0.9685512 0.9686228 0.9686932 0.9687625 0.9688305 0.9688974 0.9689630
  [8] 0.9690275 0.9690907 0.9691527 0.9692136 0.9692732 0.9693316 0.9693887
 [15] 0.9694446 0.9694994 0.9695528 0.9696051 0.9696561 0.9697058 0.9697544
 [22] 0.9698016 0.9698476 0.9698924 0.9699359 0.9699782 0.9700192 0.9700589
 [29] 0.9700974 0.9701346 0.9701705 0.9702051 0.9702385 0.9702706 0.9703014
 [36] 0.9703309 0.9703591 0.9703861 0.9704117 0.9704361 0.9704591 0.9704809
 [43] 0.9705013 0.9705205 0.9705383 0.9705548 0.9705700 0.9705839 0.9705965
 [50] 0.9706077 0.9706177 0.9706263 0.9706335 0.9706395 0.9706441 0.9706474
 [57] 0.9706493 0.9706499 0.9706492 0.9706471 0.9706437 0.9706389 0.9706328
 [64] 0.9706254 0.9706165 0.9706064 0.9705948 0.9705820 0.9705677 0.9705521
 [71] 0.9705351 0.9705168 0.9704971 0.9704760 0.9704536 0.9704298 0.9704046
 [78] 0.9703781 0.9703501 0.9703208 0.9702901 0.9702581 0.9702246 0.9701898
 [85] 0.9701536 0.9701160 0.9700770 0.9700366 0.9699948 0.9699517 0.9699071
 [92] 0.9698612 0.9698138 0.9697651 0.9697150 0.9696634 0.9696105 0.9695562
 [99] 0.9695005 0.9694433 0.9693848 0.9693249 0.9692635 0.9692008 0.9691367
[106] 0.9690711 0.9690042 0.9689358 0.9688661 0.9687949 0.9687223 0.9686483
[113] 0.9685729 0.9684961 0.9684179 0.9683383 0.9682573 0.9681748 0.9680910
[120] 0.9680057 0.9679190 0.9678309 0.9677414 0.9676505 0.9675582 0.9674645
[127] 0.9673693 0.9672727 0.9671748 0.9670754 0.9669746 0.9668724 0.9667688
[134] 0.9666638 0.9665574 0.9664495 0.9663403 0.9662296 0.9661175 0.9660041
[141] 0.9658892 0.9657729 0.9656552 0.9655361 0.9654156 0.9652937 0.9651704
[148] 0.9650457 0.9649196 0.9647921 0.9646631 0.9645328 0.9644011 0.9642680
[155] 0.9641335 0.9639976 0.9638603 0.9637216 0.9635815 0.9634401 0.9632972
[162] 0.9631530 0.9630073 0.9628603 0.9627119 0.9625621 0.9624110 0.9622584
[169] 0.9621045 0.9619492 0.9617925 0.9616345 0.9614750 0.9613143 0.9611521
[176] 0.9609886 0.9608237 0.9606574 0.9604898 0.9603208 0.9601505 0.9599788
[183] 0.9598057 0.9596313 0.9594556 0.9592785 0.9591001 0.9589203 0.9587391
[190] 0.9585567 0.9583729 0.9581877 0.9580013 0.9578135 0.9576243 0.9574339
[197] 0.9572421 0.9570490 0.9568546 0.9566588 0.9564618 0.9562634 0.9560638
[204] 0.9558628 0.9556605 0.9554570 0.9552521 0.9550459 0.9548385 0.9546297
[211] 0.9544197 0.9542084 0.9539958 0.9537819 0.9535667 0.9533503 0.9531326
[218] 0.9529137 0.9526934 0.9524720 0.9522492 0.9520252 0.9518000 0.9515735
[225] 0.9513458 0.9511168 0.9508866 0.9506551 0.9504225 0.9501886 0.9499535
[232] 0.9497171 0.9494795 0.9492408 0.9490008 0.9487596 0.9485172 0.9482736
[239] 0.9480288 0.9477828 0.9475357 0.9472873 0.9470378 0.9467870 0.9465352
[246] 0.9462821 0.9460279 0.9457725 0.9455159 0.9452582 0.9449994 0.9447394
[253] 0.9444782 0.9442160 0.9439525 0.9436880 0.9434223 0.9431555 0.9428876
[260] 0.9426186 0.9423484 0.9420772 0.9418048 0.9415314 0.9412568 0.9409812
[267] 0.9407044 0.9404266 0.9401478 0.9398678 0.9395868 0.9393047 0.9390215
[274] 0.9387373 0.9384521 0.9381657 0.9378784 0.9375900 0.9373006 0.9370101
[281] 0.9367186 0.9364261 0.9361326 0.9358381 0.9355425 0.9352460 0.9349484
[288] 0.9346499 0.9343504 0.9340499 0.9337484 0.9334459 0.9331425 0.9328381
[295] 0.9325327 0.9322264 0.9319191 0.9316109 0.9313017 0.9309916 0.9306805
[302] 0.9303686 0.9300557 0.9297418 0.9294271 0.9291115 0.9287949 0.9284775
[309] 0.9281591 0.9278399 0.9275198 0.9271987 0.9268769 0.9265541 0.9262305
[316] 0.9259060 0.9255807 0.9252545 0.9249274 0.9245995 0.9242708 0.9239412
[323] 0.9236109 0.9232796 0.9229476 0.9226148 0.9222811 0.9219467 0.9216114
[330] 0.9212754 0.9209385 0.9206009 0.9202625 0.9199233 0.9195834 0.9192426
[337] 0.9189012 0.9185590 0.9182160 0.9178723 0.9175278 0.9171826 0.9168367
[344] 0.9164900 0.9161426 0.9157946 0.9154458 0.9150963 0.9147461 0.9143952
[351] 0.9140436 0.9136913 0.9133384 0.9129847 0.9126304 0.9122755 0.9119198
[358] 0.9115635 0.9112066 0.9108490 0.9104908 0.9101320 0.9097725 0.9094124
[365] 0.9090516 0.9086903 0.9083283 0.9079657 0.9076026 0.9072388 0.9068745
[372] 0.9065095 0.9061440 0.9057779 0.9054112 0.9050440 0.9046762 0.9043078
[379] 0.9039389 0.9035694 0.9031994 0.9028289 0.9024578 0.9020862 0.9017141
[386] 0.9013414 0.9009683 0.9005946 0.9002204 0.8998457 0.8994706 0.8990949
[393] 0.8987188 0.8983421 0.8979650 0.8975875 0.8972094 0.8968309 0.8964520
[400] 0.8960726 0.8956927
> mx
[1] 0.97065
> mxli
[1] -1.43
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1zg8h1200496053.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/235pe1200496053.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/381dq1200496053.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/49dgc1200496053.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/5n0am1200496053.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/6jbum1200496053.tab") 
> 
> system("convert tmp/1zg8h1200496053.ps tmp/1zg8h1200496053.png")
> system("convert tmp/235pe1200496053.ps tmp/235pe1200496053.png")
> system("convert tmp/381dq1200496053.ps tmp/381dq1200496053.png")
> system("convert tmp/49dgc1200496053.ps tmp/49dgc1200496053.png")
> system("convert tmp/5n0am1200496053.ps tmp/5n0am1200496053.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.290   0.758   1.621