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(106.7,110.2,125.9,100.1,106.4,114.8,81.3,87,104.2,108,105,94.5,92,95.9,108.8,103.4,102.1,110.1,83.2,82.7,106.8,113.7,102.5,96.6,92.1,95.6,102.3,98.6,98.2,104.5,84,73.8,103.9,106,97.2,102.6,89,93.8,116.7,106.8,98.5,118.7,90,91.9,113.3,113.1,104.1,108.7,96.7,101,116.9,105.8,99,129.4,83,88.9,115.9,104.2,113.4,112.2,100.8,107.3,126.6,102.9,117.9,128.8,87.5,93.8,122.7,126.2,124.6,116.7,115.2,111.1,129.9,113.3,118.5,133.5,102.1,102.4)
> #'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] 0.2836740 0.2838440 0.2840140 0.2841838 0.2843536 0.2845232 0.2846928
  [8] 0.2848623 0.2850317 0.2852010 0.2853702 0.2855393 0.2857083 0.2858772
 [15] 0.2860460 0.2862147 0.2863833 0.2865519 0.2867203 0.2868886 0.2870568
 [22] 0.2872249 0.2873929 0.2875609 0.2877287 0.2878964 0.2880640 0.2882315
 [29] 0.2883989 0.2885662 0.2887334 0.2889005 0.2890675 0.2892344 0.2894011
 [36] 0.2895678 0.2897344 0.2899008 0.2900672 0.2902334 0.2903995 0.2905655
 [43] 0.2907315 0.2908973 0.2910630 0.2912285 0.2913940 0.2915594 0.2917246
 [50] 0.2918898 0.2920548 0.2922197 0.2923845 0.2925492 0.2927138 0.2928782
 [57] 0.2930426 0.2932068 0.2933709 0.2935349 0.2936988 0.2938625 0.2940262
 [64] 0.2941897 0.2943531 0.2945164 0.2946796 0.2948426 0.2950056 0.2951684
 [71] 0.2953311 0.2954936 0.2956561 0.2958184 0.2959806 0.2961427 0.2963047
 [78] 0.2964665 0.2966282 0.2967898 0.2969513 0.2971126 0.2972738 0.2974349
 [85] 0.2975959 0.2977567 0.2979175 0.2980780 0.2982385 0.2983988 0.2985590
 [92] 0.2987191 0.2988791 0.2990389 0.2991986 0.2993581 0.2995175 0.2996768
 [99] 0.2998360 0.2999950 0.3001539 0.3003127 0.3004713 0.3006298 0.3007882
[106] 0.3009464 0.3011045 0.3012625 0.3014203 0.3015780 0.3017356 0.3018930
[113] 0.3020503 0.3022074 0.3023644 0.3025213 0.3026780 0.3028346 0.3029911
[120] 0.3031474 0.3033036 0.3034596 0.3036156 0.3037713 0.3039269 0.3040824
[127] 0.3042377 0.3043929 0.3045480 0.3047029 0.3048577 0.3050123 0.3051668
[134] 0.3053211 0.3054753 0.3056294 0.3057833 0.3059370 0.3060906 0.3062441
[141] 0.3063974 0.3065506 0.3067036 0.3068565 0.3070092 0.3071618 0.3073142
[148] 0.3074665 0.3076187 0.3077706 0.3079225 0.3080742 0.3082257 0.3083771
[155] 0.3085283 0.3086794 0.3088303 0.3089811 0.3091317 0.3092822 0.3094325
[162] 0.3095827 0.3097327 0.3098826 0.3100323 0.3101818 0.3103312 0.3104805
[169] 0.3106295 0.3107785 0.3109272 0.3110759 0.3112243 0.3113726 0.3115208
[176] 0.3116687 0.3118166 0.3119642 0.3121117 0.3122591 0.3124063 0.3125533
[183] 0.3127002 0.3128469 0.3129935 0.3131398 0.3132861 0.3134321 0.3135780
[190] 0.3137238 0.3138694 0.3140148 0.3141600 0.3143051 0.3144501 0.3145948
[197] 0.3147394 0.3148839 0.3150281 0.3151722 0.3153162 0.3154599 0.3156036
[204] 0.3157470 0.3158903 0.3160334 0.3161763 0.3163191 0.3164617 0.3166041
[211] 0.3167464 0.3168885 0.3170304 0.3171722 0.3173138 0.3174552 0.3175965
[218] 0.3177376 0.3178785 0.3180192 0.3181598 0.3183002 0.3184404 0.3185805
[225] 0.3187204 0.3188601 0.3189997 0.3191390 0.3192782 0.3194173 0.3195561
[232] 0.3196948 0.3198333 0.3199716 0.3201098 0.3202477 0.3203856 0.3205232
[239] 0.3206606 0.3207979 0.3209350 0.3210719 0.3212087 0.3213453 0.3214817
[246] 0.3216179 0.3217539 0.3218898 0.3220255 0.3221610 0.3222963 0.3224315
[253] 0.3225665 0.3227012 0.3228359 0.3229703 0.3231046 0.3232386 0.3233725
[260] 0.3235062 0.3236398 0.3237731 0.3239063 0.3240393 0.3241721 0.3243047
[267] 0.3244372 0.3245695 0.3247015 0.3248334 0.3249652 0.3250967 0.3252280
[274] 0.3253592 0.3254902 0.3256210 0.3257516 0.3258820 0.3260123 0.3261424
[281] 0.3262722 0.3264019 0.3265314 0.3266608 0.3267899 0.3269189 0.3270476
[288] 0.3271762 0.3273046 0.3274328 0.3275608 0.3276887 0.3278163 0.3279438
[295] 0.3280710 0.3281981 0.3283250 0.3284517 0.3285783 0.3287046 0.3288307
[302] 0.3289567 0.3290825 0.3292080 0.3293334 0.3294586 0.3295836 0.3297085
[309] 0.3298331 0.3299575 0.3300818 0.3302059 0.3303297 0.3304534 0.3305769
[316] 0.3307002 0.3308233 0.3309462 0.3310689 0.3311915 0.3313138 0.3314360
[323] 0.3315579 0.3316797 0.3318013 0.3319226 0.3320438 0.3321648 0.3322856
[330] 0.3324062 0.3325267 0.3326469 0.3327669 0.3328868 0.3330064 0.3331258
[337] 0.3332451 0.3333642 0.3334830 0.3336017 0.3337202 0.3338385 0.3339566
[344] 0.3340745 0.3341922 0.3343097 0.3344270 0.3345441 0.3346610 0.3347777
[351] 0.3348943 0.3350106 0.3351267 0.3352427 0.3353584 0.3354740 0.3355893
[358] 0.3357045 0.3358194 0.3359342 0.3360487 0.3361631 0.3362773 0.3363913
[365] 0.3365050 0.3366186 0.3367320 0.3368452 0.3369582 0.3370710 0.3371836
[372] 0.3372960 0.3374082 0.3375202 0.3376320 0.3377436 0.3378550 0.3379662
[379] 0.3380772 0.3381880 0.3382986 0.3384090 0.3385192 0.3386292 0.3387391
[386] 0.3388487 0.3389581 0.3390673 0.3391763 0.3392852 0.3393938 0.3395022
[393] 0.3396104 0.3397184 0.3398263 0.3399339 0.3400413 0.3401485 0.3402556
[400] 0.3403624 0.3404690
> mx
[1] 0.340469
> mxli
[1] 2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1vies1194297012.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/2x7mw1194297012.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/3tvhf1194297012.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/45drc1194297012.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/5vpj51194297012.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/6758j1194297012.tab") 
> 
> system("convert tmp/1vies1194297012.ps tmp/1vies1194297012.png")
> system("convert tmp/2x7mw1194297012.ps tmp/2x7mw1194297012.png")
> system("convert tmp/3tvhf1194297012.ps tmp/3tvhf1194297012.png")
> system("convert tmp/45drc1194297012.ps tmp/45drc1194297012.png")
> system("convert tmp/5vpj51194297012.ps tmp/5vpj51194297012.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.397   1.314   2.592