R version 3.4.1 (2017-06-30) -- "Single Candle"
Copyright (C) 2017 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> source('/home/pw/wessanet/cretab')
> 
> 
> 
> myrfcuid = ''
> 
> x <- c(503334,503737,504101,504504,504894,505297,505687,506090,506493,506883,507286,507676,508079,508482,508846,509249,509639,510042,510432,510835,511238,511628,512031,512421,512824,513227,513604,514007,514397,514800,515190,515593,515996,516386,516789,517179,517582,517985,518349,518752,519142,519545,519935,520338,520741,521131,521534,521924,522327,522730,523094,523497,523887,524290,524680,525083,525486,525876,526279,526669,527072,527475,527839,528242,528632,529035,529425,529828,530231,530621,531024,531414,531817,532220,532597,533000,533390,533793,534183,534586,534989,535379,535782,536172,536575,536978,537342,537745,538135,538538,538928,539331,539734,540124,540527,540917,541320,541723,542087,542490,542880,543283,543673,544076,544479,544869,545272,545662)
> par1 = '12'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 (Sat, 15 Jul 2017 00:11:16 +0200)
> #Author: root
> #To cite this work: Wessa P. (2017), Standard Deviation-Mean Plot (v1.0.7) in Free Statistics Software (v$_version), Office for Research Development and Education, URL https://www.wessa.net/rwasp_smp.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 108
> (np <- floor(n / par1))
[1] 9
> arr <- array(NA,dim=c(par1,np))
> j <- 0
> k <- 1
> for (i in 1:(np*par1))
+ {
+ j = j + 1
+ arr[j,k] <- x[i]
+ if (j == par1) {
+ j = 0
+ k=k+1
+ }
+ }
> arr
        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]   [,7]   [,8]   [,9]
 [1,] 503334 508079 512824 517582 522327 527072 531817 536575 541320
 [2,] 503737 508482 513227 517985 522730 527475 532220 536978 541723
 [3,] 504101 508846 513604 518349 523094 527839 532597 537342 542087
 [4,] 504504 509249 514007 518752 523497 528242 533000 537745 542490
 [5,] 504894 509639 514397 519142 523887 528632 533390 538135 542880
 [6,] 505297 510042 514800 519545 524290 529035 533793 538538 543283
 [7,] 505687 510432 515190 519935 524680 529425 534183 538928 543673
 [8,] 506090 510835 515593 520338 525083 529828 534586 539331 544076
 [9,] 506493 511238 515996 520741 525486 530231 534989 539734 544479
[10,] 506883 511628 516386 521131 525876 530621 535379 540124 544869
[11,] 507286 512031 516789 521534 526279 531024 535782 540527 545272
[12,] 507676 512421 517179 521924 526669 531414 536172 540917 545662
> arr.mean <- array(NA,dim=np)
> arr.sd <- array(NA,dim=np)
> arr.range <- array(NA,dim=np)
> for (j in 1:np)
+ {
+ arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
+ arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
+ arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
+ }
> arr.mean
[1] 505498.5 510243.5 514999.3 519746.5 524491.5 529236.5 533992.3 538739.5
[9] 543484.5
> arr.sd
[1] 1425.863 1425.863 1429.122 1425.863 1425.863 1425.863 1429.122 1425.863
[9] 1425.863
> arr.range
[1] 4342 4342 4355 4342 4342 4342 4355 4342 4342
> (lm1 <- lm(arr.sd~arr.mean))

Call:
lm(formula = arr.sd ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
  1.427e+03    1.624e-08  

> (lnlm1 <- lm(log(arr.sd)~log(arr.mean)))

Call:
lm(formula = log(arr.sd) ~ log(arr.mean))

Coefficients:
  (Intercept)  log(arr.mean)  
    7.2616245      0.0001075  

> (lm2 <- lm(arr.range~arr.mean))

Call:
lm(formula = arr.range ~ arr.mean)

Coefficients:
(Intercept)     arr.mean  
  4.345e+03    6.477e-08  

> postscript(file="/home/pw/wessanet/rcomp/tmp/10zwg1502825152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
> dev.off()
null device 
          1 
> postscript(file="/home/pw/wessanet/rcomp/tmp/2q4g11502825152.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
> dev.off()
null device 
          1 
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Section',header=TRUE)
> a<-table.element(a,'Mean',header=TRUE)
> a<-table.element(a,'Standard Deviation',header=TRUE)
> a<-table.element(a,'Range',header=TRUE)
> a<-table.row.end(a)
> for (j in 1:np) {
+ a<-table.row.start(a)
+ a<-table.element(a,j,header=TRUE)
+ a<-table.element(a,arr.mean[j])
+ a<-table.element(a,arr.sd[j] )
+ a<-table.element(a,arr.range[j] )
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/3k2w51502825152.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/47csp1502825152.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[1]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'beta',header=TRUE)
> a<-table.element(a,lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'T-STAT',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'p-value',header=TRUE)
> a<-table.element(a,summary(lnlm1)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Lambda',header=TRUE)
> a<-table.element(a,1-lnlm1$coefficients[[2]])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/home/pw/wessanet/rcomp/tmp/580ln1502825152.tab") 
> 
> try(system("convert /home/pw/wessanet/rcomp/tmp/10zwg1502825152.ps /home/pw/wessanet/rcomp/tmp/10zwg1502825152.png",intern=TRUE))
character(0)
> try(system("convert /home/pw/wessanet/rcomp/tmp/2q4g11502825152.ps /home/pw/wessanet/rcomp/tmp/2q4g11502825152.png",intern=TRUE))
character(0)
> 
> proc.time()
   user  system elapsed 
  0.956   0.132   1.162