R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(3207324
+ ,3178086
+ ,3148485
+ ,3087246
+ ,3693267
+ ,3661164
+ ,3207324
+ ,2905563
+ ,2934699
+ ,2934699
+ ,2967168
+ ,3025542
+ ,3207324
+ ,3148485
+ ,3239325
+ ,3388641
+ ,4238049
+ ,4238049
+ ,4056732
+ ,3874950
+ ,4024266
+ ,4205949
+ ,4238049
+ ,4328892
+ ,4601499
+ ,4419732
+ ,4419732
+ ,4692342
+ ,5448042
+ ,5509281
+ ,5357202
+ ,4993734
+ ,5266260
+ ,5266260
+ ,5295495
+ ,5448042
+ ,5568120
+ ,5629359
+ ,5629359
+ ,5811042
+ ,6508356
+ ,6689670
+ ,6718809
+ ,6264966
+ ,6508356
+ ,6417513
+ ,6235746
+ ,6628434
+ ,6718809
+ ,6566727
+ ,6598830
+ ,6809748
+ ,7597920
+ ,7990056
+ ,7990056
+ ,7808739
+ ,8080980
+ ,7808739
+ ,7656276
+ ,8233527
+ ,8323902
+ ,8110134
+ ,8654901
+ ,8868684
+ ,9504309
+ ,9926133
+ ,9867774
+ ,9835290
+ ,10078677
+ ,10049076
+ ,9686076
+ ,10230759
+ ,10412541
+ ,10230759
+ ,10986462
+ ,11349927
+ ,12196086
+ ,12529950
+ ,12439476
+ ,12257691
+ ,12409872
+ ,12591555
+ ,11985168
+ ,12468612
+ ,12773337
+ ,12650394
+ ,13438098
+ ,13710240
+ ,14861511
+ ,15072414
+ ,14800254
+ ,14952351
+ ,15043194
+ ,15134034
+ ,14556783
+ ,15101568
+ ,15403311
+ ,15101568
+ ,15980562
+ ,16252821
+ ,17433111
+ ,17614893
+ ,17673267
+ ,17977893
+ ,17977893
+ ,18097971
+ ,17553189
+ ,17825814
+ ,18007131
+ ,17673267
+ ,18642738
+ ,18824436
+ ,20034063
+ ,20247849
+ ,20549592
+ ,20822217
+ ,20851353
+ ,20883456
+ ,20338689
+ ,20883456)
> par1 = '12'
> par1 <- '12'
> #'GNU S' R Code compiled by R2WASP v. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P. (2012), Standard Deviation-Mean Plot (v1.0.6) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_smp.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 120
> (np <- floor(n / par1))
[1] 10
> 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]
 [1,] 3207324 3207324 4601499 5568120 6718809  8323902 10412541 12773337
 [2,] 3178086 3148485 4419732 5629359 6566727  8110134 10230759 12650394
 [3,] 3148485 3239325 4419732 5629359 6598830  8654901 10986462 13438098
 [4,] 3087246 3388641 4692342 5811042 6809748  8868684 11349927 13710240
 [5,] 3693267 4238049 5448042 6508356 7597920  9504309 12196086 14861511
 [6,] 3661164 4238049 5509281 6689670 7990056  9926133 12529950 15072414
 [7,] 3207324 4056732 5357202 6718809 7990056  9867774 12439476 14800254
 [8,] 2905563 3874950 4993734 6264966 7808739  9835290 12257691 14952351
 [9,] 2934699 4024266 5266260 6508356 8080980 10078677 12409872 15043194
[10,] 2934699 4205949 5266260 6417513 7808739 10049076 12591555 15134034
[11,] 2967168 4238049 5295495 6235746 7656276  9686076 11985168 14556783
[12,] 3025542 4328892 5448042 6628434 8233527 10230759 12468612 15101568
          [,9]    [,10]
 [1,] 15403311 18007131
 [2,] 15101568 17673267
 [3,] 15980562 18642738
 [4,] 16252821 18824436
 [5,] 17433111 20034063
 [6,] 17614893 20247849
 [7,] 17673267 20549592
 [8,] 17977893 20822217
 [9,] 17977893 20851353
[10,] 18097971 20883456
[11,] 17553189 20338689
[12,] 17825814 20883456
> 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]  3162547  3849059  5059802  6217478  7488367  9427976 11821508 14341182
 [9] 17074358 19813187
> arr.sd
 [1]  264096.3  464409.1  415774.5  440416.1  628761.3  738530.9  854633.0
 [8]  937422.7 1079127.8 1192084.5
> arr.range
 [1]  787704 1180407 1089549 1150689 1666800 2120625 2360796 2483640 2996403
[10] 3210189
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  1.809e+05    5.299e-02  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       1.4696         0.7465  

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

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

Coefficients:
(Intercept)     arr.mean  
  4.769e+05    1.453e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/1lmdj1375886601.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="/var/wessaorg/rcomp/tmp/2nhv91375886601.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 
> 
> #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,'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="/var/wessaorg/rcomp/tmp/3tges1375886601.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4r3lt1375886601.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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="/var/wessaorg/rcomp/tmp/5pocq1375886601.tab") 
> 
> try(system("convert tmp/1lmdj1375886601.ps tmp/1lmdj1375886601.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nhv91375886601.ps tmp/2nhv91375886601.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.854   0.503   2.327