R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(178421,139871,118159,109763,97415,119190,97903,96953,87888,84637,90549,95680,99371,79984,86752,85733,84906,78356,108895,101768,73285,65724,67457,67203,69273,80807,75129,74991,68157,73858,71349,85634,91624,116014,120033,108651,105378,138939,132974,135277,152741,158417,157460,193997,154089,147570,162924,153629,155907,197675,250708,266652,209842,165826,137152,150581,145973,126532,115437,119526,110856,97243,103876,116370,109616,98365,90440,88899,92358,88394,98219,113546,107168,77540,74944,75641,75910,87384,84615,80420,80784,79933,82118,91420,112426,114528,131025,116460,111258,155318,155078,134794,139985,198778,172436,169585,203702,282392,220658,194472,269246,215340,218319,195724,174614,172085,152347,189615,173804,145683,133550,121156,112040,120767,127019,136295,113425,107815,100298,97048,98750,98235,101254,139589,134921,80355,80396,82183,79709,90781)
> par1 = '4'
> par1 <- '4'
> #'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] 130
> (np <- floor(n / par1))
[1] 32
> 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]  [,10]  [,11]
[1,] 178421  97415 87888 99371  84906 73285 69273 68157  91624 105378 152741
[2,] 139871 119190 84637 79984  78356 65724 80807 73858 116014 138939 158417
[3,] 118159  97903 90549 86752 108895 67457 75129 71349 120033 132974 157460
[4,] 109763  96953 95680 85733 101768 67203 74991 85634 108651 135277 193997
      [,12]  [,13]  [,14]  [,15]  [,16]  [,17]  [,18]  [,19] [,20] [,21]  [,22]
[1,] 154089 155907 209842 145973 110856 109616  92358 107168 75910 80784 112426
[2,] 147570 197675 165826 126532  97243  98365  88394  77540 87384 79933 114528
[3,] 162924 250708 137152 115437 103876  90440  98219  74944 84615 82118 131025
[4,] 153629 266652 150581 119526 116370  88899 113546  75641 80420 91420 116460
      [,23]  [,24]  [,25]  [,26]  [,27]  [,28]  [,29]  [,30]  [,31]  [,32]
[1,] 111258 139985 203702 269246 174614 173804 112040 113425  98750 134921
[2,] 155318 198778 282392 215340 172085 145683 120767 107815  98235  80355
[3,] 155078 172436 220658 218319 152347 133550 127019 100298 101254  80396
[4,] 134794 169585 194472 195724 189615 121156 136295  97048 139589  82183
> 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] 136553.50 102865.25  89688.50  87960.00  93481.25  68417.25  75050.00
 [8]  74749.50 109080.50 128142.00 165653.75 154553.00 217735.50 165850.25
[15] 126867.00 107086.25  96830.00  98129.25  83823.25  82082.25  83563.75
[22] 118609.75 139112.00 170196.00 225306.00 224657.25 172165.25 143548.25
[29] 124030.25 104646.50 109457.00  94463.75
> arr.sd
 [1] 30659.321 10890.077  4668.975  8169.992 14242.683  3333.920  4709.087
 [8]  7622.151 12555.803 15373.477 19057.693  6322.051 50679.374 31580.660
[15] 13536.206  8318.540  9479.291 11041.839 15601.783  5012.754  5314.139
[22]  8439.181 20912.819 24048.743 39572.228 31370.231 15310.776 22519.217
[29] 10226.952  7388.011 20131.234 26984.961
> arr.range
 [1]  68658  22237  11043  19387  30539   7561  11534  17477  28409  33561
[11]  41256  15354 110745  72690  30536  19127  20717  25152  32224  11474
[21]  11487  18599  44060  58793  87920  73522  37268  52648  24255  16377
[31]  41354  54566
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
 -9373.0657       0.2051  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       -9.390          1.615  

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

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

Coefficients:
(Intercept)     arr.mean  
 -2.260e+04    4.715e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/1j25g1354565961.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/29ity1354565961.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/30j9e1354565961.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/44emq1354565961.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5dg981354565961.tab") 
> 
> try(system("convert tmp/1j25g1354565961.ps tmp/1j25g1354565961.png",intern=TRUE))
character(0)
> try(system("convert tmp/29ity1354565961.ps tmp/29ity1354565961.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.154   0.472   2.728