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(1165010
+ ,1160665
+ ,1156265
+ ,1147162.5
+ ,1237238.75
+ ,1232481.25
+ ,1165010
+ ,1120157.5
+ ,1124488.75
+ ,1124488.75
+ ,1129328.75
+ ,1138005
+ ,1151507.5
+ ,1151507.5
+ ,1142831.25
+ ,1120157.5
+ ,1237238.75
+ ,1255086.25
+ ,1228136.25
+ ,1165010
+ ,1192015
+ ,1151507.5
+ ,1169781.25
+ ,1178512.5
+ ,1187615
+ ,1165010
+ ,1169781.25
+ ,1138005
+ ,1237238.75
+ ,1268588.75
+ ,1241638.75
+ ,1192015
+ ,1245983.75
+ ,1187615
+ ,1241638.75
+ ,1237238.75
+ ,1250755
+ ,1201131.25
+ ,1255086.25
+ ,1250755
+ ,1331715
+ ,1313441.25
+ ,1241638.75
+ ,1205462.5
+ ,1255086.25
+ ,1187615
+ ,1237238.75
+ ,1245983.75
+ ,1264257.5
+ ,1223805
+ ,1245983.75
+ ,1259486.25
+ ,1309110
+ ,1268588.75
+ ,1214633.75
+ ,1156265
+ ,1210288.75
+ ,1061788.75
+ ,1133660
+ ,1174112.5
+ ,1214633.75
+ ,1156265
+ ,1156265
+ ,1156265
+ ,1187615
+ ,1142831.25
+ ,1084036.25
+ ,1034838.75
+ ,1070533.75
+ ,931205
+ ,1016578.75
+ ,1066188.75
+ ,1075305
+ ,1025681.25
+ ,1030012.5
+ ,1016578.75
+ ,1061788.75
+ ,1030012.5
+ ,967381.25
+ ,922102.5
+ ,998662.5
+ ,832383.75
+ ,940362.5
+ ,989560
+ ,989560
+ ,931205
+ ,877236.25
+ ,872905
+ ,922102.5
+ ,877236.25
+ ,791931.25
+ ,733136.25
+ ,796276.25
+ ,647831.25
+ ,782760
+ ,854562.5
+ ,877236.25
+ ,827626.25
+ ,764926.25
+ ,809778.75
+ ,827626.25
+ ,814110
+ ,679126.25
+ ,616481.25
+ ,661278.75
+ ,526350
+ ,665678.75
+ ,715302.5
+ ,755755
+ ,688283.75
+ ,625157.5
+ ,661278.75
+ ,679126.25
+ ,643431.25
+ ,508502.5
+ ,449707.5
+ ,503676.25
+ ,355231.25
+ ,517178.75
+ ,616481.25)
> 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,] 1165010 1151508 1187615 1250755 1264258 1214634 1075305.0 989560.0
 [2,] 1160665 1151508 1165010 1201131 1223805 1156265 1025681.2 931205.0
 [3,] 1156265 1142831 1169781 1255086 1245984 1156265 1030012.5 877236.2
 [4,] 1147162 1120158 1138005 1250755 1259486 1156265 1016578.8 872905.0
 [5,] 1237239 1237239 1237239 1331715 1309110 1187615 1061788.8 922102.5
 [6,] 1232481 1255086 1268589 1313441 1268589 1142831 1030012.5 877236.2
 [7,] 1165010 1228136 1241639 1241639 1214634 1084036  967381.2 791931.2
 [8,] 1120158 1165010 1192015 1205462 1156265 1034839  922102.5 733136.2
 [9,] 1124489 1192015 1245984 1255086 1210289 1070534  998662.5 796276.2
[10,] 1124489 1151508 1187615 1187615 1061789  931205  832383.8 647831.2
[11,] 1129329 1169781 1241639 1237239 1133660 1016579  940362.5 782760.0
[12,] 1138005 1178512 1237239 1245984 1174112 1066189  989560.0 854562.5
          [,9]    [,10]
 [1,] 877236.2 755755.0
 [2,] 827626.2 688283.8
 [3,] 764926.2 625157.5
 [4,] 809778.8 661278.8
 [5,] 827626.2 679126.2
 [6,] 814110.0 643431.2
 [7,] 679126.2 508502.5
 [8,] 616481.2 449707.5
 [9,] 661278.8 503676.2
[10,] 526350.0 355231.2
[11,] 665678.8 517178.8
[12,] 715302.5 616481.2
> 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] 1158358.4 1178607.6 1209364.1 1247992.4 1210165.0 1101438.0  990819.3
 [8]  839728.5  732126.8  583650.8
> arr.sd
 [1]  39254.93  41649.90  40853.51  41799.49  68837.47  82068.37  67492.61
 [8]  94070.50 105040.60 116252.84
> arr.range
 [1] 117081.2 134928.8 130583.8 144100.0 247321.2 283428.8 242921.2 341728.8
 [9] 350886.2 400523.8
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
  1.857e+05   -1.132e-01  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       30.217         -1.386  

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

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

Coefficients:
(Intercept)     arr.mean  
  6.477e+05   -3.983e-01  

> postscript(file="/var/wessaorg/rcomp/tmp/1o6c71377065085.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/2czju1377065085.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/3vgt91377065085.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4voa51377065085.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5mdgw1377065085.tab") 
> 
> try(system("convert tmp/1o6c71377065085.ps tmp/1o6c71377065085.png",intern=TRUE))
character(0)
> try(system("convert tmp/2czju1377065085.ps tmp/2czju1377065085.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.463   0.406   1.848