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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(1.6,2,2.6,3,2.6,2.9,2.5,2.4,1.5,1.1,0.6,0.9,1.1,1.5,1.7,1.2,0.4,-0.7,-1.4,-1.6,-1.2,-0.4,-0.2,-0.3,-0.5,0,-0.5,0.2,0.7,1.6,2.6,3.3,3.3,3.2,3.5,3.9,4.5,4.6,6.6,7.1,8.9,8.8,8.5,7.6,7.5,7.5,6.1,6.3,8.4,7.1,5.6,4.2,2.1,1.2,0.9,1.4,1.7,1.7,1.9,1.3,-0.7,0.3,0.8,0.9,1.1,2.5,2.7,3.3,4.2,3.8,3.8,3.2,2.9,1.9,1.7,1.6,1.7,1.2,0.7,-0.2,-1.5,-1.2,-1,0,-0.6,0.7,1.3,0.8,1,0.5,0.3,1,1,1.1,1.5,1.5,2,1.7,0.6,1.2,1.5,2.1,3.2,3.9,4.6,4.2,4.4,3.7,3.7,2.8,2.9,3.9,3.1,3,2.8,2.4,2.1,3.1,3,3.1,3.3,3.3,3.8,3.1,3.9,4,4.4,3.7,3.6,3.4,2.8,2.8,2.6,3.3,2.4,1.6,0.7,0,-1.1,-1.2,-1.3,-1.6,-1.3,-1.6,-1.1,-1,0.3,1.2,0.7,1.1,2.1,2.5,2.3,2.3,2.6,3.2,2.2,2.7,2.2,1.4,2.4,2,1.3,1.1,1.4,1.8,1.9,1.6)
> 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] 168
> (np <- floor(n / par1))
[1] 14
> 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] [,12] [,13]
 [1,]  1.6  1.1 -0.5  4.5  8.4 -0.7  2.9 -0.6  2.0   3.7   3.3   2.6  -1.1
 [2,]  2.0  1.5  0.0  4.6  7.1  0.3  1.9  0.7  1.7   2.8   3.3   3.3  -1.0
 [3,]  2.6  1.7 -0.5  6.6  5.6  0.8  1.7  1.3  0.6   2.9   3.8   2.4   0.3
 [4,]  3.0  1.2  0.2  7.1  4.2  0.9  1.6  0.8  1.2   3.9   3.1   1.6   1.2
 [5,]  2.6  0.4  0.7  8.9  2.1  1.1  1.7  1.0  1.5   3.1   3.9   0.7   0.7
 [6,]  2.9 -0.7  1.6  8.8  1.2  2.5  1.2  0.5  2.1   3.0   4.0   0.0   1.1
 [7,]  2.5 -1.4  2.6  8.5  0.9  2.7  0.7  0.3  3.2   2.8   4.4  -1.1   2.1
 [8,]  2.4 -1.6  3.3  7.6  1.4  3.3 -0.2  1.0  3.9   2.4   3.7  -1.2   2.5
 [9,]  1.5 -1.2  3.3  7.5  1.7  4.2 -1.5  1.0  4.6   2.1   3.6  -1.3   2.3
[10,]  1.1 -0.4  3.2  7.5  1.7  3.8 -1.2  1.1  4.2   3.1   3.4  -1.6   2.3
[11,]  0.6 -0.2  3.5  6.1  1.9  3.8 -1.0  1.5  4.4   3.0   2.8  -1.3   2.6
[12,]  0.9 -0.3  3.9  6.3  1.3  3.2  0.0  1.5  3.7   3.1   2.8  -1.6   3.2
      [,14]
 [1,]   2.2
 [2,]   2.7
 [3,]   2.2
 [4,]   1.4
 [5,]   2.4
 [6,]   2.0
 [7,]   1.3
 [8,]   1.1
 [9,]   1.4
[10,]   1.8
[11,]   1.9
[12,]   1.6
> 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] 1.975000000 0.008333333 1.775000000 7.000000000 3.125000000 2.158333333
 [7] 0.650000000 0.841666667 2.758333333 2.991666667 3.508333333 0.208333333
[13] 1.350000000 1.833333333
> arr.sd
 [1] 0.8158933 1.1555859 1.7062053 1.4597634 2.5669667 1.6104535 1.4126056
 [8] 0.5822501 1.3931379 0.4851585 0.4851585 1.8362305 1.4080935 0.4886593
> arr.range
 [1] 2.4 3.3 4.4 4.4 7.5 4.9 4.4 2.1 4.0 1.8 1.6 4.9 4.3 1.6
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
     1.2209       0.0104  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
      0.09152       -0.03505  

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

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

Coefficients:
(Intercept)     arr.mean  
    3.54975      0.06306  

> postscript(file="/var/wessaorg/rcomp/tmp/18cwd1355323411.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/2oofs1355323411.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/3dwoh1355323411.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/4gj7u1355323411.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/5khme1355323411.tab") 
> 
> try(system("convert tmp/18cwd1355323411.ps tmp/18cwd1355323411.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oofs1355323411.ps tmp/2oofs1355323411.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.388   0.221   1.591