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(88.1,101.7,114.8,103.4,96.4,110,71.1,79.4,119.2,99.1,113.2,103.6,97.5,102.4,120.8,89.5,101.7,112.5,72.4,84.7,117.2,112.8,111.3,102.3,95.2,103,116.4,95.1,100.7,112.4,75.3,93.3,118.6,118.7,110.7,113.3,89.5,106.3,115.1,105.7,95.8,114.7,79.6,80.6,125,127.5,99.5,104.3,90,96,108.9,95.8,87.2,108.4,74.9,80.8,119.1,107.9,106.9,96.8,93.7,95.2,112.7,98.5,91.5,112,76.7,84.7,114.9,108.4,104.6,111.3,90.8,109.1,121,95.2,110.5,102.4,86.7,99.1,126,110.3,104.6,103.1,102)
> par1 = '12'
> par1 <- as.numeric(par1)
> (n <- length(x))
[1] 85
> (np <- floor(n / par1))
[1] 7
> 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]
 [1,]  88.1  97.5  95.2  89.5  90.0  93.7  90.8
 [2,] 101.7 102.4 103.0 106.3  96.0  95.2 109.1
 [3,] 114.8 120.8 116.4 115.1 108.9 112.7 121.0
 [4,] 103.4  89.5  95.1 105.7  95.8  98.5  95.2
 [5,]  96.4 101.7 100.7  95.8  87.2  91.5 110.5
 [6,] 110.0 112.5 112.4 114.7 108.4 112.0 102.4
 [7,]  71.1  72.4  75.3  79.6  74.9  76.7  86.7
 [8,]  79.4  84.7  93.3  80.6  80.8  84.7  99.1
 [9,] 119.2 117.2 118.6 125.0 119.1 114.9 126.0
[10,]  99.1 112.8 118.7 127.5 107.9 108.4 110.3
[11,] 113.2 111.3 110.7  99.5 106.9 104.6 104.6
[12,] 103.6 102.3 113.3 104.3  96.8 111.3 103.1
> 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] 100.0000 102.0917 104.3917 103.6333  97.7250 100.3500 104.9000
> arr.sd
[1] 14.47342 14.30210 13.10735 15.60381 13.03444 12.28247 11.48097
> arr.range
[1] 48.1 48.4 43.4 47.9 44.2 38.2 39.3
> (lm1 <- lm(arr.sd~arr.mean))

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

Coefficients:
(Intercept)     arr.mean  
   16.54863     -0.03023  

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

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

Coefficients:
  (Intercept)  log(arr.mean)  
       4.0616        -0.3171  

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

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

Coefficients:
(Intercept)     arr.mean  
    62.7426      -0.1819  

> postscript(file="/var/fisher/rcomp/tmp/1iyg71354448099.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/fisher/rcomp/tmp/2uavd1354448099.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/3e9w31354448099.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)$coefficients[2,4])
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/4hynb1354448099.tab") 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'alpha',header=TRUE)
> a<-table.element(a,lnlm1$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/fisher/rcomp/tmp/5gz0f1354448099.tab") 
> 
> try(system("convert tmp/1iyg71354448099.ps tmp/1iyg71354448099.png",intern=TRUE))
character(0)
> try(system("convert tmp/2uavd1354448099.ps tmp/2uavd1354448099.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.253   0.365   1.606