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Type 'q()' to quit R. > x <- c(3.43,3.43,3.43,3.43,3.43,3.43,3.43,3.43,3.5,3.52,3.53,3.53,3.53,3.53,3.53,3.53,3.53,3.53,3.53,3.53,3.58,3.58,3.59,3.59,3.59,3.59,3.59,3.59,3.59,3.59,3.59,3.61,3.71,3.83,3.83,3.83,3.83,3.83,3.83,3.83,3.83,3.83,3.83,3.83,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.92,3.98,3.98,3.98,3.98,3.98,3.98,3.98,3.98,3.98,3.98,3.98,3.98,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.09,4.21,4.21,4.21,4.21) > 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] 84 > (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,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [2,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [3,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [4,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [5,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [6,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [7,] 3.43 3.53 3.59 3.83 3.92 3.98 4.09 [8,] 3.43 3.53 3.61 3.83 3.92 3.98 4.09 [9,] 3.50 3.58 3.71 3.92 3.98 4.09 4.21 [10,] 3.52 3.58 3.83 3.92 3.98 4.09 4.21 [11,] 3.53 3.59 3.83 3.92 3.98 4.09 4.21 [12,] 3.53 3.59 3.83 3.92 3.98 4.09 4.21 > 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] 3.460000 3.548333 3.661667 3.860000 3.940000 4.016667 4.130000 > arr.sd [1] 0.04492418 0.02724746 0.10701175 0.04431294 0.02954196 0.05416026 0.05908392 > arr.range [1] 0.10 0.06 0.24 0.09 0.06 0.11 0.12 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 0.0501059 0.0005839 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -4.0753 0.7716 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 0.18125 -0.01836 > postscript(file="/var/wessaorg/rcomp/tmp/1uyoa1386245135.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/2vbos1386245135.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/3kshe1386245135.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/4dawn1386245135.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/5w0ay1386245135.tab") > > try(system("convert tmp/1uyoa1386245135.ps tmp/1uyoa1386245135.png",intern=TRUE)) character(0) > try(system("convert tmp/2vbos1386245135.ps tmp/2vbos1386245135.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.104 0.484 2.549