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Type 'q()' to quit R. > x <- c(102.42,102.46,102.76,102.4,102.47,102.27,102.17,101.84,102.13,103.34,103.43,103.59,104.21,105.42,105.95,106.28,106.49,106.49,106.49,107.38,108.69,108.76,108.84,108.67,108.79,109.96,110.86,111,111.84,112.21,112.4,113.76,114.85,115.23,115.39,115.29,115.53,116.26,116.85,117.37,118.03,118.49,119.32,119.4,122.26,122.91,123.78,123.99,124.7,125.89,127.57,128.97,130.65,130.73,130.95,131.36,132.85,133.08,133.13,133.27,133.9,134.85,135.49,136.21,136.31,136.22,136.22,135.51,137.3,138.42,138.92,138.67) > 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] 72 > (np <- floor(n / par1)) [1] 6 > 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] [1,] 102.42 104.21 108.79 115.53 124.70 133.90 [2,] 102.46 105.42 109.96 116.26 125.89 134.85 [3,] 102.76 105.95 110.86 116.85 127.57 135.49 [4,] 102.40 106.28 111.00 117.37 128.97 136.21 [5,] 102.47 106.49 111.84 118.03 130.65 136.31 [6,] 102.27 106.49 112.21 118.49 130.73 136.22 [7,] 102.17 106.49 112.40 119.32 130.95 136.22 [8,] 101.84 107.38 113.76 119.40 131.36 135.51 [9,] 102.13 108.69 114.85 122.26 132.85 137.30 [10,] 103.34 108.76 115.23 122.91 133.08 138.42 [11,] 103.43 108.84 115.39 123.78 133.13 138.92 [12,] 103.59 108.67 115.29 123.99 133.27 138.67 > 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] 102.6067 106.9725 112.6317 119.5158 130.2625 136.5017 > arr.sd [1] 0.5594532 1.5059043 2.2598505 2.9963992 2.9065322 1.5565512 > arr.range [1] 1.75 4.63 6.60 8.46 8.57 5.02 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean -2.14027 0.03476 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -14.875 3.235 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean -6.8087 0.1071 > postscript(file="/var/fisher/rcomp/tmp/1quuw1367153039.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/2h4my1367153039.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/3liud1367153039.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/4sg5o1367153039.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/5vo4s1367153040.tab") > > try(system("convert tmp/1quuw1367153039.ps tmp/1quuw1367153039.png",intern=TRUE)) character(0) > try(system("convert tmp/2h4my1367153039.ps tmp/2h4my1367153039.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.204 0.243 1.431