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Type 'q()' to quit R. > x <- c(0.43,0.45,0.44,0.44,0.44,0.48,0.47,0.47,0.47,0.49,0.49,0.46,0.45,0.44,0.42,0.43,0.43,0.47,0.47,0.47,0.47,0.48,0.48,0.48,0.49,0.49,0.47,0.5,0.51,0.5,0.49,0.5,0.51,0.51,0.5,0.53,0.5,0.49,0.46,0.46,0.47,0.49,0.5,0.5,0.51,0.5,0.52,0.5,0.48,0.47,0.43,0.42,0.45,0.5,0.52,0.52,0.51,0.52,0.52,0.51,0.51,0.51,0.48,0.49,0.47,0.51,0.5,0.51,0.51,0.52,0.51,0.52,0.48,0.49,0.47,0.44,0.44,0.47,0.51,0.51,0.52,0.52,0.52,0.52) > 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,] 0.43 0.45 0.49 0.50 0.48 0.51 0.48 [2,] 0.45 0.44 0.49 0.49 0.47 0.51 0.49 [3,] 0.44 0.42 0.47 0.46 0.43 0.48 0.47 [4,] 0.44 0.43 0.50 0.46 0.42 0.49 0.44 [5,] 0.44 0.43 0.51 0.47 0.45 0.47 0.44 [6,] 0.48 0.47 0.50 0.49 0.50 0.51 0.47 [7,] 0.47 0.47 0.49 0.50 0.52 0.50 0.51 [8,] 0.47 0.47 0.50 0.50 0.52 0.51 0.51 [9,] 0.47 0.47 0.51 0.51 0.51 0.51 0.52 [10,] 0.49 0.48 0.51 0.50 0.52 0.52 0.52 [11,] 0.49 0.48 0.50 0.52 0.52 0.51 0.52 [12,] 0.46 0.48 0.53 0.50 0.51 0.52 0.52 > 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] 0.4608333 0.4575000 0.5000000 0.4916667 0.4875000 0.5033333 0.4908333 > arr.sd [1] 0.02065224 0.02220770 0.01477098 0.01898963 0.03695821 0.01556998 0.03058768 > arr.range [1] 0.06 0.06 0.06 0.06 0.10 0.05 0.08 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 0.05439 -0.06516 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -5.324 -2.059 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 0.05319 0.02879 > postscript(file="/var/fisher/rcomp/tmp/1rrrn1387726521.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/2uw6d1387726521.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/3dcj21387726522.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/4hz151387726522.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/5zoml1387726522.tab") > > try(system("convert tmp/1rrrn1387726521.ps tmp/1rrrn1387726521.png",intern=TRUE)) character(0) > try(system("convert tmp/2uw6d1387726521.ps tmp/2uw6d1387726521.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.181 0.653 2.755