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Type 'q()' to quit R. > x <- c(16896.2,16698,19691.6,15930.7,17444.6,17699.4,15189.8,15672.7,17180.8,17664.9,17862.9,16162.3,17463.6,16772.1,19106.9,16721.3,18161.3,18509.9,17802.7,16409.9,17967.7,20286.6,19537.3,18021.9,20194.3,19049.6,20244.7,21473.3,19673.6,21053.2,20159.5,18203.6,21289.5,20432.3,17180.4,15816.8,15076.6,14531.6,15761.3,14345.5,13916.8,15496.8,14285.6,13597.3,16263.1,16773.3,15986.9,16842.6,16014.6,15878.6,18664.9,17690.5,17107.6,19165.7,17203.6,16579,18885.1) > par1 = '12' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > par1 <- as.numeric(par1) > (n <- length(x)) [1] 57 > (np <- floor(n / par1)) [1] 4 > 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] [1,] 16896.2 17463.6 20194.3 15076.6 [2,] 16698.0 16772.1 19049.6 14531.6 [3,] 19691.6 19106.9 20244.7 15761.3 [4,] 15930.7 16721.3 21473.3 14345.5 [5,] 17444.6 18161.3 19673.6 13916.8 [6,] 17699.4 18509.9 21053.2 15496.8 [7,] 15189.8 17802.7 20159.5 14285.6 [8,] 15672.7 16409.9 18203.6 13597.3 [9,] 17180.8 17967.7 21289.5 16263.1 [10,] 17664.9 20286.6 20432.3 16773.3 [11,] 17862.9 19537.3 17180.4 15986.9 [12,] 16162.3 18021.9 15816.8 16842.6 > 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] 17007.83 18063.43 19564.23 15239.78 > arr.sd [1] 1211.155 1169.856 1723.106 1109.893 > arr.range [1] 4501.8 3876.7 5656.5 3245.3 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean -938.5840 0.1283 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -8.016 1.554 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean -4213.5939 0.4885 > postscript(file="/var/yougetitorg/rcomp/tmp/1tntz1293658746.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/yougetitorg/rcomp/tmp/2tntz1293658746.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/yougetitorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/yougetitorg/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/yougetitorg/rcomp/tmp/37gui1293658747.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/yougetitorg/rcomp/tmp/4byto1293658747.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/yougetitorg/rcomp/tmp/5wzrt1293658747.tab") > > try(system("convert tmp/1tntz1293658746.ps tmp/1tntz1293658746.png",intern=TRUE)) character(0) > try(system("convert tmp/2tntz1293658746.ps tmp/2tntz1293658746.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.590 0.320 0.808