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Type 'q()' to quit R. > x <- c(71.42,71.47,71.55,71.6,71.62,71.64,71.65,71.65,71.65,71.7,71.73,71.83,71.83,71.87,71.91,71.99,72.1,72.12,72.12,72.12,72.25,72.59,72.72,72.76,72.76,72.91,73,73.11,73.11,73.16,73.16,73.33,73.51,73.65,73.65,73.65,73.65,73.66,73.71,73.73,73.77,73.77,73.78,73.85,73.88,74.3,74.53,74.53,74.53,74.53,74.65,74.65,74.65,74.71,74.71,74.78,74.85,74.9,74.96,74.96,74.96,74.98,75.19,75.22,75.33,75.33,75.37,75.44,75.54,75.58,75.59,75.61) > 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] 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,] 71.42 71.83 72.76 73.65 74.53 74.96 [2,] 71.47 71.87 72.91 73.66 74.53 74.98 [3,] 71.55 71.91 73.00 73.71 74.65 75.19 [4,] 71.60 71.99 73.11 73.73 74.65 75.22 [5,] 71.62 72.10 73.11 73.77 74.65 75.33 [6,] 71.64 72.12 73.16 73.77 74.71 75.33 [7,] 71.65 72.12 73.16 73.78 74.71 75.37 [8,] 71.65 72.12 73.33 73.85 74.78 75.44 [9,] 71.65 72.25 73.51 73.88 74.85 75.54 [10,] 71.70 72.59 73.65 74.30 74.90 75.58 [11,] 71.73 72.72 73.65 74.53 74.96 75.59 [12,] 71.83 72.76 73.65 74.53 74.96 75.61 > 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] 71.62583 72.19833 73.25000 73.93000 74.74000 75.34500 > arr.sd [1] 0.1099966 0.3221754 0.3056439 0.3274141 0.1508762 0.2245602 > arr.range [1] 0.41 0.93 0.89 0.88 0.43 0.65 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 0.106792 0.001813 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -15.966 3.365 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 1.77954 -0.01471 > postscript(file="/var/yougetitorg/rcomp/tmp/171ux1304971583.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/2d9e51304971583.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/32taw1304971583.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/4y5fi1304971584.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/5tz3r1304971584.tab") > > try(system("convert tmp/171ux1304971583.ps tmp/171ux1304971583.png",intern=TRUE)) character(0) > try(system("convert tmp/2d9e51304971583.ps tmp/2d9e51304971583.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.580 0.290 0.802