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Type 'q()' to quit R. > x <- c(3004,3080,3017,3114,3057,3032,3127,3050,2910,2671,2638,2672,2654,2568,2467,2419,2363,2291,2560,2527,2370,2310,2231,2367,2346,2286,2249,2226,2108,2131,2387,2358,2284,2312,2293,2576,2665,2749,2926,2886,2893,2944,3060,3045,2894,2955,2954,3243,3120,3074,3034,2981,2876,2835,2978,2881,2768,2722,2630,2753,2771,2652,2584,2501,2449,2445,2620,2579,2460,2434,2392,1037,1212,1232,1174,1158,1140,1118,1212,1207,1186,608,627,626,649,619,612,643,623,649,699,693,659,669,668,693) > 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] 96 > (np <- floor(n / par1)) [1] 8 > 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] [,8] [1,] 3004 2654 2346 2665 3120 2771 1212 649 [2,] 3080 2568 2286 2749 3074 2652 1232 619 [3,] 3017 2467 2249 2926 3034 2584 1174 612 [4,] 3114 2419 2226 2886 2981 2501 1158 643 [5,] 3057 2363 2108 2893 2876 2449 1140 623 [6,] 3032 2291 2131 2944 2835 2445 1118 649 [7,] 3127 2560 2387 3060 2978 2620 1212 699 [8,] 3050 2527 2358 3045 2881 2579 1207 693 [9,] 2910 2370 2284 2894 2768 2460 1186 659 [10,] 2671 2310 2312 2955 2722 2434 608 669 [11,] 2638 2231 2293 2954 2630 2392 627 668 [12,] 2672 2367 2576 3243 2753 1037 626 693 > 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] 2947.6667 2427.2500 2296.3333 2934.5000 2887.6667 2410.3333 1041.6667 [8] 656.3333 > arr.sd [1] 182.08556 128.73802 121.80859 146.73755 152.62273 446.33626 256.15277 [8] 29.49371 > arr.range [1] 489 423 468 578 490 1734 624 87 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 125.48525 0.02614 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -0.8299 0.7669 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 295.9768 0.1435 > postscript(file="/var/wessaorg/rcomp/tmp/1x3fq1416514983.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/2usyh1416514983.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/3hlgj1416514983.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/4yn6q1416514983.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/5akj71416514983.tab") > > try(system("convert tmp/1x3fq1416514983.ps tmp/1x3fq1416514983.png",intern=TRUE)) character(0) > try(system("convert tmp/2usyh1416514983.ps tmp/2usyh1416514983.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.989 0.142 1.140