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Type 'q()' to quit R. > x <- c(2194,2419,2742,2137,2710,2173,2363,2126,1905,2121,1983,1734,2074,2049,2406,2558,2251,2059,2397,1747,1707,2319,1631,1627,1791,2034,1997,2169,2028,2253,2218,1855,2187,1852,1570,1851,1954,1828,2251,2277,2085,2282,2266,1878,2267,2069,1746,2299,2360,2214,2825,2355,2333,3016,2155,2172,2150,2533,2058,2160,2260,2498,2695,2799,2945,2930,2318,2540,2570,2669,2450,2842,3440,2678,2981,2259,2844,2546,2456,2295,2379,2479,2057,2280,2351,2275,2543,2305,2188,2720,2398,2147,1898,2538,2081,2057,2497,2460,2195,2823,2100,2640,2342,2171,2482) > par1 = '4' > #'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] 105 > (np <- floor(n / par1)) [1] 26 > 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] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 2194 2710 1905 2074 2251 1707 1791 2028 2187 1954 2085 2267 2360 2333 [2,] 2419 2173 2121 2049 2059 2319 2034 2253 1852 1828 2282 2069 2214 3016 [3,] 2742 2363 1983 2406 2397 1631 1997 2218 1570 2251 2266 1746 2825 2155 [4,] 2137 2126 1734 2558 1747 1627 2169 1855 1851 2277 1878 2299 2355 2172 [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25] [,26] [1,] 2150 2260 2945 2570 3440 2844 2379 2351 2188 1898 2497 2100 [2,] 2533 2498 2930 2669 2678 2546 2479 2275 2720 2538 2460 2640 [3,] 2058 2695 2318 2450 2981 2456 2057 2543 2398 2081 2195 2342 [4,] 2160 2799 2540 2842 2259 2295 2280 2305 2147 2057 2823 2171 > 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] 2373.00 2343.00 1935.75 2271.75 2113.50 1821.00 1997.75 2088.50 1865.00 [10] 2077.50 2127.75 2095.25 2438.50 2419.00 2225.25 2563.00 2683.25 2632.75 [19] 2839.50 2535.25 2298.75 2368.50 2363.25 2143.50 2493.75 2313.25 > arr.sd [1] 274.4777 265.2533 161.4505 250.7886 280.8149 334.0339 156.4041 184.4026 [9] 252.3714 221.6642 188.9522 254.0845 266.4063 406.0008 210.2401 237.4546 [17] 307.3146 165.7656 497.8906 230.5376 180.4852 120.4588 262.0145 275.2508 [25] 257.4275 240.3489 > arr.range [1] 605 584 387 509 650 692 378 398 617 449 404 553 611 861 475 [16] 539 627 392 1181 549 422 268 573 640 628 540 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean -1.1069 0.1096 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -0.1724 0.7307 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean -48.8532 0.2659 > postscript(file="/var/www/html/rcomp/tmp/1mfiv1243503636.ps",horizontal=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/www/html/rcomp/tmp/2u9041243503636.ps",horizontal=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/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/www/html/rcomp/tmp/3uzsa1243503636.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/www/html/rcomp/tmp/4jint1243503636.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/www/html/rcomp/tmp/5vx441243503636.tab") > > system("convert tmp/1mfiv1243503636.ps tmp/1mfiv1243503636.png") > system("convert tmp/2u9041243503636.ps tmp/2u9041243503636.png") > > > proc.time() user system elapsed 0.500 0.287 3.083