R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(25.7300 + ,26.2400 + ,25.5150 + ,26.4400 + ,27.0800 + ,26.8000 + ,26.2850 + ,25.4600 + ,24.6500 + ,23.7050 + ,21.6500 + ,20.7500 + ,23.6300 + ,21.8650 + ,22.1050 + ,22.8900 + ,22.1000 + ,23.2300 + ,23.0150 + ,22.6900 + ,21.5300 + ,20.8050 + ,20.5050 + ,22.6250 + ,21.9250 + ,22.3300 + ,22.0300 + ,22.7250 + ,23.0150 + ,22.3650 + ,22.2450 + ,23.1250 + ,22.9500 + ,22.2200 + ,21.9150 + ,23.1400 + ,22.5100 + ,21.9850 + ,21.7450 + ,21.1350 + ,20.5350 + ,22.0000 + ,22.6150 + ,22.5350 + ,22.6900 + ,23.4350 + ,22.1650 + ,21.9650 + ,23.0700 + ,22.3750 + ,22.9900 + ,22.9900 + ,22.4150 + ,22.6550 + ,22.1100 + ,22.2850 + ,22.4850 + ,23.2300 + ,22.7400 + ,22.5900 + ,22.2150 + ,22.3300 + ,21.9850 + ,22.0500 + ,22.2900 + ,22.2050 + ,22.4650 + ,22.6350 + ,22.9500 + ,22.7200 + ,22.3550 + ,22.4650 + ,22.6200 + ,22.3000 + ,21.8650 + ,22.0100 + ,21.3100 + ,21.6800 + ,21.9200 + ,21.4400 + ,21.6450 + ,21.5600 + ,21.1000 + ,21.5150 + ,21.6650 + ,21.9300 + ,21.7750 + ,21.3600 + 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,37.7700 + ,37.8700 + ,38.0700 + ,38.2000 + ,38.1200 + ,38.3100 + ,38.3500 + ,38.4700 + ,38.5500 + ,38.6500 + ,38.8000 + ,38.4700 + ,37.9700 + ,38.1700 + ,38.2700 + ,38.4000 + ,38.1400 + ,38.4500 + ,38.4400 + ,38.4000 + ,38.6200 + ,38.6100 + ,38.8200 + ,38.9500 + ,38.6600 + ,38.4100 + ,38.3300 + ,38.0700 + ,38.2200 + ,38.6200 + ,38.9000 + ,38.9900 + ,38.8900 + ,38.6300 + ,38.7300 + ,40.1800 + ,40.5900 + ,40.7200 + ,40.7500 + ,40.7100 + ,40.7300 + ,40.7000 + ,41.0100 + ,41.0300 + ,40.5700 + ,40.7900 + ,40.7800 + ,40.9500 + ,40.7600 + ,40.4900 + ,40.9100 + ,40.7300 + ,40.8700 + ,40.8200 + ,41.1100 + ,40.8900 + ,40.5200 + ,40.8900 + ,40.7100 + ,40.5800 + ,40.8700 + ,40.5800 + ,40.5800 + ,40.7700 + ,40.5800 + ,40.6600 + ,40.9100 + ,40.8600 + ,40.8800 + ,40.7900 + ,40.8900 + ,40.9900 + ,40.9100 + ,41.0700 + ,40.8600 + ,40.4200 + ,40.3700 + ,40.6600 + ,40.9200 + ,41.5600 + ,41.7900 + ,41.6900 + ,41.7300 + ,41.8500 + ,41.9600 + ,42.0300 + ,42.1900 + ,42.3600 + ,42.2900 + ,42.2900 + ,42.2300 + ,42.1400 + ,41.9400 + ,41.9500 + ,42.2600 + ,41.9700 + ,42.3800 + ,42.1000 + ,42.1200 + ,42.0200 + ,42.4300 + ,42.4000 + ,41.1900 + ,40.8100 + ,40.9700 + ,41.0000 + ,40.6800 + ,40.3500 + ,39.6200 + ,39.2900 + ,39.2900 + ,39.4000 + ,39.1800 + ,39.9200 + ,39.3500 + ,39.4500 + ,39.5700 + ,39.6800 + ,39.9400 + ,40.1800 + ,40.8800 + ,41.3500 + ,41.2600 + ,41.2500 + ,41.4100 + ,41.1200 + ,41.4100 + ,41.6000 + ,41.6000 + ,41.6300 + ,41.7200 + ,41.6400 + ,41.7800 + ,41.8700 + ,41.8400 + ,41.7800 + ,41.9400 + ,42.1700 + ,41.9500 + ,41.4600 + ,41.5000 + ,41.6400 + ,41.6100 + ,41.7900 + ,42.0500 + ,42.2200 + ,41.8900 + ,42.2700 + ,41.7800) > 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] 1511 > (np <- floor(n / par1)) [1] 125 > 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] [1,] 25.730 23.630 21.925 22.510 23.070 22.215 22.620 21.665 22.195 19.415 [2,] 26.240 21.865 22.330 21.985 22.375 22.330 22.300 21.930 21.925 19.865 [3,] 25.515 22.105 22.030 21.745 22.990 21.985 21.865 21.775 21.300 18.925 [4,] 26.440 22.890 22.725 21.135 22.990 22.050 22.010 21.360 21.340 19.550 [5,] 27.080 22.100 23.015 20.535 22.415 22.290 21.310 21.120 21.650 19.375 [6,] 26.800 23.230 22.365 22.000 22.655 22.205 21.680 21.660 21.420 19.580 [7,] 26.285 23.015 22.245 22.615 22.110 22.465 21.920 21.200 21.045 19.785 [8,] 25.460 22.690 23.125 22.535 22.285 22.635 21.440 21.345 21.530 20.420 [9,] 24.650 21.530 22.950 22.690 22.485 22.950 21.645 21.775 21.185 20.610 [10,] 23.705 20.805 22.220 23.435 23.230 22.720 21.560 21.155 20.535 20.635 [11,] 21.650 20.505 21.915 22.165 22.740 22.355 21.100 20.335 20.425 20.725 [12,] 20.750 22.625 23.140 21.965 22.590 22.465 21.515 20.635 19.845 20.825 [,11] [,12] [,13] [,14] [,15] [,16] [,17] [,18] [,19] [,20] [1,] 21.090 22.495 21.460 21.375 23.450 24.055 24.590 25.515 24.585 24.955 [2,] 21.320 22.310 21.395 22.200 24.580 23.915 24.440 25.175 24.695 24.530 [3,] 22.070 22.495 21.120 21.815 24.510 23.965 24.500 24.565 24.670 24.370 [4,] 22.010 22.495 21.140 22.450 24.830 24.775 24.320 24.590 24.720 24.590 [5,] 22.375 22.365 21.355 22.480 24.545 24.405 24.255 24.680 24.520 24.720 [6,] 22.425 22.105 21.525 23.035 24.880 23.995 24.355 24.760 24.230 24.530 [7,] 22.550 22.515 21.235 23.320 24.720 23.900 24.155 24.695 24.060 24.385 [8,] 21.960 22.550 21.585 23.500 24.690 23.750 24.525 24.640 24.235 24.265 [9,] 21.975 22.510 21.570 23.340 24.545 23.925 24.645 24.835 24.350 24.900 [10,] 22.430 22.165 21.470 23.650 24.205 24.065 25.265 24.920 24.305 24.790 [11,] 22.670 21.545 21.400 23.605 24.290 24.145 25.395 24.800 24.680 24.975 [12,] 22.485 21.465 21.460 23.315 24.445 23.995 25.160 24.750 24.710 25.205 [,21] [,22] [,23] [,24] [,25] [,26] [,27] [,28] [,29] [,30] [1,] 25.315 26.485 27.400 27.045 28.370 28.635 28.590 27.575 27.255 26.990 [2,] 25.290 26.565 27.420 26.655 28.435 28.745 28.635 28.135 27.090 27.410 [3,] 25.755 26.655 27.460 26.860 28.290 28.935 28.645 28.440 27.125 27.665 [4,] 26.080 26.850 27.505 26.560 28.440 28.840 28.720 28.515 27.190 27.955 [5,] 26.225 26.560 27.395 26.745 28.740 28.915 28.720 28.565 27.525 27.860 [6,] 26.335 26.935 27.035 27.200 29.120 29.290 28.870 28.145 27.435 27.690 [7,] 26.710 26.900 27.035 27.245 29.095 29.555 28.840 28.210 26.715 27.415 [8,] 26.880 27.160 27.075 27.740 29.055 29.520 28.500 27.750 26.545 27.580 [9,] 26.500 27.405 26.620 27.905 28.590 29.530 28.520 27.185 26.325 26.560 [10,] 26.425 27.295 26.615 28.065 28.600 29.210 28.175 27.120 27.005 26.360 [11,] 26.290 27.325 26.730 27.970 29.040 28.490 28.165 27.175 26.895 26.635 [12,] 26.170 27.395 26.755 28.230 28.980 28.460 28.095 27.070 27.110 26.650 [,31] [,32] [,33] [,34] [,35] [,36] [,37] [,38] [,39] [,40] [1,] 26.965 27.375 26.910 26.940 26.805 25.615 26.090 25.015 26.620 28.185 [2,] 27.235 27.270 26.880 26.865 27.010 25.700 26.200 25.025 27.040 28.375 [3,] 27.350 27.650 27.295 26.435 26.745 25.645 26.180 25.215 27.130 28.555 [4,] 27.230 27.310 27.380 26.680 26.670 26.205 26.155 25.735 27.375 28.490 [5,] 27.090 27.400 27.510 26.870 26.705 26.375 26.240 26.210 27.470 28.020 [6,] 27.100 27.325 27.475 26.725 26.550 25.635 26.230 26.200 27.525 27.845 [7,] 26.800 27.385 27.130 26.880 26.560 25.400 26.120 26.225 27.440 27.865 [8,] 26.675 27.435 27.485 26.590 25.770 25.780 25.900 26.360 27.495 27.940 [9,] 26.825 27.500 27.660 26.830 25.795 25.675 25.130 26.410 27.555 28.060 [10,] 26.850 27.650 27.235 26.150 25.730 26.225 25.540 26.425 28.205 27.920 [11,] 26.920 27.430 27.095 26.335 25.310 25.825 25.165 26.185 28.190 27.640 [12,] 26.975 27.145 27.155 27.020 25.040 25.755 25.060 26.135 28.275 27.650 [,41] [,42] [,43] [,44] [,45] [,46] [,47] [,48] [,49] [,50] [1,] 27.8000 29.145 29.300 29.970 30.660 31.040 32.250 32.745 31.460 31.480 [2,] 27.8300 29.260 29.550 29.955 30.855 31.260 31.995 32.795 31.530 31.350 [3,] 27.6950 29.015 29.380 29.970 30.930 31.920 32.070 32.790 31.345 31.105 [4,] 27.5800 28.795 29.260 30.000 30.975 32.160 32.195 32.535 31.520 31.425 [5,] 28.0800 28.710 29.560 30.170 31.225 32.135 32.415 32.680 31.700 31.525 [6,] 27.7750 28.755 29.440 30.575 31.290 31.810 32.325 32.725 31.565 31.430 [7,] 27.9413 28.780 29.830 30.735 31.230 32.305 32.425 32.750 31.740 31.285 [8,] 28.6550 28.985 29.900 30.805 31.320 32.055 31.950 32.885 31.710 31.280 [9,] 28.6900 29.055 29.720 30.505 31.275 31.695 32.370 32.610 31.465 31.260 [10,] 28.7800 29.115 29.705 30.620 31.400 31.585 32.670 31.935 31.385 31.435 [11,] 28.8150 28.935 29.780 30.595 31.460 32.350 32.850 31.745 31.625 31.575 [12,] 28.9150 29.310 29.800 30.605 31.480 32.450 32.655 31.515 31.480 31.770 [,51] [,52] [,53] [,54] [,55] [,56] [,57] [,58] [,59] [,60] [1,] 31.785 32.215 31.350 33.735 33.940 33.670 33.350 32.805 33.645 34.660 [2,] 31.570 32.785 31.785 33.815 33.870 33.660 33.255 32.485 34.045 34.435 [3,] 31.595 32.605 31.755 33.720 33.465 34.155 33.405 32.745 34.150 34.670 [4,] 31.700 32.610 32.135 33.635 33.500 34.090 33.380 32.810 34.265 34.865 [5,] 32.275 32.820 32.345 33.700 33.705 33.915 33.020 32.915 34.375 34.630 [6,] 32.275 32.810 32.610 33.450 33.730 33.995 32.765 33.130 34.340 34.595 [7,] 31.880 32.645 32.515 33.640 33.860 34.150 32.710 33.200 34.050 34.345 [8,] 31.955 32.405 32.860 34.155 33.940 34.230 32.600 32.490 33.925 34.405 [9,] 31.940 31.970 33.020 34.005 33.730 34.150 32.700 32.465 34.030 34.005 [10,] 32.155 31.515 33.170 33.655 33.350 33.745 32.945 32.605 33.835 33.865 [11,] 31.960 30.800 33.610 33.545 33.450 33.735 32.695 33.015 33.765 33.535 [12,] 32.455 31.140 33.830 33.925 33.475 33.455 32.560 33.355 33.560 34.200 [,61] [,62] [,63] [,64] [,65] [,66] [,67] [,68] [,69] [,70] [1,] 32.910 33.635 35.585 34.380 33.740 34.465 34.000 33.190 34.200 34.465 [2,] 33.385 34.530 34.685 34.785 33.700 34.160 33.505 33.155 34.785 34.670 [3,] 32.560 34.840 34.690 34.030 33.925 33.520 33.310 33.340 34.595 34.030 [4,] 33.340 33.985 34.550 34.525 33.500 33.885 33.695 33.390 34.970 33.785 [5,] 31.980 34.250 34.885 33.780 33.370 34.325 32.975 33.365 34.940 33.495 [6,] 33.235 34.865 35.510 32.710 33.515 33.890 32.985 33.785 34.755 33.675 [7,] 33.570 34.930 35.615 32.615 33.560 34.105 32.435 33.445 35.080 33.795 [8,] 34.100 35.225 35.245 32.765 34.095 34.325 32.370 33.240 34.985 33.725 [9,] 34.085 35.225 35.325 32.695 33.935 33.515 32.585 33.130 35.070 34.045 [10,] 34.640 34.870 34.640 32.950 33.475 33.695 33.095 33.445 34.850 34.265 [11,] 33.880 34.715 33.910 33.450 33.735 34.060 33.615 33.720 34.685 33.950 [12,] 33.550 35.400 33.710 33.400 34.285 33.895 33.415 33.685 34.465 34.215 [,71] [,72] [,73] [,74] [,75] [,76] [,77] [,78] [,79] [,80] [1,] 34.005 34.220 34.800 35.200 36.730 36.845 38.390 37.615 37.345 39.095 [2,] 33.720 34.450 34.590 35.295 36.735 37.060 38.705 37.730 37.870 39.460 [3,] 33.730 34.200 34.615 35.560 36.435 37.465 38.735 37.530 38.045 39.580 [4,] 33.765 34.430 34.380 35.710 36.010 37.855 38.435 37.365 37.990 39.225 [5,] 33.925 34.525 34.395 35.745 36.060 38.315 38.285 36.545 37.870 39.075 [6,] 33.915 34.410 34.635 35.950 36.110 38.160 38.165 36.820 37.780 38.990 [7,] 34.040 34.625 34.755 35.935 35.970 38.465 37.560 36.620 37.335 38.990 [8,] 34.015 34.590 35.075 36.330 36.220 38.580 37.025 37.305 37.470 38.730 [9,] 34.275 34.500 35.125 36.905 36.975 38.720 37.255 37.140 37.385 38.320 [10,] 34.165 34.415 35.110 37.005 37.085 38.500 37.095 37.620 37.540 38.640 [11,] 33.985 34.425 35.165 37.070 36.910 38.640 37.275 37.330 38.170 38.240 [12,] 33.970 34.930 35.080 36.880 37.065 38.570 37.780 37.600 38.285 38.845 [,81] [,82] [,83] [,84] [,85] [,86] [,87] [,88] [,89] [,90] [,91] [,92] [1,] 38.720 40.415 39.26 37.90 37.68 38.11 37.18 36.56 37.31 36.89 36.96 37.42 [2,] 38.775 40.320 38.77 37.66 37.76 38.23 37.33 37.24 37.38 36.73 36.91 37.50 [3,] 38.515 39.885 38.11 37.77 38.31 38.13 37.08 37.25 37.71 36.42 36.99 37.24 [4,] 38.440 39.780 38.47 37.55 37.93 37.90 36.77 37.39 37.64 36.42 37.32 37.54 [5,] 38.330 39.620 38.17 38.35 38.38 37.74 37.42 37.93 37.86 35.97 37.32 37.35 [6,] 38.510 39.395 38.00 38.12 38.34 37.84 36.72 37.36 37.64 36.25 37.51 38.14 [7,] 39.425 39.300 37.46 38.35 38.24 37.40 36.36 37.42 37.60 37.60 37.70 38.31 [8,] 40.005 39.380 37.14 38.62 38.33 37.23 36.29 37.56 37.66 37.60 37.13 38.91 [9,] 40.560 39.350 37.40 38.52 38.58 36.61 36.08 37.97 37.50 37.66 37.13 38.77 [10,] 40.400 39.550 37.28 38.64 38.58 36.88 36.16 37.92 37.28 37.30 37.11 38.61 [11,] 40.505 39.530 37.51 38.03 38.56 37.09 36.01 37.38 36.78 37.04 37.05 37.56 [12,] 39.875 39.470 38.15 38.12 38.09 37.04 36.43 37.15 37.05 37.03 37.01 37.21 [,93] [,94] [,95] [,96] [,97] [,98] [,99] [,100] [,101] [,102] [,103] [1,] 36.84 38.68 40.07 40.71 42.35 42.19 40.77 40.68 40.49 40.84 40.15 [2,] 37.42 39.02 40.04 41.07 42.10 42.52 39.99 40.93 40.52 40.96 40.20 [3,] 37.67 39.12 40.12 41.18 42.24 42.92 40.81 40.41 40.54 40.86 40.16 [4,] 37.73 39.22 40.69 41.08 42.33 43.09 41.42 39.13 40.83 40.84 39.83 [5,] 37.71 39.31 40.22 40.09 42.21 42.97 40.65 39.76 40.68 40.64 39.65 [6,] 38.52 38.96 40.44 42.37 41.96 42.38 40.79 39.53 41.03 40.28 39.53 [7,] 37.72 38.59 40.45 42.55 42.24 42.34 41.41 39.72 41.03 40.32 39.09 [8,] 38.11 39.02 40.72 42.10 42.08 42.25 41.18 40.33 41.01 40.08 39.05 [9,] 38.45 38.83 40.17 42.66 42.70 41.93 40.79 40.26 40.23 40.57 38.78 [10,] 38.72 38.76 40.54 42.72 42.46 42.24 40.39 40.11 40.84 40.22 38.65 [11,] 38.70 39.33 40.08 42.70 42.12 42.55 40.41 40.46 40.81 40.29 38.28 [12,] 38.82 39.87 40.86 42.15 42.15 41.40 40.34 40.37 41.09 40.37 38.31 [,104] [,105] [,106] [,107] [,108] [,109] [,110] [,111] [,112] [,113] [1,] 38.52 38.78 38.40 37.66 39.57 40.17 39.83 40.16 40.13 37.90 [2,] 38.12 38.44 37.88 38.00 39.61 40.14 40.46 40.19 39.53 38.17 [3,] 38.15 38.69 37.95 38.55 39.51 40.06 40.40 40.49 39.69 37.82 [4,] 38.35 38.86 37.43 38.78 39.50 40.43 39.85 40.66 39.76 37.20 [5,] 38.10 38.79 37.16 38.82 40.05 40.43 40.13 41.09 39.71 37.48 [6,] 38.18 39.59 37.20 39.09 39.83 40.37 39.21 41.31 39.28 37.61 [7,] 37.90 39.31 37.05 39.09 40.05 40.25 39.23 40.66 39.92 38.03 [8,] 38.54 39.40 37.28 38.80 39.87 40.19 39.27 40.46 39.90 37.95 [9,] 38.24 38.63 37.08 39.03 39.88 40.19 39.10 40.27 39.24 38.57 [10,] 38.35 38.53 37.78 39.61 40.12 40.08 40.02 40.39 38.84 38.64 [11,] 38.52 38.33 37.77 39.80 40.21 40.35 39.86 39.94 38.73 38.51 [12,] 38.63 38.74 37.91 39.62 40.22 40.37 40.04 39.73 38.87 38.65 [,114] [,115] [,116] [,117] [,118] [,119] [,120] [,121] [,122] [,123] [1,] 38.93 38.35 38.44 38.90 41.01 41.11 40.91 40.92 42.23 41.19 [2,] 37.47 38.47 38.40 38.99 41.03 40.89 40.86 41.56 42.14 40.81 [3,] 37.10 38.55 38.62 38.89 40.57 40.52 40.88 41.79 41.94 40.97 [4,] 37.30 38.65 38.61 38.63 40.79 40.89 40.79 41.69 41.95 41.00 [5,] 37.18 38.80 38.82 38.73 40.78 40.71 40.89 41.73 42.26 40.68 [6,] 37.50 38.47 38.95 40.18 40.95 40.58 40.99 41.85 41.97 40.35 [7,] 37.77 37.97 38.66 40.59 40.76 40.87 40.91 41.96 42.38 39.62 [8,] 37.87 38.17 38.41 40.72 40.49 40.58 41.07 42.03 42.10 39.29 [9,] 38.07 38.27 38.33 40.75 40.91 40.58 40.86 42.19 42.12 39.29 [10,] 38.20 38.40 38.07 40.71 40.73 40.77 40.42 42.36 42.02 39.40 [11,] 38.12 38.14 38.22 40.73 40.87 40.58 40.37 42.29 42.43 39.18 [12,] 38.31 38.45 38.62 40.70 40.82 40.66 40.66 42.29 42.40 39.92 [,124] [,125] [1,] 39.35 41.41 [2,] 39.45 41.60 [3,] 39.57 41.60 [4,] 39.68 41.63 [5,] 39.94 41.72 [6,] 40.18 41.64 [7,] 40.88 41.78 [8,] 41.35 41.87 [9,] 41.26 41.84 [10,] 41.25 41.78 [11,] 41.41 41.94 [12,] 41.12 42.17 > 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] 25.02542 22.24917 22.49875 22.10958 22.66125 22.38875 21.74708 21.32958 [9] 21.19958 19.97583 22.11333 22.25125 21.39292 22.84042 24.47417 24.07417 [17] 24.63375 24.82708 24.48000 24.68458 26.16458 26.96083 27.08708 27.35167 [25] 28.72958 29.01042 28.53958 27.82375 27.01792 27.23083 27.00125 27.40625 [33] 27.26750 26.69333 26.22417 25.81958 25.83417 25.92833 27.52667 28.04542 [41] 28.21302 28.98833 29.60208 30.37542 31.17500 31.89708 32.34750 32.47583 [49] 31.54375 31.41000 31.96208 32.19333 32.58208 33.74833 33.66792 33.91250 [57] 32.94875 32.83500 33.99875 34.35083 33.43625 34.70583 34.86250 33.50708 [65] 33.73625 33.98667 33.16542 33.40750 34.78167 34.00958 33.95917 34.47667 [73] 34.81042 36.13208 36.52542 38.09792 37.89208 37.26833 37.75708 38.93250 [81] 39.33833 39.66625 37.97667 38.13583 38.23167 37.51667 36.65250 37.42750 [89] 37.45083 36.90917 37.17833 37.88000 38.03417 39.05917 40.36667 41.78167 [97] 42.24500 42.39833 40.74583 40.14083 40.75833 40.52250 39.30667 38.30000 [105] 38.84083 37.57417 38.90417 39.86833 40.25250 39.78333 40.44583 39.46667 [113] 38.04417 37.81833 38.39083 38.51250 39.87667 40.80917 40.72833 40.80083 [121] 41.88833 42.16167 40.14167 40.45333 41.74833 > arr.sd [1] 2.0206957 0.9552268 0.4673772 0.7570231 0.3486475 0.2784790 0.4251709 [8] 0.4808111 0.6611404 0.6383780 0.4895607 0.3773359 0.1553655 0.7573352 [15] 0.3786329 0.2719528 0.4126418 0.2718828 0.2319679 0.2873979 0.4954037 [22] 0.3468942 0.3439969 0.6007508 0.3134084 0.4014998 0.2628728 0.5812140 [29] 0.3506971 0.5613532 0.2035383 0.1455885 0.2443870 0.2669638 0.6569691 [36] 0.2930751 0.4727811 0.5403001 0.4965258 0.3025006 0.5107227 0.1996740 [43] 0.2183352 0.3330060 0.2597726 0.4384604 0.2787431 0.4661927 0.1280470 [50] 0.1726399 0.2815255 0.6925786 0.7584298 0.1986012 0.2110629 0.2526541 [57] 0.3224630 0.2970155 0.2632068 0.3876962 0.7233763 0.5262683 0.6261117 [64] 0.7830896 0.2770799 0.3105079 0.5182552 0.2221333 0.2632345 0.3451512 [71] 0.1665674 0.1905653 0.2916525 0.6831992 0.4374381 0.6459787 0.6352933 [78] 0.4057503 0.3341642 0.4073557 0.8846220 0.3706513 0.6472365 0.3691134 [85] 0.3071817 0.5452995 0.5024508 0.3944415 0.3073777 0.5665118 0.2396146 [92] 0.6289819 0.6163007 0.3492969 0.2881077 0.9065302 0.1970002 0.4695614 [99] 0.4345627 0.5141888 0.2692020 0.2996096 0.7101771 0.2218107 0.3926937 [106] 0.4314976 0.6340556 0.2681870 0.1328790 0.4727162 0.4477308 0.4680780 [113] 0.4796677 0.5391379 0.2298402 0.2492580 0.9418486 0.1628975 0.1799411 [120] 0.2140288 0.4025336 0.1777554 0.7715726 0.8296586 0.1965074 > arr.range [1] 6.330 3.125 1.225 2.900 1.120 0.965 1.520 1.595 2.350 1.900 1.580 1.085 [13] 0.465 2.275 1.430 1.025 1.240 0.950 0.660 0.940 1.590 0.920 0.890 1.670 [25] 0.830 1.095 0.775 1.495 1.200 1.595 0.675 0.505 0.780 0.870 1.970 0.975 [37] 1.180 1.410 1.655 0.915 1.335 0.600 0.640 0.850 0.820 1.410 0.900 1.370 [49] 0.395 0.665 0.885 2.020 2.480 0.705 0.590 0.775 0.845 0.890 0.815 1.330 [61] 2.660 1.765 1.905 2.170 0.915 0.950 1.630 0.655 0.880 1.175 0.555 0.730 [73] 0.785 1.870 1.115 1.875 1.710 1.185 0.950 1.340 2.230 1.115 2.120 1.090 [85] 0.900 1.620 1.410 1.410 1.080 1.690 0.790 1.700 1.980 1.280 0.820 2.630 [97] 0.740 1.690 1.430 1.800 0.860 0.880 1.920 0.730 1.260 1.350 2.140 0.720 [109] 0.370 1.360 1.580 1.400 1.450 1.830 0.830 0.880 2.120 0.540 0.590 0.700 [121] 1.440 0.490 2.010 2.060 0.760 > (lm1 <- lm(arr.sd~arr.mean)) Call: lm(formula = arr.sd ~ arr.mean) Coefficients: (Intercept) arr.mean 0.514388 -0.002578 > (lnlm1 <- lm(log(arr.sd)~log(arr.mean))) Call: lm(formula = log(arr.sd) ~ log(arr.mean)) Coefficients: (Intercept) log(arr.mean) -0.3813 -0.1694 > (lm2 <- lm(arr.range~arr.mean)) Call: lm(formula = arr.range ~ arr.mean) Coefficients: (Intercept) arr.mean 1.69338 -0.01142 > postscript(file="/var/wessaorg/rcomp/tmp/1gn6m1418394628.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/2qo1n1418394628.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/3rqef1418394628.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/4nv161418394628.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/5dle11418394628.tab") > > try(system("convert tmp/1gn6m1418394628.ps tmp/1gn6m1418394628.png",intern=TRUE)) character(0) > try(system("convert tmp/2qo1n1418394628.ps tmp/2qo1n1418394628.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.879 0.172 2.057