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39 35 35 32 NA 49 51 34 38 37 39 42 38 21 32 49 76 49 49 37 50 31 49 44 42 42 50 26 79 46 61 43 68 50 41 32 32 44 49 32 41 33 12 32 32 46 78 49 41 113 NA 28 33 NA 20 31 33 8 12 83 37 80 52 21 25 41 43 53 NA NA 12 32 33 NA 37 27 29 67 NA NA 18 54 36 67 51 25 27 29 30 30 34 34 71 NA 57 50 NA 21 23 23 30 41 50 53 53 54 54 55 56 56 65 67 68 72 77 78 78 NA NA NA NA NA NA NA NA 113 113 18 2 8 8 29 50 10 72 16 35 21 24 34 44 16 18 18 24 26 28 28 40 41 52 52 53 53 53 53 53 53 54 54 55 57 58 58 59 59 60 61 65 65 68 70 76 NA NA NA NA NA NA NA NA NA 29 59 NA NA NA NA NA NA 10 12 12 14 17 17 17 17 20 22 23 23 23 23 23 23 23 24 24 25 25 25 26 26 27 27 27 27 28 29 30 30 31 32 32 32 32 32 32 33 33 33 34 36 36 37 37 38 38 38 39 40 40 40 41 41 42 43 43 44 44 44 45 45 46 46 47 47 48 48 48 49 51 53 53 53 54 54 55 55 56 57 57 57 58 58 59 59 61 63 63 64 64 65 66 66 67 68 68 72 73 74 75 NA NA NA NA NA NA NA NA 5 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 10 10 10 11 11 11 11 11 11 11 11 11 11 12 12 12 12 12 12 12 13 13 13 13 13 14 15 15 15 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 18 18 18 18 18 19 19 19 20 20 21 21 22 22 22 22 22 23 23 23 23 23 24 24 24 24 24 25 25 25 26 26 26 26 26 27 27 28 29 29 29 29 29 30 30 30 30 31 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 32 33 33 33 33 33 33 34 34 34 35 35 35 35 36 36 36 37 37 37 37 37 37 37 37 37 38 38 38 38 39 40 41 41 42 42 42 44 44 45 46 46 46 47 47 47 48 48 48 48 49 49 49 50 50 50 50 50 50 50 50 50 51 51 51 51 52 53 53 53 54 54 55 55 55 56 56 56 56 56 57 57 58 58 58 59 60 60 60 60 60 60 61 61 62 62 63 63 63 63 64 64 65 65 65 66 67 67 68 68 68 68 68 68 69 69 69 70 70 70 71 72 73 74 74 77 80 80 81 82 83 84 113 113 113 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 8 9 10 11 12 16 17 31 39 42 42 42 43 43 44 48 50 53 67 68 69 69 71 77 113 NA NA NA NA 10 14 14 17 17 22 23 28 29 36 37 40 41 42 43 44 45 45 47 47 47 48 50 10 12 12 17 17 19 20 23 24 24 24 25 25 30 38 39 40 40 40 42 43 43 44 45 46 46 48 48 50 51 51 52 52 52 54 54 55 56 57 60 62 63 63 67 70 71 76 82 NA 16 17 17 17 24 25 27 29 32 32 32 34 37 41 41 45 52 54 55 58 79 NA NA 8 9 11 14 14 14 16 16 17 17 17 17 20 20 23 23 24 24 27 27 27 28 28 29 30 30 30 30 31 33 33 33 34 34 35 36 38 39 40 40 40 41 41 42 42 45 46 46 46 46 46 46 47 48 49 49 50 50 51 51 52 52 54 54 54 56 57 59 60 61 67 70 73 76 77 77 82 NA NA 7 9 11 12 12 13 13 13 14 15 15 16 17 18 20 20 21 21 22 22 23 23 24 25 26 26 28 28 28 28 29 29 31 31 31 32 32 32 32 33 33 34 34 34 35 35 35 35 36 37 37 37 37 37 38 39 39 39 40 41 41 41 41 41 42 42 43 44 44 45 45 46 47 47 47 47 50 50 50 50 50 50 50 51 51 51 52 52 53 54 54 55 55 56 57 57 58 58 58 58 58 59 59 59 59 59 60 61 61 62 62 63 64 65 65 65 66 67 67 68 68 68 68 70 70 70 71 72 72 72 73 73 74 74 75 75 77 78 85 NA NA NA NA NA 12 12 15 16 20 20 22 23 25 27 27 31 36 40 40 43 43 44 44 44 45 46 48 50 51 52 56 57 66 67 7 11 17 18 18 20 21 22 23 24 25 25 25 28 28 34 34 34 36 36 36 37 39 44 46 48 48 49 50 51 51 51 52 54 54 56 56 57 57 58 62 63 63 64 66 66 66 67 67 68 72 74 84 NA NA NA NA 12 15 16 17 20 21 26 28 29 35 35 37 39 39 43 46 48 48 49 50 52 53 56 56 58 58 60 61 65 68 74 77 81 NA NA 12 15 15 16 16 16 17 18 21 21 22 22 27 29 32 39 39 44 45 46 47 48 48 48 49 51 53 56 58 59 66 68 71 113 NA NA NA NA NA 6 8 9 12 13 13 14 14 14 14 14 16 16 17 18 18 19 19 19 20 21 21 21 22 22 23 24 25 25 25 26 27 27 28 28 28 29 30 31 31 32 33 34 35 36 37 39 39 40 40 40 41 42 42 43 46 47 48 48 48 48 49 49 49 50 50 50 51 52 53 53 53 54 54 54 55 55 56 57 58 60 62 63 64 64 67 67 70 82 83 84 NA NA NA 10 12 13 13 13 15 16 16 17 17 17 20 21 21 21 23 23 23 23 24 25 25 26 27 27 28 28 28 29 29 29 30 30 30 31 31 32 32 33 33 33 33 34 34 35 35 36 36 36 37 37 37 38 38 38 39 39 39 40 41 41 42 42 42 43 43 44 44 44 44 45 45 45 45 45 47 47 47 47 47 47 48 48 48 49 50 50 51 52 52 52 52 53 53 54 54 55 55 55 55 56 57 57 60 60 62 63 63 64 64 65 65 67 68 68 68 69 69 70 71 74 75 81 113 113 113 NA 14 16 29 29 39 39 69 29 34 45 59 NA 56 59 46 51 26 58 66 20 20 26 33 52 54 57 66 67 NA 23 31 61 67 NA 32 26 44 45 52 52 52 53 61 NA 10 19 21 40 45 47 51 57 53 34 NA 9 14 18 31 35 52 54 56 58 NA 23 27 28 31 39 53 42 55 54 NA 28 43 68 15 24 26 36 45 73 38 53 61 29 16 48 NA 30 31 44 58 38 31 16 16 26 28 45 47 51 54 55 56 60 10 28 31 60 66 61 62 72 72 28 43 NA 27 68 68 NA NA 13 14 23 43 47 49 53 53 54 14 20 22 24 28 32 35 36 36 44 44 45 45 45 48 49 50 50 52 54 58 59 71 15 15 16 20 23 24 26 35 35 36 36 36 38 38 39 41 45 46 47 48 48 48 54 58 60 66 76 78 NA 12 13 14 15 16 18 18 18 20 21 21 23 25 30 33 37 39 43 46 52 52 59 59 64 69 74 11 46 49 52 NA 9 10 13 15 17 18 18 19 20 21 22 23 24 24 25 28 32 33 36 37 45 45 50 54 54 63 66 66 67 14 17 18 19 21 21 21 23 23 24 32 33 38 40 42 47 47 48 52 53 59 70 10 11 12 13 13 13 14 14 14 15 16 16 16 16 17 18 18 18 18 18 18 18 19 19 20 21 21 22 23 23 24 24 25 25 26 26 27 28 29 29 30 30 30 31 32 33 33 33 33 33 36 36 36 37 40 41 42 42 43 44 45 45 45 45 45 46 49 49 49 50 52 52 52 53 53 54 54 55 58 59 59 60 62 63 63 66 68 69 69 70 72 79 NA 27 28 37 39 40 14 18 25 32 38 46 51 51 52 54 64 42 44 55 25 32 50 8 9 9 9 10 11 12 14 15 15 16 16 16 17 17 17 17 17 18 18 18 19 19 19 19 19 19 19 20 20 20 20 21 21 21 21 22 23 23 24 24 25 25 25 26 26 26 27 28 28 29 29 29 30 31 31 31 32 32 34 34 35 35 36 36 37 37 37 38 41 42 43 43 43 46 47 47 47 48 49 49 50 50 51 52 52 52 52 53 53 53 53 54 54 54 54 54 54 55 55 56 57 58 62 64 64 65 65 71 72 73 74 75 76 76 77 83 113 113 NA NA NA NA NA NA NA NA 9 11 11 12 12 13 13 13 14 14 14 14 14 14 15 15 15 15 15 16 16 16 16 16 16 16 16 16 16 16 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 19 19 19 19 19 19 19 19 20 20 20 20 20 21 21 21 21 21 21 21 21 21 21 21 22 22 22 22 22 22 22 22 22 22 23 23 23 23 23 24 24 24 24 24 24 24 24 24 24 25 25 25 25 26 26 26 26 26 26 26 26 26 27 27 27 27 27 27 27 27 28 28 28 28 28 28 28 28 28 29 29 29 29 29 29 29 29 29 29 29 30 30 30 30 30 31 31 31 31 31 31 32 32 32 32 32 32 32 32 32 33 33 33 33 33 33 33 34 34 34 34 34 34 34 34 34 35 35 35 35 35 35 36 36 37 37 37 37 37 37 37 37 37 38 38 38 38 38 38 38 38 38 39 39 39 39 39 40 40 40 40 40 41 41 41 41 41 41 41 41 41 42 42 42 42 42 42 42 42 42 42 43 43 43 43 43 43 43 44 44 44 44 44 44 44 44 44 44 45 45 45 45 45 46 46 46 46 46 46 46 46 46 46 46 47 47 47 47 47 47 47 48 48 48 48 49 49 49 50 50 50 50 50 50 50 50 51 51 51 51 52 52 52 52 52 52 52 53 53 53 53 53 53 53 53 53 54 54 54 55 55 55 55 55 55 55 55 56 56 56 56 57 57 57 58 58 58 59 59 59 59 59 59 59 59 60 60 61 61 62 62 62 62 62 63 64 64 64 64 65 65 65 65 66 66 66 66 66 67 67 67 67 67 68 68 68 68 68 69 69 70 71 71 72 72 72 72 73 73 74 75 75 76 76 76 76 77 78 78 79 80 80 83 86 113 113 113 113 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 9 9 10 11 11 14 14 15 17 17 17 18 18 18 20 20 20 20 20 20 22 24 25 25 25 25 26 26 27 27 27 27 28 28 29 29 29 30 31 31 31 33 34 34 35 35 35 36 36 36 36 37 38 38 38 39 39 39 40 41 41 41 42 42 42 42 43 43 43 43 44 45 46 46 46 47 47 47 47 48 48 48 49 49 49 52 52 55 55 55 55 59 60 62 63 63 64 65 65 65 65 67 67 67 73 77 79 82 NA NA NA NA NA NA 4 8 16 17 17 17 19 19 24 24 24 27 29 31 31 31 32 33 33 36 36 38 38 40 40 41 42 42 42 44 44 45 45 45 46 46 46 46 48 48 48 48 49 51 52 53 55 56 57 58 58 58 59 59 59 62 62 63 64 70 6 8 9 11 13 13 15 16 16 17 19 19 19 20 20 20 21 21 21 22 22 22 23 24 26 26 27 27 27 28 28 28 29 29 29 30 30 30 32 33 33 35 35 35 36 36 37 37 38 38 38 38 38 40 41 41 43 43 43 43 44 44 44 44 45 45 45 47 47 47 47 49 49 49 49 49 49 52 52 52 54 54 54 55 55 55 57 57 57 58 58 59 59 60 61 61 61 61 65 65 65 66 67 68 68 69 69 76 77 77 113 NA NA NA NA NA NA NA NA NA NA 7 12 13 13 14 14 14 15 15 16 16 17 17 17 17 17 18 18 18 18 19 20 21 22 22 23 23 23 26 26 26 29 29 30 30 30 30 31 32 32 34 35 35 36 37 39 39 40 40 42 42 43 44 44 44 44 44 45 45 45 46 46 46 46 47 47 47 47 47 48 48 48 48 48 49 49 52 53 53 53 53 56 56 59 61 62 64 65 65 65 68 71 72 72 73 75 75 76 79 80 NA NA 6 6 9 13 13 16 16 16 16 17 17 17 18 18 18 19 19 19 21 22 22 22 23 24 24 24 24 24 24 24 24 25 25 25 25 26 26 26 26 27 29 29 29 31 32 33 33 34 34 34 34 35 37 38 38 39 40 41 41 42 42 43 43 44 44 45 46 46 47 49 49 51 51 51 52 53 54 54 54 54 55 55 56 56 56 57 58 58 59 59 59 59 59 60 60 61 61 62 62 64 64 65 66 67 67 69 71 71 75 113 NA NA NA NA 7 8 8 8 8 8 9 9 10 10 10 10 10 10 11 11 11 11 11 12 12 12 13 13 13 13 13 13 13 14 14 15 15 15 15 15 15 16 16 16 16 17 17 17 17 17 17 17 17 17 17 18 18 18 18 18 18 18 18 19 19 19 19 19 19 20 20 20 20 21 21 21 21 22 22 22 22 23 23 23 23 23 23 23 23 24 24 24 24 24 24 24 24 25 25 25 25 25 25 25 26 26 26 26 26 26 26 26 27 27 27 27 27 27 27 27 27 27 28 28 28 28 28 29 29 29 29 29 29 29 30 30 30 30 30 30 30 30 30 30 30 30 30 31 31 31 31 32 32 32 32 32 32 32 32 33 33 33 33 33 33 33 33 33 34 34 34 34 34 35 35 35 35 35 35 35 35 36 36 36 36 36 36 36 36 36 36 36 37 37 37 37 37 37 37 38 38 38 38 38 38 38 38 38 39 39 39 39 39 39 39 40 40 40 40 40 40 40 40 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 41 42 42 42 42 42 42 42 42 42 42 42 42 42 42 42 43 43 43 43 43 43 43 43 43 43 43 43 43 43 43 44 44 44 44 44 44 44 44 44 44 44 44 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 45 46 46 46 46 46 46 46 46 46 46 46 46 46 46 46 47 47 47 47 47 47 48 48 48 48 48 48 48 48 48 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 49 50 50 50 50 50 50 50 50 50 50 51 51 51 51 51 51 51 52 52 52 52 52 52 52 53 53 53 53 53 53 53 53 53 54 54 54 55 55 55 55 55 55 55 56 56 56 56 56 56 57 57 58 58 58 58 58 58 59 59 59 59 59 59 60 60 60 60 60 60 61 61 61 62 63 63 63 64 64 64 64 64 65 65 65 65 65 65 66 66 66 66 66 67 67 67 67 67 67 68 68 68 69 69 69 70 70 70 70 70 70 70 73 73 74 75 75 77 77 77 77 77 78 79 79 79 79 80 80 80 81 82 82 113 NA NA NA NA NA NA NA NA NA 36 15 24 32 38 38 43 47 47 47 47 48 50 50 62 68 68 74 NA 25 57 21 28 32 39 46 66 NA 7 9 10 12 13 13 18 19 22 23 31 32 35 39 41 47 48 50 56 57 86 NA NA NA NA NA 8 8 12 12 13 16 17 18 18 18 18 18 19 20 22 24 24 24 29 30 31 32 33 34 35 36 37 37 37 37 38 42 44 46 48 51 64 64 65 66 66 68 77 79 113 NA NA 9 12 12 14 16 17 17 18 19 19 19 20 21 23 23 24 24 24 26 26 28 29 31 32 33 35 37 38 38 42 42 42 45 47 47 50 52 52 55 56 57 58 60 64 65 66 69 7 12 12 14 20 22 23 24 29 29 29 31 34 34 35 38 38 38 38 39 40 40 41 41 42 44 51 51 55 58 NA 7 14 17 39 41 54 5 17 17 21 22 22 22 23 23 27 29 30 35 36 36 41 42 43 44 46 47 48 50 50 53 59 68 NA NA 8 19 24 25 25 26 29 31 31 32 35 35 37 37 37 38 39 40 40 43 45 47 48 48 49 53 53 57 58 58 66 67 67 76 77 NA 11 11 11 15 18 21 22 27 28 30 31 37 40 42 42 44 44 48 51 55 77 8 15 15 16 17 17 17 18 18 19 21 21 23 23 25 27 29 29 29 30 31 31 32 32 32 33 33 35 37 38 39 40 42 43 43 43 44 45 45 45 46 46 47 47 49 50 51 53 53 55 56 56 57 57 57 57 57 58 59 60 61 68 68 68 113 113 113 NA NA NA NA 10 16 19 20 21 24 24 26 33 34 36 37 40 46 48 50 54 57 NA 12 13 17 33 34 36 37 38 41 42 43 43 43 43 43 45 46 46 48 51 62 63 NA NA NA NA NA 9 13 13 13 15 15 15 16 16 16 17 18 18 18 19 19 19 20 20 21 21 21 21 21 21 21 21 22 22 22 23 23 23 23 23 23 24 24 24 24 24 25 25 25 26 26 26 26 26 26 27 27 27 27 27 28 28 28 28 30 31 31 32 32 32 33 33 34 35 35 35 35 36 36 38 39 39 39 40 40 40 40 41 42 42 43 44 44 45 46 46 46 47 47 47 48 50 50 50 50 51 52 53 53 55 55 56 56 56 58 59 63 63 63 64 64 68 70 71 72 73 77 81 113 113 113 NA NA NA 8 8 12 13 13 14 15 15 16 17 17 17 17 18 18 18 18 20 22 22 22 24 24 24 24 24 26 27 27 28 29 29 29 31 32 33 33 34 34 35 36 36 36 37 38 39 39 41 41 42 42 43 43 43 44 45 46 46 46 46 46 47 48 48 48 50 50 50 50 53 53 55 55 55 59 59 60 61 64 65 68 71 74 6 7 9 10 10 10 14 15 15 15 15 17 18 18 18 18 19 19 19 19 21 21 23 24 24 25 25 26 27 27 28 28 28 30 34 37 38 39 39 40 41 41 41 43 44 44 46 46 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
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R Code
num <- 50 res <- array(NA,dim=c(num,3)) q1 <- function(data,n,p,i,f) { np <- n*p; i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q2 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i qvalue <- (1-f)*data[i] + f*data[i+1] } q3 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- data[i+1] } } q4 <- function(data,n,p,i,f) { np <- n*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- (data[i]+data[i+1])/2 } else { qvalue <- data[i+1] } } q5 <- function(data,n,p,i,f) { np <- (n-1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i+1] } else { qvalue <- data[i+1] + f*(data[i+2]-data[i+1]) } } q6 <- function(data,n,p,i,f) { np <- n*p+0.5 i <<- floor(np) f <<- np - i qvalue <- data[i] } q7 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { qvalue <- f*data[i] + (1-f)*data[i+1] } } q8 <- function(data,n,p,i,f) { np <- (n+1)*p i <<- floor(np) f <<- np - i if (f==0) { qvalue <- data[i] } else { if (f == 0.5) { qvalue <- (data[i]+data[i+1])/2 } else { if (f < 0.5) { qvalue <- data[i] } else { qvalue <- data[i+1] } } } } iqd <- function(x,def) { x <-sort(x[!is.na(x)]) n<-length(x) if (def==1) { qvalue1 <- q1(x,n,0.25,i,f) qvalue3 <- q1(x,n,0.75,i,f) } if (def==2) { qvalue1 <- q2(x,n,0.25,i,f) qvalue3 <- q2(x,n,0.75,i,f) } if (def==3) { qvalue1 <- q3(x,n,0.25,i,f) qvalue3 <- q3(x,n,0.75,i,f) } if (def==4) { qvalue1 <- q4(x,n,0.25,i,f) qvalue3 <- q4(x,n,0.75,i,f) } if (def==5) { qvalue1 <- q5(x,n,0.25,i,f) qvalue3 <- q5(x,n,0.75,i,f) } if (def==6) { qvalue1 <- q6(x,n,0.25,i,f) qvalue3 <- q6(x,n,0.75,i,f) } if (def==7) { qvalue1 <- q7(x,n,0.25,i,f) qvalue3 <- q7(x,n,0.75,i,f) } if (def==8) { qvalue1 <- q8(x,n,0.25,i,f) qvalue3 <- q8(x,n,0.75,i,f) } iqdiff <- qvalue3 - qvalue1 return(c(iqdiff,iqdiff/2,iqdiff/(qvalue3 + qvalue1))) } range <- max(x) - min(x) lx <- length(x) biasf <- (lx-1)/lx varx <- var(x) bvarx <- varx*biasf sdx <- sqrt(varx) mx <- mean(x) bsdx <- sqrt(bvarx) x2 <- x*x mse0 <- sum(x2)/lx xmm <- x-mx xmm2 <- xmm*xmm msem <- sum(xmm2)/lx axmm <- abs(x - mx) medx <- median(x) axmmed <- abs(x - medx) xmmed <- x - medx xmmed2 <- xmmed*xmmed msemed <- sum(xmmed2)/lx qarr <- array(NA,dim=c(8,3)) for (j in 1:8) { qarr[j,] <- iqd(x,j) } sdpo <- 0 adpo <- 0 for (i in 1:(lx-1)) { for (j in (i+1):lx) { ldi <- x[i]-x[j] aldi <- abs(ldi) sdpo = sdpo + ldi * ldi adpo = adpo + aldi } } denom <- (lx*(lx-1)/2) sdpo = sdpo / denom adpo = adpo / denom gmd <- 0 for (i in 1:lx) { for (j in 1:lx) { ldi <- abs(x[i]-x[j]) gmd = gmd + ldi } } gmd <- gmd / (lx*(lx-1)) sumx <- sum(x) pk <- x / sumx ck <- cumsum(pk) dk <- array(NA,dim=lx) for (i in 1:lx) { if (ck[i] <= 0.5) dk[i] <- ck[i] else dk[i] <- 1 - ck[i] } bigd <- sum(dk) * 2 / (lx-1) iod <- 1 - sum(pk*pk) res[1,] <- c('Absolute range','http://www.xycoon.com/absolute.htm', range) res[2,] <- c('Relative range (unbiased)','http://www.xycoon.com/relative.htm', range/sd(x)) res[3,] <- c('Relative range (biased)','http://www.xycoon.com/relative.htm', range/sqrt(varx*biasf)) res[4,] <- c('Variance (unbiased)','http://www.xycoon.com/unbiased.htm', varx) res[5,] <- c('Variance (biased)','http://www.xycoon.com/biased.htm', bvarx) res[6,] <- c('Standard Deviation (unbiased)','http://www.xycoon.com/unbiased1.htm', sdx) res[7,] <- c('Standard Deviation (biased)','http://www.xycoon.com/biased1.htm', bsdx) res[8,] <- c('Coefficient of Variation (unbiased)','http://www.xycoon.com/variation.htm', sdx/mx) res[9,] <- c('Coefficient of Variation (biased)','http://www.xycoon.com/variation.htm', bsdx/mx) res[10,] <- c('Mean Squared Error (MSE versus 0)','http://www.xycoon.com/mse.htm', mse0) res[11,] <- c('Mean Squared Error (MSE versus Mean)','http://www.xycoon.com/mse.htm', msem) res[12,] <- c('Mean Absolute Deviation from Mean (MAD Mean)', 'http://www.xycoon.com/mean2.htm', sum(axmm)/lx) res[13,] <- c('Mean Absolute Deviation from Median (MAD Median)', 'http://www.xycoon.com/median1.htm', sum(axmmed)/lx) res[14,] <- c('Median Absolute Deviation from Mean', 'http://www.xycoon.com/mean3.htm', median(axmm)) res[15,] <- c('Median Absolute Deviation from Median', 'http://www.xycoon.com/median2.htm', median(axmmed)) res[16,] <- c('Mean Squared Deviation from Mean', 'http://www.xycoon.com/mean1.htm', msem) res[17,] <- c('Mean Squared Deviation from Median', 'http://www.xycoon.com/median.htm', msemed) load(file='createtable') mylink1 <- hyperlink('http://www.xycoon.com/difference.htm','Interquartile Difference','') mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ') res[18,] <- c('', mylink2, qarr[1,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ') res[19,] <- c('', mylink2, qarr[2,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ') res[20,] <- c('', mylink2, qarr[3,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ') res[21,] <- c('', mylink2, qarr[4,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ') res[22,] <- c('', mylink2, qarr[5,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ') res[23,] <- c('', mylink2, qarr[6,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ') res[24,] <- c('', mylink2, qarr[7,1]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ') res[25,] <- c('', mylink2, qarr[8,1]) mylink1 <- hyperlink('http://www.xycoon.com/deviation.htm','Semi Interquartile Difference','') mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ') res[26,] <- c('', mylink2, qarr[1,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ') res[27,] <- c('', mylink2, qarr[2,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ') res[28,] <- c('', mylink2, qarr[3,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ') res[29,] <- c('', mylink2, qarr[4,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ') res[30,] <- c('', mylink2, qarr[5,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ') res[31,] <- c('', mylink2, qarr[6,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ') res[32,] <- c('', mylink2, qarr[7,2]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ') res[33,] <- c('', mylink2, qarr[8,2]) mylink1 <- hyperlink('http://www.xycoon.com/variation1.htm','Coefficient of Quartile Variation','') mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_1.htm','(Weighted Average at Xnp)',''),sep=' ') res[34,] <- c('', mylink2, qarr[1,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_2.htm','(Weighted Average at X(n+1)p)',''),sep=' ') res[35,] <- c('', mylink2, qarr[2,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_3.htm','(Empirical Distribution Function)',''),sep=' ') res[36,] <- c('', mylink2, qarr[3,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_4.htm','(Empirical Distribution Function - Averaging)',''),sep=' ') res[37,] <- c('', mylink2, qarr[4,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_5.htm','(Empirical Distribution Function - Interpolation)',''),sep=' ') res[38,] <- c('', mylink2, qarr[5,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_6.htm','(Closest Observation)',''),sep=' ') res[39,] <- c('', mylink2, qarr[6,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_7.htm','(True Basic - Statistics Graphics Toolkit)',''),sep=' ') res[40,] <- c('', mylink2, qarr[7,3]) mylink2 <- paste(mylink1,hyperlink('http://www.xycoon.com/method_8.htm','(MS Excel (old versions))',''),sep=' ') res[41,] <- c('', mylink2, qarr[8,3]) res[42,] <- c('Number of all Pairs of Observations', 'http://www.xycoon.com/pair_numbers.htm', lx*(lx-1)/2) res[43,] <- c('Squared Differences between all Pairs of Observations', 'http://www.xycoon.com/squared_differences.htm', sdpo) res[44,] <- c('Mean Absolute Differences between all Pairs of Observations', 'http://www.xycoon.com/mean_abs_differences.htm', adpo) res[45,] <- c('Gini Mean Difference', 'http://www.xycoon.com/gini_mean_difference.htm', gmd) res[46,] <- c('Leik Measure of Dispersion', 'http://www.xycoon.com/leiks_d.htm', bigd) res[47,] <- c('Index of Diversity', 'http://www.xycoon.com/diversity.htm', iod) res[48,] <- c('Index of Qualitative Variation', 'http://www.xycoon.com/qualitative_variation.htm', iod*lx/(lx-1)) res[49,] <- c('Coefficient of Dispersion', 'http://www.xycoon.com/dispersion.htm', sum(axmm)/lx/medx) res[50,] <- c('Observations', '', lx) res a<-table.start() a<-table.row.start(a) a<-table.element(a,'Variability - Ungrouped Data',2,TRUE) a<-table.row.end(a) for (i in 1:num) { a<-table.row.start(a) if (res[i,1] != '') { a<-table.element(a,hyperlink(res[i,2],res[i,1],''),header=TRUE) } else { a<-table.element(a,res[i,2],header=TRUE) } a<-table.element(a,res[i,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab')
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