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Data X:
12.9 1 0 11 8 7 18 12 20 4 0 13 21 149 12.2 1 0 19 18 20 23 20 19 4 1 8 22 139 12.8 1 0 16 12 9 22 14 18 5 0 14 22 148 7.4 1 0 24 24 19 22 25 24 4 1 16 18 158 6.7 1 0 15 16 12 19 15 20 4 1 14 23 128 12.6 1 0 17 19 16 25 20 20 9 1 13 12 224 14.8 1 0 19 16 17 28 21 24 8 0 15 20 159 13.3 1 0 19 15 9 16 15 21 11 1 13 22 105 11.1 1 0 28 28 28 28 28 28 4 1 20 21 159 8.2 1 0 26 21 20 21 11 10 4 1 17 19 167 11.4 1 0 15 18 16 22 22 22 6 1 15 22 165 6.4 1 0 26 22 22 24 22 19 4 1 16 15 159 10.6 1 0 16 19 17 24 27 27 8 1 12 20 119 12.0 1 0 24 22 12 26 24 23 4 0 17 19 176 6.3 1 0 25 25 18 28 23 24 4 0 11 18 54 11.3 1 1 22 20 20 24 24 24 11 0 16 15 91 11.9 1 0 15 16 12 20 21 25 4 1 16 20 163 9.3 1 0 21 19 16 26 20 24 4 0 15 21 124 9.6 1 1 22 18 16 21 19 21 6 1 13 21 137 10.0 1 0 27 26 21 28 25 28 6 0 14 15 121 6.4 1 0 26 24 15 27 16 28 4 1 19 16 153 13.8 1 0 26 20 17 23 24 22 8 1 16 23 148 10.8 1 0 22 19 17 24 21 26 5 0 17 21 221 13.8 1 0 21 19 17 24 22 26 4 1 10 18 188 11.7 1 0 22 23 18 22 25 21 9 1 15 25 149 10.9 1 0 20 18 15 21 23 26 4 1 14 9 244 16.1 1 1 21 16 20 25 20 23 7 1 14 30 148 13.4 1 1 20 18 13 20 21 20 10 0 16 20 92 9.9 1 0 22 21 21 21 22 24 4 1 15 23 150 11.5 1 0 21 20 12 26 25 25 4 0 17 16 153 8.3 1 0 8 15 6 23 23 24 7 0 14 16 94 11.7 1 0 22 19 13 21 19 20 12 0 16 19 156 9.0 1 0 20 19 19 27 21 24 7 1 15 25 132 9.7 1 0 24 7 12 25 19 25 5 1 16 18 161 10.8 1 0 17 20 14 23 25 23 8 1 16 23 105 10.3 1 0 20 20 13 25 16 21 5 1 10 21 97 10.4 1 0 23 19 12 23 24 23 4 0 8 10 151 12.7 1 1 20 19 17 19 24 21 9 1 17 14 131 9.3 1 0 22 20 19 22 18 18 7 1 14 22 166 11.8 1 0 19 18 10 24 28 24 4 0 10 26 157 5.9 1 0 15 14 10 19 15 18 4 1 14 23 111 11.4 1 0 20 17 11 21 17 21 4 1 12 23 145 13.0 1 0 22 17 11 27 18 23 4 1 16 24 162 10.8 1 0 17 8 10 25 26 25 4 1 16 24 163 12.3 1 1 14 9 7 25 18 22 7 1 16 18 59 11.3 1 0 24 22 22 23 22 22 4 0 8 23 187 11.8 1 0 17 20 12 17 19 23 7 1 16 15 109 7.9 1 1 23 20 18 28 17 24 4 1 15 19 90 12.7 1 0 25 22 20 25 26 25 4 0 8 16 105 12.3 1 1 16 22 9 20 21 22 4 1 13 25 83 11.6 1 1 18 22 16 25 26 24 4 1 14 23 116 6.7 1 1 20 16 14 21 21 21 8 1 13 17 42 10.9 1 0 18 14 11 24 12 24 4 1 16 19 148 12.1 1 1 23 24 20 28 20 25 4 1 19 21 155 13.3 1 0 24 21 17 20 20 23 4 1 19 18 125 10.1 1 0 23 20 14 19 24 27 4 1 14 27 116 5.7 1 1 13 20 8 24 24 27 7 0 15 21 128 14.3 1 0 20 18 16 21 22 23 12 1 13 13 138 8.0 1 1 20 14 11 24 21 18 4 0 10 8 49 13.3 1 1 19 19 10 23 20 20 4 1 16 29 96 9.3 1 0 22 24 15 18 23 23 4 1 15 28 164 12.5 1 0 22 19 15 27 19 24 5 0 11 23 162 7.6 1 0 15 16 10 25 24 26 15 0 9 21 99 15.9 1 0 17 16 10 20 21 20 5 1 16 19 202 9.2 1 0 19 16 18 21 16 23 10 0 12 19 186 9.1 1 1 20 14 10 23 17 22 9 1 12 20 66 11.1 1 0 22 22 22 27 23 23 8 0 14 18 183 13.0 1 0 21 21 16 24 20 17 4 1 14 19 214 14.5 1 0 21 15 10 27 19 20 5 1 13 17 188 12.2 1 1 16 14 7 24 18 22 4 0 15 19 104 12.3 1 0 20 15 16 23 18 18 9 0 17 25 177 11.4 1 0 21 14 16 24 21 19 4 0 14 19 126 8.8 1 1 20 20 16 21 20 19 10 0 11 22 76 14.6 1 1 23 21 22 23 17 16 4 1 9 23 99 12.6 1 0 18 14 5 27 25 26 4 0 7 14 139 NA 1 0 22 19 18 24 15 14 6 1 13 28 78 13.0 1 0 16 16 10 25 17 25 7 0 15 16 162 12.6 1 1 17 13 8 19 17 23 5 1 12 24 108 13.2 1 0 24 26 16 24 24 18 4 0 15 20 159 9.9 1 1 13 13 8 25 21 22 4 0 14 12 74 7.7 1 0 19 18 16 23 22 26 4 1 16 24 110 10.5 1 1 20 15 14 23 18 25 4 0 14 22 96 13.4 1 1 22 18 15 25 22 26 4 0 13 12 116 10.9 1 1 19 21 9 26 20 26 4 0 16 22 87 4.3 1 1 21 17 21 26 21 24 6 1 13 20 97 10.3 1 1 15 18 7 16 21 22 10 0 16 10 127 11.8 1 1 21 20 17 23 20 21 7 1 16 23 106 11.2 1 1 24 18 18 26 18 22 4 1 16 17 80 11.4 1 1 22 25 16 25 25 28 4 0 10 22 74 8.6 1 1 20 20 16 23 23 22 7 0 12 24 91 13.2 1 1 21 19 14 26 21 26 4 0 12 18 133 12.6 1 1 19 18 15 22 20 20 8 1 12 21 74 5.6 1 1 14 12 8 20 21 24 11 1 12 20 114 9.9 1 1 25 22 22 27 20 21 6 1 19 20 140 8.8 1 1 11 16 5 20 22 23 14 0 14 22 95 7.7 1 1 17 18 13 22 15 23 5 1 13 19 98 9.0 1 1 22 23 22 24 24 23 4 0 16 20 121 7.3 1 1 20 20 18 21 22 22 8 1 15 26 126 11.4 1 1 22 20 15 24 21 23 9 1 12 23 98 13.6 1 1 15 16 11 26 17 21 4 1 8 24 95 7.9 1 1 23 22 19 24 23 27 4 1 10 21 110 10.7 1 1 20 19 19 24 22 23 5 1 16 21 70 10.3 1 1 22 23 21 27 23 26 4 0 16 19 102 8.3 1 1 16 6 4 25 16 27 5 1 10 8 86 9.6 1 1 25 19 17 27 18 27 4 1 18 17 130 14.2 1 1 18 24 10 19 25 23 4 1 12 20 96 8.5 1 1 19 19 13 22 18 23 7 0 16 11 102 13.5 1 1 25 15 15 22 14 23 10 0 10 8 100 4.9 1 1 21 18 11 25 20 28 4 0 14 15 94 6.4 1 1 22 18 20 23 19 24 5 0 12 18 52 9.6 1 1 21 22 13 24 18 20 4 0 11 18 98 11.6 1 1 22 23 18 24 22 23 4 0 15 19 118 11.1 1 1 23 18 20 23 21 22 4 1 7 19 99 4.35 0 0 20 17 15 22 14 15 6 1 16 23 48 12.7 0 0 6 6 4 24 5 27 4 1 16 22 50 18.1 0 0 15 22 9 19 25 23 8 1 16 21 150 17.85 0 0 18 20 18 25 21 23 5 1 16 25 154 16.6 0 1 24 16 12 26 11 20 4 0 12 30 109 12.6 0 1 22 16 17 18 20 18 17 1 15 17 68 17.1 0 0 21 17 12 24 9 22 4 1 14 27 194 19.1 0 0 23 20 16 28 15 20 4 0 15 23 158 16.1 0 0 20 23 17 23 23 21 8 1 16 23 159 13.35 0 0 20 18 14 19 21 25 4 0 13 18 67 18.4 0 0 18 13 13 19 9 19 7 0 10 18 147 14.7 0 0 25 22 20 27 24 25 4 1 17 23 39 10.6 0 0 16 20 16 24 16 24 4 1 15 19 100 12.6 0 0 20 20 15 26 20 22 5 1 18 15 111 16.2 0 0 14 13 10 21 15 28 7 1 16 20 138 13.6 0 0 22 16 16 25 18 22 4 1 20 16 101 18.9 0 1 26 25 21 28 22 21 4 1 16 24 131 14.1 0 0 20 16 15 19 21 23 7 1 17 25 101 14.5 0 0 17 15 16 20 21 19 11 1 16 25 114 16.15 0 0 22 19 19 26 21 21 7 0 15 19 165 14.75 0 0 22 19 9 27 20 25 4 1 13 19 114 14.8 0 0 20 24 19 23 24 23 4 1 16 16 111 12.45 0 0 17 9 7 18 15 28 4 1 16 19 75 12.65 0 0 22 22 23 23 24 14 4 1 16 19 82 17.35 0 0 17 15 14 21 18 23 4 1 17 23 121 8.6 0 0 22 22 10 23 24 24 4 1 20 21 32 18.4 0 0 21 22 16 22 24 25 6 0 14 22 150 16.1 0 0 25 24 12 21 15 15 8 1 17 19 117 11.6 0 1 11 12 10 14 19 23 23 1 6 20 71 17.75 0 0 19 21 7 24 20 26 4 1 16 20 165 15.25 0 0 24 25 20 26 26 21 8 1 15 3 154 17.65 0 0 17 26 9 24 26 26 6 1 16 23 126 16.35 0 0 22 21 12 22 23 23 4 0 16 23 149 17.65 0 0 17 14 10 20 13 15 7 0 14 20 145 13.6 0 0 26 28 19 20 16 16 4 1 16 15 120 14.35 0 0 20 21 11 18 22 20 4 0 16 16 109 14.75 0 0 19 16 15 18 21 20 4 0 16 7 132 18.25 0 0 21 16 14 25 11 21 10 1 14 24 172 9.9 0 0 24 25 11 28 23 28 6 0 14 17 169 16 0 0 21 21 14 23 18 19 5 1 16 24 114 18.25 0 0 19 22 15 20 19 21 5 1 16 24 156 16.85 0 0 13 9 7 22 15 22 4 0 15 19 172 14.6 0 1 24 20 22 27 8 27 4 1 16 25 68 13.85 0 1 28 19 19 24 15 20 5 1 16 20 89 18.95 0 0 27 24 22 23 21 17 5 1 18 28 167 15.6 0 0 22 22 11 20 25 26 5 0 15 23 113 14.85 0 1 23 22 19 22 14 21 5 0 16 27 115 11.75 0 1 19 12 9 21 21 24 4 0 16 18 78 18.45 0 1 18 17 11 24 18 21 6 0 16 28 118 15.9 0 1 23 18 17 26 18 25 4 1 17 21 87 17.1 0 0 21 10 12 24 12 22 4 0 14 19 173 16.1 0 0 22 22 17 18 24 17 4 1 18 23 2 19.9 0 1 17 24 10 17 17 14 9 0 9 27 162 10.95 0 1 15 18 17 23 20 23 18 1 15 22 49 18.45 0 1 21 18 13 21 24 28 6 0 14 28 122 15.1 0 1 20 23 11 21 22 24 5 1 15 25 96 15 0 1 26 21 19 24 15 22 4 0 13 21 100 11.35 0 1 19 21 21 22 22 24 11 0 16 22 82 15.95 0 1 28 28 24 24 26 25 4 1 20 28 100 18.1 0 1 21 17 13 24 17 21 10 0 14 20 115 14.6 0 1 19 21 16 24 23 22 6 1 12 29 141 15.4 0 0 22 21 13 23 19 16 8 1 15 25 165 15.4 0 0 21 20 15 21 21 18 8 1 15 25 165 17.6 0 1 20 18 15 24 23 27 6 1 15 20 110 13.35 0 0 19 17 11 19 19 17 8 1 16 20 118 19.1 0 0 11 7 7 19 18 25 4 0 11 16 158 15.35 0 1 17 17 13 23 16 24 4 1 16 20 146 7.6 0 0 19 14 13 25 23 21 9 0 7 20 49 13.4 0 1 20 18 12 24 13 21 9 0 11 23 90 13.9 0 1 17 14 8 21 18 19 5 0 9 18 121 19.1 0 0 21 23 7 18 23 27 4 1 15 25 155 15.25 0 1 21 20 17 23 21 28 4 0 16 18 104 12.9 0 1 12 14 9 20 23 19 15 1 14 19 147 16.1 0 1 23 17 18 23 16 23 10 0 15 25 110 17.35 0 1 22 21 17 23 17 25 9 0 13 25 108 13.15 0 1 22 23 17 23 20 26 7 0 13 25 113 12.15 0 1 21 24 18 23 18 25 9 0 12 24 115 12.6 0 1 20 21 12 27 20 25 6 1 16 19 61 10.35 0 1 18 14 14 19 19 24 4 1 14 26 60 15.4 0 1 21 24 22 25 26 24 7 1 16 10 109 9.6 0 1 24 16 19 25 9 24 4 1 14 17 68 18.2 0 1 22 21 21 21 23 22 7 0 15 13 111 13.6 0 1 20 8 10 25 9 21 4 0 10 17 77 14.85 0 1 17 17 16 17 13 17 15 1 16 30 73 14.75 0 0 19 18 11 22 27 23 4 0 14 25 151 14.1 0 1 16 17 15 23 22 17 9 0 16 4 89 14.9 0 1 19 16 12 27 12 25 4 0 12 16 78 16.25 0 1 23 22 21 27 18 19 4 0 16 21 110 19.25 0 0 8 17 22 5 6 8 28 1 16 23 220 13.6 0 1 22 21 20 19 17 14 4 1 15 22 65 13.6 0 0 23 20 15 24 22 22 4 0 14 17 141 15.65 0 1 15 20 9 23 22 25 4 0 16 20 117 12.75 0 0 17 19 15 28 23 28 5 1 11 20 122 14.6 0 1 21 8 14 25 19 25 4 0 15 22 63 9.85 0 0 25 19 11 27 20 24 4 1 18 16 44 12.65 0 1 18 11 9 16 17 15 12 1 13 23 52 19.2 0 1 20 13 12 25 24 24 4 0 7 0 131 16.6 0 1 21 18 11 26 20 28 6 1 7 18 101 11.2 0 1 21 19 14 24 18 24 6 1 17 25 42 15.25 0 0 24 23 10 23 23 25 5 1 18 23 152 11.9 0 0 22 20 18 24 27 23 4 0 15 12 107 13.2 0 1 22 22 11 27 25 26 4 0 8 18 77 16.35 0 0 23 19 14 25 24 26 4 0 13 24 154 12.4 0 0 17 16 16 19 12 22 10 1 13 11 103 15.85 0 1 15 11 11 19 16 25 7 1 15 18 96 18.15 0 0 22 21 16 24 24 22 4 1 18 23 175 11.15 0 1 19 14 13 20 23 26 7 1 16 24 57 15.65 0 1 18 21 12 21 24 20 4 0 14 29 112 17.75 0 0 21 20 17 28 24 26 4 0 15 18 143 7.65 0 1 20 21 23 26 26 26 12 0 19 15 49 12.35 0 0 19 20 14 19 19 21 5 1 16 29 110 15.6 0 0 19 19 10 23 28 21 8 1 12 16 131 19.3 0 0 16 19 16 23 23 24 6 0 16 19 167 15.2 0 1 18 18 11 21 21 21 17 0 11 22 56 17.1 0 0 23 20 16 26 19 18 4 0 16 16 137 15.6 0 1 22 21 19 25 23 23 5 1 15 23 86 18.4 0 0 23 22 17 25 23 26 4 1 19 23 121 19.05 0 0 20 19 12 24 20 23 5 0 15 19 149 18.55 0 0 24 23 17 23 18 25 5 0 14 4 168 19.1 0 0 25 16 11 22 20 20 6 0 14 20 140 13.1 0 1 25 23 19 27 28 25 4 1 17 24 88 12.85 0 0 20 18 12 26 21 26 4 1 16 20 168 9.5 0 0 23 23 8 23 25 19 4 1 20 4 94 4.5 0 0 21 20 17 22 18 21 6 1 16 24 51 11.85 0 1 23 20 13 26 24 23 8 0 9 22 48 13.6 0 0 23 23 17 22 28 24 10 1 13 16 145 11.7 0 0 11 13 7 17 9 6 4 1 15 3 66 12.4 0 1 21 21 23 25 22 22 5 1 19 15 85 13.35 0 0 27 26 18 22 26 21 4 0 16 24 109 11.4 0 1 19 18 13 28 28 28 4 0 17 17 63 14.9 0 1 21 19 17 22 18 24 4 1 16 20 102 19.9 0 1 16 18 13 21 23 14 16 0 9 27 162 11.2 0 1 21 18 8 24 15 20 7 1 11 26 86 14.6 0 1 22 19 16 26 24 28 4 1 14 23 114 17.6 0 0 16 13 14 26 12 19 4 0 19 17 164 14.05 0 0 18 10 13 24 12 24 14 1 13 20 119 16.1 0 0 23 21 19 27 20 21 5 0 14 22 126 13.35 0 0 24 24 15 22 25 21 5 1 15 19 132 11.85 0 0 20 21 15 23 24 26 5 1 15 24 142 11.95 0 0 20 23 8 22 23 24 5 0 14 19 83 14.75 0 1 18 18 14 23 18 26 7 1 16 23 94 15.15 0 1 4 11 7 15 20 25 19 0 17 15 81 13.2 0 0 14 16 11 20 22 23 16 1 12 27 166 16.85 0 1 22 20 17 22 20 24 4 0 15 26 110 7.85 0 1 17 20 19 25 25 24 4 1 17 22 64 7.7 0 0 23 26 17 27 28 26 7 0 15 22 93 12.6 0 1 20 21 12 24 25 23 9 0 10 18 104 7.85 0 1 18 12 12 21 14 20 5 1 16 15 105 10.95 0 1 19 15 18 17 16 16 14 1 15 22 49 12.35 0 1 20 18 16 26 24 24 4 0 11 27 88 9.95 0 1 15 14 15 20 13 20 16 1 16 10 95 14.9 0 1 24 18 20 22 19 23 10 1 16 20 102 16.65 0 1 21 16 16 24 18 23 5 0 16 17 99 13.4 0 1 19 19 12 23 16 18 6 1 14 23 63 13.95 0 1 19 7 10 22 8 21 4 0 14 19 76 15.7 0 1 27 21 28 28 27 25 4 0 16 13 109 16.85 0 1 23 24 19 21 23 23 4 1 16 27 117 10.95 0 1 23 21 18 24 20 26 5 1 18 23 57 15.35 0 1 20 20 19 28 20 26 4 0 14 16 120 12.2 0 1 17 22 8 25 26 24 4 1 20 25 73 15.1 0 1 21 17 17 24 23 23 5 0 15 2 91 17.75 0 1 23 19 16 24 24 21 4 0 16 26 108 15.2 0 1 22 20 18 21 21 23 4 1 16 20 105 14.6 0 0 16 16 12 20 15 20 5 0 16 23 117 16.65 0 1 20 20 17 26 22 23 8 0 12 22 119 8.1 0 1 16 16 13 16 25 24 15 1 8 24 31
Names of X columns:
TOT jaar_bin group_bin AMS.I1 AMS.I2 AMS.I3 AMS.E1 AMS.E2 AMS.E3 AMS.A gender_bin CONFSTATTOT NUMERACYTOT LFM
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
grey
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
FALSE
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,signif(mysum$coefficients[i,1],6)) a<-table.element(a, signif(mysum$coefficients[i,2],6)) a<-table.element(a, signif(mysum$coefficients[i,3],4)) a<-table.element(a, signif(mysum$coefficients[i,4],6)) a<-table.element(a, signif(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, signif(sqrt(mysum$r.squared),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, signif(mysum$r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, signif(mysum$adj.r.squared,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[1],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[2],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, signif(mysum$fstatistic[3],6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, signif(mysum$sigma,6)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, signif(sum(myerror*myerror),6)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,signif(x[i],6)) a<-table.element(a,signif(x[i]-mysum$resid[i],6)) a<-table.element(a,signif(mysum$resid[i],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6)) a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant1,6)) a<-table.element(a,signif(numsignificant1/numgqtests,6)) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant5,6)) a<-table.element(a,signif(numsignificant5/numgqtests,6)) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,signif(numsignificant10,6)) a<-table.element(a,signif(numsignificant10/numgqtests,6)) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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