Workshop 7

*The author of this computation has been verified*

R Software Module: /rwasp_regression_trees1.wasp (opens new window with default values)

Title produced by software: Recursive Partitioning (Regression Trees)

Date of computation: Tue, 21 Dec 2010 13:25:42 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd.htm/, Retrieved Tue, 21 Dec 2010 14:23:50 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

162556 807 213118 6282154 29790 444 81767 4321023 87550 412 153198 4111912 84738 428 -26007 223193 54660 315 126942 1491348 42634 168 157214 1629616 40949 263 129352 1398893 45187 267 234817 1926517 37704 228 60448 983660 16275 129 47818 1443586 25830 104 245546 1073089 12679 122 48020 984885 18014 393 -1710 1405225 43556 190 32648 227132 24811 280 95350 929118 6575 63 151352 1071292 7123 102 288170 638830 21950 265 114337 856956 37597 234 37884 992426 17821 277 122844 444477 12988 73 82340 857217 22330 67 79801 711969 13326 103 165548 702380 16189 290 116384 358589 7146 83 134028 297978 15824 56 63838 585715 27664 236 74996 657954 11920 73 31080 209458 8568 34 32168 786690 14416 139 49857 439798 3369 26 87161 688779 11819 70 106113 574339 6984 40 80570 741409 4519 42 102129 597793 2220 12 301670 644190 18562 211 102313 377934 10327 74 88577 640273 5336 80 112477 697458 2365 83 191778 550608 4069 131 79804 207393 8636 203 128294 301607 13718 56 96448 345783 4525 89 93 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 17 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.8124 R-squared 0.66 RMSE 6884.0103 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 162556 64785.2857142857 97770.7142857143 2 29790 64785.2857142857 -34995.2857142857 3 87550 64785.2857142857 22764.7142857143 4 84738 64785.2857142857 19952.7142857143 5 54660 64785.2857142857 -10125.2857142857 6 42634 27826 14808 7 40949 27826 13123 8 45187 27826 17361 9 37704 27826 9878 10 16275 15714.7142857143 560.285714285714 11 25830 15714.7142857143 10115.2857142857 12 12679 15714.7142857143 -3035.71428571429 13 18014 64785.2857142857 -46771.2857142857 14 43556 27826 15730 15 24811 27826 -3015 16 6575 15714.7142857143 -9139.71428571429 17 7123 6579.875 543.125 18 21950 27826 -5876 19 37597 27826 9771 20 17821 27826 -10005 21 12988 15714.7142857143 -2726.71428571429 22 22330 15714.7142857143 6615.28571428571 23 13326 15714.7142857143 -2388.71428571429 24 16189 64785.2857142857 -48596.2857142857 25 7146 6579.875 566.125 26 15824 6579.875 9244.125 27 27664 27826 -162 28 11920 6579.875 5340.125 29 8568 5195.71428571429 3372.28571428571 30 14416 6579.875 7836.125 31 3369 1974.71111111111 1394.28888888889 32 11819 6579.875 5239.125 33 6984 5195.71428571429 1788.28571428571 34 4519 5195.71428571429 -676.714285714285 35 2220 1950.71428571429 269.285714285714 36 18562 27826 -9264 37 10327 6579.875 3747.125 38 5336 6579.875 -1243.875 39 2365 6579.875 -4214.875 40 4069 6579.875 -2510.875 41 8636 27826 -19190 42 13718 6579.875 7138.125 43 4525 6579.875 -2054.875 44 6869 6579.875 289.125 45 4628 2895.42857142857 1732.57142857143 46 3689 1974.71111111111 1714.28888888889 47 4891 2895.42857142857 1995.57142857143 48 7489 6579.875 909.125 49 4901 6579.875 -1678.875 50 2284 2895.42857142857 -611.428571428572 51 3160 6579.875 -3419.875 52 4150 6579.875 -2429.875 53 7285 6579.875 705.125 54 1134 6579.875 -5445.875 55 4658 6579.875 -1921.875 56 2384 6579.875 -4195.875 57 3748 1950.71428571429 1797.28571428571 58 5371 5195.71428571429 175.285714285715 59 1285 2895.42857142857 -1610.42857142857 60 9327 6579.875 2747.125 61 5565 5195.71428571429 369.285714285715 62 1528 2895.42857142857 -1367.42857142857 63 3122 1974.71111111111 1147.28888888889 64 7561 6579.875 981.125 65 2675 2895.42857142857 -220.428571428572 66 13253 27826 -14573 67 880 1221.28571428571 -341.285714285714 68 2053 5195.71428571429 -3142.71428571429 69 1424 6579.875 -5155.875 70 4036 6579.875 -2543.875 71 3045 2895.42857142857 149.571428571428 72 5119 2895.42857142857 2223.57142857143 73 1431 2895.42857142857 -1464.42857142857 74 554 648.731707317073 -94.7317073170732 75 1975 2895.42857142857 -920.428571428572 76 1765 1974.71111111111 -209.711111111111 77 1012 1974.71111111111 -962.711111111111 78 810 1221.28571428571 -411.285714285714 79 1280 2895.42857142857 -1615.42857142857 80 666 648.731707317073 17.2682926829268 81 1380 1974.71111111111 -594.711111111111 82 4677 6579.875 -1902.875 83 876 2895.42857142857 -2019.42857142857 84 814 462.708333333333 351.291666666667 85 514 648.731707317073 -134.731707317073 86 5692 6579.875 -887.875 87 3642 6579.875 -2937.875 88 540 648.731707317073 -108.731707317073 89 2099 2895.42857142857 -796.428571428572 90 567 1950.71428571429 -1383.71428571429 91 2001 2895.42857142857 -894.428571428572 92 2949 2895.42857142857 53.5714285714284 93 2253 6579.875 -4326.875 94 6533 1974.71111111111 4558.28888888889 95 1889 2895.42857142857 -1006.42857142857 96 3055 1974.71111111111 1080.28888888889 97 272 1950.71428571429 -1678.71428571429 98 1414 1974.71111111111 -560.711111111111 99 2564 1950.71428571429 613.285714285714 100 1383 648.731707317073 734.268292682927 101 1261 1974.71111111111 -713.711111111111 102 975 1221.28571428571 -246.285714285714 103 3366 2895.42857142857 470.571428571428 104 576 334.142857142857 241.857142857143 105 1686 1974.71111111111 -288.711111111111 106 746 1974.71111111111 -1228.71111111111 107 3192 2895.42857142857 296.571428571428 108 2045 1950.71428571429 94.2857142857142 109 5702 2895.42857142857 2806.57142857143 110 1932 1974.71111111111 -42.7111111111112 111 936 1974.71111111111 -1038.71111111111 112 3437 2895.42857142857 541.571428571428 113 5131 2895.42857142857 2235.57142857143 114 2397 1221.28571428571 1175.71428571429 115 1389 648.731707317073 740.268292682927 116 1503 2895.42857142857 -1392.42857142857 117 402 648.731707317073 -246.731707317073 118 2239 1950.71428571429 288.285714285714 119 2234 1221.28571428571 1012.71428571429 120 837 1974.71111111111 -1137.71111111111 121 10579 6579.875 3999.125 122 875 648.731707317073 226.268292682927 123 1585 6579.875 -4994.875 124 1659 1974.71111111111 -315.711111111111 125 2647 648.731707317073 1998.26829268293 126 3294 1974.71111111111 1319.28888888889 127 0 1.03478260869565 -1.03478260869565 128 94 81.3076923076923 12.6923076923077 129 422 1974.71111111111 -1552.71111111111 130 0 1.03478260869565 -1.03478260869565 131 34 188.25 -154.25 132 1558 1974.71111111111 -416.711111111111 133 0 71.578947368421 -71.578947368421 134 43 1974.71111111111 -1931.71111111111 135 645 210.518518518519 434.481481481482 136 316 210.518518518519 105.481481481481 137 115 648.731707317073 -533.731707317073 138 5 71.578947368421 -66.578947368421 139 897 210.518518518519 686.481481481482 140 0 1.03478260869565 -1.03478260869565 141 389 648.731707317073 -259.731707317073 142 0 1.03478260869565 -1.03478260869565 143 1002 462.708333333333 539.291666666667 144 36 210.518518518519 -174.518518518519 145 460 648.731707317073 -188.731707317073 146 309 188.25 120.75 147 0 1.03478260869565 -1.03478260869565 148 9 81.3076923076923 -72.3076923076923 149 271 71.578947368421 199.421052631579 150 14 1.03478260869565 12.9652173913043 151 520 462.708333333333 57.2916666666667 152 1766 2895.42857142857 -1129.42857142857 153 0 210.518518518519 -210.518518518519 154 458 462.708333333333 -4.70833333333331 155 20 71.578947368421 -51.5789473684211 156 0 1.03478260869565 -1.03478260869565 157 0 1.03478260869565 -1.03478260869565 158 98 71.578947368421 26.4210526315789 159 405 210.518518518519 194.481481481481 160 0 1.03478260869565 -1.03478260869565 161 0 1.03478260869565 -1.03478260869565 162 0 1.03478260869565 -1.03478260869565 163 0 1.03478260869565 -1.03478260869565 164 483 462.708333333333 20.2916666666667 165 454 1974.71111111111 -1520.71111111111 166 47 71.578947368421 -24.5789473684211 167 0 1.03478260869565 -1.03478260869565 168 757 648.731707317073 108.268292682927 169 4655 2895.42857142857 1759.57142857143 170 0 1.03478260869565 -1.03478260869565 171 0 1.03478260869565 -1.03478260869565 172 36 210.518518518519 -174.518518518519 173 0 1.03478260869565 -1.03478260869565 174 203 81.3076923076923 121.692307692308 175 0 1.03478260869565 -1.03478260869565 176 126 81.3076923076923 44.6923076923077 177 400 6579.875 -6179.875 178 71 1.03478260869565 69.9652173913043 179 0 1.03478260869565 -1.03478260869565 180 0 1.03478260869565 -1.03478260869565 181 972 648.731707317073 323.268292682927 182 531 648.731707317073 -117.731707317073 183 2461 1974.71111111111 486.288888888889 184 378 462.708333333333 -84.7083333333333 185 23 210.518518518519 -187.518518518519 186 638 648.731707317073 -10.7317073170732 187 2300 1974.71111111111 325.288888888889 188 149 334.142857142857 -185.142857142857 189 226 210.518518518519 15.4814814814815 190 0 1.03478260869565 -1.03478260869565 191 275 648.731707317073 -373.731707317073 192 0 1.03478260869565 -1.03478260869565 193 141 81.3076923076923 59.6923076923077 194 0 1.03478260869565 -1.03478260869565 195 28 210.518518518519 -182.518518518519 196 0 1.03478260869565 -1.03478260869565 197 4980 6579.875 -1599.875 198 0 1.03478260869565 -1.03478260869565 199 0 1.03478260869565 -1.03478260869565 200 472 1221.28571428571 -749.285714285714 201 0 1.03478260869565 -1.03478260869565 202 0 1.03478260869565 -1.03478260869565 203 0 1.03478260869565 -1.03478260869565 204 203 462.708333333333 -259.708333333333 205 496 648.731707317073 -152.731707317073 206 10 210.518518518519 -200.518518518519 207 63 71.578947368421 -8.57894736842105 208 0 1.03478260869565 -1.03478260869565 209 1136 1974.71111111111 -838.711111111111 210 265 210.518518518519 54.4814814814815 211 0 1.03478260869565 -1.03478260869565 212 0 1.03478260869565 -1.03478260869565 213 267 648.731707317073 -381.731707317073 214 474 1221.28571428571 -747.285714285714 215 534 210.518518518519 323.481481481482 216 0 71.578947368421 -71.578947368421 217 15 81.3076923076923 -66.3076923076923 218 397 188.25 208.75 219 0 71.578947368421 -71.578947368421 220 1866 1974.71111111111 -108.711111111111 221 288 210.518518518519 77.4814814814815 222 0 1.03478260869565 -1.03478260869565 223 3 71.578947368421 -68.578947368421 224 468 1974.71111111111 -1506.71111111111 225 20 71.578947368421 -51.5789473684211 226 278 1974.71111111111 -1696.71111111111 227 61 81.3076923076923 -20.3076923076923 228 0 1.03478260869565 -1.03478260869565 229 192 71.578947368421 120.421052631579 230 0 1.03478260869565 -1.03478260869565 231 317 462.708333333333 -145.708333333333 232 738 648.731707317073 89.2682926829268 233 0 1.03478260869565 -1.03478260869565 234 368 462.708333333333 -94.7083333333333 235 0 1.03478260869565 -1.03478260869565 236 2 1.03478260869565 0.965217391304348 237 0 1.03478260869565 -1.03478260869565 238 53 188.25 -135.25 239 0 1.03478260869565 -1.03478260869565 240 0 1.03478260869565 -1.03478260869565 241 0 1.03478260869565 -1.03478260869565 242 94 210.518518518519 -116.518518518519 243 0 1.03478260869565 -1.03478260869565 244 24 188.25 -164.25 245 2332 984.428571428571 1347.57142857143 246 0 1.03478260869565 -1.03478260869565 247 0 1.03478260869565 -1.03478260869565 248 131 462.708333333333 -331.708333333333 249 0 1.03478260869565 -1.03478260869565 250 0 1.03478260869565 -1.03478260869565 251 206 462.708333333333 -256.708333333333 252 0 1.03478260869565 -1.03478260869565 253 167 71.578947368421 95.421052631579 254 622 2895.42857142857 -2273.42857142857 255 2328 6579.875 -4251.875 256 0 1.03478260869565 -1.03478260869565 257 365 462.708333333333 -97.7083333333333 258 364 1974.71111111111 -1610.71111111111 259 0 1.03478260869565 -1.03478260869565 260 0 1.03478260869565 -1.03478260869565 261 0 1.03478260869565 -1.03478260869565 262 0 1.03478260869565 -1.03478260869565 263 226 648.731707317073 -422.731707317073 264 307 648.731707317073 -341.731707317073 265 0 1.03478260869565 -1.03478260869565 266 0 1.03478260869565 -1.03478260869565 267 0 1.03478260869565 -1.03478260869565 268 188 648.731707317073 -460.731707317073 269 0 1.03478260869565 -1.03478260869565 270 138 1974.71111111111 -1836.71111111111 271 0 1.03478260869565 -1.03478260869565 272 0 1.03478260869565 -1.03478260869565 273 0 1.03478260869565 -1.03478260869565 274 125 81.3076923076923 43.6923076923077 275 0 1.03478260869565 -1.03478260869565 276 282 648.731707317073 -366.731707317073 277 335 1974.71111111111 -1639.71111111111 278 0 1.03478260869565 -1.03478260869565 279 1324 1974.71111111111 -650.711111111111 280 176 334.142857142857 -158.142857142857 281 0 1.03478260869565 -1.03478260869565 282 0 1.03478260869565 -1.03478260869565 283 249 1974.71111111111 -1725.71111111111 284 0 1.03478260869565 -1.03478260869565 285 333 648.731707317073 -315.731707317073 286 0 1.03478260869565 -1.03478260869565 287 601 210.518518518519 390.481481481482 288 30 210.518518518519 -180.518518518519 289 0 1.03478260869565 -1.03478260869565 290 249 462.708333333333 -213.708333333333 291 0 1.03478260869565 -1.03478260869565 292 165 648.731707317073 -483.731707317073 293 453 648.731707317073 -195.731707317073 294 0 1.03478260869565 -1.03478260869565 295 53 462.708333333333 -409.708333333333 296 382 648.731707317073 -266.731707317073 297 0 1.03478260869565 -1.03478260869565 298 0 1.03478260869565 -1.03478260869565 299 0 1.03478260869565 -1.03478260869565 300 0 81.3076923076923 -81.3076923076923 301 30 210.518518518519 -180.518518518519 302 290 71.578947368421 218.421052631579 303 0 71.578947368421 -71.578947368421 304 0 1.03478260869565 -1.03478260869565 305 366 648.731707317073 -282.731707317073 306 2 81.3076923076923 -79.3076923076923 307 0 1.03478260869565 -1.03478260869565 308 209 334.142857142857 -125.142857142857 309 384 188.25 195.75 310 0 1.03478260869565 -1.03478260869565 311 0 1.03478260869565 -1.03478260869565 312 365 2895.42857142857 -2530.42857142857 313 0 1.03478260869565 -1.03478260869565 314 49 648.731707317073 -599.731707317073 315 3 81.3076923076923 -78.3076923076923 316 133 984.428571428571 -851.428571428571 317 32 1.03478260869565 30.9652173913043 318 368 1974.71111111111 -1606.71111111111 319 1 210.518518518519 -209.518518518519 320 0 1.03478260869565 -1.03478260869565 321 0 1.03478260869565 -1.03478260869565 322 0 1.03478260869565 -1.03478260869565 323 0 1.03478260869565 -1.03478260869565 324 0 1.03478260869565 -1.03478260869565 325 0 1.03478260869565 -1.03478260869565 326 22 71.578947368421 -49.5789473684211 327 0 1.03478260869565 -1.03478260869565 328 0 1.03478260869565 -1.03478260869565 329 0 1.03478260869565 -1.03478260869565 330 0 1.03478260869565 -1.03478260869565 331 0 1.03478260869565 -1.03478260869565 332 0 1.03478260869565 -1.03478260869565 333 0 1.03478260869565 -1.03478260869565 334 96 210.518518518519 -114.518518518519 335 1 71.578947368421 -70.578947368421 336 314 210.518518518519 103.481481481481 337 844 1974.71111111111 -1130.71111111111 338 0 1.03478260869565 -1.03478260869565 339 26 71.578947368421 -45.5789473684211 340 125 188.25 -63.25 341 304 462.708333333333 -158.708333333333 342 0 1.03478260869565 -1.03478260869565 343 0 1.03478260869565 -1.03478260869565 344 0 1.03478260869565 -1.03478260869565 345 621 462.708333333333 158.291666666667 346 0 1.03478260869565 -1.03478260869565 347 119 210.518518518519 -91.5185185185185 348 0 1.03478260869565 -1.03478260869565 349 0 1.03478260869565 -1.03478260869565 350 1595 648.731707317073 946.268292682927 351 312 210.518518518519 101.481481481481 352 60 81.3076923076923 -21.3076923076923 353 587 648.731707317073 -61.7317073170732 354 135 71.578947368421 63.421052631579 355 0 1.03478260869565 -1.03478260869565 356 0 1.03478260869565 -1.03478260869565 357 514 648.731707317073 -134.731707317073 358 0 1.03478260869565 -1.03478260869565 359 0 1.03478260869565 -1.03478260869565 360 0 1.03478260869565 -1.03478260869565 361 1 210.518518518519 -209.518518518519 362 0 1.03478260869565 -1.03478260869565 363 0 1.03478260869565 -1.03478260869565 364 1763 1974.71111111111 -211.711111111111 365 180 188.25 -8.25 366 0 1.03478260869565 -1.03478260869565 367 0 1.03478260869565 -1.03478260869565 368 0 1.03478260869565 -1.03478260869565 369 0 1.03478260869565 -1.03478260869565 370 218 81.3076923076923 136.692307692308 371 0 1.03478260869565 -1.03478260869565 372 448 648.731707317073 -200.731707317073 373 227 648.731707317073 -421.731707317073 374 174 210.518518518519 -36.5185185185185 375 0 1.03478260869565 -1.03478260869565 376 0 1.03478260869565 -1.03478260869565 377 121 462.708333333333 -341.708333333333 378 607 648.731707317073 -41.7317073170732 379 2212 648.731707317073 1563.26829268293 380 0 1.03478260869565 -1.03478260869565 381 0 1.03478260869565 -1.03478260869565 382 530 334.142857142857 195.857142857143 383 571 1221.28571428571 -650.285714285714 384 0 1.03478260869565 -1.03478260869565 385 78 334.142857142857 -256.142857142857 386 2489 1974.71111111111 514.288888888889 387 131 210.518518518519 -79.5185185185185 388 923 462.708333333333 460.291666666667 389 72 210.518518518519 -138.518518518519 390 572 462.708333333333 109.291666666667 391 397 648.731707317073 -251.731707317073 392 450 462.708333333333 -12.7083333333333 393 622 1221.28571428571 -599.285714285714 394 694 648.731707317073 45.2682926829268 395 3425 1221.28571428571 2203.71428571429 396 562 1221.28571428571 -659.285714285714 397 4917 1974.71111111111 2942.28888888889 398 1442 27826 -26384 399 529 462.708333333333 66.2916666666667 400 2126 2895.42857142857 -769.428571428572 401 1061 984.428571428571 76.5714285714286 402 776 648.731707317073 127.268292682927 403 611 462.708333333333 148.291666666667 404 1526 1974.71111111111 -448.711111111111 405 592 1221.28571428571 -629.285714285714 406 1182 648.731707317073 533.268292682927 407 621 334.142857142857 286.857142857143 408 989 462.708333333333 526.291666666667 409 438 462.708333333333 -24.7083333333333 410 726 984.428571428571 -258.428571428571 411 1303 6579.875 -5276.875 412 7419 1974.71111111111 5444.28888888889 413 1164 1221.28571428571 -57.2857142857142 414 3310 5195.71428571429 -1885.71428571429 415 1920 1221.28571428571 698.714285714286 416 965 984.428571428571 -19.4285714285714 417 3256 1974.71111111111 1281.28888888889 418 1135 1974.71111111111 -839.711111111111 419 1270 1974.71111111111 -704.711111111111 420 661 984.428571428571 -323.428571428571 421 1013 984.428571428571 28.5714285714286 422 2844 6579.875 -3735.875 423 11528 6579.875 4948.125 424 6526 1974.71111111111 4551.28888888889 425 2264 1974.71111111111 289.288888888889 426 5109 6579.875 -1470.875 427 3999 1974.71111111111 2024.28888888889 428 35624 27826 7798 429 9252 2895.42857142857 6356.57142857143 430 15236 6579.875 8656.125 431 18073 6579.875 11493.125

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/2r03r1292937924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/2r03r1292937924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/32slu1292937924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/32slu1292937924.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/4uj2f1292937924.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937826cpn8izbstkfi4xd/4uj2f1292937924.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = quantiles ; par3 = 2 ; par4 = yes ;

Parameters (R input):

par1 = 1 ; par2 = none ; par3 = 2 ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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