Costs ws10

*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 13:19:54 +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/t1292937498h1ijzznn5wu3ab4.htm/, Retrieved Tue, 21 Dec 2010 14:18:22 +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/t1292937498h1ijzznn5wu3ab4.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 «

6282154 162556 807 213118 1 1 4321023 29790 444 81767 1 1 4111912 87550 412 153198 1 1 223193 84738 428 -26007 0 1 1491348 54660 315 126942 1 1 1629616 42634 168 157214 1 0 1398893 40949 263 129352 0 1 1926517 45187 267 234817 1 0 983660 37704 228 60448 1 1 1443586 16275 129 47818 1 1 1073089 25830 104 245546 0 1 984885 12679 122 48020 0 1 1405225 18014 393 -1710 1 1 227132 43556 190 32648 0 1 929118 24811 280 95350 1 1 1071292 6575 63 151352 0 0 638830 7123 102 288170 0 0 856956 21950 265 114337 1 1 992426 37597 234 37884 1 1 444477 17821 277 122844 0 1 857217 12988 73 82340 1 1 711969 22330 67 79801 1 0 702380 13326 103 165548 0 0 358589 16189 290 116384 0 1 297978 7146 83 134028 0 1 585715 15824 56 63838 0 1 657954 27664 236 74996 1 1 209458 11920 73 31080 0 1 786690 8568 34 32168 0 1 439798 14416 139 49857 0 1 688779 3369 26 87161 1 1 574339 11819 70 106113 1 1 741409 6984 40 80570 1 0 597793 4519 42 102129 1 1 644190 2220 12 301670 0 1 377934 18562 211 102313 0 1 64027 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 33 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.8072 R-squared 0.6516 RMSE 6968.151 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 7940.3829787234 8334.6170212766 11 25830 7940.3829787234 17889.6170212766 12 12679 7940.3829787234 4738.61702127660 13 18014 64785.2857142857 -46771.2857142857 14 43556 27826 15730 15 24811 27826 -3015 16 6575 7940.3829787234 -1365.38297872340 17 7123 7940.3829787234 -817.382978723404 18 21950 27826 -5876 19 37597 27826 9771 20 17821 27826 -10005 21 12988 7940.3829787234 5047.61702127660 22 22330 7940.3829787234 14389.6170212766 23 13326 7940.3829787234 5385.61702127660 24 16189 64785.2857142857 -48596.2857142857 25 7146 7940.3829787234 -794.382978723404 26 15824 7940.3829787234 7883.6170212766 27 27664 27826 -162 28 11920 7940.3829787234 3979.61702127660 29 8568 5195.71428571429 3372.28571428571 30 14416 7940.3829787234 6475.6170212766 31 3369 1974.71111111111 1394.28888888889 32 11819 7940.3829787234 3878.61702127660 33 6984 5195.71428571429 1788.28571428571 34 4519 5195.71428571429 -676.714285714285 35 2220 1950.71428571429 269.285714285714 36 18562 27826 -9264 37 10327 7940.3829787234 2386.61702127660 38 5336 7940.3829787234 -2604.38297872340 39 2365 7940.3829787234 -5575.38297872340 40 4069 7940.3829787234 -3871.38297872340 41 8636 27826 -19190 42 13718 7940.3829787234 5777.6170212766 43 4525 7940.3829787234 -3415.38297872340 44 6869 7940.3829787234 -1071.38297872340 45 4628 2895.42857142857 1732.57142857143 46 3689 1974.71111111111 1714.28888888889 47 4891 2895.42857142857 1995.57142857143 48 7489 7940.3829787234 -451.382978723404 49 4901 7940.3829787234 -3039.38297872340 50 2284 2895.42857142857 -611.428571428572 51 3160 7940.3829787234 -4780.3829787234 52 4150 7940.3829787234 -3790.38297872340 53 7285 7940.3829787234 -655.382978723404 54 1134 7940.3829787234 -6806.3829787234 55 4658 7940.3829787234 -3282.38297872340 56 2384 7940.3829787234 -5556.38297872340 57 3748 1950.71428571429 1797.28571428571 58 5371 5195.71428571429 175.285714285715 59 1285 2895.42857142857 -1610.42857142857 60 9327 7940.3829787234 1386.61702127660 61 5565 5195.71428571429 369.285714285715 62 1528 2895.42857142857 -1367.42857142857 63 3122 1974.71111111111 1147.28888888889 64 7561 7940.3829787234 -379.382978723404 65 2675 2895.42857142857 -220.428571428572 66 13253 27826 -14573 67 880 456 424 68 2053 5195.71428571429 -3142.71428571429 69 1424 7940.3829787234 -6516.3829787234 70 4036 7940.3829787234 -3904.38297872340 71 3045 2895.42857142857 149.571428571428 72 5119 2895.42857142857 2223.57142857143 73 1431 2895.42857142857 -1464.42857142857 74 554 456 98 75 1975 2895.42857142857 -920.428571428572 76 1765 1974.71111111111 -209.711111111111 77 1012 1974.71111111111 -962.711111111111 78 810 456 354 79 1280 2895.42857142857 -1615.42857142857 80 666 1097.82352941176 -431.823529411765 81 1380 1974.71111111111 -594.711111111111 82 4677 7940.3829787234 -3263.38297872340 83 876 2895.42857142857 -2019.42857142857 84 814 456 358 85 514 456 58 86 5692 7940.3829787234 -2248.38297872340 87 3642 7940.3829787234 -4298.38297872340 88 540 456 84 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 7940.3829787234 -5687.38297872340 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 1097.82352941176 285.176470588235 101 1261 1974.71111111111 -713.711111111111 102 975 456 519 103 3366 2895.42857142857 470.571428571428 104 576 456 120 105 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 1097.82352941176 1299.17647058824 115 1389 1097.82352941176 291.176470588235 116 1503 2895.42857142857 -1392.42857142857 117 402 456 -54 118 2239 1950.71428571429 288.285714285714 119 2234 1097.82352941176 1136.17647058824 120 837 1974.71111111111 -1137.71111111111 121 10579 7940.3829787234 2638.61702127660 122 875 456 419 123 1585 7940.3829787234 -6355.3829787234 124 1659 1974.71111111111 -315.711111111111 125 2647 1097.82352941176 1549.17647058824 126 3294 1974.71111111111 1319.28888888889 127 0 1.03478260869565 -1.03478260869565 128 94 456 -362 129 422 1974.71111111111 -1552.71111111111 130 0 1.03478260869565 -1.03478260869565 131 34 456 -422 132 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 137 115 456 -341 138 5 71.578947368421 -66.578947368421 139 897 210.518518518519 686.481481481482 140 0 1.03478260869565 -1.03478260869565 141 389 456 -67 142 0 1.03478260869565 -1.03478260869565 143 1002 456 546 144 36 210.518518518519 -174.518518518519 145 460 456 4 146 309 456 -147 147 0 1.03478260869565 -1.03478260869565 148 9 456 -447 149 271 71.578947368421 199.421052631579 150 14 1.03478260869565 12.9652173913043 151 520 456 64 152 1766 2895.42857142857 -1129.42857142857 153 0 210.518518518519 -210.518518518519 154 458 456 2 155 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 164 483 456 27 165 454 1974.71111111111 -1520.71111111111 166 47 71.578947368421 -24.5789473684211 167 0 1.03478260869565 -1.03478260869565 168 757 456 301 169 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 174 203 456 -253 175 0 1.03478260869565 -1.03478260869565 176 126 456 -330 177 400 7940.3829787234 -7540.3829787234 178 71 1.03478260869565 69.9652173913043 179 0 1.03478260869565 -1.03478260869565 180 0 1.03478260869565 -1.03478260869565 181 972 456 516 182 531 1097.82352941176 -566.823529411765 183 2461 1974.71111111111 486.288888888889 184 378 1097.82352941176 -719.823529411765 185 23 210.518518518519 -187.518518518519 186 638 1097.82352941176 -459.823529411765 187 2300 1974.71111111111 325.288888888889 188 149 456 -307 189 226 210.518518518519 15.4814814814815 190 0 1.03478260869565 -1.03478260869565 191 275 456 -181 192 0 1.03478260869565 -1.03478260869565 193 141 456 -315 194 0 1.03478260869565 -1.03478260869565 195 28 210.518518518519 -182.518518518519 196 0 1.03478260869565 -1.03478260869565 197 4980 7940.3829787234 -2960.38297872340 198 0 1.03478260869565 -1.03478260869565 199 0 1.03478260869565 -1.03478260869565 200 472 1097.82352941176 -625.823529411765 201 0 1.03478260869565 -1.03478260869565 202 0 1.03478260869565 -1.03478260869565 203 0 1.03478260869565 -1.03478260869565 204 203 1097.82352941176 -894.823529411765 205 496 1097.82352941176 -601.823529411765 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 213 267 456 -189 214 474 456 18 215 534 210.518518518519 323.481481481482 216 0 71.578947368421 -71.578947368421 217 15 456 -441 218 397 456 -59 219 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 61 456 -395 228 0 1.03478260869565 -1.03478260869565 229 192 71.578947368421 120.421052631579 230 0 1.03478260869565 -1.03478260869565 231 317 456 -139 232 738 456 282 233 0 1.03478260869565 -1.03478260869565 234 368 456 -88 235 0 1.03478260869565 -1.03478260869565 236 2 1.03478260869565 0.965217391304348 237 0 1.03478260869565 -1.03478260869565 238 53 456 -403 239 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 24 456 -432 245 2332 984.428571428571 1347.57142857143 246 0 1.03478260869565 -1.03478260869565 247 0 1.03478260869565 -1.03478260869565 248 131 456 -325 249 0 1.03478260869565 -1.03478260869565 250 0 1.03478260869565 -1.03478260869565 251 206 456 -250 252 0 1.03478260869565 -1.03478260869565 253 167 71.578947368421 95.421052631579 254 622 2895.42857142857 -2273.42857142857 255 2328 7940.3829787234 -5612.38297872340 256 0 1.03478260869565 -1.03478260869565 257 365 456 -91 258 364 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 263 226 456 -230 264 307 456 -149 265 0 1.03478260869565 -1.03478260869565 266 0 1.03478260869565 -1.03478260869565 267 0 1.03478260869565 -1.03478260869565 268 188 456 -268 269 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 274 125 456 -331 275 0 1.03478260869565 -1.03478260869565 276 282 456 -174 277 335 1974.71111111111 -1639.71111111111 278 0 1.03478260869565 -1.03478260869565 279 1324 1974.71111111111 -650.711111111111 280 176 456 -280 281 0 1.03478260869565 -1.03478260869565 282 0 1.03478260869565 -1.03478260869565 283 249 1974.71111111111 -1725.71111111111 284 0 1.03478260869565 -1.03478260869565 285 333 456 -123 286 0 1.03478260869565 -1.03478260869565 287 601 210.518518518519 390.481481481482 288 30 210.518518518519 -180.518518518519 289 0 1.03478260869565 -1.03478260869565 290 249 456 -207 291 0 1.03478260869565 -1.03478260869565 292 165 456 -291 293 453 456 -3 294 0 1.03478260869565 -1.03478260869565 295 53 456 -403 296 382 1097.82352941176 -715.823529411765 297 0 1.03478260869565 -1.03478260869565 298 0 1.03478260869565 -1.03478260869565 299 0 1.03478260869565 -1.03478260869565 300 0 456 -456 301 30 210.518518518519 -180.518518518519 302 290 71.578947368421 218.421052631579 303 0 71.578947368421 -71.578947368421 304 0 1.03478260869565 -1.03478260869565 305 366 456 -90 306 2 456 -454 307 0 1.03478260869565 -1.03478260869565 308 209 1097.82352941176 -888.823529411765 309 384 456 -72 310 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 1097.82352941176 -1048.82352941176 315 3 456 -453 316 133 984.428571428571 -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 340 125 456 -331 341 304 456 -152 342 0 1.03478260869565 -1.03478260869565 343 0 1.03478260869565 -1.03478260869565 344 0 1.03478260869565 -1.03478260869565 345 621 456 165 346 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 456 1139 351 312 210.518518518519 101.481481481481 352 60 456 -396 353 587 456 131 354 135 71.578947368421 63.421052631579 355 0 1.03478260869565 -1.03478260869565 356 0 1.03478260869565 -1.03478260869565 357 514 456 58 358 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 365 180 456 -276 366 0 1.03478260869565 -1.03478260869565 367 0 1.03478260869565 -1.03478260869565 368 0 1.03478260869565 -1.03478260869565 369 0 1.03478260869565 -1.03478260869565 370 218 456 -238 371 0 1.03478260869565 -1.03478260869565 372 448 456 -8 373 227 456 -229 374 174 210.518518518519 -36.5185185185185 375 0 1.03478260869565 -1.03478260869565 376 0 1.03478260869565 -1.03478260869565 377 121 456 -335 378 607 456 151 379 2212 456 1756 380 0 1.03478260869565 -1.03478260869565 381 0 1.03478260869565 -1.03478260869565 382 530 456 74 383 571 456 115 384 0 1.03478260869565 -1.03478260869565 385 78 456 -378 386 2489 1974.71111111111 514.288888888889 387 131 210.518518518519 -79.5185185185185 388 923 456 467 389 72 210.518518518519 -138.518518518519 390 572 456 116 391 397 456 -59 392 450 456 -6 393 622 456 166 394 694 456 238 395 3425 1097.82352941176 2327.17647058824 396 562 456 106 397 4917 1974.71111111111 2942.28888888889 398 1442 27826 -26384 399 529 456 73 400 2126 2895.42857142857 -769.428571428572 401 1061 984.428571428571 76.5714285714286 402 776 456 320 403 611 456 155 404 1526 1974.71111111111 -448.711111111111 405 592 456 136 406 1182 456 726 407 621 456 165 408 989 456 533 409 438 456 -18 410 726 984.428571428571 -258.428571428571 411 1303 7940.3829787234 -6637.3829787234 412 7419 1974.71111111111 5444.28888888889 413 1164 1097.82352941176 66.1764705882354 414 3310 5195.71428571429 -1885.71428571429 415 1920 456 1464 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 7940.3829787234 -5096.38297872340 423 11528 7940.3829787234 3587.61702127660 424 6526 1974.71111111111 4551.28888888889 425 2264 1974.71111111111 289.288888888889 426 5109 7940.3829787234 -2831.38297872340 427 3999 1974.71111111111 2024.28888888889 428 35624 27826 7798 429 9252 2895.42857142857 6356.57142857143 430 15236 7940.3829787234 7295.6170212766 431 18073 7940.3829787234 10132.6170212766

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/2ucfv1292937560.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/2ucfv1292937560.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/3ucfv1292937560.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/3ucfv1292937560.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/4m4ef1292937560.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292937498h1ijzznn5wu3ab4/4m4ef1292937560.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 2 ; par2 = none ; par3 = 3 ; 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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Software written by Ed van Stee & Patrick Wessa

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