Workshop 7 Costs RP

*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: Fri, 24 Dec 2010 14:48:42 +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/24/t1293202013uvug1fs5msh0h78.htm/, Retrieved Fri, 24 Dec 2010 15:46:54 +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/24/t1293202013uvug1fs5msh0h78.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 «

1 162556 1081 213118 6282929 1 29790 309 81767 4324047 1 87550 458 153198 4108272 0 84738 588 -26007 -1212617 1 54660 299 126942 1485329 1 42634 156 157214 1779876 0 40949 481 129352 1367203 1 42312 323 234817 2519076 1 37704 452 60448 912684 1 16275 109 47818 1443586 0 25830 115 245546 1220017 0 12679 110 48020 984885 1 18014 239 -1710 1457425 0 43556 247 32648 -572920 1 24524 497 95350 929144 0 6532 103 151352 1151176 0 7123 109 288170 790090 1 20813 502 114337 774497 1 37597 248 37884 990576 0 17821 373 122844 454195 1 12988 119 82340 876607 1 22330 84 79801 711969 0 13326 102 165548 702380 0 16189 295 116384 264449 0 7146 105 134028 450033 0 15824 64 63838 541063 1 26088 267 74996 588864 0 11326 129 31080 -37216 0 8568 37 32168 783310 0 14416 361 49857 467359 1 3369 28 87161 688779 1 11819 85 106113 608419 1 6620 44 80570 696348 1 4519 49 102129 597793 0 2220 22 301670 821730 0 18562 155 102313 377934 0 10327 91 88577 651939 1 5336 81 112477 697458 1 2365 79 191778 70 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 10 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk 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 0.7251 R-squared 0.5257 RMSE 8111.4854 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 162556 40511.3157894737 122044.684210526 2 29790 40511.3157894737 -10721.3157894737 3 87550 40511.3157894737 47038.6842105263 4 84738 40511.3157894737 44226.6842105263 5 54660 40511.3157894737 14148.6842105263 6 42634 17773.1 24860.9 7 40949 40511.3157894737 437.684210526313 8 42312 40511.3157894737 1800.68421052631 9 37704 40511.3157894737 -2807.31578947369 10 16275 17773.1 -1498.1 11 25830 17773.1 8056.9 12 12679 17773.1 -5094.1 13 18014 17773.1 240.900000000001 14 43556 40511.3157894737 3044.68421052631 15 24524 40511.3157894737 -15987.3157894737 16 6532 17773.1 -11241.1 17 7123 17773.1 -10650.1 18 20813 40511.3157894737 -19698.3157894737 19 37597 40511.3157894737 -2914.31578947369 20 17821 40511.3157894737 -22690.3157894737 21 12988 17773.1 -4785.1 22 22330 17773.1 4556.9 23 13326 17773.1 -4447.1 24 16189 40511.3157894737 -24322.3157894737 25 7146 6689.21212121212 456.787878787879 26 15824 3358.64406779661 12465.3559322034 27 26088 40511.3157894737 -14423.3157894737 28 11326 6689.21212121212 4636.78787878788 29 8568 3358.64406779661 5209.35593220339 30 14416 40511.3157894737 -26095.3157894737 31 3369 1535.93617021277 1833.06382978723 32 11819 6689.21212121212 5129.78787878788 33 6620 3358.64406779661 3261.35593220339 34 4519 3358.64406779661 1160.35593220339 35 2220 1535.93617021277 684.063829787234 36 18562 6689.21212121212 11872.7878787879 37 10327 6689.21212121212 3637.78787878788 38 5336 3358.64406779661 1977.35593220339 39 2365 3358.64406779661 -993.64406779661 40 4069 6689.21212121212 -2620.21212121212 41 7710 40511.3157894737 -32801.3157894737 42 13718 3358.64406779661 10359.3559322034 43 4525 6689.21212121212 -2164.21212121212 44 6869 6689.21212121212 179.787878787879 45 4628 3358.64406779661 1269.35593220339 46 3653 1535.93617021277 2117.06382978723 47 1265 1535.93617021277 -270.936170212766 48 7489 40511.3157894737 -33022.3157894737 49 4901 6689.21212121212 -1788.21212121212 50 2284 1535.93617021277 748.063829787234 51 3160 6689.21212121212 -3529.21212121212 52 4150 6689.21212121212 -2539.21212121212 53 7285 6689.21212121212 595.787878787879 54 1134 6689.21212121212 -5555.21212121212 55 4658 6689.21212121212 -2031.21212121212 56 2384 6689.21212121212 -4305.21212121212 57 3748 873.1 2874.9 58 5371 3358.64406779661 2012.35593220339 59 1285 6689.21212121212 -5404.21212121212 60 9327 6689.21212121212 2637.78787878788 61 5565 3358.64406779661 2206.35593220339 62 1528 3358.64406779661 -1830.64406779661 63 3122 1535.93617021277 1586.06382978723 64 7317 6689.21212121212 627.787878787879 65 2675 3358.64406779661 -683.64406779661 66 13253 40511.3157894737 -27258.3157894737 67 880 3358.64406779661 -2478.64406779661 68 2053 3358.64406779661 -1305.64406779661 69 1424 6689.21212121212 -5265.21212121212 70 4036 3358.64406779661 677.35593220339 71 3045 3358.64406779661 -313.64406779661 72 5119 3358.64406779661 1760.35593220339 73 1431 1535.93617021277 -104.936170212766 74 554 401.089285714286 152.910714285714 75 1975 1535.93617021277 439.063829787234 76 1286 1154.53333333333 131.466666666667 77 1012 3358.64406779661 -2346.64406779661 78 810 3358.64406779661 -2548.64406779661 79 1280 6689.21212121212 -5409.21212121212 80 666 6689.21212121212 -6023.21212121212 81 1380 1535.93617021277 -155.936170212766 82 4608 3358.64406779661 1249.35593220339 83 876 3358.64406779661 -2482.64406779661 84 814 401.089285714286 412.910714285714 85 514 873.1 -359.1 86 5692 3358.64406779661 2333.35593220339 87 3642 6689.21212121212 -3047.21212121212 88 540 873.1 -333.1 89 2099 3358.64406779661 -1259.64406779661 90 567 873.1 -306.1 91 2001 3358.64406779661 -1357.64406779661 92 2949 3358.64406779661 -409.64406779661 93 2253 6689.21212121212 -4436.21212121212 94 6533 3358.64406779661 3174.35593220339 95 1889 3358.64406779661 -1469.64406779661 96 3055 3358.64406779661 -303.64406779661 97 272 873.1 -601.1 98 1414 6689.21212121212 -5275.21212121212 99 2564 1535.93617021277 1028.06382978723 100 1383 1154.53333333333 228.466666666667 101 1261 1535.93617021277 -274.936170212766 102 975 1535.93617021277 -560.936170212766 103 3366 3358.64406779661 7.35593220338978 104 576 873.1 -297.1 105 1306 1535.93617021277 -229.936170212766 106 746 1535.93617021277 -789.936170212766 107 3192 3358.64406779661 -166.64406779661 108 2045 1535.93617021277 509.063829787234 109 5477 3358.64406779661 2118.35593220339 110 1932 3358.64406779661 -1426.64406779661 111 936 1535.93617021277 -599.936170212766 112 3437 3358.64406779661 78.3559322033898 113 5131 3358.64406779661 1772.35593220339 114 2397 1154.53333333333 1242.46666666667 115 1389 1154.53333333333 234.466666666667 116 1503 3358.64406779661 -1855.64406779661 117 402 1535.93617021277 -1133.93617021277 118 2239 1535.93617021277 703.063829787234 119 2234 1154.53333333333 1079.46666666667 120 837 3358.64406779661 -2521.64406779661 121 10579 6689.21212121212 3889.78787878788 122 875 873.1 1.89999999999998 123 1395 6689.21212121212 -5294.21212121212 124 1659 1535.93617021277 123.063829787234 125 2647 3358.64406779661 -711.64406779661 126 3294 1535.93617021277 1758.06382978723 127 0 0 0 128 94 401.089285714286 -307.089285714286 129 422 1535.93617021277 -1113.93617021277 130 0 0 0 131 34 401.089285714286 -367.089285714286 132 1558 3358.64406779661 -1800.64406779661 133 0 0 0 134 43 32.88 10.12 135 0 0 0 136 316 401.089285714286 -85.0892857142857 137 115 401.089285714286 -286.089285714286 138 0 0 0 139 0 0 0 140 0 0 0 141 389 401.089285714286 -12.0892857142857 142 0 0 0 143 1002 401.089285714286 600.910714285714 144 36 32.88 3.12 145 460 401.089285714286 58.9107142857143 146 309 401.089285714286 -92.0892857142857 147 0 0 0 148 9 32.88 -23.88 149 0 0 0 150 14 32.88 -18.88 151 520 401.089285714286 118.910714285714 152 1766 3358.64406779661 -1592.64406779661 153 0 0 0 154 458 401.089285714286 56.9107142857143 155 20 32.88 -12.88 156 0 0 0 157 0 0 0 158 98 111.555555555556 -13.5555555555556 159 405 401.089285714286 3.91071428571428 160 0 0 0 161 0 0 0 162 0 0 0 163 0 0 0 164 483 1535.93617021277 -1052.93617021277 165 454 3358.64406779661 -2904.64406779661 166 0 0 0 167 0 0 0 168 757 873.1 -116.1 169 0 0 0 170 0 0 0 171 0 0 0 172 36 32.88 3.12 173 0 0 0 174 203 401.089285714286 -198.089285714286 175 0 0 0 176 90 401.089285714286 -311.089285714286 177 0 0 0 178 71 32.88 38.12 179 0 0 0 180 0 0 0 181 972 1535.93617021277 -563.936170212766 182 531 1154.53333333333 -623.533333333333 183 604 3358.64406779661 -2754.64406779661 184 283 1154.53333333333 -871.533333333333 185 23 111.555555555556 -88.5555555555556 186 638 1154.53333333333 -516.533333333333 187 699 1154.53333333333 -455.533333333333 188 149 111.555555555556 37.4444444444444 189 226 208 18 190 0 0 0 191 275 111.555555555556 163.444444444444 192 0 0 0 193 141 111.555555555556 29.4444444444444 194 0 0 0 195 28 32.88 -4.88 196 0 0 0 197 2566 3358.64406779661 -792.64406779661 198 0 0 0 199 0 0 0 200 472 1535.93617021277 -1063.93617021277 201 0 0 0 202 0 0 0 203 0 0 0 204 203 1535.93617021277 -1332.93617021277 205 496 1154.53333333333 -658.533333333333 206 10 32.88 -22.88 207 63 111.555555555556 -48.5555555555556 208 0 0 0 209 1136 1535.93617021277 -399.936170212766 210 0 0 0 211 0 0 0 212 0 0 0 213 267 1535.93617021277 -1268.93617021277 214 474 1535.93617021277 -1061.93617021277 215 534 401.089285714286 132.910714285714 216 0 0 0 217 15 401.089285714286 -386.089285714286 218 397 1535.93617021277 -1138.93617021277 219 0 0 0 220 1061 1535.93617021277 -474.936170212766 221 288 208 80 222 0 0 0 223 3 32.88 -29.88 224 0 0 0 225 20 32.88 -12.88 226 278 1535.93617021277 -1257.93617021277 227 0 0 0 228 0 0 0 229 192 208 -16 230 0 0 0 231 317 401.089285714286 -84.0892857142857 232 0 0 0 233 0 0 0 234 368 401.089285714286 -33.0892857142857 235 0 0 0 236 2 32.88 -30.88 237 0 0 0 238 53 32.88 20.12 239 0 0 0 240 0 0 0 241 0 0 0 242 94 111.555555555556 -17.5555555555556 243 0 0 0 244 24 401.089285714286 -377.089285714286 245 2332 985.142857142857 1346.85714285714 246 0 0 0 247 0 0 0 248 131 401.089285714286 -270.089285714286 249 0 0 0 250 0 0 0 251 206 401.089285714286 -195.089285714286 252 0 0 0 253 167 208 -41 254 622 3358.64406779661 -2736.64406779661 255 885 6689.21212121212 -5804.21212121212 256 0 0 0 257 365 401.089285714286 -36.0892857142857 258 364 1535.93617021277 -1171.93617021277 259 0 0 0 260 0 0 0 261 0 0 0 262 0 0 0 263 226 401.089285714286 -175.089285714286 264 307 401.089285714286 -94.0892857142857 265 0 0 0 266 0 0 0 267 0 0 0 268 188 873.1 -685.1 269 0 0 0 270 138 985.142857142857 -847.142857142857 271 0 0 0 272 0 0 0 273 0 0 0 274 125 401.089285714286 -276.089285714286 275 0 0 0 276 282 1535.93617021277 -1253.93617021277 277 335 1535.93617021277 -1200.93617021277 278 0 0 0 279 813 3358.64406779661 -2545.64406779661 280 176 1535.93617021277 -1359.93617021277 281 0 0 0 282 0 0 0 283 249 3358.64406779661 -3109.64406779661 284 0 0 0 285 333 401.089285714286 -68.0892857142857 286 0 0 0 287 0 0 0 288 30 111.555555555556 -81.5555555555556 289 0 0 0 290 249 3358.64406779661 -3109.64406779661 291 0 0 0 292 165 1535.93617021277 -1370.93617021277 293 453 1535.93617021277 -1082.93617021277 294 0 0 0 295 53 401.089285714286 -348.089285714286 296 382 1535.93617021277 -1153.93617021277 297 0 0 0 298 0 0 0 299 0 0 0 300 0 0 0 301 30 32.88 -2.88 302 290 208 82 303 0 0 0 304 0 0 0 305 366 3358.64406779661 -2992.64406779661 306 2 401.089285714286 -399.089285714286 307 0 0 0 308 209 1154.53333333333 -945.533333333333 309 384 1535.93617021277 -1151.93617021277 310 0 0 0 311 0 0 0 312 365 3358.64406779661 -2993.64406779661 313 0 0 0 314 49 1154.53333333333 -1105.53333333333 315 3 32.88 -29.88 316 133 401.089285714286 -268.089285714286 317 32 32.88 -0.880000000000003 318 368 401.089285714286 -33.0892857142857 319 1 32.88 -31.88 320 0 0 0 321 0 0 0 322 0 0 0 323 0 0 0 324 0 0 0 325 0 0 0 326 22 32.88 -10.88 327 0 0 0 328 0 0 0 329 0 0 0 330 0 0 0 331 0 0 0 332 0 0 0 333 0 0 0 334 96 32.88 63.12 335 1 32.88 -31.88 336 0 0 0 337 81 401.089285714286 -320.089285714286 338 0 0 0 339 26 32.88 -6.88 340 125 401.089285714286 -276.089285714286 341 304 401.089285714286 -97.0892857142857 342 0 0 0 343 0 0 0 344 0 0 0 345 0 0 0 346 0 0 0 347 119 208 -89 348 0 0 0 349 0 0 0 350 0 0 0 351 312 401.089285714286 -89.0892857142857 352 60 401.089285714286 -341.089285714286 353 587 401.089285714286 185.910714285714 354 135 32.88 102.12 355 0 0 0 356 0 0 0 357 514 3358.64406779661 -2844.64406779661 358 0 0 0 359 0 0 0 360 0 0 0 361 1 32.88 -31.88 362 0 0 0 363 0 0 0 364 58 32.88 25.12 365 180 401.089285714286 -221.089285714286 366 0 0 0 367 0 0 0 368 0 0 0 369 0 0 0 370 0 0 0 371 0 0 0 372 448 3358.64406779661 -2910.64406779661 373 227 401.089285714286 -174.089285714286 374 174 208 -34 375 0 0 0 376 0 0 0 377 121 1535.93617021277 -1414.93617021277 378 607 401.089285714286 205.910714285714 379 0 0 0 380 0 0 0 381 0 0 0 382 530 3358.64406779661 -2828.64406779661 383 571 3358.64406779661 -2787.64406779661 384 0 0 0 385 78 401.089285714286 -323.089285714286 386 2489 3358.64406779661 -869.64406779661 387 131 111.555555555556 19.4444444444444 388 923 401.089285714286 521.910714285714 389 72 32.88 39.12 390 572 401.089285714286 170.910714285714 391 397 401.089285714286 -4.08928571428572 392 450 401.089285714286 48.9107142857143 393 622 401.089285714286 220.910714285714 394 694 873.1 -179.1 395 3425 1154.53333333333 2270.46666666667 396 562 401.089285714286 160.910714285714 397 4917 3358.64406779661 1558.35593220339 398 1442 6689.21212121212 -5247.21212121212 399 529 401.089285714286 127.910714285714 400 2126 6689.21212121212 -4563.21212121212 401 1061 985.142857142857 75.8571428571429 402 776 401.089285714286 374.910714285714 403 611 401.089285714286 209.910714285714 404 1526 1535.93617021277 -9.936170212766 405 592 401.089285714286 190.910714285714 406 1182 401.089285714286 780.910714285714 407 621 401.089285714286 219.910714285714 408 989 401.089285714286 587.910714285714 409 438 401.089285714286 36.9107142857143 410 726 985.142857142857 -259.142857142857 411 1303 3358.64406779661 -2055.64406779661 412 6341 1535.93617021277 4805.06382978723 413 1164 1154.53333333333 9.4666666666667 414 3310 1535.93617021277 1774.06382978723 415 1366 401.089285714286 964.910714285714 416 965 985.142857142857 -20.1428571428571 417 3256 3358.64406779661 -102.64406779661 418 1135 1154.53333333333 -19.5333333333333 419 1270 1535.93617021277 -265.936170212766 420 661 985.142857142857 -324.142857142857 421 1013 985.142857142857 27.8571428571429 422 2844 3358.64406779661 -514.64406779661 423 11528 3358.64406779661 8169.35593220339 424 6526 1535.93617021277 4990.06382978723 425 2264 1535.93617021277 728.063829787234 426 4461 6689.21212121212 -2228.21212121212 427 3999 1535.93617021277 2463.06382978723 428 35624 6689.21212121212 28934.7878787879 429 9252 3358.64406779661 5893.35593220339 430 15236 6689.21212121212 8546.78787878788 431 18073 6689.21212121212 11383.7878787879

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/2gb8p1293202109.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/2gb8p1293202109.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/3gb8p1293202109.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/3gb8p1293202109.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/4r27s1293202109.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/24/t1293202013uvug1fs5msh0h78/4r27s1293202109.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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