workshop 10 recursive partitioning

*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 13:09:51 +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/t1292936920xq9tyjhwz2q2fzj.htm/, Retrieved Tue, 21 Dec 2010 14:08:44 +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/t1292936920xq9tyjhwz2q2fzj.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 21 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.8561 R-squared 0.733 RMSE 38.4445 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 807 369.571428571429 437.428571428571 2 444 233.375 210.625 3 412 369.571428571429 42.4285714285714 4 428 369.571428571429 58.4285714285714 5 315 369.571428571429 -54.5714285714286 6 168 369.571428571429 -201.571428571429 7 263 233.375 29.625 8 267 369.571428571429 -102.571428571429 9 228 233.375 -5.375 10 129 233.375 -104.375 11 104 233.375 -129.375 12 122 77.8367346938775 44.1632653061225 13 393 233.375 159.625 14 190 369.571428571429 -179.571428571429 15 280 233.375 46.625 16 63 77.8367346938775 -14.8367346938775 17 102 77.8367346938775 24.1632653061225 18 265 233.375 31.625 19 234 233.375 0.625 20 277 233.375 43.625 21 73 77.8367346938775 -4.83673469387755 22 67 233.375 -166.375 23 103 77.8367346938775 25.1632653061225 24 290 233.375 56.625 25 83 77.8367346938775 5.16326530612245 26 56 77.8367346938775 -21.8367346938775 27 236 233.375 2.625 28 73 77.8367346938775 -4.83673469387755 29 34 77.8367346938775 -43.8367346938775 30 139 77.8367346938775 61.1632653061225 31 26 38.0735294117647 -12.0735294117647 32 70 77.8367346938775 -7.83673469387755 33 40 77.8367346938775 -37.8367346938775 34 42 77.8367346938775 -35.8367346938775 35 12 38.0735294117647 -26.0735294117647 36 211 233.375 -22.375 37 74 77.8367346938775 -3.83673469387755 38 80 77.8367346938775 2.16326530612245 39 83 38.0735294117647 44.9264705882353 40 131 77.8367346938775 53.1632653061225 41 203 77.8367346938775 125.163265306122 42 56 77.8367346938775 -21.8367346938775 43 89 77.8367346938775 11.1632653061225 44 88 77.8367346938775 10.1632653061225 45 39 77.8367346938775 -38.8367346938775 46 25 38.0735294117647 -13.0735294117647 47 49 77.8367346938775 -28.8367346938775 48 149 77.8367346938775 71.1632653061225 49 58 77.8367346938775 -19.8367346938775 50 41 38.0735294117647 2.9264705882353 51 90 38.0735294117647 51.9264705882353 52 136 77.8367346938775 58.1632653061225 53 97 77.8367346938775 19.1632653061225 54 63 20.8235294117647 42.1764705882353 55 114 77.8367346938775 36.1632653061225 56 77 38.0735294117647 38.9264705882353 57 6 38.0735294117647 -32.0735294117647 58 47 77.8367346938775 -30.8367346938775 59 51 38.0735294117647 12.9264705882353 60 85 77.8367346938775 7.16326530612245 61 43 77.8367346938775 -34.8367346938775 62 32 38.0735294117647 -6.0735294117647 63 25 38.0735294117647 -13.0735294117647 64 77 77.8367346938775 -0.836734693877546 65 54 38.0735294117647 15.9264705882353 66 251 77.8367346938775 173.163265306122 67 15 11.8086956521739 3.19130434782609 68 44 38.0735294117647 5.9264705882353 69 73 38.0735294117647 34.9264705882353 70 85 77.8367346938775 7.16326530612245 71 49 38.0735294117647 10.9264705882353 72 38 77.8367346938775 -39.8367346938775 73 35 38.0735294117647 -3.0735294117647 74 9 11.8086956521739 -2.80869565217391 75 34 38.0735294117647 -4.0735294117647 76 20 38.0735294117647 -18.0735294117647 77 29 20.8235294117647 8.1764705882353 78 11 11.8086956521739 -0.808695652173913 79 52 38.0735294117647 13.9264705882353 80 13 11.8086956521739 1.19130434782609 81 29 38.0735294117647 -9.0735294117647 82 66 77.8367346938775 -11.8367346938775 83 33 20.8235294117647 12.1764705882353 84 15 11.8086956521739 3.19130434782609 85 15 20.8235294117647 -5.82352941176471 86 68 77.8367346938775 -9.83673469387755 87 100 38.0735294117647 61.9264705882353 88 13 20.8235294117647 -7.8235294117647 89 45 38.0735294117647 6.9264705882353 90 14 20.8235294117647 -6.82352941176471 91 36 38.0735294117647 -2.07352941176470 92 40 38.0735294117647 1.92647058823530 93 68 38.0735294117647 29.9264705882353 94 29 77.8367346938775 -48.8367346938775 95 43 38.0735294117647 4.9264705882353 96 30 38.0735294117647 -8.0735294117647 97 9 20.8235294117647 -11.8235294117647 98 22 38.0735294117647 -16.0735294117647 99 19 38.0735294117647 -19.0735294117647 100 9 38.0735294117647 -29.0735294117647 101 31 11.8086956521739 19.1913043478261 102 19 11.8086956521739 7.19130434782609 103 55 38.0735294117647 16.9264705882353 104 8 11.8086956521739 -3.80869565217391 105 28 38.0735294117647 -10.0735294117647 106 29 11.8086956521739 17.1913043478261 107 48 38.0735294117647 9.9264705882353 108 16 38.0735294117647 -22.0735294117647 109 47 77.8367346938775 -30.8367346938775 110 20 38.0735294117647 -18.0735294117647 111 22 20.8235294117647 1.17647058823529 112 33 38.0735294117647 -5.0735294117647 113 44 77.8367346938775 -33.8367346938775 114 13 38.0735294117647 -25.0735294117647 115 6 38.0735294117647 -32.0735294117647 116 35 38.0735294117647 -3.0735294117647 117 8 11.8086956521739 -3.80869565217391 118 17 38.0735294117647 -21.0735294117647 119 11 38.0735294117647 -27.0735294117647 120 21 11.8086956521739 9.19130434782609 121 92 77.8367346938775 14.1632653061225 122 12 11.8086956521739 0.191304347826087 123 112 38.0735294117647 73.9264705882353 124 25 38.0735294117647 -13.0735294117647 125 17 38.0735294117647 -21.0735294117647 126 23 38.0735294117647 -15.0735294117647 127 0 0.170940170940171 -0.170940170940171 128 10 4.92857142857143 5.07142857142857 129 23 11.8086956521739 11.1913043478261 130 0 0.170940170940171 -0.170940170940171 131 7 4.92857142857143 2.07142857142857 132 25 38.0735294117647 -13.0735294117647 133 1 0.170940170940171 0.829059829059829 134 20 4.92857142857143 15.0714285714286 135 4 11.8086956521739 -7.80869565217391 136 4 11.8086956521739 -7.80869565217391 137 10 4.92857142857143 5.07142857142857 138 1 4.92857142857143 -3.92857142857143 139 4 11.8086956521739 -7.80869565217391 140 0 0.170940170940171 -0.170940170940171 141 8 11.8086956521739 -3.80869565217391 142 0 0.170940170940171 -0.170940170940171 143 11 11.8086956521739 -0.808695652173913 144 4 4.92857142857143 -0.92857142857143 145 15 11.8086956521739 3.19130434782609 146 9 11.8086956521739 -2.80869565217391 147 0 0.170940170940171 -0.170940170940171 148 7 4.92857142857143 2.07142857142857 149 2 11.8086956521739 -9.80869565217391 150 0 4.92857142857143 -4.92857142857143 151 7 11.8086956521739 -4.80869565217391 152 46 38.0735294117647 7.9264705882353 153 5 0.170940170940171 4.82905982905983 154 7 11.8086956521739 -4.80869565217391 155 2 4.92857142857143 -2.92857142857143 156 0 0.170940170940171 -0.170940170940171 157 0 0.170940170940171 -0.170940170940171 158 2 4.92857142857143 -2.92857142857143 159 5 11.8086956521739 -6.80869565217391 160 0 0.170940170940171 -0.170940170940171 161 0 0.170940170940171 -0.170940170940171 162 0 0.170940170940171 -0.170940170940171 163 0 0.170940170940171 -0.170940170940171 164 7 11.8086956521739 -4.80869565217391 165 24 11.8086956521739 12.1913043478261 166 1 4.92857142857143 -3.92857142857143 167 0 0.170940170940171 -0.170940170940171 168 18 20.8235294117647 -2.82352941176471 169 55 77.8367346938775 -22.8367346938775 170 0 0.170940170940171 -0.170940170940171 171 0 0.170940170940171 -0.170940170940171 172 3 4.92857142857143 -1.92857142857143 173 0 0.170940170940171 -0.170940170940171 174 9 11.8086956521739 -2.80869565217391 175 0 0.170940170940171 -0.170940170940171 176 8 11.8086956521739 -3.80869565217391 177 113 11.8086956521739 101.191304347826 178 0 4.92857142857143 -4.92857142857143 179 0 0.170940170940171 -0.170940170940171 180 0 0.170940170940171 -0.170940170940171 181 19 20.8235294117647 -1.82352941176471 182 11 11.8086956521739 -0.808695652173913 183 25 38.0735294117647 -13.0735294117647 184 16 11.8086956521739 4.19130434782609 185 5 4.92857142857143 0.0714285714285712 186 11 11.8086956521739 -0.808695652173913 187 23 38.0735294117647 -15.0735294117647 188 6 11.8086956521739 -5.80869565217391 189 5 11.8086956521739 -6.80869565217391 190 0 0.170940170940171 -0.170940170940171 191 7 11.8086956521739 -4.80869565217391 192 0 0.170940170940171 -0.170940170940171 193 7 11.8086956521739 -4.80869565217391 194 0 0.170940170940171 -0.170940170940171 195 3 4.92857142857143 -1.92857142857143 196 0 0.170940170940171 -0.170940170940171 197 89 77.8367346938775 11.1632653061225 198 0 0.170940170940171 -0.170940170940171 199 0 0.170940170940171 -0.170940170940171 200 19 11.8086956521739 7.19130434782609 201 0 0.170940170940171 -0.170940170940171 202 0 0.170940170940171 -0.170940170940171 203 0 0.170940170940171 -0.170940170940171 204 12 11.8086956521739 0.191304347826087 205 12 11.8086956521739 0.191304347826087 206 5 4.92857142857143 0.0714285714285712 207 2 4.92857142857143 -2.92857142857143 208 0 0.170940170940171 -0.170940170940171 209 26 20.8235294117647 5.17647058823529 210 3 11.8086956521739 -8.80869565217391 211 0 0.170940170940171 -0.170940170940171 212 0 0.170940170940171 -0.170940170940171 213 11 11.8086956521739 -0.808695652173913 214 10 11.8086956521739 -1.80869565217391 215 5 11.8086956521739 -6.80869565217391 216 2 0.170940170940171 1.82905982905983 217 6 4.92857142857143 1.07142857142857 218 7 11.8086956521739 -4.80869565217391 219 2 0.170940170940171 1.82905982905983 220 28 38.0735294117647 -10.0735294117647 221 3 11.8086956521739 -8.80869565217391 222 0 0.170940170940171 -0.170940170940171 223 1 4.92857142857143 -3.92857142857143 224 20 11.8086956521739 8.19130434782609 225 1 4.92857142857143 -3.92857142857143 226 22 11.8086956521739 10.1913043478261 227 9 4.92857142857143 4.07142857142857 228 0 0.170940170940171 -0.170940170940171 229 2 11.8086956521739 -9.80869565217391 230 0 0.170940170940171 -0.170940170940171 231 7 11.8086956521739 -4.80869565217391 232 9 11.8086956521739 -2.80869565217391 233 0 0.170940170940171 -0.170940170940171 234 13 11.8086956521739 1.19130434782609 235 0 0.170940170940171 -0.170940170940171 236 0 4.92857142857143 -4.92857142857143 237 0 0.170940170940171 -0.170940170940171 238 6 4.92857142857143 1.07142857142857 239 0 0.170940170940171 -0.170940170940171 240 0 0.170940170940171 -0.170940170940171 241 0 0.170940170940171 -0.170940170940171 242 3 4.92857142857143 -1.92857142857143 243 0 0.170940170940171 -0.170940170940171 244 7 4.92857142857143 2.07142857142857 245 2 38.0735294117647 -36.0735294117647 246 0 0.170940170940171 -0.170940170940171 247 0 0.170940170940171 -0.170940170940171 248 15 11.8086956521739 3.19130434782609 249 0 0.170940170940171 -0.170940170940171 250 0 0.170940170940171 -0.170940170940171 251 9 11.8086956521739 -2.80869565217391 252 0 0.170940170940171 -0.170940170940171 253 1 11.8086956521739 -10.8086956521739 254 38 11.8086956521739 26.1913043478261 255 57 38.0735294117647 18.9264705882353 256 0 0.170940170940171 -0.170940170940171 257 7 11.8086956521739 -4.80869565217391 258 26 11.8086956521739 14.1913043478261 259 0 0.170940170940171 -0.170940170940171 260 0 0.170940170940171 -0.170940170940171 261 0 0.170940170940171 -0.170940170940171 262 0 0.170940170940171 -0.170940170940171 263 13 20.8235294117647 -7.8235294117647 264 10 11.8086956521739 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4.92857142857143 -3.92857142857143 336 4 11.8086956521739 -7.80869565217391 337 20 11.8086956521739 8.19130434782609 338 0 0.170940170940171 -0.170940170940171 339 1 4.92857142857143 -3.92857142857143 340 10 11.8086956521739 -1.80869565217391 341 12 11.8086956521739 0.191304347826087 342 0 0.170940170940171 -0.170940170940171 343 0 0.170940170940171 -0.170940170940171 344 0 0.170940170940171 -0.170940170940171 345 13 11.8086956521739 1.19130434782609 346 0 0.170940170940171 -0.170940170940171 347 3 4.92857142857143 -1.92857142857143 348 0 0.170940170940171 -0.170940170940171 349 0 0.170940170940171 -0.170940170940171 350 10 38.0735294117647 -28.0735294117647 351 3 11.8086956521739 -8.80869565217391 352 7 4.92857142857143 2.07142857142857 353 10 11.8086956521739 -1.80869565217391 354 1 11.8086956521739 -10.8086956521739 355 0 0.170940170940171 -0.170940170940171 356 0 0.170940170940171 -0.170940170940171 357 15 11.8086956521739 3.19130434782609 358 0 0.170940170940171 -0.170940170940171 359 0 0.170940170940171 -0.170940170940171 360 0 0.170940170940171 -0.170940170940171 361 4 4.92857142857143 -0.92857142857143 362 0 0.170940170940171 -0.170940170940171 363 0 0.170940170940171 -0.170940170940171 364 28 38.0735294117647 -10.0735294117647 365 9 11.8086956521739 -2.80869565217391 366 0 0.170940170940171 -0.170940170940171 367 0 0.170940170940171 -0.170940170940171 368 0 0.170940170940171 -0.170940170940171 369 0 0.170940170940171 -0.170940170940171 370 7 11.8086956521739 -4.80869565217391 371 0 0.170940170940171 -0.170940170940171 372 7 11.8086956521739 -4.80869565217391 373 7 11.8086956521739 -4.80869565217391 374 3 11.8086956521739 -8.80869565217391 375 0 0.170940170940171 -0.170940170940171 376 0 0.170940170940171 -0.170940170940171 377 11 11.8086956521739 -0.808695652173913 378 7 11.8086956521739 -4.80869565217391 379 10 38.0735294117647 -28.0735294117647 380 0 0.170940170940171 -0.170940170940171 381 0 0.170940170940171 -0.170940170940171 382 18 11.8086956521739 6.19130434782609 383 14 11.8086956521739 2.19130434782609 384 0 0.170940170940171 -0.170940170940171 385 12 4.92857142857143 7.07142857142857 386 29 38.0735294117647 -9.0735294117647 387 3 11.8086956521739 -8.80869565217391 388 6 11.8086956521739 -5.80869565217391 389 3 4.92857142857143 -1.92857142857143 390 8 11.8086956521739 -3.80869565217391 391 10 11.8086956521739 -1.80869565217391 392 6 11.8086956521739 -5.80869565217391 393 8 11.8086956521739 -3.80869565217391 394 6 20.8235294117647 -14.8235294117647 395 9 38.0735294117647 -29.0735294117647 396 8 11.8086956521739 -3.80869565217391 397 26 77.8367346938775 -51.8367346938775 398 239 38.0735294117647 200.926470588235 399 7 11.8086956521739 -4.80869565217391 400 41 38.0735294117647 2.9264705882353 401 3 11.8086956521739 -8.80869565217391 402 8 11.8086956521739 -3.80869565217391 403 6 11.8086956521739 -5.80869565217391 404 21 38.0735294117647 -17.0735294117647 405 7 11.8086956521739 -4.80869565217391 406 11 11.8086956521739 -0.808695652173913 407 11 11.8086956521739 -0.808695652173913 408 12 11.8086956521739 0.191304347826087 409 9 11.8086956521739 -2.80869565217391 410 3 11.8086956521739 -8.80869565217391 411 57 38.0735294117647 18.9264705882353 412 21 77.8367346938775 -56.8367346938775 413 15 11.8086956521739 3.19130434782609 414 32 38.0735294117647 -6.0735294117647 415 11 38.0735294117647 -27.0735294117647 416 2 11.8086956521739 -9.80869565217391 417 23 38.0735294117647 -15.0735294117647 418 20 11.8086956521739 8.19130434782609 419 24 11.8086956521739 12.1913043478261 420 1 11.8086956521739 -10.8086956521739 421 1 11.8086956521739 -10.8086956521739 422 74 38.0735294117647 35.9264705882353 423 68 77.8367346938775 -9.83673469387755 424 20 77.8367346938775 -57.8367346938775 425 20 38.0735294117647 -18.0735294117647 426 82 77.8367346938775 4.16326530612245 427 21 38.0735294117647 -17.0735294117647 428 244 233.375 10.625 429 32 77.8367346938775 -45.8367346938775 430 86 77.8367346938775 8.16326530612245 431 69 233.375 -164.375

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/2f4bw1292936969.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/2f4bw1292936969.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/3f4bw1292936969.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/3f4bw1292936969.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/4qdsz1292936969.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292936920xq9tyjhwz2q2fzj/4qdsz1292936969.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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