orders 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 12:25:58 +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/t1292934237u5fvzphop5pqyzi.htm/, Retrieved Tue, 21 Dec 2010 13:24:07 +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/t1292934237u5fvzphop5pqyzi.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 «

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 640273 10327 74 88577 0 1 697458 53 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 18 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 0.871 R-squared 0.7586 RMSE 31.6689 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 444 250.636363636364 193.363636363636 2 412 250.636363636364 161.363636363636 3 428 250.636363636364 177.363636363636 4 315 250.636363636364 64.3636363636364 5 168 250.636363636364 -82.6363636363636 6 263 250.636363636364 12.3636363636364 7 267 250.636363636364 16.3636363636364 8 228 250.636363636364 -22.6363636363636 9 129 250.636363636364 -121.636363636364 10 104 250.636363636364 -146.636363636364 11 122 77.8367346938775 44.1632653061225 12 393 250.636363636364 142.363636363636 13 190 250.636363636364 -60.6363636363636 14 280 250.636363636364 29.3636363636364 15 63 77.8367346938775 -14.8367346938775 16 102 77.8367346938775 24.1632653061225 17 265 250.636363636364 14.3636363636364 18 234 250.636363636364 -16.6363636363636 19 277 250.636363636364 26.3636363636364 20 73 77.8367346938775 -4.83673469387755 21 67 250.636363636364 -183.636363636364 22 103 77.8367346938775 25.1632653061225 23 290 250.636363636364 39.3636363636364 24 83 77.8367346938775 5.16326530612245 25 56 77.8367346938775 -21.8367346938775 26 236 250.636363636364 -14.6363636363636 27 73 77.8367346938775 -4.83673469387755 28 34 77.8367346938775 -43.8367346938775 29 139 77.8367346938775 61.1632653061225 30 26 38.0735294117647 -12.0735294117647 31 70 77.8367346938775 -7.83673469387755 32 40 77.8367346938775 -37.8367346938775 33 42 77.8367346938775 -35.8367346938775 34 12 38.0735294117647 -26.0735294117647 35 211 250.636363636364 -39.6363636363636 36 74 77.8367346938775 -3.83673469387755 37 80 77.8367346938775 2.16326530612245 38 83 38.0735294117647 44.9264705882353 39 131 77.8367346938775 53.1632653061225 40 203 77.8367346938775 125.163265306122 41 56 77.8367346938775 -21.8367346938775 42 89 77.8367346938775 11.1632653061225 43 88 77.8367346938775 10.1632653061225 44 39 77.8367346938775 -38.8367346938775 45 25 38.0735294117647 -13.0735294117647 46 49 77.8367346938775 -28.8367346938775 47 149 77.8367346938775 71.1632653061225 48 58 77.8367346938775 -19.8367346938775 49 41 38.0735294117647 2.9264705882353 50 90 38.0735294117647 51.9264705882353 51 136 77.8367346938775 58.1632653061225 52 97 77.8367346938775 19.1632653061225 53 63 20.8235294117647 42.1764705882353 54 114 77.8367346938775 36.1632653061225 55 77 38.0735294117647 38.9264705882353 56 6 38.0735294117647 -32.0735294117647 57 47 77.8367346938775 -30.8367346938775 58 51 38.0735294117647 12.9264705882353 59 85 77.8367346938775 7.16326530612245 60 43 77.8367346938775 -34.8367346938775 61 32 38.0735294117647 -6.0735294117647 62 25 38.0735294117647 -13.0735294117647 63 77 77.8367346938775 -0.836734693877546 64 54 38.0735294117647 15.9264705882353 65 251 77.8367346938775 173.163265306122 66 15 11.8086956521739 3.19130434782609 67 44 38.0735294117647 5.9264705882353 68 73 38.0735294117647 34.9264705882353 69 85 77.8367346938775 7.16326530612245 70 49 38.0735294117647 10.9264705882353 71 38 77.8367346938775 -39.8367346938775 72 35 38.0735294117647 -3.0735294117647 73 9 11.8086956521739 -2.80869565217391 74 34 38.0735294117647 -4.0735294117647 75 20 38.0735294117647 -18.0735294117647 76 29 20.8235294117647 8.1764705882353 77 11 11.8086956521739 -0.808695652173913 78 52 38.0735294117647 13.9264705882353 79 13 11.8086956521739 1.19130434782609 80 29 38.0735294117647 -9.0735294117647 81 66 77.8367346938775 -11.8367346938775 82 33 20.8235294117647 12.1764705882353 83 15 11.8086956521739 3.19130434782609 84 15 20.8235294117647 -5.82352941176471 85 68 77.8367346938775 -9.83673469387755 86 100 38.0735294117647 61.9264705882353 87 13 20.8235294117647 -7.8235294117647 88 45 38.0735294117647 6.9264705882353 89 14 20.8235294117647 -6.82352941176471 90 36 38.0735294117647 -2.07352941176470 91 40 38.0735294117647 1.92647058823530 92 68 38.0735294117647 29.9264705882353 93 29 77.8367346938775 -48.8367346938775 94 43 38.0735294117647 4.9264705882353 95 30 38.0735294117647 -8.0735294117647 96 9 20.8235294117647 -11.8235294117647 97 22 38.0735294117647 -16.0735294117647 98 19 38.0735294117647 -19.0735294117647 99 9 38.0735294117647 -29.0735294117647 100 31 11.8086956521739 19.1913043478261 101 19 11.8086956521739 7.19130434782609 102 55 38.0735294117647 16.9264705882353 103 8 11.8086956521739 -3.80869565217391 104 28 38.0735294117647 -10.0735294117647 105 29 11.8086956521739 17.1913043478261 106 48 38.0735294117647 9.9264705882353 107 16 38.0735294117647 -22.0735294117647 108 47 77.8367346938775 -30.8367346938775 109 20 38.0735294117647 -18.0735294117647 110 22 20.8235294117647 1.17647058823529 111 33 38.0735294117647 -5.0735294117647 112 44 77.8367346938775 -33.8367346938775 113 13 38.0735294117647 -25.0735294117647 114 6 38.0735294117647 -32.0735294117647 115 35 38.0735294117647 -3.0735294117647 116 8 11.8086956521739 -3.80869565217391 117 17 38.0735294117647 -21.0735294117647 118 11 38.0735294117647 -27.0735294117647 119 21 11.8086956521739 9.19130434782609 120 92 77.8367346938775 14.1632653061225 121 12 11.8086956521739 0.191304347826087 122 112 38.0735294117647 73.9264705882353 123 25 38.0735294117647 -13.0735294117647 124 17 38.0735294117647 -21.0735294117647 125 23 38.0735294117647 -15.0735294117647 126 0 0.170940170940171 -0.170940170940171 127 10 4.92857142857143 5.07142857142857 128 23 11.8086956521739 11.1913043478261 129 0 0.170940170940171 -0.170940170940171 130 7 4.92857142857143 2.07142857142857 131 25 38.0735294117647 -13.0735294117647 132 1 0.170940170940171 0.829059829059829 133 20 4.92857142857143 15.0714285714286 134 4 11.8086956521739 -7.80869565217391 135 4 11.8086956521739 -7.80869565217391 136 10 4.92857142857143 5.07142857142857 137 1 4.92857142857143 -3.92857142857143 138 4 11.8086956521739 -7.80869565217391 139 0 0.170940170940171 -0.170940170940171 140 8 11.8086956521739 -3.80869565217391 141 0 0.170940170940171 -0.170940170940171 142 11 11.8086956521739 -0.808695652173913 143 4 4.92857142857143 -0.92857142857143 144 15 11.8086956521739 3.19130434782609 145 9 11.8086956521739 -2.80869565217391 146 0 0.170940170940171 -0.170940170940171 147 7 4.92857142857143 2.07142857142857 148 2 11.8086956521739 -9.80869565217391 149 0 4.92857142857143 -4.92857142857143 150 7 11.8086956521739 -4.80869565217391 151 46 38.0735294117647 7.9264705882353 152 5 0.170940170940171 4.82905982905983 153 7 11.8086956521739 -4.80869565217391 154 2 4.92857142857143 -2.92857142857143 155 0 0.170940170940171 -0.170940170940171 156 0 0.170940170940171 -0.170940170940171 157 2 4.92857142857143 -2.92857142857143 158 5 11.8086956521739 -6.80869565217391 159 0 0.170940170940171 -0.170940170940171 160 0 0.170940170940171 -0.170940170940171 161 0 0.170940170940171 -0.170940170940171 162 0 0.170940170940171 -0.170940170940171 163 7 11.8086956521739 -4.80869565217391 164 24 11.8086956521739 12.1913043478261 165 1 4.92857142857143 -3.92857142857143 166 0 0.170940170940171 -0.170940170940171 167 18 20.8235294117647 -2.82352941176471 168 55 77.8367346938775 -22.8367346938775 169 0 0.170940170940171 -0.170940170940171 170 0 0.170940170940171 -0.170940170940171 171 3 4.92857142857143 -1.92857142857143 172 0 0.170940170940171 -0.170940170940171 173 9 11.8086956521739 -2.80869565217391 174 0 0.170940170940171 -0.170940170940171 175 8 11.8086956521739 -3.80869565217391 176 113 11.8086956521739 101.191304347826 177 0 4.92857142857143 -4.92857142857143 178 0 0.170940170940171 -0.170940170940171 179 0 0.170940170940171 -0.170940170940171 180 19 20.8235294117647 -1.82352941176471 181 11 11.8086956521739 -0.808695652173913 182 25 38.0735294117647 -13.0735294117647 183 16 11.8086956521739 4.19130434782609 184 5 4.92857142857143 0.0714285714285712 185 11 11.8086956521739 -0.808695652173913 186 23 38.0735294117647 -15.0735294117647 187 6 11.8086956521739 -5.80869565217391 188 5 11.8086956521739 -6.80869565217391 189 0 0.170940170940171 -0.170940170940171 190 7 11.8086956521739 -4.80869565217391 191 0 0.170940170940171 -0.170940170940171 192 7 11.8086956521739 -4.80869565217391 193 0 0.170940170940171 -0.170940170940171 194 3 4.92857142857143 -1.92857142857143 195 0 0.170940170940171 -0.170940170940171 196 89 77.8367346938775 11.1632653061225 197 0 0.170940170940171 -0.170940170940171 198 0 0.170940170940171 -0.170940170940171 199 19 11.8086956521739 7.19130434782609 200 0 0.170940170940171 -0.170940170940171 201 0 0.170940170940171 -0.170940170940171 202 0 0.170940170940171 -0.170940170940171 203 12 11.8086956521739 0.191304347826087 204 12 11.8086956521739 0.191304347826087 205 5 4.92857142857143 0.0714285714285712 206 2 4.92857142857143 -2.92857142857143 207 0 0.170940170940171 -0.170940170940171 208 26 20.8235294117647 5.17647058823529 209 3 11.8086956521739 -8.80869565217391 210 0 0.170940170940171 -0.170940170940171 211 0 0.170940170940171 -0.170940170940171 212 11 11.8086956521739 -0.808695652173913 213 10 11.8086956521739 -1.80869565217391 214 5 11.8086956521739 -6.80869565217391 215 2 0.170940170940171 1.82905982905983 216 6 4.92857142857143 1.07142857142857 217 7 11.8086956521739 -4.80869565217391 218 2 0.170940170940171 1.82905982905983 219 28 38.0735294117647 -10.0735294117647 220 3 11.8086956521739 -8.80869565217391 221 0 0.170940170940171 -0.170940170940171 222 1 4.92857142857143 -3.92857142857143 223 20 11.8086956521739 8.19130434782609 224 1 4.92857142857143 -3.92857142857143 225 22 11.8086956521739 10.1913043478261 226 9 4.92857142857143 4.07142857142857 227 0 0.170940170940171 -0.170940170940171 228 2 11.8086956521739 -9.80869565217391 229 0 0.170940170940171 -0.170940170940171 230 7 11.8086956521739 -4.80869565217391 231 9 11.8086956521739 -2.80869565217391 232 0 0.170940170940171 -0.170940170940171 233 13 11.8086956521739 1.19130434782609 234 0 0.170940170940171 -0.170940170940171 235 0 4.92857142857143 -4.92857142857143 236 0 0.170940170940171 -0.170940170940171 237 6 4.92857142857143 1.07142857142857 238 0 0.170940170940171 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20.8235294117647 -7.8235294117647 263 10 11.8086956521739 -1.80869565217391 264 0 0.170940170940171 -0.170940170940171 265 0 0.170940170940171 -0.170940170940171 266 0 0.170940170940171 -0.170940170940171 267 9 20.8235294117647 -11.8235294117647 268 0 0.170940170940171 -0.170940170940171 269 26 11.8086956521739 14.1913043478261 270 0 0.170940170940171 -0.170940170940171 271 0 0.170940170940171 -0.170940170940171 272 0 0.170940170940171 -0.170940170940171 273 19 11.8086956521739 7.19130434782609 274 0 0.170940170940171 -0.170940170940171 275 12 11.8086956521739 0.191304347826087 276 23 11.8086956521739 11.1913043478261 277 0 0.170940170940171 -0.170940170940171 278 29 38.0735294117647 -9.0735294117647 279 8 11.8086956521739 -3.80869565217391 280 0 0.170940170940171 -0.170940170940171 281 0 0.170940170940171 -0.170940170940171 282 26 11.8086956521739 14.1913043478261 283 0 0.170940170940171 -0.170940170940171 284 9 11.8086956521739 -2.80869565217391 285 0 0.170940170940171 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0.170940170940171 -0.170940170940171 310 0 0.170940170940171 -0.170940170940171 311 32 20.8235294117647 11.1764705882353 312 0 0.170940170940171 -0.170940170940171 313 14 4.92857142857143 9.07142857142857 314 8 4.92857142857143 3.07142857142857 315 4 11.8086956521739 -7.80869565217391 316 0 4.92857142857143 -4.92857142857143 317 20 11.8086956521739 8.19130434782609 318 5 4.92857142857143 0.0714285714285712 319 0 0.170940170940171 -0.170940170940171 320 0 0.170940170940171 -0.170940170940171 321 0 0.170940170940171 -0.170940170940171 322 0 0.170940170940171 -0.170940170940171 323 0 0.170940170940171 -0.170940170940171 324 0 0.170940170940171 -0.170940170940171 325 1 4.92857142857143 -3.92857142857143 326 0 0.170940170940171 -0.170940170940171 327 0 0.170940170940171 -0.170940170940171 328 0 0.170940170940171 -0.170940170940171 329 0 0.170940170940171 -0.170940170940171 330 0 0.170940170940171 -0.170940170940171 331 0 0.170940170940171 -0.170940170940171 332 0 0.170940170940171 -0.170940170940171 333 4 4.92857142857143 -0.92857142857143 334 1 4.92857142857143 -3.92857142857143 335 4 11.8086956521739 -7.80869565217391 336 20 11.8086956521739 8.19130434782609 337 0 0.170940170940171 -0.170940170940171 338 1 4.92857142857143 -3.92857142857143 339 10 11.8086956521739 -1.80869565217391 340 12 11.8086956521739 0.191304347826087 341 0 0.170940170940171 -0.170940170940171 342 0 0.170940170940171 -0.170940170940171 343 0 0.170940170940171 -0.170940170940171 344 13 11.8086956521739 1.19130434782609 345 0 0.170940170940171 -0.170940170940171 346 3 4.92857142857143 -1.92857142857143 347 0 0.170940170940171 -0.170940170940171 348 0 0.170940170940171 -0.170940170940171 349 10 38.0735294117647 -28.0735294117647 350 3 11.8086956521739 -8.80869565217391 351 7 4.92857142857143 2.07142857142857 352 10 11.8086956521739 -1.80869565217391 353 1 11.8086956521739 -10.8086956521739 354 0 0.170940170940171 -0.170940170940171 355 0 0.170940170940171 -0.170940170940171 356 15 11.8086956521739 3.19130434782609 357 0 0.170940170940171 -0.170940170940171 358 0 0.170940170940171 -0.170940170940171 359 0 0.170940170940171 -0.170940170940171 360 4 4.92857142857143 -0.92857142857143 361 0 0.170940170940171 -0.170940170940171 362 0 0.170940170940171 -0.170940170940171 363 28 38.0735294117647 -10.0735294117647 364 9 11.8086956521739 -2.80869565217391 365 0 0.170940170940171 -0.170940170940171 366 0 0.170940170940171 -0.170940170940171 367 0 0.170940170940171 -0.170940170940171 368 0 0.170940170940171 -0.170940170940171 369 7 11.8086956521739 -4.80869565217391 370 0 0.170940170940171 -0.170940170940171 371 7 11.8086956521739 -4.80869565217391 372 7 11.8086956521739 -4.80869565217391 373 3 11.8086956521739 -8.80869565217391 374 0 0.170940170940171 -0.170940170940171 375 0 0.170940170940171 -0.170940170940171 376 11 11.8086956521739 -0.808695652173913 377 7 11.8086956521739 -4.80869565217391 378 10 38.0735294117647 -28.0735294117647 379 0 0.170940170940171 -0.170940170940171 380 0 0.170940170940171 -0.170940170940171 381 18 11.8086956521739 6.19130434782609 382 14 11.8086956521739 2.19130434782609 383 0 0.170940170940171 -0.170940170940171 384 12 4.92857142857143 7.07142857142857 385 29 38.0735294117647 -9.0735294117647 386 3 11.8086956521739 -8.80869565217391 387 6 11.8086956521739 -5.80869565217391 388 3 4.92857142857143 -1.92857142857143 389 8 11.8086956521739 -3.80869565217391 390 10 11.8086956521739 -1.80869565217391 391 6 11.8086956521739 -5.80869565217391 392 8 11.8086956521739 -3.80869565217391 393 6 20.8235294117647 -14.8235294117647 394 9 38.0735294117647 -29.0735294117647 395 8 11.8086956521739 -3.80869565217391 396 26 77.8367346938775 -51.8367346938775 397 239 38.0735294117647 200.926470588235 398 7 11.8086956521739 -4.80869565217391 399 41 38.0735294117647 2.9264705882353 400 3 11.8086956521739 -8.80869565217391 401 8 11.8086956521739 -3.80869565217391 402 6 11.8086956521739 -5.80869565217391 403 21 38.0735294117647 -17.0735294117647 404 7 11.8086956521739 -4.80869565217391 405 11 11.8086956521739 -0.808695652173913 406 11 11.8086956521739 -0.808695652173913 407 12 11.8086956521739 0.191304347826087 408 9 11.8086956521739 -2.80869565217391 409 3 11.8086956521739 -8.80869565217391 410 57 38.0735294117647 18.9264705882353 411 21 77.8367346938775 -56.8367346938775 412 15 11.8086956521739 3.19130434782609 413 32 38.0735294117647 -6.0735294117647 414 11 38.0735294117647 -27.0735294117647 415 2 11.8086956521739 -9.80869565217391 416 23 38.0735294117647 -15.0735294117647 417 20 11.8086956521739 8.19130434782609 418 24 11.8086956521739 12.1913043478261 419 1 11.8086956521739 -10.8086956521739 420 1 11.8086956521739 -10.8086956521739 421 74 38.0735294117647 35.9264705882353 422 68 77.8367346938775 -9.83673469387755 423 20 77.8367346938775 -57.8367346938775 424 20 38.0735294117647 -18.0735294117647 425 82 77.8367346938775 4.16326530612245 426 21 38.0735294117647 -17.0735294117647 427 244 250.636363636364 -6.63636363636363 428 32 77.8367346938775 -45.8367346938775 429 86 77.8367346938775 8.16326530612245 430 69 250.636363636364 -181.636363636364

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/2d7651292934339.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/2d7651292934339.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/3d7651292934339.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/3d7651292934339.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/4oy581292934339.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292934237u5fvzphop5pqyzi/4oy581292934339.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 3 ; 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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