paper - regression tree without categories

*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: Wed, 22 Dec 2010 18:12:26 +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/22/t1293041663kyazqoi7n68fg15.htm/, Retrieved Wed, 22 Dec 2010 19:14:23 +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/22/t1293041663kyazqoi7n68fg15.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 «

30.00 1 1 2 0 0 5.00 1 1 1 1 2 5.00 0 1 2 1 2 30.00 0 1 1 1 2 25.00 0 1 2 1 2 50.00 1 1 1 0 0 65.00 1 1 2 1 0 40.00 0 1 1 0 0 55.00 1 1 2 2 0 75.00 0 1 1 0 0 50.00 0 1 1 1 0 20.00 1 1 2 1 0 25.00 1 1 2 0 0 30.00 1 1 2 0 0 80.00 1 1 1 0 0 NA 0 1 1 0 0 25.00 0 1 1 0 1 50.00 1 1 2 0 1 35.00 1 1 2 0 0 40.00 0 1 1 0 0 40.00 1 1 1 1 1 50.00 1 1 2 1 1 30.00 1 1 1 0 0 45.00 1 1 2 0 0 30.00 1 1 2 0 0 25.00 0 1 1 0 0 25.00 1 1 1 1 0 20.00 1 1 2 1 0 35.00 1 1 2 0 0 30.00 1 1 1 0 0 45.00 0 1 1 0 0 40.00 1 1 1 0 0 30.00 1 1 2 0 0 60.00 1 1 2 0 0 45.00 0 1 1 0 0 45.00 1 1 2 0 0 25.00 1 1 2 0 1 NA 1 1 1 0 0 45.00 0 1 1 0 0 50.00 0 1 1 0 0 NA 0 1 1 0 0 45.00 1 1 2 0 0 60.00 1 1 2 2 0 60.00 1 1 2 0 0 45.00 1 1 2 0 0 45.00 0 1 1 0 0 NA 0 1 1 0 0 45.00 1 1 1 0 0 55.00 1 1 2 0 0 40.00 1 1 1 0 1 60.00 1 1 2 0 1 35.00 0 1 1 0 0 30.00 0 1 1 0 0 20.00 0 1 1 0 0 15.00 1 1 1 1 2 15.00 1 1 2 1 2 40.00 1 1 1 1 2 40.00 1 1 2 1 2 50.00 0 1 1 0 0 NA 1 1 2 0 0 40.00 0 1 1 1 0 80.0 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 68 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 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 0.6469 R-squared 0.4184 RMSE 0.3705 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 1 0.932 0.068 2 1 0.374193548387097 0.625806451612903 3 0 0.932 -0.932 4 0 0.374193548387097 -0.374193548387097 5 0 0.932 -0.932 6 1 0.374193548387097 0.625806451612903 7 1 0.932 0.068 8 0 0.374193548387097 -0.374193548387097 9 1 0.932 0.068 10 0 0.125 -0.125 11 0 0.374193548387097 -0.374193548387097 12 1 0.932 0.068 13 1 0.932 0.068 14 1 0.932 0.068 15 1 0.125 0.875 16 0 0.374193548387097 -0.374193548387097 17 0 0.374193548387097 -0.374193548387097 18 1 0.932 0.068 19 1 0.932 0.068 20 0 0.374193548387097 -0.374193548387097 21 1 0.374193548387097 0.625806451612903 22 1 0.932 0.068 23 1 0.374193548387097 0.625806451612903 24 1 0.932 0.068 25 1 0.932 0.068 26 0 0.374193548387097 -0.374193548387097 27 1 0.374193548387097 0.625806451612903 28 1 0.932 0.068 29 1 0.932 0.068 30 1 0.374193548387097 0.625806451612903 31 0 0.374193548387097 -0.374193548387097 32 1 0.374193548387097 0.625806451612903 33 1 0.932 0.068 34 1 0.932 0.068 35 0 0.374193548387097 -0.374193548387097 36 1 0.932 0.068 37 1 0.932 0.068 38 1 0.374193548387097 0.625806451612903 39 0 0.374193548387097 -0.374193548387097 40 0 0.374193548387097 -0.374193548387097 41 0 0.374193548387097 -0.374193548387097 42 1 0.932 0.068 43 1 0.932 0.068 44 1 0.932 0.068 45 1 0.932 0.068 46 0 0.374193548387097 -0.374193548387097 47 0 0.374193548387097 -0.374193548387097 48 1 0.374193548387097 0.625806451612903 49 1 0.932 0.068 50 1 0.374193548387097 0.625806451612903 51 1 0.932 0.068 52 0 0.374193548387097 -0.374193548387097 53 0 0.374193548387097 -0.374193548387097 54 0 0.374193548387097 -0.374193548387097 55 1 0.374193548387097 0.625806451612903 56 1 0.932 0.068 57 1 0.374193548387097 0.625806451612903 58 1 0.932 0.068 59 0 0.374193548387097 -0.374193548387097 60 1 0.932 0.068 61 0 0.374193548387097 -0.374193548387097 62 1 0.932 0.068 63 0 0.374193548387097 -0.374193548387097 64 1 0.932 0.068 65 1 0.374193548387097 0.625806451612903 66 1 0.932 0.068 67 1 0.932 0.068 68 1 0.932 0.068 69 1 0.374193548387097 0.625806451612903 70 1 0.932 0.068 71 0 0.374193548387097 -0.374193548387097 72 0 0.374193548387097 -0.374193548387097 73 1 0.932 0.068 74 1 0.932 0.068 75 0 0.374193548387097 -0.374193548387097 76 0 0.374193548387097 -0.374193548387097 77 1 0.932 0.068 78 0 0.374193548387097 -0.374193548387097 79 1 0.932 0.068 80 1 0.932 0.068 81 0 0.374193548387097 -0.374193548387097 82 0 0.125 -0.125 83 1 0.932 0.068 84 1 0.932 0.068 85 0 0.374193548387097 -0.374193548387097 86 1 0.932 0.068 87 1 0.374193548387097 0.625806451612903 88 1 0.374193548387097 0.625806451612903 89 1 0.932 0.068 90 0 0.374193548387097 -0.374193548387097 91 1 0.932 0.068 92 1 0.374193548387097 0.625806451612903 93 1 0.932 0.068 94 1 0.374193548387097 0.625806451612903 95 1 0.374193548387097 0.625806451612903 96 1 0.932 0.068 97 0 0.374193548387097 -0.374193548387097 98 1 0.932 0.068 99 1 0.932 0.068 100 1 0.932 0.068 101 1 0.374193548387097 0.625806451612903 102 0 0.374193548387097 -0.374193548387097 103 1 0.932 0.068 104 1 0.932 0.068 105 1 0.932 0.068 106 0 0.932 -0.932 107 0 0.374193548387097 -0.374193548387097 108 1 0.932 0.068 109 1 0.932 0.068 110 1 0.374193548387097 0.625806451612903 111 0 0.374193548387097 -0.374193548387097 112 1 0.932 0.068 113 1 0.932 0.068 114 1 0.932 0.068 115 0 0.374193548387097 -0.374193548387097 116 0 0.125 -0.125 117 1 0.932 0.068 118 1 0.932 0.068 119 0 0.374193548387097 -0.374193548387097 120 1 0.374193548387097 0.625806451612903 121 1 0.374193548387097 0.625806451612903 122 1 0.932 0.068 123 1 0.932 0.068 124 1 0.125 0.875 125 1 0.932 0.068 126 0 0.374193548387097 -0.374193548387097 127 0 0.374193548387097 -0.374193548387097 128 1 0.932 0.068 129 0 0.374193548387097 -0.374193548387097 130 1 0.932 0.068 131 1 0.932 0.068 132 1 0.932 0.068 133 0 0.374193548387097 -0.374193548387097 134 1 0.374193548387097 0.625806451612903 135 1 0.932 0.068 136 0 0.125 -0.125 137 1 0.374193548387097 0.625806451612903 138 1 0.932 0.068 139 0 0.374193548387097 -0.374193548387097 140 1 0.932 0.068 141 1 0.374193548387097 0.625806451612903 142 1 0.932 0.068 143 0 0.374193548387097 -0.374193548387097 144 1 0.374193548387097 0.625806451612903 145 1 0.932 0.068 146 1 0.374193548387097 0.625806451612903 147 1 0.932 0.068 148 0 0.374193548387097 -0.374193548387097 149 0 0.374193548387097 -0.374193548387097 150 1 0.932 0.068 151 0 0.374193548387097 -0.374193548387097 152 1 0.374193548387097 0.625806451612903 153 1 0.374193548387097 0.625806451612903 154 1 0.932 0.068 155 0 0.374193548387097 -0.374193548387097 156 1 0.932 0.068 157 0 0.374193548387097 -0.374193548387097 158 0 0.374193548387097 -0.374193548387097 159 0 0.374193548387097 -0.374193548387097 160 1 0.932 0.068 161 1 0.932 0.068 162 1 0.932 0.068 163 0 0.374193548387097 -0.374193548387097 164 1 0.932 0.068 165 1 0.374193548387097 0.625806451612903 166 1 0.374193548387097 0.625806451612903 167 0 0.374193548387097 -0.374193548387097 168 1 0.932 0.068 169 1 0.932 0.068 170 0 0.932 -0.932 171 1 0.374193548387097 0.625806451612903 172 0 0.374193548387097 -0.374193548387097 173 0 0.374193548387097 -0.374193548387097 174 0 0.374193548387097 -0.374193548387097 175 0 0.125 -0.125 176 0 0.374193548387097 -0.374193548387097 177 1 0.932 0.068 178 1 0.374193548387097 0.625806451612903 179 1 0.932 0.068 180 0 0.374193548387097 -0.374193548387097 181 1 0.932 0.068 182 1 0.932 0.068 183 1 0.932 0.068 184 1 0.374193548387097 0.625806451612903 185 0 0.374193548387097 -0.374193548387097 186 0 0.374193548387097 -0.374193548387097 187 1 0.932 0.068 188 1 0.932 0.068 189 1 0.932 0.068 190 0 0.374193548387097 -0.374193548387097 191 1 0.932 0.068 192 0 0.374193548387097 -0.374193548387097 193 1 0.932 0.068 194 1 0.932 0.068 195 0 0.374193548387097 -0.374193548387097 196 1 0.932 0.068 197 1 0.374193548387097 0.625806451612903 198 0 0.374193548387097 -0.374193548387097 199 1 0.932 0.068 200 1 0.932 0.068 201 0 0.374193548387097 -0.374193548387097 202 0 0.374193548387097 -0.374193548387097 203 1 0.374193548387097 0.625806451612903 204 0 0.374193548387097 -0.374193548387097 205 1 0.932 0.068 206 0 0.125 -0.125 207 0 0.374193548387097 -0.374193548387097 208 1 0.932 0.068 209 1 0.932 0.068 210 1 0.374193548387097 0.625806451612903 211 0 0.374193548387097 -0.374193548387097 212 0 0.374193548387097 -0.374193548387097 213 0 0.374193548387097 -0.374193548387097 214 1 0.932 0.068 215 1 0.932 0.068 216 0 0.125 -0.125 217 1 0.932 0.068 218 0 0.125 -0.125 219 1 0.932 0.068 220 1 0.374193548387097 0.625806451612903 221 1 0.932 0.068 222 0 0.125 -0.125 223 0 0.374193548387097 -0.374193548387097 224 0 0.374193548387097 -0.374193548387097 225 0 0.374193548387097 -0.374193548387097 226 0 0.374193548387097 -0.374193548387097 227 0 0.374193548387097 -0.374193548387097 228 1 0.932 0.068 229 0 0.374193548387097 -0.374193548387097 230 1 0.932 0.068 231 1 0.932 0.068 232 1 0.374193548387097 0.625806451612903 233 0 0.374193548387097 -0.374193548387097 234 1 0.932 0.068 235 0 0.374193548387097 -0.374193548387097 236 1 0.374193548387097 0.625806451612903 237 0 0.374193548387097 -0.374193548387097 238 0 0.374193548387097 -0.374193548387097 239 1 0.932 0.068 240 0 0.374193548387097 -0.374193548387097 241 1 0.374193548387097 0.625806451612903 242 0 0.374193548387097 -0.374193548387097 243 1 0.932 0.068 244 0 0.374193548387097 -0.374193548387097 245 0 0.374193548387097 -0.374193548387097 246 1 0.932 0.068 247 0 0.374193548387097 -0.374193548387097 248 1 0.932 0.068 249 0 0.374193548387097 -0.374193548387097 250 1 0.374193548387097 0.625806451612903 251 1 0.932 0.068 252 1 0.932 0.068 253 0 0.125 -0.125 254 1 0.932 0.068 255 1 0.374193548387097 0.625806451612903 256 1 0.932 0.068 257 1 0.374193548387097 0.625806451612903 258 1 0.932 0.068 259 1 0.932 0.068 260 1 0.374193548387097 0.625806451612903 261 1 0.932 0.068 262 1 0.374193548387097 0.625806451612903 263 0 0.374193548387097 -0.374193548387097 264 1 0.932 0.068 265 1 0.125 0.875 266 1 0.374193548387097 0.625806451612903 267 0 0.125 -0.125 268 0 0.125 -0.125 269 0 0.374193548387097 -0.374193548387097 270 0 0.374193548387097 -0.374193548387097 271 1 0.932 0.068 272 1 0.374193548387097 0.625806451612903 273 1 0.932 0.068 274 1 0.374193548387097 0.625806451612903 275 1 0.374193548387097 0.625806451612903 276 1 0.932 0.068 277 0 0.125 -0.125 278 1 0.932 0.068 279 1 0.374193548387097 0.625806451612903 280 0 0.125 -0.125 281 1 0.374193548387097 0.625806451612903 282 1 0.932 0.068 283 1 0.932 0.068 284 0 0.374193548387097 -0.374193548387097 285 1 0.932 0.068 286 0 0.125 -0.125 287 0 0.932 -0.932 288 0 0.125 -0.125 289 1 0.932 0.068 290 1 0.932 0.068 291 0 0.374193548387097 -0.374193548387097 292 1 0.932 0.068 293 1 0.374193548387097 0.625806451612903 294 1 0.932 0.068 295 0 0.374193548387097 -0.374193548387097 296 1 0.374193548387097 0.625806451612903 297 1 0.932 0.068 298 1 0.932 0.068 299 1 0.374193548387097 0.625806451612903 300 0 0.374193548387097 -0.374193548387097 301 0 0.125 -0.125 302 0 0.374193548387097 -0.374193548387097 303 1 0.932 0.068 304 0 0.125 -0.125 305 1 0.932 0.068 306 0 0.125 -0.125 307 0 0.374193548387097 -0.374193548387097 308 0 0.374193548387097 -0.374193548387097 309 1 0.932 0.068 310 1 0.932 0.068 311 0 0.125 -0.125 312 1 0.932 0.068 313 0 0.374193548387097 -0.374193548387097 314 0 0.374193548387097 -0.374193548387097 315 1 0.932 0.068 316 1 0.932 0.068 317 0 0.374193548387097 -0.374193548387097 318 1 0.374193548387097 0.625806451612903 319 0 0.374193548387097 -0.374193548387097 320 1 0.932 0.068 321 1 0.374193548387097 0.625806451612903 322 0 0.125 -0.125 323 1 0.932 0.068 324 0 0.126009693053312 -0.126009693053312 325 1 0.932 0.068 326 0 0.126009693053312 -0.126009693053312 327 0 0.126009693053312 -0.126009693053312 328 0 0.126009693053312 -0.126009693053312 329 0 0.126009693053312 -0.126009693053312 330 1 0.932 0.068 331 0 0.126009693053312 -0.126009693053312 332 0 0.126009693053312 -0.126009693053312 333 0 0.126009693053312 -0.126009693053312 334 1 0.932 0.068 335 0 0.126009693053312 -0.126009693053312 336 0 0.126009693053312 -0.126009693053312 337 1 0.126009693053312 0.873990306946688 338 1 0.932 0.068 339 0 0.126009693053312 -0.126009693053312 340 1 0.777777777777778 0.222222222222222 341 1 0.932 0.068 342 1 0.932 0.068 343 1 0.932 0.068 344 1 0.126009693053312 0.873990306946688 345 1 0.932 0.068 346 0 0.126009693053312 -0.126009693053312 347 0 0.126009693053312 -0.126009693053312 348 0 0.126009693053312 -0.126009693053312 349 0 0.126009693053312 -0.126009693053312 350 1 0.932 0.068 351 1 0.932 0.068 352 0 0.126009693053312 -0.126009693053312 353 1 0.932 0.068 354 1 0.932 0.068 355 0 0.126009693053312 -0.126009693053312 356 1 0.932 0.068 357 0 0.126009693053312 -0.126009693053312 358 0 0.126009693053312 -0.126009693053312 359 1 0.932 0.068 360 1 0.777777777777778 0.222222222222222 361 1 0.126009693053312 0.873990306946688 362 1 0.932 0.068 363 1 0.932 0.068 364 0 0.126009693053312 -0.126009693053312 365 0 0.126009693053312 -0.126009693053312 366 0 0.932 -0.932 367 0 0.126009693053312 -0.126009693053312 368 0 0.126009693053312 -0.126009693053312 369 0 0.126009693053312 -0.126009693053312 370 0 0.932 -0.932 371 1 0.932 0.068 372 1 0.932 0.068 373 0 0.126009693053312 -0.126009693053312 374 1 0.932 0.068 375 0 0.126009693053312 -0.126009693053312 376 0 0.126009693053312 -0.126009693053312 377 1 0.126009693053312 0.873990306946688 378 1 0.932 0.068 379 0 0.126009693053312 -0.126009693053312 380 1 0.932 0.068 381 1 0.932 0.068 382 0 0.932 -0.932 383 0 0.932 -0.932 384 0 0.126009693053312 -0.126009693053312 385 0 0.126009693053312 -0.126009693053312 386 1 0.777777777777778 0.222222222222222 387 0 0.126009693053312 -0.126009693053312 388 1 0.932 0.068 389 1 0.932 0.068 390 0 0.126009693053312 -0.126009693053312 391 0 0.126009693053312 -0.126009693053312 392 0 0.126009693053312 -0.126009693053312 393 1 0.932 0.068 394 0 0.126009693053312 -0.126009693053312 395 0 0.126009693053312 -0.126009693053312 396 1 0.932 0.068 397 1 0.932 0.068 398 0 0.126009693053312 -0.126009693053312 399 1 0.777777777777778 0.222222222222222 400 0 0.126009693053312 -0.126009693053312 401 1 0.932 0.068 402 1 0.932 0.068 403 1 0.932 0.068 404 0 0.126009693053312 -0.126009693053312 405 0 0.126009693053312 -0.126009693053312 406 0 0.126009693053312 -0.126009693053312 407 0 0.126009693053312 -0.126009693053312 408 1 0.932 0.068 409 0 0.126009693053312 -0.126009693053312 410 0 0.126009693053312 -0.126009693053312 411 0 0.126009693053312 -0.126009693053312 412 0 0.932 -0.932 413 0 0.126009693053312 -0.126009693053312 414 0 0.126009693053312 -0.126009693053312 415 0 0.126009693053312 -0.126009693053312 416 1 0.932 0.068 417 0 0.126009693053312 -0.126009693053312 418 0 0.126009693053312 -0.126009693053312 419 0 0.126009693053312 -0.126009693053312 420 0 0.126009693053312 -0.126009693053312 421 0 0.126009693053312 -0.126009693053312 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0.528205128205128 0.471794871794872 1246 0 0.126009693053312 -0.126009693053312 1247 0 0.126009693053312 -0.126009693053312 1248 1 0.528205128205128 0.471794871794872 1249 0 0.126009693053312 -0.126009693053312 1250 0 0.126009693053312 -0.126009693053312 1251 0 0.126009693053312 -0.126009693053312 1252 0 0.126009693053312 -0.126009693053312 1253 0 0.126009693053312 -0.126009693053312 1254 0 0.777777777777778 -0.777777777777778 1255 1 0.126009693053312 0.873990306946688 1256 0 0.126009693053312 -0.126009693053312 1257 1 0.777777777777778 0.222222222222222 1258 1 0.528205128205128 0.471794871794872 1259 1 0.528205128205128 0.471794871794872 1260 0 0.126009693053312 -0.126009693053312 1261 1 0.528205128205128 0.471794871794872 1262 1 0.528205128205128 0.471794871794872 1263 0 0.126009693053312 -0.126009693053312 1264 0 0.126009693053312 -0.126009693053312 1265 0 0.126009693053312 -0.126009693053312 1266 0 0.528205128205128 -0.528205128205128 1267 0 0.126009693053312 -0.126009693053312 1268 0 0.528205128205128 -0.528205128205128 1269 0 0.126009693053312 -0.126009693053312 1270 0 0.126009693053312 -0.126009693053312 1271 0 0.126009693053312 -0.126009693053312 1272 0 0.126009693053312 -0.126009693053312 1273 0 0.126009693053312 -0.126009693053312 1274 0 0.528205128205128 -0.528205128205128 1275 0 0.126009693053312 -0.126009693053312 1276 0 0.126009693053312 -0.126009693053312 1277 0 0.528205128205128 -0.528205128205128 1278 1 0.126009693053312 0.873990306946688 1279 0 0.126009693053312 -0.126009693053312 1280 0 0.528205128205128 -0.528205128205128 1281 0 0.126009693053312 -0.126009693053312 1282 0 0.126009693053312 -0.126009693053312 1283 0 0.126009693053312 -0.126009693053312 1284 0 0.126009693053312 -0.126009693053312 1285 0 0.126009693053312 -0.126009693053312 1286 0 0.126009693053312 -0.126009693053312 1287 1 0.528205128205128 0.471794871794872 1288 0 0.126009693053312 -0.126009693053312 1289 0 0.126009693053312 -0.126009693053312 1290 0 0.126009693053312 -0.126009693053312 1291 1 0.528205128205128 0.471794871794872 1292 0 0.126009693053312 -0.126009693053312 1293 0 0.126009693053312 -0.126009693053312 1294 0 0.126009693053312 -0.126009693053312 1295 0 0.126009693053312 -0.126009693053312 1296 0 0.126009693053312 -0.126009693053312 1297 0 0.126009693053312 -0.126009693053312 1298 0 0.777777777777778 -0.777777777777778 1299 0 0.126009693053312 -0.126009693053312 1300 0 0.126009693053312 -0.126009693053312 1301 1 0.528205128205128 0.471794871794872 1302 0 0.126009693053312 -0.126009693053312 1303 0 0.126009693053312 -0.126009693053312 1304 0 0.126009693053312 -0.126009693053312 1305 0 0.528205128205128 -0.528205128205128 1306 0 0.528205128205128 -0.528205128205128 1307 0 0.126009693053312 -0.126009693053312 1308 0 0.126009693053312 -0.126009693053312 1309 0 0.126009693053312 -0.126009693053312

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/2agev1293041474.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/2agev1293041474.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/3k7vz1293041474.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/3k7vz1293041474.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/4vgcj1293041474.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/22/t1293041663kyazqoi7n68fg15/4vgcj1293041474.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') }

Copyright

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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