*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 19:59:56 +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/t129296150385znwhrk89h7zs7.htm/, Retrieved Tue, 21 Dec 2010 20:58:23 +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/21/t129296150385znwhrk89h7zs7.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 «

8 350 165 3693 11.5 8 318 150 3436 11 8 302 140 3449 10.5 8 429 198 4341 10 8 440 215 4312 8.5 8 455 225 4425 10 8 383 170 3563 10 8 340 160 3609 8 8 455 225 3086 10 4 113 95 2372 15 6 199 97 2774 15.5 4 97 46 1835 20.5 4 110 87 2672 17.5 4 104 95 2375 17.5 4 121 113 2234 12.5 8 360 215 4615 14 8 307 200 4376 15 8 304 193 4732 18.5 4 97 88 2130 14.5 4 113 95 2228 14 6 250 100 3329 15.5 6 232 100 3288 15.5 8 350 165 4209 12 8 318 150 4096 13 8 400 170 4746 12 8 400 175 5140 12 4 140 72 2408 19 6 250 100 3282 15 4 122 86 2220 14 4 116 90 2123 14 4 88 76 2065 14.5 4 71 65 1773 19 4 97 60 1834 19 4 91 70 1955 20.5 4 97,5 80 2126 17 4 122 86 2226 16.5 8 350 165 4274 12 8 318 150 4135 13.5 8 351 153 4129 13 8 429 208 4633 11 8 350 155 4502 13.5 8 400 190 4422 12.5 3 70 97 2330 13.5 8 307 130 4098 14 8 302 140 4294 16 4 121 112 2933 14.5 4 121 76 2511 18 4 122 86 2395 16 4 120 97 2506 14.5 4 98 80 2164 15 8 350 175 4100 13 8 304 150 3672 11.5 8 302 137 4042 14.5 8 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 20 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 0.8469 R-squared 0.7173 RMSE 1.4455 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 11.5 11.26 0.24 2 11 12.1038461538462 -1.10384615384615 3 10.5 12.1038461538462 -1.60384615384615 4 10 9.94 0.0600000000000005 5 8.5 9.94 -1.44 6 10 10.8888888888889 -0.88888888888889 7 10 9.94 0.0600000000000005 8 8 11.26 -3.26 9 10 9.94 0.0600000000000005 10 15 15.5136363636364 -0.513636363636364 11 15.5 15.5083333333333 -0.00833333333333286 12 20.5 21.4055555555556 -0.905555555555555 13 17.5 16.3450980392157 1.15490196078431 14 17.5 15.5136363636364 1.98636363636364 15 12.5 13.0846153846154 -0.584615384615384 16 14 12.53 1.47 17 15 15.4142857142857 -0.414285714285715 18 18.5 15.4142857142857 3.08571428571429 19 14.5 16.3450980392157 -1.84509803921569 20 14 15.5136363636364 -1.51363636363636 21 15.5 15.6821428571429 -0.182142857142857 22 15.5 15.6821428571429 -0.182142857142857 23 12 11.26 0.74 24 13 13.7887096774194 -0.788709677419355 25 12 12.53 -0.529999999999999 26 12 12.53 -0.529999999999999 27 19 16.3450980392157 2.65490196078431 28 15 15.6821428571429 -0.682142857142857 29 14 16.3450980392157 -2.34509803921569 30 14 15.5136363636364 -1.51363636363636 31 14.5 16.3450980392157 -1.84509803921569 32 19 16.3450980392157 2.65490196078431 33 19 17.5166666666667 1.48333333333333 34 20.5 16.3450980392157 4.15490196078431 35 17 16.3450980392157 0.654901960784315 36 16.5 16.3450980392157 0.154901960784315 37 12 11.26 0.74 38 13.5 13.7887096774194 -0.288709677419355 39 13 13.7887096774194 -0.788709677419355 40 11 10.8888888888889 0.111111111111111 41 13.5 13.7887096774194 -0.288709677419355 42 12.5 12.53 -0.0299999999999994 43 13.5 14.6733333333333 -1.17333333333333 44 14 13.7887096774194 0.211290322580645 45 16 15.4 0.6 46 14.5 15.5083333333333 -1.00833333333333 47 18 16.3450980392157 1.65490196078431 48 16 16.3450980392157 -0.345098039215685 49 14.5 14.6733333333333 -0.173333333333334 50 15 16.3450980392157 -1.34509803921569 51 13 11.26 1.74 52 11.5 12.1038461538462 -0.603846153846154 53 14.5 15.4 -0.9 54 12.5 12.1038461538462 0.396153846153846 55 12 13.7887096774194 -1.78870967741936 56 13 13.7887096774194 -0.788709677419355 57 11 10.8888888888889 0.111111111111111 58 11 10.8888888888889 0.111111111111111 59 16.5 15.6821428571429 0.817857142857143 60 18 15.6821428571429 2.31785714285714 61 16.5 17.12 -0.620000000000001 62 16 15.5136363636364 0.486363636363636 63 14 13.7887096774194 0.211290322580645 64 12.5 12.53 -0.0299999999999994 65 15 15.5083333333333 -0.508333333333333 66 19.5 16.3450980392157 3.15490196078431 67 16.5 15.5136363636364 0.986363636363636 68 18.5 16.3450980392157 2.15490196078431 69 14 13.6307692307692 0.36923076923077 70 13 13.7887096774194 -0.788709677419355 71 9.5 9.94 -0.440000000000000 72 15.5 16.3450980392157 -0.845098039215685 73 14 15.5136363636364 -1.51363636363636 74 11 12.1038461538462 -1.10384615384615 75 14 13.6307692307692 0.36923076923077 76 11 11.26 -0.26 77 16.5 17.12 -0.620000000000001 78 16 15.5083333333333 0.491666666666667 79 16.5 16.3450980392157 0.154901960784315 80 21 16.3450980392157 4.65490196078431 81 17 18.1485714285714 -1.14857142857143 82 18 18.1485714285714 -0.148571428571429 83 14 15.4 -1.4 84 14.5 13.7887096774194 0.711290322580645 85 16 15.4 0.6 86 15.5 13.7887096774194 1.71129032258064 87 15.5 16.3450980392157 -0.845098039215685 88 14.5 16.3450980392157 -1.84509803921569 89 19 21.4055555555556 -2.40555555555556 90 14.5 16.3450980392157 -1.84509803921569 91 14 16.3450980392157 -2.34509803921569 92 15 14.6733333333333 0.326666666666666 93 16 16.3450980392157 -0.345098039215685 94 16 17.12 -1.12 95 19.5 21.0461538461538 -1.54615384615385 96 11.5 12.53 -1.03 97 14 13.7887096774194 0.211290322580645 98 13.5 13.7887096774194 -0.288709677419355 99 21 18.1485714285714 2.85142857142857 100 19 18.1485714285714 0.85142857142857 101 19 18.1485714285714 0.85142857142857 102 13.5 15.6821428571429 -2.18214285714286 103 12 12.1038461538462 -0.103846153846154 104 17 16.3450980392157 0.654901960784315 105 16 15.5083333333333 0.491666666666667 106 13.5 14.6733333333333 -1.17333333333333 107 16.5 16.3450980392157 0.154901960784315 108 14.5 15.6821428571429 -1.18214285714286 109 15 15.5136363636364 -0.513636363636364 110 17 18.8 -1.8 111 13.5 13.0846153846154 0.415384615384616 112 17.5 17.5166666666667 -0.0166666666666657 113 16.9 16.3450980392157 0.554901960784314 114 14.9 15.5136363636364 -0.613636363636363 115 15.3 16.3450980392157 -1.04509803921568 116 13 13.7887096774194 -0.788709677419355 117 13.9 13.7887096774194 0.111290322580645 118 12.8 13.7887096774194 -0.988709677419354 119 14.5 15.6821428571429 -1.18214285714286 120 17.6 17.12 0.48 121 22.2 21.4055555555556 0.794444444444444 122 22.1 21.4055555555556 0.694444444444446 123 17.7 18.1485714285714 -0.44857142857143 124 16.2 18.1485714285714 -1.94857142857143 125 17.8 17.12 0.68 126 17 16.3450980392157 0.654901960784315 127 16.4 16.3450980392157 0.0549019607843135 128 15.7 15.6821428571429 0.0178571428571423 129 13.2 13.7887096774194 -0.588709677419356 130 16.7 15.4 1.3 131 12.1 12.53 -0.43 132 15 15.4 -0.4 133 14 12.1038461538462 1.89615384615385 134 14.8 16.3450980392157 -1.54509803921568 135 18.6 17.5166666666667 1.08333333333334 136 16.8 16.3450980392157 0.454901960784316 137 12.5 13.7887096774194 -1.28870967741936 138 13.7 13.7887096774194 -0.0887096774193559 139 16.9 18.1485714285714 -1.24857142857143 140 17.7 18.1485714285714 -0.44857142857143 141 11.1 9.94 1.16 142 11.4 11.26 0.140000000000001 143 14.5 13.7887096774194 0.711290322580645 144 14.5 16.3450980392157 -1.84509803921569 145 18.2 16.3450980392157 1.85490196078431 146 15.8 16.3450980392157 -0.545098039215684 147 15.9 16.3450980392157 -0.445098039215685 148 16.4 16.3450980392157 0.0549019607843135 149 14.5 15.5083333333333 -1.00833333333333 150 12.8 13.6307692307692 -0.83076923076923 151 21.5 21.4055555555556 0.094444444444445 152 14.4 16.3450980392157 -1.94509803921568 153 18.6 16.3450980392157 2.25490196078432 154 13.2 12.1038461538462 1.09615384615384 155 12.8 12.1038461538462 0.696153846153846 156 18.2 17.12 1.08000000000000 157 15.8 17.12 -1.32 158 17.2 18.1485714285714 -0.94857142857143 159 17.2 17.12 0.0799999999999983 160 16.7 17.12 -0.420000000000002 161 18.7 18.1485714285714 0.55142857142857 162 13.2 12.1038461538462 1.09615384615384 163 13.4 15.4142857142857 -2.01428571428571 164 13.7 13.7887096774194 -0.0887096774193559 165 16.5 16.3450980392157 0.154901960784315 166 14.7 14.6733333333333 0.0266666666666655 167 14.5 16.3450980392157 -1.84509803921569 168 17.6 16.3450980392157 1.25490196078432 169 15.9 15.5083333333333 0.391666666666667 170 13.6 14.925 -1.325 171 15.8 14.925 0.875 172 14.9 16.3450980392157 -1.44509803921568 173 16.6 16.3450980392157 0.254901960784316 174 18.2 17.12 1.08000000000000 175 17.3 16.3450980392157 0.954901960784316 176 16.6 15.6821428571429 0.917857142857144 177 15.4 13.7887096774194 1.61129032258065 178 13.2 13.7887096774194 -0.588709677419356 179 15.2 13.7887096774194 1.41129032258064 180 14.3 13.7887096774194 0.511290322580646 181 15 14.925 0.0749999999999993 182 14 16.3450980392157 -2.34509803921569 183 15.2 16.3450980392157 -1.14509803921569 184 15 16.3450980392157 -1.34509803921569 185 24.8 21.0461538461538 3.75384615384615 186 22.2 18.1485714285714 4.05142857142857 187 14.9 16.3450980392157 -1.44509803921568 188 19.2 16.3450980392157 2.85490196078431 189 16 15.5136363636364 0.486363636363636 190 11.3 13.0846153846154 -1.78461538461538 191 13.2 15.5136363636364 -2.31363636363636 192 14.7 16.3450980392157 -1.64509803921569 193 15.5 16.3450980392157 -0.845098039215685 194 16.4 16.3450980392157 0.0549019607843135 195 18.1 16.3450980392157 1.75490196078432 196 20.1 18.8 1.3 197 15.8 16.3450980392157 -0.545098039215684 198 15.5 15.5136363636364 -0.0136363636363637 199 15 15.5136363636364 -0.513636363636364 200 15.2 16.3450980392157 -1.14509803921569 201 14.4 15.5083333333333 -1.10833333333333 202 19.2 16.3450980392157 2.85490196078431 203 19.9 21.0461538461538 -1.14615384615385 204 13.8 16.3450980392157 -2.54509803921568 205 15.3 16.3450980392157 -1.04509803921568 206 15.1 16.3450980392157 -1.24509803921569 207 15.7 16.3450980392157 -0.645098039215686 208 16.4 16.3450980392157 0.0549019607843135 209 12.6 13.6307692307692 -1.03076923076923 210 12.9 16.3450980392157 -3.44509803921568 211 16.4 16.3450980392157 0.0549019607843135 212 16.1 17.5166666666667 -1.41666666666666 213 19.4 16.3450980392157 3.05490196078431 214 17.3 16.3450980392157 0.954901960784316 215 14.9 16.3450980392157 -1.44509803921568 216 16.2 16.3450980392157 -0.145098039215686 217 14.2 16.3450980392157 -2.14509803921569 218 14.8 13.6307692307692 1.16923076923077 219 20.4 21.0461538461538 -0.646153846153847 220 13.8 13.0846153846154 0.715384615384616 221 15.8 15.6821428571429 0.117857142857144 222 17.1 17.12 -0.0199999999999996 223 16.6 18.1485714285714 -1.54857142857143 224 18.6 16.3450980392157 2.25490196078432 225 18 16.3450980392157 1.65490196078431 226 16 16.3450980392157 -0.345098039215685 227 18 15.5136363636364 2.48636363636364 228 15.3 16.3450980392157 -1.04509803921568 229 17.6 16.3450980392157 1.25490196078432 230 14.7 16.3450980392157 -1.64509803921569 231 14.5 16.3450980392157 -1.84509803921569 232 14.5 16.3450980392157 -1.84509803921569 233 15.7 16.3450980392157 -0.645098039215686 234 16.4 15.5083333333333 0.891666666666666 235 17 17.12 -0.120000000000001 236 13.9 14.6733333333333 -0.773333333333333 237 17.3 18.8 -1.5 238 15.6 16.3450980392157 -0.745098039215685 239 11.6 16.3450980392157 -4.74509803921569 240 18.6 16.3450980392157 2.25490196078432 241 18 16.3450980392157 1.65490196078431 242 17 17.5166666666667 -0.516666666666666 243 17 17.5166666666667 -0.516666666666666 244 16 17.5166666666667 -1.51666666666667 245 19 16.3450980392157 2.65490196078431 246 18 17.5166666666667 0.483333333333334 247 17 17.5166666666667 -0.516666666666666 248 14 16.3450980392157 -2.34509803921569 249 16 17.5166666666667 -1.51666666666667 250 12 16.3450980392157 -4.34509803921569 251 19 17.5166666666667 1.48333333333333 252 19 17.5166666666667 1.48333333333333 253 21 21.4055555555556 -0.405555555555555 254 21 16.3450980392157 4.65490196078431 255 15 16.3450980392157 -1.34509803921569 256 14 16.3450980392157 -2.34509803921569 257 20 21.4055555555556 -1.40555555555556 258 16 16.3450980392157 -0.345098039215685 259 14 16.3450980392157 -2.34509803921569 260 14 16.3450980392157 -2.34509803921569 261 14 16.3450980392157 -2.34509803921569 262 14 16.3450980392157 -2.34509803921569 263 15 16.3450980392157 -1.34509803921569 264 17 16.3450980392157 0.654901960784315 265 21 21.4055555555556 -0.405555555555555 266 19 16.3450980392157 2.65490196078431 267 21 16.3450980392157 4.65490196078431 268 16 16.3450980392157 -0.345098039215685 269 15 16.3450980392157 -1.34509803921569 270 16 16.3450980392157 -0.345098039215685 271 19 21.4055555555556 -2.40555555555556 272 18 16.3450980392157 1.65490196078431 273 15 16.3450980392157 -1.34509803921569 274 19 16.3450980392157 2.65490196078431 275 15 16.3450980392157 -1.34509803921569 276 22 21.4055555555556 0.594444444444445 277 16 16.3450980392157 -0.345098039215685 278 19 21.4055555555556 -2.40555555555556 279 16 16.3450980392157 -0.345098039215685 280 15 16.3450980392157 -1.34509803921569 281 17 16.3450980392157 0.654901960784315 282 16 16.3450980392157 -0.345098039215685 283 16 16.3450980392157 -0.345098039215685 284 19 21.4055555555556 -2.40555555555556 285 16 16.3450980392157 -0.345098039215685 286 19 16.3450980392157 2.65490196078431 287 18 16.3450980392157 1.65490196078431 288 22 21.4055555555556 0.594444444444445 289 16 16.3450980392157 -0.345098039215685 290 19 16.3450980392157 2.65490196078431 291 17 16.3450980392157 0.654901960784315 292 17 16.3450980392157 0.654901960784315 293 15 16.3450980392157 -1.34509803921569 294 18 16.3450980392157 1.65490196078431 295 19 16.3450980392157 2.65490196078431 296 20 16.3450980392157 3.65490196078431 297 16 16.3450980392157 -0.345098039215685 298 22 21.4055555555556 0.594444444444445 299 17 16.3450980392157 0.654901960784315 300 16 16.3450980392157 -0.345098039215685 301 18 16.3450980392157 1.65490196078431 302 19 16.3450980392157 2.65490196078431 303 16 16.3450980392157 -0.345098039215685 304 14 15.5136363636364 -1.51363636363636 305 14 15.5136363636364 -1.51363636363636 306 15 16.3450980392157 -1.34509803921569 307 15 16.3450980392157 -1.34509803921569 308 17 16.3450980392157 0.654901960784315 309 17 16.3450980392157 0.654901960784315 310 15 16.3450980392157 -1.34509803921569 311 25 21.4055555555556 3.59444444444444 312 15 16.3450980392157 -1.34509803921569 313 15 16.3450980392157 -1.34509803921569 314 17 16.3450980392157 0.654901960784315 315 15 16.3450980392157 -1.34509803921569 316 18 16.3450980392157 1.65490196078431 317 15 16.3450980392157 -1.34509803921569 318 15 16.3450980392157 -1.34509803921569 319 17 16.3450980392157 0.654901960784315 320 16 16.3450980392157 -0.345098039215685 321 16 16.3450980392157 -0.345098039215685 322 15 16.3450980392157 -1.34509803921569 323 15 16.3450980392157 -1.34509803921569 324 22 21.4055555555556 0.594444444444445 325 16 16.3450980392157 -0.345098039215685 326 16 16.3450980392157 -0.345098039215685 327 18 16.3450980392157 1.65490196078431 328 14 16.3450980392157 -2.34509803921569 329 14 16.3450980392157 -2.34509803921569 330 13 16.3450980392157 -3.34509803921569 331 15 16.3450980392157 -1.34509803921569 332 15 16.3450980392157 -1.34509803921569 333 14 16.3450980392157 -2.34509803921569 334 15 16.3450980392157 -1.34509803921569 335 17 16.3450980392157 0.654901960784315 336 14 16.3450980392157 -2.34509803921569 337 17 16.3450980392157 0.654901960784315 338 17 16.3450980392157 0.654901960784315 339 17 16.3450980392157 0.654901960784315 340 14 15.5136363636364 -1.51363636363636 341 15 16.3450980392157 -1.34509803921569 342 13 13.0846153846154 -0.0846153846153843 343 17 16.3450980392157 0.654901960784315 344 14 16.3450980392157 -2.34509803921569 345 24 21.4055555555556 2.59444444444444 346 18 16.3450980392157 1.65490196078431 347 16 15.5136363636364 0.486363636363636 348 16 15.5136363636364 0.486363636363636 349 18 16.3450980392157 1.65490196078431 350 15 16.3450980392157 -1.34509803921569 351 16 15.5136363636364 0.486363636363636 352 19 16.3450980392157 2.65490196078431 353 17 15.5136363636364 1.48636363636364 354 17 16.3450980392157 0.654901960784315 355 12 16.3450980392157 -4.34509803921569 356 16 14.6733333333333 1.32666666666667 357 15 16.3450980392157 -1.34509803921569 358 15 14.6733333333333 0.326666666666666 359 19 16.3450980392157 2.65490196078431 360 14 14.6733333333333 -0.673333333333334 361 24 21.4055555555556 2.59444444444444 362 17 16.3450980392157 0.654901960784315 363 13 16.3450980392157 -3.34509803921569 364 15 15.5136363636364 -0.513636363636364 365 18 15.5136363636364 2.48636363636364 366 17 15.5136363636364 1.48636363636364 367 21 16.3450980392157 4.65490196078431 368 13 16.3450980392157 -3.34509803921569 369 16 15.5136363636364 0.486363636363636 370 16 16.3450980392157 -0.345098039215685 371 18 16.3450980392157 1.65490196078431 372 20 16.3450980392157 3.65490196078431 373 15 14.6733333333333 0.326666666666666 374 19 16.3450980392157 2.65490196078431 375 20 15.5136363636364 4.48636363636364 376 13 13.6307692307692 -0.63076923076923 377 15 15.5136363636364 -0.513636363636364 378 15 15.5136363636364 -0.513636363636364 379 17 16.3450980392157 0.654901960784315 380 16 16.3450980392157 -0.345098039215685 381 14 13.6307692307692 0.36923076923077 382 15 14.6733333333333 0.326666666666666 383 16 16.3450980392157 -0.345098039215685 384 15 16.3450980392157 -1.34509803921569 385 15 14.6733333333333 0.326666666666666 386 18 16.3450980392157 1.65490196078431 387 15 15.5136363636364 -0.513636363636364 388 16 16.3450980392157 -0.345098039215685 389 17 16.3450980392157 0.654901960784315 390 18 16.3450980392157 1.65490196078431 391 17 14.6733333333333 2.32666666666667 392 13 15.5136363636364 -2.51363636363636 393 14 15.5136363636364 -1.51363636363636 394 14 16.3450980392157 -2.34509803921569 395 15 15.5136363636364 -0.513636363636364 396 16 16.3450980392157 -0.345098039215685 397 14 15.5136363636364 -1.51363636363636 398 15 15.5136363636364 -0.513636363636364 399 16 16.3450980392157 -0.345098039215685 400 19 16.3450980392157 2.65490196078431 401 11 13.0846153846154 -2.08461538461538 402 13 13.6307692307692 -0.63076923076923 403 20 16.3450980392157 3.65490196078431 404 15 13.6307692307692 1.36923076923077 405 14 15.5136363636364 -1.51363636363636 406 19 16.3450980392157 2.65490196078431 407 13 13.6307692307692 -0.63076923076923 408 16 16.3450980392157 -0.345098039215685 409 18 16.3450980392157 1.65490196078431 410 17 16.3450980392157 0.654901960784315 411 19 16.3450980392157 2.65490196078431 412 15 15.5136363636364 -0.513636363636364 413 14 13.6307692307692 0.36923076923077 414 14 14.6733333333333 -0.673333333333334 415 15 16.3450980392157 -1.34509803921569 416 16 15.5136363636364 0.486363636363636 417 14 13.0846153846154 0.915384615384616 418 18 16.3450980392157 1.65490196078431 419 17 15.5136363636364 1.48636363636364 420 15 15.5136363636364 -0.513636363636364 421 13 13.0846153846154 -0.0846153846153843 422 14 14.6733333333333 -0.673333333333334 423 16 15.5136363636364 0.486363636363636 424 15 16.3450980392157 -1.34509803921569 425 19 16.3450980392157 2.65490196078431 426 14 13.6307692307692 0.36923076923077 427 13 13.6307692307692 -0.63076923076923 428 18 15.5136363636364 2.48636363636364 429 16 16.3450980392157 -0.345098039215685 430 17 15.5083333333333 1.49166666666667 431 16 16.3450980392157 -0.345098039215685 432 16 15.5083333333333 0.491666666666667 433 15 15.5083333333333 -0.508333333333333 434 16 16.3450980392157 -0.345098039215685 435 16 13.0846153846154 2.91538461538462 436 14 15.5083333333333 -1.50833333333333 437 14 13.0846153846154 0.915384615384616 438 15 15.5083333333333 -0.508333333333333 439 16 15.5083333333333 0.491666666666667 440 16 15.5136363636364 0.486363636363636 441 15 15.5083333333333 -0.508333333333333 442 18 16.3450980392157 1.65490196078431 443 16 15.5136363636364 0.486363636363636 444 16 15.5083333333333 0.491666666666667 445 18 16.3450980392157 1.65490196078431 446 17 16.3450980392157 0.654901960784315 447 13 13.0846153846154 -0.0846153846153843 448 16 15.5083333333333 0.491666666666667 449 16 15.5136363636364 0.486363636363636 450 11 13.0846153846154 -2.08461538461538 451 16 15.5083333333333 0.491666666666667 452 16 15.5083333333333 0.491666666666667 453 14 13.0846153846154 0.915384615384616 454 15 15.5083333333333 -0.508333333333333 455 16 15.5083333333333 0.491666666666667 456 16 15.5083333333333 0.491666666666667 457 15 15.5083333333333 -0.508333333333333 458 17 18.8 -1.8 459 20 21.0461538461538 -1.04615384615385 460 17 18.8 -1.8 461 14 15.6821428571429 -1.68214285714286 462 16 17.12 -1.12 463 20 18.8 1.2 464 15 15.6821428571429 -0.682142857142857 465 18 17.12 0.879999999999999 466 20 18.8 1.2 467 18 17.12 0.879999999999999 468 17 17.12 -0.120000000000001 469 17 17.12 -0.120000000000001 470 15 15.6821428571429 -0.682142857142857 471 17 17.12 -0.120000000000001 472 17 17.12 -0.120000000000001 473 18 17.12 0.879999999999999 474 10 9.94 0.0600000000000005 475 17 17.12 -0.120000000000001 476 17 15.6821428571429 1.31785714285714 477 15 17.12 -2.12 478 14 14.925 -0.925 479 16 15.6821428571429 0.317857142857143 480 18 17.12 0.879999999999999 481 20 21.0461538461538 -1.04615384615385 482 20 21.0461538461538 -1.04615384615385 483 12 12.1038461538462 -0.103846153846154 484 25 21.0461538461538 3.95384615384615 485 18 17.12 0.879999999999999 486 11 12.1038461538462 -1.10384615384615 487 17 17.12 -0.120000000000001 488 17 17.12 -0.120000000000001 489 14 15.6821428571429 -1.68214285714286 490 20 21.0461538461538 -1.04615384615385 491 15 15.6821428571429 -0.682142857142857 492 15 14.925 0.0749999999999993 493 22 21.0461538461538 0.953846153846154 494 16 17.12 -1.12 495 18 17.12 0.879999999999999 496 22 18.8 3.2 497 18 15.6821428571429 2.31785714285714 498 15 15.6821428571429 -0.682142857142857 499 16 15.6821428571429 0.317857142857143 500 16 17.12 -1.12 501 16 15.6821428571429 0.317857142857143 502 17 15.6821428571429 1.31785714285714 503 15 15.6821428571429 -0.682142857142857 504 17 15.6821428571429 1.31785714285714 505 16 15.6821428571429 0.317857142857143 506 16 15.6821428571429 0.317857142857143 507 19 17.12 1.88 508 11 12.1038461538462 -1.10384615384615 509 15 14.925 0.0749999999999993 510 16 14.925 1.075 511 16 15.6821428571429 0.317857142857143 512 22 18.1485714285714 3.85142857142857 513 17 18.1485714285714 -1.14857142857143 514 13 12.1038461538462 0.896153846153846 515 17 18.1485714285714 -1.14857142857143 516 21 21.0461538461538 -0.046153846153846 517 12 12.1038461538462 -0.103846153846154 518 11 12.1038461538462 -1.10384615384615 519 16 18.1485714285714 -2.14857142857143 520 13 15.4142857142857 -2.41428571428571 521 11 12.1038461538462 -1.10384615384615 522 16 18.1485714285714 -2.14857142857143 523 17 18.1485714285714 -1.14857142857143 524 12 12.1038461538462 -0.103846153846154 525 16 18.1485714285714 -2.14857142857143 526 19 18.1485714285714 0.85142857142857 527 20 21.0461538461538 -1.04615384615385 528 19 18.1485714285714 0.85142857142857 529 10 9.94 0.0600000000000005 530 13 12.1038461538462 0.896153846153846 531 21 21.0461538461538 -0.046153846153846 532 15 14.925 0.0749999999999993 533 8 11.26 -3.26 534 17 18.1485714285714 -1.14857142857143 535 19 18.1485714285714 0.85142857142857 536 18 18.1485714285714 -0.148571428571429 537 18 18.1485714285714 -0.148571428571429 538 16 18.1485714285714 -2.14857142857143 539 18 18.1485714285714 -0.148571428571429 540 11 11.26 -0.26 541 12 12.1038461538462 -0.103846153846154 542 12 12.1038461538462 -0.103846153846154 543 12 11.26 0.74 544 13 12.1038461538462 0.896153846153846 545 19 18.1485714285714 0.85142857142857 546 19 18.1485714285714 0.85142857142857 547 13 12.1038461538462 0.896153846153846 548 14 12.1038461538462 1.89615384615385 549 10 12.1038461538462 -2.10384615384615 550 13 12.1038461538462 0.896153846153846 551 17 18.1485714285714 -1.14857142857143 552 19 18.1485714285714 0.85142857142857 553 17 15.4 1.6 554 11 11.26 -0.26 555 15 13.7887096774194 1.21129032258064 556 15 13.7887096774194 1.21129032258064 557 9 9.94 -0.94 558 15 15.4 -0.4 559 13 13.7887096774194 -0.788709677419355 560 13 13.7887096774194 -0.788709677419355 561 19 18.1485714285714 0.85142857142857 562 17 13.7887096774194 3.21129032258064 563 21 18.1485714285714 2.85142857142857 564 13 13.7887096774194 -0.788709677419355 565 13 13.7887096774194 -0.788709677419355 566 13 13.7887096774194 -0.788709677419355 567 14 13.7887096774194 0.211290322580645 568 13 13.7887096774194 -0.788709677419355 569 15 15.4 -0.4 570 14 13.7887096774194 0.211290322580645 571 12 13.7887096774194 -1.78870967741936 572 19 18.1485714285714 0.85142857142857 573 14 13.7887096774194 0.211290322580645 574 14 13.7887096774194 0.211290322580645 575 13 13.7887096774194 -0.788709677419355 576 13 13.7887096774194 -0.788709677419355 577 14 13.7887096774194 0.211290322580645 578 13 11.26 1.74 579 13 13.7887096774194 -0.788709677419355 580 14 13.7887096774194 0.211290322580645 581 14 13.7887096774194 0.211290322580645 582 14 15.4 -1.4 583 14 13.7887096774194 0.211290322580645 584 11 11.26 -0.26 585 13 13.7887096774194 -0.788709677419355 586 12 11.26 0.74 587 13 13.7887096774194 -0.788709677419355 588 13 13.7887096774194 -0.788709677419355 589 11 9.94 1.06 590 15 13.7887096774194 1.21129032258064 591 16 13.7887096774194 2.21129032258064 592 12 11.26 0.74 593 10 9.94 0.0600000000000005 594 16 15.4 0.6 595 15 15.4 -0.4 596 9 9.94 -0.94 597 12 9.94 2.06 598 15 13.7887096774194 1.21129032258064 599 10 9.94 0.0600000000000005 600 9 9.94 -0.94 601 15 13.7887096774194 1.21129032258064 602 13 13.7887096774194 -0.788709677419355 603 15 15.4142857142857 -0.414285714285715 604 12 12.53 -0.529999999999999 605 14 15.4142857142857 -1.41428571428571 606 12 12.53 -0.529999999999999 607 13 12.53 0.470000000000001 608 10 10.8888888888889 -0.88888888888889 609 14 13.7887096774194 0.211290322580645 610 14 12.53 1.47 611 14 13.7887096774194 0.211290322580645 612 12 12.53 -0.529999999999999 613 12 13.7887096774194 -1.78870967741936 614 15 13.7887096774194 1.21129032258064 615 13 12.53 0.470000000000001 616 14 13.7887096774194 0.211290322580645 617 14 12.53 1.47 618 11 10.8888888888889 0.111111111111111 619 16 15.4 0.6 620 13 12.53 0.470000000000001 621 14 13.7887096774194 0.211290322580645 622 12 12.53 -0.529999999999999 623 15 13.7887096774194 1.21129032258064 624 19 15.4142857142857 3.58571428571429 625 11 10.8888888888889 0.111111111111111 626 12 12.53 -0.529999999999999 627 13 12.53 0.470000000000001 628 11 10.8888888888889 0.111111111111111 629 12 10.8888888888889 1.11111111111111 630 12 12.53 -0.529999999999999 631 14 13.7887096774194 0.211290322580645 632 12 12.53 -0.529999999999999

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/2qvpc1292961572.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/2qvpc1292961572.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/3qvpc1292961572.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/3qvpc1292961572.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/40m6f1292961572.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t129296150385znwhrk89h7zs7/40m6f1292961572.ps (open in new window)

Parameters (Session):

par1 = kendall ;

Parameters (R input):

par1 = 5 ; 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

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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