*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: Sat, 25 Dec 2010 12:54:19 +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/25/t1293281526qjka1bh5y4cjl1z.htm/, Retrieved Sat, 25 Dec 2010 13:52:07 +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/25/t1293281526qjka1bh5y4cjl1z.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 «

0.903 2.544 2.217 3.567 1.061 0.903 2.502 2.176 3.536 1.041 0.903 2.480 2.146 3.538 1.021 0.903 2.632 2.297 3.638 1.000 0.903 2.643 2.332 3.635 0.929 0.903 2.658 2.352 3.646 1.000 0.903 2.583 2.230 3.552 1.000 0.903 2.531 2.204 3.557 0.903 0.903 2.658 2.352 3.489 1.000 0.602 2.053 1.978 3.375 1.176 0.778 2.299 1.987 3.443 1.190 0.602 1.987 1.663 3.264 1.312 0.602 2.041 1.940 3.427 1.243 0.602 2.017 1.978 3.376 1.243 0.602 2.083 2.053 3.349 1.097 0.903 2.556 2.332 3.664 1.146 0.903 2.487 2.301 3.641 1.176 0.903 2.483 2.286 3.675 1.267 0.602 1.987 1.944 3.328 1.161 0.602 2.053 1.978 3.348 1.146 0.778 2.398 2.000 3.522 1.190 0.778 2.365 2.000 3.517 1.190 0.903 2.544 2.217 3.624 1.079 0.903 2.502 2.176 3.612 1.114 0.903 2.602 2.230 3.676 1.079 0.903 2.602 2.243 3.711 1.079 0.602 2.146 1.857 3.382 1.279 0.778 2.398 2.000 3.516 1.176 0.602 2.086 1.934 3.346 1.146 0.602 2. 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 16 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Goodness of Fit Correlation 0.9642 R-squared 0.9298 RMSE 0.0396 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 2.217 2.20238095238095 0.0146190476190475 2 2.176 2.16704545454545 0.00895454545454566 3 2.146 2.13047619047619 0.0155238095238093 4 2.297 2.3318125 -0.0348124999999997 5 2.332 2.3318125 0.000187500000000007 6 2.352 2.3318125 0.0201875 7 2.23 2.24576666666667 -0.0157666666666665 8 2.204 2.20238095238095 0.00161904761904763 9 2.352 2.3318125 0.0201875 10 1.978 1.95546808510638 0.0225319148936169 11 1.987 1.98732786885246 -0.000327868852458835 12 1.663 1.74041176470588 -0.0774117647058823 13 1.94 1.95146341463415 -0.0114634146341464 14 1.978 1.91759649122807 0.0604035087719299 15 2.053 1.95546808510638 0.097531914893617 16 2.332 2.24576666666667 0.0862333333333334 17 2.301 2.264 0.0369999999999999 18 2.286 2.264 0.0219999999999998 19 1.944 1.88185185185185 0.062148148148148 20 1.978 1.95546808510638 0.0225319148936169 21 2 2.01523076923077 -0.0152307692307692 22 2 1.98732786885246 0.0126721311475411 23 2.217 2.20238095238095 0.0146190476190475 24 2.176 2.16704545454545 0.00895454545454566 25 2.23 2.24576666666667 -0.0157666666666665 26 2.243 2.24576666666667 -0.00276666666666658 27 1.857 1.91759649122807 -0.0605964912280701 28 2 1.98732786885246 0.0126721311475411 29 1.934 1.95546808510638 -0.0214680851063831 30 1.954 1.88185185185185 0.072148148148148 31 1.881 1.88185185185185 -0.000851851851851881 32 1.813 1.80261290322581 0.0103870967741935 33 1.778 1.80261290322581 -0.0246129032258064 34 1.845 1.74041176470588 0.104588235294118 35 1.903 1.88185185185185 0.0211481481481481 36 1.934 1.91759649122807 0.0164035087719299 37 2.217 2.2315 -0.0145 38 2.176 2.1496 0.0264000000000002 39 2.185 2.20238095238095 -0.0173809523809525 40 2.318 2.3318125 -0.0138124999999998 41 2.19 2.17783333333333 0.0121666666666664 42 2.279 2.24576666666667 0.0332333333333334 43 1.987 1.95546808510638 0.0315319148936171 44 2.114 2.1496 -0.0356000000000001 45 2.146 2.13047619047619 0.0155238095238093 46 2.049 1.98732786885246 0.061672131147541 47 1.881 1.91759649122807 -0.0365964912280701 48 1.934 1.91759649122807 0.0164035087719299 49 1.987 1.95546808510638 0.0315319148936171 50 1.903 1.88185185185185 0.0211481481481481 51 2.243 2.20238095238095 0.0406190476190473 52 2.176 2.16704545454545 0.00895454545454566 53 2.137 2.13047619047619 0.00652380952380938 54 2.176 2.16704545454545 0.00895454545454566 55 2.176 2.24576666666667 -0.0697666666666663 56 2.199 2.2315 -0.0325000000000002 57 2.332 2.3318125 0.000187500000000007 58 2.352 2.3318125 0.0201875 59 2.021 1.98732786885246 0.033672131147541 60 2 1.95146341463415 0.0485365853658537 61 1.944 1.98732786885246 -0.043327868852459 62 1.978 1.98732786885246 -0.00932786885245895 63 2.176 2.24576666666667 -0.0697666666666663 64 2.255 2.2315 0.0234999999999999 65 2 1.98732786885246 0.0126721311475411 66 1.857 1.83222222222222 0.0247777777777778 67 1.973 1.91759649122807 0.05540350877193 68 1.929 1.91759649122807 0.01140350877193 69 2.029 1.95546808510638 0.0735319148936169 70 2.161 2.20238095238095 -0.0413809523809525 71 2.362 2.24576666666667 0.116233333333334 72 1.875 1.88185185185185 -0.00685185185185189 73 1.959 1.95546808510638 0.00353191489361704 74 2.176 2.16704545454545 0.00895454545454566 75 2.041 2.02592307692308 0.015076923076923 76 2.255 2.20238095238095 0.0526190476190473 77 1.978 1.98732786885246 -0.00932786885245895 78 2 1.98732786885246 0.0126721311475411 79 1.903 1.91759649122807 -0.0145964912280701 80 1.813 1.74041176470588 0.0725882352941176 81 2 2.01523076923077 -0.0152307692307692 82 2.041 2.04642857142857 -0.00542857142857134 83 2.146 2.13047619047619 0.0155238095238093 84 2.176 2.17783333333333 -0.00183333333333335 85 2.146 2.13047619047619 0.0155238095238093 86 2.176 2.126 0.0500000000000003 87 1.826 1.82304 0.00296000000000007 88 1.892 1.95546808510638 -0.0634680851063831 89 1.785 1.80261290322581 -0.0176129032258066 90 1.875 1.88185185185185 -0.00685185185185189 91 1.875 1.95546808510638 -0.080468085106383 92 1.987 1.95546808510638 0.0315319148936171 93 1.826 1.82304 0.00296000000000007 94 1.978 1.98732786885246 -0.00932786885245895 95 1.857 1.91294736842105 -0.0559473684210525 96 2.23 2.24576666666667 -0.0157666666666665 97 2.161 2.17783333333333 -0.0168333333333335 98 2.17 2.17783333333333 -0.00783333333333358 99 2.041 1.91294736842105 0.128052631578947 100 2.041 2.04642857142857 -0.00542857142857134 101 1.978 2.02022222222222 -0.0422222222222224 102 2.041 2.09833333333333 -0.0573333333333332 103 2.111 2.13047619047619 -0.0194761904761904 104 1.919 1.91759649122807 0.00140350877192996 105 2 1.98732786885246 0.0126721311475411 106 1.982 2.02592307692308 -0.043923076923077 107 1.851 1.91759649122807 -0.0665964912280701 108 1.987 1.98732786885246 -0.000327868852458835 109 1.978 1.98732786885246 -0.00932786885245895 110 1.944 1.95146341463415 -0.00746341463414635 111 2.061 2.02592307692308 0.035076923076923 112 1.724 1.80261290322581 -0.0786129032258065 113 1.908 1.91759649122807 -0.00959649122807016 114 1.964 1.95546808510638 0.00853191489361693 115 1.919 1.88185185185185 0.0371481481481482 116 2.146 2.16704545454545 -0.0210454545454546 117 2.079 2.1496 -0.0705999999999998 118 2.182 2.20238095238095 -0.0203809523809526 119 2.021 2.01523076923077 0.00576923076923075 120 1.908 1.95146341463415 -0.0434634146341464 121 1.716 1.74041176470588 -0.0244117647058824 122 1.778 1.74041176470588 0.0375882352941177 123 2 2.02022222222222 -0.0202222222222224 124 2.041 2.01523076923077 0.0257692307692308 125 1.978 1.95146341463415 0.0265365853658537 126 1.845 1.82304 0.02196 127 1.875 1.88185185185185 -0.00685185185185189 128 2.009 1.98732786885246 0.021672131147541 129 2.176 2.1496 0.0264000000000002 130 2.079 2.02022222222222 0.0587777777777778 131 2.255 2.2315 0.0234999999999999 132 2.114 2.13047619047619 -0.0164761904761908 133 2.176 2.1496 0.0264000000000002 134 1.903 1.88185185185185 0.0211481481481481 135 1.763 1.80261290322581 -0.0396129032258066 136 1.845 1.82304 0.02196 137 2.161 2.16704545454545 -0.00604545454545447 138 2.161 2.1496 0.0114000000000001 139 2.021 2.02022222222222 0.000777777777777544 140 2 2.02022222222222 -0.0202222222222224 141 2.255 2.24576666666667 0.00923333333333343 142 2.23 2.20238095238095 0.0276190476190474 143 2.173 2.17783333333333 -0.00483333333333347 144 1.892 1.849 0.0429999999999999 145 1.875 1.91759649122807 -0.0425964912280701 146 1.949 1.98732786885246 -0.0383278688524589 147 1.919 1.88185185185185 0.0371481481481482 148 1.826 1.82304 0.00296000000000007 149 1.987 1.98732786885246 -0.000327868852458835 150 2.041 2.01355555555556 0.0274444444444444 151 1.681 1.74041176470588 -0.0594117647058823 152 1.82 1.772125 0.0478750000000001 153 1.845 1.80261290322581 0.0423870967741935 154 2.146 2.1496 -0.00360000000000005 155 2.143 2.13047619047619 0.0125238095238092 156 1.978 1.95146341463415 0.0265365853658537 157 1.929 1.98732786885246 -0.0583278688524589 158 2 2.02022222222222 -0.0202222222222224 159 1.954 1.95146341463415 0.00253658536585366 160 1.929 1.95146341463415 -0.0224634146341463 161 2.041 2.02022222222222 0.0207777777777776 162 2.161 2.1496 0.0114000000000001 163 2.217 2.09833333333333 0.118666666666667 164 2.146 2.1496 -0.00360000000000005 165 1.833 1.88185185185185 -0.0488518518518519 166 1.987 1.95546808510638 0.0315319148936171 167 1.875 1.95546808510638 -0.080468085106383 168 1.929 1.95146341463415 -0.0224634146341463 169 2.013 1.98732786885246 0.025672131147541 170 2.097 2.09833333333333 -0.00133333333333319 171 2.124 2.02022222222222 0.103777777777778 172 1.851 1.849 0.002 173 1.833 1.88185185185185 -0.0488518518518519 174 1.929 1.95146341463415 -0.0224634146341463 175 1.944 1.95146341463415 -0.00746341463414635 176 2.041 2.02022222222222 0.0207777777777776 177 2.114 2.126 -0.012 178 2.14 2.1496 -0.00959999999999983 179 2.13 2.126 0.004 180 2.152 2.1496 0.00240000000000018 181 2.097 2.13047619047619 -0.0334761904761907 182 1.851 1.849 0.002 183 1.813 1.82304 -0.01004 184 1.903 1.98732786885246 -0.0843278688524589 185 1.851 1.91294736842105 -0.0619473684210525 186 1.954 1.91294736842105 0.0410526315789475 187 1.845 1.88185185185185 -0.0368518518518519 188 1.813 1.80261290322581 0.0103870967741935 189 1.954 1.98732786885246 -0.033327868852459 190 2.061 2.01355555555556 0.0474444444444444 191 1.954 1.95546808510638 -0.00146808510638308 192 1.881 1.88185185185185 -0.000851851851851881 193 1.845 1.88185185185185 -0.0368518518518519 194 1.813 1.82304 -0.01004 195 1.944 1.95146341463415 -0.00746341463414635 196 1.954 1.91294736842105 0.0410526315789475 197 1.892 1.88185185185185 0.010148148148148 198 1.954 1.98732786885246 -0.033327868852459 199 1.964 1.95546808510638 0.00853191489361693 200 1.875 1.91759649122807 -0.0425964912280701 201 2.021 1.98732786885246 0.033672131147541 202 1.813 1.80261290322581 0.0103870967741935 203 1.826 1.83222222222222 -0.00622222222222213 204 1.826 1.849 -0.0229999999999999 205 1.792 1.82304 -0.03104 206 1.944 1.95546808510638 -0.0114680851063831 207 1.924 1.91759649122807 0.00640350877192986 208 1.924 1.91759649122807 0.00640350877192986 209 2.041 2.02592307692308 0.015076923076923 210 1.924 1.95546808510638 -0.0314680851063831 211 1.806 1.82304 -0.0170399999999999 212 1.778 1.772125 0.00587500000000007 213 1.813 1.80261290322581 0.0103870967741935 214 1.792 1.80261290322581 -0.0106129032258064 215 1.799 1.88185185185185 -0.082851851851852 216 1.813 1.82304 -0.01004 217 1.869 1.88185185185185 -0.0128518518518519 218 2 2.01355555555556 -0.0135555555555555 219 1.903 1.91294736842105 -0.00994736842105248 220 2.079 2.09833333333333 -0.019333333333333 221 2.041 2.02022222222222 0.0207777777777776 222 1.944 1.95146341463415 -0.00746341463414635 223 1.929 2.02022222222222 -0.0912222222222223 224 1.944 1.91759649122807 0.0264035087719299 225 1.944 1.91759649122807 0.0264035087719299 226 1.924 1.91759649122807 0.00640350877192986 227 1.954 1.95146341463415 0.00253658536585366 228 1.869 1.82304 0.04596 229 1.833 1.80261290322581 0.0303870967741935 230 1.799 1.88185185185185 -0.082851851851852 231 1.944 1.88185185185185 0.062148148148148 232 1.875 1.88185185185185 -0.00685185185185189 233 1.826 1.82304 0.00296000000000007 234 2.041 1.98732786885246 0.053672131147541 235 1.929 1.95146341463415 -0.0224634146341463 236 1.982 2.02592307692308 -0.043923076923077 237 1.954 1.95146341463415 0.00253658536585366 238 1.934 1.98732786885246 -0.053327868852459 239 1.924 1.95546808510638 -0.0314680851063831 240 1.898 1.91759649122807 -0.0195964912280702 241 1.839 1.80261290322581 0.0363870967741935 242 1.716 1.772125 -0.056125 243 1.763 1.772125 -0.00912500000000005 244 1.778 1.772125 0.00587500000000007 245 1.813 1.80261290322581 0.0103870967741935 246 1.724 1.80261290322581 -0.0786129032258065 247 1.724 1.772125 -0.048125 248 1.82 1.772125 0.0478750000000001 249 1.778 1.772125 0.00587500000000007 250 1.851 1.849 0.002 251 1.763 1.80261290322581 -0.0396129032258066 252 1.778 1.80261290322581 -0.0246129032258064 253 1.663 1.74041176470588 -0.0774117647058823 254 1.813 1.74041176470588 0.0725882352941176 255 1.792 1.849 -0.057 256 1.826 1.849 -0.0229999999999999 257 1.69 1.74041176470588 -0.0504117647058824 258 1.806 1.82304 -0.0170399999999999 259 1.903 1.849 0.054 260 1.851 1.849 0.002 261 1.845 1.849 -0.004 262 1.845 1.849 -0.004 263 1.892 1.849 0.0429999999999999 264 1.845 1.82304 0.02196 265 1.663 1.74041176470588 -0.0774117647058823 266 1.826 1.80261290322581 0.0233870967741936 267 1.845 1.74041176470588 0.104588235294118 268 1.826 1.82304 0.00296000000000007 269 1.826 1.849 -0.0229999999999999 270 1.826 1.82304 0.00296000000000007 271 1.778 1.80261290322581 -0.0246129032258064 272 1.833 1.80261290322581 0.0303870967741935 273 1.813 1.849 -0.036 274 1.813 1.80261290322581 0.0103870967741935 275 1.869 1.849 0.02 276 1.681 1.74041176470588 -0.0594117647058823 277 1.826 1.82304 0.00296000000000007 278 1.716 1.80261290322581 -0.0866129032258065 279 1.833 1.82304 0.00995999999999997 280 1.851 1.849 0.002 281 1.845 1.82304 0.02196 282 1.826 1.82304 0.00296000000000007 283 1.826 1.82304 0.00296000000000007 284 1.785 1.80261290322581 -0.0176129032258066 285 1.813 1.82304 -0.01004 286 1.813 1.80261290322581 0.0103870967741935 287 1.833 1.80261290322581 0.0303870967741935 288 1.716 1.74041176470588 -0.0244117647058824 289 1.813 1.82304 -0.01004 290 1.833 1.80261290322581 0.0303870967741935 291 1.792 1.82304 -0.03104 292 1.799 1.82304 -0.0240400000000001 293 1.881 1.88185185185185 -0.000851851851851881 294 1.826 1.80261290322581 0.0233870967741936 295 1.845 1.80261290322581 0.0423870967741935 296 1.845 1.74041176470588 0.104588235294118 297 1.919 1.88185185185185 0.0371481481481482 298 1.681 1.74041176470588 -0.0594117647058823 299 1.944 1.88185185185185 0.062148148148148 300 1.875 1.88185185185185 -0.00685185185185189 301 1.813 1.80261290322581 0.0103870967741935 302 1.813 1.80261290322581 0.0103870967741935 303 1.845 1.88185185185185 -0.0368518518518519 304 1.954 1.88185185185185 0.072148148148148 305 1.954 1.88185185185185 0.072148148148148 306 1.875 1.88185185185185 -0.00685185185185189 307 1.799 1.88185185185185 -0.082851851851852 308 1.845 1.88185185185185 -0.0368518518518519 309 1.903 1.88185185185185 0.0211481481481481 310 1.839 1.88185185185185 -0.0428518518518519 311 1.716 1.74041176470588 -0.0244117647058824 312 1.944 1.88185185185185 0.062148148148148 313 1.944 1.88185185185185 0.062148148148148 314 1.833 1.88185185185185 -0.0488518518518519 315 1.881 1.88185185185185 -0.000851851851851881 316 1.826 1.80261290322581 0.0233870967741936 317 1.845 1.88185185185185 -0.0368518518518519 318 1.903 1.88185185185185 0.0211481481481481 319 1.833 1.88185185185185 -0.0488518518518519 320 1.875 1.88185185185185 -0.00685185185185189 321 1.875 1.88185185185185 -0.00685185185185189 322 1.944 1.88185185185185 0.062148148148148 323 1.903 1.88185185185185 0.0211481481481481 324 1.778 1.74041176470588 0.0375882352941177 325 1.875 1.88185185185185 -0.00685185185185189 326 1.892 1.88185185185185 0.010148148148148 327 1.839 1.80261290322581 0.0363870967741935 328 1.892 1.88185185185185 0.010148148148148 329 1.869 1.88185185185185 -0.0128518518518519 330 1.845 1.88185185185185 -0.0368518518518519 331 1.919 1.88185185185185 0.0371481481481482 332 1.875 1.88185185185185 -0.00685185185185189 333 1.875 1.88185185185185 -0.00685185185185189 334 1.799 1.88185185185185 -0.082851851851852 335 1.919 1.91759649122807 0.00140350877192996 336 1.934 1.95546808510638 -0.0214680851063831 337 1.908 1.91759649122807 -0.00959649122807016 338 1.851 1.91759649122807 -0.0665964912280701 339 1.934 1.91759649122807 0.0164035087719299 340 1.978 1.95546808510638 0.0225319148936169 341 1.875 1.95546808510638 -0.080468085106383 342 2.053 1.95546808510638 0.097531914893617 343 1.845 1.91759649122807 -0.0725964912280701 344 1.875 1.95546808510638 -0.080468085106383 345 1.732 1.83222222222222 -0.100222222222222 346 1.898 1.91759649122807 -0.0195964912280702 347 1.954 1.91759649122807 0.0364035087719299 348 1.954 1.91759649122807 0.0364035087719299 349 1.875 1.91759649122807 -0.0425964912280701 350 1.875 1.95546808510638 -0.080468085106383 351 1.978 1.91759649122807 0.0604035087719299 352 1.944 1.91759649122807 0.0264035087719299 353 1.964 1.91759649122807 0.0464035087719299 354 1.857 1.91759649122807 -0.0605964912280701 355 1.924 1.95546808510638 -0.0314680851063831 356 1.982 1.91759649122807 0.0644035087719299 357 1.892 1.95546808510638 -0.0634680851063831 358 1.987 1.95546808510638 0.0315319148936171 359 1.929 1.91759649122807 0.01140350877193 360 1.987 1.95546808510638 0.0315319148936171 361 1.681 1.83222222222222 -0.151222222222222 362 1.875 1.91759649122807 -0.0425964912280701 363 1.924 1.95546808510638 -0.0314680851063831 364 1.978 1.95546808510638 0.0225319148936169 365 1.978 1.91759649122807 0.0604035087719299 366 1.973 1.91759649122807 0.05540350877193 367 1.813 1.83222222222222 -0.0192222222222223 368 1.924 1.95546808510638 -0.0314680851063831 369 1.968 1.91759649122807 0.0504035087719299 370 1.934 1.91759649122807 0.0164035087719299 371 1.944 1.91759649122807 0.0264035087719299 372 1.857 1.83222222222222 0.0247777777777778 373 1.987 1.95546808510638 0.0315319148936171 374 1.857 1.91759649122807 -0.0605964912280701 375 1.954 1.83222222222222 0.121777777777778 376 2 1.95546808510638 0.044531914893617 377 1.954 1.95546808510638 -0.00146808510638308 378 1.964 1.95546808510638 0.00853191489361693 379 1.903 1.91759649122807 -0.0145964912280701 380 1.934 1.91759649122807 0.0164035087719299 381 2.029 1.95546808510638 0.0735319148936169 382 1.987 1.95546808510638 0.0315319148936171 383 1.924 1.91759649122807 0.00640350877192986 384 1.944 1.95546808510638 -0.0114680851063831 385 1.987 1.95546808510638 0.0315319148936171 386 1.881 1.91759649122807 -0.0365964912280701 387 1.978 1.95546808510638 0.0225319148936169 388 1.924 1.91759649122807 0.00640350877192986 389 1.875 1.91759649122807 -0.0425964912280701 390 1.875 1.91759649122807 -0.0425964912280701 391 1.987 1.91759649122807 0.06940350877193 392 1.954 1.95546808510638 -0.00146808510638308 393 1.978 1.95546808510638 0.0225319148936169 394 1.857 1.95546808510638 -0.098468085106383 395 1.964 1.95546808510638 0.00853191489361693 396 1.929 1.91759649122807 0.01140350877193 397 1.959 1.95546808510638 0.00353191489361704 398 1.964 1.95546808510638 0.00853191489361693 399 1.929 1.91759649122807 0.01140350877193 400 1.892 1.91759649122807 -0.0255964912280702 401 2.061 2.01355555555556 0.0474444444444444 402 2.041 2.01355555555556 0.0274444444444444 403 1.944 1.83222222222222 0.111777777777778 404 2 2.01355555555556 -0.0135555555555555 405 1.964 2.01355555555556 -0.0495555555555556 406 1.898 1.91759649122807 -0.0195964912280702 407 2 2.01355555555556 -0.0135555555555555 408 1.924 1.91759649122807 0.00640350877192986 409 1.869 1.91759649122807 -0.0485964912280701 410 1.919 1.91759649122807 0.00140350877192996 411 1.944 1.91759649122807 0.0264035087719299 412 1.954 2.01355555555556 -0.0595555555555556 413 2.041 2.02592307692308 0.015076923076923 414 1.982 2.02592307692308 -0.043923076923077 415 1.903 1.98732786885246 -0.0843278688524589 416 1.954 1.98732786885246 -0.033327868852459 417 2.061 2.02592307692308 0.035076923076923 418 1.94 1.95146341463415 -0.0114634146341464 419 1.954 1.95146341463415 0.00253658536585366 420 1.978 1.98732786885246 -0.00932786885245895 421 2.061 2.02592307692308 0.035076923076923 422 1.982 2.02592307692308 -0.043923076923077 423 1.954 1.98732786885246 -0.033327868852459 424 1.944 1.98732786885246 -0.043327868852459 425 1.914 1.95146341463415 -0.0374634146341464 426 2.041 2.02592307692308 0.015076923076923 427 2.041 2.02592307692308 0.015076923076923 428 1.954 1.95146341463415 0.00253658536585366 429 1.944 1.98732786885246 -0.043327868852459 430 2.021 1.95146341463415 0.0695365853658536 431 1.949 1.98732786885246 -0.0383278688524589 432 1.987 1.98732786885246 -0.000327868852458835 433 2 1.98732786885246 0.0126721311475411 434 1.934 1.98732786885246 -0.053327868852459 435 2.061 1.98732786885246 0.073672131147541 436 2.021 2.02592307692308 -0.00492307692307703 437 2.086 2.09833333333333 -0.0123333333333333 438 1.987 1.98732786885246 -0.000327868852458835 439 2.013 1.98732786885246 0.025672131147541 440 1.978 1.98732786885246 -0.00932786885245895 441 2.049 1.98732786885246 0.061672131147541 442 1.929 1.95146341463415 -0.0224634146341463 443 1.964 1.98732786885246 -0.023327868852459 444 2.049 1.98732786885246 0.061672131147541 445 1.944 1.95146341463415 -0.00746341463414635 446 1.944 1.95146341463415 -0.00746341463414635 447 2.064 2.09833333333333 -0.0343333333333331 448 2 1.98732786885246 0.0126721311475411 449 1.978 1.98732786885246 -0.00932786885245895 450 2.121 2.09833333333333 0.0226666666666668 451 2 1.98732786885246 0.0126721311475411 452 2.033 1.98732786885246 0.045672131147541 453 2.079 2.09833333333333 -0.019333333333333 454 2.049 1.98732786885246 0.061672131147541 455 2 1.98732786885246 0.0126721311475411 456 2.041 1.98732786885246 0.053672131147541 457 1.991 1.98732786885246 0.00367213114754117 458 1.954 1.95146341463415 0.00253658536585366 459 1.826 1.83222222222222 -0.00622222222222213 460 1.944 1.95146341463415 -0.00746341463414635 461 2.041 2.09833333333333 -0.0573333333333332 462 1.929 1.98732786885246 -0.0583278688524589 463 1.94 1.91294736842105 0.0270526315789474 464 1.987 1.98732786885246 -0.000327868852458835 465 1.929 1.95146341463415 -0.0224634146341463 466 1.954 1.91294736842105 0.0410526315789475 467 1.908 1.95146341463415 -0.0434634146341464 468 1.929 1.95146341463415 -0.0224634146341463 469 1.944 1.95146341463415 -0.00746341463414635 470 2.041 1.98732786885246 0.053672131147541 471 1.944 1.95146341463415 -0.00746341463414635 472 1.929 1.95146341463415 -0.0224634146341463 473 1.954 1.95146341463415 0.00253658536585366 474 2.352 2.3318125 0.0201875 475 1.978 1.95146341463415 0.0265365853658537 476 2.021 1.95146341463415 0.0695365853658536 477 1.944 1.98732786885246 -0.043327868852459 478 2.097 2.09833333333333 -0.00133333333333319 479 2.009 1.98732786885246 0.021672131147541 480 1.978 1.95146341463415 0.0265365853658537 481 1.857 1.91294736842105 -0.0559473684210525 482 1.881 1.91294736842105 -0.0319473684210525 483 2.111 2.13047619047619 -0.0194761904761904 484 1.851 1.91294736842105 -0.0619473684210525 485 1.978 1.95146341463415 0.0265365853658537 486 2.143 2.13047619047619 0.0125238095238092 487 1.954 1.95146341463415 0.00253658536585366 488 1.954 1.95146341463415 0.00253658536585366 489 2.041 2.09833333333333 -0.0573333333333332 490 1.903 1.91294736842105 -0.00994736842105248 491 2 1.98732786885246 0.0126721311475411 492 2.061 1.98732786885246 0.073672131147541 493 1.826 1.91294736842105 -0.0869473684210524 494 1.978 1.98732786885246 -0.00932786885245895 495 1.954 1.95146341463415 0.00253658536585366 496 1.944 1.91294736842105 0.0310526315789474 497 2 1.95146341463415 0.0485365853658537 498 2 1.98732786885246 0.0126721311475411 499 2 1.98732786885246 0.0126721311475411 500 1.944 1.98732786885246 -0.043327868852459 501 2 2.01523076923077 -0.0152307692307692 502 2 2.01523076923077 -0.0152307692307692 503 2.021 2.01523076923077 0.00576923076923075 504 2.041 2.02022222222222 0.0207777777777776 505 2.041 2.04642857142857 -0.00542857142857134 506 2.021 2.02022222222222 0.000777777777777544 507 1.954 2.02022222222222 -0.0662222222222224 508 2.176 2.16704545454545 0.00895454545454566 509 2.079 2.04642857142857 0.0325714285714289 510 2.124 2.02022222222222 0.103777777777778 511 2.041 2.02022222222222 0.0207777777777776 512 1.954 1.91294736842105 0.0410526315789475 513 2.021 2.02022222222222 0.000777777777777544 514 2.161 2.16704545454545 -0.00604545454545447 515 2 2.02022222222222 -0.0202222222222224 516 1.857 1.91294736842105 -0.0559473684210525 517 2.176 2.16704545454545 0.00895454545454566 518 2.176 2.16704545454545 0.00895454545454566 519 2.021 2.02022222222222 0.000777777777777544 520 2.217 2.09833333333333 0.118666666666667 521 2.146 2.13047619047619 0.0155238095238093 522 2.021 2.01523076923077 0.00576923076923075 523 1.929 2.02022222222222 -0.0912222222222223 524 2.114 2.16704545454545 -0.0530454545454546 525 2.041 2.01523076923077 0.0257692307692308 526 1.991 2.01523076923077 -0.0242307692307691 527 1.886 1.91294736842105 -0.0269473684210526 528 2.021 2.02022222222222 0.000777777777777544 529 2.23 2.24576666666667 -0.0157666666666665 530 2.143 2.13047619047619 0.0125238095238092 531 1.892 1.91294736842105 -0.0209473684210526 532 2.097 2.13047619047619 -0.0334761904761907 533 2.204 2.20238095238095 0.00161904761904763 534 2.021 2.02022222222222 0.000777777777777544 535 2.041 2.02022222222222 0.0207777777777776 536 2 2.02022222222222 -0.0202222222222224 537 2.041 2.04642857142857 -0.00542857142857134 538 2.041 2.01523076923077 0.0257692307692308 539 2 2.02022222222222 -0.0202222222222224 540 2.255 2.20238095238095 0.0526190476190473 541 2.176 2.16704545454545 0.00895454545454566 542 2.176 2.16704545454545 0.00895454545454566 543 2.217 2.20238095238095 0.0146190476190475 544 2.111 2.13047619047619 -0.0194761904761904 545 2.021 2.126 -0.105 546 2.041 2.04642857142857 -0.00542857142857134 547 2.146 2.16704545454545 -0.0210454545454546 548 2.176 2.1496 0.0264000000000002 549 2.176 2.24576666666667 -0.0697666666666663 550 2.176 2.16704545454545 0.00895454545454566 551 2 2.01523076923077 -0.0152307692307692 552 1.978 2.02022222222222 -0.0422222222222224 553 2.079 2.02022222222222 0.0587777777777778 554 2.243 2.24576666666667 -0.00276666666666658 555 2.13 2.126 0.004 556 2.114 2.126 -0.012 557 2.279 2.24576666666667 0.0332333333333334 558 2.114 2.13047619047619 -0.0164761904761908 559 2.161 2.16704545454545 -0.00604545454545447 560 2.176 2.16704545454545 0.00895454545454566 561 2.021 2.01523076923077 0.00576923076923075 562 2.097 2.126 -0.0289999999999999 563 2.041 1.91294736842105 0.128052631578947 564 2.176 2.16704545454545 0.00895454545454566 565 2.176 2.24576666666667 -0.0697666666666663 566 2.14 2.20238095238095 -0.0623809523809524 567 2.079 2.1496 -0.0705999999999998 568 2.161 2.20238095238095 -0.0413809523809525 569 2.137 2.13047619047619 0.00652380952380938 570 2.152 2.1496 0.00240000000000018 571 2.161 2.20238095238095 -0.0413809523809525 572 2.041 2.04642857142857 -0.00542857142857134 573 2.176 2.1496 0.0264000000000002 574 2.146 2.1496 -0.00360000000000005 575 2.161 2.20238095238095 -0.0413809523809525 576 2.176 2.16704545454545 0.00895454545454566 577 2.114 2.1496 -0.0356000000000001 578 2.243 2.20238095238095 0.0406190476190473 579 2.185 2.20238095238095 -0.0173809523809525 580 2.176 2.1496 0.0264000000000002 581 2.161 2.1496 0.0114000000000001 582 2.146 2.13047619047619 0.0155238095238093 583 2.185 2.1496 0.0354000000000001 584 2.23 2.20238095238095 0.0276190476190474 585 2.176 2.16704545454545 0.00895454545454566 586 2.217 2.20238095238095 0.0146190476190475 587 2.146 2.16704545454545 -0.0210454545454546 588 2.182 2.20238095238095 -0.0203809523809526 589 2.255 2.24576666666667 0.00923333333333343 590 2.176 2.126 0.0500000000000003 591 2.176 2.126 0.0500000000000003 592 2.217 2.2315 -0.0145 593 2.362 2.24576666666667 0.116233333333334 594 2.146 2.13047619047619 0.0155238095238093 595 2.114 2.13047619047619 -0.0164761904761908 596 2.332 2.3318125 0.000187500000000007 597 2.279 2.24576666666667 0.0332333333333334 598 2.173 2.17783333333333 -0.00483333333333347 599 2.297 2.3318125 -0.0348124999999997 600 2.342 2.3318125 0.0101875000000002 601 2.19 2.17783333333333 0.0121666666666664 602 2.199 2.2315 -0.0325000000000002 603 2.301 2.264 0.0369999999999999 604 2.255 2.2315 0.0234999999999999 605 2.322 2.264 0.0579999999999998 606 2.243 2.24576666666667 -0.00276666666666658 607 2.279 2.24576666666667 0.0332333333333334 608 2.352 2.3318125 0.0201875 609 2.161 2.17783333333333 -0.0168333333333335 610 2.204 2.17783333333333 0.0261666666666667 611 2.176 2.264 -0.088 612 2.243 2.24576666666667 -0.00276666666666658 613 2.176 2.24576666666667 -0.0697666666666663 614 2.176 2.264 -0.088 615 2.255 2.2315 0.0234999999999999 616 2.19 2.17783333333333 0.0121666666666664 617 2.332 2.24576666666667 0.0862333333333334 618 2.318 2.3318125 -0.0138124999999998 619 2.146 2.13047619047619 0.0155238095238093 620 2.23 2.24576666666667 -0.0157666666666665 621 2.17 2.17783333333333 -0.00783333333333358 622 2.23 2.24576666666667 -0.0157666666666665 623 2.176 2.17783333333333 -0.00183333333333335 624 2.286 2.264 0.0219999999999998 625 2.332 2.3318125 0.000187500000000007 626 2.23 2.24576666666667 -0.0157666666666665 627 2.223 2.24576666666667 -0.0227666666666666 628 2.352 2.3318125 0.0201875 629 2.297 2.3318125 -0.0348124999999997 630 2.255 2.24576666666667 0.00923333333333343 631 2.176 2.24576666666667 -0.0697666666666663 632 2.243 2.24576666666667 -0.00276666666666658

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/2fhnr1293281640.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/2fhnr1293281640.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/3fhnr1293281640.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/3fhnr1293281640.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/4884c1293281640.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/25/t1293281526qjka1bh5y4cjl1z/4884c1293281640.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = none ; par4 = no ;

Parameters (R input):

par1 = 3 ; par2 = none ; 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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