Workshop10Recursive Partitioning (no categorization)

*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 22:32:48 +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/t12929707203znjbetatj5bmui.htm/, Retrieved Tue, 21 Dec 2010 23:32:00 +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/t12929707203znjbetatj5bmui.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 «

1,3866 126,64 40,7819 1,3582 126,81 39,5915 1,3332 125,84 38,8859 1,3595 126,77 39,9068 1,3617 124,34 41,47 1,3684 124,4 41,5613 1,3394 120,48 41,6005 1,3262 118,54 41,4113 1,3173 117,66 41,84 1,3085 116,97 42,2892 1,327 120,11 43,1521 1,3182 119,16 43,5998 1,293 116,9 43,116 1,291 116,11 42,4185 1,2984 114,98 42,3687 1,2795 113,65 42,2975 1,299 115,82 42,8528 1,3174 117,59 43,535 1,326 118,57 44,7265 1,3111 118,07 45,7293 1,2816 114,98 45,7585 1,276 114,04 46,1685 1,2849 115,02 46,5075 1,2818 114,28 46,527 1,2829 115,04 46,601 1,2796 116,7 46,4607 1,3008 119,21 46,7135 1,2967 118,39 46,4113 1,2938 116,5 45,55 1,2833 115,46 44,6081 1,2823 117,59 44,4395 1,2765 117,33 44,9847 1,2634 116,2 45,7558 1,2596 116,83 45,3942 1,2705 118,99 45,697 1,2591 118,62 45,5664 1,2798 121,09 46,0205 1,2763 122,4 45,9195 1,2795 123,76 45,8005 1,2782 125,33 45,535 1,2644 123,23 45,4977 1,2596 122,52 45,5782 1,2615 123,64 45,7697 1,2555 124,67 45,2445 1,2555 124,71 45,0615 1,2658 122,53 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 12 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.7345 R-squared 0.5395 RMSE 1.6255 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 40.7819 41.2651800865801 -0.483280086580088 2 39.5915 41.2651800865801 -1.67368008658008 3 38.8859 41.2651800865801 -2.37928008658009 4 39.9068 41.2651800865801 -1.35838008658009 5 41.47 41.2651800865801 0.204819913419911 6 41.5613 41.2651800865801 0.296119913419915 7 41.6005 41.2651800865801 0.335319913419909 8 41.4113 41.2651800865801 0.146119913419909 9 41.84 41.2651800865801 0.574819913419915 10 42.2892 41.2651800865801 1.02401991341991 11 43.1521 41.2651800865801 1.88691991341991 12 43.5998 41.2651800865801 2.33461991341991 13 43.116 41.2651800865801 1.85081991341991 14 42.4185 41.2651800865801 1.15331991341991 15 42.3687 41.2651800865801 1.10351991341991 16 42.2975 41.2651800865801 1.03231991341991 17 42.8528 41.2651800865801 1.58761991341991 18 43.535 41.2651800865801 2.26981991341991 19 44.7265 41.2651800865801 3.46131991341991 20 45.7293 41.2651800865801 4.46411991341991 21 45.7585 41.2651800865801 4.49331991341991 22 46.1685 41.2651800865801 4.90331991341991 23 46.5075 41.2651800865801 5.24231991341991 24 46.527 41.2651800865801 5.26181991341991 25 46.601 41.2651800865801 5.33581991341991 26 46.4607 41.2651800865801 5.19551991341991 27 46.7135 41.2651800865801 5.44831991341992 28 46.4113 41.2651800865801 5.14611991341991 29 45.55 41.2651800865801 4.28481991341991 30 44.6081 41.2651800865801 3.34291991341991 31 44.4395 41.2651800865801 3.17431991341991 32 44.9847 41.2651800865801 3.71951991341991 33 45.7558 41.2651800865801 4.49061991341991 34 45.3942 41.2651800865801 4.12901991341991 35 45.697 41.2651800865801 4.43181991341991 36 45.5664 41.2651800865801 4.30121991341991 37 46.0205 41.2651800865801 4.75531991341991 38 45.9195 41.2651800865801 4.65431991341991 39 45.8005 41.2651800865801 4.53531991341991 40 45.535 41.2651800865801 4.26981991341991 41 45.4977 41.2651800865801 4.23251991341991 42 45.5782 41.2651800865801 4.31301991341991 43 45.7697 41.2651800865801 4.50451991341991 44 45.2445 41.2651800865801 3.97931991341991 45 45.0615 41.2651800865801 3.79631991341991 46 45.2865 41.2651800865801 4.02131991341991 47 44.791 41.2651800865801 3.52581991341991 48 44.7625 41.2651800865801 3.49731991341991 49 44.7644 41.2651800865801 3.49921991341991 50 44.9973 41.2651800865801 3.73211991341991 51 44.7265 42.549775 2.17672500000000 52 45.1465 44.139245505618 1.00725449438202 53 44.7465 44.139245505618 0.607254494382019 54 45.1795 44.139245505618 1.04025449438202 55 45.6515 44.139245505618 1.51225449438202 56 45.492 44.139245505618 1.35275449438202 57 45.2775 44.139245505618 1.13825449438202 58 45.2115 44.139245505618 1.07225449438202 59 45.411 44.139245505618 1.27175449438202 60 45.4005 44.139245505618 1.26125449438202 61 44.7692 44.139245505618 0.62995449438202 62 44.8913 44.139245505618 0.752054494382023 63 45.032 44.139245505618 0.892754494382018 64 44.879 44.139245505618 0.73975449438202 65 44.833 44.139245505618 0.69375449438202 66 44.8257 44.139245505618 0.686454494382019 67 44.7815 44.139245505618 0.642254494382023 68 44.479 44.139245505618 0.339754494382021 69 44.6317 44.139245505618 0.492454494382024 70 44.5043 44.139245505618 0.365054494382022 71 44.3217 44.139245505618 0.182454494382021 72 44.1005 44.139245505618 -0.0387455056179817 73 44.047 44.139245505618 -0.0922455056179814 74 43.6835 44.139245505618 -0.455745505617976 75 43.7864 44.139245505618 -0.352845505617978 76 44.1807 42.549775 1.63092500000000 77 43.9595 42.549775 1.40972500000000 78 43.937 44.139245505618 -0.202245505617981 79 43.991 44.139245505618 -0.148245505617979 80 43.865 42.549775 1.31522500000001 81 43.671 41.2651800865801 2.40581991341991 82 43.93 44.139245505618 -0.209245505617979 83 43.863 44.139245505618 -0.276245505617979 84 43.7095 44.139245505618 -0.42974550561798 85 43.9435 44.139245505618 -0.195745505617978 86 43.736 44.139245505618 -0.403245505617981 87 43.6295 44.139245505618 -0.509745505617978 88 43.598 44.139245505618 -0.54124550561798 89 43.8726 44.139245505618 -0.26664550561798 90 43.8935 44.139245505618 -0.245745505617975 91 43.5957 44.139245505618 -0.543545505617978 92 43.7155 44.139245505618 -0.42374550561798 93 43.528 44.139245505618 -0.61124550561798 94 43.3415 44.139245505618 -0.797745505617975 95 43.3374 44.139245505618 -0.801845505617976 96 43.332 44.139245505618 -0.807245505617978 97 43.3869 44.139245505618 -0.752345505617981 98 43.5016 44.139245505618 -0.637645505617975 99 43.4875 44.139245505618 -0.651745505617981 100 43.6023 44.139245505618 -0.536945505617979 101 43.3886 44.139245505618 -0.750645505617982 102 43.3105 44.139245505618 -0.828745505617981 103 43.4455 44.139245505618 -0.693745505617976 104 43.5185 44.139245505618 -0.620745505617975 105 43.5755 44.139245505618 -0.56374550561798 106 43.6217 44.139245505618 -0.517545505617981 107 43.644 44.139245505618 -0.49524550561798 108 43.5789 44.139245505618 -0.560345505617981 109 43.5215 44.139245505618 -0.617745505617975 110 43.5033 44.139245505618 -0.635945505617975 111 43.632 44.139245505618 -0.507245505617981 112 43.263 44.139245505618 -0.87624550561798 113 43.3717 44.139245505618 -0.767545505617981 114 43.2745 44.139245505618 -0.864745505617975 115 43.2647 44.139245505618 -0.87454550561798 116 43.324 44.139245505618 -0.81524550561798 117 43.4455 44.139245505618 -0.693745505617976 118 43.4098 44.139245505618 -0.729445505617981 119 43.41 44.139245505618 -0.729245505617982 120 43.93 44.139245505618 -0.209245505617979 121 43.8104 44.139245505618 -0.328845505617977 122 43.54 44.139245505618 -0.599245505617979 123 43.858 44.139245505618 -0.281245505617981 124 43.8375 44.139245505618 -0.30174550561798 125 43.881 44.139245505618 -0.258245505617978 126 43.887 44.139245505618 -0.252245505617978 127 43.8009 44.139245505618 -0.33834550561798 128 43.7877 44.139245505618 -0.351545505617977 129 43.811 44.139245505618 -0.328245505617978 130 44.0625 44.139245505618 -0.0767455056179784 131 44.125 44.139245505618 -0.0142455056179784 132 44.52 44.139245505618 0.380754494382025 133 45.4005 44.139245505618 1.26125449438202 134 45.89 44.139245505618 1.75075449438202 135 45.189 44.139245505618 1.04975449438202 136 44.9035 44.139245505618 0.764254494382023 137 44.9351 44.139245505618 0.79585449438202 138 44.801 44.139245505618 0.661754494382023 139 43.98 44.139245505618 -0.159245505617982 140 44.11 44.139245505618 -0.029245505617979 141 44.2661 44.139245505618 0.126854494382023 142 44.361 44.139245505618 0.221754494382019 143 44.099 44.139245505618 -0.0402455056179818 144 43.8435 44.139245505618 -0.295745505617980 145 43.8914 44.139245505618 -0.247845505617981 146 44.217 44.139245505618 0.0777544943820203 147 44.506 44.139245505618 0.366754494382022 148 44.54 44.139245505618 0.400754494382021 149 44.4465 44.139245505618 0.307254494382022 150 44.842 44.139245505618 0.70275449438202 151 44.8946 44.139245505618 0.755354494382019 152 44.951 44.139245505618 0.811754494382022 153 45.445 44.139245505618 1.30575449438202 154 45.0035 44.139245505618 0.864254494382024 155 45.769 44.139245505618 1.62975449438202 156 46.09 44.139245505618 1.95075449438202 157 45.412 44.139245505618 1.27275449438202 158 45.12 44.139245505618 0.980754494382019 159 45.48 44.139245505618 1.34075449438202 160 45.105 44.139245505618 0.965754494382018 161 45.056 44.139245505618 0.916754494382019 162 45.22 44.139245505618 1.08075449438202 163 45.39 44.139245505618 1.25075449438202 164 45.041 44.139245505618 0.901754494382018 165 44.9399 44.139245505618 0.800654494382023 166 44.9315 44.139245505618 0.792254494382021 167 45.1935 44.139245505618 1.05425449438202 168 45.3466 44.139245505618 1.20735449438202 169 45.4645 44.139245505618 1.32525449438202 170 45.5685 44.139245505618 1.42925449438202 171 45.3921 44.139245505618 1.25285449438202 172 45.34 44.139245505618 1.20075449438202 173 45.1308 44.139245505618 0.991554494382022 174 45.1005 44.139245505618 0.961254494382018 175 45.37 44.139245505618 1.23075449438202 176 45.2 44.139245505618 1.06075449438202 177 44.9614 44.139245505618 0.822154494382019 178 44.8015 44.139245505618 0.662254494382019 179 44.9152 44.139245505618 0.77595449438202 180 45.095 44.139245505618 0.95575449438202 181 44.9271 44.139245505618 0.787854494382024 182 44.6026 44.139245505618 0.463354494382024 183 44.5 44.139245505618 0.360754494382022 184 44.54 44.139245505618 0.400754494382021 185 44.5532 44.139245505618 0.413954494382018 186 44.407 44.139245505618 0.267754494382018 187 44.259 44.139245505618 0.119754494382022 188 44.1365 44.139245505618 -0.00274550561798037 189 44.112 44.139245505618 -0.0272455056179766 190 43.8814 44.139245505618 -0.257845505617979 191 43.98 44.139245505618 -0.159245505617982 192 43.7294 44.139245505618 -0.40984550561798 193 43.9119 44.139245505618 -0.227345505617976 194 43.955 44.139245505618 -0.18424550561798 195 43.9 44.139245505618 -0.23924550561798 196 43.7065 44.139245505618 -0.43274550561798 197 43.6939 44.139245505618 -0.445345505617979 198 43.6587 44.139245505618 -0.480545505617975 199 43.5885 44.139245505618 -0.550745505617975 200 43.8885 43.4404135135135 0.448086486486488 201 43.8216 43.4404135135135 0.381186486486484 202 43.751 43.4404135135135 0.310586486486486 203 43.699 43.4404135135135 0.258586486486486 204 43.7425 43.4404135135135 0.302086486486488 205 43.639 43.4404135135135 0.198586486486491 206 43.589 43.4404135135135 0.148586486486487 207 43.606 43.4404135135135 0.165586486486490 208 43.5325 43.4404135135135 0.0920864864864868 209 43.385 43.4404135135135 -0.0554135135135141 210 43.3745 43.4404135135135 -0.0659135135135145 211 43.236 43.4404135135135 -0.204413513513515 212 43.1957 43.4404135135135 -0.24471351351351 213 43.01 43.4404135135135 -0.430413513513514 214 43.1401 44.139245505618 -0.999145505617982 215 43.0487 44.139245505618 -1.09054550561798 216 43.1972 44.139245505618 -0.942045505617976 217 43.2461 43.4404135135135 -0.194313513513514 218 43.0866 43.4404135135135 -0.353813513513515 219 43.0865 43.4404135135135 -0.353913513513511 220 43.0194 43.4404135135135 -0.421013513513515 221 43.08 43.4404135135135 -0.360413513513514 222 43.007 43.4404135135135 -0.433413513513514 223 42.9278 43.4404135135135 -0.512613513513514 224 42.9545 43.4404135135135 -0.485913513513509 225 42.7995 43.4404135135135 -0.64091351351351 226 42.9048 43.4404135135135 -0.53561351351351 227 42.9468 43.4404135135135 -0.493613513513509 228 43.08 43.4404135135135 -0.360413513513514 229 43.1274 43.4404135135135 -0.313013513513511 230 43.1625 43.4404135135135 -0.277913513513511 231 43.45 43.4404135135135 0.00958648648649074 232 43.831 43.4404135135135 0.390586486486491 233 43.7769 43.4404135135135 0.336486486486486 234 43.98 43.4404135135135 0.539586486486485 235 43.92 43.4404135135135 0.47958648648649 236 44.11 43.4404135135135 0.669586486486487 237 44.03 43.4404135135135 0.589586486486489 238 44.1582 43.4404135135135 0.717786486486489 239 44.14 43.4404135135135 0.699586486486488 240 45.07 44.139245505618 0.930754494382022 241 44.8737 44.139245505618 0.734454494382021 242 44.8505 44.139245505618 0.711254494382018 243 44.373 44.139245505618 0.233754494382019 244 44.075 44.139245505618 -0.0642455056179756 245 43.9725 44.139245505618 -0.166745505617982 246 44.094 44.139245505618 -0.0452455056179772 247 44.191 44.139245505618 0.0517544943820241 248 43.9685 44.139245505618 -0.170745505617980 249 43.79 44.139245505618 -0.349245505617979 250 43.6041 44.139245505618 -0.535145505617976 251 43.1707 44.139245505618 -0.968545505617982 252 42.71 44.139245505618 -1.42924550561798 253 42.755 44.139245505618 -1.38424550561798 254 43.3316 44.139245505618 -0.807645505617977 255 43.5 44.139245505618 -0.639245505617978 256 43.154 44.139245505618 -0.985245505617975 257 43.16 44.139245505618 -0.979245505617982 258 43.1 44.139245505618 -1.03924550561798 259 42.85 44.139245505618 -1.28924550561798 260 42.6175 44.139245505618 -1.52174550561798 261 42.5 44.139245505618 -1.63924550561798 262 42.6285 44.139245505618 -1.51074550561798 263 42.6974 44.139245505618 -1.44184550561798 264 43.04 44.139245505618 -1.09924550561798 265 42.673 44.139245505618 -1.46624550561798 266 42.5015 44.139245505618 -1.63774550561798 267 42.538 44.139245505618 -1.60124550561798 268 42.3735 44.139245505618 -1.76574550561798 269 42.014 44.139245505618 -2.12524550561798 270 41.8618 44.139245505618 -2.27744550561798 271 42.1824 42.549775 -0.367374999999996 272 42.605 42.549775 0.0552250000000001 273 42.7345 41.2651800865801 1.46931991341991 274 42.615 41.2651800865801 1.34981991341991 275 42.465 41.2651800865801 1.19981991341992 276 42.34 41.2651800865801 1.07481991341992 277 42.251 41.2651800865801 0.98581991341991 278 42.0475 41.2651800865801 0.782319913419911 279 41.86 41.2651800865801 0.594819913419911 280 41.685 41.2651800865801 0.419819913419914 281 41.735 41.2651800865801 0.469819913419911 282 41.706 41.2651800865801 0.440819913419915 283 41.764 41.2651800865801 0.498819913419915 284 41.58 41.2651800865801 0.314819913419910 285 41.373 41.2651800865801 0.107819913419910 286 41.088 41.2651800865801 -0.177180086580087 287 41.137 41.2651800865801 -0.128180086580088 288 41.1587 41.2651800865801 -0.106480086580085 289 41.185 41.2651800865801 -0.0801800865800857 290 40.819 41.2651800865801 -0.446180086580085 291 40.633 41.2651800865801 -0.632180086580085 292 40.858 41.2651800865801 -0.407180086580091 293 40.794 41.2651800865801 -0.471180086580091 294 40.69 41.2651800865801 -0.57518008658009 295 40.595 41.2651800865801 -0.670180086580089 296 40.7305 41.2651800865801 -0.534680086580089 297 40.5471 41.2651800865801 -0.718080086580088 298 40.5145 41.2651800865801 -0.75068008658009 299 40.7 41.2651800865801 -0.565180086580085 300 40.7 41.2651800865801 -0.565180086580085 301 40.522 41.2651800865801 -0.74318008658009 302 40.6165 41.2651800865801 -0.648680086580086 303 40.3985 41.2651800865801 -0.86668008658009 304 40.2815 41.2651800865801 -0.983680086580087 305 40.245 41.2651800865801 -1.02018008658009 306 40.3055 41.2651800865801 -0.959680086580086 307 40.2696 41.2651800865801 -0.995580086580091 308 40.251 41.2651800865801 -1.01418008658009 309 40.127 41.2651800865801 -1.13818008658009 310 39.95 41.2651800865801 -1.31518008658009 311 39.675 41.2651800865801 -1.59018008658009 312 39.954 41.2651800865801 -1.31118008658009 313 39.8828 41.2651800865801 -1.38238008658008 314 39.62 41.2651800865801 -1.64518008658009 315 39.5415 41.2651800865801 -1.72368008658009 316 39.525 41.2651800865801 -1.74018008658009 317 39.8145 41.2651800865801 -1.45068008658009 318 39.6675 41.2651800865801 -1.59768008658009 319 39.695 41.2651800865801 -1.57018008658009 320 39.5985 41.2651800865801 -1.66668008658009 321 39.2735 41.2651800865801 -1.99168008658009 322 39.1435 41.2651800865801 -2.12168008658008 323 39.1742 41.2651800865801 -2.09098008658009 324 39.2025 41.2651800865801 -2.06268008658009 325 39.3946 42.549775 -3.155175 326 39.5025 41.2651800865801 -1.76268008658009 327 39.4845 42.549775 -3.065275 328 39.33 41.2651800865801 -1.93518008658009 329 39.295 41.2651800865801 -1.97018008658009 330 39.2675 41.2651800865801 -1.99768008658009 331 39.2535 41.2651800865801 -2.01168008658009 332 38.9845 41.2651800865801 -2.28068008658009 333 38.9285 41.2651800865801 -2.33668008658009 334 38.8592 41.2651800865801 -2.40598008658009 335 38.77 41.2651800865801 -2.49518008658008 336 38.79 41.2651800865801 -2.47518008658009 337 38.8205 41.2651800865801 -2.44468008658009 338 38.7577 41.2651800865801 -2.50748008658009 339 38.839 41.2651800865801 -2.42618008658009 340 38.78 41.2651800865801 -2.48518008658009 341 38.54 41.2651800865801 -2.72518008658009 342 38.511 41.2651800865801 -2.75418008658009 343 38.615 41.2651800865801 -2.65018008658009 344 38.898 41.2651800865801 -2.36718008658008 345 38.8691 41.2651800865801 -2.39608008658008 346 38.384 41.2651800865801 -2.88118008658009 347 38.0277 41.2651800865801 -3.23748008658009 348 37.72 41.2651800865801 -3.54518008658009 349 37.7325 38.3119833333333 -0.579483333333329 350 37.626 38.3119833333333 -0.685983333333333 351 37.603 38.3119833333333 -0.708983333333329 352 37.78 38.0914421052632 -0.311442105263154 353 38.559 38.3119833333333 0.247016666666667 354 39.0459 38.3119833333333 0.733916666666673 355 38.45 38.3119833333333 0.138016666666672 356 38.505 38.0914421052632 0.413557894736847 357 38.2885 38.3119833333333 -0.0234833333333313 358 37.795 38.0914421052632 -0.296442105263154 359 37.92 38.3119833333333 -0.391983333333329 360 38.034 38.3119833333333 -0.277983333333331 361 38.029 38.0914421052632 -0.0624421052631519 362 38.063 38.0914421052632 -0.0284421052631529 363 37.9828 38.0914421052632 -0.108642105263158 364 37.745 38.0914421052632 -0.346442105263158 365 37.969 38.0914421052632 -0.122442105263154 366 38.007 38.0914421052632 -0.0844421052631574 367 38.0615 38.0914421052632 -0.029942105263153 368 38.0912 38.0914421052632 -0.000242105263154713 369 38.091 38.0914421052632 -0.000442105263154247 370 38.431 38.0914421052632 0.339557894736842 371 38.48 38.0914421052632 0.388557894736842 372 38.35 38.3119833333333 0.038016666666671 373 38.214 38.3119833333333 -0.0979833333333318 374 38.384 38.3119833333333 0.07201666666667 375 38.1375 38.3119833333333 -0.174483333333328 376 38.0075 38.0914421052632 -0.083942105263155 377 38.0524 38.0914421052632 -0.0390421052631567 378 38.235 38.0914421052632 0.143557894736844 379 38.31 38.3119833333333 -0.00198333333332812 380 38.2615 38.3119833333333 -0.0504833333333323 381 38.13 38.0914421052632 0.0385578947368472 382 38.282 38.0914421052632 0.190557894736841 383 38.581 38.3119833333333 0.269016666666673 384 39.0801 38.3119833333333 0.768116666666671 385 39.0387 38.3119833333333 0.726716666666668 386 39.1015 41.2651800865801 -2.16368008658009 387 39.1503 41.2651800865801 -2.11488008658009 388 39.14 41.2651800865801 -2.12518008658009 389 39.0275 41.2651800865801 -2.23768008658008 390 38.7665 41.2651800865801 -2.49868008658009 391 38.691 41.2651800865801 -2.57418008658009 392 38.849 41.2651800865801 -2.41618008658009 393 39.1644 41.2651800865801 -2.10078008658009 394 39.4907 41.2651800865801 -1.77448008658009 395 39.5095 41.2651800865801 -1.75568008658009 396 39.2795 41.2651800865801 -1.98568008658009 397 39.0437 41.2651800865801 -2.22148008658009 398 39.1355 41.2651800865801 -2.12968008658009 399 39.143 41.2651800865801 -2.12218008658009 400 39.185 41.2651800865801 -2.08018008658009 401 39.355 41.2651800865801 -1.91018008658009 402 39.297 41.2651800865801 -1.96818008658009 403 39.4514 41.2651800865801 -1.81378008658009 404 39.4173 41.2651800865801 -1.84788008658009 405 39.4305 41.2651800865801 -1.83468008658009 406 39.384 41.2651800865801 -1.88118008658009 407 39.3261 41.2651800865801 -1.93908008658009 408 39.301 41.2651800865801 -1.96418008658009 409 39.35 41.2651800865801 -1.91518008658009 410 39.64 41.2651800865801 -1.62518008658009 411 39.4723 41.2651800865801 -1.79288008658009 412 39.3685 41.2651800865801 -1.89668008658009 413 39.1906 41.2651800865801 -2.07458008658008 414 39.1183 41.2651800865801 -2.14688008658009 415 39.1325 41.2651800865801 -2.13268008658009 416 39.1144 41.2651800865801 -2.15078008658008 417 39.1614 41.2651800865801 -2.10378008658009 418 39.0908 41.2651800865801 -2.17438008658009 419 38.9199 41.2651800865801 -2.34528008658009 420 38.913 41.2651800865801 -2.35218008658009 421 38.9655 41.2651800865801 -2.29968008658009 422 39.029 41.2651800865801 -2.23618008658008 423 39.089 41.2651800865801 -2.17618008658009 424 39.07 41.2651800865801 -2.19518008658009 425 39.0046 41.2651800865801 -2.26058008658008 426 39.1038 41.2651800865801 -2.16138008658009 427 39.3572 41.2651800865801 -1.90798008658009 428 39.388 41.2651800865801 -1.87718008658009 429 39.382 41.2651800865801 -1.88318008658009 430 39.4398 41.2651800865801 -1.82538008658009 431 39.2537 41.2651800865801 -2.01148008658009 432 39.2301 41.2651800865801 -2.03508008658009 433 39.2763 41.2651800865801 -1.98888008658009 434 39.282 41.2651800865801 -1.98318008658009 435 39.3325 41.2651800865801 -1.93268008658008 436 39.557 41.2651800865801 -1.70818008658009 437 40.1 41.2651800865801 -1.16518008658009 438 40.5875 41.2651800865801 -0.67768008658009 439 40.485 41.2651800865801 -0.780180086580089 440 40.55 41.2651800865801 -0.715180086580091 441 40.7955 41.2651800865801 -0.469680086580091 442 41.456 41.2651800865801 0.190819913419915 443 41.3557 41.2651800865801 0.0905199134199108 444 41.374 41.2651800865801 0.108819913419914 445 41.2235 41.2651800865801 -0.0416800865800866 446 41.15 41.2651800865801 -0.115180086580089 447 41.3725 41.2651800865801 0.107319913419914 448 41.6923 41.2651800865801 0.427119913419915 449 41.8 41.2651800865801 0.534819913419909 450 41.8045 41.2651800865801 0.539319913419909 451 41.64 41.2651800865801 0.374819913419913 452 41.36 41.2651800865801 0.0948199134199115 453 41.5745 41.2651800865801 0.309319913419912 454 41.593 41.2651800865801 0.327819913419916 455 41.575 41.2651800865801 0.309819913419915 456 41.68 41.2651800865801 0.414819913419912 457 42.0055 41.2651800865801 0.74031991341991 458 42.3188 41.2651800865801 1.05361991341992 459 42.565 41.2651800865801 1.29981991341991 460 42.3575 41.2651800865801 1.09231991341991 461 42.29 41.2651800865801 1.02481991341991 462 42.695 41.2651800865801 1.42981991341991 463 43.0028 41.2651800865801 1.73761991341991 464 42.4507 41.2651800865801 1.18551991341991 465 42.4705 41.2651800865801 1.20531991341991 466 42.2875 41.2651800865801 1.02231991341991 467 42.3172 41.2651800865801 1.05201991341991 468 42.55 41.2651800865801 1.28481991341991 469 42.7523 41.2651800865801 1.48711991341991 470 42.8993 41.2651800865801 1.63411991341991 471 43.1555 41.2651800865801 1.89031991341992 472 43.1885 41.2651800865801 1.92331991341991 473 43.43 41.2651800865801 2.16481991341991 474 43.31 41.2651800865801 2.04481991341991 475 42.815 41.2651800865801 1.54981991341991 476 42.7017 41.2651800865801 1.43651991341991 477 42.28 41.2651800865801 1.01481991341991 478 41.922 41.2651800865801 0.656819913419909 479 42.17 41.2651800865801 0.904819913419914 480 42.1962 41.2651800865801 0.93101991341991 481 42.3215 41.2651800865801 1.05631991341991 482 42.3173 41.2651800865801 1.05211991341992 483 42.391 41.2651800865801 1.12581991341991 484 42.463 41.2651800865801 1.19781991341991 485 42.4125 41.2651800865801 1.14731991341991 486 42.304 41.2651800865801 1.03881991341991 487 41.813 41.2651800865801 0.547819913419914 488 41.651 41.2651800865801 0.385819913419915 489 41.539 41.2651800865801 0.273819913419914 490 41.1575 41.2651800865801 -0.107680086580089 491 40.9545 41.2651800865801 -0.310680086580085

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/2l7dh1292970753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/2l7dh1292970753.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/3ezu21292970753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/3ezu21292970753.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/46qt41292970753.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929707203znjbetatj5bmui/46qt41292970753.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 3 ; par2 = none ; par3 = 3 ; par4 = no ;

R code (references can be found in the software module):

library(party) library(Hmisc) par1 <- as.numeric(par1) par3 <- as.numeric(par3) x <- data.frame(t(y)) is.data.frame(x) x <- x[!is.na(x[,par1]),] k <- length(x[1,]) n <- length(x[,1]) colnames(x)[par1] x[,par1] if (par2 == 'kmeans') { cl <- kmeans(x[,par1], par3) print(cl) clm <- matrix(cbind(cl$centers,1:par3),ncol=2) clm <- clm[sort.list(clm[,1]),] for (i in 1:par3) { cl$cluster[cl$cluster==clm[i,2]] <- paste('C',i,sep='') } cl$cluster <- as.factor(cl$cluster) print(cl$cluster) x[,par1] <- cl$cluster } if (par2 == 'quantiles') { x[,par1] <- cut2(x[,par1],g=par3) } if (par2 == 'hclust') { hc <- hclust(dist(x[,par1])^2, 'cen') print(hc) memb <- cutree(hc, k = par3) dum <- c(mean(x[memb==1,par1])) for (i in 2:par3) { dum <- c(dum, mean(x[memb==i,par1])) } hcm <- matrix(cbind(dum,1:par3),ncol=2) hcm <- hcm[sort.list(hcm[,1]),] for (i in 1:par3) { memb[memb==hcm[i,2]] <- paste('C',i,sep='') } memb <- as.factor(memb) print(memb) x[,par1] <- memb } if (par2=='equal') { ed <- cut(as.numeric(x[,par1]),par3,labels=paste('C',1:par3,sep='')) x[,par1] <- as.factor(ed) } table(x[,par1]) colnames(x) colnames(x)[par1] x[,par1] if (par2 == 'none') { m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) } load(file='createtable') if (par2 != 'none') { m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) if (par4=='yes') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) a<-table.element(a,'Prediction (training)',par3+1,TRUE) a<-table.element(a,'Prediction (testing)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Actual',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) a<-table.element(a,'CV',1,TRUE) a<-table.row.end(a) for (i in 1:10) { ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) if (i==1) { m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) m.ct.i.actu <- x[ind==1,par1] m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) m.ct.x.actu <- x[ind==2,par1] } else { m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) } } print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) numer <- numer + m.ct.i.tab[i,i] } print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) numer <- 0 for (i in 1:par3) { print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) numer <- numer + m.ct.x.tab[i,i] } print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) a<-table.row.end(a) } a<-table.row.start(a) a<-table.element(a,'Overall',1,TRUE) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.i.cp,4)) for (jjj in 1:par3) a<-table.element(a,'-') a<-table.element(a,round(m.ct.x.cp,4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') } } m bitmap(file='test1.png') plot(m) dev.off() bitmap(file='test1a.png') plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') dev.off() if (par2 == 'none') { forec <- predict(m) result <- as.data.frame(cbind(x[,par1],forec,x[,par1]-forec)) colnames(result) <- c('Actuals','Forecasts','Residuals') print(result) } if (par2 != 'none') { print(cbind(as.factor(x[,par1]),predict(m))) myt <- table(as.factor(x[,par1]),predict(m)) print(myt) } bitmap(file='test2.png') if(par2=='none') { op <- par(mfrow=c(2,2)) plot(density(result$Actuals),main='Kernel Density Plot of Actuals') plot(density(result$Residuals),main='Kernel Density Plot of Residuals') plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') par(op) } if(par2!='none') { plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') } dev.off() if (par2 == 'none') { detcoef <- cor(result$Forecasts,result$Actuals) a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goodness of Fit',2,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Correlation',1,TRUE) a<-table.element(a,round(detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'R-squared',1,TRUE) a<-table.element(a,round(detcoef*detcoef,4)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'RMSE',1,TRUE) a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'#',header=TRUE) a<-table.element(a,'Actuals',header=TRUE) a<-table.element(a,'Forecasts',header=TRUE) a<-table.element(a,'Residuals',header=TRUE) a<-table.row.end(a) for (i in 1:length(result$Actuals)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,result$Actuals[i]) a<-table.element(a,result$Forecasts[i]) a<-table.element(a,result$Residuals[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.tab') } if (par2 != 'none') { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'',1,TRUE) for (i in 1:par3) { a<-table.element(a,paste('C',i,sep=''),1,TRUE) } a<-table.row.end(a) for (i in 1:par3) { a<-table.row.start(a) a<-table.element(a,paste('C',i,sep=''),1,TRUE) for (j in 1:par3) { a<-table.element(a,myt[i,j]) } a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') }

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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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