*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: Sun, 19 Dec 2010 14:33:57 +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/19/t1292769110275oy4jboxuusnt.htm/, Retrieved Sun, 19 Dec 2010 15:31:51 +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/19/t1292769110275oy4jboxuusnt.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 1 41 38 13 12 14 1 1 39 32 16 11 18 1 1 30 35 19 15 11 1 0 31 33 15 6 12 1 1 34 37 14 13 16 1 1 35 29 13 10 18 1 1 39 31 19 12 14 1 1 34 36 15 14 14 1 1 36 35 14 12 15 1 1 37 38 15 9 15 1 0 38 31 16 10 17 1 1 36 34 16 12 19 1 0 38 35 16 12 10 1 1 39 38 16 11 16 1 1 33 37 17 15 18 1 0 32 33 15 12 14 1 0 36 32 15 10 14 1 1 38 38 20 12 17 1 0 39 38 18 11 14 1 1 32 32 16 12 16 1 0 32 33 16 11 18 1 1 31 31 16 12 11 1 1 39 38 19 13 14 1 1 37 39 16 11 12 1 0 39 32 17 12 17 1 1 41 32 17 13 9 1 0 36 35 16 10 16 1 1 33 37 15 14 14 1 1 33 33 16 12 15 1 0 34 33 14 10 11 1 1 31 31 15 12 16 1 0 27 32 12 8 13 1 1 37 31 14 10 17 1 1 34 37 16 12 15 1 0 34 30 14 12 14 1 0 32 33 10 7 16 1 0 29 31 10 9 9 1 0 36 33 14 12 15 1 1 29 31 16 10 17 1 0 35 33 16 10 13 1 0 37 32 16 10 15 1 1 34 33 14 12 16 1 0 38 32 20 15 16 1 0 35 33 14 10 12 1 1 38 28 14 10 15 1 1 37 35 11 12 11 1 1 38 39 14 13 15 1 1 33 34 15 11 15 1 1 36 38 16 11 17 1 0 38 32 14 12 13 1 1 32 38 16 14 16 1 0 32 30 1 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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.6903 R-squared 0.4765 RMSE 1.8931 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 13 15.0631578947368 -2.06315789473684 2 16 15.0631578947368 0.936842105263159 3 19 18.125 0.875 4 15 11.6818181818182 3.31818181818182 5 14 16.25 -2.25 6 13 15.0631578947368 -2.06315789473684 7 19 15.0631578947368 3.93684210526316 8 15 16.25 -1.25 9 14 15.0631578947368 -1.06315789473684 10 15 12.9473684210526 2.05263157894737 11 16 15.0631578947368 0.936842105263159 12 16 15.0631578947368 0.936842105263159 13 16 15.0631578947368 0.936842105263159 14 16 15.0631578947368 0.936842105263159 15 17 18.125 -1.125 16 15 15.0631578947368 -0.0631578947368414 17 15 15.0631578947368 -0.0631578947368414 18 20 15.0631578947368 4.93684210526316 19 18 15.0631578947368 2.93684210526316 20 16 15.0631578947368 0.936842105263159 21 16 15.0631578947368 0.936842105263159 22 16 15.0631578947368 0.936842105263159 23 19 16.25 2.75 24 16 15.0631578947368 0.936842105263159 25 17 15.0631578947368 1.93684210526316 26 17 16.25 0.75 27 16 15.0631578947368 0.936842105263159 28 15 16.25 -1.25 29 16 15.0631578947368 0.936842105263159 30 14 15.0631578947368 -1.06315789473684 31 15 15.0631578947368 -0.0631578947368414 32 12 12.9473684210526 -0.947368421052632 33 14 15.0631578947368 -1.06315789473684 34 16 15.0631578947368 0.936842105263159 35 14 15.0631578947368 -1.06315789473684 36 10 11.6818181818182 -1.68181818181818 37 10 12.9473684210526 -2.94736842105263 38 14 15.0631578947368 -1.06315789473684 39 16 15.0631578947368 0.936842105263159 40 16 15.0631578947368 0.936842105263159 41 16 15.0631578947368 0.936842105263159 42 14 15.0631578947368 -1.06315789473684 43 20 18.125 1.875 44 14 15.0631578947368 -1.06315789473684 45 14 15.0631578947368 -1.06315789473684 46 11 15.0631578947368 -4.06315789473684 47 14 16.25 -2.25 48 15 15.0631578947368 -0.0631578947368414 49 16 15.0631578947368 0.936842105263159 50 14 15.0631578947368 -1.06315789473684 51 16 16.25 -0.25 52 14 15.0631578947368 -1.06315789473684 53 12 15.0631578947368 -3.06315789473684 54 16 16.25 -0.25 55 9 11.6818181818182 -2.68181818181818 56 14 11.6818181818182 2.31818181818182 57 16 15.0631578947368 0.936842105263159 58 16 15.0631578947368 0.936842105263159 59 15 15.0631578947368 -0.0631578947368414 60 16 15.0631578947368 0.936842105263159 61 12 8.41666666666667 3.58333333333333 62 16 15.0631578947368 0.936842105263159 63 16 16.25 -0.25 64 14 15.0631578947368 -1.06315789473684 65 16 15.0631578947368 0.936842105263159 66 17 16.25 0.75 67 18 16.25 1.75 68 18 15.0631578947368 2.93684210526316 69 12 15.0631578947368 -3.06315789473684 70 16 15.0631578947368 0.936842105263159 71 10 12.9473684210526 -2.94736842105263 72 14 15.0631578947368 -1.06315789473684 73 18 16.25 1.75 74 18 16.25 1.75 75 16 15.0631578947368 0.936842105263159 76 17 12.9473684210526 4.05263157894737 77 16 16.25 -0.25 78 16 15.0631578947368 0.936842105263159 79 13 15.0631578947368 -2.06315789473684 80 16 15.0631578947368 0.936842105263159 81 16 15.0631578947368 0.936842105263159 82 16 15.0631578947368 0.936842105263159 83 15 15.0631578947368 -0.0631578947368414 84 15 15.0631578947368 -0.0631578947368414 85 16 15.0631578947368 0.936842105263159 86 14 12.9473684210526 1.05263157894737 87 16 15.0631578947368 0.936842105263159 88 16 15.0631578947368 0.936842105263159 89 15 15.0631578947368 -0.0631578947368414 90 12 12.9473684210526 -0.947368421052632 91 17 18.125 -1.125 92 16 15.0631578947368 0.936842105263159 93 15 15.0631578947368 -0.0631578947368414 94 13 15.0631578947368 -2.06315789473684 95 16 15.0631578947368 0.936842105263159 96 16 16.25 -0.25 97 16 12.9473684210526 3.05263157894737 98 16 15.0631578947368 0.936842105263159 99 14 15.0631578947368 -1.06315789473684 100 16 16.25 -0.25 101 16 15.0631578947368 0.936842105263159 102 20 18.125 1.875 103 15 15.0631578947368 -0.0631578947368414 104 16 15.0631578947368 0.936842105263159 105 13 15.0631578947368 -2.06315789473684 106 17 15.0631578947368 1.93684210526316 107 16 15.0631578947368 0.936842105263159 108 16 15.0631578947368 0.936842105263159 109 12 11.6818181818182 0.318181818181818 110 16 15.0631578947368 0.936842105263159 111 16 16.25 -0.25 112 17 15.0631578947368 1.93684210526316 113 12 15.0631578947368 -3.06315789473684 114 18 16.25 1.75 115 14 16.25 -2.25 116 14 12.9473684210526 1.05263157894737 117 13 15.0631578947368 -2.06315789473684 118 16 15.0631578947368 0.936842105263159 119 13 15.0631578947368 -2.06315789473684 120 16 16.25 -0.25 121 13 15.0631578947368 -2.06315789473684 122 16 16.25 -0.25 123 15 16.25 -1.25 124 16 18.125 -2.125 125 15 15.0631578947368 -0.0631578947368414 126 17 15.0631578947368 1.93684210526316 127 15 12.9473684210526 2.05263157894737 128 12 15.0631578947368 -3.06315789473684 129 16 15.0631578947368 0.936842105263159 130 10 15.0631578947368 -5.06315789473684 131 16 12.9473684210526 3.05263157894737 132 12 15.0631578947368 -3.06315789473684 133 14 15.0631578947368 -1.06315789473684 134 15 15.0631578947368 -0.0631578947368414 135 13 12.9473684210526 0.0526315789473681 136 15 15.0631578947368 -0.0631578947368414 137 11 15.0631578947368 -4.06315789473684 138 12 12.9473684210526 -0.947368421052632 139 11 12.9473684210526 -1.94736842105263 140 16 12.9473684210526 3.05263157894737 141 15 12.9473684210526 2.05263157894737 142 17 18.125 -1.125 143 16 15.0631578947368 0.936842105263159 144 10 12.9473684210526 -2.94736842105263 145 18 16.25 1.75 146 13 15.0631578947368 -2.06315789473684 147 16 15.0631578947368 0.936842105263159 148 13 12.9473684210526 0.0526315789473681 149 10 11.6818181818182 -1.68181818181818 150 15 16.25 -1.25 151 16 12.9473684210526 3.05263157894737 152 16 11.6818181818182 4.31818181818182 153 14 12.9473684210526 1.05263157894737 154 10 12.9473684210526 -2.94736842105263 155 13 11.6818181818182 1.31818181818182 156 15 12.9473684210526 2.05263157894737 157 16 15.0631578947368 0.936842105263159 158 12 12.9473684210526 -0.947368421052632 159 13 14.1818181818182 -1.18181818181818 160 12 12.9473684210526 -0.947368421052632 161 17 16.25 0.75 162 15 14.1818181818182 0.818181818181818 163 10 12.9473684210526 -2.94736842105263 164 14 14.1818181818182 -0.181818181818182 165 11 14.1818181818182 -3.18181818181818 166 13 14.1818181818182 -1.18181818181818 167 16 14.1818181818182 1.81818181818182 168 12 11.6818181818182 0.318181818181818 169 16 14.1818181818182 1.81818181818182 170 12 14.1818181818182 -2.18181818181818 171 9 8.41666666666667 0.583333333333334 172 12 14.1818181818182 -2.18181818181818 173 15 14.1818181818182 0.818181818181818 174 12 12.9473684210526 -0.947368421052632 175 12 12.9473684210526 -0.947368421052632 176 14 14.1818181818182 -0.181818181818182 177 12 12.9473684210526 -0.947368421052632 178 16 14.1818181818182 1.81818181818182 179 11 8.41666666666667 2.58333333333333 180 19 18.125 0.875 181 15 14.1818181818182 0.818181818181818 182 8 14.1818181818182 -6.18181818181818 183 16 14.1818181818182 1.81818181818182 184 17 14.1818181818182 2.81818181818182 185 12 11.6818181818182 0.318181818181818 186 11 8.41666666666667 2.58333333333333 187 11 11.6818181818182 -0.681818181818182 188 14 14.1818181818182 -0.181818181818182 189 16 14.1818181818182 1.81818181818182 190 12 11.6818181818182 0.318181818181818 191 16 14.1818181818182 1.81818181818182 192 13 14.1818181818182 -1.18181818181818 193 15 14.1818181818182 0.818181818181818 194 16 12.9473684210526 3.05263157894737 195 16 14.1818181818182 1.81818181818182 196 14 12.9473684210526 1.05263157894737 197 16 14.1818181818182 1.81818181818182 198 14 14.1818181818182 -0.181818181818182 199 11 12.9473684210526 -1.94736842105263 200 12 14.1818181818182 -2.18181818181818 201 15 12.9473684210526 2.05263157894737 202 15 14.1818181818182 0.818181818181818 203 16 14.1818181818182 1.81818181818182 204 16 14.1818181818182 1.81818181818182 205 11 14.1818181818182 -3.18181818181818 206 15 14.1818181818182 0.818181818181818 207 12 14.1818181818182 -2.18181818181818 208 12 14.1818181818182 -2.18181818181818 209 15 14.1818181818182 0.818181818181818 210 15 12.9473684210526 2.05263157894737 211 16 14.1818181818182 1.81818181818182 212 14 14.1818181818182 -0.181818181818182 213 17 14.1818181818182 2.81818181818182 214 14 14.1818181818182 -0.181818181818182 215 13 12.9473684210526 0.0526315789473681 216 15 14.1818181818182 0.818181818181818 217 13 14.1818181818182 -1.18181818181818 218 14 14.1818181818182 -0.181818181818182 219 15 14.1818181818182 0.818181818181818 220 12 12.9473684210526 -0.947368421052632 221 8 11.6818181818182 -3.68181818181818 222 14 14.1818181818182 -0.181818181818182 223 14 12.9473684210526 1.05263157894737 224 11 12.9473684210526 -1.94736842105263 225 12 12.9473684210526 -0.947368421052632 226 13 8.41666666666667 4.58333333333333 227 10 14.1818181818182 -4.18181818181818 228 16 11.6818181818182 4.31818181818182 229 18 16.25 1.75 230 13 14.1818181818182 -1.18181818181818 231 11 14.1818181818182 -3.18181818181818 232 4 8.41666666666667 -4.41666666666667 233 13 14.1818181818182 -1.18181818181818 234 16 14.1818181818182 1.81818181818182 235 10 8.41666666666667 1.58333333333333 236 12 12.9473684210526 -0.947368421052632 237 12 14.1818181818182 -2.18181818181818 238 10 8.41666666666667 1.58333333333333 239 13 11.6818181818182 1.31818181818182 240 12 12.9473684210526 -0.947368421052632 241 14 12.9473684210526 1.05263157894737 242 10 12.9473684210526 -2.94736842105263 243 12 12.9473684210526 -0.947368421052632 244 12 12.9473684210526 -0.947368421052632 245 11 11.6818181818182 -0.681818181818182 246 10 11.6818181818182 -1.68181818181818 247 12 11.6818181818182 0.318181818181818 248 16 12.9473684210526 3.05263157894737 249 12 14.1818181818182 -2.18181818181818 250 14 14.1818181818182 -0.181818181818182 251 16 14.1818181818182 1.81818181818182 252 14 12.9473684210526 1.05263157894737 253 13 14.1818181818182 -1.18181818181818 254 4 8.41666666666667 -4.41666666666667 255 15 14.1818181818182 0.818181818181818 256 11 14.1818181818182 -3.18181818181818 257 11 11.6818181818182 -0.681818181818182 258 14 12.9473684210526 1.05263157894737 259 15 14.1818181818182 0.818181818181818 260 14 12.9473684210526 1.05263157894737 261 13 14.1818181818182 -1.18181818181818 262 11 12.9473684210526 -1.94736842105263 263 15 14.1818181818182 0.818181818181818 264 11 12.9473684210526 -1.94736842105263 265 13 11.6818181818182 1.31818181818182 266 13 12.9473684210526 0.0526315789473681 267 16 14.1818181818182 1.81818181818182 268 13 12.9473684210526 0.0526315789473681 269 16 14.1818181818182 1.81818181818182 270 16 16.25 -0.25 271 12 11.6818181818182 0.318181818181818 272 7 8.41666666666667 -1.41666666666667 273 16 14.1818181818182 1.81818181818182 274 5 11.6818181818182 -6.68181818181818 275 16 12.9473684210526 3.05263157894737 276 4 8.41666666666667 -4.41666666666667 277 12 12.9473684210526 -0.947368421052632 278 15 14.1818181818182 0.818181818181818 279 14 14.1818181818182 -0.181818181818182 280 11 12.9473684210526 -1.94736842105263 281 16 14.1818181818182 1.81818181818182 282 15 14.1818181818182 0.818181818181818 283 12 12.9473684210526 -0.947368421052632 284 6 8.41666666666667 -2.41666666666667 285 16 14.1818181818182 1.81818181818182 286 10 12.9473684210526 -2.94736842105263 287 15 16.25 -1.25 288 14 14.1818181818182 -0.181818181818182

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/2bpk01292769227.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/2bpk01292769227.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/3bpk01292769227.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/3bpk01292769227.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/44z2m1292769227.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292769110275oy4jboxuusnt/44z2m1292769227.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = none ; par3 = 3 ; par4 = no ;

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') }

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