*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 14:44:59 +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/t12929426127wovwipa403wtkk.htm/, Retrieved Tue, 21 Dec 2010 15:43:33 +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/t12929426127wovwipa403wtkk.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 14 41 38 13 12 1 1 18 39 32 16 11 1 1 11 30 35 19 15 1 0 12 31 33 15 6 1 1 16 34 37 14 13 1 1 18 35 29 13 10 1 1 14 39 31 19 12 1 1 14 34 36 15 14 1 1 15 36 35 14 12 1 1 15 37 38 15 9 1 0 17 38 31 16 10 1 1 19 36 34 16 12 1 0 10 38 35 16 12 1 1 16 39 38 16 11 1 1 18 33 37 17 15 1 0 14 32 33 15 12 1 0 14 36 32 15 10 1 1 17 38 38 20 12 1 0 14 39 38 18 11 1 1 16 32 32 16 12 1 0 18 32 33 16 11 1 1 11 31 31 16 12 1 1 14 39 38 19 13 1 1 12 37 39 16 11 1 0 17 39 32 17 12 1 1 9 41 32 17 13 1 0 16 36 35 16 10 1 1 14 33 37 15 14 1 1 15 33 33 16 12 1 0 11 34 33 14 10 1 1 16 31 31 15 12 1 0 13 27 32 12 8 1 1 17 37 31 14 10 1 1 15 34 37 16 12 1 0 14 34 30 14 12 1 0 16 32 33 10 7 1 0 9 29 31 10 9 1 0 15 36 33 14 12 1 1 17 29 31 16 10 1 0 13 35 33 16 10 1 0 15 37 32 16 10 1 1 16 34 33 14 12 1 0 16 38 32 20 15 1 0 12 35 33 14 10 1 1 15 38 28 14 10 1 1 11 37 35 11 12 1 1 15 38 39 14 13 1 1 15 33 34 15 11 1 1 17 36 38 16 11 1 0 13 38 32 14 12 1 1 16 32 38 16 14 1 0 14 32 3 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Goodness of Fit Correlation 0.3673 R-squared 0.1349 RMSE 2.3154 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 14 14.4489795918367 -0.448979591836734 2 18 14.4489795918367 3.55102040816327 3 11 14.4489795918367 -3.44897959183673 4 12 14.0454545454545 -2.04545454545454 5 16 14.4489795918367 1.55102040816327 6 18 14.4489795918367 3.55102040816327 7 14 14.4489795918367 -0.448979591836734 8 14 14.4489795918367 -0.448979591836734 9 15 14.4489795918367 0.551020408163266 10 15 14.4489795918367 0.551020408163266 11 17 14.0454545454545 2.95454545454546 12 19 14.4489795918367 4.55102040816327 13 10 14.0454545454545 -4.04545454545454 14 16 14.4489795918367 1.55102040816327 15 18 14.4489795918367 3.55102040816327 16 14 14.0454545454545 -0.045454545454545 17 14 14.0454545454545 -0.045454545454545 18 17 14.4489795918367 2.55102040816327 19 14 14.0454545454545 -0.045454545454545 20 16 14.4489795918367 1.55102040816327 21 18 14.0454545454545 3.95454545454546 22 11 14.4489795918367 -3.44897959183673 23 14 14.4489795918367 -0.448979591836734 24 12 14.4489795918367 -2.44897959183673 25 17 14.0454545454545 2.95454545454546 26 9 14.4489795918367 -5.44897959183673 27 16 14.0454545454545 1.95454545454546 28 14 14.4489795918367 -0.448979591836734 29 15 14.4489795918367 0.551020408163266 30 11 14.0454545454545 -3.04545454545454 31 16 14.4489795918367 1.55102040816327 32 13 11.6875 1.3125 33 17 14.4489795918367 2.55102040816327 34 15 14.4489795918367 0.551020408163266 35 14 14.0454545454545 -0.045454545454545 36 16 11.6875 4.3125 37 9 11.6875 -2.6875 38 15 14.0454545454545 0.954545454545455 39 17 14.4489795918367 2.55102040816327 40 13 14.0454545454545 -1.04545454545454 41 15 14.0454545454545 0.954545454545455 42 16 14.4489795918367 1.55102040816327 43 16 14.0454545454545 1.95454545454546 44 12 14.0454545454545 -2.04545454545454 45 15 14.4489795918367 0.551020408163266 46 11 14.4489795918367 -3.44897959183673 47 15 14.4489795918367 0.551020408163266 48 15 14.4489795918367 0.551020408163266 49 17 14.4489795918367 2.55102040816327 50 13 14.0454545454545 -1.04545454545454 51 16 14.4489795918367 1.55102040816327 52 14 14.0454545454545 -0.045454545454545 53 11 11.6875 -0.6875 54 12 14.4489795918367 -2.44897959183673 55 12 11.6875 0.3125 56 15 14.4489795918367 0.551020408163266 57 16 14.4489795918367 1.55102040816327 58 15 14.4489795918367 0.551020408163266 59 12 14.0454545454545 -2.04545454545454 60 12 14.4489795918367 -2.44897959183673 61 8 11.6875 -3.6875 62 13 14.0454545454545 -1.04545454545454 63 11 14.4489795918367 -3.44897959183673 64 14 14.4489795918367 -0.448979591836734 65 15 14.4489795918367 0.551020408163266 66 10 14.0454545454545 -4.04545454545454 67 11 14.4489795918367 -3.44897959183673 68 12 14.0454545454545 -2.04545454545454 69 15 14.4489795918367 0.551020408163266 70 15 14.0454545454545 0.954545454545455 71 14 11.6875 2.3125 72 16 14.4489795918367 1.55102040816327 73 15 14.4489795918367 0.551020408163266 74 15 14.0454545454545 0.954545454545455 75 13 14.0454545454545 -1.04545454545454 76 12 14.4489795918367 -2.44897959183673 77 17 14.4489795918367 2.55102040816327 78 13 14.4489795918367 -1.44897959183673 79 15 11.6875 3.3125 80 13 14.0454545454545 -1.04545454545454 81 15 14.0454545454545 0.954545454545455 82 15 14.4489795918367 0.551020408163266 83 16 14.0454545454545 1.95454545454546 84 15 14.4489795918367 0.551020408163266 85 14 14.4489795918367 -0.448979591836734 86 15 14.0454545454545 0.954545454545455 87 14 14.4489795918367 -0.448979591836734 88 13 14.4489795918367 -1.44897959183673 89 7 14.4489795918367 -7.44897959183673 90 17 14.4489795918367 2.55102040816327 91 13 14.4489795918367 -1.44897959183673 92 15 14.4489795918367 0.551020408163266 93 14 14.4489795918367 -0.448979591836734 94 13 14.4489795918367 -1.44897959183673 95 16 14.4489795918367 1.55102040816327 96 12 14.4489795918367 -2.44897959183673 97 14 14.4489795918367 -0.448979591836734 98 17 14.0454545454545 2.95454545454546 99 15 14.0454545454545 0.954545454545455 100 17 14.4489795918367 2.55102040816327 101 12 14.0454545454545 -2.04545454545454 102 16 14.4489795918367 1.55102040816327 103 11 14.0454545454545 -3.04545454545454 104 15 14.4489795918367 0.551020408163266 105 9 11.6875 -2.6875 106 16 14.4489795918367 1.55102040816327 107 15 14.0454545454545 0.954545454545455 108 10 14.0454545454545 -4.04545454545454 109 10 14.4489795918367 -4.44897959183673 110 15 14.4489795918367 0.551020408163266 111 11 14.4489795918367 -3.44897959183673 112 13 14.4489795918367 -1.44897959183673 113 18 14.4489795918367 3.55102040816327 114 16 14.0454545454545 1.95454545454546 115 14 14.4489795918367 -0.448979591836734 116 14 14.4489795918367 -0.448979591836734 117 14 14.4489795918367 -0.448979591836734 118 14 14.4489795918367 -0.448979591836734 119 12 11.6875 0.3125 120 14 14.4489795918367 -0.448979591836734 121 15 14.4489795918367 0.551020408163266 122 15 14.4489795918367 0.551020408163266 123 15 14.4489795918367 0.551020408163266 124 13 14.4489795918367 -1.44897959183673 125 17 14.0454545454545 2.95454545454546 126 17 14.4489795918367 2.55102040816327 127 19 14.4489795918367 4.55102040816327 128 15 14.4489795918367 0.551020408163266 129 13 14.0454545454545 -1.04545454545454 130 9 11.6875 -2.6875 131 15 14.4489795918367 0.551020408163266 132 15 11.6875 3.3125 133 15 14.0454545454545 0.954545454545455 134 16 14.4489795918367 1.55102040816327 135 11 11.6875 -0.6875 136 14 14.0454545454545 -0.045454545454545 137 11 14.4489795918367 -3.44897959183673 138 15 14.4489795918367 0.551020408163266 139 13 11.6875 1.3125 140 15 14.4489795918367 0.551020408163266 141 16 14.0454545454545 1.95454545454546 142 14 14.4489795918367 -0.448979591836734 143 15 14.0454545454545 0.954545454545455 144 16 14.4489795918367 1.55102040816327 145 16 14.4489795918367 1.55102040816327 146 11 11.6875 -0.6875 147 12 14.0454545454545 -2.04545454545454 148 9 11.6875 -2.6875 149 16 14.4489795918367 1.55102040816327 150 13 14.4489795918367 -1.44897959183673 151 16 14.0454545454545 1.95454545454546 152 12 14.4489795918367 -2.44897959183673 153 9 14.4489795918367 -5.44897959183673 154 13 14.4489795918367 -1.44897959183673 155 14 14.4489795918367 -0.448979591836734 156 19 14.4489795918367 4.55102040816327 157 13 14.4489795918367 -1.44897959183673 158 12 14.4489795918367 -2.44897959183673 159 10 12.6769230769231 -2.67692307692308 160 14 12.6769230769231 1.32307692307692 161 16 12.6769230769231 3.32307692307692 162 10 12.6769230769231 -2.67692307692308 163 11 12.6769230769231 -1.67692307692308 164 14 12.6769230769231 1.32307692307692 165 12 12.6769230769231 -0.676923076923076 166 9 12.6769230769231 -3.67692307692308 167 9 12.6769230769231 -3.67692307692308 168 11 12.6769230769231 -1.67692307692308 169 16 12.6769230769231 3.32307692307692 170 9 12.6769230769231 -3.67692307692308 171 13 12.6769230769231 0.323076923076924 172 16 12.6769230769231 3.32307692307692 173 13 12.6769230769231 0.323076923076924 174 9 12.6769230769231 -3.67692307692308 175 12 12.6769230769231 -0.676923076923076 176 16 12.6769230769231 3.32307692307692 177 11 12.6769230769231 -1.67692307692308 178 14 12.6769230769231 1.32307692307692 179 13 12.6769230769231 0.323076923076924 180 15 12.6769230769231 2.32307692307692 181 14 12.6769230769231 1.32307692307692 182 16 12.6769230769231 3.32307692307692 183 13 12.6769230769231 0.323076923076924 184 14 12.6769230769231 1.32307692307692 185 15 12.6769230769231 2.32307692307692 186 13 12.6769230769231 0.323076923076924 187 11 12.6769230769231 -1.67692307692308 188 11 12.6769230769231 -1.67692307692308 189 14 12.6769230769231 1.32307692307692 190 15 12.6769230769231 2.32307692307692 191 11 12.6769230769231 -1.67692307692308 192 15 12.6769230769231 2.32307692307692 193 12 12.6769230769231 -0.676923076923076 194 14 12.6769230769231 1.32307692307692 195 14 12.6769230769231 1.32307692307692 196 8 12.6769230769231 -4.67692307692308 197 9 12.6769230769231 -3.67692307692308 198 15 12.6769230769231 2.32307692307692 199 17 12.6769230769231 4.32307692307692 200 13 12.6769230769231 0.323076923076924 201 15 12.6769230769231 2.32307692307692 202 15 12.6769230769231 2.32307692307692 203 14 12.6769230769231 1.32307692307692 204 16 12.6769230769231 3.32307692307692 205 13 12.6769230769231 0.323076923076924 206 16 12.6769230769231 3.32307692307692 207 9 12.6769230769231 -3.67692307692308 208 16 12.6769230769231 3.32307692307692 209 11 12.6769230769231 -1.67692307692308 210 10 12.6769230769231 -2.67692307692308 211 11 12.6769230769231 -1.67692307692308 212 15 12.6769230769231 2.32307692307692 213 17 12.6769230769231 4.32307692307692 214 14 12.6769230769231 1.32307692307692 215 8 12.6769230769231 -4.67692307692308 216 15 12.6769230769231 2.32307692307692 217 11 12.6769230769231 -1.67692307692308 218 16 12.6769230769231 3.32307692307692 219 10 12.6769230769231 -2.67692307692308 220 15 12.6769230769231 2.32307692307692 221 16 12.6769230769231 3.32307692307692 222 19 12.6769230769231 6.32307692307692 223 12 12.6769230769231 -0.676923076923076 224 8 12.6769230769231 -4.67692307692308 225 11 12.6769230769231 -1.67692307692308 226 14 12.6769230769231 1.32307692307692 227 9 12.6769230769231 -3.67692307692308 228 15 12.6769230769231 2.32307692307692 229 13 12.6769230769231 0.323076923076924 230 16 12.6769230769231 3.32307692307692 231 11 12.6769230769231 -1.67692307692308 232 12 12.6769230769231 -0.676923076923076 233 13 12.6769230769231 0.323076923076924 234 10 12.6769230769231 -2.67692307692308 235 11 12.6769230769231 -1.67692307692308 236 12 12.6769230769231 -0.676923076923076 237 8 12.6769230769231 -4.67692307692308 238 12 12.6769230769231 -0.676923076923076 239 12 12.6769230769231 -0.676923076923076 240 11 12.6769230769231 -1.67692307692308 241 13 12.6769230769231 0.323076923076924 242 14 12.6769230769231 1.32307692307692 243 10 12.6769230769231 -2.67692307692308 244 12 12.6769230769231 -0.676923076923076 245 15 12.6769230769231 2.32307692307692 246 13 12.6769230769231 0.323076923076924 247 13 12.6769230769231 0.323076923076924 248 13 12.6769230769231 0.323076923076924 249 12 12.6769230769231 -0.676923076923076 250 12 12.6769230769231 -0.676923076923076 251 9 12.6769230769231 -3.67692307692308 252 9 12.6769230769231 -3.67692307692308 253 15 12.6769230769231 2.32307692307692 254 10 12.6769230769231 -2.67692307692308 255 14 12.6769230769231 1.32307692307692 256 15 12.6769230769231 2.32307692307692 257 7 12.6769230769231 -5.67692307692308 258 14 12.6769230769231 1.32307692307692 259 8 12.6769230769231 -4.67692307692308 260 10 12.6769230769231 -2.67692307692308 261 13 12.6769230769231 0.323076923076924 262 13 12.6769230769231 0.323076923076924 263 13 12.6769230769231 0.323076923076924 264 8 12.6769230769231 -4.67692307692308 265 12 12.6769230769231 -0.676923076923076 266 13 12.6769230769231 0.323076923076924 267 12 12.6769230769231 -0.676923076923076 268 10 12.6769230769231 -2.67692307692308 269 13 12.6769230769231 0.323076923076924 270 12 12.6769230769231 -0.676923076923076 271 9 12.6769230769231 -3.67692307692308 272 15 12.6769230769231 2.32307692307692 273 13 12.6769230769231 0.323076923076924 274 13 12.6769230769231 0.323076923076924 275 13 12.6769230769231 0.323076923076924 276 15 12.6769230769231 2.32307692307692 277 15 12.6769230769231 2.32307692307692 278 14 12.6769230769231 1.32307692307692 279 15 12.6769230769231 2.32307692307692 280 11 12.6769230769231 -1.67692307692308 281 15 12.6769230769231 2.32307692307692 282 14 12.6769230769231 1.32307692307692 283 13 12.6769230769231 0.323076923076924 284 12 12.6769230769231 -0.676923076923076 285 16 12.6769230769231 3.32307692307692 286 16 12.6769230769231 3.32307692307692 287 9 12.6769230769231 -3.67692307692308 288 14 12.6769230769231 1.32307692307692

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/2blgb1292942690.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/2blgb1292942690.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/3blgb1292942690.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/3blgb1292942690.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/43ufw1292942690.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t12929426127wovwipa403wtkk/43ufw1292942690.ps (open in new window)

Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; 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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