*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, 05 Dec 2010 20:21:33 +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/05/t1291580377a669eavix0owrni.htm/, Retrieved Sun, 05 Dec 2010 21:19:40 +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/05/t1291580377a669eavix0owrni.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 13 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Goodness of Fit Correlation 0.5474 R-squared 0.2997 RMSE 3.3906 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 41 36.4 4.6 2 39 34.3367346938776 4.66326530612245 3 30 34.3367346938776 -4.33673469387755 4 31 34.3367346938776 -3.33673469387755 5 34 34.3367346938776 -0.336734693877553 6 35 31.2105263157895 3.78947368421053 7 39 34.3367346938776 4.66326530612245 8 34 34.3367346938776 -0.336734693877553 9 36 34.3367346938776 1.66326530612245 10 37 36.4 0.600000000000001 11 38 34.3367346938776 3.66326530612245 12 36 34.3367346938776 1.66326530612245 13 38 34.3367346938776 3.66326530612245 14 39 36.4 2.6 15 33 34.3367346938776 -1.33673469387755 16 32 34.3367346938776 -2.33673469387755 17 36 34.3367346938776 1.66326530612245 18 38 36.4 1.6 19 39 36.4 2.6 20 32 34.3367346938776 -2.33673469387755 21 32 34.3367346938776 -2.33673469387755 22 31 34.3367346938776 -3.33673469387755 23 39 36.4 2.6 24 37 36.4 0.600000000000001 25 39 34.3367346938776 4.66326530612245 26 41 34.3367346938776 6.66326530612245 27 36 34.3367346938776 1.66326530612245 28 33 34.3367346938776 -1.33673469387755 29 33 34.3367346938776 -1.33673469387755 30 34 34.3367346938776 -0.336734693877553 31 31 34.3367346938776 -3.33673469387755 32 27 34.3367346938776 -7.33673469387755 33 37 34.3367346938776 2.66326530612245 34 34 34.3367346938776 -0.336734693877553 35 34 31.2105263157895 2.78947368421053 36 32 34.3367346938776 -2.33673469387755 37 29 34.3367346938776 -5.33673469387755 38 36 34.3367346938776 1.66326530612245 39 29 34.3367346938776 -5.33673469387755 40 35 34.3367346938776 0.663265306122447 41 37 34.3367346938776 2.66326530612245 42 34 34.3367346938776 -0.336734693877553 43 38 34.3367346938776 3.66326530612245 44 35 34.3367346938776 0.663265306122447 45 38 31.2105263157895 6.78947368421053 46 37 34.3367346938776 2.66326530612245 47 38 36.4 1.6 48 33 34.3367346938776 -1.33673469387755 49 36 36.4 -0.399999999999999 50 38 34.3367346938776 3.66326530612245 51 32 36.4 -4.4 52 32 31.2105263157895 0.789473684210527 53 32 34.3367346938776 -2.33673469387755 54 34 36.4 -2.4 55 32 34.3367346938776 -2.33673469387755 56 37 34.3367346938776 2.66326530612245 57 39 34.3367346938776 4.66326530612245 58 29 34.3367346938776 -5.33673469387755 59 37 34.3367346938776 2.66326530612245 60 35 34.3367346938776 0.663265306122447 61 30 31.2105263157895 -1.21052631578947 62 38 34.3367346938776 3.66326530612245 63 34 34.3367346938776 -0.336734693877553 64 31 34.3367346938776 -3.33673469387755 65 34 34.3367346938776 -0.336734693877553 66 35 34.3367346938776 0.663265306122447 67 36 34.3367346938776 1.66326530612245 68 30 31.2105263157895 -1.21052631578947 69 39 36.4 2.6 70 35 34.3367346938776 0.663265306122447 71 38 34.3367346938776 3.66326530612245 72 31 36.4 -5.4 73 34 36.4 -2.4 74 38 36.4 1.6 75 34 31.2105263157895 2.78947368421053 76 39 31.2105263157895 7.78947368421053 77 37 34.3367346938776 2.66326530612245 78 34 34.3367346938776 -0.336734693877553 79 28 34.3367346938776 -6.33673469387755 80 37 31.2105263157895 5.78947368421053 81 33 34.3367346938776 -1.33673469387755 82 35 34.3367346938776 0.663265306122447 83 37 34.3367346938776 2.66326530612245 84 32 34.3367346938776 -2.33673469387755 85 33 34.3367346938776 -1.33673469387755 86 38 36.4 1.6 87 33 34.3367346938776 -1.33673469387755 88 29 34.3367346938776 -5.33673469387755 89 33 34.3367346938776 -1.33673469387755 90 31 34.3367346938776 -3.33673469387755 91 36 34.3367346938776 1.66326530612245 92 35 36.4 -1.400 93 32 31.2105263157895 0.789473684210527 94 29 31.2105263157895 -2.21052631578947 95 39 34.3367346938776 4.66326530612245 96 37 34.3367346938776 2.66326530612245 97 35 34.3367346938776 0.663265306122447 98 37 34.3367346938776 2.66326530612245 99 32 34.3367346938776 -2.33673469387755 100 38 34.3367346938776 3.66326530612245 101 37 34.3367346938776 2.66326530612245 102 36 34.3367346938776 1.66326530612245 103 32 31.2105263157895 0.789473684210527 104 33 36.4 -3.4 105 40 34.3367346938776 5.66326530612245 106 38 34.3367346938776 3.66326530612245 107 41 36.4 4.6 108 36 34.3367346938776 1.66326530612245 109 43 36.4 6.6 110 30 34.3367346938776 -4.33673469387755 111 31 34.3367346938776 -3.33673469387755 112 32 36.4 -4.4 113 37 34.3367346938776 2.66326530612245 114 37 34.3367346938776 2.66326530612245 115 33 36.4 -3.4 116 34 36.4 -2.4 117 33 34.3367346938776 -1.33673469387755 118 38 34.3367346938776 3.66326530612245 119 33 34.3367346938776 -1.33673469387755 120 31 31.2105263157895 -0.210526315789473 121 38 36.4 1.6 122 37 36.4 0.600000000000001 123 36 34.3367346938776 1.66326530612245 124 31 34.3367346938776 -3.33673469387755 125 39 34.3367346938776 4.66326530612245 126 44 36.4 7.6 127 33 34.3367346938776 -1.33673469387755 128 35 34.3367346938776 0.663265306122447 129 32 34.3367346938776 -2.33673469387755 130 28 34.3367346938776 -6.33673469387755 131 40 34.3367346938776 5.66326530612245 132 27 31.2105263157895 -4.21052631578947 133 37 36.4 0.600000000000001 134 32 31.2105263157895 0.789473684210527 135 28 24.3333333333333 3.66666666666667 136 34 34.3367346938776 -0.336734693877553 137 30 34.3367346938776 -4.33673469387755 138 35 34.3367346938776 0.663265306122447 139 31 34.3367346938776 -3.33673469387755 140 32 34.3367346938776 -2.33673469387755 141 30 34.3367346938776 -4.33673469387755 142 30 34.3367346938776 -4.33673469387755 143 31 24.3333333333333 6.66666666666667 144 40 34.3367346938776 5.66326530612245 145 32 31.2105263157895 0.789473684210527 146 36 34.3367346938776 1.66326530612245 147 32 34.3367346938776 -2.33673469387755 148 35 34.3367346938776 0.663265306122447 149 38 36.4 1.6 150 42 34.3367346938776 7.66326530612245 151 34 36.4 -2.4 152 35 36.4 -1.400 153 38 34.3367346938776 3.66326530612245 154 33 34.3367346938776 -1.33673469387755 155 32 34.3367346938776 -2.33673469387755 156 33 34.3367346938776 -1.33673469387755 157 34 34.3367346938776 -0.336734693877553 158 32 36.4 -4.4 159 27 31.2105263157895 -4.21052631578947 160 31 31.2105263157895 -0.210526315789473 161 38 34.3367346938776 3.66326530612245 162 34 36.4 -2.4 163 24 31.2105263157895 -7.21052631578947 164 30 34.3367346938776 -4.33673469387755 165 26 24.3333333333333 1.66666666666667 166 34 36.4 -2.4 167 27 34.3367346938776 -7.33673469387755 168 37 34.3367346938776 2.66326530612245 169 36 34.3367346938776 1.66326530612245 170 41 34.3367346938776 6.66326530612245 171 29 24.3333333333333 4.66666666666667 172 36 31.2105263157895 4.78947368421053 173 32 34.3367346938776 -2.33673469387755 174 37 34.3367346938776 2.66326530612245 175 30 31.2105263157895 -1.21052631578947 176 31 34.3367346938776 -3.33673469387755 177 38 34.3367346938776 3.66326530612245 178 36 34.3367346938776 1.66326530612245 179 35 31.2105263157895 3.78947368421053 180 31 34.3367346938776 -3.33673469387755 181 38 34.3367346938776 3.66326530612245 182 22 31.2105263157895 -9.21052631578947 183 32 34.3367346938776 -2.33673469387755 184 36 34.3367346938776 1.66326530612245 185 39 34.3367346938776 4.66326530612245 186 28 31.2105263157895 -3.21052631578947 187 32 34.3367346938776 -2.33673469387755 188 32 34.3367346938776 -2.33673469387755 189 38 36.4 1.6 190 32 34.3367346938776 -2.33673469387755 191 35 34.3367346938776 0.663265306122447 192 32 34.3367346938776 -2.33673469387755 193 37 36.4 0.600000000000001 194 34 34.3367346938776 -0.336734693877553 195 33 34.3367346938776 -1.33673469387755 196 33 34.3367346938776 -1.33673469387755 197 30 31.2105263157895 -1.21052631578947 198 24 31.2105263157895 -7.21052631578947 199 34 34.3367346938776 -0.336734693877553 200 34 34.3367346938776 -0.336734693877553 201 33 34.3367346938776 -1.33673469387755 202 34 34.3367346938776 -0.336734693877553 203 35 34.3367346938776 0.663265306122447 204 35 34.3367346938776 0.663265306122447 205 36 34.3367346938776 1.66326530612245 206 34 34.3367346938776 -0.336734693877553 207 34 34.3367346938776 -0.336734693877553 208 41 36.4 4.6 209 32 36.4 -4.4 210 30 34.3367346938776 -4.33673469387755 211 35 34.3367346938776 0.663265306122447 212 28 24.3333333333333 3.66666666666667 213 33 34.3367346938776 -1.33673469387755 214 39 34.3367346938776 4.66326530612245 215 36 34.3367346938776 1.66326530612245 216 36 36.4 -0.399999999999999 217 35 34.3367346938776 0.663265306122447 218 38 34.3367346938776 3.66326530612245 219 33 34.3367346938776 -1.33673469387755 220 31 34.3367346938776 -3.33673469387755 221 32 34.3367346938776 -2.33673469387755 222 31 34.3367346938776 -3.33673469387755 223 33 34.3367346938776 -1.33673469387755 224 34 34.3367346938776 -0.336734693877553 225 34 34.3367346938776 -0.336734693877553 226 34 31.2105263157895 2.78947368421053 227 33 36.4 -3.4 228 32 34.3367346938776 -2.33673469387755 229 41 34.3367346938776 6.66326530612245 230 34 34.3367346938776 -0.336734693877553 231 36 34.3367346938776 1.66326530612245 232 37 31.2105263157895 5.78947368421053 233 36 31.2105263157895 4.78947368421053 234 29 34.3367346938776 -5.33673469387755 235 37 31.2105263157895 5.78947368421053 236 27 34.3367346938776 -7.33673469387755 237 35 34.3367346938776 0.663265306122447 238 28 31.2105263157895 -3.21052631578947 239 35 34.3367346938776 0.663265306122447 240 29 34.3367346938776 -5.33673469387755 241 32 34.3367346938776 -2.33673469387755 242 36 34.3367346938776 1.66326530612245 243 19 24.3333333333333 -5.33333333333333 244 21 24.3333333333333 -3.33333333333333 245 31 34.3367346938776 -3.33673469387755 246 33 34.3367346938776 -1.33673469387755 247 36 34.3367346938776 1.66326530612245 248 33 34.3367346938776 -1.33673469387755 249 37 34.3367346938776 2.66326530612245 250 34 34.3367346938776 -0.336734693877553 251 35 34.3367346938776 0.663265306122447 252 31 34.3367346938776 -3.33673469387755 253 37 34.3367346938776 2.66326530612245 254 35 31.2105263157895 3.78947368421053 255 27 31.2105263157895 -4.21052631578947 256 34 36.4 -2.4 257 40 34.3367346938776 5.66326530612245 258 29 34.3367346938776 -5.33673469387755 259 38 34.3367346938776 3.66326530612245 260 34 34.3367346938776 -0.336734693877553 261 21 31.2105263157895 -10.2105263157895 262 36 34.3367346938776 1.66326530612245 263 38 34.3367346938776 3.66326530612245 264 30 31.2105263157895 -1.21052631578947 265 35 34.3367346938776 0.663265306122447 266 30 31.2105263157895 -1.21052631578947 267 36 34.3367346938776 1.66326530612245 268 34 36.4 -2.4 269 35 34.3367346938776 0.663265306122447 270 34 34.3367346938776 -0.336734693877553 271 32 36.4 -4.4 272 33 31.2105263157895 1.78947368421053 273 33 34.3367346938776 -1.33673469387755 274 26 34.3367346938776 -8.33673469387755 275 35 36.4 -1.400 276 21 24.3333333333333 -3.33333333333333 277 38 34.3367346938776 3.66326530612245 278 35 34.3367346938776 0.663265306122447 279 33 34.3367346938776 -1.33673469387755 280 37 34.3367346938776 2.66326530612245 281 38 34.3367346938776 3.66326530612245 282 34 34.3367346938776 -0.336734693877553 283 27 31.2105263157895 -4.21052631578947 284 16 24.3333333333333 -8.33333333333333 285 40 36.4 3.6 286 36 34.3367346938776 1.66326530612245 287 42 36.4 5.6 288 30 34.3367346938776 -4.33673469387755

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/2klq71291580479.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/2klq71291580479.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/3klq71291580479.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/3klq71291580479.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/4cc7a1291580479.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/05/t1291580377a669eavix0owrni/4cc7a1291580479.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = equal ; par3 = 2 ; 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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