*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 14:09:55 +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/t1292940501mce5ngmaem47oyk.htm/, Retrieved Tue, 21 Dec 2010 15:08:21 +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/t1292940501mce5ngmaem47oyk.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 14 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Goodness of Fit Correlation 0.5624 R-squared 0.3163 RMSE 3.2245 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 38 38.1 -0.100000000000001 2 32 34.8988764044944 -2.89887640449438 3 35 31.1607142857143 3.83928571428572 4 33 31.1607142857143 1.83928571428572 5 37 33.984 3.016 6 29 33.984 -4.984 7 31 34.8988764044944 -3.89887640449438 8 36 33.984 2.016 9 35 33.984 1.01600000000000 10 38 33.984 4.016 11 31 34.8988764044944 -3.89887640449438 12 34 34.8988764044944 -0.89887640449438 13 35 34.8988764044944 0.101123595505619 14 38 34.8988764044944 3.10112359550562 15 37 34.8988764044944 2.10112359550562 16 33 33.984 -0.984000000000002 17 32 33.984 -1.984 18 38 34.8988764044944 3.10112359550562 19 38 34.8988764044944 3.10112359550562 20 32 34.8988764044944 -2.89887640449438 21 33 34.8988764044944 -1.89887640449438 22 31 31.1607142857143 -0.160714285714285 23 38 34.8988764044944 3.10112359550562 24 39 34.8988764044944 4.10112359550562 25 32 34.8988764044944 -2.89887640449438 26 32 38.1 -6.1 27 35 34.8988764044944 0.101123595505619 28 37 33.984 3.016 29 33 34.8988764044944 -1.89887640449438 30 33 33.984 -0.984000000000002 31 31 31.1607142857143 -0.160714285714285 32 32 31.1607142857143 0.839285714285715 33 31 33.984 -2.984 34 37 34.8988764044944 2.10112359550562 35 30 33.984 -3.984 36 33 33.984 -0.984000000000002 37 31 31.1607142857143 -0.160714285714285 38 33 33.984 -0.984000000000002 39 31 31.1607142857143 -0.160714285714285 40 33 34.8988764044944 -1.89887640449438 41 32 34.8988764044944 -2.89887640449438 42 33 33.984 -0.984000000000002 43 32 34.8988764044944 -2.89887640449438 44 33 33.984 -0.984000000000002 45 28 33.984 -5.984 46 35 33.984 1.01600000000000 47 39 33.984 5.016 48 34 33.984 0.0159999999999982 49 38 34.8988764044944 3.10112359550562 50 32 33.984 -1.984 51 38 34.8988764044944 3.10112359550562 52 30 33.984 -3.984 53 33 33.984 -0.984000000000002 54 38 34.8988764044944 3.10112359550562 55 32 33.984 -1.984 56 35 33.984 1.01600000000000 57 34 34.8988764044944 -0.89887640449438 58 34 31.1607142857143 2.83928571428572 59 36 33.984 2.016 60 34 34.8988764044944 -0.89887640449438 61 28 31.1607142857143 -3.16071428571428 62 34 34.8988764044944 -0.89887640449438 63 35 34.8988764044944 0.101123595505619 64 35 31.1607142857143 3.83928571428572 65 31 34.8988764044944 -3.89887640449438 66 37 34.8988764044944 2.10112359550562 67 35 34.8988764044944 0.101123595505619 68 27 31.1607142857143 -4.16071428571428 69 40 33.984 6.016 70 37 34.8988764044944 2.10112359550562 71 36 33.984 2.016 72 38 31.1607142857143 6.83928571428572 73 39 34.8988764044944 4.10112359550562 74 41 34.8988764044944 6.10112359550562 75 27 34.8988764044944 -7.89887640449438 76 30 34.8988764044944 -4.89887640449438 77 37 34.8988764044944 2.10112359550562 78 31 34.8988764044944 -3.89887640449438 79 31 31.1607142857143 -0.160714285714285 80 27 34.8988764044944 -7.89887640449438 81 36 34.8988764044944 1.10112359550562 82 37 34.8988764044944 2.10112359550562 83 33 33.984 -0.984000000000002 84 34 33.984 0.0159999999999982 85 31 34.8988764044944 -3.89887640449438 86 39 33.984 5.016 87 34 34.8988764044944 -0.89887640449438 88 32 31.1607142857143 0.839285714285715 89 33 33.984 -0.984000000000002 90 36 31.1607142857143 4.83928571428572 91 32 34.8988764044944 -2.89887640449438 92 41 34.8988764044944 6.10112359550562 93 28 33.984 -5.984 94 30 31.1607142857143 -1.16071428571428 95 36 34.8988764044944 1.10112359550562 96 35 34.8988764044944 0.101123595505619 97 31 34.8988764044944 -3.89887640449438 98 34 34.8988764044944 -0.89887640449438 99 36 33.984 2.016 100 36 34.8988764044944 1.10112359550562 101 35 34.8988764044944 0.101123595505619 102 37 34.8988764044944 2.10112359550562 103 28 33.984 -5.984 104 39 34.8988764044944 4.10112359550562 105 32 33.984 -1.984 106 35 34.8988764044944 0.101123595505619 107 39 38.1 0.899999999999999 108 35 34.8988764044944 0.101123595505619 109 42 38.1 3.9 110 34 31.1607142857143 2.83928571428572 111 33 31.1607142857143 1.83928571428572 112 41 34.8988764044944 6.10112359550562 113 34 33.984 0.0159999999999982 114 32 34.8988764044944 -2.89887640449438 115 40 33.984 6.016 116 40 33.984 6.016 117 35 33.984 1.01600000000000 118 36 34.8988764044944 1.10112359550562 119 37 33.984 3.016 120 27 31.1607142857143 -4.16071428571428 121 39 33.984 5.016 122 38 34.8988764044944 3.10112359550562 123 31 33.984 -2.984 124 33 31.1607142857143 1.83928571428572 125 32 33.984 -1.984 126 39 38.1 0.899999999999999 127 36 33.984 2.016 128 33 33.984 -0.984000000000002 129 33 34.8988764044944 -1.89887640449438 130 32 31.1607142857143 0.839285714285715 131 37 34.8988764044944 2.10112359550562 132 30 31.1607142857143 -1.16071428571428 133 38 33.984 4.016 134 29 33.984 -4.984 135 22 31.1607142857143 -9.16071428571428 136 35 33.984 1.01600000000000 137 35 31.1607142857143 3.83928571428572 138 34 33.984 0.0159999999999982 139 35 31.1607142857143 3.83928571428572 140 34 34.8988764044944 -0.89887640449438 141 37 31.1607142857143 5.83928571428572 142 35 31.1607142857143 3.83928571428572 143 23 31.1607142857143 -8.16071428571428 144 31 33.984 -2.984 145 27 34.8988764044944 -7.89887640449438 146 36 33.984 2.016 147 31 34.8988764044944 -3.89887640449438 148 32 33.984 -1.984 149 39 33.984 5.016 150 37 38.1 -1.10000000000000 151 38 34.8988764044944 3.10112359550562 152 39 34.8988764044944 4.10112359550562 153 34 33.984 0.0159999999999982 154 31 33.984 -2.984 155 37 33.984 3.016 156 36 33.984 2.016 157 32 34.8988764044944 -2.89887640449438 158 38 33.984 4.016 159 26 31.1607142857143 -5.16071428571428 160 26 31.1607142857143 -5.16071428571428 161 33 34.8988764044944 -1.89887640449438 162 39 33.984 5.016 163 30 24.375 5.625 164 33 31.1607142857143 1.83928571428572 165 25 31.1607142857143 -6.16071428571428 166 38 33.984 4.016 167 37 31.1607142857143 5.83928571428572 168 31 33.984 -2.984 169 37 34.8988764044944 2.10112359550562 170 35 38.1 -3.1 171 25 31.1607142857143 -6.16071428571428 172 28 33.984 -5.984 173 35 33.984 1.01600000000000 174 33 33.984 -0.984000000000002 175 30 31.1607142857143 -1.16071428571428 176 31 31.1607142857143 -0.160714285714285 177 37 33.984 3.016 178 36 34.8988764044944 1.10112359550562 179 30 33.984 -3.984 180 36 31.1607142857143 4.83928571428572 181 32 33.984 -1.984 182 28 24.375 3.625 183 36 34.8988764044944 1.10112359550562 184 34 34.8988764044944 -0.89887640449438 185 31 33.984 -2.984 186 28 31.1607142857143 -3.16071428571428 187 36 33.984 2.016 188 36 33.984 2.016 189 40 34.8988764044944 5.10112359550562 190 33 33.984 -0.984000000000002 191 37 34.8988764044944 2.10112359550562 192 32 33.984 -1.984 193 38 33.984 4.016 194 31 34.8988764044944 -3.89887640449438 195 37 34.8988764044944 2.10112359550562 196 33 33.984 -0.984000000000002 197 30 31.1607142857143 -1.16071428571428 198 30 24.375 5.625 199 31 33.984 -2.984 200 32 33.984 -1.984 201 34 33.984 0.0159999999999982 202 36 33.984 2.016 203 37 34.8988764044944 2.10112359550562 204 36 34.8988764044944 1.10112359550562 205 33 33.984 -0.984000000000002 206 33 33.984 -0.984000000000002 207 33 33.984 -0.984000000000002 208 44 38.1 5.9 209 39 33.984 5.016 210 32 31.1607142857143 0.839285714285715 211 35 34.8988764044944 0.101123595505619 212 25 31.1607142857143 -6.16071428571428 213 35 34.8988764044944 0.101123595505619 214 34 33.984 0.0159999999999982 215 35 33.984 1.01600000000000 216 39 33.984 5.016 217 33 33.984 -0.984000000000002 218 36 33.984 2.016 219 32 33.984 -1.984 220 32 31.1607142857143 0.839285714285715 221 36 33.984 2.016 222 32 31.1607142857143 0.839285714285715 223 34 33.984 0.0159999999999982 224 33 33.984 -0.984000000000002 225 35 33.984 1.01600000000000 226 30 33.984 -3.984 227 38 33.984 4.016 228 34 34.8988764044944 -0.89887640449438 229 33 38.1 -5.1 230 32 33.984 -1.984 231 31 33.984 -2.984 232 30 33.984 -3.984 233 27 33.984 -6.984 234 31 31.1607142857143 -0.160714285714285 235 30 33.984 -3.984 236 32 31.1607142857143 0.839285714285715 237 35 33.984 1.01600000000000 238 28 31.1607142857143 -3.16071428571428 239 33 33.984 -0.984000000000002 240 35 31.1607142857143 3.83928571428572 241 35 33.984 1.01600000000000 242 32 33.984 -1.984 243 21 24.375 -3.375 244 20 24.375 -4.375 245 34 31.1607142857143 2.83928571428572 246 32 33.984 -1.984 247 34 33.984 0.0159999999999982 248 32 34.8988764044944 -2.89887640449438 249 33 33.984 -0.984000000000002 250 33 33.984 -0.984000000000002 251 37 34.8988764044944 2.10112359550562 252 32 31.1607142857143 0.839285714285715 253 34 33.984 0.0159999999999982 254 30 33.984 -3.984 255 30 31.1607142857143 -1.16071428571428 256 38 33.984 4.016 257 36 33.984 2.016 258 32 31.1607142857143 0.839285714285715 259 34 33.984 0.0159999999999982 260 33 33.984 -0.984000000000002 261 27 24.375 2.625 262 32 33.984 -1.984 263 34 33.984 0.0159999999999982 264 29 31.1607142857143 -2.16071428571428 265 35 33.984 1.01600000000000 266 27 31.1607142857143 -4.16071428571428 267 33 34.8988764044944 -1.89887640449438 268 38 33.984 4.016 269 36 34.8988764044944 1.10112359550562 270 33 34.8988764044944 -1.89887640449438 271 39 33.984 5.016 272 29 33.984 -4.984 273 32 34.8988764044944 -2.89887640449438 274 34 31.1607142857143 2.83928571428572 275 38 34.8988764044944 3.10112359550562 276 17 24.375 -7.375 277 35 33.984 1.01600000000000 278 32 33.984 -1.984 279 34 33.984 0.0159999999999982 280 36 33.984 2.016 281 31 34.8988764044944 -3.89887640449438 282 35 33.984 1.01600000000000 283 29 31.1607142857143 -2.16071428571428 284 22 24.375 -2.375 285 41 34.8988764044944 6.10112359550562 286 36 33.984 2.016 287 42 38.1 3.9 288 33 31.1607142857143 1.83928571428572

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/2o5sh1292940578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/2o5sh1292940578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/3o5sh1292940578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/3o5sh1292940578.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/4hw911292940578.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/21/t1292940501mce5ngmaem47oyk/4hw911292940578.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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