paper RP 1

*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: Wed, 29 Dec 2010 06:10:33 +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/29/t1293602918vg0slstzyecuhdz.htm/, Retrieved Wed, 29 Dec 2010 07:08:38 +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/29/t1293602918vg0slstzyecuhdz.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 «

5.2 67.7 32.4 5 7.7 1019 86 7.9 111.5 94.7 6.3 8.4 1010.4 77 8.7 29.4 133.4 3.9 8.7 1022.6 77 8.9 62.3 196.5 3.8 8.1 1015 71 15.3 19 307.3 3 11.2 1019.8 66 15.4 95.5 128.7 2.8 13.5 1013.7 77 18.1 30.4 279.9 3.3 14.5 1019.2 71 19.7 44.3 230.2 2.1 16.4 1018.3 72 13 56.5 92.2 2.9 12.5 1017.2 84 12.6 80 121.3 4 12.4 1010.9 83 6.2 66.1 73.4 3.4 9.1 1013.1 92 3.5 96.7 24 4 7.2 1017.1 89 3.4 83.5 93.8 4.6 6.8 1022.4 85 0 37.7 76.2 3.1 5.7 1017.8 87 9.5 24.6 113.3 3.6 9.3 1013.3 77 8.9 69.4 194.2 3.9 7.2 1016.3 65 10.4 26.9 155.3 2.9 8.7 1021.8 70 13.2 100.3 114 3.3 10.8 1013.1 71 18.9 110.6 193 2.7 15.5 1015.8 72 19 15.7 250.7 2.3 14.9 1019.3 70 16.3 55.7 173 2.5 13.5 1015.6 74 10.6 24.4 112.9 2.6 10.8 1015.6 84 5.8 174.6 63.2 3.9 8.3 1012 89 3.6 70.4 51.3 3.1 6.9 1027.9 85 2.6 30.6 45.7 3.2 6.7 1029.6 88 5 46.1 80.6 3.2 7.6 1023.6 86 7.3 92.8 78.8 3.9 8.5 1014.6 83 9.2 56.5 124.9 3.5 8.7 1012.1 75 15.7 49.3 252.5 3.3 11.7 1017.7 67 16.8 116.7 188.3 2.9 13.9 1014.9 74 18.4 67.6 192.6 2. 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 9 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Goodness of Fit Correlation 0.9836 R-squared 0.9674 RMSE 1.0115 Actuals, Predictions, and Residuals # Actuals Forecasts Residuals 1 5.2 4.58666666666667 0.613333333333333 2 7.9 8.93571428571429 -1.03571428571428 3 8.7 8.93571428571429 -0.235714285714286 4 8.9 8.93571428571429 -0.0357142857142847 5 15.3 14.1222222222222 1.17777777777778 6 15.4 16.1166666666667 -0.716666666666667 7 18.1 19.7352941176471 -1.63529411764706 8 19.7 19.7352941176471 -0.0352941176470587 9 13 13.6636363636364 -0.663636363636364 10 12.6 13.6636363636364 -1.06363636363636 11 6.2 7.94705882352941 -1.74705882352941 12 3.5 3.5 0 13 3.4 3.5 -0.1 14 0 1.83529411764706 -1.83529411764706 15 9.5 11.06 -1.56 16 8.9 7.225 1.675 17 10.4 8.93571428571429 1.46428571428572 18 13.2 14.1222222222222 -0.922222222222222 19 18.9 18.2083333333333 0.691666666666666 20 19 19.7352941176471 -0.735294117647058 21 16.3 16.1166666666667 0.183333333333334 22 10.6 10.6461538461538 -0.046153846153846 23 5.8 5.75238095238095 0.0476190476190474 24 3.6 3.5 0.1 25 2.6 1.83529411764706 0.764705882352941 26 5 4.58666666666667 0.413333333333333 27 7.3 7.07777777777778 0.222222222222222 28 9.2 8.93571428571429 0.264285714285714 29 15.7 14.1222222222222 1.57777777777778 30 16.8 17.0642857142857 -0.264285714285712 31 18.4 18.2083333333333 0.191666666666666 32 18.1 18.2083333333333 -0.108333333333331 33 14.6 13.6636363636364 0.936363636363636 34 7.8 7.94705882352941 -0.147058823529412 35 7.6 7.94705882352941 -0.347058823529412 36 3.8 4.58666666666667 -0.786666666666667 37 5.6 5.75238095238095 -0.152380952380953 38 2.2 1.83529411764706 0.364705882352941 39 6.8 7.225 -0.425 40 11.8 11.06 0.74 41 14.9 14.1222222222222 0.777777777777779 42 16.7 17.0642857142857 -0.364285714285714 43 16.7 15.9307692307692 0.769230769230768 44 15.9 17.0642857142857 -1.16428571428571 45 13.6 13.6636363636364 -0.0636363636363644 46 9.2 7.94705882352941 1.25294117647059 47 2.8 7.94705882352941 -5.14705882352941 48 2.5 5.75238095238095 -3.25238095238095 49 4.8 5.75238095238095 -0.952380952380953 50 2.8 1.83529411764706 0.964705882352941 51 7.8 7.07777777777778 0.722222222222222 52 9 11.06 -2.06 53 12.9 14.1222222222222 -1.22222222222222 54 16.4 16.1166666666667 0.283333333333331 55 21.8 19.7352941176471 2.06470588235294 56 17.8 17.0642857142857 0.735714285714288 57 13.5 13.6636363636364 -0.163636363636364 58 10 10.6461538461538 -0.646153846153846 59 10.4 10.6461538461538 -0.246153846153845 60 5.5 5.75238095238095 -0.252380952380952 61 4 3.5 0.5 62 6.8 4.58666666666667 2.21333333333333 63 5.7 7.225 -1.525 64 9.1 7.94705882352941 1.15294117647059 65 13.6 14.1222222222222 -0.522222222222222 66 15 16.1166666666667 -1.11666666666667 67 20.9 19.7352941176471 1.16470588235294 68 20.4 19.7352941176471 0.66470588235294 69 14 13.6636363636364 0.336363636363636 70 13.7 13.6636363636364 0.0363636363636353 71 7.1 7.07777777777778 0.0222222222222221 72 0.8 1.83529411764706 -1.03529411764706 73 2.1 1.83529411764706 0.264705882352941 74 1.3 1.83529411764706 -0.535294117647059 75 3.9 1.83529411764706 2.06470588235294 76 10.7 7.225 3.475 77 11.1 11.06 0.0399999999999991 78 16.4 16.1166666666667 0.283333333333331 79 17.1 16.1166666666667 0.983333333333334 80 17.3 17.0642857142857 0.235714285714288 81 12.9 14.1222222222222 -1.22222222222222 82 10.9 10.6461538461538 0.253846153846155 83 5.3 5.75238095238095 -0.452380952380953 84 0.7 1.83529411764706 -1.13529411764706 85 -0.2 1.83529411764706 -2.03529411764706 86 6.5 5.75238095238095 0.747619047619048 87 8.6 7.94705882352941 0.652941176470588 88 8.5 8.93571428571429 -0.435714285714285 89 13.3 14.1222222222222 -0.822222222222221 90 16.2 17.0642857142857 -0.864285714285714 91 17.5 18.2083333333333 -0.708333333333332 92 21.2 19.7352941176471 1.46470588235294 93 14.8 16.1166666666667 -1.31666666666667 94 10.3 10.6461538461538 -0.346153846153845 95 7.3 7.94705882352941 -0.647058823529412 96 5.1 5.75238095238095 -0.652380952380953 97 4.4 4.58666666666667 -0.186666666666667 98 6.2 7.07777777777778 -0.877777777777777 99 7.7 8.93571428571429 -1.23571428571428 100 9.3 7.94705882352941 1.35294117647059 101 15.6 16.1166666666667 -0.516666666666667 102 16.3 15.9307692307692 0.36923076923077 103 16.6 15.9307692307692 0.66923076923077 104 17.4 17.0642857142857 0.335714285714285 105 15.3 15.9307692307692 -0.63076923076923 106 9.7 10.6461538461538 -0.946153846153846 107 3.7 4.58666666666667 -0.886666666666667 108 4.6 4.58666666666667 0.0133333333333328 109 5.4 5.75238095238095 -0.352380952380952 110 3.1 3.5 -0.4 111 7.9 7.07777777777778 0.822222222222223 112 10.1 11.06 -0.96 113 15 16.1166666666667 -1.11666666666667 114 15.6 16.1166666666667 -0.516666666666667 115 19.7 19.7352941176471 -0.0352941176470587 116 18.1 18.2083333333333 -0.108333333333331 117 17.7 18.2083333333333 -0.508333333333333 118 10.7 10.6461538461538 0.0538461538461537 119 6.2 5.75238095238095 0.447619047619048 120 4.2 4.58666666666667 -0.386666666666667 121 4 4.58666666666667 -0.586666666666667 122 5.9 7.07777777777778 -1.17777777777778 123 7.1 7.07777777777778 0.0222222222222221 124 10.5 11.06 -0.56 125 15.1 15.9307692307692 -0.830769230769231 126 16.8 19.7352941176471 -2.93529411764706 127 15.3 15.9307692307692 -0.63076923076923 128 18.4 19.7352941176471 -1.33529411764706 129 16.1 15.9307692307692 0.169230769230770 130 11.3 10.6461538461538 0.653846153846155 131 7.9 7.94705882352941 -0.0470588235294116 132 5.6 5.75238095238095 -0.152380952380953 133 3.4 3.5 -0.1 134 4.8 4.58666666666667 0.213333333333333 135 6.5 5.75238095238095 0.747619047619048 136 8.5 7.94705882352941 0.552941176470588 137 15.1 14.1222222222222 0.977777777777778 138 15.7 16.1166666666667 -0.416666666666668 139 18.7 18.2083333333333 0.491666666666667 140 19.2 19.7352941176471 -0.535294117647059 141 12.9 13.6636363636364 -0.763636363636364 142 14.4 15.9307692307692 -1.53076923076923 143 6.2 5.75238095238095 0.447619047619048 144 3.3 3.5 -0.2 145 4.6 4.58666666666667 0.0133333333333328 146 7.2 7.07777777777778 0.122222222222223 147 7.8 8.93571428571429 -1.13571428571429 148 9.9 8.93571428571429 0.964285714285715 149 13.6 14.1222222222222 -0.522222222222222 150 17.1 17.0642857142857 0.0357142857142883 151 17.8 18.2083333333333 -0.408333333333331 152 18.6 18.2083333333333 0.391666666666669 153 14.7 16.1166666666667 -1.41666666666667 154 10.5 10.6461538461538 -0.146153846153846 155 8.6 7.94705882352941 0.652941176470588 156 4.4 5.75238095238095 -1.35238095238095 157 2.3 1.83529411764706 0.464705882352941 158 2.8 1.83529411764706 0.964705882352941 159 8.8 8.93571428571429 -0.135714285714284 160 10.7 8.93571428571429 1.76428571428571 161 13.9 14.1222222222222 -0.222222222222221 162 19.3 19.7352941176471 -0.435294117647057 163 19.5 19.7352941176471 -0.235294117647058 164 20.4 19.7352941176471 0.66470588235294 165 15.3 16.1166666666667 -0.816666666666666 166 7.9 7.94705882352941 -0.0470588235294116 167 8.3 7.94705882352941 0.352941176470589 168 4.5 4.58666666666667 -0.0866666666666669 169 3.2 3.5 -0.3 170 5 4.58666666666667 0.413333333333333 171 6.6 7.225 -0.625 172 11.1 11.06 0.0399999999999991 173 12.8 14.1222222222222 -1.32222222222222 174 16.3 17.0642857142857 -0.764285714285712 175 17.4 17.0642857142857 0.335714285714285 176 18.9 18.2083333333333 0.691666666666666 177 15.8 15.9307692307692 -0.130769230769230 178 11.7 10.6461538461538 1.05384615384615 179 6.4 5.75238095238095 0.647619047619048 180 2.9 3.5 -0.6 181 4.7 4.58666666666667 0.113333333333333 182 2.4 1.83529411764706 0.564705882352941 183 7.2 7.07777777777778 0.122222222222223 184 10.7 11.06 -0.360000000000001 185 13.4 14.1222222222222 -0.722222222222221 186 18.5 16.1166666666667 2.38333333333333 187 18.3 18.2083333333333 0.0916666666666686 188 16.8 15.9307692307692 0.86923076923077 189 16.6 15.9307692307692 0.66923076923077 190 14.1 13.6636363636364 0.436363636363636 191 6.1 5.75238095238095 0.347619047619047 192 3.5 4.58666666666667 -1.08666666666667 193 1.7 1.83529411764706 -0.135294117647059 194 2.3 1.83529411764706 0.464705882352941 195 4.5 3.5 1 196 9.3 8.93571428571429 0.364285714285716 197 14.2 14.1222222222222 0.0777777777777775 198 17.3 16.1166666666667 1.18333333333333 199 23 19.7352941176471 3.26470588235294 200 16.3 15.9307692307692 0.36923076923077 201 18.4 16.1166666666667 2.28333333333333 202 14.2 13.6636363636364 0.536363636363635 203 9.1 7.94705882352941 1.15294117647059 204 5.9 5.75238095238095 0.147619047619048 205 7.2 7.94705882352941 -0.747058823529412 206 6.8 5.75238095238095 1.04761904761905 207 8 8.93571428571429 -0.935714285714285 208 14.3 11.06 3.24 209 14.6 14.1222222222222 0.477777777777778 210 17.5 18.2083333333333 -0.708333333333332 211 17.2 17.0642857142857 0.135714285714286 212 17.2 17.0642857142857 0.135714285714286 213 14.1 13.6636363636364 0.436363636363636 214 10.5 10.6461538461538 -0.146153846153846 215 6.8 5.75238095238095 1.04761904761905 216 4.1 3.5 0.6 217 6.5 5.75238095238095 0.747619047619048 218 6.1 7.225 -1.125 219 6.3 7.225 -0.925 220 9.3 8.93571428571429 0.364285714285716 221 16.4 14.1222222222222 2.27777777777778 222 16.1 16.1166666666667 -0.0166666666666657 223 18 17.0642857142857 0.935714285714287 224 17.6 17.0642857142857 0.535714285714288 225 14 14.1222222222222 -0.122222222222222 226 10.5 10.6461538461538 -0.146153846153846 227 6.9 5.75238095238095 1.14761904761905 228 2.8 1.83529411764706 0.964705882352941 229 0.7 1.83529411764706 -1.13529411764706 230 3.6 3.5 0.1 231 6.7 7.225 -0.524999999999999 232 12.5 11.06 1.44 233 14.4 14.1222222222222 0.277777777777779 234 16.5 16.1166666666667 0.383333333333333 235 18.7 19.7352941176471 -1.03529411764706 236 19.4 19.7352941176471 -0.335294117647059 237 15.8 15.9307692307692 -0.130769230769230 238 11.3 10.6461538461538 0.653846153846155 239 9.7 7.94705882352941 1.75294117647059 240 2.9 3.5 -0.6

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/2rkr51293603021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/2rkr51293603021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/3rkr51293603021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/3rkr51293603021.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/41b881293603021.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293602918vg0slstzyecuhdz/41b881293603021.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = none ; par3 = 3 ; par4 = no ;

Parameters (R input):

par1 = 1 ; 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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