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Data X:
58.58527778 145 30 94 112285 14.36009776 1 33.60611111 101 28 103 84786 11.30900293 1 49.03 98 38 93 83123 12.45810893 1 49.81138889 132 30 103 101193 13.50394283 1 34.21805556 60 22 51 38361 7.219358341 1 14.65166667 38 26 70 68504 7.381367182 1 107.0927778 144 25 91 119182 15.26096233 1 9.213888889 5 18 22 22807 3.165415029 1 41.40583333 84 26 93 116174 11.78143911 1 45.95722222 79 25 60 57635 9.277808364 1 65.8925 127 38 123 66198 14.22780485 1 48.14611111 78 44 148 71701 13.3753608 1 36.98083333 60 30 90 57793 9.487750499 1 71.90916667 131 40 124 80444 15.21511671 1 50.02305556 84 34 70 53855 10.42712012 1 90.22194444 133 47 168 97668 17.84978648 1 64.15666667 150 30 115 133824 15.89534882 1 65.77361111 91 31 71 101481 12.55115438 1 37.63138889 132 23 66 99645 11.43396085 1 56.36805556 136 36 134 114789 15.53507626 1 59.76305556 124 36 117 99052 14.52239325 1 95.63805556 118 30 108 67654 13.89331579 1 42.75972222 70 25 84 65553 9.712278591 1 36.92861111 107 39 156 97500 14.12942504 1 48.53444444 119 34 120 69112 12.89537363 1 48.44861111 89 31 114 82753 12.1602659 1 62.65222222 112 31 94 85323 13.01181689 1 62.12 108 33 120 72654 13.29873685 1 34.67138889 52 25 81 30727 7.775415068 1 61.58277778 112 33 110 77873 13.28429728 1 58.54638889 116 35 133 117478 15.11451039 1 47.29611111 123 42 122 74007 13.74797244 1 72.37805556 125 43 158 90183 16.42942616 1 23.57027778 27 30 109 61542 8.584377305 1 81.78444444 162 33 124 101494 16.47410685 1 59.90027778 64 32 92 55813 10.83539947 1 90.3075 92 36 126 79215 14.48035784 1 46.53944444 83 28 70 55461 9.817153316 1 29.55777778 41 14 37 31081 5.345232896 1 73.82472222 120 32 120 83122 14.39310875 1 74.90305556 105 30 93 70106 12.87630593 1 41.42 79 35 95 60578 10.76877965 1 42.46416667 70 28 90 79892 10.49694082 1 31.01805556 55 28 80 49810 8.443671407 1 32.33555556 39 39 31 71570 8.407782836 1 100.6391667 67 34 110 100708 13.94065255 1 21.88888889 21 26 66 33032 6.16326461 1 50.87972222 127 39 138 82875 14.43688293 1 77.2125 152 39 133 139077 17.77583855 1 41.84138889 113 33 113 71595 12.2486023 1 46.89138889 99 28 100 72260 11.45685566 1 6.718888889 7 4 7 5950 1.153093302 1 91.46305556 141 39 140 115762 17.35686844 1 18.06361111 21 18 61 32551 5.224652076 1 28.0825 35 14 41 31701 5.238696901 1 60.81833333 109 29 96 80670 12.60523152 1 67.79222222 133 44 164 143558 17.98404039 1 64.81333333 230 28 102 120733 15.30279334 1 71.23944444 166 35 124 105195 16.2306506 1 57.26694444 68 28 99 73107 11.17728596 1 86.52027778 147 38 129 132068 17.64250157 1 65.5 179 23 62 149193 14.43288916 1 49.4275 61 36 73 46821 9.841441509 1 57.54888889 101 32 114 87011 13.10185804 1 54.59805556 108 29 99 95260 12.78229154 1 48.38444444 90 25 70 55183 9.841353028 1 39.79055556 114 27 104 106671 12.52267132 1 52.09972222 103 36 116 73511 12.85177133 1 52.13361111 142 28 91 92945 13.17889823 1 33.06 79 23 74 78664 9.459926349 1 50.60888889 88 40 138 70054 13.14460233 1 20.435 25 23 67 22618 5.679993974 1 54.16083333 83 40 151 74011 13.621099 1 46.52444444 113 28 72 83737 11.4493559 1 39.93222222 118 34 120 69094 12.45612266 1 76.53916667 110 33 115 93133 14.52255588 1 67.55527778 129 28 105 95536 14.00916003 1 50.83305556 51 34 104 225920 12.87917848 1 37.68027778 93 30 108 62133 10.91453539 1 42.30527778 76 33 98 61370 10.67710167 1 33.39472222 49 22 69 43836 7.500506329 1 96.24583333 118 38 111 106117 15.71511893 1 40.49722222 38 26 99 38692 8.451414364 1 53.70527778 141 35 71 84651 13.04281114 1 22.48694444 58 8 27 56622 5.475735648 1 34.10388889 27 24 69 15986 6.318561991 1 36.27361111 91 29 107 95364 11.67102852 1 79.57444444 63 29 107 89691 12.88262502 1 66.96277778 56 45 93 67267 12.34103121 1 41.235 144 37 129 126846 15.31887313 1 56.86472222 73 33 69 41140 10.00450285 1 50.5775 168 33 118 102860 14.86835659 1 38.98444444 64 25 73 51715 8.706868119 1 61.25444444 97 32 119 55801 12.38401963 1 67.51666667 117 29 104 111813 14.23699191 1 45.2125 100 28 107 120293 12.98177075 1 50.72583333 149 28 99 138599 14.75112225 1 64.48277778 187 31 90 161647 15.68212424 1 73.69944444 127 52 197 115929 18.1400865 1 86.34416667 245 24 85 162901 15.91867058 1 62.51666667 87 41 139 109825 14.96179352 1 64.5325 177 33 106 129838 16.04078615 1 40.26833333 49 32 50 37510 7.958769343 1 12.02416667 49 19 64 43750 6.117022453 1 43.265 73 20 31 40652 7.408636579 1 45.7525 177 31 63 87771 12.70501195 1 56.09444444 94 31 92 85872 12.21180573 1 65.40388889 117 32 106 89275 13.7541755 1 27.62944444 55 23 69 192565 10.16398398 1 27.98611111 58 30 93 140867 11.38027871 1 62.37472222 95 31 114 120662 14.09406222 1 67.64194444 129 42 110 101338 15.38882422 1 6.371666667 11 1 0 1168 0.694512385 1 42.35388889 101 32 83 65567 10.98992814 1 17.1825 28 11 30 25162 3.847727223 1 36.80194444 89 36 98 40735 10.36537965 1 88.165 193 31 82 91413 15.07282766 1 5.848333333 4 0 0 855 0.406821823 1 58.23361111 84 24 60 97068 11.07449882 1 8.726111111 39 8 9 14116 2.651799226 1 67.98583333 101 33 115 76643 13.40079885 1 51.25277778 82 40 140 110681 14.266541 1 35.67305556 36 38 120 92696 11.1935173 1 27.1775 75 24 66 94785 9.43355457 1 10.615 16 8 21 8773 2.298582242 1 41.9725 55 35 124 83209 11.55994194 1 75.68277778 131 43 152 93815 16.70032444 1 47.915 131 43 139 86687 14.84074833 1 91.14083333 144 41 144 105547 17.38403831 1 69.60527778 139 38 120 103487 15.73039586 1 97.51861111 211 45 160 213688 19.36265885 1 43.89305556 78 31 114 71220 11.32605381 1 23.73305556 39 28 78 56926 7.851840967 1 63.67833333 90 31 119 91721 13.30225262 1 97.67194444 166 40 141 115168 17.89762797 1 23.39083333 12 30 101 111194 9.46320715 1 90.16611111 133 37 133 135777 17.41915883 1 36.40805556 69 30 83 51513 9.332238773 1 56.74194444 119 35 116 74163 13.4187927 1 45.98416667 119 32 90 51633 11.37725913 1 39.36722222 65 27 36 75345 8.702323456 1 83.27083333 101 31 97 98952 14.19611665 1 54.39944444 196 31 98 102372 14.3980936 1 48.12777778 15 21 78 37238 7.301383906 1 70.69111111 136 39 117 103772 15.72027262 1 28.99694444 89 41 148 123969 14.03405468 1 55.41 123 32 105 135400 14.75498272 1 62.31388889 163 39 132 130115 17.03707132 1 4.08 5 0 0 6023 0.498724543 1 50.45361111 96 30 73 64466 10.82410397 1 75.51555556 151 37 86 54990 13.99003257 1 1.999722222 6 0 0 1644 0.295218143 1 12.96111111 13 5 13 6179 1.833602184 1 4.874166667 3 1 4 3926 0.59944272 1 26.45194444 23 32 48 34777 6.512322257 1 42.38916667 57 24 46 73224 8.594439161 1 28.23472222 28 11 38 17140 10.17463007 0 28.05861111 32 13 39 27570 11.70685947 0 1.993333333 0 0 0 1423 0.336629956 0 26.82222222 47 17 38 22996 12.81545815 0 48.84 65 20 77 39992 18.48134929 0 94.88055556 123 21 78 117105 19.15 0 28.77694444 26 16 49 23789 11.98976509 0 31.28083333 48 20 73 26706 15.96311694 0 23.77055556 37 21 36 24266 12.36867636 0 61.33361111 60 18 63 44418 17.85359417 0 25.73916667 39 17 41 35232 13.33972078 0 37.03555556 64 20 56 40909 17.43586247 0 17.04472222 26 12 25 13294 7.971763963 0 34.98055556 64 17 65 32387 16.49423975 0 22.86555556 25 10 38 21233 9.589676646 0 28.33611111 26 13 44 44332 13.0201353 0 28.20083333 76 22 87 61056 18.30998406 0 11.54611111 2 9 27 13497 5.311440585 0 27.75638889 36 25 80 32334 16.14544025 0 6.291111111 23 13 28 44339 9.61483208 0 12.97166667 14 13 33 10288 6.97001928 0 36.58277778 78 19 59 65622 17.8328823 0 25.48194444 14 18 49 16563 10.43631816 0 22.18416667 24 22 49 29011 12.49521893 0 30.01194444 39 14 38 34553 13.13014142 0 27.46277778 50 13 39 23517 12.5995365 0 33.45694444 57 16 56 51009 16.89272827 0 32.23555556 61 20 50 33416 15.95822221 0 69.4575 49 18 61 83305 17.44624506 0 37.80111111 40 13 41 27142 13.36096345 0 25.69416667 21 18 55 21399 11.70208318 0 37.71694444 29 14 44 24874 12.6700532 0 20.66888889 35 7 21 34988 9.942922584 0 22.56666667 13 17 50 45549 12.47787034 0 37.04666667 56 16 57 32755 16.1794073 0 27.26277778 14 17 48 27114 11.34269609 0 22.11638889 43 11 32 20760 10.61458743 0 16.44277778 20 24 68 37636 13.63362964 0 38.87277778 72 22 87 65461 19.3 0 32.94777778 87 12 43 30080 14.17862044 0 20.24444444 21 19 67 24094 12.13110302 0 18.1875 56 13 46 69008 14.29122225 0 27.67861111 59 17 46 54968 15.90424527 0 19.99027778 82 15 56 46090 15.30537883 0 21.46444444 43 16 48 27507 12.71711954 0 13.69138889 25 24 44 10672 10.1041787 0 37.53638889 38 15 60 34029 15.10633448 0 30.12388889 25 17 65 46300 14.97472539 0 24.92944444 38 18 55 24760 13.14940394 0 12.30444444 12 20 38 18779 8.803370342 0 21.56888889 29 16 52 21280 11.37671367 0 50.42444444 47 16 60 40662 16.48829016 0 37.2275 45 18 54 28987 15.28690517 0 34.46222222 40 22 86 22827 15.87449367 0 25.73055556 30 8 24 18513 8.991891655 0 33.84666667 41 17 52 30594 14.52069002 0 14.69861111 25 18 49 24006 10.73329293 0 22.74222222 23 16 61 27913 12.11255989 0 16.38361111 14 23 61 42744 13.12039076 0 14.86527778 16 22 81 12934 11.17618446 0 16.89222222 26 13 43 22574 9.852452086 0 15.65972222 21 13 40 41385 10.83826353 0 18.19166667 27 16 40 18653 10.01602482 0 22.48583333 9 16 56 18472 9.98429329 0 21.195 33 20 68 30976 13.90654664 0 28.89194444 42 22 79 63339 17.43829448 0 27.25111111 68 17 47 25568 14.13651406 0 18.88583333 32 18 57 33747 12.95465875 0 8.608055556 6 17 41 4154 6.405875596 0 37.62722222 67 12 29 19474 13.00827743 0 20.41777778 33 7 3 35130 8.843896805 0 17.53416667 77 17 60 39067 14.95275123 0 17.015 46 14 30 13310 9.985399433 0 20.80944444 30 23 79 65892 15.85289071 0 8.826111111 0 17 47 4143 6.304871788 0 22.62138889 36 14 40 28579 11.70744131 0 24.21833333 46 15 48 51776 14.6847188 0 13.91388889 18 17 36 21152 9.063740747 0 18.2625 48 21 42 38084 14.09669644 0 15.73694444 29 18 49 27717 11.45807414 0 43.99972222 28 18 57 32928 14.5766967 0 12.90416667 34 17 12 11342 8.013459929 0 20.45111111 33 17 40 19499 10.91719297 0 10.66527778 34 16 43 16380 9.680449615 0 25.5275 33 15 33 36874 12.30631181 0 38.75722222 80 21 77 48259 19.15 0 14.49 32 16 43 16734 9.974038576 0 14.32416667 30 14 45 28207 10.61396958 0 19.5975 41 15 47 30143 12.39969169 0 23.57111111 41 17 43 41369 13.89235106 0 28.48277778 51 15 45 45833 15.3368091 0 24.07722222 18 15 50 29156 11.27570477 0 23.80805556 34 10 35 35944 11.46890639 0 9.628333333 31 6 7 36278 7.705473469 0 41.82777778 39 22 71 45588 17.80919895 0 27.66972222 54 21 67 45097 17.4519668 0 5.374722222 14 1 0 3895 2.080910919 0 27.60361111 24 18 62 28394 13.09820752 0 23.95277778 24 17 54 18632 11.29135539 0 8.565833333 8 4 4 2325 2.507172277 0 8.807222222 26 10 25 25139 7.823664497 0 24.94611111 19 16 40 27975 10.96662538 0 17.24666667 11 16 38 14483 8.284173862 0 11.15305556 14 9 19 13127 5.69261529 0 7.676111111 1 16 17 5839 4.687276457 0 21.38611111 39 17 67 24069 13.25987215 0 10.40555556 5 7 14 3738 3.580093526 0 15.04361111 37 15 30 18625 9.733116837 0 13.85055556 32 14 54 36341 11.88651299 0 23.42694444 38 14 35 24548 11.3271032 0 17.82638889 47 18 59 21792 12.99614065 0 16.495 47 12 24 26263 10.51715507 0 33.14111111 37 16 58 23686 13.71616844 0 21.30611111 51 21 42 49303 15.32447894 0 28.72916667 45 19 46 25659 13.82274766 0 19.54 21 16 61 28904 11.71158746 0 12.05833333 1 1 3 2781 1.908370816 0 29.12166667 42 16 52 29236 13.82088431 0 17.28194444 26 10 25 19546 8.241327352 0 19.25111111 21 19 40 22818 10.5053078 0 14.75472222 4 12 32 32689 8.180917703 0 5.49 10 2 4 5752 2.322655495 0 24.07777778 43 14 49 22197 12.28598307 0 23.3625 34 17 63 20055 12.54906448 0 21.65138889 31 19 67 25272 13.11109062 0 24.75361111 19 14 32 82206 11.7934321 0 25.27916667 34 11 23 32073 10.81286184 0 11.18 6 4 7 5444 3.068772109 0 17.82972222 11 16 54 20154 9.674855276 0 14.12694444 24 20 37 36944 11.39973018 0 15.72583333 16 12 35 8019 7.166265475 0 17.44222222 72 15 51 30884 13.46103292 0 20.14861111 21 16 39 19540 9.81357389 0
Names of X columns:
Tijd totblogs Reviews LFM totsize Score Dum
Endogenous Variable (Column Number)
Categorization
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
no
yes
Chart options
R Code
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') }
Compute
Summary of computational transaction
Raw Input
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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