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Data X:
279055 73 3 96 130 212408 75 4 75 143 233939 83 16 70 118 222117 106 2 134 146 179751 55 1 72 73 70849 28 3 8 89 605767 135 0 173 146 33186 19 0 1 22 227332 62 7 88 132 258874 48 0 98 92 359064 120 0 112 147 264989 131 7 125 203 212638 87 10 57 113 368577 85 4 139 171 269455 88 10 87 87 397992 190 0 176 208 335567 76 8 114 153 428322 172 4 121 97 182016 58 3 103 95 267365 89 8 135 197 279428 73 0 123 160 508849 111 1 99 148 206722 47 5 74 84 200004 58 9 103 227 257139 133 1 158 154 270941 138 0 116 151 324969 134 5 114 142 329962 92 0 150 148 190867 60 0 64 110 393860 79 0 150 149 327660 89 3 143 179 269239 83 6 50 149 391045 105 1 145 187 130446 49 4 56 153 430118 104 4 141 163 273950 56 0 83 127 428077 128 0 112 151 254312 93 2 79 100 120351 35 1 33 46 395643 211 2 152 156 345875 86 10 126 128 216827 82 10 97 111 224524 83 5 84 119 182485 69 6 68 148 157164 85 1 50 65 459455 157 2 101 134 78800 42 2 20 66 217932 84 0 101 201 368086 123 10 150 177 230299 70 3 129 156 244782 81 0 99 158 24188 24 0 8 7 400109 334 8 88 175 65029 17 5 21 61 101097 64 3 30 41 309810 67 1 102 133 369627 90 5 163 228 367127 204 6 132 140 377704 154 0 161 155 280106 90 12 90 141 400971 153 10 160 181 315924 122 12 139 75 291391 124 11 104 97 295075 93 8 103 142 280018 81 3 66 136 267432 71 0 163 87 217181 141 6 93 140 258166 159 10 85 169 260919 87 2 150 129 182961 73 5 143 92 256967 74 13 107 160 73566 32 6 22 67 272362 93 7 85 179 229056 62 2 101 90 229851 70 5 131 144 371391 91 4 140 144 398210 104 3 156 144 220419 111 6 81 134 231884 72 2 137 146 217714 72 0 102 121 200046 53 1 72 112 483074 131 1 161 145 146100 72 5 30 99 295224 109 2 120 96 80953 25 0 49 27 217384 63 0 121 77 179344 62 6 76 137 415550 221 1 85 151 389059 129 4 151 126 180679 106 1 165 159 299505 104 1 89 101 292260 84 3 168 144 199481 68 10 48 102 282361 78 1 149 135 329281 89 4 75 147 234577 48 5 107 155 297995 67 7 116 138 329583 89 0 173 113 416463 163 12 155 248 415683 119 13 165 116 297080 142 9 121 176 318283 70 0 156 140 224033 199 0 86 59 43287 14 4 13 64 238089 87 4 120 40 263322 160 0 117 98 299566 60 0 133 139 321797 95 0 169 135 193926 95 0 39 97 175138 105 0 125 142 354041 78 5 82 155 303273 91 1 148 115 23668 13 0 12 0 196743 79 0 146 103 61857 25 4 23 30 217543 54 0 87 130 440711 128 1 164 102 21054 16 0 4 0 252805 52 5 81 77 31961 22 0 18 9 360436 125 3 118 150 251948 77 7 76 163 187003 96 14 55 148 180842 58 3 62 94 38214 34 0 16 21 280392 56 3 98 151 358276 84 0 137 187 211775 67 0 50 171 447335 90 4 152 170 348017 99 0 163 145 441946 133 3 142 198 215177 43 0 80 152 130177 47 0 59 112 316128 363 4 94 173 466139 198 5 128 177 162279 62 16 63 153 416643 140 6 127 161 178322 86 5 60 115 292443 54 2 118 147 283913 100 1 110 124 244802 126 1 45 57 387072 125 9 96 144 246963 92 1 128 126 173260 63 3 41 78 346748 108 11 146 153 176654 59 5 147 196 268189 95 2 121 130 314070 112 1 185 159 1 0 9 0 0 14688 10 0 4 0 98 1 0 0 0 455 2 0 0 0 0 0 1 0 0 0 0 0 0 0 291650 94 2 85 94 415421 168 3 164 129 0 0 0 0 0 203 4 0 0 0 7199 5 0 7 0 46660 20 0 12 13 17547 5 0 0 4 121550 46 0 37 89 969 2 0 0 0 242774 75 2 62 71
Names of X columns:
Tijd_RFC #Logins #Gedeelde_Compendia #Blogs #Reviews+120tekens
Endogenous Variable (Column Number)
Categorization
none
none
quantiles
hclust
equal
Number of categories (only if categorization<>none)
Cross-Validation? (only if categorization<>none)
no
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
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Raw Output
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Computing time
2 seconds
R Server
Big Analytics Cloud Computing Center
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