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Data X:
1 26 21 21 23 17 23 4 127 1 20 16 15 24 17 20 4 108 1 19 19 18 22 18 20 6 110 2 19 18 11 20 21 21 8 102 1 20 16 8 24 20 24 8 104 1 25 23 19 27 28 22 4 140 2 25 17 4 28 19 23 4 112 1 22 12 20 27 22 20 8 115 1 26 19 16 24 16 25 5 121 1 22 16 14 23 18 23 4 112 2 17 19 10 24 25 27 4 118 2 22 20 13 27 17 27 4 122 1 19 13 14 27 14 22 4 105 1 24 20 8 28 11 24 4 111 1 26 27 23 27 27 25 4 151 2 21 17 11 23 20 22 8 106 1 13 8 9 24 22 28 4 100 2 26 25 24 28 22 28 4 149 2 20 26 5 27 21 27 4 122 1 22 13 15 25 23 25 8 115 2 14 19 5 19 17 16 4 86 1 21 15 19 24 24 28 7 124 1 7 5 6 20 14 21 4 69 2 23 16 13 28 17 24 4 117 1 17 14 11 26 23 27 5 113 1 25 24 17 23 24 14 4 123 1 25 24 17 23 24 14 4 123 1 19 9 5 20 8 27 4 84 2 20 19 9 11 22 20 4 97 1 23 19 15 24 23 21 4 121 2 22 25 17 25 25 22 4 132 1 22 19 17 23 21 21 4 119 1 21 18 20 18 24 12 15 98 2 15 15 12 20 15 20 10 87 2 20 12 7 20 22 24 4 101 2 22 21 16 24 21 19 8 115 1 18 12 7 23 25 28 4 109 2 20 15 14 25 16 23 4 109 2 28 28 24 28 28 27 4 159 1 22 25 15 26 23 22 4 129 1 18 19 15 26 21 27 7 119 1 23 20 10 23 21 26 4 119 1 20 24 14 22 26 22 6 122 2 25 26 18 24 22 21 5 131 2 26 25 12 21 21 19 4 120 1 15 12 9 20 18 24 16 82 2 17 12 9 22 12 19 5 86 2 23 15 8 20 25 26 12 105 1 21 17 18 25 17 22 6 114 2 13 14 10 20 24 28 9 100 1 18 16 17 22 15 21 9 100 1 19 11 14 23 13 23 4 99 1 22 20 16 25 26 28 5 132 1 16 11 10 23 16 10 4 82 2 24 22 19 23 24 24 4 132 1 18 20 10 22 21 21 5 107 1 20 19 14 24 20 21 4 114 1 24 17 10 25 14 24 4 110 2 14 21 4 21 25 24 4 105 2 22 23 19 12 25 25 5 121 1 24 18 9 17 20 25 4 109 1 18 17 12 20 22 23 6 106 1 21 27 16 23 20 21 4 124 2 23 25 11 23 26 16 4 120 1 17 19 18 20 18 17 18 91 2 22 22 11 28 22 25 4 126 2 24 24 24 24 24 24 6 138 2 21 20 17 24 17 23 4 118 1 22 19 18 24 24 25 4 128 1 16 11 9 24 20 23 5 98 1 21 22 19 28 19 28 4 133 2 23 22 18 25 20 26 4 130 2 22 16 12 21 15 22 5 103 1 24 20 23 25 23 19 10 124 1 24 24 22 25 26 26 5 142 1 16 16 14 18 22 18 8 96 1 16 16 14 17 20 18 8 93 2 21 22 16 26 24 25 5 129 2 26 24 23 28 26 27 4 150 2 15 16 7 21 21 12 4 88 2 25 27 10 27 25 15 4 125 1 18 11 12 22 13 21 5 92 0 23 21 12 21 20 23 4 0 1 20 20 12 25 22 22 4 117 2 17 20 17 22 23 21 8 112 2 25 27 21 23 28 24 4 144 1 24 20 16 26 22 27 5 130 1 17 12 11 19 20 22 14 87 1 19 8 14 25 6 28 8 92 1 20 21 13 21 21 26 8 114 1 15 18 9 13 20 10 4 81 2 27 24 19 24 18 19 4 127 1 22 16 13 25 23 22 6 115 1 23 18 19 26 20 21 4 123 1 16 20 13 25 24 24 7 115 1 19 20 13 25 22 25 7 117 2 25 19 13 22 21 21 4 117 1 19 17 14 21 18 20 6 103 2 19 16 12 23 21 21 4 108 2 26 26 22 25 23 24 7 139 1 21 15 11 24 23 23 4 113 2 20 22 5 21 15 18 4 97 1 24 17 18 21 21 24 8 117 1 22 23 19 25 24 24 4 133 2 20 21 14 22 23 19 4 115 1 18 19 15 20 21 20 10 103 2 18 14 12 20 21 18 8 95 1 24 17 19 23 20 20 6 117 1 24 12 15 28 11 27 4 113 1 22 24 17 23 22 23 4 127 1 23 18 8 28 27 26 4 126 1 22 20 10 24 25 23 5 119 1 20 16 12 18 18 17 4 97 1 18 20 12 20 20 21 6 105 1 25 22 20 28 24 25 4 140 2 18 12 12 21 10 23 5 91 1 16 16 12 21 27 27 7 112 1 20 17 14 25 21 24 8 113 2 19 22 6 19 21 20 5 102 1 15 12 10 18 18 27 8 92 1 19 14 18 21 15 21 10 98 1 19 23 18 22 24 24 8 122 1 16 15 7 24 22 21 5 100 1 17 17 18 15 14 15 12 84 1 28 28 9 28 28 25 4 142 2 23 20 17 26 18 25 5 124 1 25 23 22 23 26 22 4 137 1 20 13 11 26 17 24 6 105 2 17 18 15 20 19 21 4 106 2 23 23 17 22 22 22 4 125 1 16 19 15 20 18 23 7 104 2 23 23 22 23 24 22 7 130 2 11 12 9 22 15 20 10 79 2 18 16 13 24 18 23 4 108 2 24 23 20 23 26 25 5 136 1 23 13 14 22 11 23 8 98 1 21 22 14 26 26 22 11 120 2 16 18 12 23 21 25 7 108 2 24 23 20 27 23 26 4 139 1 23 20 20 23 23 22 8 123 1 18 10 8 21 15 24 6 90 1 20 17 17 26 22 24 7 119 1 9 18 9 23 26 25 5 105 2 24 15 18 21 16 20 4 110 1 25 23 22 27 20 26 8 135 1 20 17 10 19 18 21 4 101 2 21 17 13 23 22 26 8 114 2 25 22 15 25 16 21 6 118 2 22 20 18 23 19 22 4 120 2 21 20 18 22 20 16 9 108 1 21 19 12 22 19 26 5 114 1 22 18 12 25 23 28 6 122 1 27 22 20 25 24 18 4 132 2 24 20 12 28 25 25 4 130 2 24 22 16 28 21 23 4 130 2 21 18 16 20 21 21 5 112 1 18 16 18 25 23 20 6 114 1 16 16 16 19 27 25 16 103 1 22 16 13 25 23 22 6 115 1 20 16 17 22 18 21 6 108 2 18 17 13 18 16 16 4 94 1 20 18 17 20 16 18 4 105
Names of X columns:
G I1 I2 I3 E1 E2 E3 A T
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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