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Data X:
41 38 13 12 14 12 53 32 39 32 16 11 18 11 83 51 30 35 19 15 11 14 66 42 31 33 15 6 12 12 67 41 34 37 14 13 16 21 76 46 35 29 13 10 18 12 78 47 39 31 19 12 14 22 53 37 34 36 15 14 14 11 80 49 36 35 14 12 15 10 74 45 37 38 15 9 15 13 76 47 38 31 16 10 17 10 79 49 36 34 16 12 19 8 54 33 38 35 16 12 10 15 67 42 39 38 16 11 16 14 54 33 33 37 17 15 18 10 87 53 32 33 15 12 14 14 58 36 36 32 15 10 14 14 75 45 38 38 20 12 17 11 88 54 39 38 18 11 14 10 64 41 32 32 16 12 16 13 57 36 32 33 16 11 18 9.5 66 41 31 31 16 12 11 14 68 44 39 38 19 13 14 12 54 33 37 39 16 11 12 14 56 37 39 32 17 12 17 11 86 52 41 32 17 13 9 9 80 47 36 35 16 10 16 11 76 43 33 37 15 14 14 15 69 44 33 33 16 12 15 14 78 45 34 33 14 10 11 13 67 44 31 31 15 12 16 9 80 49 27 32 12 8 13 15 54 33 37 31 14 10 17 10 71 43 34 37 16 12 15 11 84 54 34 30 14 12 14 13 74 42 32 33 10 7 16 8 71 44 29 31 10 9 9 20 63 37 36 33 14 12 15 12 71 43 29 31 16 10 17 10 76 46 35 33 16 10 13 10 69 42 37 32 16 10 15 9 74 45 34 33 14 12 16 14 75 44 38 32 20 15 16 8 54 33 35 33 14 10 12 14 52 31 38 28 14 10 15 11 69 42 37 35 11 12 11 13 68 40 38 39 14 13 15 9 65 43 33 34 15 11 15 11 75 46 36 38 16 11 17 15 74 42 38 32 14 12 13 11 75 45 32 38 16 14 16 10 72 44 32 30 14 10 14 14 67 40 32 33 12 12 11 18 63 37 34 38 16 13 12 14 62 46 32 32 9 5 12 11 63 36 37 35 14 6 15 14.5 76 47 39 34 16 12 16 13 74 45 29 34 16 12 15 9 67 42 37 36 15 11 12 10 73 43 35 34 16 10 12 15 70 43 30 28 12 7 8 20 53 32 38 34 16 12 13 12 77 45 34 35 16 14 11 12 80 48 31 35 14 11 14 14 52 31 34 31 16 12 15 13 54 33 35 37 17 13 10 11 80 49 36 35 18 14 11 17 66 42 30 27 18 11 12 12 73 41 39 40 12 12 15 13 63 38 35 37 16 12 15 14 69 42 38 36 10 8 14 13 67 44 31 38 14 11 16 15 54 33 34 39 18 14 15 13 81 48 38 41 18 14 15 10 69 40 34 27 16 12 13 11 84 50 39 30 17 9 12 19 80 49 37 37 16 13 17 13 70 43 34 31 16 11 13 17 69 44 28 31 13 12 15 13 77 47 37 27 16 12 13 9 54 33 33 36 16 12 15 11 79 46 35 37 16 12 15 9 71 45 37 33 15 12 16 12 73 43 32 34 15 11 15 12 72 44 33 31 16 10 14 13 77 47 38 39 14 9 15 13 75 45 33 34 16 12 14 12 69 42 29 32 16 12 13 15 54 33 33 33 15 12 7 22 70 43 31 36 12 9 17 13 73 46 36 32 17 15 13 15 54 33 35 41 16 12 15 13 77 46 32 28 15 12 14 15 82 48 29 30 13 12 13 12.5 80 47 39 36 16 10 16 11 80 47 37 35 16 13 12 16 69 43 35 31 16 9 14 11 78 46 37 34 16 12 17 11 81 48 32 36 14 10 15 10 76 46 38 36 16 14 17 10 76 45 37 35 16 11 12 16 73 45 36 37 20 15 16 12 85 52 32 28 15 11 11 11 66 42 33 39 16 11 15 16 79 47 40 32 13 12 9 19 68 41 38 35 17 12 16 11 76 47 41 39 16 12 15 16 71 43 36 35 16 11 10 15 54 33 43 42 12 7 10 24 46 30 30 34 16 12 15 14 85 52 31 33 16 14 11 15 74 44 32 41 17 11 13 11 88 55 32 33 13 11 14 15 38 11 37 34 12 10 18 12 76 47 37 32 18 13 16 10 86 53 33 40 14 13 14 14 54 33 34 40 14 8 14 13 67 44 33 35 13 11 14 9 69 42 38 36 16 12 14 15 90 55 33 37 13 11 12 15 54 33 31 27 16 13 14 14 76 46 38 39 13 12 15 11 89 54 37 38 16 14 15 8 76 47 36 31 15 13 15 11 73 45 31 33 16 15 13 11 79 47 39 32 15 10 17 8 90 55 44 39 17 11 17 10 74 44 33 36 15 9 19 11 81 53 35 33 12 11 15 13 72 44 32 33 16 10 13 11 71 42 28 32 10 11 9 20 66 40 40 37 16 8 15 10 77 46 27 30 12 11 15 15 65 40 37 38 14 12 15 12 74 46 32 29 15 12 16 14 85 53 28 22 13 9 11 23 54 33 34 35 15 11 14 14 63 42 30 35 11 10 11 16 54 35 35 34 12 8 15 11 64 40 31 35 11 9 13 12 69 41 32 34 16 8 15 10 54 33 30 37 15 9 16 14 84 51 30 35 17 15 14 12 86 53 31 23 16 11 15 12 77 46 40 31 10 8 16 11 89 55 32 27 18 13 16 12 76 47 36 36 13 12 11 13 60 38 32 31 16 12 12 11 75 46 35 32 13 9 9 19 73 46 38 39 10 7 16 12 85 53 42 37 15 13 13 17 79 47 34 38 16 9 16 9 71 41 35 39 16 6 12 12 72 44 38 34 14 8 9 19 69 43 33 31 10 8 13 18 78 51 36 32 17 15 13 15 54 33 32 37 13 6 14 14 69 43 33 36 15 9 19 11 81 53 34 32 16 11 13 9 84 51 32 38 12 8 12 18 84 50 34 36 13 8 13 16 69 46 27 26 13 10 10 24 66 43 31 26 12 8 14 14 81 47 38 33 17 14 16 20 82 50 34 39 15 10 10 18 72 43 24 30 10 8 11 23 54 33 30 33 14 11 14 12 78 48 26 25 11 12 12 14 74 44 34 38 13 12 9 16 82 50 27 37 16 12 9 18 73 41 37 31 12 5 11 20 55 34 36 37 16 12 16 12 72 44 41 35 12 10 9 12 78 47 29 25 9 7 13 17 59 35 36 28 12 12 16 13 72 44 32 35 15 11 13 9 78 44 37 33 12 8 9 16 68 43 30 30 12 9 12 18 69 41 31 31 14 10 16 10 67 41 38 37 12 9 11 14 74 42 36 36 16 12 14 11 54 33 35 30 11 6 13 9 67 41 31 36 19 15 15 11 70 44 38 32 15 12 14 10 80 48 22 28 8 12 16 11 89 55 32 36 16 12 13 19 76 44 36 34 17 11 14 14 74 43 39 31 12 7 15 12 87 52 28 28 11 7 13 14 54 30 32 36 11 5 11 21 61 39 32 36 14 12 11 13 38 11 38 40 16 12 14 10 75 44 32 33 12 3 15 15 69 42 35 37 16 11 11 16 62 41 32 32 13 10 15 14 72 44 37 38 15 12 12 12 70 44 34 31 16 9 14 19 79 48 33 37 16 12 14 15 87 53 33 33 14 9 8 19 62 37 26 32 16 12 13 13 77 44 30 30 16 12 9 17 69 44 24 30 14 10 15 12 69 40 34 31 11 9 17 11 75 42 34 32 12 12 13 14 54 35 33 34 15 8 15 11 72 43 34 36 15 11 15 13 74 45 35 37 16 11 14 12 85 55 35 36 16 12 16 15 52 31 36 33 11 10 13 14 70 44 34 33 15 10 16 12 84 50 34 33 12 12 9 17 64 40 41 44 12 12 16 11 84 53 32 39 15 11 11 18 87 54 30 32 15 8 10 13 79 49 35 35 16 12 11 17 67 40 28 25 14 10 15 13 65 41 33 35 17 11 17 11 85 52 39 34 14 10 14 12 83 52 36 35 13 8 8 22 61 36 36 39 15 12 15 14 82 52 35 33 13 12 11 12 76 46 38 36 14 10 16 12 58 31 33 32 15 12 10 17 72 44 31 32 12 9 15 9 72 44 34 36 13 9 9 21 38 11 32 36 8 6 16 10 78 46 31 32 14 10 19 11 54 33 33 34 14 9 12 12 63 34 34 33 11 9 8 23 66 42 34 35 12 9 11 13 70 43 34 30 13 6 14 12 71 43 33 38 10 10 9 16 67 44 32 34 16 6 15 9 58 36 41 33 18 14 13 17 72 46 34 32 13 10 16 9 72 44 36 31 11 10 11 14 70 43 37 30 4 6 12 17 76 50 36 27 13 12 13 13 50 33 29 31 16 12 10 11 72 43 37 30 10 7 11 12 72 44 27 32 12 8 12 10 88 53 35 35 12 11 8 19 53 34 28 28 10 3 12 16 58 35 35 33 13 6 12 16 66 40 37 31 15 10 15 14 82 53 29 35 12 8 11 20 69 42 32 35 14 9 13 15 68 43 36 32 10 9 14 23 44 29 19 21 12 8 10 20 56 36 21 20 12 9 12 16 53 30 31 34 11 7 15 14 70 42 33 32 10 7 13 17 78 47 36 34 12 6 13 11 71 44 33 32 16 9 13 13 72 45 37 33 12 10 12 17 68 44 34 33 14 11 12 15 67 43 35 37 16 12 9 21 75 43 31 32 14 8 9 18 62 40 37 34 13 11 15 15 67 41 35 30 4 3 10 8 83 52 27 30 15 11 14 12 64 38 34 38 11 12 15 12 68 41 40 36 11 7 7 22 62 39 29 32 14 9 14 12 72 43
Names of X columns:
Connected Separate Learning Software Happiness Depression Sport1 Sport2
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
par4 <- 'no' par3 <- '2' par2 <- 'none' par1 <- '2' 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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Raw Input
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Raw Output
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Computing time
0 seconds
R Server
Big Analytics Cloud Computing Center
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