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Data X:
13 53 41 7 2 16 86 39 5 2 19 66 30 5 2 15 67 31 5 1 14 76 34 8 2 13 78 35 6 2 19 53 39 5 2 15 80 34 6 2 14 74 36 5 2 15 76 37 4 2 16 79 38 6 1 16 54 36 5 2 16 67 38 5 1 16 54 39 6 2 17 87 33 7 2 15 58 32 6 1 15 75 36 7 1 20 88 38 6 2 18 64 39 8 1 16 57 32 7 2 16 66 32 5 1 16 68 31 5 2 19 54 39 7 2 16 56 37 7 2 17 86 39 5 1 17 80 41 4 2 16 76 36 10 1 15 69 33 6 2 16 78 33 5 2 14 67 34 5 1 15 80 31 5 2 12 54 27 5 1 14 71 37 6 2 16 84 34 5 2 14 74 34 5 1 7 71 32 5 1 10 63 29 5 1 14 71 36 5 1 16 76 29 5 2 16 69 35 5 1 16 74 37 5 1 14 75 34 7 2 20 54 38 5 1 14 52 35 6 1 14 69 38 7 2 11 68 37 7 2 14 65 38 5 2 15 75 33 5 2 16 74 36 4 2 14 75 38 5 1 16 72 32 4 2 14 67 32 5 1 12 63 32 5 1 16 62 34 7 2 9 63 32 5 1 14 76 37 5 2 16 74 39 6 2 16 67 29 4 2 15 73 37 6 1 16 70 35 6 2 12 53 30 5 1 16 77 38 7 1 16 77 34 6 2 14 52 31 8 2 16 54 34 7 2 17 80 35 5 1 18 66 36 6 2 18 73 30 6 1 12 63 39 5 2 16 69 35 5 1 10 67 38 5 1 14 54 31 5 2 18 81 34 4 2 18 69 38 6 1 16 84 34 6 1 17 80 39 6 2 16 70 37 6 2 16 69 34 7 2 13 77 28 5 1 16 54 37 7 1 16 79 33 6 1 20 30 37 5 1 16 71 35 5 2 15 73 37 4 1 15 72 32 8 2 16 77 33 8 2 14 75 38 5 1 16 69 33 5 2 16 54 29 6 2 15 70 33 4 2 12 73 31 5 2 17 54 36 5 2 16 77 35 5 2 15 82 32 5 2 13 80 29 6 2 16 80 39 6 2 16 69 37 5 2 16 78 35 6 2 16 81 37 5 1 14 76 32 7 1 16 76 38 5 2 16 73 37 6 1 20 85 36 6 2 15 66 32 6 1 16 79 33 4 2 13 68 40 5 1 17 76 38 5 2 16 71 41 7 1 16 54 36 6 1 12 46 43 9 2 16 82 30 6 2 16 74 31 6 2 17 88 32 5 2 13 38 32 6 1 12 76 37 5 2 18 86 37 8 1 14 54 33 7 2 14 70 34 5 2 13 69 33 7 2 16 90 38 6 2 13 54 33 6 2 16 76 31 9 2 13 89 38 7 2 16 76 37 6 2 15 73 33 5 2 16 79 31 5 2 15 90 39 6 1 17 74 44 6 2 15 81 33 7 2 12 72 35 5 2 16 71 32 5 1 10 66 28 5 1 16 77 40 6 2 12 65 27 4 1 14 74 37 5 1 15 82 32 7 2 13 54 28 5 1 15 63 34 7 1 11 54 30 7 2 12 64 35 6 2 8 69 31 5 1 16 54 32 8 2 15 84 30 5 1 17 86 30 5 2 16 77 31 5 1 10 89 40 6 2 18 76 32 4 2 13 60 36 5 1 16 75 32 5 1 13 73 35 7 1 10 85 38 6 2 15 79 42 7 2 16 71 34 10 1 16 72 35 6 2 14 69 35 8 2 10 78 33 4 2 17 54 36 5 2 13 69 32 6 2 15 81 33 7 2 16 84 34 7 2 12 84 32 6 2 13 69 34 6 2
Names of X columns:
Learning Belonging Connected Age Gender
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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