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Data X:
11 6 6 4 15 16 2 40 37 15 10 77 26 16 5 4 23 24 1 29 31 9 20 63 26 13 20 10 26 22 1 37 35 12 16 73 15 7 12 6 19 21 1 32 36 15 10 76 10 10 11 5 19 23 1 39 32 17 8 90 21 10 12 8 16 23 1 32 30 14 14 67 27 15 11 9 23 21 2 35 34 9 19 69 21 9 9 9 22 20 2 35 34 12 15 70 21 12 13 8 19 22 1 28 22 11 23 54 21 8 9 11 24 20 1 37 27 13 9 54 22 9 14 6 19 12 2 32 27 16 12 76 29 10 12 8 25 23 2 34 33 16 14 75 29 15 18 11 23 23 2 37 38 15 13 76 29 11 9 5 31 30 1 35 37 10 11 80 30 12 15 10 29 22 2 40 31 16 11 89 19 9 12 7 18 21 1 37 36 12 10 73 19 10 12 7 17 21 1 37 38 15 12 74 22 13 12 13 22 15 2 33 31 13 18 78 18 8 15 10 21 22 2 37 34 18 12 76 28 14 11 8 24 24 1 35 33 13 10 69 17 9 13 6 22 23 2 36 38 17 15 74 18 12 10 8 16 15 2 32 28 14 15 82 20 8 17 7 22 24 1 38 34 13 12 77 16 8 13 5 21 24 2 34 32 13 9 84 17 9 17 9 25 21 2 33 34 15 11 75 25 14 15 11 22 21 2 33 39 15 16 79 22 11 13 11 24 18 2 42 37 13 17 79 34 16 18 11 21 20 2 33 34 14 12 69 31 9 17 9 25 19 2 32 41 13 11 88 38 11 21 7 29 29 2 32 32 16 13 57 18 13 12 6 19 20 2 33 35 14 9 69 25 12 12 7 29 23 1 35 33 12 14 52 20 9 15 6 25 24 2 39 32 18 11 86 23 14 8 5 19 27 1 28 32 9 20 66 12 4 15 4 27 28 1 38 32 16 8 54 20 8 16 10 25 24 2 36 37 16 12 85 15 14 9 8 23 29 1 38 31 17 10 79 21 10 13 6 24 24 1 34 27 13 11 84 21 13 17 11 23 22 2 33 31 15 11 73 20 10 11 4 25 25 2 37 37 17 13 70 30 14 9 9 23 14 2 34 31 15 13 54 22 13 15 10 22 22 2 34 40 14 13 70 33 14 9 6 32 24 1 36 35 10 15 54 25 14 15 9 22 24 1 31 35 13 12 69 20 14 14 10 18 24 2 37 35 11 13 68 10 5 8 6 19 24 1 36 35 16 11 76 15 11 11 8 23 22 1 34 38 16 9 71 21 9 14 13 19 21 2 30 35 11 14 66 16 9 12 8 16 21 2 29 34 15 9 67 23 10 15 10 23 21 2 35 37 15 9 71 25 14 11 5 17 15 2 33 37 12 15 54 18 6 11 8 17 26 2 29 31 17 10 76 33 11 9 6 28 22 1 28 31 15 13 77 18 13 8 9 24 24 1 32 33 16 8 71 18 12 13 9 21 13 2 33 37 14 15 69 13 8 12 7 14 19 2 31 36 17 13 73 24 14 24 20 21 10 2 43 42 10 24 46 19 11 11 8 20 28 1 32 28 11 11 66 20 11 11 8 25 25 2 35 41 15 13 77 21 11 16 7 20 24 1 31 23 15 12 77 18 16 12 7 17 22 2 33 33 7 22 70 29 14 18 10 26 30 1 39 32 17 11 86 13 16 12 5 17 22 1 32 33 14 15 38 26 14 14 8 17 24 1 32 33 18 7 66 22 9 16 9 24 23 1 36 32 14 14 75 28 8 24 20 30 20 1 39 38 14 10 64 28 11 13 6 25 22 2 41 32 9 9 80 23 8 11 10 15 22 2 30 35 14 12 86 22 14 14 11 25 19 2 30 35 11 16 54 28 8 16 12 18 24 2 32 34 15 10 54 28 8 12 7 20 22 2 39 34 16 13 74 31 10 21 12 32 26 2 38 38 17 11 88 15 8 11 8 14 12 2 38 39 16 12 85 15 8 6 6 20 25 1 32 32 12 11 63 24 10 9 6 25 29 2 34 39 15 13 81 22 9 14 9 25 23 2 36 35 15 10 74 17 9 16 5 25 23 2 39 36 16 11 80 25 7 18 11 35 17 2 31 28 16 9 80 32 16 9 6 29 26 1 36 36 11 13 60 23 14 13 10 25 27 2 34 38 12 14 62 20 11 17 8 21 23 1 34 35 14 14 63 20 9 11 7 21 20 2 38 39 15 11 89 28 16 16 8 24 24 2 38 36 17 10 76 20 7 11 9 26 22 2 33 36 19 11 81 20 11 11 8 24 26 2 32 34 15 12 72 23 14 11 10 20 29 1 30 34 16 14 84 20 11 20 13 24 20 2 31 27 14 14 76 21 8 10 7 18 17 2 34 37 16 21 76 14 11 12 7 17 16 2 35 33 15 13 72 31 8 11 8 22 24 1 37 34 17 11 81 21 12 14 9 22 24 2 35 39 12 12 72 18 8 12 9 22 19 2 35 29 18 12 78 26 13 12 8 24 29 2 31 33 13 11 79 25 8 12 7 32 25 2 31 35 14 14 52 9 13 10 6 19 25 1 38 36 14 13 67 18 9 12 8 21 24 1 34 30 14 13 74 19 12 10 8 23 29 1 30 27 12 12 73 29 11 7 4 26 22 2 32 37 14 14 69 31 14 10 8 18 23 1 31 33 12 12 67 24 9 13 10 19 15 2 37 32 15 12 76 16 10 12 7 22 29 2 34 35 11 12 77 19 9 13 8 27 21 1 32 33 11 18 63 19 9 9 7 21 23 2 34 37 15 11 84 22 8 14 10 20 20 2 38 36 14 15 90 31 16 14 9 21 25 1 38 39 15 13 75 20 10 12 8 20 28 2 38 35 16 11 76 26 11 18 5 29 18 2 39 31 14 22 53 17 6 17 8 30 25 2 33 37 18 10 87 16 9 12 9 10 13 2 34 36 13 16 69 16 8 15 9 23 24 2 35 31 14 11 78 9 6 8 11 29 23 2 36 32 13 15 54 19 20 8 7 19 25 1 32 33 14 14 58 22 10 12 8 26 27 2 34 36 14 11 80 15 8 10 4 22 24 2 44 39 17 10 74 25 16 18 16 26 24 2 37 39 12 14 56 30 9 15 9 27 26 2 32 29 16 14 82 30 12 16 10 19 18 2 35 34 15 11 64 24 14 11 12 24 26 1 38 35 10 15 67 20 10 10 8 26 23 1 38 32 13 11 75 12 7 7 4 22 28 1 38 41 15 10 69 31 14 17 11 23 20 2 32 38 16 10 72 25 11 7 8 25 23 2 39 38 14 12 54 23 13 14 12 19 24 1 27 32 13 15 54 23 10 12 8 20 21 2 37 31 17 10 71 26 9 15 6 25 25 2 41 38 14 12 53 14 15 13 8 14 16 2 31 38 16 15 54 18 12 10 8 19 23 1 36 33 15 12 71 28 12 16 14 27 22 2 38 28 12 11 69 19 9 11 10 21 27 1 37 38 16 10 30 21 15 7 5 21 24 1 30 28 8 20 53 18 10 15 8 14 17 1 40 32 9 19 68 29 13 18 12 21 21 2 34 31 13 17 69 16 11 11 11 23 21 2 36 34 19 8 54 22 10 13 8 18 19 2 36 35 11 17 66 15 12 11 8 20 25 1 33 36 15 11 79 21 9 13 9 19 24 1 34 33 11 13 67 17 14 12 6 15 21 1 37 32 15 9 74 17 9 11 5 23 26 1 37 32 16 10 86 33 14 11 8 26 25 2 39 40 15 13 63 17 11 13 7 21 25 2 37 35 12 16 69 20 11 8 4 13 13 1 37 33 16 12 73 17 9 12 9 24 25 1 35 37 15 14 69 16 11 9 5 17 23 1 32 33 13 11 71 18 10 14 9 21 26 2 33 31 14 13 77 32 12 18 12 28 22 2 31 33 11 15 74 22 10 15 6 22 20 2 30 34 15 14 82 19 6 17 8 25 14 2 32 35 12 18 84 29 16 11 6 27 24 2 33 40 14 14 54 23 14 17 7 25 21 2 29 30 13 10 80 17 8 12 9 21 24 2 37 38 15 8 76
Names of X columns:
Mistakes Doubts P-Expectations P-Criticism Person-Standards Organization Gender connected separate hapiness depression sport
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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