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Data X:
15 10 77 46 15 12 13 6 11 6 4 15 16 0 9 20 63 37 12 7 11 4 26 5 4 23 24 1 12 16 73 45 15 13 14 6 26 20 10 26 22 1 15 10 76 46 12 11 12 5 15 12 6 19 21 1 17 8 90 55 14 16 12 5 10 11 5 19 23 1 14 14 67 40 8 10 6 4 21 12 8 16 23 1 9 19 69 43 11 15 10 5 27 11 9 23 21 0 11 23 54 33 4 4 10 2 21 13 8 19 22 1 13 9 54 33 13 7 12 5 21 9 11 24 20 1 16 12 76 47 19 15 15 6 22 14 6 19 12 0 16 14 75 44 10 5 13 6 29 12 8 25 23 0 15 13 76 47 15 16 18 8 29 18 11 23 23 0 10 11 80 49 6 15 11 6 29 9 5 31 30 1 16 11 89 55 7 13 12 3 30 15 10 29 22 0 12 10 73 43 14 13 13 6 19 12 7 18 21 1 15 12 74 46 16 15 14 6 19 12 7 17 21 1 13 18 78 51 16 15 16 7 22 12 13 22 15 0 18 12 76 47 14 10 16 8 18 15 10 21 22 0 13 10 69 42 15 17 16 6 28 11 8 24 24 1 17 15 74 42 14 14 15 7 17 13 6 22 23 0 14 15 82 48 12 9 13 4 18 10 8 16 15 0 13 12 77 45 9 6 8 4 20 17 7 22 24 1 13 9 84 51 12 11 14 2 16 13 5 21 24 0 15 11 75 46 14 13 15 6 17 17 9 25 21 0 15 16 79 47 14 10 16 6 25 15 11 22 21 0 13 17 79 47 10 4 13 6 22 13 11 24 18 0 13 11 88 55 16 15 15 7 31 17 9 25 19 0 16 13 57 36 10 8 11 4 38 21 7 29 29 0 14 9 69 42 8 10 14 3 18 12 6 19 20 0 18 11 86 51 12 8 13 5 20 15 6 25 24 0 9 20 66 40 8 9 12 4 23 8 5 19 27 1 16 8 54 33 13 14 14 6 12 15 4 27 28 1 16 12 85 52 11 5 13 3 20 16 10 25 24 0 17 10 79 49 12 7 12 3 15 9 8 23 29 1 13 11 84 50 16 16 14 6 21 13 6 24 24 1 17 13 70 43 16 14 15 6 20 11 4 25 25 0 15 13 54 33 13 16 16 6 30 9 9 23 14 0 14 13 70 44 14 15 15 8 22 15 10 22 22 0 10 15 54 33 5 4 5 2 33 9 6 32 24 1 13 12 69 41 14 12 15 6 25 15 9 22 24 1 11 13 68 40 13 8 8 4 20 14 10 18 24 0 11 14 66 42 15 12 16 6 21 14 13 19 21 0 15 9 67 42 11 12 14 5 16 12 8 16 21 0 15 9 71 45 15 13 13 6 23 15 10 23 21 0 12 15 54 33 16 14 14 6 25 11 5 17 15 0 17 10 76 46 13 14 14 5 18 11 8 17 26 0 15 13 77 47 11 15 12 6 33 9 6 28 22 1 16 8 71 44 12 14 13 7 18 8 9 24 24 1 14 15 69 44 12 11 15 5 18 13 9 21 13 0 17 13 73 46 10 13 15 6 13 12 7 14 19 0 10 24 46 30 8 4 13 6 24 24 20 21 10 0 11 11 66 42 9 8 10 4 19 11 8 20 28 1 15 13 77 46 12 13 13 5 20 11 8 25 25 0 15 12 77 46 14 15 14 6 21 16 7 20 24 1 7 22 70 43 12 15 13 6 18 12 7 17 22 0 17 11 86 52 11 8 13 4 29 18 10 26 30 1 14 15 38 11 14 17 18 6 13 12 5 17 22 1 18 7 66 41 7 12 12 4 26 14 8 17 24 1 14 14 75 45 16 13 14 7 22 16 9 24 23 1 14 10 64 41 11 7 13 6 28 24 20 30 20 1 9 9 80 47 16 16 16 6 28 13 6 25 22 0 14 12 86 53 13 11 15 6 23 11 10 15 22 0 11 16 54 35 11 10 14 5 22 14 11 25 19 0 16 13 74 45 13 14 13 6 28 12 7 20 22 0 17 11 88 54 14 19 12 6 31 21 12 32 26 0 12 11 63 36 10 8 9 5 15 11 8 14 12 1 15 13 81 48 15 15 15 8 15 6 6 20 25 0 15 10 74 45 11 8 12 6 22 14 9 25 23 0 16 11 80 47 6 6 11 2 17 16 5 25 23 0 16 9 80 49 11 7 13 2 25 18 11 35 17 0 11 13 60 38 12 16 13 4 32 9 6 29 26 1 12 14 62 46 12 10 15 6 23 13 10 25 27 0 14 14 63 42 8 8 14 5 20 17 8 21 23 1 15 11 89 54 9 9 12 4 20 11 7 21 20 0 17 10 76 45 10 8 16 4 28 16 8 24 24 0 19 11 81 53 16 14 14 6 20 11 9 26 22 0 15 12 72 44 15 14 13 5 20 11 8 24 26 0 16 14 84 51 14 14 12 6 23 11 10 20 29 1 14 14 76 46 12 15 13 7 20 20 13 24 20 0 16 21 76 46 12 7 12 6 21 10 7 18 17 0 15 13 72 44 12 12 13 4 14 12 7 17 16 0 17 11 81 48 8 7 10 3 31 11 8 22 24 1 12 12 72 44 16 12 15 8 21 14 9 22 24 0 18 12 78 47 11 6 9 4 18 12 9 22 19 0 13 11 79 47 12 10 13 4 26 12 8 24 29 0 14 14 52 31 9 12 13 5 25 12 7 32 25 0 14 13 67 44 14 13 13 5 9 10 6 19 25 1 14 13 74 42 15 14 15 7 18 12 8 21 24 1 12 12 73 41 8 8 13 4 19 10 8 23 29 1 14 14 69 43 12 14 14 5 29 7 4 26 22 0 12 12 67 41 10 10 11 5 31 10 8 18 23 1 15 12 76 47 16 14 15 8 24 13 10 19 15 0 11 18 63 37 8 10 15 2 19 13 8 27 21 1 15 11 84 54 9 6 12 5 19 9 7 21 23 0 14 15 90 55 8 9 15 4 22 14 10 20 20 0 15 13 75 45 11 11 14 5 31 14 9 21 25 1 16 11 76 47 16 16 16 7 20 12 8 20 28 0 14 22 53 37 5 8 12 3 26 18 5 29 18 0 18 10 87 53 15 16 11 5 17 17 8 30 25 0 14 11 78 46 15 16 13 6 16 15 9 23 24 0 13 15 54 33 12 14 12 5 9 8 11 29 23 0 14 14 58 36 12 12 12 6 19 8 7 19 25 1 14 11 80 49 16 16 16 7 22 12 8 26 27 0 17 10 74 44 12 15 13 6 15 10 4 22 24 0 12 14 56 37 10 11 12 6 25 18 16 26 24 0 16 14 82 53 12 6 14 5 30 15 9 27 26 0 10 15 67 42 11 16 14 6 24 11 12 24 26 1 13 11 75 45 16 16 15 6 20 10 8 26 23 1 15 10 69 40 7 8 12 3 12 7 4 22 28 1 16 10 72 44 9 11 11 4 31 17 11 23 20 0 14 12 54 33 11 13 11 4 25 7 8 25 23 0 13 15 54 33 6 9 11 4 23 14 12 19 24 1 17 10 71 43 14 15 13 6 23 12 8 20 21 0 14 12 53 32 11 11 12 6 26 15 6 25 25 0 16 15 54 33 11 12 12 4 14 13 8 14 16 0 12 11 69 42 16 8 14 4 28 16 14 27 22 0 16 10 30 0 7 7 12 4 19 11 10 21 27 1 8 20 53 32 8 10 12 4 21 7 5 21 24 1 9 19 68 41 10 9 12 4 18 15 8 14 17 1 13 17 69 44 14 13 13 5 29 18 12 21 21 0 19 8 54 33 9 11 11 4 16 11 11 23 21 0 11 17 66 42 13 12 13 7 22 13 8 18 19 0 15 11 79 46 13 5 12 3 15 11 8 20 25 1 11 13 67 44 12 12 14 5 21 13 9 19 24 1 15 9 74 45 11 14 15 5 17 12 6 15 21 1 16 10 86 53 10 15 15 6 17 11 5 23 26 1 15 13 63 38 12 14 13 5 33 11 8 26 25 0 12 16 69 43 14 13 16 6 17 13 7 21 25 0 16 12 73 43 11 14 17 6 20 8 4 13 13 1 15 14 69 42 13 14 13 3 17 12 9 24 25 1 13 11 71 42 14 15 14 6 16 9 5 17 23 1 14 13 77 47 13 13 13 5 18 14 9 21 26 0 11 15 74 44 16 14 16 8 32 18 12 28 22 0 15 14 82 49 13 11 13 6 22 15 6 22 20 0 14 14 54 33 9 11 13 3 29 11 6 27 24 0 13 10 80 47 14 8 14 4 23 17 7 25 21 0 15 8 76 47 15 12 16 7 17 12 9 21 24 0
Names of X columns:
Happiness Depression Belonging BelongingFinal Popularity KnowingPeople Liked Celebrity ConcernOverMistakes ParentalExpectations ParentalCriticism PersonalStandards Organization Gender
Endogenous Variable (Column Number)
Categorization
quantiles
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
1 seconds
R Server
Big Analytics Cloud Computing Center
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