Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
10 11 16 1 24 14 33 12 24 14 11 13 2 25 11 30 8 25 18 15 16 2 17 6 30 8 30 15 9 15 1 18 12 26 8 19 11 17 15 2 16 10 24 7 22 17 16 14 2 20 10 28 4 25 19 9 11 2 16 11 24 11 23 7 12 15 2 18 16 27 7 17 12 14 13 2 17 11 28 7 21 15 4 6 2 30 12 42 10 19 14 13 11 2 23 8 31 10 15 14 12 9 2 18 12 25 8 16 16 13 14 1 12 4 23 4 27 12 15 5 2 21 9 27 9 22 12 10 8 1 15 8 23 8 14 13 9 6 1 20 8 34 7 22 9 11 15 2 27 15 36 9 23 11 15 12 2 21 9 31 13 19 12 10 10 1 31 14 39 8 18 11 9 8 1 19 11 27 8 20 14 15 16 2 16 8 27 9 23 18 12 8 2 20 9 31 6 25 11 12 12 1 21 9 31 9 19 17 14 14 2 17 9 26 6 22 14 16 13 1 22 9 34 9 24 14 5 8 2 26 11 39 5 29 12 10 11 2 25 16 39 16 26 14 9 12 2 25 8 35 7 32 15 14 13 2 17 9 30 9 25 10 5 4 1 33 14 40 6 32 11 12 16 1 32 16 38 6 29 14 14 17 1 13 16 21 5 17 11 16 14 2 32 12 45 12 28 15 11 8 2 22 9 32 9 25 16 6 6 2 17 9 29 5 25 15 11 15 1 33 11 40 6 28 16 9 11 2 31 14 44 11 23 13 16 16 1 20 10 28 8 26 15 13 5 1 15 12 24 8 20 16 10 5 2 29 10 37 8 25 13 6 9 1 23 13 33 12 19 9 12 7 1 26 16 30 4 23 14 15 14 1 18 9 26 8 21 15 15 12 2 11 6 16 4 15 14 11 7 1 28 8 48 20 30 16 16 16 2 20 10 30 8 20 13 12 10 2 26 13 35 8 24 17 11 8 1 29 14 43 10 26 16 14 15 1 15 11 22 8 23 15 7 8 1 12 7 16 4 22 16 11 12 2 14 15 25 8 14 15 13 14 1 17 9 27 9 24 13 16 16 1 21 10 31 6 24 11 17 15 2 16 10 24 7 22 16 12 14 1 18 13 25 9 24 17 14 16 1 10 10 18 5 19 10 6 15 1 29 11 36 5 31 17 8 7 1 31 8 39 8 22 11 8 10 1 19 9 29 8 27 14 14 13 1 9 13 16 6 19 15 12 13 2 20 11 29 8 25 11 13 8 2 20 14 30 10 18 15 9 6 2 19 9 26 7 21 16 12 6 2 30 9 41 9 27 16 13 14 2 28 8 37 7 20 15 15 16 2 29 15 43 11 23 14 11 11 2 26 9 37 6 25 17 14 15 2 23 10 33 8 20 12 16 12 2 21 12 31 9 22 13 14 8 2 23 14 36 7 25 12 8 8 1 19 12 26 8 23 9 16 16 2 28 11 37 6 25 17 13 14 2 18 6 26 8 17 11 4 4 1 21 12 31 8 19 16 11 5 2 20 8 32 10 25 14 16 16 2 22 10 32 8 26 9 8 9 1 23 14 29 5 19 15 14 15 1 21 11 33 7 20 17 16 14 2 20 10 28 4 25 17 12 7 1 15 14 22 8 23 15 16 15 1 19 10 28 7 17 18 7 12 1 26 14 36 8 17 13 14 15 1 16 11 23 5 17 15 13 11 2 22 10 34 6 22 12 12 10 2 23 14 34 10 25 16 7 7 1 19 9 27 10 21 17 14 19 2 31 10 47 12 32 13 14 13 2 29 13 44 12 21 15 11 11 1 31 16 43 9 21 12 14 13 1 19 9 27 7 18 11 13 12 2 22 10 32 8 18 15 15 13 2 23 10 34 10 23 15 12 11 1 15 7 24 6 19 18 14 10 2 18 8 31 10 21 16 14 14 1 23 14 31 10 20 12 16 14 2 25 14 34 5 17 16 12 7 2 21 8 28 7 18 15 16 14 2 24 9 35 10 19 15 11 14 1 17 14 27 6 15 17 10 13 2 13 8 21 7 14 16 11 7 2 25 7 38 11 35 13 12 14 2 9 6 15 11 29 13 13 7 1 21 8 29 11 24 13 14 12 1 25 14 35 9 22 16 11 14 1 20 11 25 4 13 11 11 10 2 22 14 33 11 25 15 12 12 2 14 11 23 7 17 15 15 15 2 15 8 19 6 20 9 10 9 1 18 10 30 8 14 14 12 12 1 19 20 25 7 19 14 8 8 1 20 11 33 8 21 15 15 14 2 20 11 28 8 24 14 13 13 2 18 10 29 9 21 15 12 14 2 33 14 41 8 26 14 12 14 2 29 11 33 4 26 13 10 4 2 22 11 31 11 24 15 11 12 2 16 9 25 8 16 16 10 15 1 17 9 24 5 23 14 8 10 1 21 10 31 8 16 14 8 10 2 18 13 28 6 19 14 12 11 2 18 12 27 9 21 15 9 15 1 18 12 26 8 19 15 15 12 2 17 8 26 9 21 13 16 15 2 22 13 31 13 22 15 13 16 2 30 14 37 9 23 16 7 13 2 30 12 43 10 29 10 8 4 2 24 14 43 20 21 8 8 10 1 21 15 26 5 21 14 9 11 2 29 16 37 6 27 12 16 8 2 28 12 40 14 27 13 16 15 2 31 9 45 9 25 15 9 9 2 20 9 28 7 21 14 8 9 2 22 8 32 10 20 15 14 10 2 25 14 36 11 22 19 16 14 2 20 7 27 9 26 17 12 15 2 15 8 21 4 22 16 10 8 2 38 11 55 7 29 17 10 8 2 28 16 40 8 24 13 12 11 2 16 8 26 5 21 16 19 15 2 22 9 32 6 19 14 12 15 2 20 11 35 13 24 12 15 13 1 26 13 42 10 26 12 15 5 2 21 9 27 9 22 13 15 17 1 28 14 36 8 24
Names of X columns:
Happiness Popularity KnowPeople Gender CMistakes DAction PExpectations PCriticism PStandards
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
view raw input (R code)
Raw Output
view raw output of R engine
Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation