Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
12 38 13 1 11 32 16 1 15 35 19 1 6 33 15 1 13 37 14 1 10 29 13 1 12 31 19 1 14 36 15 1 12 35 14 1 9 38 15 1 10 31 16 1 12 34 16 1 12 35 16 1 11 38 16 1 15 37 17 1 12 33 15 1 10 32 15 1 12 38 20 1 11 38 18 1 12 32 16 1 11 33 16 1 12 31 16 1 13 38 19 1 11 39 16 1 12 32 17 1 13 32 17 1 10 35 16 1 14 37 15 1 12 33 16 1 10 33 14 1 12 31 15 1 8 32 12 1 10 31 14 1 12 37 16 1 12 30 14 1 7 33 10 1 9 31 10 1 12 33 14 1 10 31 16 1 10 33 16 1 10 32 16 1 12 33 14 1 15 32 20 1 10 33 14 1 10 28 14 1 12 35 11 1 13 39 14 1 11 34 15 1 11 38 16 1 12 32 14 1 14 38 16 1 10 30 14 1 12 33 12 1 13 38 16 1 5 32 9 1 6 35 14 1 12 34 16 1 12 34 16 1 11 36 15 1 10 34 16 1 7 28 12 1 12 34 16 1 14 35 16 1 11 35 14 1 12 31 16 1 13 37 17 1 14 35 18 1 11 27 18 1 12 40 12 1 12 37 16 1 8 36 10 1 11 38 14 1 14 39 18 1 14 41 18 1 12 27 16 1 9 30 17 1 13 37 16 1 11 31 16 1 12 31 13 1 12 27 16 1 12 36 16 1 12 37 16 1 12 33 15 1 11 34 15 1 10 31 16 1 9 39 14 1 12 34 16 1 12 32 16 1 12 33 15 1 9 36 12 1 15 32 17 1 12 41 16 1 12 28 15 1 12 30 13 1 10 36 16 1 13 35 16 1 9 31 16 1 12 34 16 1 10 36 14 1 14 36 16 1 11 35 16 1 15 37 20 1 11 28 15 1 11 39 16 1 12 32 13 1 12 35 17 1 12 39 16 1 11 35 16 1 7 42 12 1 12 34 16 1 14 33 16 1 11 41 17 1 10 34 12 1 13 32 18 1 13 40 14 1 8 40 14 1 11 35 13 1 12 36 16 1 11 37 13 1 13 27 16 1 12 39 13 1 14 38 16 1 13 31 15 1 15 33 16 1 10 32 15 1 11 39 17 1 9 36 15 1 11 33 12 1 10 33 16 1 11 32 10 1 8 37 16 1 11 30 12 1 12 38 14 1 12 29 15 1 9 22 13 1 11 35 15 1 10 35 11 1 8 34 12 1 9 35 11 1 8 34 16 1 9 37 15 1 15 35 17 1 11 23 16 1 8 31 10 1 13 27 18 1 12 36 13 1 12 31 16 1 9 32 13 1 7 39 10 1 13 37 15 1 9 38 16 1 6 39 16 1 8 34 14 1 8 31 10 1 6 37 13 1 9 36 15 1 11 32 16 1 8 38 12 1 10 26 13 0 8 26 12 0 14 33 17 0 10 39 15 0 8 30 10 0 11 33 14 0 12 25 11 0 12 38 13 0 12 37 16 0 5 31 12 0 12 37 16 0 10 35 12 0 7 25 9 0 12 28 12 0 11 35 15 0 8 33 12 0 9 30 12 0 10 31 14 0 9 37 12 0 12 36 16 0 6 30 11 0 15 36 19 0 12 32 15 0 12 28 8 0 12 36 16 0 11 34 17 0 7 31 12 0 7 28 11 0 5 36 11 0 12 36 14 0 12 40 16 0 3 33 12 0 11 37 16 0 10 32 13 0 12 38 15 0 9 31 16 0 12 37 16 0 9 33 14 0 12 30 16 0 10 30 14 0 9 31 11 0 12 32 12 0 8 34 15 0 11 36 15 0 11 37 16 0 12 36 16 0 10 33 11 0 10 33 15 0 12 33 12 0 12 44 12 0 11 39 15 0 8 32 15 0 12 35 16 0 10 25 14 0 11 35 17 0 10 34 14 0 8 35 13 0 12 39 15 0 12 33 13 0 10 36 14 0 12 32 15 0 9 32 12 0 6 36 8 0 10 32 14 0 9 34 14 0 9 33 11 0 9 35 12 0 6 30 13 0 10 38 10 0 6 34 16 0 14 33 18 0 10 32 13 0 10 31 11 0 6 30 4 0 12 27 13 0 12 31 16 0 7 30 10 0 8 32 12 0 11 35 12 0 3 28 10 0 6 33 13 0 8 35 12 0 9 35 14 0 9 32 10 0 8 21 12 0 9 20 12 0 7 34 11 0 7 32 10 0 6 34 12 0 9 32 16 0 10 33 12 0 11 33 14 0 12 37 16 0 8 32 14 0 11 34 13 0 3 30 4 0 11 30 15 0 12 38 11 0 7 36 11 0 9 32 14 0 12 34 15 0 8 33 14 0 11 27 13 0 8 32 11 0 10 34 15 0 8 29 11 0 7 35 13 0 8 27 13 0 10 33 16 0 8 38 13 0 12 36 16 0 14 33 16 0 7 39 12 0 6 29 7 0 11 32 16 0 4 34 5 0 9 38 16 0 5 17 4 0 9 35 12 0 11 32 15 0 12 34 14 0 9 36 11 0 12 31 16 0 10 35 15 0 9 29 12 0 6 22 6 0 10 41 16 0 9 36 10 0 13 42 15 0 12 33 14 0
Names of X columns:
Software Separate Learning Pop
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 <- '3' par2 <- 'none' par1 <- '3' 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
0 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation