Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
24 14 11 24 5 3 25 11 7 25 4 2 17 6 17 30 4 4 18 12 10 19 4 2 18 8 12 22 2 4 16 10 12 22 5 2 20 10 11 25 5 3 16 11 11 23 4 4 18 16 12 17 3 1 17 11 13 21 4 2 23 13 14 19 4 2 30 12 16 19 NA 2 23 8 11 15 3 2 18 12 10 16 3 1 15 11 11 23 4 2 12 4 15 27 5 4 21 9 9 22 4 2 15 8 11 14 2 2 20 8 17 22 4 3 31 14 17 23 4 2 27 15 11 23 4 3 34 16 18 21 4 2 21 9 14 19 4 3 31 14 10 18 4 2 19 11 11 20 5 3 16 8 15 23 4 3 20 9 15 25 4 4 21 9 13 19 4 2 22 9 16 24 4 4 17 9 13 22 4 3 24 10 9 25 4 4 25 16 18 26 4 3 26 11 18 29 2 4 25 8 12 32 4 4 17 9 17 25 4 3 32 16 9 29 5 5 33 11 9 28 5 4 13 16 12 17 4 2 32 12 18 28 3 4 25 12 12 29 4 4 29 14 18 26 5 5 22 9 14 25 4 4 18 10 15 14 4 2 17 9 16 25 5 4 20 10 10 26 4 4 15 12 11 20 5 1 20 14 14 18 4 2 33 14 9 32 4 5 29 10 12 25 4 4 23 14 17 25 3 2 26 16 5 23 4 3 18 9 12 21 4 4 20 10 12 20 4 2 11 6 6 15 2 1 28 8 24 30 3 4 26 13 12 24 5 2 22 10 12 26 4 4 17 8 14 24 4 2 12 7 7 22 5 3 14 15 13 14 3 2 17 9 12 24 5 3 21 10 13 24 4 2 19 12 14 24 5 2 18 13 8 24 4 3 10 10 11 19 4 1 29 11 9 31 5 5 31 8 11 22 4 4 19 9 13 27 4 4 9 13 10 19 5 1 20 11 11 25 4 2 28 8 12 20 3 2 19 9 9 21 4 2 30 9 15 27 5 4 29 15 18 23 5 2 26 9 15 25 5 3 23 10 12 20 4 2 13 14 13 21 4 2 21 12 14 22 5 2 19 12 10 23 5 4 28 11 13 25 4 3 23 14 13 25 5 3 18 6 11 17 3 2 21 12 13 19 4 2 20 8 16 25 4 3 23 14 8 19 4 2 21 11 16 20 4 2 21 10 11 26 4 3 15 14 9 23 5 4 28 12 16 27 4 4 19 10 12 17 4 2 26 14 14 17 4 2 10 5 8 19 5 2 16 11 9 17 3 2 22 10 15 22 3 3 19 9 11 21 5 2 31 10 21 32 5 5 31 16 14 21 4 2 29 13 18 21 4 4 19 9 12 18 4 3 22 10 13 18 4 3 23 10 15 23 4 3 15 7 12 19 4 2 20 9 19 20 4 3 18 8 15 21 4 2 23 14 11 20 5 2 25 14 11 17 2 1 21 8 10 18 4 2 24 9 13 19 2 2 25 14 15 22 4 3 17 14 12 15 5 2 13 8 12 14 3 2 28 8 16 18 4 2 21 8 9 24 3 2 25 7 18 35 4 5 9 6 8 29 4 4 16 8 13 21 4 3 19 6 17 25 2 4 17 11 9 20 1 2 25 14 15 22 4 2 20 11 8 13 3 1 29 11 7 26 4 4 14 11 12 17 3 2 22 14 14 25 3 4 15 8 6 20 5 1 19 20 8 19 4 2 20 11 17 21 3 3 15 8 10 22 4 2 20 11 11 24 4 3 18 10 14 21 4 2 33 14 11 26 4 4 22 11 13 24 3 3 16 9 12 16 4 2 17 9 11 23 4 4 16 8 9 18 4 3 21 10 12 16 4 2 26 13 20 26 4 4 18 13 12 19 3 1 18 12 13 21 4 2 17 8 12 21 4 1 22 13 12 22 2 2 30 14 9 23 2 3 30 12 15 29 4 4 24 14 24 21 2 4 21 15 7 21 4 4 21 13 17 23 3 3 29 16 11 27 4 4 31 9 17 25 3 4 20 9 11 21 2 3 16 9 12 10 2 1 22 8 14 20 4 2 20 7 11 26 3 4 28 16 16 24 4 3 38 11 21 29 4 5 22 9 14 19 2 3 20 11 20 24 5 4 17 9 13 19 4 2 28 14 11 24 4 4 22 13 15 22 4 3 31 16 19 17 3 3
Names of X columns:
CM D PE PS Organization Goals
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
2 seconds
R Server
Big Analytics Cloud Computing Center
Click here to blog (archive) this computation