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Data X:
50 2 2 0.00616180910715386 50 5 2 0.0041489514654836 50 10 2 0.00561135416024812 50 15 2 0.00594409185203443 50 20 2 0.0060673280341775 50 2 5 0.00616180910715386 50 5 5 0.00411608848357878 50 10 5 0.00579210056072463 50 15 5 0.00568118799679586 50 20 5 0.00593587610655822 50 2 10 0.00616180910715386 50 5 10 0.00398052868322139 50 10 10 0.00552508883274796 50 15 10 0.00551276521453365 50 20 10 0.0055908147965576 50 2 15 0.00616180910715386 50 5 15 0.00406268613798345 50 10 15 0.00524986135929509 50 15 15 0.00532791094131904 50 20 15 0.00553330457822417 50 2 20 0.00616180910715386 50 5 20 0.00419824593834083 50 10 20 0.00545525499620022 50 15 20 0.00524164561381888 50 20 20 0.00537309754143817 50 2 30 0.00616180910715386 50 5 30 0.00416538295643601 50 10 30 0.00531147945036663 50 15 30 0.00534023455953335 50 20 30 0.00520467475917596 70 2 2 0.00518002752274735 70 5 2 0.0051019779407234 70 10 2 0.00551276521453365 70 15 2 0.00586604227001048 70 20 2 0.0060591122887013 70 2 5 0.00518002752274735 70 5 5 0.00527861646846181 70 10 5 0.00552098096000986 70 15 5 0.00550044159631935 70 20 5 0.00586604227001048 70 2 10 0.00518002752274735 70 5 10 0.00524575348655699 70 10 10 0.00543882350524781 70 15 10 0.00549633372358124 70 20 10 0.00550454946905745 70 2 15 0.00518002752274735 70 5 15 0.00505268346786617 70 10 15 0.00536488179596196 70 15 15 0.00543882350524781 70 20 15 0.00527861646846181 70 2 20 0.00518002752274735 70 5 20 0.00530326370489042 70 10 20 0.00528683221393801 70 15 20 0.00518002752274735 70 20 20 0.00529094008667612 70 2 30 0.00518002752274735 70 5 30 0.00490069217655637 70 10 30 0.00546757861441453 70 15 30 0.0051101936861996 70 20 30 0.00520056688643786 20 2 2 0.00916466407870684 20 5 2 0.00467065130322263 20 10 2 0.00570994310596258 20 15 2 0.00612073037977283 20 20 2 0.0062234271982254 20 2 5 0.00916466407870684 20 5 5 0.00449401277548422 20 10 5 0.00566475650584345 20 15 5 0.0060673280341775 20 20 5 0.00610840676155853 20 2 10 0.00916466407870684 20 5 10 0.0044734734117937 20 10 10 0.00565243288762914 20 15 10 0.00598927845215355 20 20 10 0.00608786739786801 20 2 15 0.00916466407870684 20 5 15 0.00453509150286524 20 10 15 0.00570172736048637 20 15 15 0.00585782652453427 20 20 15 0.00619056421632058 20 2 20 0.00916466407870684 20 5 20 0.00451455213917473 20 10 20 0.0055784911783433 20 15 20 0.00594409185203443 20 20 20 0.00595230759751063 20 2 30 0.00916466407870684 20 5 30 0.00438720808429355 20 10 30 0.00524164561381888 20 15 30 0.00604678867048699 20 20 30 0.00593176823382012 100 2 2 0.00482675046727052 100 5 2 0.00552098096000986 100 10 2 0.00551687308727176 100 15 2 0.00581263992441514 100 20 2 0.00635898699858278 100 2 5 0.00483907408548483 100 5 5 0.00550865734179555 100 10 5 0.00545114712346211 100 15 5 0.00554973606917658 100 20 5 0.00581674779715325 100 2 10 0.00486372132191345 100 5 10 0.00560724628751001 100 10 10 0.00532791094131904 100 15 10 0.00563189352393863 100 20 10 0.00540596052334299 100 2 15 0.00486372132191345 100 5 15 0.00547168648715263 100 10 15 0.00522110625012837 100 15 15 0.00522521412286647 100 20 15 0.00529915583215232 100 2 20 0.00486372132191345 100 5 20 0.00550044159631935 100 10 20 0.00532791094131904 100 15 20 0.00527861646846181 100 20 20 0.0052745085957237 100 2 30 0.00486372132191345 100 5 30 0.00554152032370037 100 10 30 0.00539774477786678 100 15 30 0.00506089921334237 100 20 30 0.00521289050465217
Names of X columns:
Ntrees Max_depth Min_rows DL120
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 <- '2' par2 <- 'quantiles' par1 <- '4' 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') } } print(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') }
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Raw Input
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Raw Output
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R Server
Big Analytics Cloud Computing Center
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