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Data X:
1 78 20 17 30 28 2 46 38 17 42 39 3 18 0 0 0 0 4 84 49 22 54 54 5 125 74 30 86 80 6 215 104 31 157 144 7 50 37 19 36 36 8 48 53 25 48 48 9 37 42 30 45 42 10 86 62 26 77 71 11 69 50 20 49 49 12 59 65 25 77 74 13 85 28 15 28 27 14 84 48 22 84 83 15 44 42 12 31 31 16 67 47 19 28 28 17 49 71 28 99 98 18 47 0 12 2 2 19 77 50 28 41 43 20 20 12 13 25 24 21 49 16 14 16 16 22 81 76 27 96 95 23 58 29 25 23 22 24 45 38 30 33 33 25 73 50 18 46 45 26 22 33 17 59 59 27 138 45 22 72 66 28 74 59 28 72 70 29 102 49 25 62 56 30 35 40 16 55 55 31 39 40 23 27 27 32 38 51 20 41 37 33 88 41 11 51 48 34 102 73 20 26 26 35 42 43 21 65 64 36 1 0 0 0 0 37 54 46 27 28 21 38 46 44 14 44 44 39 41 31 29 36 36 40 49 71 31 100 89 41 56 61 19 104 101 42 47 28 30 35 31 43 25 21 23 69 65 44 62 42 20 73 71 45 41 44 22 106 102 46 72 34 19 53 53 47 26 15 32 43 41 48 77 46 18 49 46 49 75 43 26 38 37 50 51 47 25 51 51 51 28 12 22 14 14 52 54 42 19 40 40 53 64 56 24 79 77 54 67 41 26 52 51 55 48 48 27 44 43 56 44 30 10 34 33 57 55 44 26 47 47 58 17 25 21 32 31 59 55 42 21 31 31 60 72 28 34 40 40 61 47 33 29 42 42 62 62 32 18 34 35 63 45 28 16 40 40 64 29 31 23 35 30 65 25 13 22 11 11 66 37 38 29 43 41 67 60 39 31 53 53 68 57 68 21 82 82 69 32 32 21 41 41 70 15 5 21 6 6 71 102 53 15 82 81 72 52 33 9 47 47 73 53 48 21 108 100 74 58 36 18 46 46 75 51 52 31 38 38 76 31 0 24 0 0 77 50 52 24 45 45 78 78 45 22 57 56 79 23 16 21 20 18 80 66 33 26 56 54 81 56 48 22 38 37 82 51 33 26 42 40 83 24 24 20 37 37 84 32 37 25 36 36 85 36 16 19 34 34 86 42 32 22 53 49 87 180 48 25 85 82 88 83 36 19 36 36 89 46 29 21 33 33 90 40 26 20 57 55 91 33 37 23 50 50 92 66 58 22 71 71 93 52 35 21 32 31 94 51 24 12 45 42 95 30 18 9 33 31 96 89 37 32 53 51 97 49 86 24 64 64 98 12 13 1 14 14 99 83 20 24 38 37 100 51 32 20 39 37 101 24 8 4 8 8 102 19 38 15 38 38 103 44 45 21 24 23 104 52 24 23 22 22 105 35 23 12 18 18 106 22 2 16 3 1 107 32 52 24 49 48 108 22 5 9 5 5 109 0 0 0 0 0 110 26 43 22 47 46 111 48 18 17 33 33 112 35 41 18 44 41 113 47 45 21 56 57 114 55 29 17 49 49 115 5 0 0 0 0 116 0 0 0 0 0 117 37 32 20 45 45 118 65 58 26 78 78 119 81 17 26 51 46 120 32 24 20 25 25 121 19 7 1 1 1 122 58 62 24 62 59 123 33 30 14 29 29 124 42 49 26 26 26 125 37 3 12 4 4 126 12 10 2 10 10 127 41 42 16 43 43 128 23 18 22 36 36 129 35 40 28 43 41 130 9 1 2 0 0 131 9 0 0 0 0 132 49 29 17 33 32 133 3 0 1 0 0 134 41 46 17 53 53 135 3 5 0 0 0 136 16 8 4 6 6 137 0 0 0 0 0 138 41 21 25 19 18 139 31 21 26 26 26 140 4 0 0 0 0 141 11 0 0 0 0 142 20 15 15 16 16 143 40 40 18 84 84 144 16 17 19 28 22
Names of X columns:
Ranking Logins BloggedComputations ReviewedCompendiums includedhyperlinks includedblogs
Endogenous Variable (Column Number)


Categorization


Number of categories (only if categorization<>none)


Cross-Validation? (only if categorization<>none)


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+2par3,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(detcoefdetcoef,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') }

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