Send output to:
Browser Blue - Charts White
Browser Black/White
CSV
Data X:
210907 3 79 30 94 0 120982 4 58 28 103 0 176508 12 60 38 93 1 385534 0 121 25 91 0 149061 5 43 26 93 0 165446 0 69 25 60 1 237213 0 78 38 123 1 133131 7 44 30 90 1 324799 0 158 47 168 1 230964 4 102 30 115 0 236785 3 77 31 71 1 135473 0 82 23 66 0 215147 0 101 36 117 0 344297 1 80 30 108 1 153935 5 50 25 84 0 174724 0 123 34 120 1 174415 0 73 31 114 1 225548 5 81 31 94 0 223632 0 105 33 120 1 124817 0 47 25 81 1 210767 3 94 35 133 0 170266 4 44 42 122 0 294424 2 107 33 124 0 325107 0 84 36 126 1 7176 0 0 0 0 1 106408 1 33 14 37 0 96560 0 42 17 38 0 265769 2 96 32 120 1 149112 6 56 35 95 0 175824 0 57 20 77 1 152871 5 59 28 90 0 111665 4 39 28 80 1 362301 2 76 34 110 1 183167 0 91 39 138 0 168809 0 76 28 100 1 24188 0 8 4 7 1 329267 8 79 39 140 1 218946 1 76 29 96 1 244052 5 101 44 164 1 341570 1 94 21 78 1 103597 1 27 16 49 0 256462 0 123 35 124 1 235800 8 105 23 62 0 196553 2 41 29 99 1 174184 0 72 25 70 1 143246 5 67 27 104 0 187559 8 75 36 116 1 187681 2 114 28 91 0 73566 6 22 23 67 1 167488 2 69 28 72 0 143756 0 105 34 120 0 243199 3 88 28 105 0 182999 6 73 34 104 1 152299 0 62 33 98 1 346485 0 118 38 111 1 193339 2 100 35 71 1 122774 0 24 24 69 1 130585 5 67 29 107 0 112611 0 46 20 73 1 286468 1 57 29 107 1 148446 1 135 37 129 1 182079 2 124 33 118 0 140344 6 33 25 73 1 220516 1 98 32 119 1 243060 4 58 29 104 1 162765 2 68 28 107 1 232138 0 131 31 90 1 265318 10 110 52 197 0 85574 0 37 21 36 1 310839 9 130 24 85 0 225060 7 93 41 139 0 232317 0 118 33 106 1 144966 0 39 32 50 0 164709 0 81 31 63 1 220801 1 51 18 63 1 99466 0 28 23 69 0 92661 1 40 17 41 1 133328 0 56 20 56 1 61361 0 27 12 25 1 100750 0 83 30 93 1 102010 3 28 13 44 0 101523 0 59 22 87 1 243511 0 133 42 110 1 22938 0 12 1 0 1 152474 0 106 32 83 1 99923 0 44 25 80 0 132487 0 71 36 98 1 317394 1 116 31 82 0 21054 0 4 0 0 1 209641 5 62 24 60 1 22648 0 12 13 28 0 31414 0 18 8 9 0 46698 0 14 13 33 1 131698 0 60 19 59 1 244749 2 98 33 115 1 128423 8 32 38 120 0 97839 2 25 24 66 0 272458 0 100 43 152 1 108043 1 45 14 38 1 328107 3 129 41 144 0 351067 3 136 45 160 1 158015 0 59 31 114 0 229242 4 63 31 119 1 84207 11 14 30 101 1 120445 0 36 16 56 0 324598 0 113 37 133 0 131069 4 47 30 83 0 204271 0 92 35 116 0 116048 0 50 20 50 0 250047 0 41 18 61 1 299775 9 91 31 97 1 195838 1 111 31 98 0 173260 3 41 21 78 1 254488 10 120 39 117 0 92499 0 25 18 55 1 224330 1 131 39 132 0 135781 2 45 14 44 0 74408 4 29 7 21 1 81240 0 58 17 50 0 181633 2 47 30 73 1 271856 1 109 37 86 1 95227 0 37 32 48 1 98146 0 15 17 48 0 59194 6 7 24 68 0 139942 0 54 22 87 1 118612 2 54 12 43 0 72880 0 14 19 67 1 65475 2 16 13 46 1 71965 1 32 15 56 1 135131 0 38 15 60 0 108446 1 22 17 65 0 181528 0 32 16 60 1 134019 0 32 18 54 1 121848 0 37 17 52 0 81872 0 32 16 61 0 58981 7 0 23 61 0 53515 2 5 22 81 0 56375 7 10 13 40 1 65490 3 27 16 40 1 76302 0 29 20 68 1 104011 6 25 22 79 1 98104 2 55 17 47 0 30989 0 5 17 41 1 135458 3 43 12 29 0 63123 1 34 17 60 1 74914 0 35 23 79 1 31774 1 0 17 47 0 81437 0 37 14 40 1 65745 0 26 21 42 1 56653 0 38 18 49 1 158399 0 23 18 57 1 73624 0 30 17 40 1 91899 0 18 15 33 1 139526 0 28 21 77 1 51567 2 21 14 45 0 102538 1 50 15 45 0 86678 0 12 15 50 1 150580 0 27 22 71 1 99611 0 41 21 67 1 99373 1 12 18 62 0 86230 0 21 17 54 0 30837 0 8 4 4 0 31706 0 26 10 25 1 89806 0 27 16 40 1 64175 0 37 18 59 0 59382 0 29 12 24 0 119308 0 32 16 58 0 76702 0 35 21 42 0 19764 1 10 2 4 1 84105 0 17 17 63 0 64187 0 10 16 54 1 72535 0 17 16 39 1
Names of X columns:
time_in_rfc shared_compendiums blogged_computations compendiums_reviewed feedback_messages_p120 What_is_your_gender?
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