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Data X:
20465 158258 48 18 23975 0 33629 186930 53 20 85634 1 1423 7215 0 0 1929 0 25629 129098 51 27 36294 0 54002 230632 76 31 72255 0 151036 508313 128 36 189748 1 33287 180745 62 23 61834 1 31172 185559 83 30 68167 0 28113 154581 55 30 38462 0 57803 290658 67 26 101219 1 49830 121844 50 24 43270 2 52143 184039 77 30 76183 0 21055 100324 46 22 31476 0 47007 209427 79 25 62157 4 28735 168265 56 18 46261 4 59147 154593 54 22 50063 3 78950 142018 81 33 64483 0 13497 79030 6 15 2341 5 46154 167047 74 34 48149 0 53249 27997 13 18 12743 0 10726 73019 22 15 18743 0 83700 241082 99 30 97057 0 40400 195820 38 25 17675 0 33797 141899 59 34 33106 1 36205 145433 50 21 53311 1 30165 183744 50 21 42754 0 58534 202232 61 25 59056 0 44663 190230 81 31 101621 0 92556 354924 60 31 118120 0 40078 192399 52 20 79572 0 34711 182286 61 28 42744 0 31076 181590 60 22 65931 2 74608 133801 53 17 38575 4 58092 233686 76 25 28795 0 42009 219428 63 24 94440 1 0 0 0 0 0 0 36022 223044 54 28 38229 0 23333 100129 44 14 31972 3 53349 136733 36 35 40071 9 92596 249965 83 34 132480 0 49598 242379 105 22 62797 2 44093 145794 37 34 40429 0 84205 96404 25 23 45545 2 63369 195891 64 24 57568 1 60132 117156 55 26 39019 2 37403 157787 41 22 53866 2 24460 81293 23 35 38345 1 46456 224049 67 24 50210 0 66616 223789 54 31 80947 1 41554 160344 68 26 43461 8 22346 48188 12 22 14812 0 30874 152206 86 21 37819 0 68701 294283 74 27 102738 0 35728 235223 56 30 54509 0 29010 195583 67 33 62956 1 23110 145942 40 11 55411 8 38844 208834 53 26 50611 0 27084 93764 26 26 26692 1 35139 151985 67 23 60056 0 57476 190545 36 38 25155 10 33277 148922 50 31 42840 6 31141 132856 48 20 39358 0 61281 126107 46 19 47241 11 25820 112718 53 26 49611 3 23284 160930 27 26 41833 0 35378 99184 38 33 48930 0 74990 182022 69 36 110600 8 29653 138708 93 25 52235 2 64622 114408 59 24 53986 0 4157 31970 5 21 4105 0 29245 225558 53 19 59331 3 50008 137011 40 12 47796 1 52338 113612 72 30 38302 2 13310 108641 51 21 14063 1 92901 162203 81 34 54414 0 10956 100098 27 32 9903 2 34241 174768 94 28 53987 1 75043 158459 71 28 88937 0 21152 80934 20 21 21928 0 42249 84971 34 31 29487 0 42005 80545 54 26 35334 0 41152 287191 49 29 57596 0 14399 62974 26 23 29750 1 28263 134091 48 25 41029 0 17215 75555 35 22 12416 0 48140 162154 32 26 51158 0 62897 226638 55 33 79935 0 22883 115019 58 24 26552 0 41622 105038 44 24 25807 7 40715 155537 45 21 50620 0 65897 153133 49 28 61467 5 76542 165577 72 27 65292 1 37477 151517 39 25 55516 0 53216 133686 28 15 42006 0 40911 61342 24 13 26273 0 57021 245196 52 36 90248 0 73116 195576 96 24 61476 0 3895 19349 13 1 9604 0 46609 225371 38 24 45108 3 29351 152796 41 31 47232 0 2325 59117 24 4 3439 0 31747 91762 54 21 30553 0 32665 136769 68 23 24751 0 19249 113552 28 23 34458 1 15292 85338 36 12 24649 1 5842 27676 2 16 2342 0 33994 147984 83 29 52739 0 13018 122417 29 26 6245 0 0 0 0 0 0 0 98177 91529 46 25 35381 0 37941 107205 25 21 19595 0 31032 144664 51 23 50848 0 32683 136540 59 21 39443 0 34545 76656 36 21 27023 0 0 3616 0 0 0 0 0 0 0 0 0 0 27525 183065 40 23 61022 0 66856 144636 68 33 63528 0 28549 159104 28 30 34835 2 38610 113273 36 23 37172 0 2781 43410 7 1 13 0 41211 175774 70 29 62548 1 22698 95401 30 18 31334 0 41194 118893 59 32 20839 8 32689 60493 3 12 5084 3 5752 19764 10 2 9927 1 26757 164062 46 21 53229 3 22527 132696 34 28 29877 0 44810 155367 54 29 37310 0 0 11796 1 2 0 0 0 10674 0 0 0 0 100674 142261 39 18 50067 0 0 6836 0 1 0 0 57786 154206 48 21 47708 6 0 5118 5 0 0 0 5444 40248 8 4 6012 1 0 0 0 0 0 0 28470 122641 38 25 27749 0 61849 88837 21 26 47555 0 0 7131 0 0 0 1 2179 9056 0 4 1336 0 8019 76611 15 17 11017 1 39644 132697 50 21 55184 0 23494 100681 17 22 43485 1
Names of X columns:
CWCharacters Time Blogs Reviews CWseconds Shared
Endogenous Variable (Column Number)
Categorization
quantiles
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
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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