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Data X:
158258 0 48 18 20465 23975 186930 1 53 20 33629 85634 7215 0 0 0 1423 1929 129098 0 51 27 25629 36294 230632 0 76 31 54002 72255 508313 1 128 36 151036 189748 180745 1 62 23 33287 61834 185559 0 83 30 31172 68167 154581 0 55 30 28113 38462 290658 1 67 26 57803 101219 121844 2 50 24 49830 43270 184039 0 77 30 52143 76183 100324 0 46 22 21055 31476 209427 4 79 25 47007 62157 168265 4 56 18 28735 46261 154593 3 54 22 59147 50063 142018 0 81 33 78950 64483 78604 5 6 15 13497 2341 167047 0 74 34 46154 48149 27997 0 13 18 53249 12743 73019 0 22 15 10726 18743 241082 0 99 30 83700 97057 195820 0 38 25 40400 17675 141899 1 59 34 33797 33106 145433 1 50 21 36205 53311 183744 0 50 21 30165 42754 202232 0 61 25 58534 59056 190230 0 81 31 44663 101621 354924 0 60 31 92556 118120 192399 0 52 20 40078 79572 182286 0 61 28 34711 42744 181590 2 60 22 31076 65931 133801 4 53 17 74608 38575 233686 0 76 25 58092 28795 219428 1 63 24 42009 94440 0 0 0 0 0 0 223044 0 54 28 36022 38229 100129 3 44 14 23333 31972 136733 9 36 35 53349 40071 249965 0 83 34 92596 132480 242379 2 105 22 49598 62797 145794 0 37 34 44093 40429 96404 2 25 23 84205 45545 195891 1 64 24 63369 57568 117156 2 55 26 60132 39019 157787 2 41 22 37403 53866 81293 1 23 35 24460 38345 224049 0 67 24 46456 50210 223789 1 54 31 66616 80947 160344 8 68 26 41554 43461 48188 0 12 22 22346 14812 152206 0 86 21 30874 37819 294283 0 74 27 68701 102738 235223 0 56 30 35728 54509 195583 1 67 33 29010 62956 145942 8 40 11 23110 55411 208834 0 53 26 38844 50611 93764 1 26 26 27084 26692 151985 0 67 23 35139 60056 190545 10 36 38 57476 25155 148922 6 50 31 33277 42840 132856 0 48 20 31141 39358 126107 11 46 19 61281 47241 112718 3 53 26 25820 49611 160930 0 27 26 23284 41833 99184 0 38 33 35378 48930 182022 8 69 36 74990 110600 138708 2 93 25 29653 52235 114408 0 59 24 64622 53986 31970 0 5 21 4157 4105 225558 3 53 19 29245 59331 137011 1 40 12 50008 47796 113612 2 72 30 52338 38302 108641 1 51 21 13310 14063 162203 0 81 34 92901 54414 100098 2 27 32 10956 9903 174768 1 94 28 34241 53987 158459 0 71 28 75043 88937 80934 0 20 21 21152 21928 84971 0 34 31 42249 29487 80545 0 54 26 42005 35334 287191 0 49 29 41152 57596 62974 1 26 23 14399 29750 130982 0 47 25 28263 41029 75555 0 35 22 17215 12416 162154 0 32 26 48140 51158 226638 0 55 33 62897 79935 115019 0 58 24 22883 26552 105038 7 44 24 41622 25807 155537 0 45 21 40715 50620 153133 5 49 28 65897 61467 165577 1 72 27 76542 65292 151517 0 39 25 37477 55516 133686 0 28 15 53216 42006 58128 0 24 13 40911 26273 245196 0 52 36 57021 90248 195576 0 96 24 73116 61476 19349 0 13 1 3895 9604 225371 3 38 24 46609 45108 152796 0 41 31 29351 47232 59117 0 24 4 2325 3439 91762 0 54 21 31747 30553 127987 0 59 23 32665 24751 113552 1 28 23 19249 34458 85338 1 36 12 15292 24649 27676 0 2 16 5842 2342 147984 0 83 29 33994 52739 122417 0 29 26 13018 6245 0 0 0 0 0 0 91529 0 46 25 98177 35381 107205 0 25 21 37941 19595 144664 0 51 23 31032 50848 136540 0 59 21 32683 39443 76656 0 36 21 34545 27023 3616 0 0 0 0 0 0 0 0 0 0 0 183065 0 40 23 27525 61022 144636 0 68 33 66856 63528 159104 2 28 30 28549 34835 113273 0 36 23 38610 37172 43410 0 7 1 2781 13 175774 1 70 29 41211 62548 95401 0 30 18 22698 31334 118893 8 59 32 41194 20839 60493 3 3 12 32689 5084 19764 1 10 2 5752 9927 164062 3 46 21 26757 53229 132696 0 34 28 22527 29877 155367 0 54 29 44810 37310 11796 0 1 2 0 0 10674 0 0 0 0 0 142261 0 39 18 100674 50067 6836 0 0 1 0 0 154206 6 48 21 57786 47708 5118 0 5 0 0 0 40248 1 8 4 5444 6012 0 0 0 0 0 0 122641 0 38 25 28470 27749 88837 0 21 26 61849 47555 7131 1 0 0 0 0 9056 0 0 4 2179 1336 76611 1 15 17 8019 11017 132697 0 50 21 39644 55184 100681 1 17 22 23494 43485
Names of X columns:
Time Shared Bloggs Reviews Characters Seconds
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
2 seconds
R Server
Big Analytics Cloud Computing Center
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