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Type 'q()' to quit R. > x <- array(list(0,210907,0,2,0,149061,0,0,0,237213,1,0,0,133131,1,4,0,324799,1,0,0,230964,0,-1,0,236785,1,0,0,344297,1,1,0,174724,1,0,0,174415,1,3,0,223632,1,-1,0,294424,0,4,0,325107,1,3,0,106408,0,1,0,96560,0,0,0,265769,1,-2,0,149112,0,-4,0,152871,0,2,0,362301,1,2,0,183167,0,-4,0,218946,1,2,0,244052,1,2,0,341570,1,0,0,196553,1,-3,0,143246,0,2,0,143756,0,4,0,152299,1,2,0,193339,1,2,0,130585,0,-4,0,112611,1,3,0,148446,1,3,0,182079,0,2,0,243060,1,-1,0,162765,1,-3,0,85574,1,0,0,225060,0,1,0,133328,1,-3,0,100750,1,3,0,101523,1,0,0,243511,1,0,0,152474,1,0,0,132487,1,3,0,317394,0,-3,0,244749,1,0,0,128423,0,2,0,97839,0,-1,1,229242,1,2,1,324598,0,2,1,195838,0,-2,1,254488,0,0,1,92499,1,-2,1,224330,0,0,1,181633,1,6,1,271856,1,-3,1,95227,1,3,1,98146,0,0,1,118612,0,-2,1,65475,1,1,1,108446,0,0,1,121848,0,2,1,76302,1,2,1,98104,0,-3,1,30989,1,-2,1,31774,0,1,1,150580,1,-4,1,59382,0,1,1,84105,0,0),dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67)) > y <- array(NA,dim=c(4,67),dimnames=list(c('pop','time_in_rfc','gender','total_tests'),1:67)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '3' > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "gender" > x[,par1] [1] 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 0 1 1 1 0 1 1 [39] 1 1 1 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 > 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]) 0 1 29 38 > colnames(x) [1] "pop" "time_in_rfc" "gender" "total_tests" > colnames(x)[par1] [1] "gender" > x[,par1] [1] 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 1 0 0 1 0 1 1 1 1 0 0 1 1 0 1 1 0 1 1 1 0 1 1 [39] 1 1 1 1 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 1 0 1 0 1 0 0 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/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="/var/wessaorg/rcomp/tmp/11ezv1323616366.tab") + } + } > m Conditional inference tree with 1 terminal nodes Response: gender Inputs: pop, time_in_rfc, total_tests Number of observations: 67 1)* weights = 67 > postscript(file="/var/wessaorg/rcomp/tmp/2v53n1323616366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3jtpv1323616366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(x[,par1] ~ as.factor(where(m)),main='Response by Terminal Node',xlab='Terminal Node',ylab='Response') > dev.off() null device 1 > 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) + } Actuals Forecasts Residuals 1 0 0.5671642 -0.5671642 2 0 0.5671642 -0.5671642 3 1 0.5671642 0.4328358 4 1 0.5671642 0.4328358 5 1 0.5671642 0.4328358 6 0 0.5671642 -0.5671642 7 1 0.5671642 0.4328358 8 1 0.5671642 0.4328358 9 1 0.5671642 0.4328358 10 1 0.5671642 0.4328358 11 1 0.5671642 0.4328358 12 0 0.5671642 -0.5671642 13 1 0.5671642 0.4328358 14 0 0.5671642 -0.5671642 15 0 0.5671642 -0.5671642 16 1 0.5671642 0.4328358 17 0 0.5671642 -0.5671642 18 0 0.5671642 -0.5671642 19 1 0.5671642 0.4328358 20 0 0.5671642 -0.5671642 21 1 0.5671642 0.4328358 22 1 0.5671642 0.4328358 23 1 0.5671642 0.4328358 24 1 0.5671642 0.4328358 25 0 0.5671642 -0.5671642 26 0 0.5671642 -0.5671642 27 1 0.5671642 0.4328358 28 1 0.5671642 0.4328358 29 0 0.5671642 -0.5671642 30 1 0.5671642 0.4328358 31 1 0.5671642 0.4328358 32 0 0.5671642 -0.5671642 33 1 0.5671642 0.4328358 34 1 0.5671642 0.4328358 35 1 0.5671642 0.4328358 36 0 0.5671642 -0.5671642 37 1 0.5671642 0.4328358 38 1 0.5671642 0.4328358 39 1 0.5671642 0.4328358 40 1 0.5671642 0.4328358 41 1 0.5671642 0.4328358 42 1 0.5671642 0.4328358 43 0 0.5671642 -0.5671642 44 1 0.5671642 0.4328358 45 0 0.5671642 -0.5671642 46 0 0.5671642 -0.5671642 47 1 0.5671642 0.4328358 48 0 0.5671642 -0.5671642 49 0 0.5671642 -0.5671642 50 0 0.5671642 -0.5671642 51 1 0.5671642 0.4328358 52 0 0.5671642 -0.5671642 53 1 0.5671642 0.4328358 54 1 0.5671642 0.4328358 55 1 0.5671642 0.4328358 56 0 0.5671642 -0.5671642 57 0 0.5671642 -0.5671642 58 1 0.5671642 0.4328358 59 0 0.5671642 -0.5671642 60 0 0.5671642 -0.5671642 61 1 0.5671642 0.4328358 62 0 0.5671642 -0.5671642 63 1 0.5671642 0.4328358 64 0 0.5671642 -0.5671642 65 1 0.5671642 0.4328358 66 0 0.5671642 -0.5671642 67 0 0.5671642 -0.5671642 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/43b7r1323616366.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > 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() null device 1 > 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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/526ii1323616366.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/676ru1323616366.tab") + } Warning message: In cor(result$Forecasts, result$Actuals) : the standard deviation is zero > 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="/var/wessaorg/rcomp/tmp/7uz8x1323616366.tab") + } > > try(system("convert tmp/2v53n1323616366.ps tmp/2v53n1323616366.png",intern=TRUE)) character(0) > try(system("convert tmp/3jtpv1323616366.ps tmp/3jtpv1323616366.png",intern=TRUE)) character(0) > try(system("convert tmp/43b7r1323616366.ps tmp/43b7r1323616366.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.456 0.206 2.682