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,21 + ,15 + ,7 + ,5 + ,21 + ,24 + ,21 + ,13 + ,17 + ,11 + ,23 + ,22 + ,29 + ,16 + ,11 + ,6 + ,27 + ,24 + ,31 + ,9 + ,17 + ,9 + ,25 + ,19 + ,20 + ,9 + ,11 + ,7 + ,21 + ,20 + ,16 + ,9 + ,12 + ,9 + ,10 + ,13 + ,22 + ,8 + ,14 + ,10 + ,20 + ,20 + ,20 + ,7 + ,11 + ,9 + ,26 + ,22 + ,28 + ,16 + ,16 + ,8 + ,24 + ,24 + ,38 + ,11 + ,21 + ,7 + ,29 + ,29 + ,22 + ,9 + ,14 + ,6 + ,19 + ,12 + ,20 + ,11 + ,20 + ,13 + ,24 + ,20 + ,17 + ,9 + ,13 + ,6 + ,19 + ,21 + ,28 + ,14 + ,11 + ,8 + ,24 + ,24 + ,22 + ,13 + ,15 + ,10 + ,22 + ,22 + ,31 + ,16 + ,19 + ,16 + ,17 + ,20) + ,dim=c(6 + ,159) + ,dimnames=list(c('CM' + ,'D' + ,'PE' + ,'PC' + ,'PS' + ,'O') + ,1:159)) > y <- array(NA,dim=c(6,159),dimnames=list(c('CM','D','PE','PC','PS','O'),1:159)) > 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' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "PE" > x[,par1] [1] 11 7 17 10 12 12 11 11 12 13 14 16 11 10 11 15 9 11 17 17 11 18 14 10 11 [26] 15 15 13 16 13 9 18 18 12 17 9 9 12 18 12 18 14 15 16 10 11 14 9 12 17 [51] 5 12 12 6 24 12 12 14 7 13 12 13 14 8 11 9 11 13 10 11 12 9 15 18 15 [76] 12 13 14 10 13 13 11 13 16 8 16 11 9 16 12 14 8 9 15 11 21 14 18 12 13 [101] 15 12 19 15 11 11 10 13 15 12 12 16 9 18 8 13 17 9 15 8 7 12 14 6 8 [126] 17 10 11 14 11 13 12 11 9 12 20 12 13 12 12 9 15 24 7 17 11 17 11 12 14 [151] 11 16 21 14 20 13 11 15 19 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 24 1 2 4 6 13 8 24 26 16 13 13 8 9 8 2 2 2 2 > colnames(x) [1] "CM" "D" "PE" "PC" "PS" "O" > colnames(x)[par1] [1] "PE" > x[,par1] [1] 11 7 17 10 12 12 11 11 12 13 14 16 11 10 11 15 9 11 17 17 11 18 14 10 11 [26] 15 15 13 16 13 9 18 18 12 17 9 9 12 18 12 18 14 15 16 10 11 14 9 12 17 [51] 5 12 12 6 24 12 12 14 7 13 12 13 14 8 11 9 11 13 10 11 12 9 15 18 15 [76] 12 13 14 10 13 13 11 13 16 8 16 11 9 16 12 14 8 9 15 11 21 14 18 12 13 [101] 15 12 19 15 11 11 10 13 15 12 12 16 9 18 8 13 17 9 15 8 7 12 14 6 8 [126] 17 10 11 14 11 13 12 11 9 12 20 12 13 12 12 9 15 24 7 17 11 17 11 12 14 [151] 11 16 21 14 20 13 11 15 19 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/www/html/rcomp/tmp/1zrvj1293391534.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: PE Inputs: CM, D, PC, PS, O Number of observations: 159 1) PC <= 9; criterion = 1, statistic = 55.6 2) PC <= 4; criterion = 0.997, statistic = 11.668 3)* weights = 9 2) PC > 4 4)* weights = 109 1) PC > 9 5) CM <= 23; criterion = 0.998, statistic = 12.607 6)* weights = 21 5) CM > 23 7) PC <= 11; criterion = 0.976, statistic = 7.912 8)* weights = 10 7) PC > 11 9)* weights = 10 > postscript(file="/var/www/html/rcomp/tmp/2zrvj1293391534.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/www/html/rcomp/tmp/3zrvj1293391534.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 11 18.500000 -7.5000000 2 7 12.174312 -5.1743119 3 17 12.174312 4.8256881 4 10 12.174312 -2.1743119 5 12 12.174312 -0.1743119 6 12 12.174312 -0.1743119 7 11 8.666667 2.3333333 8 11 13.619048 -2.6190476 9 12 12.174312 -0.1743119 10 13 12.174312 0.8256881 11 14 13.619048 0.3809524 12 16 16.800000 -0.8000000 13 11 13.619048 -2.6190476 14 10 12.174312 -2.1743119 15 11 12.174312 -1.1743119 16 15 8.666667 6.3333333 17 9 12.174312 -3.1743119 18 11 12.174312 -1.1743119 19 17 12.174312 4.8256881 20 17 16.800000 0.2000000 21 11 12.174312 -1.1743119 22 18 16.800000 1.2000000 23 14 13.619048 0.3809524 24 10 12.174312 -2.1743119 25 11 12.174312 -1.1743119 26 15 12.174312 2.8256881 27 15 12.174312 2.8256881 28 13 12.174312 0.8256881 29 16 12.174312 3.8256881 30 13 12.174312 0.8256881 31 9 12.174312 -3.1743119 32 18 18.500000 -0.5000000 33 18 12.174312 5.8256881 34 12 12.174312 -0.1743119 35 17 12.174312 4.8256881 36 9 12.174312 -3.1743119 37 9 12.174312 -3.1743119 38 12 12.174312 -0.1743119 39 18 18.500000 -0.5000000 40 12 12.174312 -0.1743119 41 18 16.800000 1.2000000 42 14 12.174312 1.8256881 43 15 12.174312 2.8256881 44 16 12.174312 3.8256881 45 10 12.174312 -2.1743119 46 11 12.174312 -1.1743119 47 14 13.619048 0.3809524 48 9 12.174312 -3.1743119 49 12 12.174312 -0.1743119 50 17 12.174312 4.8256881 51 5 8.666667 -3.6666667 52 12 12.174312 -0.1743119 53 12 12.174312 -0.1743119 54 6 8.666667 -2.6666667 55 24 18.500000 5.5000000 56 12 12.174312 -0.1743119 57 12 12.174312 -0.1743119 58 14 12.174312 1.8256881 59 7 8.666667 -1.6666667 60 13 12.174312 0.8256881 61 12 12.174312 -0.1743119 62 13 12.174312 0.8256881 63 14 12.174312 1.8256881 64 8 12.174312 -4.1743119 65 11 12.174312 -1.1743119 66 9 12.174312 -3.1743119 67 11 12.174312 -1.1743119 68 13 12.174312 0.8256881 69 10 12.174312 -2.1743119 70 11 12.174312 -1.1743119 71 12 12.174312 -0.1743119 72 9 12.174312 -3.1743119 73 15 12.174312 2.8256881 74 18 16.800000 1.2000000 75 15 12.174312 2.8256881 76 12 12.174312 -0.1743119 77 13 12.174312 0.8256881 78 14 12.174312 1.8256881 79 10 12.174312 -2.1743119 80 13 12.174312 0.8256881 81 13 13.619048 -0.6190476 82 11 12.174312 -1.1743119 83 13 12.174312 0.8256881 84 16 13.619048 2.3809524 85 8 12.174312 -4.1743119 86 16 12.174312 3.8256881 87 11 12.174312 -1.1743119 88 9 12.174312 -3.1743119 89 16 18.500000 -2.5000000 90 12 12.174312 -0.1743119 91 14 12.174312 1.8256881 92 8 12.174312 -4.1743119 93 9 12.174312 -3.1743119 94 15 12.174312 2.8256881 95 11 13.619048 -2.6190476 96 21 18.500000 2.5000000 97 14 12.174312 1.8256881 98 18 18.500000 -0.5000000 99 12 12.174312 -0.1743119 100 13 12.174312 0.8256881 101 15 13.619048 1.3809524 102 12 12.174312 -0.1743119 103 19 13.619048 5.3809524 104 15 13.619048 1.3809524 105 11 13.619048 -2.6190476 106 11 12.174312 -1.1743119 107 10 12.174312 -2.1743119 108 13 16.800000 -3.8000000 109 15 16.800000 -1.8000000 110 12 12.174312 -0.1743119 111 12 12.174312 -0.1743119 112 16 18.500000 -2.5000000 113 9 13.619048 -4.6190476 114 18 16.800000 1.2000000 115 8 13.619048 -5.6190476 116 13 12.174312 0.8256881 117 17 12.174312 4.8256881 118 9 12.174312 -3.1743119 119 15 12.174312 2.8256881 120 8 8.666667 -0.6666667 121 7 8.666667 -1.6666667 122 12 12.174312 -0.1743119 123 14 13.619048 0.3809524 124 6 12.174312 -6.1743119 125 8 12.174312 -4.1743119 126 17 12.174312 4.8256881 127 10 8.666667 1.3333333 128 11 12.174312 -1.1743119 129 14 12.174312 1.8256881 130 11 12.174312 -1.1743119 131 13 13.619048 -0.6190476 132 12 12.174312 -0.1743119 133 11 12.174312 -1.1743119 134 9 8.666667 0.3333333 135 12 12.174312 -0.1743119 136 20 16.800000 3.2000000 137 12 12.174312 -0.1743119 138 13 12.174312 0.8256881 139 12 12.174312 -0.1743119 140 12 13.619048 -1.6190476 141 9 12.174312 -3.1743119 142 15 16.800000 -1.8000000 143 24 18.500000 5.5000000 144 7 12.174312 -5.1743119 145 17 13.619048 3.3809524 146 11 12.174312 -1.1743119 147 17 12.174312 4.8256881 148 11 12.174312 -1.1743119 149 12 12.174312 -0.1743119 150 14 13.619048 0.3809524 151 11 12.174312 -1.1743119 152 16 12.174312 3.8256881 153 21 12.174312 8.8256881 154 14 12.174312 1.8256881 155 20 13.619048 6.3809524 156 13 12.174312 0.8256881 157 11 12.174312 -1.1743119 158 15 13.619048 1.3809524 159 19 18.500000 0.5000000 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/html/rcomp/tmp/4sjum1293391534.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/www/html/rcomp/tmp/56aav1293391534.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/www/html/rcomp/tmp/62lte1293391535.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="/var/www/html/rcomp/tmp/7dvah1293391535.tab") + } > try(system("convert tmp/2zrvj1293391534.ps tmp/2zrvj1293391534.png",intern=TRUE)) character(0) > try(system("convert tmp/3zrvj1293391534.ps tmp/3zrvj1293391534.png",intern=TRUE)) character(0) > try(system("convert tmp/4sjum1293391534.ps tmp/4sjum1293391534.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.045 0.576 6.698