R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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> y <- array(NA,dim=c(6,430),dimnames=list(c('Wealth','Costs','Orders','Dividends','Group','Gender '),1:430)) > 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 = '2' > 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] "Orders" > x[,par1] [1] 444 412 428 315 168 263 267 228 129 104 122 393 190 280 63 102 265 234 [19] 277 73 67 103 290 83 56 236 73 34 139 26 70 40 42 12 211 74 [37] 80 83 131 203 56 89 88 39 25 49 149 58 41 90 136 97 63 114 [55] 77 6 47 51 85 43 32 25 77 54 251 15 44 73 85 49 38 35 [73] 9 34 20 29 11 52 13 29 66 33 15 15 68 100 13 45 14 36 [91] 40 68 29 43 30 9 22 19 9 31 19 55 8 28 29 48 16 47 [109] 20 22 33 44 13 6 35 8 17 11 21 92 12 112 25 17 23 0 [127] 10 23 0 7 25 1 20 4 4 10 1 4 0 8 0 11 4 15 [145] 9 0 7 2 0 7 46 5 7 2 0 0 2 5 0 0 0 0 [163] 7 24 1 0 18 55 0 0 3 0 9 0 8 113 0 0 0 19 [181] 11 25 16 5 11 23 6 5 0 7 0 7 0 3 0 89 0 0 [199] 19 0 0 0 12 12 5 2 0 26 3 0 0 11 10 5 2 6 [217] 7 2 28 3 0 1 20 1 22 9 0 2 0 7 9 0 13 0 [235] 0 0 6 0 0 0 3 0 7 2 0 0 15 0 0 9 0 1 [253] 38 57 0 7 26 0 0 0 0 13 10 0 0 0 9 0 26 0 [271] 0 0 19 0 12 23 0 29 8 0 0 26 0 9 0 5 3 0 [289] 13 0 12 19 0 10 9 0 0 0 9 4 1 1 0 14 12 0 [307] 19 17 0 0 32 0 14 8 4 0 20 5 0 0 0 0 0 0 [325] 1 0 0 0 0 0 0 0 4 1 4 20 0 1 10 12 0 0 [343] 0 13 0 3 0 0 10 3 7 10 1 0 0 15 0 0 0 4 [361] 0 0 28 9 0 0 0 0 7 0 7 7 3 0 0 11 7 10 [379] 0 0 18 14 0 12 29 3 6 3 8 10 6 8 6 9 8 26 [397] 239 7 41 3 8 6 21 7 11 11 12 9 3 57 21 15 32 11 [415] 2 23 20 24 1 1 74 68 20 20 82 21 244 32 86 69 > 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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 115 14 9 13 9 8 9 18 10 15 10 10 10 7 4 7 2 3 2 7 20 21 22 23 24 25 26 28 29 30 31 32 33 34 35 36 38 39 40 41 9 4 3 5 2 5 6 3 6 1 1 4 2 2 2 1 2 1 2 2 42 43 44 45 46 47 48 49 51 52 54 55 56 57 58 63 66 67 68 69 1 2 2 1 1 2 1 2 1 1 1 2 2 2 1 2 1 1 3 1 70 73 74 77 80 82 83 85 86 88 89 90 92 97 100 102 103 104 112 113 1 3 2 2 1 1 2 2 1 1 2 1 1 1 1 1 1 1 1 1 114 122 129 131 136 139 149 168 190 203 211 228 234 236 239 244 251 263 265 267 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 277 280 290 315 393 412 428 444 1 1 1 1 1 1 1 1 > colnames(x) [1] "Wealth" "Costs" "Orders" "Dividends" "Group" "Gender." > colnames(x)[par1] [1] "Orders" > x[,par1] [1] 444 412 428 315 168 263 267 228 129 104 122 393 190 280 63 102 265 234 [19] 277 73 67 103 290 83 56 236 73 34 139 26 70 40 42 12 211 74 [37] 80 83 131 203 56 89 88 39 25 49 149 58 41 90 136 97 63 114 [55] 77 6 47 51 85 43 32 25 77 54 251 15 44 73 85 49 38 35 [73] 9 34 20 29 11 52 13 29 66 33 15 15 68 100 13 45 14 36 [91] 40 68 29 43 30 9 22 19 9 31 19 55 8 28 29 48 16 47 [109] 20 22 33 44 13 6 35 8 17 11 21 92 12 112 25 17 23 0 [127] 10 23 0 7 25 1 20 4 4 10 1 4 0 8 0 11 4 15 [145] 9 0 7 2 0 7 46 5 7 2 0 0 2 5 0 0 0 0 [163] 7 24 1 0 18 55 0 0 3 0 9 0 8 113 0 0 0 19 [181] 11 25 16 5 11 23 6 5 0 7 0 7 0 3 0 89 0 0 [199] 19 0 0 0 12 12 5 2 0 26 3 0 0 11 10 5 2 6 [217] 7 2 28 3 0 1 20 1 22 9 0 2 0 7 9 0 13 0 [235] 0 0 6 0 0 0 3 0 7 2 0 0 15 0 0 9 0 1 [253] 38 57 0 7 26 0 0 0 0 13 10 0 0 0 9 0 26 0 [271] 0 0 19 0 12 23 0 29 8 0 0 26 0 9 0 5 3 0 [289] 13 0 12 19 0 10 9 0 0 0 9 4 1 1 0 14 12 0 [307] 19 17 0 0 32 0 14 8 4 0 20 5 0 0 0 0 0 0 [325] 1 0 0 0 0 0 0 0 4 1 4 20 0 1 10 12 0 0 [343] 0 13 0 3 0 0 10 3 7 10 1 0 0 15 0 0 0 4 [361] 0 0 28 9 0 0 0 0 7 0 7 7 3 0 0 11 7 10 [379] 0 0 18 14 0 12 29 3 6 3 8 10 6 8 6 9 8 26 [397] 239 7 41 3 8 6 21 7 11 11 12 9 3 57 21 15 32 11 [415] 2 23 20 24 1 1 74 68 20 20 82 21 244 32 86 69 > 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/1d7651292934339.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: Orders Inputs: Wealth, Costs, Dividends, Group, Gender. Number of observations: 430 1) Costs <= 15824; criterion = 1, statistic = 301.599 2) Costs <= 1270; criterion = 1, statistic = 203.71 3) Costs <= 119; criterion = 1, statistic = 85.989 4) Costs <= 0; criterion = 1, statistic = 50.403 5)* weights = 117 4) Costs > 0 6)* weights = 42 3) Costs > 119 7) Wealth <= 340968; criterion = 0.988, statistic = 9.188 8)* weights = 115 7) Wealth > 340968 9)* weights = 17 2) Costs > 1270 10) Costs <= 3999; criterion = 1, statistic = 23.855 11)* weights = 68 10) Costs > 3999 12)* weights = 49 1) Costs > 15824 13)* weights = 22 > postscript(file="/var/www/html/rcomp/tmp/2d7651292934339.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/3d7651292934339.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 1 444 250.6363636 193.36363636 2 412 250.6363636 161.36363636 3 428 250.6363636 177.36363636 4 315 250.6363636 64.36363636 5 168 250.6363636 -82.63636364 6 263 250.6363636 12.36363636 7 267 250.6363636 16.36363636 8 228 250.6363636 -22.63636364 9 129 250.6363636 -121.63636364 10 104 250.6363636 -146.63636364 11 122 77.8367347 44.16326531 12 393 250.6363636 142.36363636 13 190 250.6363636 -60.63636364 14 280 250.6363636 29.36363636 15 63 77.8367347 -14.83673469 16 102 77.8367347 24.16326531 17 265 250.6363636 14.36363636 18 234 250.6363636 -16.63636364 19 277 250.6363636 26.36363636 20 73 77.8367347 -4.83673469 21 67 250.6363636 -183.63636364 22 103 77.8367347 25.16326531 23 290 250.6363636 39.36363636 24 83 77.8367347 5.16326531 25 56 77.8367347 -21.83673469 26 236 250.6363636 -14.63636364 27 73 77.8367347 -4.83673469 28 34 77.8367347 -43.83673469 29 139 77.8367347 61.16326531 30 26 38.0735294 -12.07352941 31 70 77.8367347 -7.83673469 32 40 77.8367347 -37.83673469 33 42 77.8367347 -35.83673469 34 12 38.0735294 -26.07352941 35 211 250.6363636 -39.63636364 36 74 77.8367347 -3.83673469 37 80 77.8367347 2.16326531 38 83 38.0735294 44.92647059 39 131 77.8367347 53.16326531 40 203 77.8367347 125.16326531 41 56 77.8367347 -21.83673469 42 89 77.8367347 11.16326531 43 88 77.8367347 10.16326531 44 39 77.8367347 -38.83673469 45 25 38.0735294 -13.07352941 46 49 77.8367347 -28.83673469 47 149 77.8367347 71.16326531 48 58 77.8367347 -19.83673469 49 41 38.0735294 2.92647059 50 90 38.0735294 51.92647059 51 136 77.8367347 58.16326531 52 97 77.8367347 19.16326531 53 63 20.8235294 42.17647059 54 114 77.8367347 36.16326531 55 77 38.0735294 38.92647059 56 6 38.0735294 -32.07352941 57 47 77.8367347 -30.83673469 58 51 38.0735294 12.92647059 59 85 77.8367347 7.16326531 60 43 77.8367347 -34.83673469 61 32 38.0735294 -6.07352941 62 25 38.0735294 -13.07352941 63 77 77.8367347 -0.83673469 64 54 38.0735294 15.92647059 65 251 77.8367347 173.16326531 66 15 11.8086957 3.19130435 67 44 38.0735294 5.92647059 68 73 38.0735294 34.92647059 69 85 77.8367347 7.16326531 70 49 38.0735294 10.92647059 71 38 77.8367347 -39.83673469 72 35 38.0735294 -3.07352941 73 9 11.8086957 -2.80869565 74 34 38.0735294 -4.07352941 75 20 38.0735294 -18.07352941 76 29 20.8235294 8.17647059 77 11 11.8086957 -0.80869565 78 52 38.0735294 13.92647059 79 13 11.8086957 1.19130435 80 29 38.0735294 -9.07352941 81 66 77.8367347 -11.83673469 82 33 20.8235294 12.17647059 83 15 11.8086957 3.19130435 84 15 20.8235294 -5.82352941 85 68 77.8367347 -9.83673469 86 100 38.0735294 61.92647059 87 13 20.8235294 -7.82352941 88 45 38.0735294 6.92647059 89 14 20.8235294 -6.82352941 90 36 38.0735294 -2.07352941 91 40 38.0735294 1.92647059 92 68 38.0735294 29.92647059 93 29 77.8367347 -48.83673469 94 43 38.0735294 4.92647059 95 30 38.0735294 -8.07352941 96 9 20.8235294 -11.82352941 97 22 38.0735294 -16.07352941 98 19 38.0735294 -19.07352941 99 9 38.0735294 -29.07352941 100 31 11.8086957 19.19130435 101 19 11.8086957 7.19130435 102 55 38.0735294 16.92647059 103 8 11.8086957 -3.80869565 104 28 38.0735294 -10.07352941 105 29 11.8086957 17.19130435 106 48 38.0735294 9.92647059 107 16 38.0735294 -22.07352941 108 47 77.8367347 -30.83673469 109 20 38.0735294 -18.07352941 110 22 20.8235294 1.17647059 111 33 38.0735294 -5.07352941 112 44 77.8367347 -33.83673469 113 13 38.0735294 -25.07352941 114 6 38.0735294 -32.07352941 115 35 38.0735294 -3.07352941 116 8 11.8086957 -3.80869565 117 17 38.0735294 -21.07352941 118 11 38.0735294 -27.07352941 119 21 11.8086957 9.19130435 120 92 77.8367347 14.16326531 121 12 11.8086957 0.19130435 122 112 38.0735294 73.92647059 123 25 38.0735294 -13.07352941 124 17 38.0735294 -21.07352941 125 23 38.0735294 -15.07352941 126 0 0.1709402 -0.17094017 127 10 4.9285714 5.07142857 128 23 11.8086957 11.19130435 129 0 0.1709402 -0.17094017 130 7 4.9285714 2.07142857 131 25 38.0735294 -13.07352941 132 1 0.1709402 0.82905983 133 20 4.9285714 15.07142857 134 4 11.8086957 -7.80869565 135 4 11.8086957 -7.80869565 136 10 4.9285714 5.07142857 137 1 4.9285714 -3.92857143 138 4 11.8086957 -7.80869565 139 0 0.1709402 -0.17094017 140 8 11.8086957 -3.80869565 141 0 0.1709402 -0.17094017 142 11 11.8086957 -0.80869565 143 4 4.9285714 -0.92857143 144 15 11.8086957 3.19130435 145 9 11.8086957 -2.80869565 146 0 0.1709402 -0.17094017 147 7 4.9285714 2.07142857 148 2 11.8086957 -9.80869565 149 0 4.9285714 -4.92857143 150 7 11.8086957 -4.80869565 151 46 38.0735294 7.92647059 152 5 0.1709402 4.82905983 153 7 11.8086957 -4.80869565 154 2 4.9285714 -2.92857143 155 0 0.1709402 -0.17094017 156 0 0.1709402 -0.17094017 157 2 4.9285714 -2.92857143 158 5 11.8086957 -6.80869565 159 0 0.1709402 -0.17094017 160 0 0.1709402 -0.17094017 161 0 0.1709402 -0.17094017 162 0 0.1709402 -0.17094017 163 7 11.8086957 -4.80869565 164 24 11.8086957 12.19130435 165 1 4.9285714 -3.92857143 166 0 0.1709402 -0.17094017 167 18 20.8235294 -2.82352941 168 55 77.8367347 -22.83673469 169 0 0.1709402 -0.17094017 170 0 0.1709402 -0.17094017 171 3 4.9285714 -1.92857143 172 0 0.1709402 -0.17094017 173 9 11.8086957 -2.80869565 174 0 0.1709402 -0.17094017 175 8 11.8086957 -3.80869565 176 113 11.8086957 101.19130435 177 0 4.9285714 -4.92857143 178 0 0.1709402 -0.17094017 179 0 0.1709402 -0.17094017 180 19 20.8235294 -1.82352941 181 11 11.8086957 -0.80869565 182 25 38.0735294 -13.07352941 183 16 11.8086957 4.19130435 184 5 4.9285714 0.07142857 185 11 11.8086957 -0.80869565 186 23 38.0735294 -15.07352941 187 6 11.8086957 -5.80869565 188 5 11.8086957 -6.80869565 189 0 0.1709402 -0.17094017 190 7 11.8086957 -4.80869565 191 0 0.1709402 -0.17094017 192 7 11.8086957 -4.80869565 193 0 0.1709402 -0.17094017 194 3 4.9285714 -1.92857143 195 0 0.1709402 -0.17094017 196 89 77.8367347 11.16326531 197 0 0.1709402 -0.17094017 198 0 0.1709402 -0.17094017 199 19 11.8086957 7.19130435 200 0 0.1709402 -0.17094017 201 0 0.1709402 -0.17094017 202 0 0.1709402 -0.17094017 203 12 11.8086957 0.19130435 204 12 11.8086957 0.19130435 205 5 4.9285714 0.07142857 206 2 4.9285714 -2.92857143 207 0 0.1709402 -0.17094017 208 26 20.8235294 5.17647059 209 3 11.8086957 -8.80869565 210 0 0.1709402 -0.17094017 211 0 0.1709402 -0.17094017 212 11 11.8086957 -0.80869565 213 10 11.8086957 -1.80869565 214 5 11.8086957 -6.80869565 215 2 0.1709402 1.82905983 216 6 4.9285714 1.07142857 217 7 11.8086957 -4.80869565 218 2 0.1709402 1.82905983 219 28 38.0735294 -10.07352941 220 3 11.8086957 -8.80869565 221 0 0.1709402 -0.17094017 222 1 4.9285714 -3.92857143 223 20 11.8086957 8.19130435 224 1 4.9285714 -3.92857143 225 22 11.8086957 10.19130435 226 9 4.9285714 4.07142857 227 0 0.1709402 -0.17094017 228 2 11.8086957 -9.80869565 229 0 0.1709402 -0.17094017 230 7 11.8086957 -4.80869565 231 9 11.8086957 -2.80869565 232 0 0.1709402 -0.17094017 233 13 11.8086957 1.19130435 234 0 0.1709402 -0.17094017 235 0 4.9285714 -4.92857143 236 0 0.1709402 -0.17094017 237 6 4.9285714 1.07142857 238 0 0.1709402 -0.17094017 239 0 0.1709402 -0.17094017 240 0 0.1709402 -0.17094017 241 3 4.9285714 -1.92857143 242 0 0.1709402 -0.17094017 243 7 4.9285714 2.07142857 244 2 38.0735294 -36.07352941 245 0 0.1709402 -0.17094017 246 0 0.1709402 -0.17094017 247 15 11.8086957 3.19130435 248 0 0.1709402 -0.17094017 249 0 0.1709402 -0.17094017 250 9 11.8086957 -2.80869565 251 0 0.1709402 -0.17094017 252 1 11.8086957 -10.80869565 253 38 11.8086957 26.19130435 254 57 38.0735294 18.92647059 255 0 0.1709402 -0.17094017 256 7 11.8086957 -4.80869565 257 26 11.8086957 14.19130435 258 0 0.1709402 -0.17094017 259 0 0.1709402 -0.17094017 260 0 0.1709402 -0.17094017 261 0 0.1709402 -0.17094017 262 13 20.8235294 -7.82352941 263 10 11.8086957 -1.80869565 264 0 0.1709402 -0.17094017 265 0 0.1709402 -0.17094017 266 0 0.1709402 -0.17094017 267 9 20.8235294 -11.82352941 268 0 0.1709402 -0.17094017 269 26 11.8086957 14.19130435 270 0 0.1709402 -0.17094017 271 0 0.1709402 -0.17094017 272 0 0.1709402 -0.17094017 273 19 11.8086957 7.19130435 274 0 0.1709402 -0.17094017 275 12 11.8086957 0.19130435 276 23 11.8086957 11.19130435 277 0 0.1709402 -0.17094017 278 29 38.0735294 -9.07352941 279 8 11.8086957 -3.80869565 280 0 0.1709402 -0.17094017 281 0 0.1709402 -0.17094017 282 26 11.8086957 14.19130435 283 0 0.1709402 -0.17094017 284 9 11.8086957 -2.80869565 285 0 0.1709402 -0.17094017 286 5 11.8086957 -6.80869565 287 3 4.9285714 -1.92857143 288 0 0.1709402 -0.17094017 289 13 11.8086957 1.19130435 290 0 0.1709402 -0.17094017 291 12 11.8086957 0.19130435 292 19 20.8235294 -1.82352941 293 0 0.1709402 -0.17094017 294 10 4.9285714 5.07142857 295 9 11.8086957 -2.80869565 296 0 0.1709402 -0.17094017 297 0 0.1709402 -0.17094017 298 0 0.1709402 -0.17094017 299 9 0.1709402 8.82905983 300 4 4.9285714 -0.92857143 301 1 11.8086957 -10.80869565 302 1 0.1709402 0.82905983 303 0 0.1709402 -0.17094017 304 14 20.8235294 -6.82352941 305 12 4.9285714 7.07142857 306 0 0.1709402 -0.17094017 307 19 11.8086957 7.19130435 308 17 11.8086957 5.19130435 309 0 0.1709402 -0.17094017 310 0 0.1709402 -0.17094017 311 32 20.8235294 11.17647059 312 0 0.1709402 -0.17094017 313 14 4.9285714 9.07142857 314 8 4.9285714 3.07142857 315 4 11.8086957 -7.80869565 316 0 4.9285714 -4.92857143 317 20 11.8086957 8.19130435 318 5 4.9285714 0.07142857 319 0 0.1709402 -0.17094017 320 0 0.1709402 -0.17094017 321 0 0.1709402 -0.17094017 322 0 0.1709402 -0.17094017 323 0 0.1709402 -0.17094017 324 0 0.1709402 -0.17094017 325 1 4.9285714 -3.92857143 326 0 0.1709402 -0.17094017 327 0 0.1709402 -0.17094017 328 0 0.1709402 -0.17094017 329 0 0.1709402 -0.17094017 330 0 0.1709402 -0.17094017 331 0 0.1709402 -0.17094017 332 0 0.1709402 -0.17094017 333 4 4.9285714 -0.92857143 334 1 4.9285714 -3.92857143 335 4 11.8086957 -7.80869565 336 20 11.8086957 8.19130435 337 0 0.1709402 -0.17094017 338 1 4.9285714 -3.92857143 339 10 11.8086957 -1.80869565 340 12 11.8086957 0.19130435 341 0 0.1709402 -0.17094017 342 0 0.1709402 -0.17094017 343 0 0.1709402 -0.17094017 344 13 11.8086957 1.19130435 345 0 0.1709402 -0.17094017 346 3 4.9285714 -1.92857143 347 0 0.1709402 -0.17094017 348 0 0.1709402 -0.17094017 349 10 38.0735294 -28.07352941 350 3 11.8086957 -8.80869565 351 7 4.9285714 2.07142857 352 10 11.8086957 -1.80869565 353 1 11.8086957 -10.80869565 354 0 0.1709402 -0.17094017 355 0 0.1709402 -0.17094017 356 15 11.8086957 3.19130435 357 0 0.1709402 -0.17094017 358 0 0.1709402 -0.17094017 359 0 0.1709402 -0.17094017 360 4 4.9285714 -0.92857143 361 0 0.1709402 -0.17094017 362 0 0.1709402 -0.17094017 363 28 38.0735294 -10.07352941 364 9 11.8086957 -2.80869565 365 0 0.1709402 -0.17094017 366 0 0.1709402 -0.17094017 367 0 0.1709402 -0.17094017 368 0 0.1709402 -0.17094017 369 7 11.8086957 -4.80869565 370 0 0.1709402 -0.17094017 371 7 11.8086957 -4.80869565 372 7 11.8086957 -4.80869565 373 3 11.8086957 -8.80869565 374 0 0.1709402 -0.17094017 375 0 0.1709402 -0.17094017 376 11 11.8086957 -0.80869565 377 7 11.8086957 -4.80869565 378 10 38.0735294 -28.07352941 379 0 0.1709402 -0.17094017 380 0 0.1709402 -0.17094017 381 18 11.8086957 6.19130435 382 14 11.8086957 2.19130435 383 0 0.1709402 -0.17094017 384 12 4.9285714 7.07142857 385 29 38.0735294 -9.07352941 386 3 11.8086957 -8.80869565 387 6 11.8086957 -5.80869565 388 3 4.9285714 -1.92857143 389 8 11.8086957 -3.80869565 390 10 11.8086957 -1.80869565 391 6 11.8086957 -5.80869565 392 8 11.8086957 -3.80869565 393 6 20.8235294 -14.82352941 394 9 38.0735294 -29.07352941 395 8 11.8086957 -3.80869565 396 26 77.8367347 -51.83673469 397 239 38.0735294 200.92647059 398 7 11.8086957 -4.80869565 399 41 38.0735294 2.92647059 400 3 11.8086957 -8.80869565 401 8 11.8086957 -3.80869565 402 6 11.8086957 -5.80869565 403 21 38.0735294 -17.07352941 404 7 11.8086957 -4.80869565 405 11 11.8086957 -0.80869565 406 11 11.8086957 -0.80869565 407 12 11.8086957 0.19130435 408 9 11.8086957 -2.80869565 409 3 11.8086957 -8.80869565 410 57 38.0735294 18.92647059 411 21 77.8367347 -56.83673469 412 15 11.8086957 3.19130435 413 32 38.0735294 -6.07352941 414 11 38.0735294 -27.07352941 415 2 11.8086957 -9.80869565 416 23 38.0735294 -15.07352941 417 20 11.8086957 8.19130435 418 24 11.8086957 12.19130435 419 1 11.8086957 -10.80869565 420 1 11.8086957 -10.80869565 421 74 38.0735294 35.92647059 422 68 77.8367347 -9.83673469 423 20 77.8367347 -57.83673469 424 20 38.0735294 -18.07352941 425 82 77.8367347 4.16326531 426 21 38.0735294 -17.07352941 427 244 250.6363636 -6.63636364 428 32 77.8367347 -45.83673469 429 86 77.8367347 8.16326531 430 69 250.6363636 -181.63636364 > 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/4oy581292934339.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/528lh1292934339.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/6uzkk1292934339.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/7yz181292934339.tab") + } > > try(system("convert tmp/2d7651292934339.ps tmp/2d7651292934339.png",intern=TRUE)) character(0) > try(system("convert tmp/3d7651292934339.ps tmp/3d7651292934339.png",intern=TRUE)) character(0) > try(system("convert tmp/4oy581292934339.ps tmp/4oy581292934339.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.776 0.769 17.783