R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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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,82206 + ,1182 + ,91005 + ,29 + ,3 + ,29 + ,11 + ,32073 + ,528 + ,40248 + ,16 + ,1 + ,8 + ,4 + ,5444 + ,642 + ,64187 + ,27 + ,0 + ,10 + ,16 + ,20154 + ,947 + ,50857 + ,21 + ,0 + ,15 + ,20 + ,36944 + ,819 + ,56613 + ,19 + ,1 + ,15 + ,12 + ,8019 + ,757 + ,62792 + ,35 + ,0 + ,28 + ,15 + ,30884 + ,894 + ,72535 + ,14 + ,0 + ,17 + ,16 + ,19540) + ,dim=c(7 + ,289) + ,dimnames=list(c('pageviews' + ,'time_in_rfc' + ,'logins' + ,'shared_compendiums' + ,'blogged_computations' + ,'compendiums_reviewed' + ,'totsize') + ,1:289)) > y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','time_in_rfc','logins','shared_compendiums','blogged_computations','compendiums_reviewed','totsize'),1:289)) > 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 Attaching package: 'zoo' The following object(s) are masked from 'package:base': as.Date, 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) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). 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] "logins" > x[,par1] [1] 56 56 54 89 40 25 92 18 63 44 33 84 88 55 60 66 154 53 [19] 119 41 61 58 75 33 40 92 100 112 73 40 45 60 62 75 31 77 [37] 34 46 99 17 66 30 76 146 67 56 107 58 34 61 119 42 66 89 [55] 44 66 24 259 17 64 41 68 168 43 132 105 71 112 94 82 70 57 [73] 53 103 121 62 52 52 32 62 45 46 63 75 88 46 53 37 90 63 [91] 78 25 45 46 41 144 82 91 71 63 53 62 63 32 39 62 117 34 [109] 92 93 54 144 14 61 109 38 73 75 50 61 55 77 75 72 50 32 [127] 53 42 71 10 35 65 25 66 41 86 16 42 19 19 45 65 35 95 [145] 49 37 64 38 34 32 65 52 62 65 83 95 29 18 33 247 139 29 [163] 118 110 67 42 65 94 64 81 95 67 63 83 45 30 70 32 83 31 [181] 67 66 10 70 103 5 20 5 36 34 48 40 43 31 42 46 33 18 [199] 55 35 59 19 66 60 36 25 47 54 53 40 40 39 14 45 36 28 [217] 44 30 22 17 31 55 54 21 14 81 35 43 46 30 23 38 54 20 [235] 53 45 39 20 24 31 35 151 52 30 31 29 57 40 44 25 77 35 [253] 11 63 44 19 13 42 38 29 20 27 20 19 37 26 42 49 30 49 [271] 67 28 19 49 27 30 22 12 31 20 20 39 29 16 27 21 19 35 [289] 14 > 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 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27 28 29 2 2 1 1 1 4 2 3 3 7 7 2 2 1 2 5 1 3 2 5 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 7 7 5 4 5 7 3 3 4 4 7 4 7 3 5 7 6 1 1 4 50 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 70 71 2 4 7 5 4 3 2 2 1 3 4 6 7 3 5 7 5 1 3 3 72 73 75 76 77 78 81 82 83 84 86 88 89 90 91 92 93 94 95 99 1 2 5 1 3 1 2 2 3 1 1 2 2 1 1 3 1 2 3 1 100 103 105 107 109 110 112 117 118 119 121 132 139 144 146 151 154 168 247 259 1 2 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 1 1 > colnames(x) [1] "pageviews" "time_in_rfc" "logins" [4] "shared_compendiums" "blogged_computations" "compendiums_reviewed" [7] "totsize" > colnames(x)[par1] [1] "logins" > x[,par1] [1] 56 56 54 89 40 25 92 18 63 44 33 84 88 55 60 66 154 53 [19] 119 41 61 58 75 33 40 92 100 112 73 40 45 60 62 75 31 77 [37] 34 46 99 17 66 30 76 146 67 56 107 58 34 61 119 42 66 89 [55] 44 66 24 259 17 64 41 68 168 43 132 105 71 112 94 82 70 57 [73] 53 103 121 62 52 52 32 62 45 46 63 75 88 46 53 37 90 63 [91] 78 25 45 46 41 144 82 91 71 63 53 62 63 32 39 62 117 34 [109] 92 93 54 144 14 61 109 38 73 75 50 61 55 77 75 72 50 32 [127] 53 42 71 10 35 65 25 66 41 86 16 42 19 19 45 65 35 95 [145] 49 37 64 38 34 32 65 52 62 65 83 95 29 18 33 247 139 29 [163] 118 110 67 42 65 94 64 81 95 67 63 83 45 30 70 32 83 31 [181] 67 66 10 70 103 5 20 5 36 34 48 40 43 31 42 46 33 18 [199] 55 35 59 19 66 60 36 25 47 54 53 40 40 39 14 45 36 28 [217] 44 30 22 17 31 55 54 21 14 81 35 43 46 30 23 38 54 20 [235] 53 45 39 20 24 31 35 151 52 30 31 29 57 40 44 25 77 35 [253] 11 63 44 19 13 42 38 29 20 27 20 19 37 26 42 49 30 49 [271] 67 28 19 49 27 30 22 12 31 20 20 39 29 16 27 21 19 35 [289] 14 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/fisher/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/fisher/rcomp/tmp/1gply1355246887.tab") + } + } > m Conditional inference tree with 7 terminal nodes Response: logins Inputs: pageviews, time_in_rfc, shared_compendiums, blogged_computations, compendiums_reviewed, totsize Number of observations: 289 1) pageviews <= 2146; criterion = 1, statistic = 165.814 2) pageviews <= 1182; criterion = 1, statistic = 128.737 3) pageviews <= 628; criterion = 1, statistic = 47.365 4) pageviews <= 391; criterion = 0.999, statistic = 14.311 5)* weights = 10 4) pageviews > 391 6)* weights = 25 3) pageviews > 628 7) time_in_rfc <= 98866; criterion = 0.986, statistic = 9.279 8)* weights = 65 7) time_in_rfc > 98866 9)* weights = 18 2) pageviews > 1182 10) pageviews <= 1468; criterion = 0.998, statistic = 12.582 11)* weights = 56 10) pageviews > 1468 12)* weights = 74 1) pageviews > 2146 13)* weights = 41 > postscript(file="/var/fisher/rcomp/tmp/24gcq1355246887.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/fisher/rcomp/tmp/395zp1355246887.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 56 52.73214 3.2678571 2 56 43.94444 12.0555556 3 54 66.37838 -12.3783784 4 89 106.65854 -17.6585366 5 40 43.94444 -3.9444444 6 25 22.36000 2.6400000 7 92 106.65854 -14.6585366 8 18 11.80000 6.2000000 9 63 52.73214 10.2678571 10 44 66.37838 -22.3783784 11 33 52.73214 -19.7321429 12 84 66.37838 17.6216216 13 88 66.37838 21.6216216 14 55 52.73214 2.2678571 15 60 106.65854 -46.6585366 16 66 66.37838 -0.3783784 17 154 106.65854 47.3414634 18 53 66.37838 -13.3783784 19 119 106.65854 12.3414634 20 41 43.94444 -2.9444444 21 61 66.37838 -5.3783784 22 58 66.37838 -8.3783784 23 75 106.65854 -31.6585366 24 33 52.73214 -19.7321429 25 40 52.73214 -12.7321429 26 92 106.65854 -14.6585366 27 100 106.65854 -6.6585366 28 112 106.65854 5.3414634 29 73 66.37838 6.6216216 30 40 52.73214 -12.7321429 31 45 66.37838 -21.3783784 32 60 66.37838 -6.3783784 33 62 66.37838 -4.3783784 34 75 66.37838 8.6216216 35 31 33.10769 -2.1076923 36 77 106.65854 -29.6585366 37 34 43.94444 -9.9444444 38 46 66.37838 -20.3783784 39 99 106.65854 -7.6585366 40 17 11.80000 5.2000000 41 66 66.37838 -0.3783784 42 30 43.94444 -13.9444444 43 76 66.37838 9.6216216 44 146 106.65854 39.3414634 45 67 106.65854 -39.6585366 46 56 52.73214 3.2678571 47 107 106.65854 0.3414634 48 58 52.73214 5.2678571 49 34 43.94444 -9.9444444 50 61 66.37838 -5.3783784 51 119 106.65854 12.3414634 52 42 33.10769 8.8923077 53 66 66.37838 -0.3783784 54 89 106.65854 -17.6585366 55 44 66.37838 -22.3783784 56 66 52.73214 13.2678571 57 24 22.36000 1.6400000 58 259 106.65854 152.3414634 59 17 33.10769 -16.1076923 60 64 43.94444 20.0555556 61 41 106.65854 -65.6585366 62 68 66.37838 1.6216216 63 168 106.65854 61.3414634 64 43 43.94444 -0.9444444 65 132 106.65854 25.3414634 66 105 66.37838 38.6216216 67 71 66.37838 4.6216216 68 112 106.65854 5.3414634 69 94 106.65854 -12.6585366 70 82 66.37838 15.6216216 71 70 66.37838 3.6216216 72 57 52.73214 4.2678571 73 53 52.73214 0.2678571 74 103 66.37838 36.6216216 75 121 106.65854 14.3414634 76 62 66.37838 -4.3783784 77 52 52.73214 -0.7321429 78 52 66.37838 -14.3783784 79 32 33.10769 -1.1076923 80 62 66.37838 -4.3783784 81 45 52.73214 -7.7321429 82 46 52.73214 -6.7321429 83 63 66.37838 -3.3783784 84 75 66.37838 8.6216216 85 88 66.37838 21.6216216 86 46 52.73214 -6.7321429 87 53 52.73214 0.2678571 88 37 52.73214 -15.7321429 89 90 106.65854 -16.6585366 90 63 52.73214 10.2678571 91 78 66.37838 11.6216216 92 25 33.10769 -8.1076923 93 45 66.37838 -21.3783784 94 46 43.94444 2.0555556 95 41 43.94444 -2.9444444 96 144 106.65854 37.3414634 97 82 66.37838 15.6216216 98 91 106.65854 -15.6585366 99 71 66.37838 4.6216216 100 63 66.37838 -3.3783784 101 53 52.73214 0.2678571 102 62 106.65854 -44.6585366 103 63 66.37838 -3.3783784 104 32 52.73214 -20.7321429 105 39 52.73214 -13.7321429 106 62 66.37838 -4.3783784 107 117 106.65854 10.3414634 108 34 52.73214 -18.7321429 109 92 106.65854 -14.6585366 110 93 66.37838 26.6216216 111 54 66.37838 -12.3783784 112 144 52.73214 91.2678571 113 14 22.36000 -8.3600000 114 61 66.37838 -5.3783784 115 109 66.37838 42.6216216 116 38 66.37838 -28.3783784 117 73 66.37838 6.6216216 118 75 66.37838 8.6216216 119 50 43.94444 6.0555556 120 61 66.37838 -5.3783784 121 55 66.37838 -11.3783784 122 77 52.73214 24.2678571 123 75 66.37838 8.6216216 124 72 52.73214 19.2678571 125 50 52.73214 -2.7321429 126 32 33.10769 -1.1076923 127 53 43.94444 9.0555556 128 42 52.73214 -10.7321429 129 71 66.37838 4.6216216 130 10 11.80000 -1.8000000 131 35 33.10769 1.8923077 132 65 66.37838 -1.3783784 133 25 22.36000 2.6400000 134 66 66.37838 -0.3783784 135 41 52.73214 -11.7321429 136 86 106.65854 -20.6585366 137 16 11.80000 4.2000000 138 42 66.37838 -24.3783784 139 19 22.36000 -3.3600000 140 19 22.36000 -3.3600000 141 45 33.10769 11.8923077 142 65 66.37838 -1.3783784 143 35 33.10769 1.8923077 144 95 106.65854 -11.6585366 145 49 66.37838 -17.3783784 146 37 66.37838 -29.3783784 147 64 52.73214 11.2678571 148 38 33.10769 4.8923077 149 34 22.36000 11.6400000 150 32 52.73214 -20.7321429 151 65 106.65854 -41.6585366 152 52 43.94444 8.0555556 153 62 52.73214 9.2678571 154 65 106.65854 -41.6585366 155 83 66.37838 16.6216216 156 95 106.65854 -11.6585366 157 29 43.94444 -14.9444444 158 18 33.10769 -15.1076923 159 33 33.10769 -0.1076923 160 247 106.65854 140.3414634 161 139 106.65854 32.3414634 162 29 33.10769 -4.1076923 163 118 52.73214 65.2678571 164 110 106.65854 3.3414634 165 67 66.37838 0.6216216 166 42 52.73214 -10.7321429 167 65 66.37838 -1.3783784 168 94 52.73214 41.2678571 169 64 43.94444 20.0555556 170 81 66.37838 14.6216216 171 95 106.65854 -11.6585366 172 67 66.37838 0.6216216 173 63 66.37838 -3.3783784 174 83 106.65854 -23.6585366 175 45 52.73214 -7.7321429 176 30 66.37838 -36.3783784 177 70 106.65854 -36.6585366 178 32 33.10769 -1.1076923 179 83 66.37838 16.6216216 180 31 52.73214 -21.7321429 181 67 52.73214 14.2678571 182 66 66.37838 -0.3783784 183 10 11.80000 -1.8000000 184 70 66.37838 3.6216216 185 103 106.65854 -3.6585366 186 5 11.80000 -6.8000000 187 20 22.36000 -2.3600000 188 5 11.80000 -6.8000000 189 36 66.37838 -30.3783784 190 34 33.10769 0.8923077 191 48 52.73214 -4.7321429 192 40 33.10769 6.8923077 193 43 52.73214 -9.7321429 194 31 52.73214 -21.7321429 195 42 52.73214 -10.7321429 196 46 52.73214 -6.7321429 197 33 33.10769 -0.1076923 198 18 22.36000 -4.3600000 199 55 52.73214 2.2678571 200 35 33.10769 1.8923077 201 59 33.10769 25.8923077 202 19 22.36000 -3.3600000 203 66 52.73214 13.2678571 204 60 52.73214 7.2678571 205 36 66.37838 -30.3783784 206 25 22.36000 2.6400000 207 47 33.10769 13.8923077 208 54 66.37838 -12.3783784 209 53 52.73214 0.2678571 210 40 52.73214 -12.7321429 211 40 52.73214 -12.7321429 212 39 52.73214 -13.7321429 213 14 22.36000 -8.3600000 214 45 33.10769 11.8923077 215 36 33.10769 2.8923077 216 28 33.10769 -5.1076923 217 44 33.10769 10.8923077 218 30 33.10769 -3.1076923 219 22 33.10769 -11.1076923 220 17 22.36000 -5.3600000 221 31 33.10769 -2.1076923 222 55 66.37838 -11.3783784 223 54 66.37838 -12.3783784 224 21 33.10769 -12.1076923 225 14 11.80000 2.2000000 226 81 66.37838 14.6216216 227 35 33.10769 1.8923077 228 43 33.10769 9.8923077 229 46 52.73214 -6.7321429 230 30 52.73214 -22.7321429 231 23 22.36000 0.6400000 232 38 33.10769 4.8923077 233 54 52.73214 1.2678571 234 20 33.10769 -13.1076923 235 53 33.10769 19.8923077 236 45 33.10769 11.8923077 237 39 52.73214 -13.7321429 238 20 33.10769 -13.1076923 239 24 33.10769 -9.1076923 240 31 22.36000 8.6400000 241 35 33.10769 1.8923077 242 151 66.37838 84.6216216 243 52 33.10769 18.8923077 244 30 22.36000 7.6400000 245 31 33.10769 -2.1076923 246 29 33.10769 -4.1076923 247 57 66.37838 -9.3783784 248 40 33.10769 6.8923077 249 44 33.10769 10.8923077 250 25 22.36000 2.6400000 251 77 66.37838 10.6216216 252 35 43.94444 -8.9444444 253 11 11.80000 -0.8000000 254 63 52.73214 10.2678571 255 44 33.10769 10.8923077 256 19 22.36000 -3.3600000 257 13 22.36000 -9.3600000 258 42 33.10769 8.8923077 259 38 22.36000 15.6400000 260 29 22.36000 6.6400000 261 20 22.36000 -2.3600000 262 27 33.10769 -6.1076923 263 20 22.36000 -2.3600000 264 19 33.10769 -14.1076923 265 37 33.10769 3.8923077 266 26 33.10769 -7.1076923 267 42 33.10769 8.8923077 268 49 33.10769 15.8923077 269 30 43.94444 -13.9444444 270 49 33.10769 15.8923077 271 67 52.73214 14.2678571 272 28 33.10769 -5.1076923 273 19 33.10769 -14.1076923 274 49 43.94444 5.0555556 275 27 33.10769 -6.1076923 276 30 33.10769 -3.1076923 277 22 22.36000 -0.3600000 278 12 11.80000 0.2000000 279 31 33.10769 -2.1076923 280 20 33.10769 -13.1076923 281 20 33.10769 -13.1076923 282 39 33.10769 5.8923077 283 29 33.10769 -4.1076923 284 16 22.36000 -6.3600000 285 27 33.10769 -6.1076923 286 21 33.10769 -12.1076923 287 19 33.10769 -14.1076923 288 35 33.10769 1.8923077 289 14 33.10769 -19.1076923 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/fisher/rcomp/tmp/4g6f51355246887.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/5hb6r1355246887.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/fisher/rcomp/tmp/6y4rp1355246887.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/fisher/rcomp/tmp/71s6y1355246887.tab") + } > > try(system("convert tmp/24gcq1355246887.ps tmp/24gcq1355246887.png",intern=TRUE)) character(0) > try(system("convert tmp/395zp1355246887.ps tmp/395zp1355246887.png",intern=TRUE)) character(0) > try(system("convert tmp/4g6f51355246887.ps tmp/4g6f51355246887.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.585 0.642 8.213