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. Type 'q()' to quit R. > x <- array(list(1418 + ,210907 + ,56 + ,81 + ,79 + ,30 + ,869 + ,120982 + ,56 + ,55 + ,58 + ,28 + ,1530 + ,176508 + ,54 + ,50 + ,60 + ,38 + ,2172 + ,179321 + ,89 + ,125 + ,108 + ,30 + ,901 + ,123185 + ,40 + ,40 + ,49 + ,22 + ,463 + ,52746 + ,25 + ,37 + ,0 + ,26 + ,3201 + ,385534 + ,92 + ,63 + ,121 + ,25 + ,371 + ,33170 + ,18 + ,44 + ,1 + ,18 + ,1192 + ,101645 + ,63 + ,88 + ,20 + ,11 + ,1583 + ,149061 + ,44 + ,66 + ,43 + ,26 + ,1439 + ,165446 + ,33 + ,57 + ,69 + ,25 + ,1764 + ,237213 + ,84 + ,74 + ,78 + ,38 + ,1495 + ,173326 + ,88 + ,49 + ,86 + ,44 + ,1373 + ,133131 + ,55 + ,52 + ,44 + ,30 + ,2187 + ,258873 + ,60 + ,88 + ,104 + ,40 + ,1491 + ,180083 + ,66 + ,36 + ,63 + ,34 + ,4041 + ,324799 + ,154 + ,108 + ,158 + ,47 + ,1706 + ,230964 + ,53 + ,43 + ,102 + ,30 + ,2152 + ,236785 + ,119 + ,75 + ,77 + ,31 + ,1036 + ,135473 + ,41 + ,32 + ,82 + ,23 + ,1882 + ,202925 + ,61 + ,44 + ,115 + ,36 + ,1929 + ,215147 + ,58 + ,85 + ,101 + ,36 + ,2242 + ,344297 + ,75 + ,86 + ,80 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+ ,44 + ,17 + ,16) + ,dim=c(6 + ,289) + ,dimnames=list(c('pageviews' + ,'RFC' + ,'logins' + ,'compendiumviews' + ,'bloggedcomputations' + ,'compendiumsreviewed') + ,1:289)) > y <- array(NA,dim=c(6,289),dimnames=list(c('pageviews','RFC','logins','compendiumviews','bloggedcomputations','compendiumsreviewed'),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 = '5' > 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] "bloggedcomputations" > x[,par1] [1] 79 58 60 108 49 0 121 1 20 43 69 78 86 44 104 63 158 102 [19] 77 82 115 101 80 50 83 123 73 81 105 47 105 94 44 114 38 107 [37] 30 71 84 0 59 33 42 96 106 56 57 59 39 34 76 20 91 115 [55] 85 76 8 79 21 30 76 101 94 27 92 123 75 128 105 55 56 41 [73] 72 67 75 114 118 77 22 66 69 105 116 88 73 99 62 53 118 30 [91] 100 49 24 67 46 57 75 135 68 124 33 98 58 68 81 131 110 37 [109] 130 93 118 39 13 74 81 109 151 51 28 40 56 27 37 83 54 27 [127] 28 59 133 12 0 106 23 44 71 116 4 62 12 18 14 60 7 98 [145] 64 29 32 25 16 48 100 46 45 129 130 136 59 25 32 63 95 14 [163] 36 113 47 92 70 19 50 41 91 111 41 120 135 27 87 25 131 45 [181] 29 58 4 47 109 7 12 0 37 37 46 15 42 7 54 54 14 16 [199] 33 32 21 15 38 22 28 10 31 32 32 43 27 37 20 32 0 5 [217] 26 10 27 11 29 25 55 23 5 43 23 34 36 35 0 37 28 16 [235] 26 38 23 22 30 16 18 28 32 21 23 29 50 12 21 18 27 41 [253] 13 12 21 8 26 27 13 16 2 42 5 37 17 38 37 29 32 35 [271] 17 20 7 46 24 40 3 10 37 17 28 19 29 8 10 15 15 28 [289] 17 > 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 7 8 10 11 12 13 14 15 16 17 18 19 20 21 6 1 1 1 2 3 4 3 4 1 5 3 3 4 5 4 3 2 4 5 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 3 5 2 4 3 8 7 6 4 1 8 3 2 2 2 9 4 2 2 4 42 43 44 45 46 47 48 49 50 51 53 54 55 56 57 58 59 60 62 63 3 3 3 2 4 3 1 2 3 1 1 3 2 3 2 3 4 2 2 2 64 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 1 1 2 2 2 1 2 1 2 1 3 3 2 1 2 1 3 1 2 1 85 86 87 88 91 92 93 94 95 96 98 99 100 101 102 104 105 106 107 108 1 1 1 1 2 2 1 2 1 1 2 1 2 2 1 1 4 2 1 1 109 110 111 113 114 115 116 118 120 121 123 124 128 129 130 131 133 135 136 151 2 1 1 1 2 2 2 3 1 1 2 1 1 1 2 2 1 2 1 1 158 1 > colnames(x) [1] "pageviews" "RFC" "logins" [4] "compendiumviews" "bloggedcomputations" "compendiumsreviewed" > colnames(x)[par1] [1] "bloggedcomputations" > x[,par1] [1] 79 58 60 108 49 0 121 1 20 43 69 78 86 44 104 63 158 102 [19] 77 82 115 101 80 50 83 123 73 81 105 47 105 94 44 114 38 107 [37] 30 71 84 0 59 33 42 96 106 56 57 59 39 34 76 20 91 115 [55] 85 76 8 79 21 30 76 101 94 27 92 123 75 128 105 55 56 41 [73] 72 67 75 114 118 77 22 66 69 105 116 88 73 99 62 53 118 30 [91] 100 49 24 67 46 57 75 135 68 124 33 98 58 68 81 131 110 37 [109] 130 93 118 39 13 74 81 109 151 51 28 40 56 27 37 83 54 27 [127] 28 59 133 12 0 106 23 44 71 116 4 62 12 18 14 60 7 98 [145] 64 29 32 25 16 48 100 46 45 129 130 136 59 25 32 63 95 14 [163] 36 113 47 92 70 19 50 41 91 111 41 120 135 27 87 25 131 45 [181] 29 58 4 47 109 7 12 0 37 37 46 15 42 7 54 54 14 16 [199] 33 32 21 15 38 22 28 10 31 32 32 43 27 37 20 32 0 5 [217] 26 10 27 11 29 25 55 23 5 43 23 34 36 35 0 37 28 16 [235] 26 38 23 22 30 16 18 28 32 21 23 29 50 12 21 18 27 41 [253] 13 12 21 8 26 27 13 16 2 42 5 37 17 38 37 29 32 35 [271] 17 20 7 46 24 40 3 10 37 17 28 19 29 8 10 15 15 28 [289] 17 > 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/1v2b91354877871.tab") + } + } > m Conditional inference tree with 9 terminal nodes Response: bloggedcomputations Inputs: pageviews, RFC, logins, compendiumviews, compendiumsreviewed Number of observations: 289 1) RFC <= 141722; criterion = 1, statistic = 198.82 2) RFC <= 99466; criterion = 1, statistic = 70.54 3) pageviews <= 642; criterion = 1, statistic = 47.331 4)* weights = 37 3) pageviews > 642 5) pageviews <= 1335; criterion = 0.998, statistic = 12.202 6)* weights = 71 5) pageviews > 1335 7)* weights = 9 2) RFC > 99466 8) compendiumsreviewed <= 28; criterion = 0.999, statistic = 13.109 9)* weights = 44 8) compendiumsreviewed > 28 10)* weights = 10 1) RFC > 141722 11) RFC <= 220801; criterion = 1, statistic = 31.706 12) pageviews <= 1597; criterion = 0.997, statistic = 11.544 13)* weights = 32 12) pageviews > 1597 14)* weights = 34 11) RFC > 220801 15) compendiumsreviewed <= 31; criterion = 0.96, statistic = 6.989 16)* weights = 19 15) compendiumsreviewed > 31 17)* weights = 33 > postscript(file="/var/wessaorg/rcomp/tmp/21o6j1354877871.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/3e0se1354877871.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 79 59.53125 19.4687500 2 58 41.52273 16.4772727 3 60 59.53125 0.4687500 4 108 84.08824 23.9117647 5 49 41.52273 7.4772727 6 0 10.62162 -10.6216216 7 121 88.78947 32.2105263 8 1 10.62162 -9.6216216 9 20 41.52273 -21.5227273 10 43 59.53125 -16.5312500 11 69 59.53125 9.4687500 12 78 110.54545 -32.5454545 13 86 59.53125 26.4687500 14 44 69.50000 -25.5000000 15 104 110.54545 -6.5454545 16 63 59.53125 3.4687500 17 158 110.54545 47.4545455 18 102 88.78947 13.2105263 19 77 88.78947 -11.7894737 20 82 41.52273 40.4772727 21 115 84.08824 30.9117647 22 101 84.08824 16.9117647 23 80 88.78947 -8.7894737 24 50 59.53125 -9.5312500 25 83 69.50000 13.5000000 26 123 84.08824 38.9117647 27 73 84.08824 -11.0882353 28 81 88.78947 -7.7894737 29 105 110.54545 -5.5454545 30 47 41.52273 5.4772727 31 105 110.54545 -5.5454545 32 94 84.08824 9.9117647 33 44 59.53125 -15.5312500 34 114 110.54545 3.4545455 35 38 24.60563 13.3943662 36 107 110.54545 -3.5454545 37 30 41.52273 -11.5227273 38 71 84.08824 -13.0882353 39 84 110.54545 -26.5454545 40 0 10.62162 -10.6216216 41 59 59.53125 -0.5312500 42 33 41.52273 -8.5227273 43 42 39.66667 2.3333333 44 96 110.54545 -14.5454545 45 106 88.78947 17.2105263 46 56 59.53125 -3.5312500 47 57 84.08824 -27.0882353 48 59 59.53125 -0.5312500 49 39 41.52273 -2.5227273 50 34 69.50000 -35.5000000 51 76 110.54545 -34.5454545 52 20 24.60563 -4.6056338 53 91 84.08824 6.9117647 54 115 110.54545 4.4545455 55 85 84.08824 0.9117647 56 76 59.53125 16.4687500 57 8 10.62162 -2.6216216 58 79 110.54545 -31.5454545 59 21 24.60563 -3.6056338 60 30 41.52273 -11.5227273 61 76 84.08824 -8.0882353 62 101 110.54545 -9.5454545 63 94 88.78947 5.2105263 64 27 41.52273 -14.5227273 65 92 88.78947 3.2105263 66 123 110.54545 12.4545455 67 75 84.08824 -9.0882353 68 128 110.54545 17.4545455 69 105 88.78947 16.2105263 70 55 84.08824 -29.0882353 71 56 84.08824 -28.0882353 72 41 59.53125 -18.5312500 73 72 59.53125 12.4687500 74 67 59.53125 7.4687500 75 75 84.08824 -9.0882353 76 114 84.08824 29.9117647 77 118 41.52273 76.4772727 78 77 84.08824 -7.0882353 79 22 24.60563 -2.6056338 80 66 84.08824 -18.0882353 81 69 59.53125 9.4687500 82 105 59.53125 45.4687500 83 116 110.54545 5.4545455 84 88 88.78947 -0.7894737 85 73 59.53125 13.4687500 86 99 69.50000 29.5000000 87 62 59.53125 2.4687500 88 53 41.52273 11.4772727 89 118 110.54545 7.4545455 90 30 59.53125 -29.5312500 91 100 84.08824 15.9117647 92 49 24.60563 24.3943662 93 24 41.52273 -17.5227273 94 67 69.50000 -2.5000000 95 46 41.52273 4.4772727 96 57 88.78947 -31.7894737 97 75 110.54545 -35.5454545 98 135 84.08824 50.9117647 99 68 84.08824 -16.0882353 100 124 84.08824 39.9117647 101 33 41.52273 -8.5227273 102 98 84.08824 13.9117647 103 58 88.78947 -30.7894737 104 68 59.53125 8.4687500 105 81 59.53125 21.4687500 106 131 88.78947 42.2105263 107 110 110.54545 -0.5454545 108 37 24.60563 12.3943662 109 130 88.78947 41.2105263 110 93 110.54545 -17.5454545 111 118 110.54545 7.4545455 112 39 59.53125 -20.5312500 113 13 10.62162 2.3783784 114 74 84.08824 -10.0882353 115 81 59.53125 21.4687500 116 109 84.08824 24.9117647 117 151 110.54545 40.4545455 118 51 84.08824 -33.0882353 119 28 24.60563 3.3943662 120 40 39.66667 0.3333333 121 56 41.52273 14.4772727 122 27 24.60563 2.3943662 123 37 41.52273 -4.5227273 124 83 69.50000 13.5000000 125 54 88.78947 -34.7894737 126 27 24.60563 2.3943662 127 28 41.52273 -13.5227273 128 59 41.52273 17.4772727 129 133 110.54545 22.4545455 130 12 10.62162 1.3783784 131 0 24.60563 -24.6056338 132 106 84.08824 21.9117647 133 23 10.62162 12.3783784 134 44 41.52273 2.4772727 135 71 69.50000 1.5000000 136 116 88.78947 27.2105263 137 4 10.62162 -6.6216216 138 62 84.08824 -22.0882353 139 12 10.62162 1.3783784 140 18 10.62162 7.3783784 141 14 24.60563 -10.6056338 142 60 41.52273 18.4772727 143 7 24.60563 -17.6056338 144 98 110.54545 -12.5454545 145 64 84.08824 -20.0882353 146 29 39.66667 -10.6666667 147 32 69.50000 -37.5000000 148 25 24.60563 0.3943662 149 16 10.62162 5.3783784 150 48 59.53125 -11.5312500 151 100 110.54545 -10.5454545 152 46 59.53125 -13.5312500 153 45 41.52273 3.4772727 154 129 110.54545 18.4545455 155 130 110.54545 19.4545455 156 136 110.54545 25.4545455 157 59 59.53125 -0.5312500 158 25 24.60563 0.3943662 159 32 24.60563 7.3943662 160 63 88.78947 -25.7894737 161 95 110.54545 -15.5454545 162 14 24.60563 -10.6056338 163 36 41.52273 -5.5227273 164 113 110.54545 2.4545455 165 47 69.50000 -22.5000000 166 92 59.53125 32.4687500 167 70 84.08824 -14.0882353 168 19 41.52273 -22.5227273 169 50 41.52273 8.4772727 170 41 88.78947 -47.7894737 171 91 88.78947 2.2105263 172 111 84.08824 26.9117647 173 41 84.08824 -43.0882353 174 120 110.54545 9.4545455 175 135 69.50000 65.5000000 176 27 41.52273 -14.5227273 177 87 84.08824 2.9117647 178 25 24.60563 0.3943662 179 131 110.54545 20.4545455 180 45 41.52273 3.4772727 181 29 24.60563 4.3943662 182 58 39.66667 18.3333333 183 4 10.62162 -6.6216216 184 47 84.08824 -37.0882353 185 109 110.54545 -1.5454545 186 7 10.62162 -3.6216216 187 12 10.62162 1.3783784 188 0 10.62162 -10.6216216 189 37 41.52273 -4.5227273 190 37 24.60563 12.3943662 191 46 59.53125 -13.5312500 192 15 24.60563 -9.6056338 193 42 39.66667 2.3333333 194 7 24.60563 -17.6056338 195 54 41.52273 12.4772727 196 54 41.52273 12.4772727 197 14 24.60563 -10.6056338 198 16 10.62162 5.3783784 199 33 41.52273 -8.5227273 200 32 24.60563 7.3943662 201 21 24.60563 -3.6056338 202 15 10.62162 4.3783784 203 38 41.52273 -3.5227273 204 22 41.52273 -19.5227273 205 28 39.66667 -11.6666667 206 10 10.62162 -0.6216216 207 31 24.60563 6.3943662 208 32 59.53125 -27.5312500 209 32 41.52273 -9.5227273 210 43 41.52273 1.4772727 211 27 24.60563 2.3943662 212 37 41.52273 -4.5227273 213 20 10.62162 9.3783784 214 32 24.60563 7.3943662 215 0 24.60563 -24.6056338 216 5 24.60563 -19.6056338 217 26 24.60563 1.3943662 218 10 24.60563 -14.6056338 219 27 24.60563 2.3943662 220 11 10.62162 0.3783784 221 29 24.60563 4.3943662 222 25 41.52273 -16.5227273 223 55 39.66667 15.3333333 224 23 24.60563 -1.6056338 225 5 10.62162 -5.6216216 226 43 41.52273 1.4772727 227 23 24.60563 -1.6056338 228 34 24.60563 9.3943662 229 36 24.60563 11.3943662 230 35 39.66667 -4.6666667 231 0 10.62162 -10.6216216 232 37 24.60563 12.3943662 233 28 39.66667 -11.6666667 234 16 24.60563 -8.6056338 235 26 24.60563 1.3943662 236 38 24.60563 13.3943662 237 23 59.53125 -36.5312500 238 22 24.60563 -2.6056338 239 30 24.60563 5.3943662 240 16 10.62162 5.3783784 241 18 24.60563 -6.6056338 242 28 41.52273 -13.5227273 243 32 24.60563 7.3943662 244 21 10.62162 10.3783784 245 23 24.60563 -1.6056338 246 29 24.60563 4.3943662 247 50 41.52273 8.4772727 248 12 24.60563 -12.6056338 249 21 24.60563 -3.6056338 250 18 10.62162 7.3783784 251 27 59.53125 -32.5312500 252 41 41.52273 -0.5227273 253 13 10.62162 2.3783784 254 12 24.60563 -12.6056338 255 21 24.60563 -3.6056338 256 8 10.62162 -2.6216216 257 26 10.62162 15.3783784 258 27 24.60563 2.3943662 259 13 10.62162 2.3783784 260 16 10.62162 5.3783784 261 2 10.62162 -8.6216216 262 42 24.60563 17.3943662 263 5 10.62162 -5.6216216 264 37 24.60563 12.3943662 265 17 10.62162 6.3783784 266 38 24.60563 13.3943662 267 37 24.60563 12.3943662 268 29 24.60563 4.3943662 269 32 41.52273 -9.5227273 270 35 24.60563 10.3943662 271 17 41.52273 -24.5227273 272 20 24.60563 -4.6056338 273 7 24.60563 -17.6056338 274 46 41.52273 4.4772727 275 24 24.60563 -0.6056338 276 40 24.60563 15.3943662 277 3 10.62162 -7.6216216 278 10 10.62162 -0.6216216 279 37 24.60563 12.3943662 280 17 24.60563 -7.6056338 281 28 24.60563 3.3943662 282 19 24.60563 -5.6056338 283 29 24.60563 4.3943662 284 8 10.62162 -2.6216216 285 10 10.62162 -0.6216216 286 15 24.60563 -9.6056338 287 15 24.60563 -9.6056338 288 28 24.60563 3.3943662 289 17 24.60563 -7.6056338 > 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/4o89b1354877871.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5e7mr1354877871.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6cga01354877871.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/wessaorg/rcomp/tmp/7nxbp1354877871.tab") + } > > try(system("convert tmp/21o6j1354877871.ps tmp/21o6j1354877871.png",intern=TRUE)) character(0) > try(system("convert tmp/3e0se1354877871.ps tmp/3e0se1354877871.png",intern=TRUE)) character(0) > try(system("convert tmp/4o89b1354877871.ps tmp/4o89b1354877871.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.218 0.394 7.594