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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+ ,19 + ,76 + ,39 + ,300 + ,30 + ,0 + ,19 + ,14 + ,43 + ,29 + ,450 + ,36 + ,3 + ,29 + ,11 + ,39 + ,16 + ,183 + ,34 + ,1 + ,8 + ,4 + ,14 + ,27 + ,238 + ,36 + ,0 + ,10 + ,16 + ,61 + ,21 + ,165 + ,34 + ,0 + ,15 + ,20 + ,71 + ,19 + ,234 + ,37 + ,1 + ,15 + ,12 + ,44 + ,35 + ,176 + ,46 + ,0 + ,28 + ,15 + ,60 + ,14 + ,329 + ,44 + ,0 + ,17 + ,16 + ,64) + ,dim=c(7 + ,289) + ,dimnames=list(c('logins' + ,'compendium_views_info' + ,'compendium_views_pr' + ,'shared_compendiums' + ,'blogged_computations' + ,'compendiums_reviewed' + ,'feedback_messages_p1') + ,1:289)) > y <- array(NA,dim=c(7,289),dimnames=list(c('logins','compendium_views_info','compendium_views_pr','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1'),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 = '7' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '7' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > 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] "feedback_messages_p1" > x[,par1] [1] 115 109 146 116 68 101 96 67 44 100 93 140 166 99 139 130 181 116 [19] 116 88 139 135 108 89 156 129 118 118 125 95 126 135 154 165 113 127 [37] 52 121 136 0 108 46 54 124 115 128 80 97 104 59 125 82 149 149 [55] 122 118 12 144 67 52 108 166 80 60 107 127 107 146 84 141 123 111 [73] 98 105 135 107 85 155 88 155 104 132 127 108 129 116 122 85 147 99 [91] 87 28 90 109 78 111 158 141 122 124 93 124 112 108 99 117 199 78 [109] 91 158 126 122 71 75 115 119 124 72 91 45 78 39 68 119 117 39 [127] 50 88 155 0 36 123 32 99 136 117 0 88 39 25 52 75 71 124 [145] 151 71 145 87 27 131 162 165 54 159 147 170 119 49 104 120 150 112 [163] 59 136 107 130 115 107 75 71 120 116 79 150 156 51 118 71 144 47 [181] 28 68 0 110 147 0 15 4 64 111 85 68 40 80 88 48 76 51 [199] 67 59 61 76 60 68 71 76 62 61 67 88 30 64 68 64 91 88 [217] 52 49 62 61 76 88 66 71 68 48 25 68 41 90 66 54 59 60 [235] 77 68 72 67 64 63 59 84 64 56 54 67 58 59 40 22 83 81 [253] 2 72 61 15 32 62 58 36 59 68 21 55 54 55 72 41 61 67 [271] 76 64 3 63 40 69 48 8 52 66 76 43 39 14 61 71 44 60 [289] 64 > 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 2 3 4 8 12 14 15 21 22 25 27 28 30 32 36 39 40 41 43 5 1 1 1 1 1 1 2 1 1 2 1 2 1 2 2 4 3 2 1 44 45 46 47 48 49 50 51 52 54 55 56 58 59 60 61 62 63 64 66 2 1 1 1 3 2 1 2 5 5 2 1 2 7 4 6 3 2 7 3 67 68 69 71 72 75 76 77 78 79 80 81 82 83 84 85 87 88 89 90 7 10 1 8 4 3 6 1 3 1 3 1 1 1 2 3 2 8 1 2 91 93 95 96 97 98 99 100 101 104 105 107 108 109 110 111 112 113 115 116 3 2 1 1 1 1 4 1 1 3 1 5 5 2 1 3 2 1 4 5 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 135 136 139 140 3 4 3 2 1 4 2 5 2 2 3 1 2 2 1 1 3 3 2 1 141 144 145 146 147 149 150 151 154 155 156 158 159 162 165 166 170 181 199 2 2 1 2 3 2 2 1 1 3 2 2 1 1 2 2 1 1 1 > colnames(x) [1] "logins" "compendium_views_info" "compendium_views_pr" [4] "shared_compendiums" "blogged_computations" "compendiums_reviewed" [7] "feedback_messages_p1" > colnames(x)[par1] [1] "feedback_messages_p1" > x[,par1] [1] 115 109 146 116 68 101 96 67 44 100 93 140 166 99 139 130 181 116 [19] 116 88 139 135 108 89 156 129 118 118 125 95 126 135 154 165 113 127 [37] 52 121 136 0 108 46 54 124 115 128 80 97 104 59 125 82 149 149 [55] 122 118 12 144 67 52 108 166 80 60 107 127 107 146 84 141 123 111 [73] 98 105 135 107 85 155 88 155 104 132 127 108 129 116 122 85 147 99 [91] 87 28 90 109 78 111 158 141 122 124 93 124 112 108 99 117 199 78 [109] 91 158 126 122 71 75 115 119 124 72 91 45 78 39 68 119 117 39 [127] 50 88 155 0 36 123 32 99 136 117 0 88 39 25 52 75 71 124 [145] 151 71 145 87 27 131 162 165 54 159 147 170 119 49 104 120 150 112 [163] 59 136 107 130 115 107 75 71 120 116 79 150 156 51 118 71 144 47 [181] 28 68 0 110 147 0 15 4 64 111 85 68 40 80 88 48 76 51 [199] 67 59 61 76 60 68 71 76 62 61 67 88 30 64 68 64 91 88 [217] 52 49 62 61 76 88 66 71 68 48 25 68 41 90 66 54 59 60 [235] 77 68 72 67 64 63 59 84 64 56 54 67 58 59 40 22 83 81 [253] 2 72 61 15 32 62 58 36 59 68 21 55 54 55 72 41 61 67 [271] 76 64 3 63 40 69 48 8 52 66 76 43 39 14 61 71 44 60 [289] 64 > 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/19lqv1354817500.tab") + } + } > m Conditional inference tree with 15 terminal nodes Response: feedback_messages_p1 Inputs: logins, compendium_views_info, compendium_views_pr, shared_compendiums, blogged_computations, compendiums_reviewed Number of observations: 289 1) compendiums_reviewed <= 24; criterion = 1, statistic = 278.87 2) compendiums_reviewed <= 12; criterion = 1, statistic = 155.057 3) compendiums_reviewed <= 5; criterion = 1, statistic = 34.821 4)* weights = 13 3) compendiums_reviewed > 5 5) compendiums_reviewed <= 8; criterion = 1, statistic = 18.901 6)* weights = 8 5) compendiums_reviewed > 8 7)* weights = 16 2) compendiums_reviewed > 12 8) compendiums_reviewed <= 17; criterion = 1, statistic = 107.104 9) compendiums_reviewed <= 14; criterion = 1, statistic = 42.306 10)* weights = 20 9) compendiums_reviewed > 14 11) compendiums_reviewed <= 15; criterion = 0.995, statistic = 11.322 12)* weights = 9 11) compendiums_reviewed > 15 13)* weights = 38 8) compendiums_reviewed > 17 14) compendiums_reviewed <= 21; criterion = 1, statistic = 37.025 15) compendiums_reviewed <= 18; criterion = 1, statistic = 17.655 16)* weights = 14 15) compendiums_reviewed > 18 17)* weights = 21 14) compendiums_reviewed > 21 18)* weights = 23 1) compendiums_reviewed > 24 19) compendiums_reviewed <= 35; criterion = 1, statistic = 102.185 20) compendiums_reviewed <= 30; criterion = 1, statistic = 59.866 21) compendiums_reviewed <= 26; criterion = 1, statistic = 26.484 22)* weights = 11 21) compendiums_reviewed > 26 23) compendiums_reviewed <= 28; criterion = 0.974, statistic = 8.094 24)* weights = 15 23) compendiums_reviewed > 28 25)* weights = 17 20) compendiums_reviewed > 30 26)* weights = 43 19) compendiums_reviewed > 35 27) compendiums_reviewed <= 40; criterion = 1, statistic = 17.988 28)* weights = 27 27) compendiums_reviewed > 40 29)* weights = 14 > postscript(file="/var/wessaorg/rcomp/tmp/2ghpb1354817500.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/3oqtb1354817500.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 1 115 111.588235 3.41176471 2 109 106.133333 2.86666667 3 146 141.407407 4.59259259 4 116 111.588235 4.41176471 5 68 85.260870 -17.26086957 6 101 95.000000 6.00000000 7 96 95.000000 1.00000000 8 67 70.000000 -3.00000000 9 44 40.375000 3.62500000 10 100 95.000000 5.00000000 11 93 95.000000 -2.00000000 12 140 141.407407 -1.40740741 13 166 165.285714 0.71428571 14 99 111.588235 -12.58823529 15 139 141.407407 -2.40740741 16 130 121.976744 8.02325581 17 181 165.285714 15.71428571 18 116 111.588235 4.41176471 19 116 121.976744 -5.97674419 20 88 85.260870 2.73913043 21 139 141.407407 -2.40740741 22 135 141.407407 -6.40740741 23 108 111.588235 -3.58823529 24 89 95.000000 -6.00000000 25 156 141.407407 14.59259259 26 129 121.976744 7.02325581 27 118 121.976744 -3.97674419 28 118 121.976744 -3.97674419 29 125 121.976744 3.02325581 30 95 95.000000 0.00000000 31 126 121.976744 4.02325581 32 135 121.976744 13.02325581 33 154 165.285714 -11.28571429 34 165 165.285714 -0.28571429 35 113 111.588235 1.41176471 36 127 121.976744 5.02325581 37 52 49.950000 2.05000000 38 121 121.976744 -0.97674419 39 136 141.407407 -5.40740741 40 0 5.615385 -5.61538462 41 108 106.133333 1.86666667 42 46 49.950000 -3.95000000 43 54 63.052632 -9.05263158 44 124 121.976744 2.02325581 45 115 111.588235 3.41176471 46 128 121.976744 6.02325581 47 80 76.095238 3.90476190 48 97 106.133333 -9.13333333 49 104 106.133333 -2.13333333 50 59 141.407407 -82.40740741 51 125 121.976744 3.02325581 52 82 95.000000 -13.00000000 53 149 141.407407 7.59259259 54 149 141.407407 7.59259259 55 122 121.976744 0.02325581 56 118 106.133333 11.86666667 57 12 5.615385 6.38461538 58 144 141.407407 2.59259259 59 67 70.000000 -3.00000000 60 52 49.950000 2.05000000 61 108 111.588235 -3.58823529 62 166 165.285714 0.71428571 63 80 76.095238 3.90476190 64 60 63.052632 -3.05263158 65 107 106.133333 0.86666667 66 127 121.976744 5.02325581 67 107 106.133333 0.86666667 68 146 141.407407 4.59259259 69 84 85.260870 -1.26086957 70 141 141.407407 -0.40740741 71 123 121.976744 1.02325581 72 111 111.588235 -0.58823529 73 98 95.000000 3.00000000 74 105 106.133333 -1.13333333 75 135 141.407407 -6.40740741 76 107 106.133333 0.86666667 77 85 85.260870 -0.26086957 78 155 141.407407 13.59259259 79 88 85.260870 2.73913043 80 155 141.407407 13.59259259 81 104 106.133333 -2.13333333 82 132 121.976744 10.02325581 83 127 121.976744 5.02325581 84 108 106.133333 1.86666667 85 129 121.976744 7.02325581 86 116 111.588235 4.41176471 87 122 121.976744 0.02325581 88 85 85.260870 -0.26086957 89 147 141.407407 5.59259259 90 99 95.000000 4.00000000 91 87 121.976744 -34.97674419 92 28 25.750000 2.25000000 93 90 85.260870 4.73913043 94 109 111.588235 -2.58823529 95 78 76.095238 1.90476190 96 111 111.588235 -0.58823529 97 158 165.285714 -7.28571429 98 141 141.407407 -0.40740741 99 122 121.976744 0.02325581 100 124 121.976744 2.02325581 101 93 95.000000 -2.00000000 102 124 121.976744 2.02325581 103 112 111.588235 0.41176471 104 108 106.133333 1.86666667 105 99 106.133333 -7.13333333 106 117 121.976744 -4.97674419 107 199 165.285714 33.71428571 108 78 76.095238 1.90476190 109 91 85.260870 5.73913043 110 158 165.285714 -7.28571429 111 126 121.976744 4.02325581 112 122 121.976744 0.02325581 113 71 76.095238 -5.09523810 114 75 76.095238 -1.09523810 115 115 121.976744 -6.97674419 116 119 121.976744 -2.97674419 117 124 121.976744 2.02325581 118 72 70.000000 2.00000000 119 91 85.260870 5.73913043 120 45 63.052632 -18.05263158 121 78 76.095238 1.90476190 122 39 40.375000 -1.37500000 123 68 63.052632 4.94736842 124 119 111.588235 7.41176471 125 117 121.976744 -4.97674419 126 39 40.375000 -1.37500000 127 50 49.950000 0.05000000 128 88 85.260870 2.73913043 129 155 165.285714 -10.28571429 130 0 5.615385 -5.61538462 131 36 40.375000 -4.37500000 132 123 121.976744 1.02325581 133 32 40.375000 -8.37500000 134 99 95.000000 4.00000000 135 136 141.407407 -5.40740741 136 117 121.976744 -4.97674419 137 0 5.615385 -5.61538462 138 88 85.260870 2.73913043 139 39 49.950000 -10.95000000 140 25 25.750000 -0.75000000 141 52 49.950000 2.05000000 142 75 76.095238 -1.09523810 143 71 70.000000 1.00000000 144 124 121.976744 2.02325581 145 151 141.407407 9.59259259 146 71 85.260870 -14.26086957 147 145 141.407407 3.59259259 148 87 85.260870 1.73913043 149 27 25.750000 1.25000000 150 131 121.976744 9.02325581 151 162 165.285714 -3.28571429 152 165 165.285714 -0.28571429 153 54 49.950000 4.05000000 154 159 165.285714 -6.28571429 155 147 141.407407 5.59259259 156 170 165.285714 4.71428571 157 119 121.976744 -2.97674419 158 49 49.950000 -0.95000000 159 104 106.133333 -2.13333333 160 120 121.976744 -1.97674419 161 150 141.407407 8.59259259 162 112 111.588235 0.41176471 163 59 63.052632 -4.05263158 164 136 141.407407 -5.40740741 165 107 111.588235 -4.58823529 166 130 121.976744 8.02325581 167 115 121.976744 -6.97674419 168 107 106.133333 0.86666667 169 75 76.095238 -1.09523810 170 71 70.000000 1.00000000 171 120 121.976744 -1.97674419 172 116 121.976744 -5.97674419 173 79 76.095238 2.90476190 174 150 141.407407 8.59259259 175 156 165.285714 -9.28571429 176 51 49.950000 1.05000000 177 118 121.976744 -3.97674419 178 71 70.000000 1.00000000 179 144 141.407407 2.59259259 180 47 49.950000 -2.95000000 181 28 25.750000 2.25000000 182 68 63.052632 4.94736842 183 0 5.615385 -5.61538462 184 110 111.588235 -1.58823529 185 147 141.407407 5.59259259 186 0 5.615385 -5.61538462 187 15 5.615385 9.38461538 188 4 5.615385 -1.61538462 189 64 63.052632 0.94736842 190 111 121.976744 -10.97674419 191 85 85.260870 -0.26086957 192 68 63.052632 4.94736842 193 40 40.375000 -0.37500000 194 80 85.260870 -5.26086957 195 88 85.260870 2.73913043 196 48 40.375000 7.62500000 197 76 76.095238 -0.09523810 198 51 49.950000 1.05000000 199 67 63.052632 3.94736842 200 59 58.111111 0.88888889 201 61 63.052632 -2.05263158 202 76 85.260870 -9.26086957 203 60 58.111111 1.88888889 204 68 63.052632 4.94736842 205 71 70.000000 1.00000000 206 76 76.095238 -0.09523810 207 62 63.052632 -1.05263158 208 61 63.052632 -2.05263158 209 67 70.000000 -3.00000000 210 88 85.260870 2.73913043 211 30 25.750000 4.25000000 212 64 63.052632 0.94736842 213 68 70.000000 -2.00000000 214 64 63.052632 0.94736842 215 91 85.260870 5.73913043 216 88 85.260870 2.73913043 217 52 49.950000 2.05000000 218 49 49.950000 -0.95000000 219 62 63.052632 -1.05263158 220 61 63.052632 -2.05263158 221 76 76.095238 -0.09523810 222 88 85.260870 2.73913043 223 66 63.052632 2.94736842 224 71 70.000000 1.00000000 225 68 63.052632 4.94736842 226 48 40.375000 7.62500000 227 25 25.750000 -0.75000000 228 68 63.052632 4.94736842 229 41 49.950000 -8.95000000 230 90 85.260870 4.73913043 231 66 63.052632 2.94736842 232 54 49.950000 4.05000000 233 59 58.111111 0.88888889 234 60 63.052632 -3.05263158 235 77 76.095238 0.90476190 236 68 70.000000 -2.00000000 237 72 70.000000 2.00000000 238 67 63.052632 3.94736842 239 64 63.052632 0.94736842 240 63 63.052632 -0.05263158 241 59 58.111111 0.88888889 242 84 76.095238 7.90476190 243 64 63.052632 0.94736842 244 56 49.950000 6.05000000 245 54 58.111111 -4.11111111 246 67 63.052632 3.94736842 247 58 58.111111 -0.11111111 248 59 58.111111 0.88888889 249 40 40.375000 -0.37500000 250 22 25.750000 -3.75000000 251 83 85.260870 -2.26086957 252 81 76.095238 4.90476190 253 2 5.615385 -3.61538462 254 72 70.000000 2.00000000 255 61 63.052632 -2.05263158 256 15 5.615385 9.38461538 257 32 40.375000 -8.37500000 258 62 63.052632 -1.05263158 259 58 63.052632 -5.05263158 260 36 40.375000 -4.37500000 261 59 63.052632 -4.05263158 262 68 63.052632 4.94736842 263 21 25.750000 -4.75000000 264 55 58.111111 -3.11111111 265 54 49.950000 4.05000000 266 55 49.950000 5.05000000 267 72 70.000000 2.00000000 268 41 40.375000 0.62500000 269 61 63.052632 -2.05263158 270 67 76.095238 -9.09523810 271 76 76.095238 -0.09523810 272 64 63.052632 0.94736842 273 3 5.615385 -2.61538462 274 63 63.052632 -0.05263158 275 40 40.375000 -0.37500000 276 69 76.095238 -7.09523810 277 48 40.375000 7.62500000 278 8 5.615385 2.38461538 279 52 49.950000 2.05000000 280 66 63.052632 2.94736842 281 76 76.095238 -0.09523810 282 43 49.950000 -6.95000000 283 39 40.375000 -1.37500000 284 14 5.615385 8.38461538 285 61 63.052632 -2.05263158 286 71 76.095238 -5.09523810 287 44 40.375000 3.62500000 288 60 58.111111 1.88888889 289 64 63.052632 0.94736842 > 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/4yo8z1354817500.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/55bet1354817501.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/6j4yi1354817501.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/7n6791354817501.tab") + } > > try(system("convert tmp/2ghpb1354817500.ps tmp/2ghpb1354817500.png",intern=TRUE)) character(0) > try(system("convert tmp/3oqtb1354817500.ps tmp/3oqtb1354817500.png",intern=TRUE)) character(0) > try(system("convert tmp/4yo8z1354817500.ps tmp/4yo8z1354817500.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.055 0.632 10.666