R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing 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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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > 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 = '2' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '2' > #'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 objects 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 is masked from 'package:survival': untangle.specials The following objects 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] "Gender" > x[,par1] [1] 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 1 0 0 0 [38] 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 0 [75] 0 1 1 1 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0 0 1 1 1 [112] 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 [149] 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 [186] 0 0 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 [223] 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 [260] 1 0 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 > 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 1 135 153 > colnames(x) [1] "Pop" "Gender" "Connected" "Separate" "Learning" "Software" [7] "Happiness" > colnames(x)[par1] [1] "Gender" > x[,par1] [1] 1 1 1 0 1 1 1 1 1 1 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 1 1 0 1 0 1 1 0 0 0 [38] 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 0 1 0 1 1 1 0 1 0 0 1 1 1 0 1 0 1 0 0 1 1 0 [75] 0 1 1 1 0 0 0 1 0 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 0 1 0 1 0 0 1 1 1 [112] 1 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 [149] 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 [186] 0 0 1 1 1 1 1 0 1 1 0 0 0 0 0 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 0 0 1 0 1 0 [223] 0 0 0 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 [260] 1 0 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 1 > 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/1tbnp1386667919.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: Gender Inputs: Pop, Connected, Separate, Learning, Software, Happiness Number of observations: 288 1) Happiness <= 13; criterion = 0.998, statistic = 13.154 2)* weights = 134 1) Happiness > 13 3)* weights = 154 > postscript(file="/var/fisher/rcomp/tmp/2t4bq1386667919.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/3bqjh1386667919.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 1 0.6363636 0.3636364 2 1 0.6363636 0.3636364 3 1 0.4104478 0.5895522 4 0 0.4104478 -0.4104478 5 1 0.6363636 0.3636364 6 1 0.6363636 0.3636364 7 1 0.6363636 0.3636364 8 1 0.6363636 0.3636364 9 1 0.6363636 0.3636364 10 1 0.6363636 0.3636364 11 0 0.6363636 -0.6363636 12 1 0.6363636 0.3636364 13 0 0.4104478 -0.4104478 14 1 0.6363636 0.3636364 15 1 0.6363636 0.3636364 16 0 0.6363636 -0.6363636 17 0 0.6363636 -0.6363636 18 1 0.6363636 0.3636364 19 0 0.6363636 -0.6363636 20 1 0.6363636 0.3636364 21 0 0.6363636 -0.6363636 22 1 0.4104478 0.5895522 23 1 0.6363636 0.3636364 24 1 0.4104478 0.5895522 25 0 0.6363636 -0.6363636 26 1 0.4104478 0.5895522 27 0 0.6363636 -0.6363636 28 1 0.6363636 0.3636364 29 1 0.6363636 0.3636364 30 0 0.4104478 -0.4104478 31 1 0.6363636 0.3636364 32 0 0.4104478 -0.4104478 33 1 0.6363636 0.3636364 34 1 0.6363636 0.3636364 35 0 0.6363636 -0.6363636 36 0 0.6363636 -0.6363636 37 0 0.4104478 -0.4104478 38 0 0.6363636 -0.6363636 39 1 0.6363636 0.3636364 40 0 0.4104478 -0.4104478 41 0 0.6363636 -0.6363636 42 1 0.6363636 0.3636364 43 0 0.6363636 -0.6363636 44 0 0.4104478 -0.4104478 45 1 0.6363636 0.3636364 46 1 0.4104478 0.5895522 47 1 0.6363636 0.3636364 48 1 0.6363636 0.3636364 49 1 0.6363636 0.3636364 50 0 0.4104478 -0.4104478 51 1 0.6363636 0.3636364 52 0 0.6363636 -0.6363636 53 0 0.4104478 -0.4104478 54 1 0.4104478 0.5895522 55 0 0.4104478 -0.4104478 56 1 0.6363636 0.3636364 57 1 0.6363636 0.3636364 58 1 0.6363636 0.3636364 59 0 0.4104478 -0.4104478 60 1 0.4104478 0.5895522 61 0 0.4104478 -0.4104478 62 0 0.4104478 -0.4104478 63 1 0.4104478 0.5895522 64 1 0.6363636 0.3636364 65 1 0.6363636 0.3636364 66 0 0.4104478 -0.4104478 67 1 0.4104478 0.5895522 68 0 0.4104478 -0.4104478 69 1 0.6363636 0.3636364 70 0 0.6363636 -0.6363636 71 0 0.6363636 -0.6363636 72 1 0.6363636 0.3636364 73 1 0.6363636 0.3636364 74 0 0.6363636 -0.6363636 75 0 0.4104478 -0.4104478 76 1 0.4104478 0.5895522 77 1 0.6363636 0.3636364 78 1 0.4104478 0.5895522 79 0 0.6363636 -0.6363636 80 0 0.4104478 -0.4104478 81 0 0.6363636 -0.6363636 82 1 0.6363636 0.3636364 83 0 0.6363636 -0.6363636 84 1 0.6363636 0.3636364 85 1 0.6363636 0.3636364 86 0 0.6363636 -0.6363636 87 1 0.6363636 0.3636364 88 1 0.4104478 0.5895522 89 1 0.4104478 0.5895522 90 1 0.6363636 0.3636364 91 1 0.4104478 0.5895522 92 1 0.6363636 0.3636364 93 1 0.6363636 0.3636364 94 1 0.4104478 0.5895522 95 1 0.6363636 0.3636364 96 1 0.4104478 0.5895522 97 1 0.6363636 0.3636364 98 0 0.6363636 -0.6363636 99 0 0.6363636 -0.6363636 100 1 0.6363636 0.3636364 101 0 0.4104478 -0.4104478 102 1 0.6363636 0.3636364 103 0 0.4104478 -0.4104478 104 1 0.6363636 0.3636364 105 0 0.4104478 -0.4104478 106 1 0.6363636 0.3636364 107 0 0.6363636 -0.6363636 108 0 0.4104478 -0.4104478 109 1 0.4104478 0.5895522 110 1 0.6363636 0.3636364 111 1 0.4104478 0.5895522 112 1 0.4104478 0.5895522 113 1 0.6363636 0.3636364 114 0 0.6363636 -0.6363636 115 1 0.6363636 0.3636364 116 1 0.6363636 0.3636364 117 1 0.6363636 0.3636364 118 1 0.6363636 0.3636364 119 0 0.4104478 -0.4104478 120 1 0.6363636 0.3636364 121 1 0.6363636 0.3636364 122 1 0.6363636 0.3636364 123 1 0.6363636 0.3636364 124 1 0.4104478 0.5895522 125 0 0.6363636 -0.6363636 126 1 0.6363636 0.3636364 127 1 0.6363636 0.3636364 128 1 0.6363636 0.3636364 129 0 0.4104478 -0.4104478 130 0 0.4104478 -0.4104478 131 1 0.6363636 0.3636364 132 0 0.6363636 -0.6363636 133 0 0.6363636 -0.6363636 134 1 0.6363636 0.3636364 135 0 0.4104478 -0.4104478 136 0 0.6363636 -0.6363636 137 1 0.4104478 0.5895522 138 1 0.6363636 0.3636364 139 0 0.4104478 -0.4104478 140 1 0.6363636 0.3636364 141 0 0.6363636 -0.6363636 142 1 0.6363636 0.3636364 143 0 0.6363636 -0.6363636 144 1 0.6363636 0.3636364 145 1 0.6363636 0.3636364 146 0 0.4104478 -0.4104478 147 0 0.4104478 -0.4104478 148 0 0.4104478 -0.4104478 149 1 0.6363636 0.3636364 150 1 0.4104478 0.5895522 151 0 0.6363636 -0.6363636 152 1 0.4104478 0.5895522 153 1 0.4104478 0.5895522 154 1 0.4104478 0.5895522 155 1 0.6363636 0.3636364 156 1 0.6363636 0.3636364 157 1 0.4104478 0.5895522 158 1 0.4104478 0.5895522 159 0 0.4104478 -0.4104478 160 0 0.6363636 -0.6363636 161 0 0.6363636 -0.6363636 162 1 0.4104478 0.5895522 163 0 0.4104478 -0.4104478 164 0 0.6363636 -0.6363636 165 1 0.4104478 0.5895522 166 1 0.4104478 0.5895522 167 0 0.4104478 -0.4104478 168 0 0.4104478 -0.4104478 169 1 0.6363636 0.3636364 170 0 0.4104478 -0.4104478 171 1 0.4104478 0.5895522 172 1 0.6363636 0.3636364 173 0 0.4104478 -0.4104478 174 1 0.4104478 0.5895522 175 0 0.4104478 -0.4104478 176 1 0.6363636 0.3636364 177 1 0.4104478 0.5895522 178 1 0.6363636 0.3636364 179 0 0.4104478 -0.4104478 180 0 0.6363636 -0.6363636 181 0 0.6363636 -0.6363636 182 1 0.6363636 0.3636364 183 1 0.4104478 0.5895522 184 0 0.6363636 -0.6363636 185 1 0.6363636 0.3636364 186 0 0.4104478 -0.4104478 187 0 0.4104478 -0.4104478 188 1 0.4104478 0.5895522 189 1 0.6363636 0.3636364 190 1 0.6363636 0.3636364 191 1 0.4104478 0.5895522 192 1 0.6363636 0.3636364 193 0 0.4104478 -0.4104478 194 1 0.6363636 0.3636364 195 1 0.6363636 0.3636364 196 0 0.4104478 -0.4104478 197 0 0.4104478 -0.4104478 198 0 0.6363636 -0.6363636 199 0 0.6363636 -0.6363636 200 0 0.4104478 -0.4104478 201 1 0.6363636 0.3636364 202 1 0.6363636 0.3636364 203 1 0.6363636 0.3636364 204 0 0.6363636 -0.6363636 205 0 0.4104478 -0.4104478 206 0 0.6363636 -0.6363636 207 1 0.4104478 0.5895522 208 0 0.6363636 -0.6363636 209 0 0.4104478 -0.4104478 210 0 0.4104478 -0.4104478 211 1 0.4104478 0.5895522 212 0 0.6363636 -0.6363636 213 1 0.6363636 0.3636364 214 1 0.6363636 0.3636364 215 0 0.4104478 -0.4104478 216 1 0.6363636 0.3636364 217 0 0.4104478 -0.4104478 218 0 0.6363636 -0.6363636 219 1 0.4104478 0.5895522 220 0 0.6363636 -0.6363636 221 1 0.6363636 0.3636364 222 0 0.6363636 -0.6363636 223 0 0.4104478 -0.4104478 224 0 0.4104478 -0.4104478 225 0 0.4104478 -0.4104478 226 1 0.6363636 0.3636364 227 0 0.4104478 -0.4104478 228 0 0.6363636 -0.6363636 229 1 0.4104478 0.5895522 230 1 0.6363636 0.3636364 231 0 0.4104478 -0.4104478 232 0 0.4104478 -0.4104478 233 0 0.4104478 -0.4104478 234 1 0.4104478 0.5895522 235 0 0.4104478 -0.4104478 236 0 0.4104478 -0.4104478 237 0 0.4104478 -0.4104478 238 0 0.4104478 -0.4104478 239 0 0.4104478 -0.4104478 240 0 0.4104478 -0.4104478 241 0 0.4104478 -0.4104478 242 1 0.6363636 0.3636364 243 1 0.4104478 0.5895522 244 1 0.4104478 0.5895522 245 0 0.6363636 -0.6363636 246 0 0.4104478 -0.4104478 247 1 0.4104478 0.5895522 248 1 0.4104478 0.5895522 249 0 0.4104478 -0.4104478 250 0 0.4104478 -0.4104478 251 0 0.4104478 -0.4104478 252 1 0.4104478 0.5895522 253 1 0.6363636 0.3636364 254 1 0.4104478 0.5895522 255 1 0.6363636 0.3636364 256 0 0.6363636 -0.6363636 257 0 0.4104478 -0.4104478 258 0 0.6363636 -0.6363636 259 0 0.4104478 -0.4104478 260 1 0.4104478 0.5895522 261 0 0.4104478 -0.4104478 262 0 0.4104478 -0.4104478 263 1 0.4104478 0.5895522 264 0 0.4104478 -0.4104478 265 0 0.4104478 -0.4104478 266 1 0.4104478 0.5895522 267 1 0.4104478 0.5895522 268 0 0.4104478 -0.4104478 269 1 0.4104478 0.5895522 270 0 0.4104478 -0.4104478 271 0 0.4104478 -0.4104478 272 1 0.6363636 0.3636364 273 0 0.4104478 -0.4104478 274 1 0.4104478 0.5895522 275 0 0.4104478 -0.4104478 276 0 0.6363636 -0.6363636 277 0 0.6363636 -0.6363636 278 0 0.6363636 -0.6363636 279 1 0.6363636 0.3636364 280 0 0.4104478 -0.4104478 281 0 0.6363636 -0.6363636 282 1 0.6363636 0.3636364 283 0 0.4104478 -0.4104478 284 1 0.4104478 0.5895522 285 0 0.6363636 -0.6363636 286 0 0.6363636 -0.6363636 287 1 0.4104478 0.5895522 288 1 0.6363636 0.3636364 > 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/4khpk1386667919.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/5u6iw1386667919.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/6bct91386667919.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/7h4w41386667919.tab") + } > > try(system("convert tmp/2t4bq1386667919.ps tmp/2t4bq1386667919.png",intern=TRUE)) character(0) > try(system("convert tmp/3bqjh1386667919.ps tmp/3bqjh1386667919.png",intern=TRUE)) character(0) > try(system("convert tmp/4khpk1386667919.ps tmp/4khpk1386667919.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 15.866 2.755 18.608