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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,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' + ,'Seperate' + ,'Learning' + ,'Software' + ,'Happines') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Seperate','Learning','Software','Happines'),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 = '10' > par2 = 'none' > par1 = '5' > par4 <- 'no' > par3 <- '10' > par2 <- 'none' > par1 <- '4' > 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] "Seperate" > x[,par1] [1] 38 32 35 33 37 29 31 36 35 38 31 34 35 38 37 33 32 38 38 32 33 31 38 39 32 [26] 32 35 37 33 33 31 32 31 37 30 33 31 33 31 33 32 33 32 33 28 35 39 34 38 32 [51] 38 30 33 38 32 35 34 34 36 34 28 34 35 35 31 37 35 27 40 37 36 38 39 41 27 [76] 30 37 31 31 27 36 37 33 34 31 39 34 32 33 36 32 41 28 30 36 35 31 34 36 36 [101] 35 37 28 39 32 35 39 35 42 34 33 41 34 32 40 40 35 36 37 27 39 38 31 33 32 [126] 39 36 33 33 32 37 30 38 29 22 35 35 34 35 34 37 35 23 31 27 36 31 32 39 37 [151] 38 39 34 31 37 36 32 38 26 26 33 39 30 33 25 38 37 31 37 35 25 28 35 33 30 [176] 31 37 36 30 36 32 28 36 34 31 28 36 36 40 33 37 32 38 31 37 33 30 30 31 32 [201] 34 36 37 36 33 33 33 44 39 32 35 25 35 34 35 39 33 36 32 32 36 32 34 33 35 [226] 30 38 34 33 32 31 30 27 31 30 32 35 28 33 35 35 32 21 20 34 32 34 32 33 33 [251] 37 32 34 30 30 38 36 32 34 33 27 32 34 29 35 27 33 38 36 33 39 29 32 34 38 [276] 17 35 32 34 36 31 35 29 22 41 36 42 33 > 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]) 17 20 21 22 23 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 44 1 1 1 2 1 3 2 8 8 5 15 24 35 35 26 30 25 21 20 14 4 4 2 1 > colnames(x) [1] "Pop" "Gender" "Connected" "Seperate" "Learning" "Software" [7] "Happines" > colnames(x)[par1] [1] "Seperate" > x[,par1] [1] 38 32 35 33 37 29 31 36 35 38 31 34 35 38 37 33 32 38 38 32 33 31 38 39 32 [26] 32 35 37 33 33 31 32 31 37 30 33 31 33 31 33 32 33 32 33 28 35 39 34 38 32 [51] 38 30 33 38 32 35 34 34 36 34 28 34 35 35 31 37 35 27 40 37 36 38 39 41 27 [76] 30 37 31 31 27 36 37 33 34 31 39 34 32 33 36 32 41 28 30 36 35 31 34 36 36 [101] 35 37 28 39 32 35 39 35 42 34 33 41 34 32 40 40 35 36 37 27 39 38 31 33 32 [126] 39 36 33 33 32 37 30 38 29 22 35 35 34 35 34 37 35 23 31 27 36 31 32 39 37 [151] 38 39 34 31 37 36 32 38 26 26 33 39 30 33 25 38 37 31 37 35 25 28 35 33 30 [176] 31 37 36 30 36 32 28 36 34 31 28 36 36 40 33 37 32 38 31 37 33 30 30 31 32 [201] 34 36 37 36 33 33 33 44 39 32 35 25 35 34 35 39 33 36 32 32 36 32 34 33 35 [226] 30 38 34 33 32 31 30 27 31 30 32 35 28 33 35 35 32 21 20 34 32 34 32 33 33 [251] 37 32 34 30 30 38 36 32 34 33 27 32 34 29 35 27 33 38 36 33 39 29 32 34 38 [276] 17 35 32 34 36 31 35 29 22 41 36 42 33 > 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/19qhn1355221202.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: Seperate Inputs: Pop, Gender, Connected, Learning, Software, Happines Number of observations: 288 1) Connected <= 31; criterion = 1, statistic = 78.931 2) Connected <= 24; criterion = 1, statistic = 21.962 3)* weights = 8 2) Connected > 24 4)* weights = 56 1) Connected > 31 5) Connected <= 40; criterion = 0.972, statistic = 7.978 6) Learning <= 15; criterion = 0.972, statistic = 7.995 7)* weights = 125 6) Learning > 15 8)* weights = 89 5) Connected > 40 9)* weights = 10 > postscript(file="/var/fisher/rcomp/tmp/2agp71355221202.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/36oc61355221202.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 38 38.10000 -0.1000000 2 32 34.89888 -2.8988764 3 35 31.16071 3.8392857 4 33 31.16071 1.8392857 5 37 33.98400 3.0160000 6 29 33.98400 -4.9840000 7 31 34.89888 -3.8988764 8 36 33.98400 2.0160000 9 35 33.98400 1.0160000 10 38 33.98400 4.0160000 11 31 34.89888 -3.8988764 12 34 34.89888 -0.8988764 13 35 34.89888 0.1011236 14 38 34.89888 3.1011236 15 37 34.89888 2.1011236 16 33 33.98400 -0.9840000 17 32 33.98400 -1.9840000 18 38 34.89888 3.1011236 19 38 34.89888 3.1011236 20 32 34.89888 -2.8988764 21 33 34.89888 -1.8988764 22 31 31.16071 -0.1607143 23 38 34.89888 3.1011236 24 39 34.89888 4.1011236 25 32 34.89888 -2.8988764 26 32 38.10000 -6.1000000 27 35 34.89888 0.1011236 28 37 33.98400 3.0160000 29 33 34.89888 -1.8988764 30 33 33.98400 -0.9840000 31 31 31.16071 -0.1607143 32 32 31.16071 0.8392857 33 31 33.98400 -2.9840000 34 37 34.89888 2.1011236 35 30 33.98400 -3.9840000 36 33 33.98400 -0.9840000 37 31 31.16071 -0.1607143 38 33 33.98400 -0.9840000 39 31 31.16071 -0.1607143 40 33 34.89888 -1.8988764 41 32 34.89888 -2.8988764 42 33 33.98400 -0.9840000 43 32 34.89888 -2.8988764 44 33 33.98400 -0.9840000 45 28 33.98400 -5.9840000 46 35 33.98400 1.0160000 47 39 33.98400 5.0160000 48 34 33.98400 0.0160000 49 38 34.89888 3.1011236 50 32 33.98400 -1.9840000 51 38 34.89888 3.1011236 52 30 33.98400 -3.9840000 53 33 33.98400 -0.9840000 54 38 34.89888 3.1011236 55 32 33.98400 -1.9840000 56 35 33.98400 1.0160000 57 34 34.89888 -0.8988764 58 34 31.16071 2.8392857 59 36 33.98400 2.0160000 60 34 34.89888 -0.8988764 61 28 31.16071 -3.1607143 62 34 34.89888 -0.8988764 63 35 34.89888 0.1011236 64 35 31.16071 3.8392857 65 31 34.89888 -3.8988764 66 37 34.89888 2.1011236 67 35 34.89888 0.1011236 68 27 31.16071 -4.1607143 69 40 33.98400 6.0160000 70 37 34.89888 2.1011236 71 36 33.98400 2.0160000 72 38 31.16071 6.8392857 73 39 34.89888 4.1011236 74 41 34.89888 6.1011236 75 27 34.89888 -7.8988764 76 30 34.89888 -4.8988764 77 37 34.89888 2.1011236 78 31 34.89888 -3.8988764 79 31 31.16071 -0.1607143 80 27 34.89888 -7.8988764 81 36 34.89888 1.1011236 82 37 34.89888 2.1011236 83 33 33.98400 -0.9840000 84 34 33.98400 0.0160000 85 31 34.89888 -3.8988764 86 39 33.98400 5.0160000 87 34 34.89888 -0.8988764 88 32 31.16071 0.8392857 89 33 33.98400 -0.9840000 90 36 31.16071 4.8392857 91 32 34.89888 -2.8988764 92 41 34.89888 6.1011236 93 28 33.98400 -5.9840000 94 30 31.16071 -1.1607143 95 36 34.89888 1.1011236 96 35 34.89888 0.1011236 97 31 34.89888 -3.8988764 98 34 34.89888 -0.8988764 99 36 33.98400 2.0160000 100 36 34.89888 1.1011236 101 35 34.89888 0.1011236 102 37 34.89888 2.1011236 103 28 33.98400 -5.9840000 104 39 34.89888 4.1011236 105 32 33.98400 -1.9840000 106 35 34.89888 0.1011236 107 39 38.10000 0.9000000 108 35 34.89888 0.1011236 109 42 38.10000 3.9000000 110 34 31.16071 2.8392857 111 33 31.16071 1.8392857 112 41 34.89888 6.1011236 113 34 33.98400 0.0160000 114 32 34.89888 -2.8988764 115 40 33.98400 6.0160000 116 40 33.98400 6.0160000 117 35 33.98400 1.0160000 118 36 34.89888 1.1011236 119 37 33.98400 3.0160000 120 27 31.16071 -4.1607143 121 39 33.98400 5.0160000 122 38 34.89888 3.1011236 123 31 33.98400 -2.9840000 124 33 31.16071 1.8392857 125 32 33.98400 -1.9840000 126 39 38.10000 0.9000000 127 36 33.98400 2.0160000 128 33 33.98400 -0.9840000 129 33 34.89888 -1.8988764 130 32 31.16071 0.8392857 131 37 34.89888 2.1011236 132 30 31.16071 -1.1607143 133 38 33.98400 4.0160000 134 29 33.98400 -4.9840000 135 22 31.16071 -9.1607143 136 35 33.98400 1.0160000 137 35 31.16071 3.8392857 138 34 33.98400 0.0160000 139 35 31.16071 3.8392857 140 34 34.89888 -0.8988764 141 37 31.16071 5.8392857 142 35 31.16071 3.8392857 143 23 31.16071 -8.1607143 144 31 33.98400 -2.9840000 145 27 34.89888 -7.8988764 146 36 33.98400 2.0160000 147 31 34.89888 -3.8988764 148 32 33.98400 -1.9840000 149 39 33.98400 5.0160000 150 37 38.10000 -1.1000000 151 38 34.89888 3.1011236 152 39 34.89888 4.1011236 153 34 33.98400 0.0160000 154 31 33.98400 -2.9840000 155 37 33.98400 3.0160000 156 36 33.98400 2.0160000 157 32 34.89888 -2.8988764 158 38 33.98400 4.0160000 159 26 31.16071 -5.1607143 160 26 31.16071 -5.1607143 161 33 34.89888 -1.8988764 162 39 33.98400 5.0160000 163 30 24.37500 5.6250000 164 33 31.16071 1.8392857 165 25 31.16071 -6.1607143 166 38 33.98400 4.0160000 167 37 31.16071 5.8392857 168 31 33.98400 -2.9840000 169 37 34.89888 2.1011236 170 35 38.10000 -3.1000000 171 25 31.16071 -6.1607143 172 28 33.98400 -5.9840000 173 35 33.98400 1.0160000 174 33 33.98400 -0.9840000 175 30 31.16071 -1.1607143 176 31 31.16071 -0.1607143 177 37 33.98400 3.0160000 178 36 34.89888 1.1011236 179 30 33.98400 -3.9840000 180 36 31.16071 4.8392857 181 32 33.98400 -1.9840000 182 28 24.37500 3.6250000 183 36 34.89888 1.1011236 184 34 34.89888 -0.8988764 185 31 33.98400 -2.9840000 186 28 31.16071 -3.1607143 187 36 33.98400 2.0160000 188 36 33.98400 2.0160000 189 40 34.89888 5.1011236 190 33 33.98400 -0.9840000 191 37 34.89888 2.1011236 192 32 33.98400 -1.9840000 193 38 33.98400 4.0160000 194 31 34.89888 -3.8988764 195 37 34.89888 2.1011236 196 33 33.98400 -0.9840000 197 30 31.16071 -1.1607143 198 30 24.37500 5.6250000 199 31 33.98400 -2.9840000 200 32 33.98400 -1.9840000 201 34 33.98400 0.0160000 202 36 33.98400 2.0160000 203 37 34.89888 2.1011236 204 36 34.89888 1.1011236 205 33 33.98400 -0.9840000 206 33 33.98400 -0.9840000 207 33 33.98400 -0.9840000 208 44 38.10000 5.9000000 209 39 33.98400 5.0160000 210 32 31.16071 0.8392857 211 35 34.89888 0.1011236 212 25 31.16071 -6.1607143 213 35 34.89888 0.1011236 214 34 33.98400 0.0160000 215 35 33.98400 1.0160000 216 39 33.98400 5.0160000 217 33 33.98400 -0.9840000 218 36 33.98400 2.0160000 219 32 33.98400 -1.9840000 220 32 31.16071 0.8392857 221 36 33.98400 2.0160000 222 32 31.16071 0.8392857 223 34 33.98400 0.0160000 224 33 33.98400 -0.9840000 225 35 33.98400 1.0160000 226 30 33.98400 -3.9840000 227 38 33.98400 4.0160000 228 34 34.89888 -0.8988764 229 33 38.10000 -5.1000000 230 32 33.98400 -1.9840000 231 31 33.98400 -2.9840000 232 30 33.98400 -3.9840000 233 27 33.98400 -6.9840000 234 31 31.16071 -0.1607143 235 30 33.98400 -3.9840000 236 32 31.16071 0.8392857 237 35 33.98400 1.0160000 238 28 31.16071 -3.1607143 239 33 33.98400 -0.9840000 240 35 31.16071 3.8392857 241 35 33.98400 1.0160000 242 32 33.98400 -1.9840000 243 21 24.37500 -3.3750000 244 20 24.37500 -4.3750000 245 34 31.16071 2.8392857 246 32 33.98400 -1.9840000 247 34 33.98400 0.0160000 248 32 34.89888 -2.8988764 249 33 33.98400 -0.9840000 250 33 33.98400 -0.9840000 251 37 34.89888 2.1011236 252 32 31.16071 0.8392857 253 34 33.98400 0.0160000 254 30 33.98400 -3.9840000 255 30 31.16071 -1.1607143 256 38 33.98400 4.0160000 257 36 33.98400 2.0160000 258 32 31.16071 0.8392857 259 34 33.98400 0.0160000 260 33 33.98400 -0.9840000 261 27 24.37500 2.6250000 262 32 33.98400 -1.9840000 263 34 33.98400 0.0160000 264 29 31.16071 -2.1607143 265 35 33.98400 1.0160000 266 27 31.16071 -4.1607143 267 33 34.89888 -1.8988764 268 38 33.98400 4.0160000 269 36 34.89888 1.1011236 270 33 34.89888 -1.8988764 271 39 33.98400 5.0160000 272 29 33.98400 -4.9840000 273 32 34.89888 -2.8988764 274 34 31.16071 2.8392857 275 38 34.89888 3.1011236 276 17 24.37500 -7.3750000 277 35 33.98400 1.0160000 278 32 33.98400 -1.9840000 279 34 33.98400 0.0160000 280 36 33.98400 2.0160000 281 31 34.89888 -3.8988764 282 35 33.98400 1.0160000 283 29 31.16071 -2.1607143 284 22 24.37500 -2.3750000 285 41 34.89888 6.1011236 286 36 33.98400 2.0160000 287 42 38.10000 3.9000000 288 33 31.16071 1.8392857 > 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/4nnp91355221202.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/fisher/rcomp/tmp/5e8921355221203.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6kj2n1355221203.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/70td11355221203.tab") + } > > try(system("convert tmp/2agp71355221202.ps tmp/2agp71355221202.png",intern=TRUE)) character(0) > try(system("convert tmp/36oc61355221202.ps tmp/36oc61355221202.png",intern=TRUE)) character(0) > try(system("convert tmp/4nnp91355221202.ps tmp/4nnp91355221202.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.669 0.706 9.349