R version 2.12.0 (2010-10-15) Copyright (C) 2010 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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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,1.146 + ,0.903 + ,2.602 + ,2.230 + ,3.669 + ,1.079 + ,0.903 + ,2.544 + ,2.176 + ,3.672 + ,1.176 + ,0.903 + ,2.483 + ,2.286 + ,3.675 + ,1.279 + ,0.903 + ,2.643 + ,2.332 + ,3.675 + ,1.041 + ,0.903 + ,2.602 + ,2.230 + ,3.676 + ,1.079 + ,0.903 + ,2.602 + ,2.223 + ,3.691 + ,1.114 + ,0.903 + ,2.658 + ,2.352 + ,3.695 + ,1.041 + ,0.903 + ,2.632 + ,2.297 + ,3.695 + ,1.079 + ,0.903 + ,2.583 + ,2.255 + ,3.695 + ,1.079 + ,0.903 + ,2.602 + ,2.176 + ,3.699 + ,1.146 + ,0.903 + ,2.602 + ,2.243 + ,3.711 + ,1.079) + ,dim=c(5 + ,632) + ,dimnames=list(c('cylinders' + ,'engine.displacement' + ,'horsepower' + ,'weight' + ,'acceleration ') + ,1:632)) > y <- array(NA,dim=c(5,632),dimnames=list(c('cylinders','engine.displacement','horsepower','weight','acceleration '),1:632)) > 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 = '' > par2 = 'none' > par1 = '3' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > 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 Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) 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] "horsepower" > x[,par1] [1] 2.217 2.176 2.146 2.297 2.332 2.352 2.230 2.204 2.352 1.978 1.987 1.663 [13] 1.940 1.978 2.053 2.332 2.301 2.286 1.944 1.978 2.000 2.000 2.217 2.176 [25] 2.230 2.243 1.857 2.000 1.934 1.954 1.881 1.813 1.778 1.845 1.903 1.934 [37] 2.217 2.176 2.185 2.318 2.190 2.279 1.987 2.114 2.146 2.049 1.881 1.934 [49] 1.987 1.903 2.243 2.176 2.137 2.176 2.176 2.199 2.332 2.352 2.021 2.000 [61] 1.944 1.978 2.176 2.255 2.000 1.857 1.973 1.929 2.029 2.161 2.362 1.875 [73] 1.959 2.176 2.041 2.255 1.978 2.000 1.903 1.813 2.000 2.041 2.146 2.176 [85] 2.146 2.176 1.826 1.892 1.785 1.875 1.875 1.987 1.826 1.978 1.857 2.230 [97] 2.161 2.170 2.041 2.041 1.978 2.041 2.111 1.919 2.000 1.982 1.851 1.987 [109] 1.978 1.944 2.061 1.724 1.908 1.964 1.919 2.146 2.079 2.182 2.021 1.908 [121] 1.716 1.778 2.000 2.041 1.978 1.845 1.875 2.009 2.176 2.079 2.255 2.114 [133] 2.176 1.903 1.763 1.845 2.161 2.161 2.021 2.000 2.255 2.230 2.173 1.892 [145] 1.875 1.949 1.919 1.826 1.987 2.041 1.681 1.820 1.845 2.146 2.143 1.978 [157] 1.929 2.000 1.954 1.929 2.041 2.161 2.217 2.146 1.833 1.987 1.875 1.929 [169] 2.013 2.097 2.124 1.851 1.833 1.929 1.944 2.041 2.114 2.140 2.130 2.152 [181] 2.097 1.851 1.813 1.903 1.851 1.954 1.845 1.813 1.954 2.061 1.954 1.881 [193] 1.845 1.813 1.944 1.954 1.892 1.954 1.964 1.875 2.021 1.813 1.826 1.826 [205] 1.792 1.944 1.924 1.924 2.041 1.924 1.806 1.778 1.813 1.792 1.799 1.813 [217] 1.869 2.000 1.903 2.079 2.041 1.944 1.929 1.944 1.944 1.924 1.954 1.869 [229] 1.833 1.799 1.944 1.875 1.826 2.041 1.929 1.982 1.954 1.934 1.924 1.898 [241] 1.839 1.716 1.763 1.778 1.813 1.724 1.724 1.820 1.778 1.851 1.763 1.778 [253] 1.663 1.813 1.792 1.826 1.690 1.806 1.903 1.851 1.845 1.845 1.892 1.845 [265] 1.663 1.826 1.845 1.826 1.826 1.826 1.778 1.833 1.813 1.813 1.869 1.681 [277] 1.826 1.716 1.833 1.851 1.845 1.826 1.826 1.785 1.813 1.813 1.833 1.716 [289] 1.813 1.833 1.792 1.799 1.881 1.826 1.845 1.845 1.919 1.681 1.944 1.875 [301] 1.813 1.813 1.845 1.954 1.954 1.875 1.799 1.845 1.903 1.839 1.716 1.944 [313] 1.944 1.833 1.881 1.826 1.845 1.903 1.833 1.875 1.875 1.944 1.903 1.778 [325] 1.875 1.892 1.839 1.892 1.869 1.845 1.919 1.875 1.875 1.799 1.919 1.934 [337] 1.908 1.851 1.934 1.978 1.875 2.053 1.845 1.875 1.732 1.898 1.954 1.954 [349] 1.875 1.875 1.978 1.944 1.964 1.857 1.924 1.982 1.892 1.987 1.929 1.987 [361] 1.681 1.875 1.924 1.978 1.978 1.973 1.813 1.924 1.968 1.934 1.944 1.857 [373] 1.987 1.857 1.954 2.000 1.954 1.964 1.903 1.934 2.029 1.987 1.924 1.944 [385] 1.987 1.881 1.978 1.924 1.875 1.875 1.987 1.954 1.978 1.857 1.964 1.929 [397] 1.959 1.964 1.929 1.892 2.061 2.041 1.944 2.000 1.964 1.898 2.000 1.924 [409] 1.869 1.919 1.944 1.954 2.041 1.982 1.903 1.954 2.061 1.940 1.954 1.978 [421] 2.061 1.982 1.954 1.944 1.914 2.041 2.041 1.954 1.944 2.021 1.949 1.987 [433] 2.000 1.934 2.061 2.021 2.086 1.987 2.013 1.978 2.049 1.929 1.964 2.049 [445] 1.944 1.944 2.064 2.000 1.978 2.121 2.000 2.033 2.079 2.049 2.000 2.041 [457] 1.991 1.954 1.826 1.944 2.041 1.929 1.940 1.987 1.929 1.954 1.908 1.929 [469] 1.944 2.041 1.944 1.929 1.954 2.352 1.978 2.021 1.944 2.097 2.009 1.978 [481] 1.857 1.881 2.111 1.851 1.978 2.143 1.954 1.954 2.041 1.903 2.000 2.061 [493] 1.826 1.978 1.954 1.944 2.000 2.000 2.000 1.944 2.000 2.000 2.021 2.041 [505] 2.041 2.021 1.954 2.176 2.079 2.124 2.041 1.954 2.021 2.161 2.000 1.857 [517] 2.176 2.176 2.021 2.217 2.146 2.021 1.929 2.114 2.041 1.991 1.886 2.021 [529] 2.230 2.143 1.892 2.097 2.204 2.021 2.041 2.000 2.041 2.041 2.000 2.255 [541] 2.176 2.176 2.217 2.111 2.021 2.041 2.146 2.176 2.176 2.176 2.000 1.978 [553] 2.079 2.243 2.130 2.114 2.279 2.114 2.161 2.176 2.021 2.097 2.041 2.176 [565] 2.176 2.140 2.079 2.161 2.137 2.152 2.161 2.041 2.176 2.146 2.161 2.176 [577] 2.114 2.243 2.185 2.176 2.161 2.146 2.185 2.230 2.176 2.217 2.146 2.182 [589] 2.255 2.176 2.176 2.217 2.362 2.146 2.114 2.332 2.279 2.173 2.297 2.342 [601] 2.190 2.199 2.301 2.255 2.322 2.243 2.279 2.352 2.161 2.204 2.176 2.243 [613] 2.176 2.176 2.255 2.190 2.332 2.318 2.146 2.230 2.170 2.230 2.176 2.286 [625] 2.332 2.230 2.223 2.352 2.297 2.255 2.176 2.243 > 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]) 1.663 1.681 1.69 1.716 1.724 1.732 1.763 1.778 1.785 1.792 1.799 1.806 1.813 3 4 1 5 3 1 3 8 2 4 5 2 18 1.82 1.826 1.833 1.839 1.845 1.851 1.857 1.869 1.875 1.881 1.886 1.892 1.898 2 18 9 3 18 9 9 5 22 7 1 9 3 1.903 1.908 1.914 1.919 1.924 1.929 1.934 1.94 1.944 1.949 1.954 1.959 1.964 13 4 1 7 11 16 9 3 29 2 29 2 8 1.968 1.973 1.978 1.982 1.987 1.991 2 2.009 2.013 2.021 2.029 2.033 2.041 1 2 24 5 16 2 29 2 2 16 2 1 30 2.049 2.053 2.061 2.064 2.079 2.086 2.097 2.111 2.114 2.121 2.124 2.13 2.137 4 2 7 1 7 1 5 3 8 1 2 2 2 2.14 2.143 2.146 2.152 2.161 2.17 2.173 2.176 2.182 2.185 2.19 2.199 2.204 2 3 14 2 12 2 2 34 2 3 3 2 3 2.217 2.223 2.23 2.243 2.255 2.279 2.286 2.297 2.301 2.318 2.322 2.332 2.342 8 1 9 7 9 4 2 3 2 2 1 6 1 2.352 2.362 6 2 > colnames(x) [1] "cylinders" "engine.displacement" "horsepower" [4] "weight" "acceleration..." > colnames(x)[par1] [1] "horsepower" > x[,par1] [1] 2.217 2.176 2.146 2.297 2.332 2.352 2.230 2.204 2.352 1.978 1.987 1.663 [13] 1.940 1.978 2.053 2.332 2.301 2.286 1.944 1.978 2.000 2.000 2.217 2.176 [25] 2.230 2.243 1.857 2.000 1.934 1.954 1.881 1.813 1.778 1.845 1.903 1.934 [37] 2.217 2.176 2.185 2.318 2.190 2.279 1.987 2.114 2.146 2.049 1.881 1.934 [49] 1.987 1.903 2.243 2.176 2.137 2.176 2.176 2.199 2.332 2.352 2.021 2.000 [61] 1.944 1.978 2.176 2.255 2.000 1.857 1.973 1.929 2.029 2.161 2.362 1.875 [73] 1.959 2.176 2.041 2.255 1.978 2.000 1.903 1.813 2.000 2.041 2.146 2.176 [85] 2.146 2.176 1.826 1.892 1.785 1.875 1.875 1.987 1.826 1.978 1.857 2.230 [97] 2.161 2.170 2.041 2.041 1.978 2.041 2.111 1.919 2.000 1.982 1.851 1.987 [109] 1.978 1.944 2.061 1.724 1.908 1.964 1.919 2.146 2.079 2.182 2.021 1.908 [121] 1.716 1.778 2.000 2.041 1.978 1.845 1.875 2.009 2.176 2.079 2.255 2.114 [133] 2.176 1.903 1.763 1.845 2.161 2.161 2.021 2.000 2.255 2.230 2.173 1.892 [145] 1.875 1.949 1.919 1.826 1.987 2.041 1.681 1.820 1.845 2.146 2.143 1.978 [157] 1.929 2.000 1.954 1.929 2.041 2.161 2.217 2.146 1.833 1.987 1.875 1.929 [169] 2.013 2.097 2.124 1.851 1.833 1.929 1.944 2.041 2.114 2.140 2.130 2.152 [181] 2.097 1.851 1.813 1.903 1.851 1.954 1.845 1.813 1.954 2.061 1.954 1.881 [193] 1.845 1.813 1.944 1.954 1.892 1.954 1.964 1.875 2.021 1.813 1.826 1.826 [205] 1.792 1.944 1.924 1.924 2.041 1.924 1.806 1.778 1.813 1.792 1.799 1.813 [217] 1.869 2.000 1.903 2.079 2.041 1.944 1.929 1.944 1.944 1.924 1.954 1.869 [229] 1.833 1.799 1.944 1.875 1.826 2.041 1.929 1.982 1.954 1.934 1.924 1.898 [241] 1.839 1.716 1.763 1.778 1.813 1.724 1.724 1.820 1.778 1.851 1.763 1.778 [253] 1.663 1.813 1.792 1.826 1.690 1.806 1.903 1.851 1.845 1.845 1.892 1.845 [265] 1.663 1.826 1.845 1.826 1.826 1.826 1.778 1.833 1.813 1.813 1.869 1.681 [277] 1.826 1.716 1.833 1.851 1.845 1.826 1.826 1.785 1.813 1.813 1.833 1.716 [289] 1.813 1.833 1.792 1.799 1.881 1.826 1.845 1.845 1.919 1.681 1.944 1.875 [301] 1.813 1.813 1.845 1.954 1.954 1.875 1.799 1.845 1.903 1.839 1.716 1.944 [313] 1.944 1.833 1.881 1.826 1.845 1.903 1.833 1.875 1.875 1.944 1.903 1.778 [325] 1.875 1.892 1.839 1.892 1.869 1.845 1.919 1.875 1.875 1.799 1.919 1.934 [337] 1.908 1.851 1.934 1.978 1.875 2.053 1.845 1.875 1.732 1.898 1.954 1.954 [349] 1.875 1.875 1.978 1.944 1.964 1.857 1.924 1.982 1.892 1.987 1.929 1.987 [361] 1.681 1.875 1.924 1.978 1.978 1.973 1.813 1.924 1.968 1.934 1.944 1.857 [373] 1.987 1.857 1.954 2.000 1.954 1.964 1.903 1.934 2.029 1.987 1.924 1.944 [385] 1.987 1.881 1.978 1.924 1.875 1.875 1.987 1.954 1.978 1.857 1.964 1.929 [397] 1.959 1.964 1.929 1.892 2.061 2.041 1.944 2.000 1.964 1.898 2.000 1.924 [409] 1.869 1.919 1.944 1.954 2.041 1.982 1.903 1.954 2.061 1.940 1.954 1.978 [421] 2.061 1.982 1.954 1.944 1.914 2.041 2.041 1.954 1.944 2.021 1.949 1.987 [433] 2.000 1.934 2.061 2.021 2.086 1.987 2.013 1.978 2.049 1.929 1.964 2.049 [445] 1.944 1.944 2.064 2.000 1.978 2.121 2.000 2.033 2.079 2.049 2.000 2.041 [457] 1.991 1.954 1.826 1.944 2.041 1.929 1.940 1.987 1.929 1.954 1.908 1.929 [469] 1.944 2.041 1.944 1.929 1.954 2.352 1.978 2.021 1.944 2.097 2.009 1.978 [481] 1.857 1.881 2.111 1.851 1.978 2.143 1.954 1.954 2.041 1.903 2.000 2.061 [493] 1.826 1.978 1.954 1.944 2.000 2.000 2.000 1.944 2.000 2.000 2.021 2.041 [505] 2.041 2.021 1.954 2.176 2.079 2.124 2.041 1.954 2.021 2.161 2.000 1.857 [517] 2.176 2.176 2.021 2.217 2.146 2.021 1.929 2.114 2.041 1.991 1.886 2.021 [529] 2.230 2.143 1.892 2.097 2.204 2.021 2.041 2.000 2.041 2.041 2.000 2.255 [541] 2.176 2.176 2.217 2.111 2.021 2.041 2.146 2.176 2.176 2.176 2.000 1.978 [553] 2.079 2.243 2.130 2.114 2.279 2.114 2.161 2.176 2.021 2.097 2.041 2.176 [565] 2.176 2.140 2.079 2.161 2.137 2.152 2.161 2.041 2.176 2.146 2.161 2.176 [577] 2.114 2.243 2.185 2.176 2.161 2.146 2.185 2.230 2.176 2.217 2.146 2.182 [589] 2.255 2.176 2.176 2.217 2.362 2.146 2.114 2.332 2.279 2.173 2.297 2.342 [601] 2.190 2.199 2.301 2.255 2.322 2.243 2.279 2.352 2.161 2.204 2.176 2.243 [613] 2.176 2.176 2.255 2.190 2.332 2.318 2.146 2.230 2.170 2.230 2.176 2.286 [625] 2.332 2.230 2.223 2.352 2.297 2.255 2.176 2.243 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/www/rcomp/tmp/1mq6o1293281640.tab") + } + } > m Conditional inference tree with 28 terminal nodes Response: horsepower Inputs: cylinders, engine.displacement, weight, acceleration... Number of observations: 632 1) engine.displacement <= 2.418; criterion = 1, statistic = 487.313 2) weight <= 3.345; criterion = 1, statistic = 252.553 3) acceleration... <= 1.23; criterion = 1, statistic = 61.637 4) weight <= 3.312; criterion = 1, statistic = 38.482 5) weight <= 3.255; criterion = 0.999, statistic = 13.213 6)* weights = 8 5) weight > 3.255 7) acceleration... <= 1.176; criterion = 0.962, statistic = 6.713 8)* weights = 16 7) acceleration... > 1.176 9)* weights = 25 4) weight > 3.312 10)* weights = 54 3) acceleration... > 1.23 11) acceleration... <= 1.288; criterion = 0.998, statistic = 12.162 12)* weights = 31 11) acceleration... > 1.288 13)* weights = 17 2) weight > 3.345 14) acceleration... <= 1.279; criterion = 1, statistic = 71.005 15) weight <= 3.423; criterion = 1, statistic = 80.397 16) acceleration... <= 1.179; criterion = 1, statistic = 31.285 17) weight <= 3.412; criterion = 0.981, statistic = 7.999 18)* weights = 47 17) weight > 3.412 19)* weights = 9 16) acceleration... > 1.179 20)* weights = 57 15) weight > 3.423 21) acceleration... <= 1.146; criterion = 1, statistic = 35.464 22) weight <= 3.447; criterion = 0.999, statistic = 12.983 23)* weights = 13 22) weight > 3.447 24)* weights = 12 21) acceleration... > 1.146 25) weight <= 3.519; criterion = 1, statistic = 36.4 26) acceleration... <= 1.217; criterion = 1, statistic = 19.814 27)* weights = 61 26) acceleration... > 1.217 28)* weights = 41 25) weight > 3.519 29) engine.displacement <= 2.398; criterion = 0.978, statistic = 7.659 30) engine.displacement <= 2.364; criterion = 0.999, statistic = 13.481 31)* weights = 27 30) engine.displacement > 2.364 32)* weights = 13 29) engine.displacement > 2.398 33)* weights = 7 14) acceleration... > 1.279 34) weight <= 3.47; criterion = 0.991, statistic = 9.329 35)* weights = 9 34) weight > 3.47 36)* weights = 19 1) engine.displacement > 2.418 37) engine.displacement <= 2.545; criterion = 1, statistic = 91.586 38) engine.displacement <= 2.48; criterion = 1, statistic = 17.965 39)* weights = 21 38) engine.displacement > 2.48 40) weight <= 3.629; criterion = 0.996, statistic = 10.919 41) acceleration... <= 1.114; criterion = 1, statistic = 25.456 42) engine.displacement <= 2.502; criterion = 0.999, statistic = 13.47 43)* weights = 22 42) engine.displacement > 2.502 44)* weights = 21 41) acceleration... > 1.114 45) acceleration... <= 1.155; criterion = 0.981, statistic = 7.94 46)* weights = 20 45) acceleration... > 1.155 47)* weights = 9 40) weight > 3.629 48) engine.displacement <= 2.502; criterion = 0.995, statistic = 10.514 49)* weights = 7 48) engine.displacement > 2.502 50) acceleration... <= 1.114; criterion = 0.997, statistic = 11.109 51)* weights = 8 50) acceleration... > 1.114 52)* weights = 12 37) engine.displacement > 2.545 53) engine.displacement <= 2.602; criterion = 1, statistic = 15.578 54)* weights = 30 53) engine.displacement > 2.602 55)* weights = 16 > postscript(file="/var/www/rcomp/tmp/2fhnr1293281640.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/www/rcomp/tmp/3fhnr1293281640.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 2.217 2.202381 0.0146190476 2 2.176 2.167045 0.0089545455 3 2.146 2.130476 0.0155238095 4 2.297 2.331812 -0.0348125000 5 2.332 2.331812 0.0001875000 6 2.352 2.331812 0.0201875000 7 2.230 2.245767 -0.0157666667 8 2.204 2.202381 0.0016190476 9 2.352 2.331812 0.0201875000 10 1.978 1.955468 0.0225319149 11 1.987 1.987328 -0.0003278689 12 1.663 1.740412 -0.0774117647 13 1.940 1.951463 -0.0114634146 14 1.978 1.917596 0.0604035088 15 2.053 1.955468 0.0975319149 16 2.332 2.245767 0.0862333333 17 2.301 2.264000 0.0370000000 18 2.286 2.264000 0.0220000000 19 1.944 1.881852 0.0621481481 20 1.978 1.955468 0.0225319149 21 2.000 2.015231 -0.0152307692 22 2.000 1.987328 0.0126721311 23 2.217 2.202381 0.0146190476 24 2.176 2.167045 0.0089545455 25 2.230 2.245767 -0.0157666667 26 2.243 2.245767 -0.0027666667 27 1.857 1.917596 -0.0605964912 28 2.000 1.987328 0.0126721311 29 1.934 1.955468 -0.0214680851 30 1.954 1.881852 0.0721481481 31 1.881 1.881852 -0.0008518519 32 1.813 1.802613 0.0103870968 33 1.778 1.802613 -0.0246129032 34 1.845 1.740412 0.1045882353 35 1.903 1.881852 0.0211481481 36 1.934 1.917596 0.0164035088 37 2.217 2.231500 -0.0145000000 38 2.176 2.149600 0.0264000000 39 2.185 2.202381 -0.0173809524 40 2.318 2.331812 -0.0138125000 41 2.190 2.177833 0.0121666667 42 2.279 2.245767 0.0332333333 43 1.987 1.955468 0.0315319149 44 2.114 2.149600 -0.0356000000 45 2.146 2.130476 0.0155238095 46 2.049 1.987328 0.0616721311 47 1.881 1.917596 -0.0365964912 48 1.934 1.917596 0.0164035088 49 1.987 1.955468 0.0315319149 50 1.903 1.881852 0.0211481481 51 2.243 2.202381 0.0406190476 52 2.176 2.167045 0.0089545455 53 2.137 2.130476 0.0065238095 54 2.176 2.167045 0.0089545455 55 2.176 2.245767 -0.0697666667 56 2.199 2.231500 -0.0325000000 57 2.332 2.331812 0.0001875000 58 2.352 2.331812 0.0201875000 59 2.021 1.987328 0.0336721311 60 2.000 1.951463 0.0485365854 61 1.944 1.987328 -0.0433278689 62 1.978 1.987328 -0.0093278689 63 2.176 2.245767 -0.0697666667 64 2.255 2.231500 0.0235000000 65 2.000 1.987328 0.0126721311 66 1.857 1.832222 0.0247777778 67 1.973 1.917596 0.0554035088 68 1.929 1.917596 0.0114035088 69 2.029 1.955468 0.0735319149 70 2.161 2.202381 -0.0413809524 71 2.362 2.245767 0.1162333333 72 1.875 1.881852 -0.0068518519 73 1.959 1.955468 0.0035319149 74 2.176 2.167045 0.0089545455 75 2.041 2.025923 0.0150769231 76 2.255 2.202381 0.0526190476 77 1.978 1.987328 -0.0093278689 78 2.000 1.987328 0.0126721311 79 1.903 1.917596 -0.0145964912 80 1.813 1.740412 0.0725882353 81 2.000 2.015231 -0.0152307692 82 2.041 2.046429 -0.0054285714 83 2.146 2.130476 0.0155238095 84 2.176 2.177833 -0.0018333333 85 2.146 2.130476 0.0155238095 86 2.176 2.126000 0.0500000000 87 1.826 1.823040 0.0029600000 88 1.892 1.955468 -0.0634680851 89 1.785 1.802613 -0.0176129032 90 1.875 1.881852 -0.0068518519 91 1.875 1.955468 -0.0804680851 92 1.987 1.955468 0.0315319149 93 1.826 1.823040 0.0029600000 94 1.978 1.987328 -0.0093278689 95 1.857 1.912947 -0.0559473684 96 2.230 2.245767 -0.0157666667 97 2.161 2.177833 -0.0168333333 98 2.170 2.177833 -0.0078333333 99 2.041 1.912947 0.1280526316 100 2.041 2.046429 -0.0054285714 101 1.978 2.020222 -0.0422222222 102 2.041 2.098333 -0.0573333333 103 2.111 2.130476 -0.0194761905 104 1.919 1.917596 0.0014035088 105 2.000 1.987328 0.0126721311 106 1.982 2.025923 -0.0439230769 107 1.851 1.917596 -0.0665964912 108 1.987 1.987328 -0.0003278689 109 1.978 1.987328 -0.0093278689 110 1.944 1.951463 -0.0074634146 111 2.061 2.025923 0.0350769231 112 1.724 1.802613 -0.0786129032 113 1.908 1.917596 -0.0095964912 114 1.964 1.955468 0.0085319149 115 1.919 1.881852 0.0371481481 116 2.146 2.167045 -0.0210454545 117 2.079 2.149600 -0.0706000000 118 2.182 2.202381 -0.0203809524 119 2.021 2.015231 0.0057692308 120 1.908 1.951463 -0.0434634146 121 1.716 1.740412 -0.0244117647 122 1.778 1.740412 0.0375882353 123 2.000 2.020222 -0.0202222222 124 2.041 2.015231 0.0257692308 125 1.978 1.951463 0.0265365854 126 1.845 1.823040 0.0219600000 127 1.875 1.881852 -0.0068518519 128 2.009 1.987328 0.0216721311 129 2.176 2.149600 0.0264000000 130 2.079 2.020222 0.0587777778 131 2.255 2.231500 0.0235000000 132 2.114 2.130476 -0.0164761905 133 2.176 2.149600 0.0264000000 134 1.903 1.881852 0.0211481481 135 1.763 1.802613 -0.0396129032 136 1.845 1.823040 0.0219600000 137 2.161 2.167045 -0.0060454545 138 2.161 2.149600 0.0114000000 139 2.021 2.020222 0.0007777778 140 2.000 2.020222 -0.0202222222 141 2.255 2.245767 0.0092333333 142 2.230 2.202381 0.0276190476 143 2.173 2.177833 -0.0048333333 144 1.892 1.849000 0.0430000000 145 1.875 1.917596 -0.0425964912 146 1.949 1.987328 -0.0383278689 147 1.919 1.881852 0.0371481481 148 1.826 1.823040 0.0029600000 149 1.987 1.987328 -0.0003278689 150 2.041 2.013556 0.0274444444 151 1.681 1.740412 -0.0594117647 152 1.820 1.772125 0.0478750000 153 1.845 1.802613 0.0423870968 154 2.146 2.149600 -0.0036000000 155 2.143 2.130476 0.0125238095 156 1.978 1.951463 0.0265365854 157 1.929 1.987328 -0.0583278689 158 2.000 2.020222 -0.0202222222 159 1.954 1.951463 0.0025365854 160 1.929 1.951463 -0.0224634146 161 2.041 2.020222 0.0207777778 162 2.161 2.149600 0.0114000000 163 2.217 2.098333 0.1186666667 164 2.146 2.149600 -0.0036000000 165 1.833 1.881852 -0.0488518519 166 1.987 1.955468 0.0315319149 167 1.875 1.955468 -0.0804680851 168 1.929 1.951463 -0.0224634146 169 2.013 1.987328 0.0256721311 170 2.097 2.098333 -0.0013333333 171 2.124 2.020222 0.1037777778 172 1.851 1.849000 0.0020000000 173 1.833 1.881852 -0.0488518519 174 1.929 1.951463 -0.0224634146 175 1.944 1.951463 -0.0074634146 176 2.041 2.020222 0.0207777778 177 2.114 2.126000 -0.0120000000 178 2.140 2.149600 -0.0096000000 179 2.130 2.126000 0.0040000000 180 2.152 2.149600 0.0024000000 181 2.097 2.130476 -0.0334761905 182 1.851 1.849000 0.0020000000 183 1.813 1.823040 -0.0100400000 184 1.903 1.987328 -0.0843278689 185 1.851 1.912947 -0.0619473684 186 1.954 1.912947 0.0410526316 187 1.845 1.881852 -0.0368518519 188 1.813 1.802613 0.0103870968 189 1.954 1.987328 -0.0333278689 190 2.061 2.013556 0.0474444444 191 1.954 1.955468 -0.0014680851 192 1.881 1.881852 -0.0008518519 193 1.845 1.881852 -0.0368518519 194 1.813 1.823040 -0.0100400000 195 1.944 1.951463 -0.0074634146 196 1.954 1.912947 0.0410526316 197 1.892 1.881852 0.0101481481 198 1.954 1.987328 -0.0333278689 199 1.964 1.955468 0.0085319149 200 1.875 1.917596 -0.0425964912 201 2.021 1.987328 0.0336721311 202 1.813 1.802613 0.0103870968 203 1.826 1.832222 -0.0062222222 204 1.826 1.849000 -0.0230000000 205 1.792 1.823040 -0.0310400000 206 1.944 1.955468 -0.0114680851 207 1.924 1.917596 0.0064035088 208 1.924 1.917596 0.0064035088 209 2.041 2.025923 0.0150769231 210 1.924 1.955468 -0.0314680851 211 1.806 1.823040 -0.0170400000 212 1.778 1.772125 0.0058750000 213 1.813 1.802613 0.0103870968 214 1.792 1.802613 -0.0106129032 215 1.799 1.881852 -0.0828518519 216 1.813 1.823040 -0.0100400000 217 1.869 1.881852 -0.0128518519 218 2.000 2.013556 -0.0135555556 219 1.903 1.912947 -0.0099473684 220 2.079 2.098333 -0.0193333333 221 2.041 2.020222 0.0207777778 222 1.944 1.951463 -0.0074634146 223 1.929 2.020222 -0.0912222222 224 1.944 1.917596 0.0264035088 225 1.944 1.917596 0.0264035088 226 1.924 1.917596 0.0064035088 227 1.954 1.951463 0.0025365854 228 1.869 1.823040 0.0459600000 229 1.833 1.802613 0.0303870968 230 1.799 1.881852 -0.0828518519 231 1.944 1.881852 0.0621481481 232 1.875 1.881852 -0.0068518519 233 1.826 1.823040 0.0029600000 234 2.041 1.987328 0.0536721311 235 1.929 1.951463 -0.0224634146 236 1.982 2.025923 -0.0439230769 237 1.954 1.951463 0.0025365854 238 1.934 1.987328 -0.0533278689 239 1.924 1.955468 -0.0314680851 240 1.898 1.917596 -0.0195964912 241 1.839 1.802613 0.0363870968 242 1.716 1.772125 -0.0561250000 243 1.763 1.772125 -0.0091250000 244 1.778 1.772125 0.0058750000 245 1.813 1.802613 0.0103870968 246 1.724 1.802613 -0.0786129032 247 1.724 1.772125 -0.0481250000 248 1.820 1.772125 0.0478750000 249 1.778 1.772125 0.0058750000 250 1.851 1.849000 0.0020000000 251 1.763 1.802613 -0.0396129032 252 1.778 1.802613 -0.0246129032 253 1.663 1.740412 -0.0774117647 254 1.813 1.740412 0.0725882353 255 1.792 1.849000 -0.0570000000 256 1.826 1.849000 -0.0230000000 257 1.690 1.740412 -0.0504117647 258 1.806 1.823040 -0.0170400000 259 1.903 1.849000 0.0540000000 260 1.851 1.849000 0.0020000000 261 1.845 1.849000 -0.0040000000 262 1.845 1.849000 -0.0040000000 263 1.892 1.849000 0.0430000000 264 1.845 1.823040 0.0219600000 265 1.663 1.740412 -0.0774117647 266 1.826 1.802613 0.0233870968 267 1.845 1.740412 0.1045882353 268 1.826 1.823040 0.0029600000 269 1.826 1.849000 -0.0230000000 270 1.826 1.823040 0.0029600000 271 1.778 1.802613 -0.0246129032 272 1.833 1.802613 0.0303870968 273 1.813 1.849000 -0.0360000000 274 1.813 1.802613 0.0103870968 275 1.869 1.849000 0.0200000000 276 1.681 1.740412 -0.0594117647 277 1.826 1.823040 0.0029600000 278 1.716 1.802613 -0.0866129032 279 1.833 1.823040 0.0099600000 280 1.851 1.849000 0.0020000000 281 1.845 1.823040 0.0219600000 282 1.826 1.823040 0.0029600000 283 1.826 1.823040 0.0029600000 284 1.785 1.802613 -0.0176129032 285 1.813 1.823040 -0.0100400000 286 1.813 1.802613 0.0103870968 287 1.833 1.802613 0.0303870968 288 1.716 1.740412 -0.0244117647 289 1.813 1.823040 -0.0100400000 290 1.833 1.802613 0.0303870968 291 1.792 1.823040 -0.0310400000 292 1.799 1.823040 -0.0240400000 293 1.881 1.881852 -0.0008518519 294 1.826 1.802613 0.0233870968 295 1.845 1.802613 0.0423870968 296 1.845 1.740412 0.1045882353 297 1.919 1.881852 0.0371481481 298 1.681 1.740412 -0.0594117647 299 1.944 1.881852 0.0621481481 300 1.875 1.881852 -0.0068518519 301 1.813 1.802613 0.0103870968 302 1.813 1.802613 0.0103870968 303 1.845 1.881852 -0.0368518519 304 1.954 1.881852 0.0721481481 305 1.954 1.881852 0.0721481481 306 1.875 1.881852 -0.0068518519 307 1.799 1.881852 -0.0828518519 308 1.845 1.881852 -0.0368518519 309 1.903 1.881852 0.0211481481 310 1.839 1.881852 -0.0428518519 311 1.716 1.740412 -0.0244117647 312 1.944 1.881852 0.0621481481 313 1.944 1.881852 0.0621481481 314 1.833 1.881852 -0.0488518519 315 1.881 1.881852 -0.0008518519 316 1.826 1.802613 0.0233870968 317 1.845 1.881852 -0.0368518519 318 1.903 1.881852 0.0211481481 319 1.833 1.881852 -0.0488518519 320 1.875 1.881852 -0.0068518519 321 1.875 1.881852 -0.0068518519 322 1.944 1.881852 0.0621481481 323 1.903 1.881852 0.0211481481 324 1.778 1.740412 0.0375882353 325 1.875 1.881852 -0.0068518519 326 1.892 1.881852 0.0101481481 327 1.839 1.802613 0.0363870968 328 1.892 1.881852 0.0101481481 329 1.869 1.881852 -0.0128518519 330 1.845 1.881852 -0.0368518519 331 1.919 1.881852 0.0371481481 332 1.875 1.881852 -0.0068518519 333 1.875 1.881852 -0.0068518519 334 1.799 1.881852 -0.0828518519 335 1.919 1.917596 0.0014035088 336 1.934 1.955468 -0.0214680851 337 1.908 1.917596 -0.0095964912 338 1.851 1.917596 -0.0665964912 339 1.934 1.917596 0.0164035088 340 1.978 1.955468 0.0225319149 341 1.875 1.955468 -0.0804680851 342 2.053 1.955468 0.0975319149 343 1.845 1.917596 -0.0725964912 344 1.875 1.955468 -0.0804680851 345 1.732 1.832222 -0.1002222222 346 1.898 1.917596 -0.0195964912 347 1.954 1.917596 0.0364035088 348 1.954 1.917596 0.0364035088 349 1.875 1.917596 -0.0425964912 350 1.875 1.955468 -0.0804680851 351 1.978 1.917596 0.0604035088 352 1.944 1.917596 0.0264035088 353 1.964 1.917596 0.0464035088 354 1.857 1.917596 -0.0605964912 355 1.924 1.955468 -0.0314680851 356 1.982 1.917596 0.0644035088 357 1.892 1.955468 -0.0634680851 358 1.987 1.955468 0.0315319149 359 1.929 1.917596 0.0114035088 360 1.987 1.955468 0.0315319149 361 1.681 1.832222 -0.1512222222 362 1.875 1.917596 -0.0425964912 363 1.924 1.955468 -0.0314680851 364 1.978 1.955468 0.0225319149 365 1.978 1.917596 0.0604035088 366 1.973 1.917596 0.0554035088 367 1.813 1.832222 -0.0192222222 368 1.924 1.955468 -0.0314680851 369 1.968 1.917596 0.0504035088 370 1.934 1.917596 0.0164035088 371 1.944 1.917596 0.0264035088 372 1.857 1.832222 0.0247777778 373 1.987 1.955468 0.0315319149 374 1.857 1.917596 -0.0605964912 375 1.954 1.832222 0.1217777778 376 2.000 1.955468 0.0445319149 377 1.954 1.955468 -0.0014680851 378 1.964 1.955468 0.0085319149 379 1.903 1.917596 -0.0145964912 380 1.934 1.917596 0.0164035088 381 2.029 1.955468 0.0735319149 382 1.987 1.955468 0.0315319149 383 1.924 1.917596 0.0064035088 384 1.944 1.955468 -0.0114680851 385 1.987 1.955468 0.0315319149 386 1.881 1.917596 -0.0365964912 387 1.978 1.955468 0.0225319149 388 1.924 1.917596 0.0064035088 389 1.875 1.917596 -0.0425964912 390 1.875 1.917596 -0.0425964912 391 1.987 1.917596 0.0694035088 392 1.954 1.955468 -0.0014680851 393 1.978 1.955468 0.0225319149 394 1.857 1.955468 -0.0984680851 395 1.964 1.955468 0.0085319149 396 1.929 1.917596 0.0114035088 397 1.959 1.955468 0.0035319149 398 1.964 1.955468 0.0085319149 399 1.929 1.917596 0.0114035088 400 1.892 1.917596 -0.0255964912 401 2.061 2.013556 0.0474444444 402 2.041 2.013556 0.0274444444 403 1.944 1.832222 0.1117777778 404 2.000 2.013556 -0.0135555556 405 1.964 2.013556 -0.0495555556 406 1.898 1.917596 -0.0195964912 407 2.000 2.013556 -0.0135555556 408 1.924 1.917596 0.0064035088 409 1.869 1.917596 -0.0485964912 410 1.919 1.917596 0.0014035088 411 1.944 1.917596 0.0264035088 412 1.954 2.013556 -0.0595555556 413 2.041 2.025923 0.0150769231 414 1.982 2.025923 -0.0439230769 415 1.903 1.987328 -0.0843278689 416 1.954 1.987328 -0.0333278689 417 2.061 2.025923 0.0350769231 418 1.940 1.951463 -0.0114634146 419 1.954 1.951463 0.0025365854 420 1.978 1.987328 -0.0093278689 421 2.061 2.025923 0.0350769231 422 1.982 2.025923 -0.0439230769 423 1.954 1.987328 -0.0333278689 424 1.944 1.987328 -0.0433278689 425 1.914 1.951463 -0.0374634146 426 2.041 2.025923 0.0150769231 427 2.041 2.025923 0.0150769231 428 1.954 1.951463 0.0025365854 429 1.944 1.987328 -0.0433278689 430 2.021 1.951463 0.0695365854 431 1.949 1.987328 -0.0383278689 432 1.987 1.987328 -0.0003278689 433 2.000 1.987328 0.0126721311 434 1.934 1.987328 -0.0533278689 435 2.061 1.987328 0.0736721311 436 2.021 2.025923 -0.0049230769 437 2.086 2.098333 -0.0123333333 438 1.987 1.987328 -0.0003278689 439 2.013 1.987328 0.0256721311 440 1.978 1.987328 -0.0093278689 441 2.049 1.987328 0.0616721311 442 1.929 1.951463 -0.0224634146 443 1.964 1.987328 -0.0233278689 444 2.049 1.987328 0.0616721311 445 1.944 1.951463 -0.0074634146 446 1.944 1.951463 -0.0074634146 447 2.064 2.098333 -0.0343333333 448 2.000 1.987328 0.0126721311 449 1.978 1.987328 -0.0093278689 450 2.121 2.098333 0.0226666667 451 2.000 1.987328 0.0126721311 452 2.033 1.987328 0.0456721311 453 2.079 2.098333 -0.0193333333 454 2.049 1.987328 0.0616721311 455 2.000 1.987328 0.0126721311 456 2.041 1.987328 0.0536721311 457 1.991 1.987328 0.0036721311 458 1.954 1.951463 0.0025365854 459 1.826 1.832222 -0.0062222222 460 1.944 1.951463 -0.0074634146 461 2.041 2.098333 -0.0573333333 462 1.929 1.987328 -0.0583278689 463 1.940 1.912947 0.0270526316 464 1.987 1.987328 -0.0003278689 465 1.929 1.951463 -0.0224634146 466 1.954 1.912947 0.0410526316 467 1.908 1.951463 -0.0434634146 468 1.929 1.951463 -0.0224634146 469 1.944 1.951463 -0.0074634146 470 2.041 1.987328 0.0536721311 471 1.944 1.951463 -0.0074634146 472 1.929 1.951463 -0.0224634146 473 1.954 1.951463 0.0025365854 474 2.352 2.331812 0.0201875000 475 1.978 1.951463 0.0265365854 476 2.021 1.951463 0.0695365854 477 1.944 1.987328 -0.0433278689 478 2.097 2.098333 -0.0013333333 479 2.009 1.987328 0.0216721311 480 1.978 1.951463 0.0265365854 481 1.857 1.912947 -0.0559473684 482 1.881 1.912947 -0.0319473684 483 2.111 2.130476 -0.0194761905 484 1.851 1.912947 -0.0619473684 485 1.978 1.951463 0.0265365854 486 2.143 2.130476 0.0125238095 487 1.954 1.951463 0.0025365854 488 1.954 1.951463 0.0025365854 489 2.041 2.098333 -0.0573333333 490 1.903 1.912947 -0.0099473684 491 2.000 1.987328 0.0126721311 492 2.061 1.987328 0.0736721311 493 1.826 1.912947 -0.0869473684 494 1.978 1.987328 -0.0093278689 495 1.954 1.951463 0.0025365854 496 1.944 1.912947 0.0310526316 497 2.000 1.951463 0.0485365854 498 2.000 1.987328 0.0126721311 499 2.000 1.987328 0.0126721311 500 1.944 1.987328 -0.0433278689 501 2.000 2.015231 -0.0152307692 502 2.000 2.015231 -0.0152307692 503 2.021 2.015231 0.0057692308 504 2.041 2.020222 0.0207777778 505 2.041 2.046429 -0.0054285714 506 2.021 2.020222 0.0007777778 507 1.954 2.020222 -0.0662222222 508 2.176 2.167045 0.0089545455 509 2.079 2.046429 0.0325714286 510 2.124 2.020222 0.1037777778 511 2.041 2.020222 0.0207777778 512 1.954 1.912947 0.0410526316 513 2.021 2.020222 0.0007777778 514 2.161 2.167045 -0.0060454545 515 2.000 2.020222 -0.0202222222 516 1.857 1.912947 -0.0559473684 517 2.176 2.167045 0.0089545455 518 2.176 2.167045 0.0089545455 519 2.021 2.020222 0.0007777778 520 2.217 2.098333 0.1186666667 521 2.146 2.130476 0.0155238095 522 2.021 2.015231 0.0057692308 523 1.929 2.020222 -0.0912222222 524 2.114 2.167045 -0.0530454545 525 2.041 2.015231 0.0257692308 526 1.991 2.015231 -0.0242307692 527 1.886 1.912947 -0.0269473684 528 2.021 2.020222 0.0007777778 529 2.230 2.245767 -0.0157666667 530 2.143 2.130476 0.0125238095 531 1.892 1.912947 -0.0209473684 532 2.097 2.130476 -0.0334761905 533 2.204 2.202381 0.0016190476 534 2.021 2.020222 0.0007777778 535 2.041 2.020222 0.0207777778 536 2.000 2.020222 -0.0202222222 537 2.041 2.046429 -0.0054285714 538 2.041 2.015231 0.0257692308 539 2.000 2.020222 -0.0202222222 540 2.255 2.202381 0.0526190476 541 2.176 2.167045 0.0089545455 542 2.176 2.167045 0.0089545455 543 2.217 2.202381 0.0146190476 544 2.111 2.130476 -0.0194761905 545 2.021 2.126000 -0.1050000000 546 2.041 2.046429 -0.0054285714 547 2.146 2.167045 -0.0210454545 548 2.176 2.149600 0.0264000000 549 2.176 2.245767 -0.0697666667 550 2.176 2.167045 0.0089545455 551 2.000 2.015231 -0.0152307692 552 1.978 2.020222 -0.0422222222 553 2.079 2.020222 0.0587777778 554 2.243 2.245767 -0.0027666667 555 2.130 2.126000 0.0040000000 556 2.114 2.126000 -0.0120000000 557 2.279 2.245767 0.0332333333 558 2.114 2.130476 -0.0164761905 559 2.161 2.167045 -0.0060454545 560 2.176 2.167045 0.0089545455 561 2.021 2.015231 0.0057692308 562 2.097 2.126000 -0.0290000000 563 2.041 1.912947 0.1280526316 564 2.176 2.167045 0.0089545455 565 2.176 2.245767 -0.0697666667 566 2.140 2.202381 -0.0623809524 567 2.079 2.149600 -0.0706000000 568 2.161 2.202381 -0.0413809524 569 2.137 2.130476 0.0065238095 570 2.152 2.149600 0.0024000000 571 2.161 2.202381 -0.0413809524 572 2.041 2.046429 -0.0054285714 573 2.176 2.149600 0.0264000000 574 2.146 2.149600 -0.0036000000 575 2.161 2.202381 -0.0413809524 576 2.176 2.167045 0.0089545455 577 2.114 2.149600 -0.0356000000 578 2.243 2.202381 0.0406190476 579 2.185 2.202381 -0.0173809524 580 2.176 2.149600 0.0264000000 581 2.161 2.149600 0.0114000000 582 2.146 2.130476 0.0155238095 583 2.185 2.149600 0.0354000000 584 2.230 2.202381 0.0276190476 585 2.176 2.167045 0.0089545455 586 2.217 2.202381 0.0146190476 587 2.146 2.167045 -0.0210454545 588 2.182 2.202381 -0.0203809524 589 2.255 2.245767 0.0092333333 590 2.176 2.126000 0.0500000000 591 2.176 2.126000 0.0500000000 592 2.217 2.231500 -0.0145000000 593 2.362 2.245767 0.1162333333 594 2.146 2.130476 0.0155238095 595 2.114 2.130476 -0.0164761905 596 2.332 2.331812 0.0001875000 597 2.279 2.245767 0.0332333333 598 2.173 2.177833 -0.0048333333 599 2.297 2.331812 -0.0348125000 600 2.342 2.331812 0.0101875000 601 2.190 2.177833 0.0121666667 602 2.199 2.231500 -0.0325000000 603 2.301 2.264000 0.0370000000 604 2.255 2.231500 0.0235000000 605 2.322 2.264000 0.0580000000 606 2.243 2.245767 -0.0027666667 607 2.279 2.245767 0.0332333333 608 2.352 2.331812 0.0201875000 609 2.161 2.177833 -0.0168333333 610 2.204 2.177833 0.0261666667 611 2.176 2.264000 -0.0880000000 612 2.243 2.245767 -0.0027666667 613 2.176 2.245767 -0.0697666667 614 2.176 2.264000 -0.0880000000 615 2.255 2.231500 0.0235000000 616 2.190 2.177833 0.0121666667 617 2.332 2.245767 0.0862333333 618 2.318 2.331812 -0.0138125000 619 2.146 2.130476 0.0155238095 620 2.230 2.245767 -0.0157666667 621 2.170 2.177833 -0.0078333333 622 2.230 2.245767 -0.0157666667 623 2.176 2.177833 -0.0018333333 624 2.286 2.264000 0.0220000000 625 2.332 2.331812 0.0001875000 626 2.230 2.245767 -0.0157666667 627 2.223 2.245767 -0.0227666667 628 2.352 2.331812 0.0201875000 629 2.297 2.331812 -0.0348125000 630 2.255 2.245767 0.0092333333 631 2.176 2.245767 -0.0697666667 632 2.243 2.245767 -0.0027666667 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/rcomp/tmp/4884c1293281640.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/www/rcomp/tmp/5trli1293281640.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/www/rcomp/tmp/6fa161293281640.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/www/rcomp/tmp/70azu1293281640.tab") + } > > try(system("convert tmp/2fhnr1293281640.ps tmp/2fhnr1293281640.png",intern=TRUE)) character(0) > try(system("convert tmp/3fhnr1293281640.ps tmp/3fhnr1293281640.png",intern=TRUE)) character(0) > try(system("convert tmp/4884c1293281640.ps tmp/4884c1293281640.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 13.660 0.900 14.533