R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 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. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- array(list(3.567 + ,2.217 + ,1.061 + ,3.536 + ,2.176 + ,1.041 + ,3.538 + ,2.146 + ,1.021 + ,3.638 + ,2.297 + ,1.000 + ,3.635 + ,2.332 + ,0.929 + ,3.646 + ,2.352 + ,1.000 + ,3.552 + ,2.230 + ,1.000 + ,3.557 + ,2.204 + ,0.903 + ,3.489 + ,2.352 + ,1.000 + ,3.375 + ,1.978 + ,1.176 + ,3.443 + ,1.987 + ,1.190 + ,3.264 + ,1.663 + ,1.312 + ,3.427 + ,1.940 + ,1.243 + ,3.376 + ,1.978 + ,1.243 + ,3.349 + ,2.053 + ,1.097 + ,3.664 + ,2.332 + ,1.146 + ,3.641 + ,2.301 + ,1.176 + ,3.675 + ,2.286 + ,1.267 + ,3.328 + ,1.944 + ,1.161 + ,3.348 + ,1.978 + ,1.146 + ,3.522 + ,2.000 + ,1.190 + ,3.517 + ,2.000 + ,1.190 + ,3.624 + ,2.217 + ,1.079 + ,3.612 + ,2.176 + ,1.114 + ,3.676 + ,2.230 + ,1.079 + ,3.711 + ,2.243 + ,1.079 + ,3.382 + ,1.857 + ,1.279 + ,3.516 + ,2.000 + ,1.176 + ,3.346 + ,1.934 + ,1.146 + ,3.327 + ,1.954 + ,1.146 + ,3.315 + ,1.881 + ,1.161 + ,3.249 + ,1.813 + ,1.279 + ,3.263 + ,1.778 + ,1.279 + ,3.291 + ,1.845 + ,1.312 + ,3.328 + ,1.903 + ,1.230 + ,3.348 + ,1.934 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,1.204 + ,3.631 + ,2.217 + ,1.079 + ,3.631 + ,2.362 + ,1.000 + ,3.633 + ,2.146 + ,1.204 + ,3.633 + ,2.114 + ,1.176 + ,3.635 + ,2.332 + ,0.954 + ,3.636 + ,2.279 + ,1.079 + ,3.637 + ,2.173 + ,1.176 + ,3.638 + ,2.297 + ,1.000 + ,3.639 + ,2.342 + ,0.954 + ,3.639 + ,2.190 + ,1.176 + ,3.640 + ,2.199 + ,1.114 + ,3.641 + ,2.301 + ,1.176 + ,3.641 + ,2.255 + ,1.079 + ,3.642 + ,2.322 + ,1.146 + ,3.642 + ,2.243 + ,1.079 + ,3.646 + ,2.279 + ,1.114 + ,3.646 + ,2.352 + ,1.000 + ,3.647 + ,2.161 + ,1.146 + ,3.649 + ,2.204 + ,1.146 + ,3.649 + ,2.176 + ,1.146 + ,3.650 + ,2.243 + ,1.079 + ,3.650 + ,2.176 + ,1.079 + ,3.653 + ,2.176 + ,1.176 + ,3.653 + ,2.255 + ,1.114 + ,3.653 + ,2.190 + ,1.146 + ,3.664 + ,2.332 + ,1.146 + ,3.666 + ,2.318 + ,1.041 + ,3.666 + ,2.146 + ,1.204 + ,3.668 + ,2.230 + ,1.114 + ,3.668 + ,2.170 + ,1.146 + ,3.669 + ,2.230 + ,1.079 + ,3.672 + ,2.176 + ,1.176 + ,3.675 + ,2.286 + ,1.279 + ,3.675 + ,2.332 + ,1.041 + ,3.676 + ,2.230 + ,1.079 + ,3.691 + ,2.223 + ,1.114 + ,3.695 + ,2.352 + ,1.041 + ,3.695 + ,2.297 + ,1.079 + ,3.695 + ,2.255 + ,1.079 + ,3.699 + ,2.176 + ,1.146 + ,3.711 + ,2.243 + ,1.079) + ,dim=c(3 + ,632) + ,dimnames=list(c('weight' + ,'horsepower' + ,'acceleration ') + ,1:632)) > y <- array(NA,dim=c(3,632),dimnames=list(c('weight','horsepower','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 = '3' > 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 Attaching package: 'zoo' The following object(s) are masked from package:base : 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) 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] "acceleration.." > x[,par1] [1] 1.061 1.041 1.021 1.000 0.929 1.000 1.000 0.903 1.000 1.176 1.190 1.312 [13] 1.243 1.243 1.097 1.146 1.176 1.267 1.161 1.146 1.190 1.190 1.079 1.114 [25] 1.079 1.079 1.279 1.176 1.146 1.146 1.161 1.279 1.279 1.312 1.230 1.217 [37] 1.079 1.130 1.114 1.041 1.130 1.097 1.130 1.146 1.204 1.161 1.255 1.204 [49] 1.161 1.176 1.114 1.061 1.161 1.097 1.079 1.114 1.041 1.041 1.217 1.255 [61] 1.217 1.204 1.146 1.097 1.176 1.290 1.217 1.267 1.146 1.114 0.978 1.190 [73] 1.146 1.041 1.146 1.041 1.217 1.204 1.217 1.322 1.230 1.255 1.146 1.161 [85] 1.204 1.190 1.190 1.161 1.279 1.161 1.146 1.176 1.204 1.204 1.290 1.061 [97] 1.146 1.130 1.322 1.279 1.279 1.130 1.079 1.230 1.204 1.130 1.217 1.161 [109] 1.176 1.230 1.130 1.243 1.228 1.173 1.185 1.114 1.143 1.107 1.161 1.246 [121] 1.346 1.344 1.248 1.210 1.250 1.230 1.215 1.196 1.121 1.223 1.083 1.176 [133] 1.146 1.170 1.270 1.225 1.097 1.137 1.228 1.248 1.045 1.057 1.161 1.161 [145] 1.260 1.199 1.201 1.215 1.161 1.107 1.332 1.158 1.270 1.121 1.107 1.260 [157] 1.199 1.236 1.236 1.223 1.272 1.121 1.127 1.137 1.217 1.167 1.161 1.246 [169] 1.201 1.134 1.199 1.173 1.220 1.260 1.238 1.220 1.188 1.121 1.182 1.155 [181] 1.176 1.146 1.182 1.176 1.394 1.346 1.173 1.283 1.204 1.053 1.121 1.167 [193] 1.190 1.215 1.258 1.303 1.199 1.190 1.176 1.182 1.158 1.283 1.299 1.140 [205] 1.185 1.179 1.196 1.215 1.100 1.111 1.215 1.207 1.288 1.238 1.173 1.210 [217] 1.152 1.170 1.310 1.140 1.199 1.233 1.220 1.270 1.255 1.204 1.255 1.185 [229] 1.246 1.167 1.161 1.161 1.196 1.215 1.230 1.143 1.238 1.193 1.064 1.270 [241] 1.255 1.230 1.230 1.204 1.279 1.255 1.230 1.146 1.204 1.079 1.279 1.279 [253] 1.322 1.322 1.176 1.146 1.301 1.204 1.146 1.146 1.146 1.146 1.176 1.230 [265] 1.322 1.279 1.322 1.204 1.176 1.204 1.279 1.255 1.176 1.279 1.176 1.342 [277] 1.204 1.279 1.204 1.176 1.230 1.204 1.204 1.279 1.204 1.279 1.255 1.342 [289] 1.204 1.279 1.230 1.230 1.176 1.255 1.279 1.301 1.204 1.342 1.230 1.204 [301] 1.255 1.279 1.204 1.146 1.146 1.176 1.176 1.230 1.230 1.176 1.398 1.176 [313] 1.176 1.230 1.176 1.255 1.176 1.176 1.230 1.204 1.204 1.176 1.176 1.342 [325] 1.204 1.204 1.255 1.146 1.146 1.114 1.176 1.176 1.146 1.176 1.230 1.146 [337] 1.230 1.230 1.230 1.146 1.176 1.114 1.230 1.146 1.380 1.255 1.204 1.204 [349] 1.255 1.176 1.204 1.279 1.230 1.230 1.079 1.204 1.176 1.176 1.279 1.146 [361] 1.380 1.230 1.114 1.176 1.255 1.230 1.322 1.114 1.204 1.204 1.255 1.301 [373] 1.176 1.279 1.301 1.114 1.176 1.176 1.230 1.204 1.146 1.176 1.204 1.176 [385] 1.176 1.255 1.176 1.204 1.230 1.255 1.230 1.114 1.146 1.146 1.176 1.204 [397] 1.146 1.176 1.204 1.279 1.041 1.114 1.301 1.176 1.146 1.279 1.114 1.204 [409] 1.255 1.230 1.279 1.176 1.146 1.146 1.176 1.204 1.146 1.255 1.230 1.176 [421] 1.114 1.146 1.204 1.176 1.279 1.146 1.114 1.255 1.204 1.230 1.204 1.204 [433] 1.176 1.204 1.204 1.146 1.146 1.176 1.204 1.204 1.176 1.255 1.204 1.204 [445] 1.255 1.230 1.114 1.204 1.204 1.041 1.204 1.204 1.146 1.176 1.204 1.204 [457] 1.176 1.230 1.301 1.230 1.146 1.204 1.301 1.176 1.255 1.301 1.255 1.230 [469] 1.230 1.176 1.230 1.230 1.255 1.000 1.230 1.230 1.176 1.146 1.204 1.255 [481] 1.301 1.301 1.079 1.398 1.255 1.041 1.230 1.230 1.146 1.301 1.176 1.176 [493] 1.342 1.204 1.255 1.342 1.255 1.176 1.204 1.204 1.204 1.230 1.176 1.230 [505] 1.204 1.204 1.279 1.041 1.176 1.204 1.204 1.342 1.230 1.114 1.230 1.322 [517] 1.079 1.041 1.204 1.114 1.041 1.204 1.230 1.079 1.204 1.279 1.301 1.279 [529] 1.000 1.114 1.322 1.176 0.903 1.230 1.279 1.255 1.255 1.204 1.255 1.041 [541] 1.079 1.079 1.079 1.114 1.279 1.279 1.114 1.146 1.000 1.114 1.230 1.279 [553] 1.230 1.041 1.176 1.176 0.954 1.176 1.114 1.114 1.279 1.230 1.322 1.114 [565] 1.114 1.114 1.146 1.114 1.176 1.146 1.079 1.279 1.146 1.146 1.114 1.114 [577] 1.146 1.114 1.114 1.146 1.146 1.146 1.146 1.041 1.114 1.079 1.114 1.114 [589] 1.041 1.176 1.204 1.079 1.000 1.204 1.176 0.954 1.079 1.176 1.000 0.954 [601] 1.176 1.114 1.176 1.079 1.146 1.079 1.114 1.000 1.146 1.146 1.146 1.079 [613] 1.079 1.176 1.114 1.146 1.146 1.041 1.204 1.114 1.146 1.079 1.176 1.279 [625] 1.041 1.079 1.114 1.041 1.079 1.079 1.146 1.079 > 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]) 0.903 0.929 0.954 0.978 1 1.021 1.041 1.045 1.053 1.057 1.061 1.064 1.079 2 1 3 1 10 1 19 1 1 1 3 1 27 1.083 1.097 1.1 1.107 1.111 1.114 1.121 1.127 1.13 1.134 1.137 1.14 1.143 1 5 1 3 1 41 5 1 7 1 2 2 2 1.146 1.152 1.155 1.158 1.161 1.167 1.17 1.173 1.176 1.179 1.182 1.185 1.188 63 1 1 2 16 3 2 4 75 1 3 3 1 1.19 1.193 1.196 1.199 1.201 1.204 1.207 1.21 1.215 1.217 1.22 1.223 1.225 8 1 3 5 2 74 1 2 6 8 3 2 1 1.228 1.23 1.233 1.236 1.238 1.243 1.246 1.248 1.25 1.255 1.258 1.26 1.267 2 53 1 2 3 3 3 2 1 34 1 3 2 1.27 1.272 1.279 1.283 1.288 1.29 1.299 1.301 1.303 1.31 1.312 1.322 1.332 4 1 35 2 1 2 1 12 1 1 2 10 1 1.342 1.344 1.346 1.38 1.394 1.398 7 1 2 2 1 2 > colnames(x) [1] "weight" "horsepower" "acceleration.." > colnames(x)[par1] [1] "acceleration.." > x[,par1] [1] 1.061 1.041 1.021 1.000 0.929 1.000 1.000 0.903 1.000 1.176 1.190 1.312 [13] 1.243 1.243 1.097 1.146 1.176 1.267 1.161 1.146 1.190 1.190 1.079 1.114 [25] 1.079 1.079 1.279 1.176 1.146 1.146 1.161 1.279 1.279 1.312 1.230 1.217 [37] 1.079 1.130 1.114 1.041 1.130 1.097 1.130 1.146 1.204 1.161 1.255 1.204 [49] 1.161 1.176 1.114 1.061 1.161 1.097 1.079 1.114 1.041 1.041 1.217 1.255 [61] 1.217 1.204 1.146 1.097 1.176 1.290 1.217 1.267 1.146 1.114 0.978 1.190 [73] 1.146 1.041 1.146 1.041 1.217 1.204 1.217 1.322 1.230 1.255 1.146 1.161 [85] 1.204 1.190 1.190 1.161 1.279 1.161 1.146 1.176 1.204 1.204 1.290 1.061 [97] 1.146 1.130 1.322 1.279 1.279 1.130 1.079 1.230 1.204 1.130 1.217 1.161 [109] 1.176 1.230 1.130 1.243 1.228 1.173 1.185 1.114 1.143 1.107 1.161 1.246 [121] 1.346 1.344 1.248 1.210 1.250 1.230 1.215 1.196 1.121 1.223 1.083 1.176 [133] 1.146 1.170 1.270 1.225 1.097 1.137 1.228 1.248 1.045 1.057 1.161 1.161 [145] 1.260 1.199 1.201 1.215 1.161 1.107 1.332 1.158 1.270 1.121 1.107 1.260 [157] 1.199 1.236 1.236 1.223 1.272 1.121 1.127 1.137 1.217 1.167 1.161 1.246 [169] 1.201 1.134 1.199 1.173 1.220 1.260 1.238 1.220 1.188 1.121 1.182 1.155 [181] 1.176 1.146 1.182 1.176 1.394 1.346 1.173 1.283 1.204 1.053 1.121 1.167 [193] 1.190 1.215 1.258 1.303 1.199 1.190 1.176 1.182 1.158 1.283 1.299 1.140 [205] 1.185 1.179 1.196 1.215 1.100 1.111 1.215 1.207 1.288 1.238 1.173 1.210 [217] 1.152 1.170 1.310 1.140 1.199 1.233 1.220 1.270 1.255 1.204 1.255 1.185 [229] 1.246 1.167 1.161 1.161 1.196 1.215 1.230 1.143 1.238 1.193 1.064 1.270 [241] 1.255 1.230 1.230 1.204 1.279 1.255 1.230 1.146 1.204 1.079 1.279 1.279 [253] 1.322 1.322 1.176 1.146 1.301 1.204 1.146 1.146 1.146 1.146 1.176 1.230 [265] 1.322 1.279 1.322 1.204 1.176 1.204 1.279 1.255 1.176 1.279 1.176 1.342 [277] 1.204 1.279 1.204 1.176 1.230 1.204 1.204 1.279 1.204 1.279 1.255 1.342 [289] 1.204 1.279 1.230 1.230 1.176 1.255 1.279 1.301 1.204 1.342 1.230 1.204 [301] 1.255 1.279 1.204 1.146 1.146 1.176 1.176 1.230 1.230 1.176 1.398 1.176 [313] 1.176 1.230 1.176 1.255 1.176 1.176 1.230 1.204 1.204 1.176 1.176 1.342 [325] 1.204 1.204 1.255 1.146 1.146 1.114 1.176 1.176 1.146 1.176 1.230 1.146 [337] 1.230 1.230 1.230 1.146 1.176 1.114 1.230 1.146 1.380 1.255 1.204 1.204 [349] 1.255 1.176 1.204 1.279 1.230 1.230 1.079 1.204 1.176 1.176 1.279 1.146 [361] 1.380 1.230 1.114 1.176 1.255 1.230 1.322 1.114 1.204 1.204 1.255 1.301 [373] 1.176 1.279 1.301 1.114 1.176 1.176 1.230 1.204 1.146 1.176 1.204 1.176 [385] 1.176 1.255 1.176 1.204 1.230 1.255 1.230 1.114 1.146 1.146 1.176 1.204 [397] 1.146 1.176 1.204 1.279 1.041 1.114 1.301 1.176 1.146 1.279 1.114 1.204 [409] 1.255 1.230 1.279 1.176 1.146 1.146 1.176 1.204 1.146 1.255 1.230 1.176 [421] 1.114 1.146 1.204 1.176 1.279 1.146 1.114 1.255 1.204 1.230 1.204 1.204 [433] 1.176 1.204 1.204 1.146 1.146 1.176 1.204 1.204 1.176 1.255 1.204 1.204 [445] 1.255 1.230 1.114 1.204 1.204 1.041 1.204 1.204 1.146 1.176 1.204 1.204 [457] 1.176 1.230 1.301 1.230 1.146 1.204 1.301 1.176 1.255 1.301 1.255 1.230 [469] 1.230 1.176 1.230 1.230 1.255 1.000 1.230 1.230 1.176 1.146 1.204 1.255 [481] 1.301 1.301 1.079 1.398 1.255 1.041 1.230 1.230 1.146 1.301 1.176 1.176 [493] 1.342 1.204 1.255 1.342 1.255 1.176 1.204 1.204 1.204 1.230 1.176 1.230 [505] 1.204 1.204 1.279 1.041 1.176 1.204 1.204 1.342 1.230 1.114 1.230 1.322 [517] 1.079 1.041 1.204 1.114 1.041 1.204 1.230 1.079 1.204 1.279 1.301 1.279 [529] 1.000 1.114 1.322 1.176 0.903 1.230 1.279 1.255 1.255 1.204 1.255 1.041 [541] 1.079 1.079 1.079 1.114 1.279 1.279 1.114 1.146 1.000 1.114 1.230 1.279 [553] 1.230 1.041 1.176 1.176 0.954 1.176 1.114 1.114 1.279 1.230 1.322 1.114 [565] 1.114 1.114 1.146 1.114 1.176 1.146 1.079 1.279 1.146 1.146 1.114 1.114 [577] 1.146 1.114 1.114 1.146 1.146 1.146 1.146 1.041 1.114 1.079 1.114 1.114 [589] 1.041 1.176 1.204 1.079 1.000 1.204 1.176 0.954 1.079 1.176 1.000 0.954 [601] 1.176 1.114 1.176 1.079 1.146 1.079 1.114 1.000 1.146 1.146 1.146 1.079 [613] 1.079 1.176 1.114 1.146 1.146 1.041 1.204 1.114 1.146 1.079 1.176 1.279 [625] 1.041 1.079 1.114 1.041 1.079 1.079 1.146 1.079 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/html/freestat/rcomp/tmp/1n2xk1293036445.tab") + } + } > m Conditional inference tree with 19 terminal nodes Response: acceleration.. Inputs: weight, horsepower Number of observations: 632 1) horsepower <= 2.049; criterion = 1, statistic = 316.549 2) horsepower <= 1.785; criterion = 1, statistic = 54.789 3) weight <= 3.263; criterion = 1, statistic = 20.999 4)* weights = 12 3) weight > 3.263 5)* weights = 18 2) horsepower > 1.785 6) weight <= 3.469; criterion = 1, statistic = 31.917 7) horsepower <= 1.949; criterion = 1, statistic = 34.05 8)* weights = 204 7) horsepower > 1.949 9) horsepower <= 1.978; criterion = 0.977, statistic = 6.395 10)* weights = 44 9) horsepower > 1.978 11) weight <= 3.435; criterion = 0.976, statistic = 6.334 12) horsepower <= 1.987; criterion = 0.989, statistic = 7.749 13)* weights = 15 12) horsepower > 1.987 14)* weights = 13 11) weight > 3.435 15)* weights = 24 6) weight > 3.469 16) horsepower <= 1.903; criterion = 1, statistic = 29.473 17)* weights = 13 16) horsepower > 1.903 18) weight <= 3.533; criterion = 0.998, statistic = 10.766 19) horsepower <= 1.978; criterion = 1, statistic = 19.013 20)* weights = 38 19) horsepower > 1.978 21)* weights = 28 18) weight > 3.533 22)* weights = 35 1) horsepower > 2.049 23) horsepower <= 2.199; criterion = 1, statistic = 47.05 24) weight <= 3.577; criterion = 0.999, statistic = 11.949 25) horsepower <= 2.124; criterion = 0.992, statistic = 8.221 26)* weights = 25 25) horsepower > 2.124 27)* weights = 22 24) weight > 3.577 28) horsepower <= 2.146; criterion = 1, statistic = 17.631 29)* weights = 28 28) horsepower > 2.146 30) weight <= 3.625; criterion = 0.964, statistic = 5.588 31)* weights = 25 30) weight > 3.625 32)* weights = 22 23) horsepower > 2.199 33) weight <= 3.639; criterion = 0.991, statistic = 8.105 34) horsepower <= 2.279; criterion = 0.972, statistic = 6.023 35)* weights = 23 34) horsepower > 2.279 36)* weights = 9 33) weight > 3.639 37)* weights = 34 > postscript(file="/var/www/html/freestat/rcomp/tmp/2n2xk1293036445.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/html/freestat/rcomp/tmp/3gbwn1293036445.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 1.061 1.0465652 1.443478e-02 2 1.041 1.0799545 -3.895455e-02 3 1.021 1.0799545 -5.895455e-02 4 1.000 0.9794444 2.055556e-02 5 0.929 0.9794444 -5.044444e-02 6 1.000 1.0974118 -9.741176e-02 7 1.000 1.0465652 -4.656522e-02 8 0.903 1.0465652 -1.435652e-01 9 1.000 0.9794444 2.055556e-02 10 1.176 1.1888182 -1.281818e-02 11 1.190 1.1899167 8.333333e-05 12 1.312 1.3289444 -1.694444e-02 13 1.243 1.2105147 3.248529e-02 14 1.243 1.1888182 5.418182e-02 15 1.097 1.1289600 -3.196000e-02 16 1.146 1.0974118 4.858824e-02 17 1.176 1.0974118 7.858824e-02 18 1.267 1.0974118 1.695882e-01 19 1.161 1.2105147 -4.951471e-02 20 1.146 1.1888182 -4.281818e-02 21 1.190 1.1941429 -4.142857e-03 22 1.190 1.1941429 -4.142857e-03 23 1.079 1.0465652 3.243478e-02 24 1.114 1.1215200 -7.520000e-03 25 1.079 1.0974118 -1.841176e-02 26 1.079 1.0974118 -1.841176e-02 27 1.279 1.2105147 6.848529e-02 28 1.176 1.1941429 -1.814286e-02 29 1.146 1.2105147 -6.451471e-02 30 1.146 1.1888182 -4.281818e-02 31 1.161 1.2105147 -4.951471e-02 32 1.279 1.2105147 6.848529e-02 33 1.279 1.2425000 3.650000e-02 34 1.312 1.2105147 1.014853e-01 35 1.230 1.2105147 1.948529e-02 36 1.217 1.2105147 6.485294e-03 37 1.079 1.0465652 3.243478e-02 38 1.130 1.1215200 8.480000e-03 39 1.114 1.1215200 -7.520000e-03 40 1.041 1.0974118 -5.641176e-02 41 1.130 1.1483636 -1.836364e-02 42 1.097 1.0974118 -4.117647e-04 43 1.130 1.1655333 -3.553333e-02 44 1.146 1.1680357 -2.203571e-02 45 1.204 1.1680357 3.596429e-02 46 1.161 1.1899167 -2.891667e-02 47 1.255 1.2105147 4.448529e-02 48 1.204 1.2105147 -6.514706e-03 49 1.161 1.1655333 -4.533333e-03 50 1.176 1.2105147 -3.451471e-02 51 1.114 1.0465652 6.743478e-02 52 1.061 1.0799545 -1.895455e-02 53 1.161 1.1680357 -7.035714e-03 54 1.097 1.0799545 1.704545e-02 55 1.079 1.1483636 -6.936364e-02 56 1.114 1.1483636 -3.436364e-02 57 1.041 1.0974118 -5.641176e-02 58 1.041 1.0974118 -5.641176e-02 59 1.217 1.1941429 2.285714e-02 60 1.255 1.1941429 6.085714e-02 61 1.217 1.2409737 -2.397368e-02 62 1.204 1.1888182 1.518182e-02 63 1.146 1.1483636 -2.363636e-03 64 1.097 1.0974118 -4.117647e-04 65 1.176 1.1899167 -1.391667e-02 66 1.290 1.2105147 7.948529e-02 67 1.217 1.1888182 2.818182e-02 68 1.267 1.2105147 5.648529e-02 69 1.146 1.1337692 1.223077e-02 70 1.114 1.1215200 -7.520000e-03 71 0.978 0.9794444 -1.444444e-03 72 1.190 1.2105147 -2.051471e-02 73 1.146 1.1888182 -4.281818e-02 74 1.041 1.0799545 -3.895455e-02 75 1.146 1.1337692 1.223077e-02 76 1.041 1.0465652 -5.565217e-03 77 1.217 1.2409737 -2.397368e-02 78 1.204 1.1899167 1.408333e-02 79 1.217 1.2105147 6.485294e-03 80 1.322 1.2105147 1.114853e-01 81 1.230 1.2571429 -2.714286e-02 82 1.255 1.2571429 -2.142857e-03 83 1.146 1.1680357 -2.203571e-02 84 1.161 1.1483636 1.263636e-02 85 1.204 1.1680357 3.596429e-02 86 1.190 1.1483636 4.163636e-02 87 1.190 1.2105147 -2.051471e-02 88 1.161 1.2105147 -4.951471e-02 89 1.279 1.3289444 -4.994444e-02 90 1.161 1.2105147 -4.951471e-02 91 1.146 1.2105147 -6.451471e-02 92 1.176 1.1655333 1.046667e-02 93 1.204 1.2105147 -6.514706e-03 94 1.204 1.2409737 -3.697368e-02 95 1.290 1.3216923 -3.169231e-02 96 1.061 1.0974118 -3.641176e-02 97 1.146 1.1483636 -2.363636e-03 98 1.130 1.1483636 -1.836364e-02 99 1.322 1.2571429 6.485714e-02 100 1.279 1.2571429 2.185714e-02 101 1.279 1.2571429 2.185714e-02 102 1.130 1.1941429 -6.414286e-02 103 1.079 1.1289600 -4.996000e-02 104 1.230 1.2105147 1.948529e-02 105 1.204 1.1899167 1.408333e-02 106 1.130 1.1655333 -3.553333e-02 107 1.217 1.2105147 6.485294e-03 108 1.161 1.1941429 -3.314286e-02 109 1.176 1.1888182 -1.281818e-02 110 1.230 1.2409737 -1.097368e-02 111 1.130 1.1289600 1.040000e-03 112 1.243 1.2425000 5.000000e-04 113 1.228 1.2105147 1.748529e-02 114 1.173 1.1888182 -1.581818e-02 115 1.185 1.2105147 -2.551471e-02 116 1.114 1.1680357 -5.403571e-02 117 1.143 1.1680357 -2.503571e-02 118 1.107 1.1215200 -1.452000e-02 119 1.161 1.1941429 -3.314286e-02 120 1.246 1.2409737 5.026316e-03 121 1.346 1.3289444 1.705556e-02 122 1.344 1.3289444 1.505556e-02 123 1.248 1.2571429 -9.142857e-03 124 1.210 1.2571429 -4.714286e-02 125 1.250 1.2409737 9.026316e-03 126 1.230 1.2105147 1.948529e-02 127 1.215 1.2105147 4.485294e-03 128 1.196 1.1941429 1.857143e-03 129 1.121 1.1215200 -5.200000e-04 130 1.223 1.1680357 5.496429e-02 131 1.083 1.0974118 -1.441176e-02 132 1.176 1.1680357 7.964286e-03 133 1.146 1.0799545 6.604545e-02 134 1.170 1.2105147 -4.051471e-02 135 1.270 1.2425000 2.750000e-02 136 1.225 1.2105147 1.448529e-02 137 1.097 1.1215200 -2.452000e-02 138 1.137 1.1215200 1.548000e-02 139 1.228 1.2571429 -2.914286e-02 140 1.248 1.2571429 -9.142857e-03 141 1.045 1.0465652 -1.565217e-03 142 1.057 1.0465652 1.043478e-02 143 1.161 1.1483636 1.263636e-02 144 1.161 1.2105147 -4.951471e-02 145 1.260 1.2105147 4.948529e-02 146 1.199 1.2105147 -1.151471e-02 147 1.201 1.2105147 -9.514706e-03 148 1.215 1.2105147 4.485294e-03 149 1.161 1.1899167 -2.891667e-02 150 1.107 1.1337692 -2.676923e-02 151 1.332 1.3289444 3.055556e-03 152 1.158 1.2105147 -5.251471e-02 153 1.270 1.2105147 5.948529e-02 154 1.121 1.0799545 4.104545e-02 155 1.107 1.0799545 2.704545e-02 156 1.260 1.2409737 1.902632e-02 157 1.199 1.2409737 -4.197368e-02 158 1.236 1.2571429 -2.114286e-02 159 1.236 1.2409737 -4.973684e-03 160 1.223 1.2409737 -1.797368e-02 161 1.272 1.2571429 1.485714e-02 162 1.121 1.0799545 4.104545e-02 163 1.127 1.0465652 8.043478e-02 164 1.137 1.1680357 -3.103571e-02 165 1.217 1.2105147 6.485294e-03 166 1.167 1.1655333 1.466667e-03 167 1.161 1.2105147 -4.951471e-02 168 1.246 1.2105147 3.548529e-02 169 1.201 1.1899167 1.108333e-02 170 1.134 1.1289600 5.040000e-03 171 1.199 1.1289600 7.004000e-02 172 1.173 1.2105147 -3.751471e-02 173 1.220 1.2105147 9.485294e-03 174 1.260 1.2409737 1.902632e-02 175 1.238 1.2105147 2.748529e-02 176 1.220 1.1941429 2.585714e-02 177 1.188 1.1680357 1.996429e-02 178 1.121 1.1680357 -4.703571e-02 179 1.182 1.1680357 1.396429e-02 180 1.155 1.1215200 3.348000e-02 181 1.176 1.1289600 4.704000e-02 182 1.146 1.2105147 -6.451471e-02 183 1.182 1.2105147 -2.851471e-02 184 1.176 1.2105147 -3.451471e-02 185 1.394 1.3216923 7.230769e-02 186 1.346 1.2571429 8.885714e-02 187 1.173 1.2105147 -3.751471e-02 188 1.283 1.2105147 7.248529e-02 189 1.204 1.1888182 1.518182e-02 190 1.053 1.1289600 -7.596000e-02 191 1.121 1.1888182 -6.781818e-02 192 1.167 1.2105147 -4.351471e-02 193 1.190 1.2105147 -2.051471e-02 194 1.215 1.2105147 4.485294e-03 195 1.258 1.2105147 4.748529e-02 196 1.303 1.2409737 6.202632e-02 197 1.199 1.2105147 -1.151471e-02 198 1.190 1.1888182 1.181818e-03 199 1.176 1.1888182 -1.281818e-02 200 1.182 1.2105147 -2.851471e-02 201 1.158 1.1899167 -3.191667e-02 202 1.283 1.2105147 7.248529e-02 203 1.299 1.3216923 -2.269231e-02 204 1.140 1.2105147 -7.051471e-02 205 1.185 1.2105147 -2.551471e-02 206 1.179 1.2105147 -3.151471e-02 207 1.196 1.2105147 -1.451471e-02 208 1.215 1.2105147 4.485294e-03 209 1.100 1.1337692 -3.376923e-02 210 1.111 1.2105147 -9.951471e-02 211 1.215 1.2105147 4.485294e-03 212 1.207 1.2425000 -3.550000e-02 213 1.288 1.2105147 7.748529e-02 214 1.238 1.2105147 2.748529e-02 215 1.173 1.2105147 -3.751471e-02 216 1.210 1.2105147 -5.147059e-04 217 1.152 1.2105147 -5.851471e-02 218 1.170 1.1337692 3.623077e-02 219 1.310 1.3216923 -1.169231e-02 220 1.140 1.1289600 1.104000e-02 221 1.199 1.1941429 4.857143e-03 222 1.233 1.2409737 -7.973684e-03 223 1.220 1.2571429 -3.714286e-02 224 1.270 1.2105147 5.948529e-02 225 1.255 1.2105147 4.448529e-02 226 1.204 1.2105147 -6.514706e-03 227 1.255 1.1888182 6.618182e-02 228 1.185 1.2105147 -2.551471e-02 229 1.246 1.2105147 3.548529e-02 230 1.167 1.2105147 -4.351471e-02 231 1.161 1.2105147 -4.951471e-02 232 1.161 1.2105147 -4.951471e-02 233 1.196 1.2105147 -1.451471e-02 234 1.215 1.1899167 2.508333e-02 235 1.230 1.2409737 -1.097368e-02 236 1.143 1.1655333 -2.253333e-02 237 1.238 1.2409737 -2.973684e-03 238 1.193 1.2105147 -1.751471e-02 239 1.064 1.2105147 -1.465147e-01 240 1.270 1.2105147 5.948529e-02 241 1.255 1.2105147 4.448529e-02 242 1.230 1.2425000 -1.250000e-02 243 1.230 1.2425000 -1.250000e-02 244 1.204 1.2425000 -3.850000e-02 245 1.279 1.2105147 6.848529e-02 246 1.255 1.2425000 1.250000e-02 247 1.230 1.2425000 -1.250000e-02 248 1.146 1.2105147 -6.451471e-02 249 1.204 1.2425000 -3.850000e-02 250 1.079 1.2105147 -1.315147e-01 251 1.279 1.2425000 3.650000e-02 252 1.279 1.2425000 3.650000e-02 253 1.322 1.3289444 -6.944444e-03 254 1.322 1.2105147 1.114853e-01 255 1.176 1.2105147 -3.451471e-02 256 1.146 1.2105147 -6.451471e-02 257 1.301 1.3289444 -2.794444e-02 258 1.204 1.2105147 -6.514706e-03 259 1.146 1.2105147 -6.451471e-02 260 1.146 1.2105147 -6.451471e-02 261 1.146 1.2105147 -6.451471e-02 262 1.146 1.2105147 -6.451471e-02 263 1.176 1.2105147 -3.451471e-02 264 1.230 1.2105147 1.948529e-02 265 1.322 1.3289444 -6.944444e-03 266 1.279 1.2105147 6.848529e-02 267 1.322 1.2105147 1.114853e-01 268 1.204 1.2105147 -6.514706e-03 269 1.176 1.2105147 -3.451471e-02 270 1.204 1.2105147 -6.514706e-03 271 1.279 1.3289444 -4.994444e-02 272 1.255 1.2105147 4.448529e-02 273 1.176 1.2105147 -3.451471e-02 274 1.279 1.2105147 6.848529e-02 275 1.176 1.2105147 -3.451471e-02 276 1.342 1.3289444 1.305556e-02 277 1.204 1.2105147 -6.514706e-03 278 1.279 1.3289444 -4.994444e-02 279 1.204 1.2105147 -6.514706e-03 280 1.176 1.2105147 -3.451471e-02 281 1.230 1.2105147 1.948529e-02 282 1.204 1.2105147 -6.514706e-03 283 1.204 1.2105147 -6.514706e-03 284 1.279 1.3289444 -4.994444e-02 285 1.204 1.2105147 -6.514706e-03 286 1.279 1.2105147 6.848529e-02 287 1.255 1.2105147 4.448529e-02 288 1.342 1.3289444 1.305556e-02 289 1.204 1.2105147 -6.514706e-03 290 1.279 1.2105147 6.848529e-02 291 1.230 1.2105147 1.948529e-02 292 1.230 1.2105147 1.948529e-02 293 1.176 1.2105147 -3.451471e-02 294 1.255 1.2105147 4.448529e-02 295 1.279 1.2105147 6.848529e-02 296 1.301 1.2105147 9.048529e-02 297 1.204 1.2105147 -6.514706e-03 298 1.342 1.3289444 1.305556e-02 299 1.230 1.2105147 1.948529e-02 300 1.204 1.2105147 -6.514706e-03 301 1.255 1.2105147 4.448529e-02 302 1.279 1.2105147 6.848529e-02 303 1.204 1.2105147 -6.514706e-03 304 1.146 1.1888182 -4.281818e-02 305 1.146 1.1888182 -4.281818e-02 306 1.176 1.2105147 -3.451471e-02 307 1.176 1.2105147 -3.451471e-02 308 1.230 1.2105147 1.948529e-02 309 1.230 1.2105147 1.948529e-02 310 1.176 1.2105147 -3.451471e-02 311 1.398 1.3289444 6.905556e-02 312 1.176 1.2105147 -3.451471e-02 313 1.176 1.2105147 -3.451471e-02 314 1.230 1.2105147 1.948529e-02 315 1.176 1.2105147 -3.451471e-02 316 1.255 1.2105147 4.448529e-02 317 1.176 1.2105147 -3.451471e-02 318 1.176 1.2105147 -3.451471e-02 319 1.230 1.2105147 1.948529e-02 320 1.204 1.2105147 -6.514706e-03 321 1.204 1.2105147 -6.514706e-03 322 1.176 1.2105147 -3.451471e-02 323 1.176 1.2105147 -3.451471e-02 324 1.342 1.3289444 1.305556e-02 325 1.204 1.2105147 -6.514706e-03 326 1.204 1.2105147 -6.514706e-03 327 1.255 1.2105147 4.448529e-02 328 1.146 1.2105147 -6.451471e-02 329 1.146 1.2105147 -6.451471e-02 330 1.114 1.2105147 -9.651471e-02 331 1.176 1.2105147 -3.451471e-02 332 1.176 1.2105147 -3.451471e-02 333 1.146 1.2105147 -6.451471e-02 334 1.176 1.2105147 -3.451471e-02 335 1.230 1.2105147 1.948529e-02 336 1.146 1.2105147 -6.451471e-02 337 1.230 1.2105147 1.948529e-02 338 1.230 1.2105147 1.948529e-02 339 1.230 1.2105147 1.948529e-02 340 1.146 1.1888182 -4.281818e-02 341 1.176 1.2105147 -3.451471e-02 342 1.114 1.1289600 -1.496000e-02 343 1.230 1.2105147 1.948529e-02 344 1.146 1.2105147 -6.451471e-02 345 1.380 1.3289444 5.105556e-02 346 1.255 1.2105147 4.448529e-02 347 1.204 1.1888182 1.518182e-02 348 1.204 1.1888182 1.518182e-02 349 1.255 1.2105147 4.448529e-02 350 1.176 1.2105147 -3.451471e-02 351 1.204 1.1888182 1.518182e-02 352 1.279 1.2105147 6.848529e-02 353 1.230 1.1888182 4.118182e-02 354 1.230 1.2105147 1.948529e-02 355 1.079 1.2105147 -1.315147e-01 356 1.204 1.1655333 3.846667e-02 357 1.176 1.2105147 -3.451471e-02 358 1.176 1.1655333 1.046667e-02 359 1.279 1.2105147 6.848529e-02 360 1.146 1.1655333 -1.953333e-02 361 1.380 1.3289444 5.105556e-02 362 1.230 1.2105147 1.948529e-02 363 1.114 1.2105147 -9.651471e-02 364 1.176 1.1888182 -1.281818e-02 365 1.255 1.1888182 6.618182e-02 366 1.230 1.1888182 4.118182e-02 367 1.322 1.2105147 1.114853e-01 368 1.114 1.2105147 -9.651471e-02 369 1.204 1.1888182 1.518182e-02 370 1.204 1.2105147 -6.514706e-03 371 1.255 1.2105147 4.448529e-02 372 1.301 1.2105147 9.048529e-02 373 1.176 1.1655333 1.046667e-02 374 1.279 1.2105147 6.848529e-02 375 1.301 1.1888182 1.121818e-01 376 1.114 1.1337692 -1.976923e-02 377 1.176 1.1888182 -1.281818e-02 378 1.176 1.1888182 -1.281818e-02 379 1.230 1.2105147 1.948529e-02 380 1.204 1.2105147 -6.514706e-03 381 1.146 1.1337692 1.223077e-02 382 1.176 1.1655333 1.046667e-02 383 1.204 1.2105147 -6.514706e-03 384 1.176 1.2105147 -3.451471e-02 385 1.176 1.1655333 1.046667e-02 386 1.255 1.2105147 4.448529e-02 387 1.176 1.1888182 -1.281818e-02 388 1.204 1.2105147 -6.514706e-03 389 1.230 1.2105147 1.948529e-02 390 1.255 1.2105147 4.448529e-02 391 1.230 1.1655333 6.446667e-02 392 1.114 1.1888182 -7.481818e-02 393 1.146 1.1888182 -4.281818e-02 394 1.146 1.2105147 -6.451471e-02 395 1.176 1.1888182 -1.281818e-02 396 1.204 1.2105147 -6.514706e-03 397 1.146 1.1888182 -4.281818e-02 398 1.176 1.1888182 -1.281818e-02 399 1.204 1.2105147 -6.514706e-03 400 1.279 1.2105147 6.848529e-02 401 1.041 1.1289600 -8.796000e-02 402 1.114 1.1337692 -1.976923e-02 403 1.301 1.2105147 9.048529e-02 404 1.176 1.1337692 4.223077e-02 405 1.146 1.1888182 -4.281818e-02 406 1.279 1.2105147 6.848529e-02 407 1.114 1.1337692 -1.976923e-02 408 1.204 1.2105147 -6.514706e-03 409 1.255 1.2105147 4.448529e-02 410 1.230 1.2105147 1.948529e-02 411 1.279 1.2105147 6.848529e-02 412 1.176 1.1888182 -1.281818e-02 413 1.146 1.1337692 1.223077e-02 414 1.146 1.1655333 -1.953333e-02 415 1.176 1.2105147 -3.451471e-02 416 1.204 1.1888182 1.518182e-02 417 1.146 1.1289600 1.704000e-02 418 1.255 1.2105147 4.448529e-02 419 1.230 1.1888182 4.118182e-02 420 1.176 1.1888182 -1.281818e-02 421 1.114 1.1289600 -1.496000e-02 422 1.146 1.1655333 -1.953333e-02 423 1.204 1.1888182 1.518182e-02 424 1.176 1.2105147 -3.451471e-02 425 1.279 1.2105147 6.848529e-02 426 1.146 1.1337692 1.223077e-02 427 1.114 1.1337692 -1.976923e-02 428 1.255 1.1888182 6.618182e-02 429 1.204 1.2105147 -6.514706e-03 430 1.230 1.1899167 4.008333e-02 431 1.204 1.2105147 -6.514706e-03 432 1.204 1.1899167 1.408333e-02 433 1.176 1.1899167 -1.391667e-02 434 1.204 1.2105147 -6.514706e-03 435 1.204 1.1289600 7.504000e-02 436 1.146 1.1899167 -4.391667e-02 437 1.146 1.1289600 1.704000e-02 438 1.176 1.1899167 -1.391667e-02 439 1.204 1.1899167 1.408333e-02 440 1.204 1.1888182 1.518182e-02 441 1.176 1.1899167 -1.391667e-02 442 1.255 1.2105147 4.448529e-02 443 1.204 1.1888182 1.518182e-02 444 1.204 1.1899167 1.408333e-02 445 1.255 1.2105147 4.448529e-02 446 1.230 1.2105147 1.948529e-02 447 1.114 1.1289600 -1.496000e-02 448 1.204 1.1899167 1.408333e-02 449 1.204 1.1888182 1.518182e-02 450 1.041 1.1289600 -8.796000e-02 451 1.204 1.1899167 1.408333e-02 452 1.204 1.1899167 1.408333e-02 453 1.146 1.1289600 1.704000e-02 454 1.176 1.1899167 -1.391667e-02 455 1.204 1.1899167 1.408333e-02 456 1.204 1.1899167 1.408333e-02 457 1.176 1.1899167 -1.391667e-02 458 1.230 1.2409737 -1.097368e-02 459 1.301 1.3216923 -2.069231e-02 460 1.230 1.2409737 -1.097368e-02 461 1.146 1.1941429 -4.814286e-02 462 1.204 1.2409737 -3.697368e-02 463 1.301 1.2409737 6.002632e-02 464 1.176 1.1941429 -1.814286e-02 465 1.255 1.2409737 1.402632e-02 466 1.301 1.2409737 6.002632e-02 467 1.255 1.2409737 1.402632e-02 468 1.230 1.2409737 -1.097368e-02 469 1.230 1.2409737 -1.097368e-02 470 1.176 1.1941429 -1.814286e-02 471 1.230 1.2409737 -1.097368e-02 472 1.230 1.2409737 -1.097368e-02 473 1.255 1.2409737 1.402632e-02 474 1.000 0.9794444 2.055556e-02 475 1.230 1.2409737 -1.097368e-02 476 1.230 1.1941429 3.585714e-02 477 1.176 1.2409737 -6.497368e-02 478 1.146 1.1289600 1.704000e-02 479 1.204 1.1941429 9.857143e-03 480 1.255 1.2409737 1.402632e-02 481 1.301 1.3216923 -2.069231e-02 482 1.301 1.3216923 -2.069231e-02 483 1.079 1.1289600 -4.996000e-02 484 1.398 1.3216923 7.630769e-02 485 1.255 1.2409737 1.402632e-02 486 1.041 1.0799545 -3.895455e-02 487 1.230 1.2409737 -1.097368e-02 488 1.230 1.2409737 -1.097368e-02 489 1.146 1.1941429 -4.814286e-02 490 1.301 1.3216923 -2.069231e-02 491 1.176 1.1941429 -1.814286e-02 492 1.176 1.1289600 4.704000e-02 493 1.342 1.3216923 2.030769e-02 494 1.204 1.2409737 -3.697368e-02 495 1.255 1.2409737 1.402632e-02 496 1.342 1.2409737 1.010263e-01 497 1.255 1.1941429 6.085714e-02 498 1.176 1.1941429 -1.814286e-02 499 1.204 1.1941429 9.857143e-03 500 1.204 1.2409737 -3.697368e-02 501 1.204 1.1941429 9.857143e-03 502 1.230 1.1941429 3.585714e-02 503 1.176 1.1941429 -1.814286e-02 504 1.230 1.1941429 3.585714e-02 505 1.204 1.1941429 9.857143e-03 506 1.204 1.1941429 9.857143e-03 507 1.279 1.2409737 3.802632e-02 508 1.041 1.0799545 -3.895455e-02 509 1.176 1.1289600 4.704000e-02 510 1.204 1.1289600 7.504000e-02 511 1.204 1.1941429 9.857143e-03 512 1.342 1.2571429 8.485714e-02 513 1.230 1.2571429 -2.714286e-02 514 1.114 1.0799545 3.404545e-02 515 1.230 1.2571429 -2.714286e-02 516 1.322 1.3216923 3.076923e-04 517 1.079 1.0799545 -9.545455e-04 518 1.041 1.0799545 -3.895455e-02 519 1.204 1.2571429 -5.314286e-02 520 1.114 1.0465652 6.743478e-02 521 1.041 1.0799545 -3.895455e-02 522 1.204 1.2571429 -5.314286e-02 523 1.230 1.2571429 -2.714286e-02 524 1.079 1.1289600 -4.996000e-02 525 1.204 1.2571429 -5.314286e-02 526 1.279 1.2571429 2.185714e-02 527 1.301 1.3216923 -2.069231e-02 528 1.279 1.2571429 2.185714e-02 529 1.000 1.0465652 -4.656522e-02 530 1.114 1.0799545 3.404545e-02 531 1.322 1.3216923 3.076923e-04 532 1.176 1.1289600 4.704000e-02 533 0.903 1.0465652 -1.435652e-01 534 1.230 1.2571429 -2.714286e-02 535 1.279 1.2571429 2.185714e-02 536 1.255 1.2571429 -2.142857e-03 537 1.255 1.2571429 -2.142857e-03 538 1.204 1.2571429 -5.314286e-02 539 1.255 1.2571429 -2.142857e-03 540 1.041 1.0465652 -5.565217e-03 541 1.079 1.0799545 -9.545455e-04 542 1.079 1.0799545 -9.545455e-04 543 1.079 1.0465652 3.243478e-02 544 1.114 1.1289600 -1.496000e-02 545 1.279 1.2571429 2.185714e-02 546 1.279 1.2571429 2.185714e-02 547 1.114 1.0799545 3.404545e-02 548 1.146 1.0799545 6.604545e-02 549 1.000 1.0799545 -7.995455e-02 550 1.114 1.0799545 3.404545e-02 551 1.230 1.2571429 -2.714286e-02 552 1.279 1.2571429 2.185714e-02 553 1.230 1.1680357 6.196429e-02 554 1.041 1.0465652 -5.565217e-03 555 1.176 1.1680357 7.964286e-03 556 1.176 1.1680357 7.964286e-03 557 0.954 1.0465652 -9.256522e-02 558 1.176 1.1680357 7.964286e-03 559 1.114 1.1215200 -7.520000e-03 560 1.114 1.1215200 -7.520000e-03 561 1.279 1.2571429 2.185714e-02 562 1.230 1.1680357 6.196429e-02 563 1.322 1.2571429 6.485714e-02 564 1.114 1.1215200 -7.520000e-03 565 1.114 1.1215200 -7.520000e-03 566 1.114 1.1680357 -5.403571e-02 567 1.146 1.1680357 -2.203571e-02 568 1.114 1.1215200 -7.520000e-03 569 1.176 1.1680357 7.964286e-03 570 1.146 1.1215200 2.448000e-02 571 1.079 1.1215200 -4.252000e-02 572 1.279 1.2571429 2.185714e-02 573 1.146 1.1215200 2.448000e-02 574 1.146 1.1680357 -2.203571e-02 575 1.114 1.1215200 -7.520000e-03 576 1.114 1.1215200 -7.520000e-03 577 1.146 1.1680357 -2.203571e-02 578 1.114 1.0465652 6.743478e-02 579 1.114 1.1215200 -7.520000e-03 580 1.146 1.1215200 2.448000e-02 581 1.146 1.1215200 2.448000e-02 582 1.146 1.1680357 -2.203571e-02 583 1.146 1.1215200 2.448000e-02 584 1.041 1.0465652 -5.565217e-03 585 1.114 1.1215200 -7.520000e-03 586 1.079 1.0465652 3.243478e-02 587 1.114 1.1680357 -5.403571e-02 588 1.114 1.1215200 -7.520000e-03 589 1.041 1.0465652 -5.565217e-03 590 1.176 1.1483636 2.763636e-02 591 1.204 1.1483636 5.563636e-02 592 1.079 1.0465652 3.243478e-02 593 1.000 0.9794444 2.055556e-02 594 1.204 1.1680357 3.596429e-02 595 1.176 1.1680357 7.964286e-03 596 0.954 0.9794444 -2.544444e-02 597 1.079 1.0465652 3.243478e-02 598 1.176 1.1483636 2.763636e-02 599 1.000 0.9794444 2.055556e-02 600 0.954 0.9794444 -2.544444e-02 601 1.176 1.1483636 2.763636e-02 602 1.114 1.1483636 -3.436364e-02 603 1.176 1.0974118 7.858824e-02 604 1.079 1.0974118 -1.841176e-02 605 1.146 1.0974118 4.858824e-02 606 1.079 1.0974118 -1.841176e-02 607 1.114 1.0974118 1.658824e-02 608 1.000 1.0974118 -9.741176e-02 609 1.146 1.1483636 -2.363636e-03 610 1.146 1.0974118 4.858824e-02 611 1.146 1.1483636 -2.363636e-03 612 1.079 1.0974118 -1.841176e-02 613 1.079 1.1483636 -6.936364e-02 614 1.176 1.1483636 2.763636e-02 615 1.114 1.0974118 1.658824e-02 616 1.146 1.1483636 -2.363636e-03 617 1.146 1.0974118 4.858824e-02 618 1.041 1.0974118 -5.641176e-02 619 1.204 1.1680357 3.596429e-02 620 1.114 1.0974118 1.658824e-02 621 1.146 1.1483636 -2.363636e-03 622 1.079 1.0974118 -1.841176e-02 623 1.176 1.1483636 2.763636e-02 624 1.279 1.0974118 1.815882e-01 625 1.041 1.0974118 -5.641176e-02 626 1.079 1.0974118 -1.841176e-02 627 1.114 1.0974118 1.658824e-02 628 1.041 1.0974118 -5.641176e-02 629 1.079 1.0974118 -1.841176e-02 630 1.079 1.0974118 -1.841176e-02 631 1.146 1.1483636 -2.363636e-03 632 1.079 1.0974118 -1.841176e-02 > 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/html/freestat/rcomp/tmp/4q3wq1293036445.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/html/freestat/rcomp/tmp/5uluw1293036445.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/html/freestat/rcomp/tmp/6nucz1293036445.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/html/freestat/rcomp/tmp/78vs51293036445.tab") + } > > try(system("convert tmp/2n2xk1293036445.ps tmp/2n2xk1293036445.png",intern=TRUE)) character(0) > try(system("convert tmp/3gbwn1293036445.ps tmp/3gbwn1293036445.png",intern=TRUE)) character(0) > try(system("convert tmp/4q3wq1293036445.ps tmp/4q3wq1293036445.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.039 0.974 16.277