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. 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+ ,8 + ,318 + ,150 + ,4457 + ,14 + ,8 + ,400 + ,175 + ,4464 + ,12 + ,8 + ,400 + ,150 + ,4464 + ,12 + ,8 + ,318 + ,150 + ,4498 + ,15 + ,8 + ,350 + ,180 + ,4499 + ,13 + ,8 + ,350 + ,155 + ,4502 + ,14 + ,8 + ,360 + ,215 + ,4615 + ,14 + ,8 + ,429 + ,208 + ,4633 + ,11 + ,8 + ,302 + ,140 + ,4638 + ,16 + ,8 + ,360 + ,170 + ,4654 + ,13 + ,8 + ,351 + ,148 + ,4657 + ,14 + ,8 + ,400 + ,170 + ,4668 + ,12 + ,8 + ,350 + ,150 + ,4699 + ,15 + ,8 + ,304 + ,193 + ,4732 + ,19 + ,8 + ,440 + ,215 + ,4735 + ,11 + ,8 + ,400 + ,170 + ,4746 + ,12 + ,8 + ,400 + ,167 + ,4906 + ,13 + ,8 + ,455 + ,225 + ,4951 + ,11 + ,8 + ,429 + ,198 + ,4952 + ,12 + ,8 + ,383 + ,180 + ,4955 + ,12 + ,8 + ,400 + ,150 + ,4997 + ,14 + ,8 + ,400 + ,175 + ,5140 + ,12) + ,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 = '3' > par2 = 'none' > par1 = '5' > #'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] 11.5 11.0 10.5 10.0 8.5 10.0 10.0 8.0 10.0 15.0 15.5 20.5 17.5 17.5 12.5 [16] 14.0 15.0 18.5 14.5 14.0 15.5 15.5 12.0 13.0 12.0 12.0 19.0 15.0 14.0 14.0 [31] 14.5 19.0 19.0 20.5 17.0 16.5 12.0 13.5 13.0 11.0 13.5 12.5 13.5 14.0 16.0 [46] 14.5 18.0 16.0 14.5 15.0 13.0 11.5 14.5 12.5 12.0 13.0 11.0 11.0 16.5 18.0 [61] 16.5 16.0 14.0 12.5 15.0 19.5 16.5 18.5 14.0 13.0 9.5 15.5 14.0 11.0 14.0 [76] 11.0 16.5 16.0 16.5 21.0 17.0 18.0 14.0 14.5 16.0 15.5 15.5 14.5 19.0 14.5 [91] 14.0 15.0 16.0 16.0 19.5 11.5 14.0 13.5 21.0 19.0 19.0 13.5 12.0 17.0 16.0 [106] 13.5 16.5 14.5 15.0 17.0 13.5 17.5 16.9 14.9 15.3 13.0 13.9 12.8 14.5 17.6 [121] 22.2 22.1 17.7 16.2 17.8 17.0 16.4 15.7 13.2 16.7 12.1 15.0 14.0 14.8 18.6 [136] 16.8 12.5 13.7 16.9 17.7 11.1 11.4 14.5 14.5 18.2 15.8 15.9 16.4 14.5 12.8 [151] 21.5 14.4 18.6 13.2 12.8 18.2 15.8 17.2 17.2 16.7 18.7 13.2 13.4 13.7 16.5 [166] 14.7 14.5 17.6 15.9 13.6 15.8 14.9 16.6 18.2 17.3 16.6 15.4 13.2 15.2 14.3 [181] 15.0 14.0 15.2 15.0 24.8 22.2 14.9 19.2 16.0 11.3 13.2 14.7 15.5 16.4 18.1 [196] 20.1 15.8 15.5 15.0 15.2 14.4 19.2 19.9 13.8 15.3 15.1 15.7 16.4 12.6 12.9 [211] 16.4 16.1 19.4 17.3 14.9 16.2 14.2 14.8 20.4 13.8 15.8 17.1 16.6 18.6 18.0 [226] 16.0 18.0 15.3 17.6 14.7 14.5 14.5 15.7 16.4 17.0 13.9 17.3 15.6 11.6 18.6 [241] 18.0 17.0 17.0 16.0 19.0 18.0 17.0 14.0 16.0 12.0 19.0 19.0 21.0 21.0 15.0 [256] 14.0 20.0 16.0 14.0 14.0 14.0 14.0 15.0 17.0 21.0 19.0 21.0 16.0 15.0 16.0 [271] 19.0 18.0 15.0 19.0 15.0 22.0 16.0 19.0 16.0 15.0 17.0 16.0 16.0 19.0 16.0 [286] 19.0 18.0 22.0 16.0 19.0 17.0 17.0 15.0 18.0 19.0 20.0 16.0 22.0 17.0 16.0 [301] 18.0 19.0 16.0 14.0 14.0 15.0 15.0 17.0 17.0 15.0 25.0 15.0 15.0 17.0 15.0 [316] 18.0 15.0 15.0 17.0 16.0 16.0 15.0 15.0 22.0 16.0 16.0 18.0 14.0 14.0 13.0 [331] 15.0 15.0 14.0 15.0 17.0 14.0 17.0 17.0 17.0 14.0 15.0 13.0 17.0 14.0 24.0 [346] 18.0 16.0 16.0 18.0 15.0 16.0 19.0 17.0 17.0 12.0 16.0 15.0 15.0 19.0 14.0 [361] 24.0 17.0 13.0 15.0 18.0 17.0 21.0 13.0 16.0 16.0 18.0 20.0 15.0 19.0 20.0 [376] 13.0 15.0 15.0 17.0 16.0 14.0 15.0 16.0 15.0 15.0 18.0 15.0 16.0 17.0 18.0 [391] 17.0 13.0 14.0 14.0 15.0 16.0 14.0 15.0 16.0 19.0 11.0 13.0 20.0 15.0 14.0 [406] 19.0 13.0 16.0 18.0 17.0 19.0 15.0 14.0 14.0 15.0 16.0 14.0 18.0 17.0 15.0 [421] 13.0 14.0 16.0 15.0 19.0 14.0 13.0 18.0 16.0 17.0 16.0 16.0 15.0 16.0 16.0 [436] 14.0 14.0 15.0 16.0 16.0 15.0 18.0 16.0 16.0 18.0 17.0 13.0 16.0 16.0 11.0 [451] 16.0 16.0 14.0 15.0 16.0 16.0 15.0 17.0 20.0 17.0 14.0 16.0 20.0 15.0 18.0 [466] 20.0 18.0 17.0 17.0 15.0 17.0 17.0 18.0 10.0 17.0 17.0 15.0 14.0 16.0 18.0 [481] 20.0 20.0 12.0 25.0 18.0 11.0 17.0 17.0 14.0 20.0 15.0 15.0 22.0 16.0 18.0 [496] 22.0 18.0 15.0 16.0 16.0 16.0 17.0 15.0 17.0 16.0 16.0 19.0 11.0 15.0 16.0 [511] 16.0 22.0 17.0 13.0 17.0 21.0 12.0 11.0 16.0 13.0 11.0 16.0 17.0 12.0 16.0 [526] 19.0 20.0 19.0 10.0 13.0 21.0 15.0 8.0 17.0 19.0 18.0 18.0 16.0 18.0 11.0 [541] 12.0 12.0 12.0 13.0 19.0 19.0 13.0 14.0 10.0 13.0 17.0 19.0 17.0 11.0 15.0 [556] 15.0 9.0 15.0 13.0 13.0 19.0 17.0 21.0 13.0 13.0 13.0 14.0 13.0 15.0 14.0 [571] 12.0 19.0 14.0 14.0 13.0 13.0 14.0 13.0 13.0 14.0 14.0 14.0 14.0 11.0 13.0 [586] 12.0 13.0 13.0 11.0 15.0 16.0 12.0 10.0 16.0 15.0 9.0 12.0 15.0 10.0 9.0 [601] 15.0 13.0 15.0 12.0 14.0 12.0 13.0 10.0 14.0 14.0 14.0 12.0 12.0 15.0 13.0 [616] 14.0 14.0 11.0 16.0 13.0 14.0 12.0 15.0 19.0 11.0 12.0 13.0 11.0 12.0 12.0 [631] 14.0 12.0 > 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]) 8 8.5 9 9.5 10 10.5 11 11.1 11.3 11.4 11.5 11.6 12 12.1 12.5 12.6 2 1 3 1 10 1 19 1 1 1 3 1 27 1 5 1 12.8 12.9 13 13.2 13.4 13.5 13.6 13.7 13.8 13.9 14 14.2 14.3 14.4 14.5 14.7 3 1 41 5 1 7 1 2 2 2 63 1 1 2 16 3 14.8 14.9 15 15.1 15.2 15.3 15.4 15.5 15.6 15.7 15.8 15.9 16 16.1 16.2 16.4 2 4 75 1 3 3 1 8 1 3 5 2 74 1 2 6 16.5 16.6 16.7 16.8 16.9 17 17.1 17.2 17.3 17.5 17.6 17.7 17.8 18 18.1 18.2 8 3 2 1 2 53 1 2 3 3 3 2 1 34 1 3 18.5 18.6 18.7 19 19.2 19.4 19.5 19.9 20 20.1 20.4 20.5 21 21.5 22 22.1 2 4 1 35 2 1 2 1 12 1 1 2 10 1 7 1 22.2 24 24.8 25 2 2 1 2 > colnames(x) [1] "cylinders" "engine.displacement" "horsepower" [4] "weight" "acceleration." > colnames(x)[par1] [1] "acceleration." > x[,par1] [1] 11.5 11.0 10.5 10.0 8.5 10.0 10.0 8.0 10.0 15.0 15.5 20.5 17.5 17.5 12.5 [16] 14.0 15.0 18.5 14.5 14.0 15.5 15.5 12.0 13.0 12.0 12.0 19.0 15.0 14.0 14.0 [31] 14.5 19.0 19.0 20.5 17.0 16.5 12.0 13.5 13.0 11.0 13.5 12.5 13.5 14.0 16.0 [46] 14.5 18.0 16.0 14.5 15.0 13.0 11.5 14.5 12.5 12.0 13.0 11.0 11.0 16.5 18.0 [61] 16.5 16.0 14.0 12.5 15.0 19.5 16.5 18.5 14.0 13.0 9.5 15.5 14.0 11.0 14.0 [76] 11.0 16.5 16.0 16.5 21.0 17.0 18.0 14.0 14.5 16.0 15.5 15.5 14.5 19.0 14.5 [91] 14.0 15.0 16.0 16.0 19.5 11.5 14.0 13.5 21.0 19.0 19.0 13.5 12.0 17.0 16.0 [106] 13.5 16.5 14.5 15.0 17.0 13.5 17.5 16.9 14.9 15.3 13.0 13.9 12.8 14.5 17.6 [121] 22.2 22.1 17.7 16.2 17.8 17.0 16.4 15.7 13.2 16.7 12.1 15.0 14.0 14.8 18.6 [136] 16.8 12.5 13.7 16.9 17.7 11.1 11.4 14.5 14.5 18.2 15.8 15.9 16.4 14.5 12.8 [151] 21.5 14.4 18.6 13.2 12.8 18.2 15.8 17.2 17.2 16.7 18.7 13.2 13.4 13.7 16.5 [166] 14.7 14.5 17.6 15.9 13.6 15.8 14.9 16.6 18.2 17.3 16.6 15.4 13.2 15.2 14.3 [181] 15.0 14.0 15.2 15.0 24.8 22.2 14.9 19.2 16.0 11.3 13.2 14.7 15.5 16.4 18.1 [196] 20.1 15.8 15.5 15.0 15.2 14.4 19.2 19.9 13.8 15.3 15.1 15.7 16.4 12.6 12.9 [211] 16.4 16.1 19.4 17.3 14.9 16.2 14.2 14.8 20.4 13.8 15.8 17.1 16.6 18.6 18.0 [226] 16.0 18.0 15.3 17.6 14.7 14.5 14.5 15.7 16.4 17.0 13.9 17.3 15.6 11.6 18.6 [241] 18.0 17.0 17.0 16.0 19.0 18.0 17.0 14.0 16.0 12.0 19.0 19.0 21.0 21.0 15.0 [256] 14.0 20.0 16.0 14.0 14.0 14.0 14.0 15.0 17.0 21.0 19.0 21.0 16.0 15.0 16.0 [271] 19.0 18.0 15.0 19.0 15.0 22.0 16.0 19.0 16.0 15.0 17.0 16.0 16.0 19.0 16.0 [286] 19.0 18.0 22.0 16.0 19.0 17.0 17.0 15.0 18.0 19.0 20.0 16.0 22.0 17.0 16.0 [301] 18.0 19.0 16.0 14.0 14.0 15.0 15.0 17.0 17.0 15.0 25.0 15.0 15.0 17.0 15.0 [316] 18.0 15.0 15.0 17.0 16.0 16.0 15.0 15.0 22.0 16.0 16.0 18.0 14.0 14.0 13.0 [331] 15.0 15.0 14.0 15.0 17.0 14.0 17.0 17.0 17.0 14.0 15.0 13.0 17.0 14.0 24.0 [346] 18.0 16.0 16.0 18.0 15.0 16.0 19.0 17.0 17.0 12.0 16.0 15.0 15.0 19.0 14.0 [361] 24.0 17.0 13.0 15.0 18.0 17.0 21.0 13.0 16.0 16.0 18.0 20.0 15.0 19.0 20.0 [376] 13.0 15.0 15.0 17.0 16.0 14.0 15.0 16.0 15.0 15.0 18.0 15.0 16.0 17.0 18.0 [391] 17.0 13.0 14.0 14.0 15.0 16.0 14.0 15.0 16.0 19.0 11.0 13.0 20.0 15.0 14.0 [406] 19.0 13.0 16.0 18.0 17.0 19.0 15.0 14.0 14.0 15.0 16.0 14.0 18.0 17.0 15.0 [421] 13.0 14.0 16.0 15.0 19.0 14.0 13.0 18.0 16.0 17.0 16.0 16.0 15.0 16.0 16.0 [436] 14.0 14.0 15.0 16.0 16.0 15.0 18.0 16.0 16.0 18.0 17.0 13.0 16.0 16.0 11.0 [451] 16.0 16.0 14.0 15.0 16.0 16.0 15.0 17.0 20.0 17.0 14.0 16.0 20.0 15.0 18.0 [466] 20.0 18.0 17.0 17.0 15.0 17.0 17.0 18.0 10.0 17.0 17.0 15.0 14.0 16.0 18.0 [481] 20.0 20.0 12.0 25.0 18.0 11.0 17.0 17.0 14.0 20.0 15.0 15.0 22.0 16.0 18.0 [496] 22.0 18.0 15.0 16.0 16.0 16.0 17.0 15.0 17.0 16.0 16.0 19.0 11.0 15.0 16.0 [511] 16.0 22.0 17.0 13.0 17.0 21.0 12.0 11.0 16.0 13.0 11.0 16.0 17.0 12.0 16.0 [526] 19.0 20.0 19.0 10.0 13.0 21.0 15.0 8.0 17.0 19.0 18.0 18.0 16.0 18.0 11.0 [541] 12.0 12.0 12.0 13.0 19.0 19.0 13.0 14.0 10.0 13.0 17.0 19.0 17.0 11.0 15.0 [556] 15.0 9.0 15.0 13.0 13.0 19.0 17.0 21.0 13.0 13.0 13.0 14.0 13.0 15.0 14.0 [571] 12.0 19.0 14.0 14.0 13.0 13.0 14.0 13.0 13.0 14.0 14.0 14.0 14.0 11.0 13.0 [586] 12.0 13.0 13.0 11.0 15.0 16.0 12.0 10.0 16.0 15.0 9.0 12.0 15.0 10.0 9.0 [601] 15.0 13.0 15.0 12.0 14.0 12.0 13.0 10.0 14.0 14.0 14.0 12.0 12.0 15.0 13.0 [616] 14.0 14.0 11.0 16.0 13.0 14.0 12.0 15.0 19.0 11.0 12.0 13.0 11.0 12.0 12.0 [631] 14.0 12.0 > 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/1qvpc1292961572.tab") + } + } > m Conditional inference tree with 22 terminal nodes Response: acceleration. Inputs: cylinders, engine.displacement, horsepower, weight Number of observations: 632 1) horsepower <= 112; criterion = 1, statistic = 294.013 2) horsepower <= 61; criterion = 1, statistic = 50.753 3) weight <= 1834; criterion = 1, statistic = 21.221 4)* weights = 12 3) weight > 1834 5)* weights = 18 2) horsepower > 61 6) weight <= 2945; criterion = 1, statistic = 37.376 7) horsepower <= 89; criterion = 1, statistic = 35.208 8)* weights = 204 7) horsepower > 89 9) horsepower <= 95; criterion = 0.954, statistic = 6.345 10)* weights = 44 9) horsepower > 95 11) weight <= 2725; criterion = 0.966, statistic = 6.91 12) horsepower <= 97; criterion = 0.976, statistic = 7.558 13)* weights = 15 12) horsepower > 97 14)* weights = 13 11) weight > 2725 15)* weights = 24 6) weight > 2945 16) horsepower <= 80; criterion = 1, statistic = 28.068 17)* weights = 13 16) horsepower > 80 18) weight <= 3415; criterion = 0.997, statistic = 11.267 19) horsepower <= 95; criterion = 1, statistic = 18.313 20) cylinders <= 4; criterion = 0.993, statistic = 9.68 21)* weights = 8 20) cylinders > 4 22)* weights = 30 19) horsepower > 95 23)* weights = 28 18) weight > 3415 24)* weights = 35 1) horsepower > 112 25) horsepower <= 158; criterion = 1, statistic = 43.985 26) weight <= 3777; criterion = 0.998, statistic = 11.875 27) engine.displacement <= 267; criterion = 0.993, statistic = 9.877 28) weight <= 2930; criterion = 0.985, statistic = 8.411 29)* weights = 13 28) weight > 2930 30)* weights = 8 27) engine.displacement > 267 31)* weights = 26 26) weight > 3777 32) engine.displacement <= 302; criterion = 1, statistic = 21.054 33)* weights = 13 32) engine.displacement > 302 34)* weights = 62 25) horsepower > 158 35) engine.displacement <= 318; criterion = 1, statistic = 20.784 36)* weights = 7 35) engine.displacement > 318 37) weight <= 4354; criterion = 0.996, statistic = 11.039 38) engine.displacement <= 360; criterion = 0.954, statistic = 6.375 39)* weights = 15 38) engine.displacement > 360 40)* weights = 15 37) weight > 4354 41) engine.displacement <= 400; criterion = 1, statistic = 18.492 42)* weights = 20 41) engine.displacement > 400 43)* weights = 9 > postscript(file="/var/www/html/freestat/rcomp/tmp/2qvpc1292961572.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/3qvpc1292961572.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 11.5 11.26000 0.240000000 2 11.0 12.10385 -1.103846154 3 10.5 12.10385 -1.603846154 4 10.0 9.94000 0.060000000 5 8.5 9.94000 -1.440000000 6 10.0 10.88889 -0.888888889 7 10.0 9.94000 0.060000000 8 8.0 11.26000 -3.260000000 9 10.0 9.94000 0.060000000 10 15.0 15.51364 -0.513636364 11 15.5 15.50833 -0.008333333 12 20.5 21.40556 -0.905555556 13 17.5 16.34510 1.154901961 14 17.5 15.51364 1.986363636 15 12.5 13.08462 -0.584615385 16 14.0 12.53000 1.470000000 17 15.0 15.41429 -0.414285714 18 18.5 15.41429 3.085714286 19 14.5 16.34510 -1.845098039 20 14.0 15.51364 -1.513636364 21 15.5 15.68214 -0.182142857 22 15.5 15.68214 -0.182142857 23 12.0 11.26000 0.740000000 24 13.0 13.78871 -0.788709677 25 12.0 12.53000 -0.530000000 26 12.0 12.53000 -0.530000000 27 19.0 16.34510 2.654901961 28 15.0 15.68214 -0.682142857 29 14.0 16.34510 -2.345098039 30 14.0 15.51364 -1.513636364 31 14.5 16.34510 -1.845098039 32 19.0 16.34510 2.654901961 33 19.0 17.51667 1.483333333 34 20.5 16.34510 4.154901961 35 17.0 16.34510 0.654901961 36 16.5 16.34510 0.154901961 37 12.0 11.26000 0.740000000 38 13.5 13.78871 -0.288709677 39 13.0 13.78871 -0.788709677 40 11.0 10.88889 0.111111111 41 13.5 13.78871 -0.288709677 42 12.5 12.53000 -0.030000000 43 13.5 14.67333 -1.173333333 44 14.0 13.78871 0.211290323 45 16.0 15.40000 0.600000000 46 14.5 15.50833 -1.008333333 47 18.0 16.34510 1.654901961 48 16.0 16.34510 -0.345098039 49 14.5 14.67333 -0.173333333 50 15.0 16.34510 -1.345098039 51 13.0 11.26000 1.740000000 52 11.5 12.10385 -0.603846154 53 14.5 15.40000 -0.900000000 54 12.5 12.10385 0.396153846 55 12.0 13.78871 -1.788709677 56 13.0 13.78871 -0.788709677 57 11.0 10.88889 0.111111111 58 11.0 10.88889 0.111111111 59 16.5 15.68214 0.817857143 60 18.0 15.68214 2.317857143 61 16.5 17.12000 -0.620000000 62 16.0 15.51364 0.486363636 63 14.0 13.78871 0.211290323 64 12.5 12.53000 -0.030000000 65 15.0 15.50833 -0.508333333 66 19.5 16.34510 3.154901961 67 16.5 15.51364 0.986363636 68 18.5 16.34510 2.154901961 69 14.0 13.63077 0.369230769 70 13.0 13.78871 -0.788709677 71 9.5 9.94000 -0.440000000 72 15.5 16.34510 -0.845098039 73 14.0 15.51364 -1.513636364 74 11.0 12.10385 -1.103846154 75 14.0 13.63077 0.369230769 76 11.0 11.26000 -0.260000000 77 16.5 17.12000 -0.620000000 78 16.0 15.50833 0.491666667 79 16.5 16.34510 0.154901961 80 21.0 16.34510 4.654901961 81 17.0 18.14857 -1.148571429 82 18.0 18.14857 -0.148571429 83 14.0 15.40000 -1.400000000 84 14.5 13.78871 0.711290323 85 16.0 15.40000 0.600000000 86 15.5 13.78871 1.711290323 87 15.5 16.34510 -0.845098039 88 14.5 16.34510 -1.845098039 89 19.0 21.40556 -2.405555556 90 14.5 16.34510 -1.845098039 91 14.0 16.34510 -2.345098039 92 15.0 14.67333 0.326666667 93 16.0 16.34510 -0.345098039 94 16.0 17.12000 -1.120000000 95 19.5 21.04615 -1.546153846 96 11.5 12.53000 -1.030000000 97 14.0 13.78871 0.211290323 98 13.5 13.78871 -0.288709677 99 21.0 18.14857 2.851428571 100 19.0 18.14857 0.851428571 101 19.0 18.14857 0.851428571 102 13.5 15.68214 -2.182142857 103 12.0 12.10385 -0.103846154 104 17.0 16.34510 0.654901961 105 16.0 15.50833 0.491666667 106 13.5 14.67333 -1.173333333 107 16.5 16.34510 0.154901961 108 14.5 15.68214 -1.182142857 109 15.0 15.51364 -0.513636364 110 17.0 18.80000 -1.800000000 111 13.5 13.08462 0.415384615 112 17.5 17.51667 -0.016666667 113 16.9 16.34510 0.554901961 114 14.9 15.51364 -0.613636364 115 15.3 16.34510 -1.045098039 116 13.0 13.78871 -0.788709677 117 13.9 13.78871 0.111290323 118 12.8 13.78871 -0.988709677 119 14.5 15.68214 -1.182142857 120 17.6 17.12000 0.480000000 121 22.2 21.40556 0.794444444 122 22.1 21.40556 0.694444444 123 17.7 18.14857 -0.448571429 124 16.2 18.14857 -1.948571429 125 17.8 17.12000 0.680000000 126 17.0 16.34510 0.654901961 127 16.4 16.34510 0.054901961 128 15.7 15.68214 0.017857143 129 13.2 13.78871 -0.588709677 130 16.7 15.40000 1.300000000 131 12.1 12.53000 -0.430000000 132 15.0 15.40000 -0.400000000 133 14.0 12.10385 1.896153846 134 14.8 16.34510 -1.545098039 135 18.6 17.51667 1.083333333 136 16.8 16.34510 0.454901961 137 12.5 13.78871 -1.288709677 138 13.7 13.78871 -0.088709677 139 16.9 18.14857 -1.248571429 140 17.7 18.14857 -0.448571429 141 11.1 9.94000 1.160000000 142 11.4 11.26000 0.140000000 143 14.5 13.78871 0.711290323 144 14.5 16.34510 -1.845098039 145 18.2 16.34510 1.854901961 146 15.8 16.34510 -0.545098039 147 15.9 16.34510 -0.445098039 148 16.4 16.34510 0.054901961 149 14.5 15.50833 -1.008333333 150 12.8 13.63077 -0.830769231 151 21.5 21.40556 0.094444444 152 14.4 16.34510 -1.945098039 153 18.6 16.34510 2.254901961 154 13.2 12.10385 1.096153846 155 12.8 12.10385 0.696153846 156 18.2 17.12000 1.080000000 157 15.8 17.12000 -1.320000000 158 17.2 18.14857 -0.948571429 159 17.2 17.12000 0.080000000 160 16.7 17.12000 -0.420000000 161 18.7 18.14857 0.551428571 162 13.2 12.10385 1.096153846 163 13.4 15.41429 -2.014285714 164 13.7 13.78871 -0.088709677 165 16.5 16.34510 0.154901961 166 14.7 14.67333 0.026666667 167 14.5 16.34510 -1.845098039 168 17.6 16.34510 1.254901961 169 15.9 15.50833 0.391666667 170 13.6 14.92500 -1.325000000 171 15.8 14.92500 0.875000000 172 14.9 16.34510 -1.445098039 173 16.6 16.34510 0.254901961 174 18.2 17.12000 1.080000000 175 17.3 16.34510 0.954901961 176 16.6 15.68214 0.917857143 177 15.4 13.78871 1.611290323 178 13.2 13.78871 -0.588709677 179 15.2 13.78871 1.411290323 180 14.3 13.78871 0.511290323 181 15.0 14.92500 0.075000000 182 14.0 16.34510 -2.345098039 183 15.2 16.34510 -1.145098039 184 15.0 16.34510 -1.345098039 185 24.8 21.04615 3.753846154 186 22.2 18.14857 4.051428571 187 14.9 16.34510 -1.445098039 188 19.2 16.34510 2.854901961 189 16.0 15.51364 0.486363636 190 11.3 13.08462 -1.784615385 191 13.2 15.51364 -2.313636364 192 14.7 16.34510 -1.645098039 193 15.5 16.34510 -0.845098039 194 16.4 16.34510 0.054901961 195 18.1 16.34510 1.754901961 196 20.1 18.80000 1.300000000 197 15.8 16.34510 -0.545098039 198 15.5 15.51364 -0.013636364 199 15.0 15.51364 -0.513636364 200 15.2 16.34510 -1.145098039 201 14.4 15.50833 -1.108333333 202 19.2 16.34510 2.854901961 203 19.9 21.04615 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15.0 15.40000 -0.400000000 596 9.0 9.94000 -0.940000000 597 12.0 9.94000 2.060000000 598 15.0 13.78871 1.211290323 599 10.0 9.94000 0.060000000 600 9.0 9.94000 -0.940000000 601 15.0 13.78871 1.211290323 602 13.0 13.78871 -0.788709677 603 15.0 15.41429 -0.414285714 604 12.0 12.53000 -0.530000000 605 14.0 15.41429 -1.414285714 606 12.0 12.53000 -0.530000000 607 13.0 12.53000 0.470000000 608 10.0 10.88889 -0.888888889 609 14.0 13.78871 0.211290323 610 14.0 12.53000 1.470000000 611 14.0 13.78871 0.211290323 612 12.0 12.53000 -0.530000000 613 12.0 13.78871 -1.788709677 614 15.0 13.78871 1.211290323 615 13.0 12.53000 0.470000000 616 14.0 13.78871 0.211290323 617 14.0 12.53000 1.470000000 618 11.0 10.88889 0.111111111 619 16.0 15.40000 0.600000000 620 13.0 12.53000 0.470000000 621 14.0 13.78871 0.211290323 622 12.0 12.53000 -0.530000000 623 15.0 13.78871 1.211290323 624 19.0 15.41429 3.585714286 625 11.0 10.88889 0.111111111 626 12.0 12.53000 -0.530000000 627 13.0 12.53000 0.470000000 628 11.0 10.88889 0.111111111 629 12.0 10.88889 1.111111111 630 12.0 12.53000 -0.530000000 631 14.0 13.78871 0.211290323 632 12.0 12.53000 -0.530000000 > 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/40m6f1292961572.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/5wemn1292961572.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/6p5381292961572.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/7a6je1292961572.tab") + } > > try(system("convert tmp/2qvpc1292961572.ps tmp/2qvpc1292961572.png",intern=TRUE)) character(0) > try(system("convert tmp/3qvpc1292961572.ps tmp/3qvpc1292961572.png",intern=TRUE)) character(0) > try(system("convert tmp/40m6f1292961572.ps tmp/40m6f1292961572.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 18.760 1.048 19.419