R version 2.13.0 (2011-04-13) Copyright (C) 2011 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. Type 'q()' to quit R. > par9 = 'Exam Items' > par8 = 'ATTLES all' > par7 = 'all' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'Exam Items' > par8 <- 'ATTLES all' > par7 <- 'all' > par6 <- 'all' > par5 <- 'all' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > par1 <- '0' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) in Information Management (v1.0.8) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from 'package:survival': untangle.specials The following object(s) are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- as.data.frame(read.table(file='http://www.wessa.net/download/utaut.csv',sep=',',header=T)) > x$U25 <- 6-x$U25 > if(par5 == 'female') x <- x[x$Gender==0,] > if(par5 == 'male') x <- x[x$Gender==1,] > if(par6 == 'prep') x <- x[x$Pop==1,] > if(par6 == 'bachelor') x <- x[x$Pop==0,] > if(par7 != 'all') { + x <- x[x$Year==as.numeric(par7),] + } > cAc <- with(x,cbind( A1, A2, A3, A4, A5, A6, A7, A8, A9,A10)) > cAs <- with(x,cbind(A11,A12,A13,A14,A15,A16,A17,A18,A19,A20)) > cA <- cbind(cAc,cAs) > cCa <- with(x,cbind(C1,C3,C5,C7, C9,C11,C13,C15,C17,C19,C21,C23,C25,C27,C29,C31,C33,C35,C37,C39,C41,C43,C45,C47)) > cCp <- with(x,cbind(C2,C4,C6,C8,C10,C12,C14,C16,C18,C20,C22,C24,C26,C28,C30,C32,C34,C36,C38,C40,C42,C44,C46,C48)) > cC <- cbind(cCa,cCp) > cU <- with(x,cbind(U1,U2,U3,U4,U5,U6,U7,U8,U9,U10,U11,U12,U13,U14,U15,U16,U17,U18,U19,U20,U21,U22,U23,U24,U25,U26,U27,U28,U29,U30,U31,U32,U33)) > cE <- with(x,cbind(BC,NNZFG,MRT,AFL,LPM,LPC,W,WPA)) > cX <- with(x,cbind(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14,X15,X16,X17,X18)) > if (par8=='ATTLES connected') x <- cAc > if (par8=='ATTLES separate') x <- cAs > if (par8=='ATTLES all') x <- cA > if (par8=='COLLES actuals') x <- cCa > if (par8=='COLLES preferred') x <- cCp > if (par8=='COLLES all') x <- cC > if (par8=='CSUQ') x <- cU > if (par8=='Learning Activities') x <- cE > if (par8=='Exam Items') x <- cX > if (par9=='ATTLES connected') y <- cAc > if (par9=='ATTLES separate') y <- cAs > if (par9=='ATTLES all') y <- cA > if (par9=='COLLES actuals') y <- cCa > if (par9=='COLLES preferred') y <- cCp > if (par9=='COLLES all') y <- cC > if (par9=='CSUQ') y <- cU > if (par9=='Learning Activities') y <- cE > if (par9=='Exam Items') y <- cX > if (par1==0) { + nr <- length(y[,1]) + nc <- length(y[1,]) + mysum <- array(0,dim=nr) + for(jjj in 1:nr) { + for(iii in 1:nc) { + mysum[jjj] = mysum[jjj] + y[jjj,iii] + } + } + y <- mysum + } else { + y <- y[,par1] + } > nx <- cbind(y,x) > colnames(nx) <- c('endo',colnames(x)) > x <- nx > par1=1 > ncol <- length(x[1,]) > for (jjj in 1:ncol) { + x <- x[!is.na(x[,jjj]),] + } > x <- as.data.frame(x) > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "endo" > x[,par1] [1] 12 8 1 2 5 12 11 9 2 4 5 4 5 4 6 6 0 2 [19] 11 5 7 6 7 10 11 9 3 5 4 7 5 1 4 4 5 7 [37] 8 2 0 9 8 3 5 3 6 7 7 3 7 8 8 6 9 5 [55] 6 7 5 8 4 14 4 3 11 8 8 9 12 2 4 2 3 11 [73] 11 9 4 1 5 2 2 5 -1 7 4 3 7 7 5 8 7 5 [91] 10 7 5 9 4 2 5 12 4 4 6 5 7 8 11 3 1 13 [109] 7 2 2 5 3 -1 0 6 10 -2 8 11 11 10 9 10 0 4 [127] 2 8 12 10 2 0 9 -3 -2 6 10 6 6 12 9 4 6 6 [145] 3 4 8 10 6 6 4 6 5 9 2 7 2 -1 3 6 10 10 [163] 2 10 2 4 7 9 5 10 8 8 4 9 6 0 0 9 8 9 [181] 4 9 10 2 8 -2 3 -1 12 9 5 6 4 13 11 3 5 9 [199] 7 11 7 1 13 3 6 7 5 2 3 12 14 11 4 4 10 10 [217] 12 6 7 9 2 8 2 5 10 4 6 0 6 12 11 10 10 4 [235] 10 8 3 11 8 4 2 6 5 5 5 9 6 8 8 10 3 8 [253] 9 11 0 4 11 4 8 9 9 8 4 1 5 3 3 5 8 0 [271] 7 -1 2 8 8 0 4 1 9 7 4 11 4 11 12 4 15 13 [289] 7 14 10 16 14 14 11 14 0 14 3 14 4 8 7 6 10 11 [307] 3 10 12 10 15 4 10 13 12 10 4 11 6 -4 0 6 11 13 [325] 6 10 5 0 7 8 6 11 0 8 0 17 9 6 6 2 14 4 [343] -1 10 -1 8 8 8 2 8 5 6 6 9 15 11 5 2 11 11 [361] 9 11 12 5 4 -2 7 13 6 9 9 8 11 3 5 8 13 3 [379] 12 9 15 10 14 8 7 9 13 8 11 8 9 10 11 -3 7 3 [397] 15 7 4 10 10 15 4 15 6 6 14 13 6 11 9 8 3 8 [415] 7 7 -2 6 10 8 2 2 14 8 8 0 11 7 12 10 -1 10 [433] 9 11 10 11 16 9 8 6 10 13 10 10 6 8 7 10 10 16 [451] 7 13 14 1 11 10 8 7 12 12 6 15 12 9 11 14 11 12 [469] 0 12 7 5 0 8 10 7 8 1 7 11 4 16 6 16 6 3 [487] 8 7 0 5 11 5 11 -2 18 12 14 3 8 4 7 8 11 8 [505] 8 7 11 8 10 6 9 5 8 1 9 6 11 12 -4 2 6 5 [523] 11 3 5 11 6 11 0 4 10 8 4 -2 -2 5 14 0 6 3 [541] 5 3 9 3 4 1 6 3 12 9 14 9 7 4 4 5 6 1 [559] 4 10 0 6 4 7 6 14 7 -1 9 1 4 2 3 2 2 4 [577] -3 2 8 5 12 9 -2 -12 5 -2 -2 5 -1 4 6 -4 5 7 [595] 8 2 10 4 9 2 4 4 2 9 2 8 8 6 5 10 10 9 [613] 1 10 12 12 7 -4 -4 -4 5 8 -1 9 11 0 -1 5 9 3 [631] -2 1 6 -3 1 5 7 2 0 6 9 8 10 3 1 4 11 14 [649] 7 4 8 6 0 7 5 2 3 8 -3 2 3 7 7 9 2 3 [667] 4 9 4 1 5 8 3 -5 3 7 1 7 1 13 7 1 6 11 [685] 12 3 2 5 5 4 5 3 4 5 6 0 6 13 4 -2 8 12 [703] 9 12 10 1 0 0 8 4 11 7 8 3 3 10 3 6 10 3 [721] -2 11 4 2 8 8 2 4 14 3 10 6 5 7 1 4 8 13 [739] 5 7 5 9 11 1 8 14 4 10 11 14 5 10 9 6 15 9 [757] 11 8 12 10 5 5 14 6 2 6 16 7 12 13 8 11 2 10 [775] 6 5 0 10 5 6 4 13 9 6 9 16 10 4 4 6 6 9 [793] 12 14 3 8 16 7 4 5 8 7 11 5 10 7 11 10 8 8 [811] 10 8 8 5 7 7 -3 3 7 12 9 5 2 12 6 12 12 9 [829] 6 10 8 16 6 12 11 6 10 8 6 10 13 6 11 2 8 8 [847] 9 6 7 2 11 10 9 11 11 6 3 4 10 3 8 12 13 6 [865] 11 8 9 6 16 4 7 11 6 2 12 13 10 13 7 11 2 14 [883] 12 12 14 4 8 10 15 10 4 4 7 7 11 8 6 10 15 7 [901] 6 8 16 14 8 14 5 7 12 11 8 9 8 0 13 5 3 13 [919] 8 14 12 8 10 5 0 16 8 6 7 6 10 14 7 14 9 6 [937] 12 3 0 10 10 6 8 4 14 12 6 0 12 4 4 2 12 14 [955] 14 6 11 12 6 10 11 2 3 10 7 > 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]) -12 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 1 1 6 6 14 12 34 24 53 54 80 72 91 75 98 65 79 69 49 23 14 15 16 17 18 34 11 12 1 1 > colnames(x) [1] "endo" "A1" "A2" "A3" "A4" "A5" "A6" "A7" "A8" "A9" [11] "A10" "A11" "A12" "A13" "A14" "A15" "A16" "A17" "A18" "A19" [21] "A20" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 12 8 1 2 5 12 11 9 2 4 5 4 5 4 6 6 0 2 [19] 11 5 7 6 7 10 11 9 3 5 4 7 5 1 4 4 5 7 [37] 8 2 0 9 8 3 5 3 6 7 7 3 7 8 8 6 9 5 [55] 6 7 5 8 4 14 4 3 11 8 8 9 12 2 4 2 3 11 [73] 11 9 4 1 5 2 2 5 -1 7 4 3 7 7 5 8 7 5 [91] 10 7 5 9 4 2 5 12 4 4 6 5 7 8 11 3 1 13 [109] 7 2 2 5 3 -1 0 6 10 -2 8 11 11 10 9 10 0 4 [127] 2 8 12 10 2 0 9 -3 -2 6 10 6 6 12 9 4 6 6 [145] 3 4 8 10 6 6 4 6 5 9 2 7 2 -1 3 6 10 10 [163] 2 10 2 4 7 9 5 10 8 8 4 9 6 0 0 9 8 9 [181] 4 9 10 2 8 -2 3 -1 12 9 5 6 4 13 11 3 5 9 [199] 7 11 7 1 13 3 6 7 5 2 3 12 14 11 4 4 10 10 [217] 12 6 7 9 2 8 2 5 10 4 6 0 6 12 11 10 10 4 [235] 10 8 3 11 8 4 2 6 5 5 5 9 6 8 8 10 3 8 [253] 9 11 0 4 11 4 8 9 9 8 4 1 5 3 3 5 8 0 [271] 7 -1 2 8 8 0 4 1 9 7 4 11 4 11 12 4 15 13 [289] 7 14 10 16 14 14 11 14 0 14 3 14 4 8 7 6 10 11 [307] 3 10 12 10 15 4 10 13 12 10 4 11 6 -4 0 6 11 13 [325] 6 10 5 0 7 8 6 11 0 8 0 17 9 6 6 2 14 4 [343] -1 10 -1 8 8 8 2 8 5 6 6 9 15 11 5 2 11 11 [361] 9 11 12 5 4 -2 7 13 6 9 9 8 11 3 5 8 13 3 [379] 12 9 15 10 14 8 7 9 13 8 11 8 9 10 11 -3 7 3 [397] 15 7 4 10 10 15 4 15 6 6 14 13 6 11 9 8 3 8 [415] 7 7 -2 6 10 8 2 2 14 8 8 0 11 7 12 10 -1 10 [433] 9 11 10 11 16 9 8 6 10 13 10 10 6 8 7 10 10 16 [451] 7 13 14 1 11 10 8 7 12 12 6 15 12 9 11 14 11 12 [469] 0 12 7 5 0 8 10 7 8 1 7 11 4 16 6 16 6 3 [487] 8 7 0 5 11 5 11 -2 18 12 14 3 8 4 7 8 11 8 [505] 8 7 11 8 10 6 9 5 8 1 9 6 11 12 -4 2 6 5 [523] 11 3 5 11 6 11 0 4 10 8 4 -2 -2 5 14 0 6 3 [541] 5 3 9 3 4 1 6 3 12 9 14 9 7 4 4 5 6 1 [559] 4 10 0 6 4 7 6 14 7 -1 9 1 4 2 3 2 2 4 [577] -3 2 8 5 12 9 -2 -12 5 -2 -2 5 -1 4 6 -4 5 7 [595] 8 2 10 4 9 2 4 4 2 9 2 8 8 6 5 10 10 9 [613] 1 10 12 12 7 -4 -4 -4 5 8 -1 9 11 0 -1 5 9 3 [631] -2 1 6 -3 1 5 7 2 0 6 9 8 10 3 1 4 11 14 [649] 7 4 8 6 0 7 5 2 3 8 -3 2 3 7 7 9 2 3 [667] 4 9 4 1 5 8 3 -5 3 7 1 7 1 13 7 1 6 11 [685] 12 3 2 5 5 4 5 3 4 5 6 0 6 13 4 -2 8 12 [703] 9 12 10 1 0 0 8 4 11 7 8 3 3 10 3 6 10 3 [721] -2 11 4 2 8 8 2 4 14 3 10 6 5 7 1 4 8 13 [739] 5 7 5 9 11 1 8 14 4 10 11 14 5 10 9 6 15 9 [757] 11 8 12 10 5 5 14 6 2 6 16 7 12 13 8 11 2 10 [775] 6 5 0 10 5 6 4 13 9 6 9 16 10 4 4 6 6 9 [793] 12 14 3 8 16 7 4 5 8 7 11 5 10 7 11 10 8 8 [811] 10 8 8 5 7 7 -3 3 7 12 9 5 2 12 6 12 12 9 [829] 6 10 8 16 6 12 11 6 10 8 6 10 13 6 11 2 8 8 [847] 9 6 7 2 11 10 9 11 11 6 3 4 10 3 8 12 13 6 [865] 11 8 9 6 16 4 7 11 6 2 12 13 10 13 7 11 2 14 [883] 12 12 14 4 8 10 15 10 4 4 7 7 11 8 6 10 15 7 [901] 6 8 16 14 8 14 5 7 12 11 8 9 8 0 13 5 3 13 [919] 8 14 12 8 10 5 0 16 8 6 7 6 10 14 7 14 9 6 [937] 12 3 0 10 10 6 8 4 14 12 6 0 12 4 4 2 12 14 [955] 14 6 11 12 6 10 11 2 3 10 7 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/15kjt1335452920.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: endo Inputs: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10, A11, A12, A13, A14, A15, A16, A17, A18, A19, A20 Number of observations: 965 1) A16 <= 2; criterion = 0.989, statistic = 11.973 2)* weights = 357 1) A16 > 2 3)* weights = 608 > postscript(file="/var/wessaorg/rcomp/tmp/2rdta1335452920.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/wessaorg/rcomp/tmp/3emx11335452920.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 12 7.151316 4.8486842 2 8 6.355742 1.6442577 3 1 7.151316 -6.1513158 4 2 7.151316 -5.1513158 5 5 7.151316 -2.1513158 6 12 7.151316 4.8486842 7 11 7.151316 3.8486842 8 9 7.151316 1.8486842 9 2 7.151316 -5.1513158 10 4 7.151316 -3.1513158 11 5 6.355742 -1.3557423 12 4 6.355742 -2.3557423 13 5 7.151316 -2.1513158 14 4 7.151316 -3.1513158 15 6 7.151316 -1.1513158 16 6 7.151316 -1.1513158 17 0 7.151316 -7.1513158 18 2 7.151316 -5.1513158 19 11 6.355742 4.6442577 20 5 7.151316 -2.1513158 21 7 7.151316 -0.1513158 22 6 7.151316 -1.1513158 23 7 7.151316 -0.1513158 24 10 7.151316 2.8486842 25 11 6.355742 4.6442577 26 9 7.151316 1.8486842 27 3 6.355742 -3.3557423 28 5 6.355742 -1.3557423 29 4 7.151316 -3.1513158 30 7 7.151316 -0.1513158 31 5 7.151316 -2.1513158 32 1 7.151316 -6.1513158 33 4 7.151316 -3.1513158 34 4 7.151316 -3.1513158 35 5 7.151316 -2.1513158 36 7 7.151316 -0.1513158 37 8 7.151316 0.8486842 38 2 7.151316 -5.1513158 39 0 7.151316 -7.1513158 40 9 7.151316 1.8486842 41 8 7.151316 0.8486842 42 3 7.151316 -4.1513158 43 5 7.151316 -2.1513158 44 3 7.151316 -4.1513158 45 6 7.151316 -1.1513158 46 7 7.151316 -0.1513158 47 7 7.151316 -0.1513158 48 3 6.355742 -3.3557423 49 7 7.151316 -0.1513158 50 8 7.151316 0.8486842 51 8 7.151316 0.8486842 52 6 7.151316 -1.1513158 53 9 7.151316 1.8486842 54 5 7.151316 -2.1513158 55 6 6.355742 -0.3557423 56 7 7.151316 -0.1513158 57 5 7.151316 -2.1513158 58 8 7.151316 0.8486842 59 4 7.151316 -3.1513158 60 14 7.151316 6.8486842 61 4 7.151316 -3.1513158 62 3 6.355742 -3.3557423 63 11 6.355742 4.6442577 64 8 7.151316 0.8486842 65 8 7.151316 0.8486842 66 9 7.151316 1.8486842 67 12 7.151316 4.8486842 68 2 7.151316 -5.1513158 69 4 6.355742 -2.3557423 70 2 7.151316 -5.1513158 71 3 7.151316 -4.1513158 72 11 7.151316 3.8486842 73 11 6.355742 4.6442577 74 9 7.151316 1.8486842 75 4 7.151316 -3.1513158 76 1 7.151316 -6.1513158 77 5 7.151316 -2.1513158 78 2 7.151316 -5.1513158 79 2 7.151316 -5.1513158 80 5 7.151316 -2.1513158 81 -1 6.355742 -7.3557423 82 7 7.151316 -0.1513158 83 4 6.355742 -2.3557423 84 3 6.355742 -3.3557423 85 7 7.151316 -0.1513158 86 7 7.151316 -0.1513158 87 5 7.151316 -2.1513158 88 8 7.151316 0.8486842 89 7 7.151316 -0.1513158 90 5 6.355742 -1.3557423 91 10 7.151316 2.8486842 92 7 7.151316 -0.1513158 93 5 7.151316 -2.1513158 94 9 7.151316 1.8486842 95 4 6.355742 -2.3557423 96 2 7.151316 -5.1513158 97 5 7.151316 -2.1513158 98 12 7.151316 4.8486842 99 4 7.151316 -3.1513158 100 4 7.151316 -3.1513158 101 6 7.151316 -1.1513158 102 5 7.151316 -2.1513158 103 7 7.151316 -0.1513158 104 8 7.151316 0.8486842 105 11 7.151316 3.8486842 106 3 7.151316 -4.1513158 107 1 7.151316 -6.1513158 108 13 7.151316 5.8486842 109 7 7.151316 -0.1513158 110 2 7.151316 -5.1513158 111 2 6.355742 -4.3557423 112 5 7.151316 -2.1513158 113 3 6.355742 -3.3557423 114 -1 7.151316 -8.1513158 115 0 7.151316 -7.1513158 116 6 7.151316 -1.1513158 117 10 7.151316 2.8486842 118 -2 7.151316 -9.1513158 119 8 7.151316 0.8486842 120 11 6.355742 4.6442577 121 11 7.151316 3.8486842 122 10 7.151316 2.8486842 123 9 7.151316 1.8486842 124 10 7.151316 2.8486842 125 0 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2.6442577 665 2 6.355742 -4.3557423 666 3 6.355742 -3.3557423 667 4 6.355742 -2.3557423 668 9 7.151316 1.8486842 669 4 7.151316 -3.1513158 670 1 6.355742 -5.3557423 671 5 6.355742 -1.3557423 672 8 6.355742 1.6442577 673 3 7.151316 -4.1513158 674 -5 6.355742 -11.3557423 675 3 6.355742 -3.3557423 676 7 6.355742 0.6442577 677 1 6.355742 -5.3557423 678 7 6.355742 0.6442577 679 1 7.151316 -6.1513158 680 13 7.151316 5.8486842 681 7 6.355742 0.6442577 682 1 7.151316 -6.1513158 683 6 7.151316 -1.1513158 684 11 6.355742 4.6442577 685 12 7.151316 4.8486842 686 3 6.355742 -3.3557423 687 2 7.151316 -5.1513158 688 5 6.355742 -1.3557423 689 5 7.151316 -2.1513158 690 4 7.151316 -3.1513158 691 5 6.355742 -1.3557423 692 3 6.355742 -3.3557423 693 4 7.151316 -3.1513158 694 5 6.355742 -1.3557423 695 6 6.355742 -0.3557423 696 0 6.355742 -6.3557423 697 6 6.355742 -0.3557423 698 13 6.355742 6.6442577 699 4 7.151316 -3.1513158 700 -2 6.355742 -8.3557423 701 8 6.355742 1.6442577 702 12 7.151316 4.8486842 703 9 6.355742 2.6442577 704 12 6.355742 5.6442577 705 10 7.151316 2.8486842 706 1 7.151316 -6.1513158 707 0 6.355742 -6.3557423 708 0 7.151316 -7.1513158 709 8 6.355742 1.6442577 710 4 7.151316 -3.1513158 711 11 6.355742 4.6442577 712 7 7.151316 -0.1513158 713 8 6.355742 1.6442577 714 3 7.151316 -4.1513158 715 3 6.355742 -3.3557423 716 10 7.151316 2.8486842 717 3 6.355742 -3.3557423 718 6 6.355742 -0.3557423 719 10 6.355742 3.6442577 720 3 6.355742 -3.3557423 721 -2 7.151316 -9.1513158 722 11 6.355742 4.6442577 723 4 7.151316 -3.1513158 724 2 6.355742 -4.3557423 725 8 7.151316 0.8486842 726 8 6.355742 1.6442577 727 2 7.151316 -5.1513158 728 4 6.355742 -2.3557423 729 14 7.151316 6.8486842 730 3 6.355742 -3.3557423 731 10 7.151316 2.8486842 732 6 6.355742 -0.3557423 733 5 6.355742 -1.3557423 734 7 7.151316 -0.1513158 735 1 6.355742 -5.3557423 736 4 6.355742 -2.3557423 737 8 6.355742 1.6442577 738 13 7.151316 5.8486842 739 5 6.355742 -1.3557423 740 7 6.355742 0.6442577 741 5 6.355742 -1.3557423 742 9 6.355742 2.6442577 743 11 7.151316 3.8486842 744 1 6.355742 -5.3557423 745 8 6.355742 1.6442577 746 14 7.151316 6.8486842 747 4 7.151316 -3.1513158 748 10 6.355742 3.6442577 749 11 7.151316 3.8486842 750 14 7.151316 6.8486842 751 5 6.355742 -1.3557423 752 10 6.355742 3.6442577 753 9 6.355742 2.6442577 754 6 6.355742 -0.3557423 755 15 6.355742 8.6442577 756 9 7.151316 1.8486842 757 11 6.355742 4.6442577 758 8 7.151316 0.8486842 759 12 7.151316 4.8486842 760 10 6.355742 3.6442577 761 5 7.151316 -2.1513158 762 5 6.355742 -1.3557423 763 14 6.355742 7.6442577 764 6 7.151316 -1.1513158 765 2 6.355742 -4.3557423 766 6 6.355742 -0.3557423 767 16 7.151316 8.8486842 768 7 7.151316 -0.1513158 769 12 6.355742 5.6442577 770 13 6.355742 6.6442577 771 8 7.151316 0.8486842 772 11 6.355742 4.6442577 773 2 6.355742 -4.3557423 774 10 6.355742 3.6442577 775 6 7.151316 -1.1513158 776 5 6.355742 -1.3557423 777 0 6.355742 -6.3557423 778 10 7.151316 2.8486842 779 5 6.355742 -1.3557423 780 6 6.355742 -0.3557423 781 4 6.355742 -2.3557423 782 13 6.355742 6.6442577 783 9 7.151316 1.8486842 784 6 7.151316 -1.1513158 785 9 6.355742 2.6442577 786 16 7.151316 8.8486842 787 10 7.151316 2.8486842 788 4 6.355742 -2.3557423 789 4 7.151316 -3.1513158 790 6 6.355742 -0.3557423 791 6 7.151316 -1.1513158 792 9 6.355742 2.6442577 793 12 6.355742 5.6442577 794 14 7.151316 6.8486842 795 3 6.355742 -3.3557423 796 8 6.355742 1.6442577 797 16 7.151316 8.8486842 798 7 7.151316 -0.1513158 799 4 7.151316 -3.1513158 800 5 6.355742 -1.3557423 801 8 6.355742 1.6442577 802 7 7.151316 -0.1513158 803 11 7.151316 3.8486842 804 5 7.151316 -2.1513158 805 10 7.151316 2.8486842 806 7 7.151316 -0.1513158 807 11 6.355742 4.6442577 808 10 7.151316 2.8486842 809 8 6.355742 1.6442577 810 8 6.355742 1.6442577 811 10 7.151316 2.8486842 812 8 6.355742 1.6442577 813 8 7.151316 0.8486842 814 5 6.355742 -1.3557423 815 7 7.151316 -0.1513158 816 7 6.355742 0.6442577 817 -3 6.355742 -9.3557423 818 3 6.355742 -3.3557423 819 7 6.355742 0.6442577 820 12 7.151316 4.8486842 821 9 6.355742 2.6442577 822 5 7.151316 -2.1513158 823 2 6.355742 -4.3557423 824 12 6.355742 5.6442577 825 6 6.355742 -0.3557423 826 12 6.355742 5.6442577 827 12 6.355742 5.6442577 828 9 7.151316 1.8486842 829 6 7.151316 -1.1513158 830 10 7.151316 2.8486842 831 8 7.151316 0.8486842 832 16 6.355742 9.6442577 833 6 6.355742 -0.3557423 834 12 6.355742 5.6442577 835 11 6.355742 4.6442577 836 6 6.355742 -0.3557423 837 10 7.151316 2.8486842 838 8 7.151316 0.8486842 839 6 6.355742 -0.3557423 840 10 6.355742 3.6442577 841 13 6.355742 6.6442577 842 6 7.151316 -1.1513158 843 11 7.151316 3.8486842 844 2 7.151316 -5.1513158 845 8 6.355742 1.6442577 846 8 6.355742 1.6442577 847 9 7.151316 1.8486842 848 6 7.151316 -1.1513158 849 7 7.151316 -0.1513158 850 2 6.355742 -4.3557423 851 11 6.355742 4.6442577 852 10 6.355742 3.6442577 853 9 6.355742 2.6442577 854 11 6.355742 4.6442577 855 11 7.151316 3.8486842 856 6 7.151316 -1.1513158 857 3 6.355742 -3.3557423 858 4 7.151316 -3.1513158 859 10 7.151316 2.8486842 860 3 6.355742 -3.3557423 861 8 6.355742 1.6442577 862 12 6.355742 5.6442577 863 13 7.151316 5.8486842 864 6 7.151316 -1.1513158 865 11 6.355742 4.6442577 866 8 7.151316 0.8486842 867 9 7.151316 1.8486842 868 6 6.355742 -0.3557423 869 16 7.151316 8.8486842 870 4 6.355742 -2.3557423 871 7 6.355742 0.6442577 872 11 6.355742 4.6442577 873 6 7.151316 -1.1513158 874 2 7.151316 -5.1513158 875 12 7.151316 4.8486842 876 13 7.151316 5.8486842 877 10 6.355742 3.6442577 878 13 6.355742 6.6442577 879 7 6.355742 0.6442577 880 11 6.355742 4.6442577 881 2 6.355742 -4.3557423 882 14 7.151316 6.8486842 883 12 6.355742 5.6442577 884 12 6.355742 5.6442577 885 14 6.355742 7.6442577 886 4 6.355742 -2.3557423 887 8 7.151316 0.8486842 888 10 6.355742 3.6442577 889 15 6.355742 8.6442577 890 10 7.151316 2.8486842 891 4 7.151316 -3.1513158 892 4 6.355742 -2.3557423 893 7 6.355742 0.6442577 894 7 6.355742 0.6442577 895 11 7.151316 3.8486842 896 8 6.355742 1.6442577 897 6 6.355742 -0.3557423 898 10 6.355742 3.6442577 899 15 6.355742 8.6442577 900 7 6.355742 0.6442577 901 6 6.355742 -0.3557423 902 8 6.355742 1.6442577 903 16 6.355742 9.6442577 904 14 6.355742 7.6442577 905 8 6.355742 1.6442577 906 14 6.355742 7.6442577 907 5 6.355742 -1.3557423 908 7 6.355742 0.6442577 909 12 6.355742 5.6442577 910 11 7.151316 3.8486842 911 8 6.355742 1.6442577 912 9 6.355742 2.6442577 913 8 7.151316 0.8486842 914 0 7.151316 -7.1513158 915 13 7.151316 5.8486842 916 5 6.355742 -1.3557423 917 3 6.355742 -3.3557423 918 13 6.355742 6.6442577 919 8 7.151316 0.8486842 920 14 7.151316 6.8486842 921 12 7.151316 4.8486842 922 8 6.355742 1.6442577 923 10 6.355742 3.6442577 924 5 6.355742 -1.3557423 925 0 6.355742 -6.3557423 926 16 7.151316 8.8486842 927 8 6.355742 1.6442577 928 6 7.151316 -1.1513158 929 7 7.151316 -0.1513158 930 6 6.355742 -0.3557423 931 10 6.355742 3.6442577 932 14 7.151316 6.8486842 933 7 6.355742 0.6442577 934 14 6.355742 7.6442577 935 9 7.151316 1.8486842 936 6 7.151316 -1.1513158 937 12 7.151316 4.8486842 938 3 6.355742 -3.3557423 939 0 6.355742 -6.3557423 940 10 6.355742 3.6442577 941 10 6.355742 3.6442577 942 6 6.355742 -0.3557423 943 8 7.151316 0.8486842 944 4 6.355742 -2.3557423 945 14 7.151316 6.8486842 946 12 7.151316 4.8486842 947 6 7.151316 -1.1513158 948 0 7.151316 -7.1513158 949 12 7.151316 4.8486842 950 4 6.355742 -2.3557423 951 4 7.151316 -3.1513158 952 2 7.151316 -5.1513158 953 12 7.151316 4.8486842 954 14 6.355742 7.6442577 955 14 6.355742 7.6442577 956 6 6.355742 -0.3557423 957 11 6.355742 4.6442577 958 12 7.151316 4.8486842 959 6 6.355742 -0.3557423 960 10 7.151316 2.8486842 961 11 6.355742 4.6442577 962 2 7.151316 -5.1513158 963 3 6.355742 -3.3557423 964 10 6.355742 3.6442577 965 7 7.151316 -0.1513158 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/4kpbi1335452920.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5gwaz1335452920.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/wessaorg/rcomp/tmp/6owwi1335452920.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/wessaorg/rcomp/tmp/7u3v61335452920.tab") + } > > try(system("convert tmp/2rdta1335452920.ps tmp/2rdta1335452920.png",intern=TRUE)) character(0) > try(system("convert tmp/3emx11335452920.ps tmp/3emx11335452920.png",intern=TRUE)) character(0) > try(system("convert tmp/4kpbi1335452920.ps tmp/4kpbi1335452920.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 22.547 0.376 23.818