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Type 'q()' to quit R. > par9 = 'ATTLES separate' > par8 = 'ATTLES connected' > par7 = '1' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'ATTLES separate' > par8 <- 'ATTLES connected' > par7 <- '1' > 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] 39 34 33 25 32 28 33 38 33 37 34 34 31 37 30 29 34 30 37 39 40 38 25 38 32 [26] 35 30 27 46 31 30 31 29 37 31 32 35 30 40 36 38 41 39 45 37 40 35 30 29 36 [51] 33 34 33 37 36 32 39 29 33 34 25 34 40 35 34 36 34 40 37 42 25 35 27 34 32 [76] 38 30 31 40 33 35 30 34 36 36 39 37 24 29 24 31 38 31 38 37 36 35 35 33 38 [101] 32 26 31 35 34 44 35 29 33 38 42 36 33 34 28 35 42 26 36 29 39 37 36 35 42 [126] 34 29 33 31 30 44 28 33 36 37 34 37 31 26 31 29 34 27 36 33 25 37 32 37 30 [151] 33 35 43 30 32 30 29 32 31 32 35 30 40 36 32 31 34 36 37 37 40 35 30 39 24 [176] 33 38 40 32 32 37 29 40 28 25 33 37 36 34 33 35 38 29 48 30 29 31 30 31 32 [201] 39 32 29 30 39 34 32 30 30 29 36 23 40 33 24 36 33 41 33 25 35 30 27 32 22 [226] 32 24 31 > 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]) 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 48 1 1 5 7 3 4 4 15 20 16 18 20 19 17 17 18 11 9 11 2 4 1 2 1 1 1 > colnames(x) [1] "endo" "A1" "A2" "A3" "A4" "A5" "A6" "A7" "A8" "A9" [11] "A10" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 39 34 33 25 32 28 33 38 33 37 34 34 31 37 30 29 34 30 37 39 40 38 25 38 32 [26] 35 30 27 46 31 30 31 29 37 31 32 35 30 40 36 38 41 39 45 37 40 35 30 29 36 [51] 33 34 33 37 36 32 39 29 33 34 25 34 40 35 34 36 34 40 37 42 25 35 27 34 32 [76] 38 30 31 40 33 35 30 34 36 36 39 37 24 29 24 31 38 31 38 37 36 35 35 33 38 [101] 32 26 31 35 34 44 35 29 33 38 42 36 33 34 28 35 42 26 36 29 39 37 36 35 42 [126] 34 29 33 31 30 44 28 33 36 37 34 37 31 26 31 29 34 27 36 33 25 37 32 37 30 [151] 33 35 43 30 32 30 29 32 31 32 35 30 40 36 32 31 34 36 37 37 40 35 30 39 24 [176] 33 38 40 32 32 37 29 40 28 25 33 37 36 34 33 35 38 29 48 30 29 31 30 31 32 [201] 39 32 29 30 39 34 32 30 30 29 36 23 40 33 24 36 33 41 33 25 35 30 27 32 22 [226] 32 24 31 > 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/1yyfr1335872338.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: endo Inputs: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 Number of observations: 228 1) A8 <= 4; criterion = 1, statistic = 22.028 2) A6 <= 3; criterion = 0.998, statistic = 13.601 3)* weights = 59 2) A6 > 3 4)* weights = 88 1) A8 > 4 5)* weights = 81 > postscript(file="/var/wessaorg/rcomp/tmp/2bttq1335872338.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/3v3z61335872338.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 39 35.08642 3.91358025 2 34 33.57955 0.42045455 3 33 33.57955 -0.57954545 4 25 33.57955 -8.57954545 5 32 35.08642 -3.08641975 6 28 33.57955 -5.57954545 7 33 33.57955 -0.57954545 8 38 31.45763 6.54237288 9 33 33.57955 -0.57954545 10 37 35.08642 1.91358025 11 34 33.57955 0.42045455 12 34 31.45763 2.54237288 13 31 31.45763 -0.45762712 14 37 31.45763 5.54237288 15 30 35.08642 -5.08641975 16 29 35.08642 -6.08641975 17 34 35.08642 -1.08641975 18 30 33.57955 -3.57954545 19 37 33.57955 3.42045455 20 39 33.57955 5.42045455 21 40 35.08642 4.91358025 22 38 31.45763 6.54237288 23 25 31.45763 -6.45762712 24 38 31.45763 6.54237288 25 32 31.45763 0.54237288 26 35 33.57955 1.42045455 27 30 33.57955 -3.57954545 28 27 31.45763 -4.45762712 29 46 35.08642 10.91358025 30 31 35.08642 -4.08641975 31 30 35.08642 -5.08641975 32 31 31.45763 -0.45762712 33 29 33.57955 -4.57954545 34 37 33.57955 3.42045455 35 31 31.45763 -0.45762712 36 32 31.45763 0.54237288 37 35 35.08642 -0.08641975 38 30 31.45763 -1.45762712 39 40 33.57955 6.42045455 40 36 31.45763 4.54237288 41 38 35.08642 2.91358025 42 41 35.08642 5.91358025 43 39 35.08642 3.91358025 44 45 31.45763 13.54237288 45 37 35.08642 1.91358025 46 40 35.08642 4.91358025 47 35 33.57955 1.42045455 48 30 33.57955 -3.57954545 49 29 31.45763 -2.45762712 50 36 35.08642 0.91358025 51 33 31.45763 1.54237288 52 34 33.57955 0.42045455 53 33 35.08642 -2.08641975 54 37 33.57955 3.42045455 55 36 31.45763 4.54237288 56 32 33.57955 -1.57954545 57 39 35.08642 3.91358025 58 29 33.57955 -4.57954545 59 33 31.45763 1.54237288 60 34 35.08642 -1.08641975 61 25 35.08642 -10.08641975 62 34 33.57955 0.42045455 63 40 35.08642 4.91358025 64 35 33.57955 1.42045455 65 34 33.57955 0.42045455 66 36 35.08642 0.91358025 67 34 33.57955 0.42045455 68 40 33.57955 6.42045455 69 37 35.08642 1.91358025 70 42 31.45763 10.54237288 71 25 33.57955 -8.57954545 72 35 35.08642 -0.08641975 73 27 33.57955 -6.57954545 74 34 33.57955 0.42045455 75 32 35.08642 -3.08641975 76 38 35.08642 2.91358025 77 30 31.45763 -1.45762712 78 31 31.45763 -0.45762712 79 40 35.08642 4.91358025 80 33 33.57955 -0.57954545 81 35 33.57955 1.42045455 82 30 33.57955 -3.57954545 83 34 35.08642 -1.08641975 84 36 33.57955 2.42045455 85 36 33.57955 2.42045455 86 39 35.08642 3.91358025 87 37 33.57955 3.42045455 88 24 31.45763 -7.45762712 89 29 35.08642 -6.08641975 90 24 31.45763 -7.45762712 91 31 33.57955 -2.57954545 92 38 35.08642 2.91358025 93 31 33.57955 -2.57954545 94 38 33.57955 4.42045455 95 37 33.57955 3.42045455 96 36 35.08642 0.91358025 97 35 31.45763 3.54237288 98 35 33.57955 1.42045455 99 33 33.57955 -0.57954545 100 38 33.57955 4.42045455 101 32 31.45763 0.54237288 102 26 35.08642 -9.08641975 103 31 31.45763 -0.45762712 104 35 35.08642 -0.08641975 105 34 35.08642 -1.08641975 106 44 35.08642 8.91358025 107 35 31.45763 3.54237288 108 29 31.45763 -2.45762712 109 33 33.57955 -0.57954545 110 38 35.08642 2.91358025 111 42 35.08642 6.91358025 112 36 35.08642 0.91358025 113 33 33.57955 -0.57954545 114 34 31.45763 2.54237288 115 28 31.45763 -3.45762712 116 35 35.08642 -0.08641975 117 42 33.57955 8.42045455 118 26 35.08642 -9.08641975 119 36 33.57955 2.42045455 120 29 33.57955 -4.57954545 121 39 35.08642 3.91358025 122 37 35.08642 1.91358025 123 36 31.45763 4.54237288 124 35 33.57955 1.42045455 125 42 35.08642 6.91358025 126 34 31.45763 2.54237288 127 29 35.08642 -6.08641975 128 33 33.57955 -0.57954545 129 31 35.08642 -4.08641975 130 30 33.57955 -3.57954545 131 44 35.08642 8.91358025 132 28 33.57955 -5.57954545 133 33 33.57955 -0.57954545 134 36 33.57955 2.42045455 135 37 35.08642 1.91358025 136 34 33.57955 0.42045455 137 37 33.57955 3.42045455 138 31 31.45763 -0.45762712 139 26 31.45763 -5.45762712 140 31 31.45763 -0.45762712 141 29 35.08642 -6.08641975 142 34 35.08642 -1.08641975 143 27 31.45763 -4.45762712 144 36 31.45763 4.54237288 145 33 35.08642 -2.08641975 146 25 31.45763 -6.45762712 147 37 33.57955 3.42045455 148 32 31.45763 0.54237288 149 37 33.57955 3.42045455 150 30 33.57955 -3.57954545 151 33 33.57955 -0.57954545 152 35 35.08642 -0.08641975 153 43 33.57955 9.42045455 154 30 35.08642 -5.08641975 155 32 31.45763 0.54237288 156 30 35.08642 -5.08641975 157 29 33.57955 -4.57954545 158 32 35.08642 -3.08641975 159 31 31.45763 -0.45762712 160 32 31.45763 0.54237288 161 35 35.08642 -0.08641975 162 30 31.45763 -1.45762712 163 40 33.57955 6.42045455 164 36 31.45763 4.54237288 165 32 31.45763 0.54237288 166 31 31.45763 -0.45762712 167 34 33.57955 0.42045455 168 36 35.08642 0.91358025 169 37 33.57955 3.42045455 170 37 35.08642 1.91358025 171 40 35.08642 4.91358025 172 35 33.57955 1.42045455 173 30 33.57955 -3.57954545 174 39 31.45763 7.54237288 175 24 31.45763 -7.45762712 176 33 33.57955 -0.57954545 177 38 33.57955 4.42045455 178 40 35.08642 4.91358025 179 32 35.08642 -3.08641975 180 32 31.45763 0.54237288 181 37 33.57955 3.42045455 182 29 33.57955 -4.57954545 183 40 35.08642 4.91358025 184 28 31.45763 -3.45762712 185 25 35.08642 -10.08641975 186 33 33.57955 -0.57954545 187 37 33.57955 3.42045455 188 36 35.08642 0.91358025 189 34 33.57955 0.42045455 190 33 31.45763 1.54237288 191 35 35.08642 -0.08641975 192 38 35.08642 2.91358025 193 29 33.57955 -4.57954545 194 48 35.08642 12.91358025 195 30 33.57955 -3.57954545 196 29 33.57955 -4.57954545 197 31 33.57955 -2.57954545 198 30 31.45763 -1.45762712 199 31 33.57955 -2.57954545 200 32 35.08642 -3.08641975 201 39 33.57955 5.42045455 202 32 33.57955 -1.57954545 203 29 35.08642 -6.08641975 204 30 35.08642 -5.08641975 205 39 35.08642 3.91358025 206 34 35.08642 -1.08641975 207 32 31.45763 0.54237288 208 30 33.57955 -3.57954545 209 30 31.45763 -1.45762712 210 29 35.08642 -6.08641975 211 36 35.08642 0.91358025 212 23 33.57955 -10.57954545 213 40 33.57955 6.42045455 214 33 33.57955 -0.57954545 215 24 31.45763 -7.45762712 216 36 33.57955 2.42045455 217 33 33.57955 -0.57954545 218 41 35.08642 5.91358025 219 33 35.08642 -2.08641975 220 25 31.45763 -6.45762712 221 35 33.57955 1.42045455 222 30 35.08642 -5.08641975 223 27 35.08642 -8.08641975 224 32 33.57955 -1.57954545 225 22 31.45763 -9.45762712 226 32 35.08642 -3.08641975 227 24 31.45763 -7.45762712 228 31 35.08642 -4.08641975 > 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/4ie291335872338.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/58nex1335872338.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6ng971335872338.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/7xayw1335872338.tab") + } > > try(system("convert tmp/2bttq1335872338.ps tmp/2bttq1335872338.png",intern=TRUE)) character(0) > try(system("convert tmp/3v3z61335872338.ps tmp/3v3z61335872338.png",intern=TRUE)) character(0) > try(system("convert tmp/4ie291335872338.ps tmp/4ie291335872338.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.987 0.320 4.305