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Type 'q()' to quit R. > par9 = 'COLLES actuals' > par8 = 'ATTLES connected' > par7 = 'all' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'COLLES actuals' > par8 <- 'ATTLES connected' > 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] 90 95 96 93 102 93 82 98 89 88 102 83 108 86 97 89 103 115 [19] 90 88 81 74 75 93 87 92 95 105 93 90 87 105 92 89 85 100 [37] 83 97 95 73 94 99 99 88 86 89 95 83 72 98 85 84 80 108 [55] 72 110 93 85 95 94 80 93 83 92 106 93 95 96 90 93 102 94 [73] 85 87 77 83 79 110 90 95 80 95 105 91 69 81 75 98 101 96 [91] 86 91 72 76 95 96 92 77 90 85 81 80 91 89 78 66 79 91 [109] 88 61 77 81 91 75 87 93 85 88 86 93 77 92 80 98 109 95 [127] 75 88 87 84 82 107 94 86 89 95 81 87 87 88 89 68 117 84 [145] 94 109 108 61 107 80 92 94 95 83 92 75 83 110 80 101 81 59 [163] 90 94 83 90 82 93 110 97 95 98 78 89 93 106 71 91 96 108 [181] 93 95 89 72 107 102 88 95 94 96 92 110 94 95 72 103 89 96 [199] 92 90 86 90 99 86 89 108 94 83 76 102 90 112 89 98 81 116 [217] 101 85 104 92 94 105 94 85 96 79 106 79 101 90 114 99 79 94 [235] 93 91 101 81 116 86 95 91 95 78 70 109 109 81 94 87 73 86 [253] 93 113 97 85 103 105 97 86 118 102 77 94 91 89 67 104 100 86 [271] 109 97 77 88 88 115 103 84 106 97 87 106 103 97 110 102 78 101 [289] 93 83 104 85 111 104 70 90 92 90 83 71 90 72 83 120 90 93 [307] 90 98 85 101 89 108 96 86 115 79 103 93 107 87 84 65 72 87 [325] 80 92 87 106 87 92 88 92 96 86 94 98 96 79 93 81 72 109 [343] 72 96 74 88 97 97 92 93 89 98 109 99 99 98 93 105 88 102 [361] 101 90 93 96 83 97 68 106 86 78 99 94 103 110 86 87 104 96 [379] 100 111 82 72 88 103 80 93 88 56 120 86 82 101 89 66 87 87 [397] 94 92 75 77 85 84 53 74 89 83 103 84 69 68 98 72 84 71 [415] 87 88 76 92 91 61 66 88 98 85 82 70 78 99 74 69 90 93 [433] 104 87 89 57 91 82 71 63 93 87 86 83 71 86 81 81 73 64 [451] 81 79 76 84 68 69 68 104 70 81 80 86 85 101 74 93 75 84 [469] 100 67 89 83 80 62 77 93 82 104 101 73 91 78 83 76 89 93 [487] 93 76 87 77 67 88 76 91 82 81 93 67 85 87 65 81 103 69 [505] 83 82 102 74 95 75 76 72 84 99 95 81 98 90 93 81 72 72 [523] 75 57 68 70 90 87 100 69 78 75 71 85 88 83 72 85 79 82 [541] 79 85 89 70 95 89 72 89 89 74 90 92 88 113 89 94 76 94 [559] 85 79 77 69 88 86 82 84 96 82 88 111 67 73 105 94 96 97 [577] 70 91 110 92 68 104 87 96 88 95 75 84 72 91 89 67 81 93 [595] 106 100 63 84 81 78 76 99 75 86 92 95 85 95 94 60 62 91 [613] 88 73 83 64 75 95 81 92 81 86 94 93 95 95 87 87 91 91 [631] 79 99 89 104 87 86 88 84 74 85 90 83 88 80 81 77 90 86 [649] 83 77 88 98 95 101 75 79 88 77 93 76 107 89 92 82 84 90 [667] 82 76 95 109 90 77 76 83 86 82 96 92 110 77 86 100 98 101 [685] 76 80 86 107 78 69 92 84 83 89 74 79 52 86 79 95 88 91 [703] 80 94 68 89 65 84 88 64 82 82 75 93 87 90 90 108 66 99 [721] 65 93 100 92 100 77 85 80 69 91 53 91 86 95 66 95 75 90 [739] 90 83 92 84 86 72 73 100 79 71 82 99 85 80 85 98 99 86 [757] 73 78 90 103 87 76 102 73 101 83 80 79 63 96 75 103 89 105 [775] 78 80 95 96 71 80 92 67 97 79 79 83 77 82 79 83 71 83 [793] 73 86 77 72 73 82 65 67 78 70 78 88 69 85 88 > 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]) 52 53 56 57 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 1 2 1 2 1 1 3 2 3 3 5 5 8 8 10 8 9 19 11 9 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 17 15 18 14 20 19 23 21 29 19 25 32 28 32 31 31 21 27 36 24 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 33 20 13 16 14 10 14 10 12 10 8 8 6 7 8 9 3 1 2 1 115 116 117 118 120 3 2 1 1 2 > colnames(x) [1] "endo" "A1" "A2" "A3" "A4" "A5" "A6" "A7" "A8" "A9" [11] "A10" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 90 95 96 93 102 93 82 98 89 88 102 83 108 86 97 89 103 115 [19] 90 88 81 74 75 93 87 92 95 105 93 90 87 105 92 89 85 100 [37] 83 97 95 73 94 99 99 88 86 89 95 83 72 98 85 84 80 108 [55] 72 110 93 85 95 94 80 93 83 92 106 93 95 96 90 93 102 94 [73] 85 87 77 83 79 110 90 95 80 95 105 91 69 81 75 98 101 96 [91] 86 91 72 76 95 96 92 77 90 85 81 80 91 89 78 66 79 91 [109] 88 61 77 81 91 75 87 93 85 88 86 93 77 92 80 98 109 95 [127] 75 88 87 84 82 107 94 86 89 95 81 87 87 88 89 68 117 84 [145] 94 109 108 61 107 80 92 94 95 83 92 75 83 110 80 101 81 59 [163] 90 94 83 90 82 93 110 97 95 98 78 89 93 106 71 91 96 108 [181] 93 95 89 72 107 102 88 95 94 96 92 110 94 95 72 103 89 96 [199] 92 90 86 90 99 86 89 108 94 83 76 102 90 112 89 98 81 116 [217] 101 85 104 92 94 105 94 85 96 79 106 79 101 90 114 99 79 94 [235] 93 91 101 81 116 86 95 91 95 78 70 109 109 81 94 87 73 86 [253] 93 113 97 85 103 105 97 86 118 102 77 94 91 89 67 104 100 86 [271] 109 97 77 88 88 115 103 84 106 97 87 106 103 97 110 102 78 101 [289] 93 83 104 85 111 104 70 90 92 90 83 71 90 72 83 120 90 93 [307] 90 98 85 101 89 108 96 86 115 79 103 93 107 87 84 65 72 87 [325] 80 92 87 106 87 92 88 92 96 86 94 98 96 79 93 81 72 109 [343] 72 96 74 88 97 97 92 93 89 98 109 99 99 98 93 105 88 102 [361] 101 90 93 96 83 97 68 106 86 78 99 94 103 110 86 87 104 96 [379] 100 111 82 72 88 103 80 93 88 56 120 86 82 101 89 66 87 87 [397] 94 92 75 77 85 84 53 74 89 83 103 84 69 68 98 72 84 71 [415] 87 88 76 92 91 61 66 88 98 85 82 70 78 99 74 69 90 93 [433] 104 87 89 57 91 82 71 63 93 87 86 83 71 86 81 81 73 64 [451] 81 79 76 84 68 69 68 104 70 81 80 86 85 101 74 93 75 84 [469] 100 67 89 83 80 62 77 93 82 104 101 73 91 78 83 76 89 93 [487] 93 76 87 77 67 88 76 91 82 81 93 67 85 87 65 81 103 69 [505] 83 82 102 74 95 75 76 72 84 99 95 81 98 90 93 81 72 72 [523] 75 57 68 70 90 87 100 69 78 75 71 85 88 83 72 85 79 82 [541] 79 85 89 70 95 89 72 89 89 74 90 92 88 113 89 94 76 94 [559] 85 79 77 69 88 86 82 84 96 82 88 111 67 73 105 94 96 97 [577] 70 91 110 92 68 104 87 96 88 95 75 84 72 91 89 67 81 93 [595] 106 100 63 84 81 78 76 99 75 86 92 95 85 95 94 60 62 91 [613] 88 73 83 64 75 95 81 92 81 86 94 93 95 95 87 87 91 91 [631] 79 99 89 104 87 86 88 84 74 85 90 83 88 80 81 77 90 86 [649] 83 77 88 98 95 101 75 79 88 77 93 76 107 89 92 82 84 90 [667] 82 76 95 109 90 77 76 83 86 82 96 92 110 77 86 100 98 101 [685] 76 80 86 107 78 69 92 84 83 89 74 79 52 86 79 95 88 91 [703] 80 94 68 89 65 84 88 64 82 82 75 93 87 90 90 108 66 99 [721] 65 93 100 92 100 77 85 80 69 91 53 91 86 95 66 95 75 90 [739] 90 83 92 84 86 72 73 100 79 71 82 99 85 80 85 98 99 86 [757] 73 78 90 103 87 76 102 73 101 83 80 79 63 96 75 103 89 105 [775] 78 80 95 96 71 80 92 67 97 79 79 83 77 82 79 83 71 83 [793] 73 86 77 72 73 82 65 67 78 70 78 88 69 85 88 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/1e9cq1335906597.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: endo Inputs: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 Number of observations: 807 1) A1 <= 3; criterion = 1, statistic = 50.586 2) A10 <= 3; criterion = 0.997, statistic = 13.248 3)* weights = 101 2) A10 > 3 4)* weights = 179 1) A1 > 3 5) A3 <= 4; criterion = 1, statistic = 27.815 6) A1 <= 4; criterion = 0.993, statistic = 11.444 7) A7 <= 2; criterion = 0.981, statistic = 9.616 8)* weights = 35 7) A7 > 2 9)* weights = 260 6) A1 > 4 10)* weights = 93 5) A3 > 4 11)* weights = 139 > postscript(file="/var/www/rcomp/tmp/24r8q1335906597.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3xnhg1335906597.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 90 87.38462 2.6153846 2 95 81.18812 13.8118812 3 96 87.38462 8.6153846 4 93 81.18812 11.8118812 5 102 87.38462 14.6153846 6 93 87.38462 5.6153846 7 82 82.54286 -0.5428571 8 98 87.38462 10.6153846 9 89 87.38462 1.6153846 10 88 91.18280 -3.1827957 11 102 87.38462 14.6153846 12 83 87.38462 -4.3846154 13 108 87.38462 20.6153846 14 86 85.36313 0.6368715 15 97 87.38462 9.6153846 16 89 87.38462 1.6153846 17 103 93.88489 9.1151079 18 115 91.18280 23.8172043 19 90 81.18812 8.8118812 20 88 87.38462 0.6153846 21 81 91.18280 -10.1827957 22 74 87.38462 -13.3846154 23 75 87.38462 -12.3846154 24 93 85.36313 7.6368715 25 87 82.54286 4.4571429 26 92 91.18280 0.8172043 27 95 87.38462 7.6153846 28 105 93.88489 11.1151079 29 93 91.18280 1.8172043 30 90 93.88489 -3.8848921 31 87 91.18280 -4.1827957 32 105 87.38462 17.6153846 33 92 93.88489 -1.8848921 34 89 91.18280 -2.1827957 35 85 87.38462 -2.3846154 36 100 93.88489 6.1151079 37 83 82.54286 0.4571429 38 97 87.38462 9.6153846 39 95 87.38462 7.6153846 40 73 87.38462 -14.3846154 41 94 87.38462 6.6153846 42 99 85.36313 13.6368715 43 99 87.38462 11.6153846 44 88 87.38462 0.6153846 45 86 87.38462 -1.3846154 46 89 87.38462 1.6153846 47 95 87.38462 7.6153846 48 83 87.38462 -4.3846154 49 72 87.38462 -15.3846154 50 98 91.18280 6.8172043 51 85 87.38462 -2.3846154 52 84 91.18280 -7.1827957 53 80 81.18812 -1.1881188 54 108 87.38462 20.6153846 55 72 87.38462 -15.3846154 56 110 91.18280 18.8172043 57 93 91.18280 1.8172043 58 85 87.38462 -2.3846154 59 95 93.88489 1.1151079 60 94 91.18280 2.8172043 61 80 87.38462 -7.3846154 62 93 87.38462 5.6153846 63 83 82.54286 0.4571429 64 92 87.38462 4.6153846 65 106 87.38462 18.6153846 66 93 93.88489 -0.8848921 67 95 87.38462 7.6153846 68 96 93.88489 2.1151079 69 90 87.38462 2.6153846 70 93 87.38462 5.6153846 71 102 87.38462 14.6153846 72 94 87.38462 6.6153846 73 85 87.38462 -2.3846154 74 87 93.88489 -6.8848921 75 77 81.18812 -4.1881188 76 83 87.38462 -4.3846154 77 79 81.18812 -2.1881188 78 110 85.36313 24.6368715 79 90 87.38462 2.6153846 80 95 81.18812 13.8118812 81 80 82.54286 -2.5428571 82 95 87.38462 7.6153846 83 105 91.18280 13.8172043 84 91 87.38462 3.6153846 85 69 93.88489 -24.8848921 86 81 85.36313 -4.3631285 87 75 93.88489 -18.8848921 88 98 91.18280 6.8172043 89 101 85.36313 15.6368715 90 96 85.36313 10.6368715 91 86 87.38462 -1.3846154 92 91 81.18812 9.8118812 93 72 91.18280 -19.1827957 94 76 93.88489 -17.8848921 95 95 85.36313 9.6368715 96 96 93.88489 2.1151079 97 92 87.38462 4.6153846 98 77 85.36313 -8.3631285 99 90 81.18812 8.8118812 100 85 87.38462 -2.3846154 101 81 87.38462 -6.3846154 102 80 87.38462 -7.3846154 103 91 82.54286 8.4571429 104 89 81.18812 7.8118812 105 78 87.38462 -9.3846154 106 66 93.88489 -27.8848921 107 79 93.88489 -14.8848921 108 91 87.38462 3.6153846 109 88 93.88489 -5.8848921 110 61 82.54286 -21.5428571 111 77 81.18812 -4.1881188 112 81 87.38462 -6.3846154 113 91 81.18812 9.8118812 114 75 81.18812 -6.1881188 115 87 93.88489 -6.8848921 116 93 81.18812 11.8118812 117 85 87.38462 -2.3846154 118 88 85.36313 2.6368715 119 86 87.38462 -1.3846154 120 93 93.88489 -0.8848921 121 77 91.18280 -14.1827957 122 92 87.38462 4.6153846 123 80 87.38462 -7.3846154 124 98 93.88489 4.1151079 125 109 85.36313 23.6368715 126 95 85.36313 9.6368715 127 75 85.36313 -10.3631285 128 88 82.54286 5.4571429 129 87 81.18812 5.8118812 130 84 82.54286 1.4571429 131 82 91.18280 -9.1827957 132 107 93.88489 13.1151079 133 94 85.36313 8.6368715 134 86 81.18812 4.8118812 135 89 87.38462 1.6153846 136 95 85.36313 9.6368715 137 81 87.38462 -6.3846154 138 87 87.38462 -0.3846154 139 87 87.38462 -0.3846154 140 88 91.18280 -3.1827957 141 89 87.38462 1.6153846 142 68 85.36313 -17.3631285 143 117 85.36313 31.6368715 144 84 85.36313 -1.3631285 145 94 85.36313 8.6368715 146 109 93.88489 15.1151079 147 108 85.36313 22.6368715 148 61 87.38462 -26.3846154 149 107 87.38462 19.6153846 150 80 87.38462 -7.3846154 151 92 87.38462 4.6153846 152 94 85.36313 8.6368715 153 95 91.18280 3.8172043 154 83 85.36313 -2.3631285 155 92 81.18812 10.8118812 156 75 87.38462 -12.3846154 157 83 87.38462 -4.3846154 158 110 93.88489 16.1151079 159 80 81.18812 -1.1881188 160 101 87.38462 13.6153846 161 81 93.88489 -12.8848921 162 59 85.36313 -26.3631285 163 90 81.18812 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-10.3846154 681 86 91.18280 -5.1827957 682 100 85.36313 14.6368715 683 98 87.38462 10.6153846 684 101 93.88489 7.1151079 685 76 81.18812 -5.1881188 686 80 85.36313 -5.3631285 687 86 93.88489 -7.8848921 688 107 87.38462 19.6153846 689 78 87.38462 -9.3846154 690 69 81.18812 -12.1881188 691 92 82.54286 9.4571429 692 84 91.18280 -7.1827957 693 83 81.18812 1.8118812 694 89 93.88489 -4.8848921 695 74 87.38462 -13.3846154 696 79 87.38462 -8.3846154 697 52 87.38462 -35.3846154 698 86 87.38462 -1.3846154 699 79 85.36313 -6.3631285 700 95 87.38462 7.6153846 701 88 87.38462 0.6153846 702 91 87.38462 3.6153846 703 80 82.54286 -2.5428571 704 94 85.36313 8.6368715 705 68 87.38462 -19.3846154 706 89 85.36313 3.6368715 707 65 85.36313 -20.3631285 708 84 85.36313 -1.3631285 709 88 91.18280 -3.1827957 710 64 85.36313 -21.3631285 711 82 87.38462 -5.3846154 712 82 85.36313 -3.3631285 713 75 81.18812 -6.1881188 714 93 85.36313 7.6368715 715 87 85.36313 1.6368715 716 90 93.88489 -3.8848921 717 90 87.38462 2.6153846 718 108 85.36313 22.6368715 719 66 87.38462 -21.3846154 720 99 85.36313 13.6368715 721 65 81.18812 -16.1881188 722 93 87.38462 5.6153846 723 100 81.18812 18.8118812 724 92 93.88489 -1.8848921 725 100 91.18280 8.8172043 726 77 87.38462 -10.3846154 727 85 85.36313 -0.3631285 728 80 87.38462 -7.3846154 729 69 87.38462 -18.3846154 730 91 81.18812 9.8118812 731 53 91.18280 -38.1827957 732 91 85.36313 5.6368715 733 86 87.38462 -1.3846154 734 95 91.18280 3.8172043 735 66 85.36313 -19.3631285 736 95 81.18812 13.8118812 737 75 85.36313 -10.3631285 738 90 85.36313 4.6368715 739 90 81.18812 8.8118812 740 83 81.18812 1.8118812 741 92 87.38462 4.6153846 742 84 82.54286 1.4571429 743 86 81.18812 4.8118812 744 72 87.38462 -15.3846154 745 73 81.18812 -8.1881188 746 100 85.36313 14.6368715 747 79 93.88489 -14.8848921 748 71 87.38462 -16.3846154 749 82 87.38462 -5.3846154 750 99 85.36313 13.6368715 751 85 85.36313 -0.3631285 752 80 85.36313 -5.3631285 753 85 82.54286 2.4571429 754 98 93.88489 4.1151079 755 99 93.88489 5.1151079 756 86 85.36313 0.6368715 757 73 87.38462 -14.3846154 758 78 93.88489 -15.8848921 759 90 85.36313 4.6368715 760 103 87.38462 15.6153846 761 87 87.38462 -0.3846154 762 76 87.38462 -11.3846154 763 102 93.88489 8.1151079 764 73 87.38462 -14.3846154 765 101 87.38462 13.6153846 766 83 82.54286 0.4571429 767 80 81.18812 -1.1881188 768 79 87.38462 -8.3846154 769 63 81.18812 -18.1881188 770 96 93.88489 2.1151079 771 75 87.38462 -12.3846154 772 103 85.36313 17.6368715 773 89 85.36313 3.6368715 774 105 87.38462 17.6153846 775 78 85.36313 -7.3631285 776 80 85.36313 -5.3631285 777 95 87.38462 7.6153846 778 96 81.18812 14.8118812 779 71 87.38462 -16.3846154 780 80 93.88489 -13.8848921 781 92 87.38462 4.6153846 782 67 81.18812 -14.1881188 783 97 87.38462 9.6153846 784 79 81.18812 -2.1881188 785 79 81.18812 -2.1881188 786 83 81.18812 1.8118812 787 77 85.36313 -8.3631285 788 82 81.18812 0.8118812 789 79 85.36313 -6.3631285 790 83 87.38462 -4.3846154 791 71 87.38462 -16.3846154 792 83 87.38462 -4.3846154 793 73 81.18812 -8.1881188 794 86 87.38462 -1.3846154 795 77 81.18812 -4.1881188 796 72 81.18812 -9.1881188 797 73 81.18812 -8.1881188 798 82 93.88489 -11.8848921 799 65 91.18280 -26.1827957 800 67 81.18812 -14.1881188 801 78 87.38462 -9.3846154 802 70 93.88489 -23.8848921 803 78 82.54286 -4.5428571 804 88 87.38462 0.6153846 805 69 87.38462 -18.3846154 806 85 87.38462 -2.3846154 807 88 93.88489 -5.8848921 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/www/rcomp/tmp/4jmd21335906597.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/5wuoa1335906597.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/6112h1335906597.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/7e9yf1335906597.tab") + } > > try(system("convert tmp/24r8q1335906597.ps tmp/24r8q1335906597.png",intern=TRUE)) character(0) > try(system("convert tmp/3xnhg1335906597.ps tmp/3xnhg1335906597.png",intern=TRUE)) character(0) > try(system("convert tmp/4jmd21335906597.ps tmp/4jmd21335906597.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 17.360 0.840 19.801