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Type 'q()' to quit R. > par9 = 'COLLES preferred' > par8 = 'COLLES actuals' > par7 = 'all' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'COLLES preferred' > par8 <- 'COLLES actuals' > 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] 87 86 84 68 101 87 85 88 84 79 92 74 97 80 101 87 84 109 [19] 84 81 77 72 68 88 80 86 92 56 79 81 86 88 81 70 74 96 [37] 86 91 80 74 89 96 92 84 72 87 88 68 68 88 88 83 72 104 [55] 68 93 88 78 78 90 84 86 71 82 90 76 94 76 90 85 69 78 [73] 98 82 84 67 73 79 77 85 82 80 59 82 103 73 74 76 90 99 [91] 91 78 76 90 60 64 88 77 82 75 71 71 63 83 68 81 77 63 [109] 72 79 86 81 75 66 90 69 77 72 77 75 86 95 72 92 77 64 [127] 91 71 60 77 74 74 97 85 82 84 91 81 78 75 93 74 75 80 [145] 83 69 99 86 78 86 89 88 63 101 79 86 86 81 80 91 74 91 [163] 78 97 71 87 68 59 78 92 76 73 73 74 80 86 89 60 64 87 [181] 94 77 75 87 76 92 99 91 83 93 102 85 82 94 79 94 96 106 [199] 87 94 75 92 88 85 96 82 77 90 101 74 84 89 88 80 66 77 [217] 100 85 87 92 86 98 79 86 86 84 48 86 89 77 77 67 77 97 [235] 79 101 87 94 76 70 89 91 88 100 78 96 82 95 84 89 71 101 [253] 90 95 92 87 74 83 88 104 77 91 96 98 87 101 105 74 85 80 [271] 54 89 92 91 104 96 77 87 111 99 69 93 77 78 107 90 91 88 [289] 79 78 91 85 97 82 71 70 87 83 77 81 72 80 98 75 91 71 [307] 81 77 83 73 85 64 112 74 90 94 99 107 84 58 72 83 74 89 [325] 85 83 82 78 77 97 84 85 90 97 75 85 79 77 96 72 89 71 [343] 86 87 91 89 76 72 87 88 77 73 86 84 64 90 80 100 89 87 [361] 81 96 85 78 95 85 67 90 85 94 98 81 82 86 99 88 82 72 [379] 88 85 83 79 88 59 102 86 86 102 83 66 106 85 102 104 86 84 [397] 88 92 84 93 89 87 109 79 95 80 110 73 84 93 87 84 82 95 [415] 110 80 78 82 105 104 95 70 81 99 106 101 99 107 100 103 86 83 [433] 95 99 90 71 103 95 98 97 80 102 103 93 95 68 86 85 81 88 [451] 68 85 77 113 69 90 94 117 92 104 100 105 93 83 106 66 94 78 [469] 104 74 107 97 105 116 111 90 99 101 106 97 101 102 95 81 106 85 [487] 77 89 95 100 93 108 88 86 105 88 94 92 117 69 82 94 117 81 [505] 102 92 90 86 85 93 98 88 104 101 101 90 92 107 95 88 92 76 [523] 76 90 94 102 87 105 91 120 94 93 97 86 92 90 100 80 90 89 [541] 72 117 93 105 97 95 82 99 93 93 107 107 96 93 99 90 88 83 [559] 94 114 90 92 97 97 100 102 111 79 84 92 100 106 100 71 94 106 [577] 90 73 102 77 94 88 112 88 91 72 119 107 93 87 100 109 103 67 [595] 93 107 85 78 104 96 110 103 115 90 100 108 75 92 103 93 75 87 [613] 61 92 117 83 96 77 95 97 119 108 110 95 88 92 106 79 103 100 [631] 114 101 92 86 92 91 107 105 96 96 117 104 86 107 89 83 87 98 [649] 92 117 101 102 103 93 76 109 104 114 94 99 102 84 86 85 94 104 [667] 105 90 97 86 109 92 89 119 97 117 91 95 113 102 106 80 97 96 [685] 112 97 92 98 92 81 107 73 79 83 84 93 95 120 109 109 95 72 [703] 104 82 104 103 99 103 86 79 95 102 110 107 107 105 112 101 93 107 [721] 97 114 92 101 109 72 89 46 103 85 110 75 112 95 97 98 91 96 [739] 88 86 91 85 104 84 75 90 115 87 93 103 104 112 99 100 108 102 [757] 116 93 83 108 91 96 81 95 100 74 102 102 111 91 105 79 85 102 [775] 104 75 92 111 74 114 86 90 80 80 94 102 80 72 82 75 81 90 [793] 72 91 120 78 81 95 100 91 103 80 90 119 > 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]) 46 48 54 56 58 59 60 61 63 64 66 67 68 69 70 71 72 73 74 75 1 1 1 1 1 3 3 1 3 5 4 4 9 6 4 11 17 9 17 15 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 11 26 17 17 20 19 19 18 22 27 34 24 29 18 30 24 30 23 20 23 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 17 21 10 15 16 16 19 14 17 11 10 15 5 8 6 5 6 2 5 2 116 117 119 120 2 8 4 3 > colnames(x) [1] "endo" "C1" "C3" "C5" "C7" "C9" "C11" "C13" "C15" "C17" [11] "C19" "C21" "C23" "C25" "C27" "C29" "C31" "C33" "C35" "C37" [21] "C39" "C41" "C43" "C45" "C47" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 87 86 84 68 101 87 85 88 84 79 92 74 97 80 101 87 84 109 [19] 84 81 77 72 68 88 80 86 92 56 79 81 86 88 81 70 74 96 [37] 86 91 80 74 89 96 92 84 72 87 88 68 68 88 88 83 72 104 [55] 68 93 88 78 78 90 84 86 71 82 90 76 94 76 90 85 69 78 [73] 98 82 84 67 73 79 77 85 82 80 59 82 103 73 74 76 90 99 [91] 91 78 76 90 60 64 88 77 82 75 71 71 63 83 68 81 77 63 [109] 72 79 86 81 75 66 90 69 77 72 77 75 86 95 72 92 77 64 [127] 91 71 60 77 74 74 97 85 82 84 91 81 78 75 93 74 75 80 [145] 83 69 99 86 78 86 89 88 63 101 79 86 86 81 80 91 74 91 [163] 78 97 71 87 68 59 78 92 76 73 73 74 80 86 89 60 64 87 [181] 94 77 75 87 76 92 99 91 83 93 102 85 82 94 79 94 96 106 [199] 87 94 75 92 88 85 96 82 77 90 101 74 84 89 88 80 66 77 [217] 100 85 87 92 86 98 79 86 86 84 48 86 89 77 77 67 77 97 [235] 79 101 87 94 76 70 89 91 88 100 78 96 82 95 84 89 71 101 [253] 90 95 92 87 74 83 88 104 77 91 96 98 87 101 105 74 85 80 [271] 54 89 92 91 104 96 77 87 111 99 69 93 77 78 107 90 91 88 [289] 79 78 91 85 97 82 71 70 87 83 77 81 72 80 98 75 91 71 [307] 81 77 83 73 85 64 112 74 90 94 99 107 84 58 72 83 74 89 [325] 85 83 82 78 77 97 84 85 90 97 75 85 79 77 96 72 89 71 [343] 86 87 91 89 76 72 87 88 77 73 86 84 64 90 80 100 89 87 [361] 81 96 85 78 95 85 67 90 85 94 98 81 82 86 99 88 82 72 [379] 88 85 83 79 88 59 102 86 86 102 83 66 106 85 102 104 86 84 [397] 88 92 84 93 89 87 109 79 95 80 110 73 84 93 87 84 82 95 [415] 110 80 78 82 105 104 95 70 81 99 106 101 99 107 100 103 86 83 [433] 95 99 90 71 103 95 98 97 80 102 103 93 95 68 86 85 81 88 [451] 68 85 77 113 69 90 94 117 92 104 100 105 93 83 106 66 94 78 [469] 104 74 107 97 105 116 111 90 99 101 106 97 101 102 95 81 106 85 [487] 77 89 95 100 93 108 88 86 105 88 94 92 117 69 82 94 117 81 [505] 102 92 90 86 85 93 98 88 104 101 101 90 92 107 95 88 92 76 [523] 76 90 94 102 87 105 91 120 94 93 97 86 92 90 100 80 90 89 [541] 72 117 93 105 97 95 82 99 93 93 107 107 96 93 99 90 88 83 [559] 94 114 90 92 97 97 100 102 111 79 84 92 100 106 100 71 94 106 [577] 90 73 102 77 94 88 112 88 91 72 119 107 93 87 100 109 103 67 [595] 93 107 85 78 104 96 110 103 115 90 100 108 75 92 103 93 75 87 [613] 61 92 117 83 96 77 95 97 119 108 110 95 88 92 106 79 103 100 [631] 114 101 92 86 92 91 107 105 96 96 117 104 86 107 89 83 87 98 [649] 92 117 101 102 103 93 76 109 104 114 94 99 102 84 86 85 94 104 [667] 105 90 97 86 109 92 89 119 97 117 91 95 113 102 106 80 97 96 [685] 112 97 92 98 92 81 107 73 79 83 84 93 95 120 109 109 95 72 [703] 104 82 104 103 99 103 86 79 95 102 110 107 107 105 112 101 93 107 [721] 97 114 92 101 109 72 89 46 103 85 110 75 112 95 97 98 91 96 [739] 88 86 91 85 104 84 75 90 115 87 93 103 104 112 99 100 108 102 [757] 116 93 83 108 91 96 81 95 100 74 102 102 111 91 105 79 85 102 [775] 104 75 92 111 74 114 86 90 80 80 94 102 80 72 82 75 81 90 [793] 72 91 120 78 81 95 100 91 103 80 90 119 > 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/18d8i1337263978.tab") + } + } > m Conditional inference tree with 13 terminal nodes Response: endo Inputs: C1, C3, C5, C7, C9, C11, C13, C15, C17, C19, C21, C23, C25, C27, C29, C31, C33, C35, C37, C39, C41, C43, C45, C47 Number of observations: 804 1) C31 <= 3; criterion = 1, statistic = 137.223 2) C17 <= 3; criterion = 1, statistic = 21.984 3) C31 <= 2; criterion = 0.997, statistic = 14.887 4) C13 <= 3; criterion = 0.97, statistic = 10.388 5)* weights = 18 4) C13 > 3 6)* weights = 16 3) C31 > 2 7)* weights = 132 2) C17 > 3 8)* weights = 97 1) C31 > 3 9) C27 <= 4; criterion = 1, statistic = 57.137 10) C19 <= 3; criterion = 1, statistic = 42.21 11) C19 <= 1; criterion = 0.995, statistic = 13.837 12)* weights = 17 11) C19 > 1 13) C31 <= 4; criterion = 0.999, statistic = 17.969 14)* weights = 128 13) C31 > 4 15)* weights = 23 10) C19 > 3 16) C13 <= 4; criterion = 0.996, statistic = 14.184 17)* weights = 163 16) C13 > 4 18)* weights = 49 9) C27 > 4 19) C9 <= 4; criterion = 0.998, statistic = 15.933 20) C17 <= 3; criterion = 0.988, statistic = 12.094 21)* weights = 48 20) C17 > 3 22)* weights = 67 19) C9 > 4 23) C1 <= 3; criterion = 0.994, statistic = 13.338 24)* weights = 9 23) C1 > 3 25)* weights = 37 > postscript(file="/var/wessaorg/rcomp/tmp/2s4de1337263978.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/3xi4u1337263978.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 87 90.71779 -3.71779141 2 86 90.71779 -4.71779141 3 84 90.71779 -6.71779141 4 68 86.97938 -18.97938144 5 101 90.71779 10.28220859 6 87 90.71779 -3.71779141 7 85 85.97656 -0.97656250 8 88 90.71779 -2.71779141 9 84 90.71779 -6.71779141 10 79 86.97938 -7.97938144 11 92 85.97656 6.02343750 12 74 78.70588 -4.70588235 13 97 98.36735 -1.36734694 14 80 85.97656 -5.97656250 15 101 98.36735 2.63265306 16 87 81.46970 5.53030303 17 84 85.97656 -1.97656250 18 109 99.67568 9.32432432 19 84 86.97938 -2.97938144 20 81 85.97656 -4.97656250 21 77 81.46970 -4.46969697 22 72 85.97656 -13.97656250 23 68 85.97656 -17.97656250 24 88 90.71779 -2.71779141 25 80 86.97938 -6.97938144 26 86 86.97938 -0.97938144 27 92 99.67568 -7.67567568 28 56 98.36735 -42.36734694 29 79 86.97938 -7.97938144 30 81 92.47917 -11.47916667 31 86 85.97656 0.02343750 32 88 95.73913 -7.73913043 33 81 81.46970 -0.46969697 34 70 90.71779 -20.71779141 35 74 85.97656 -11.97656250 36 96 86.97938 9.02061856 37 86 81.46970 4.53030303 38 91 85.97656 5.02343750 39 80 81.46970 -1.46969697 40 74 81.46970 -7.46969697 41 89 85.97656 3.02343750 42 96 99.67568 -3.67567568 43 92 90.71779 1.28220859 44 84 90.71779 -6.71779141 45 72 81.46970 -9.46969697 46 87 85.97656 1.02343750 47 88 86.97938 1.02061856 48 68 85.97656 -17.97656250 49 68 81.46970 -13.46969697 50 88 85.97656 2.02343750 51 88 81.46970 6.53030303 52 83 85.97656 -2.97656250 53 72 81.46970 -9.46969697 54 104 90.71779 13.28220859 55 68 81.46970 -13.46969697 56 93 99.67568 -6.67567568 57 88 78.70588 9.29411765 58 78 85.97656 -7.97656250 59 78 86.97938 -8.97938144 60 90 90.71779 -0.71779141 61 84 85.97656 -1.97656250 62 86 98.46269 -12.46268657 63 71 81.46970 -10.46969697 64 82 85.97656 -3.97656250 65 90 90.71779 -0.71779141 66 76 85.97656 -9.97656250 67 94 86.97938 7.02061856 68 76 90.71779 -14.71779141 69 90 90.71779 -0.71779141 70 85 92.47917 -7.47916667 71 69 90.71779 -21.71779141 72 78 85.97656 -7.97656250 73 98 90.71779 7.28220859 74 82 86.97938 -4.97938144 75 84 81.46970 2.53030303 76 67 85.97656 -18.97656250 77 73 81.46970 -8.46969697 78 79 85.97656 -6.97656250 79 77 81.46970 -4.46969697 80 85 99.67568 -14.67567568 81 82 85.97656 -3.97656250 82 80 90.71779 -10.71779141 83 59 78.70588 -19.70588235 84 82 90.71779 -8.71779141 85 103 86.97938 16.02061856 86 73 90.71779 -17.71779141 87 74 78.70588 -4.70588235 88 76 80.56250 -4.56250000 89 90 81.46970 8.53030303 90 99 98.46269 0.53731343 91 91 98.46269 -7.46268657 92 78 86.97938 -8.97938144 93 76 81.46970 -5.46969697 94 90 90.71779 -0.71779141 95 60 68.33333 -8.33333333 96 64 85.97656 -21.97656250 97 88 95.73913 -7.73913043 98 77 86.97938 -9.97938144 99 82 90.71779 -8.71779141 100 75 80.56250 -5.56250000 101 71 85.97656 -14.97656250 102 71 78.70588 -7.70588235 103 63 81.46970 -18.46969697 104 83 81.46970 1.53030303 105 68 86.97938 -18.97938144 106 81 85.97656 -4.97656250 107 77 86.97938 -9.97938144 108 63 85.97656 -22.97656250 109 72 85.97656 -13.97656250 110 79 81.46970 -2.46969697 111 86 85.97656 0.02343750 112 81 80.56250 0.43750000 113 75 81.46970 -6.46969697 114 66 81.46970 -15.46969697 115 90 81.46970 8.53030303 116 69 80.56250 -11.56250000 117 77 78.70588 -1.70588235 118 72 81.46970 -9.46969697 119 77 81.46970 -4.46969697 120 75 90.71779 -15.71779141 121 86 85.97656 0.02343750 122 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726 72 68.33333 3.66666667 727 89 90.71779 -1.71779141 728 46 68.33333 -22.33333333 729 103 86.97938 16.02061856 730 85 86.97938 -1.97938144 731 110 85.97656 24.02343750 732 75 85.97656 -10.97656250 733 112 90.71779 21.28220859 734 95 81.46970 13.53030303 735 97 86.97938 10.02061856 736 98 85.97656 12.02343750 737 91 85.97656 5.02343750 738 96 98.46269 -2.46268657 739 88 86.97938 1.02061856 740 86 85.97656 0.02343750 741 91 85.97656 5.02343750 742 85 80.56250 4.43750000 743 104 98.46269 5.53731343 744 84 86.97938 -2.97938144 745 75 81.46970 -6.46969697 746 90 90.71779 -0.71779141 747 115 90.71779 24.28220859 748 87 81.46970 5.53030303 749 93 90.71779 2.28220859 750 103 92.47917 10.52083333 751 104 98.46269 5.53731343 752 112 98.46269 13.53731343 753 99 90.71779 8.28220859 754 100 81.46970 18.53030303 755 108 90.71779 17.28220859 756 102 90.71779 11.28220859 757 116 98.46269 17.53731343 758 93 90.71779 2.28220859 759 83 81.46970 1.53030303 760 108 99.67568 8.32432432 761 91 80.56250 10.43750000 762 96 98.46269 -2.46268657 763 81 92.47917 -11.47916667 764 95 90.71779 4.28220859 765 100 86.97938 13.02061856 766 74 81.46970 -7.46969697 767 102 98.46269 3.53731343 768 102 81.46970 20.53030303 769 111 98.46269 12.53731343 770 91 90.71779 0.28220859 771 105 98.36735 6.63265306 772 79 85.97656 -6.97656250 773 85 90.71779 -5.71779141 774 102 90.71779 11.28220859 775 104 92.47917 11.52083333 776 75 81.46970 -6.46969697 777 92 92.47917 -0.47916667 778 111 109.77778 1.22222222 779 74 68.33333 5.66666667 780 114 98.46269 15.53731343 781 86 86.97938 -0.97938144 782 90 86.97938 3.02061856 783 80 81.46970 -1.46969697 784 80 86.97938 -6.97938144 785 94 85.97656 8.02343750 786 102 90.71779 11.28220859 787 80 81.46970 -1.46969697 788 72 86.97938 -14.97938144 789 82 85.97656 -3.97656250 790 75 81.46970 -6.46969697 791 81 85.97656 -4.97656250 792 90 85.97656 4.02343750 793 72 98.46269 -26.46268657 794 91 85.97656 5.02343750 795 120 90.71779 29.28220859 796 78 81.46970 -3.46969697 797 81 81.46970 -0.46969697 798 95 98.36735 -3.36734694 799 100 95.73913 4.26086957 800 91 85.97656 5.02343750 801 103 85.97656 17.02343750 802 80 68.33333 11.66666667 803 90 81.46970 8.53030303 804 119 95.73913 23.26086957 > 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/48hpp1337263978.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/53q9i1337263978.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6zniw1337263978.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/7qk8b1337263978.tab") + } > > try(system("convert tmp/2s4de1337263978.ps tmp/2s4de1337263978.png",intern=TRUE)) character(0) > try(system("convert tmp/3xi4u1337263978.ps tmp/3xi4u1337263978.png",intern=TRUE)) character(0) > try(system("convert tmp/48hpp1337263978.ps tmp/48hpp1337263978.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 16.852 0.428 17.300