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Type 'q()' to quit R. > par9 = 'COLLES actuals' > par8 = 'ATTLES separate' > par7 = 'all' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'COLLES actuals' > par8 <- 'ATTLES separate' > 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 93 77 92 80 98 109 95 75 [127] 88 87 84 82 107 94 86 89 95 81 87 87 88 89 68 117 84 94 [145] 109 108 61 107 80 92 94 95 83 92 75 83 110 80 101 81 59 90 [163] 94 83 90 82 93 110 97 95 98 78 89 93 106 71 91 96 108 93 [181] 95 89 72 107 102 88 95 94 96 92 110 94 72 103 89 96 92 90 [199] 86 90 99 86 89 108 94 83 76 102 90 112 89 98 81 116 101 85 [217] 104 92 94 105 94 85 96 79 106 79 101 90 114 99 79 94 88 93 [235] 91 77 101 81 116 86 91 95 78 70 109 109 81 94 87 86 93 113 [253] 97 85 103 105 97 86 118 102 77 94 91 89 67 104 100 86 109 97 [271] 77 88 88 115 103 84 106 97 87 106 103 97 102 78 101 93 104 85 [289] 111 104 70 90 92 90 83 71 90 72 83 120 90 90 98 85 101 89 [307] 108 96 86 115 79 103 93 107 87 84 65 72 87 80 92 87 106 87 [325] 92 88 92 96 86 94 98 96 79 93 81 72 109 72 96 74 88 97 [343] 92 93 89 98 109 99 99 98 93 105 88 102 101 90 93 96 100 83 [361] 97 68 106 86 99 94 103 110 86 87 104 96 100 111 82 72 88 103 [379] 80 93 56 120 86 82 101 89 66 87 87 94 92 75 77 85 84 53 [397] 74 89 83 103 84 69 68 98 72 84 71 87 88 76 92 91 61 66 [415] 88 98 85 82 70 78 99 74 69 90 93 104 87 89 57 91 82 71 [433] 63 93 87 86 83 71 86 81 81 73 64 81 79 76 84 68 69 68 [451] 104 70 81 80 86 85 101 74 93 75 84 100 67 89 83 80 62 77 [469] 93 82 104 101 73 91 78 83 76 89 93 93 76 87 77 67 88 76 [487] 91 82 81 93 67 85 87 65 81 103 69 83 82 102 74 95 75 76 [505] 72 84 99 95 81 98 90 93 81 72 72 75 57 68 70 90 87 100 [523] 69 78 75 71 85 88 83 72 85 79 82 79 85 89 70 95 89 72 [541] 89 89 74 90 92 88 113 89 94 76 94 85 79 77 69 88 86 82 [559] 84 96 82 88 111 67 73 105 94 96 97 70 91 110 92 68 104 87 [577] 96 88 95 75 84 72 91 89 67 81 93 106 100 63 84 81 78 76 [595] 99 75 86 92 95 85 95 94 60 62 91 88 73 83 64 75 95 81 [613] 92 81 86 94 93 95 95 87 87 91 91 79 99 89 104 87 86 88 [631] 84 74 85 90 83 88 80 81 77 90 86 83 77 88 98 95 101 75 [649] 79 88 77 93 76 107 89 92 82 84 90 82 76 95 109 90 77 76 [667] 83 86 82 96 92 110 77 86 100 98 101 76 80 86 107 78 69 92 [685] 84 83 89 74 79 52 86 79 95 88 91 80 94 68 89 65 84 88 [703] 64 82 82 75 93 87 90 90 108 66 99 65 93 100 92 100 77 85 [721] 80 69 91 53 91 86 95 66 95 75 90 90 83 92 84 86 72 73 [739] 100 79 71 82 99 85 80 85 98 99 86 73 78 90 103 87 76 102 [757] 73 101 83 80 79 63 96 75 103 89 105 78 80 95 96 71 80 92 [775] 67 97 79 79 83 77 82 79 83 71 83 73 86 77 72 73 82 65 [793] 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 10 9 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 17 15 19 13 20 19 23 21 28 19 25 31 28 32 31 31 21 27 35 24 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 31 20 12 16 14 11 14 10 12 10 8 8 6 7 8 8 3 1 2 1 115 116 117 118 120 3 2 1 1 2 > colnames(x) [1] "endo" "A11" "A12" "A13" "A14" "A15" "A16" "A17" "A18" "A19" [11] "A20" > 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 93 77 92 80 98 109 95 75 [127] 88 87 84 82 107 94 86 89 95 81 87 87 88 89 68 117 84 94 [145] 109 108 61 107 80 92 94 95 83 92 75 83 110 80 101 81 59 90 [163] 94 83 90 82 93 110 97 95 98 78 89 93 106 71 91 96 108 93 [181] 95 89 72 107 102 88 95 94 96 92 110 94 72 103 89 96 92 90 [199] 86 90 99 86 89 108 94 83 76 102 90 112 89 98 81 116 101 85 [217] 104 92 94 105 94 85 96 79 106 79 101 90 114 99 79 94 88 93 [235] 91 77 101 81 116 86 91 95 78 70 109 109 81 94 87 86 93 113 [253] 97 85 103 105 97 86 118 102 77 94 91 89 67 104 100 86 109 97 [271] 77 88 88 115 103 84 106 97 87 106 103 97 102 78 101 93 104 85 [289] 111 104 70 90 92 90 83 71 90 72 83 120 90 90 98 85 101 89 [307] 108 96 86 115 79 103 93 107 87 84 65 72 87 80 92 87 106 87 [325] 92 88 92 96 86 94 98 96 79 93 81 72 109 72 96 74 88 97 [343] 92 93 89 98 109 99 99 98 93 105 88 102 101 90 93 96 100 83 [361] 97 68 106 86 99 94 103 110 86 87 104 96 100 111 82 72 88 103 [379] 80 93 56 120 86 82 101 89 66 87 87 94 92 75 77 85 84 53 [397] 74 89 83 103 84 69 68 98 72 84 71 87 88 76 92 91 61 66 [415] 88 98 85 82 70 78 99 74 69 90 93 104 87 89 57 91 82 71 [433] 63 93 87 86 83 71 86 81 81 73 64 81 79 76 84 68 69 68 [451] 104 70 81 80 86 85 101 74 93 75 84 100 67 89 83 80 62 77 [469] 93 82 104 101 73 91 78 83 76 89 93 93 76 87 77 67 88 76 [487] 91 82 81 93 67 85 87 65 81 103 69 83 82 102 74 95 75 76 [505] 72 84 99 95 81 98 90 93 81 72 72 75 57 68 70 90 87 100 [523] 69 78 75 71 85 88 83 72 85 79 82 79 85 89 70 95 89 72 [541] 89 89 74 90 92 88 113 89 94 76 94 85 79 77 69 88 86 82 [559] 84 96 82 88 111 67 73 105 94 96 97 70 91 110 92 68 104 87 [577] 96 88 95 75 84 72 91 89 67 81 93 106 100 63 84 81 78 76 [595] 99 75 86 92 95 85 95 94 60 62 91 88 73 83 64 75 95 81 [613] 92 81 86 94 93 95 95 87 87 91 91 79 99 89 104 87 86 88 [631] 84 74 85 90 83 88 80 81 77 90 86 83 77 88 98 95 101 75 [649] 79 88 77 93 76 107 89 92 82 84 90 82 76 95 109 90 77 76 [667] 83 86 82 96 92 110 77 86 100 98 101 76 80 86 107 78 69 92 [685] 84 83 89 74 79 52 86 79 95 88 91 80 94 68 89 65 84 88 [703] 64 82 82 75 93 87 90 90 108 66 99 65 93 100 92 100 77 85 [721] 80 69 91 53 91 86 95 66 95 75 90 90 83 92 84 86 72 73 [739] 100 79 71 82 99 85 80 85 98 99 86 73 78 90 103 87 76 102 [757] 73 101 83 80 79 63 96 75 103 89 105 78 80 95 96 71 80 92 [775] 67 97 79 79 83 77 82 79 83 71 83 73 86 77 72 73 82 65 [793] 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/1v3081335899939.tab") + } + } > m Conditional inference tree with 6 terminal nodes Response: endo Inputs: A11, A12, A13, A14, A15, A16, A17, A18, A19, A20 Number of observations: 800 1) A16 <= 3; criterion = 1, statistic = 56.553 2) A15 <= 3; criterion = 1, statistic = 25.638 3) A17 <= 4; criterion = 0.97, statistic = 8.777 4)* weights = 302 3) A17 > 4 5)* weights = 21 2) A15 > 3 6)* weights = 277 1) A16 > 3 7) A15 <= 3; criterion = 0.999, statistic = 15.33 8)* weights = 68 7) A15 > 3 9) A19 <= 4; criterion = 0.965, statistic = 8.507 10)* weights = 113 9) A19 > 4 11)* weights = 19 > postscript(file="/var/www/rcomp/tmp/2rih51335899939.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/38y2o1335899939.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 87.32353 2.67647059 2 95 88.81227 6.18772563 3 96 93.38053 2.61946903 4 93 93.38053 -0.38053097 5 102 93.38053 8.61946903 6 93 88.81227 4.18772563 7 82 88.81227 -6.81227437 8 98 93.38053 4.61946903 9 89 87.32353 1.67647059 10 88 82.97682 5.02317881 11 102 88.81227 13.18772563 12 83 87.32353 -4.32352941 13 108 88.81227 19.18772563 14 86 87.32353 -1.32352941 15 97 93.38053 3.61946903 16 89 87.32353 1.67647059 17 103 82.97682 20.02317881 18 115 82.97682 32.02317881 19 90 88.81227 1.18772563 20 88 88.81227 -0.81227437 21 81 93.38053 -12.38053097 22 74 82.97682 -8.97682119 23 75 82.97682 -7.97682119 24 93 88.81227 4.18772563 25 87 87.32353 -0.32352941 26 92 93.38053 -1.38053097 27 95 88.81227 6.18772563 28 105 88.81227 16.18772563 29 93 88.81227 4.18772563 30 90 88.81227 1.18772563 31 87 88.81227 -1.81227437 32 105 88.81227 16.18772563 33 92 88.81227 3.18772563 34 89 88.81227 0.18772563 35 85 82.97682 2.02317881 36 100 87.32353 12.67647059 37 83 88.81227 -5.81227437 38 97 93.38053 3.61946903 39 95 93.38053 1.61946903 40 73 82.97682 -9.97682119 41 94 102.00000 -8.00000000 42 99 87.32353 11.67647059 43 99 88.81227 10.18772563 44 88 88.81227 -0.81227437 45 86 87.32353 -1.32352941 46 89 88.81227 0.18772563 47 95 87.32353 7.67647059 48 83 88.81227 -5.81227437 49 72 88.81227 -16.81227437 50 98 88.81227 9.18772563 51 85 82.97682 2.02317881 52 84 88.81227 -4.81227437 53 80 93.38053 -13.38053097 54 108 82.97682 25.02317881 55 72 87.32353 -15.32352941 56 110 93.38053 16.61946903 57 93 82.97682 10.02317881 58 85 88.81227 -3.81227437 59 95 93.38053 1.61946903 60 94 82.97682 11.02317881 61 80 82.97682 -2.97682119 62 93 93.38053 -0.38053097 63 83 88.81227 -5.81227437 64 92 93.38053 -1.38053097 65 106 93.38053 12.61946903 66 93 87.32353 5.67647059 67 95 87.32353 7.67647059 68 96 88.81227 7.18772563 69 90 93.38053 -3.38053097 70 93 88.81227 4.18772563 71 102 93.38053 8.61946903 72 94 88.81227 5.18772563 73 85 82.97682 2.02317881 74 87 82.97682 4.02317881 75 77 82.97682 -5.97682119 76 83 88.81227 -5.81227437 77 79 82.97682 -3.97682119 78 110 88.81227 21.18772563 79 90 88.81227 1.18772563 80 95 88.81227 6.18772563 81 80 88.81227 -8.81227437 82 95 88.81227 6.18772563 83 105 88.81227 16.18772563 84 91 87.32353 3.67647059 85 69 87.32353 -18.32352941 86 81 87.32353 -6.32352941 87 75 93.38053 -18.38053097 88 98 93.38053 4.61946903 89 101 88.81227 12.18772563 90 96 88.81227 7.18772563 91 86 82.97682 3.02317881 92 91 82.97682 8.02317881 93 72 82.97682 -10.97682119 94 76 87.32353 -11.32352941 95 95 93.38053 1.61946903 96 96 87.32353 8.67647059 97 92 82.97682 9.02317881 98 77 82.97682 -5.97682119 99 90 88.81227 1.18772563 100 85 82.97682 2.02317881 101 81 82.97682 -1.97682119 102 80 82.97682 -2.97682119 103 91 82.97682 8.02317881 104 89 88.81227 0.18772563 105 78 82.97682 -4.97682119 106 66 88.81227 -22.81227437 107 79 87.32353 -8.32352941 108 91 93.38053 -2.38053097 109 88 88.81227 -0.81227437 110 61 82.97682 -21.97682119 111 77 82.97682 -5.97682119 112 81 82.97682 -1.97682119 113 91 82.97682 8.02317881 114 75 82.97682 -7.97682119 115 87 88.81227 -1.81227437 116 93 93.38053 -0.38053097 117 85 88.81227 -3.81227437 118 88 93.38053 -5.38053097 119 93 88.81227 4.18772563 120 77 93.38053 -16.38053097 121 92 82.97682 9.02317881 122 80 93.38053 -13.38053097 123 98 93.38053 4.61946903 124 109 93.38053 15.61946903 125 95 88.81227 6.18772563 126 75 82.97682 -7.97682119 127 88 88.81227 -0.81227437 128 87 88.81227 -1.81227437 129 84 93.38053 -9.38053097 130 82 88.81227 -6.81227437 131 107 93.38053 13.61946903 132 94 93.38053 0.61946903 133 86 82.97682 3.02317881 134 89 82.97682 6.02317881 135 95 88.81227 6.18772563 136 81 88.81227 -7.81227437 137 87 87.32353 -0.32352941 138 87 82.97682 4.02317881 139 88 88.81227 -0.81227437 140 89 93.38053 -4.38053097 141 68 88.81227 -20.81227437 142 117 88.81227 28.18772563 143 84 88.81227 -4.81227437 144 94 93.38053 0.61946903 145 109 88.81227 20.18772563 146 108 88.81227 19.18772563 147 61 82.97682 -21.97682119 148 107 88.81227 18.18772563 149 80 88.81227 -8.81227437 150 92 88.81227 3.18772563 151 94 88.81227 5.18772563 152 95 93.38053 1.61946903 153 83 82.97682 0.02317881 154 92 82.97682 9.02317881 155 75 82.97682 -7.97682119 156 83 82.97682 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82.97682 3.02317881 727 95 82.97682 12.02317881 728 66 82.97682 -16.97682119 729 95 82.97682 12.02317881 730 75 82.97682 -7.97682119 731 90 82.97682 7.02317881 732 90 88.81227 1.18772563 733 83 87.32353 -4.32352941 734 92 90.38095 1.61904762 735 84 82.97682 1.02317881 736 86 82.97682 3.02317881 737 72 87.32353 -15.32352941 738 73 82.97682 -9.97682119 739 100 82.97682 17.02317881 740 79 82.97682 -3.97682119 741 71 82.97682 -11.97682119 742 82 90.38095 -8.38095238 743 99 82.97682 16.02317881 744 85 82.97682 2.02317881 745 80 88.81227 -8.81227437 746 85 88.81227 -3.81227437 747 98 88.81227 9.18772563 748 99 90.38095 8.61904762 749 86 88.81227 -2.81227437 750 73 82.97682 -9.97682119 751 78 82.97682 -4.97682119 752 90 82.97682 7.02317881 753 103 88.81227 14.18772563 754 87 87.32353 -0.32352941 755 76 82.97682 -6.97682119 756 102 102.00000 0.00000000 757 73 88.81227 -15.81227437 758 101 90.38095 10.61904762 759 83 82.97682 0.02317881 760 80 87.32353 -7.32352941 761 79 82.97682 -3.97682119 762 63 82.97682 -19.97682119 763 96 88.81227 7.18772563 764 75 88.81227 -13.81227437 765 103 88.81227 14.18772563 766 89 88.81227 0.18772563 767 105 82.97682 22.02317881 768 78 82.97682 -4.97682119 769 80 82.97682 -2.97682119 770 95 82.97682 12.02317881 771 96 88.81227 7.18772563 772 71 82.97682 -11.97682119 773 80 88.81227 -8.81227437 774 92 93.38053 -1.38053097 775 67 82.97682 -15.97682119 776 97 88.81227 8.18772563 777 79 82.97682 -3.97682119 778 79 82.97682 -3.97682119 779 83 82.97682 0.02317881 780 77 82.97682 -5.97682119 781 82 88.81227 -6.81227437 782 79 82.97682 -3.97682119 783 83 82.97682 0.02317881 784 71 82.97682 -11.97682119 785 83 88.81227 -5.81227437 786 73 82.97682 -9.97682119 787 86 82.97682 3.02317881 788 77 87.32353 -10.32352941 789 72 82.97682 -10.97682119 790 73 82.97682 -9.97682119 791 82 88.81227 -6.81227437 792 65 82.97682 -17.97682119 793 67 82.97682 -15.97682119 794 78 82.97682 -4.97682119 795 70 93.38053 -23.38053097 796 78 88.81227 -10.81227437 797 88 82.97682 5.02317881 798 69 82.97682 -13.97682119 799 85 88.81227 -3.81227437 800 88 93.38053 -5.38053097 > 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/4vy2u1335899939.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/rcomp/tmp/5k9s61335899939.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6gs371335899939.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/7x7in1335899939.tab") + } > > try(system("convert tmp/2rih51335899939.ps tmp/2rih51335899939.png",intern=TRUE)) character(0) > try(system("convert tmp/38y2o1335899939.ps tmp/38y2o1335899939.png",intern=TRUE)) character(0) > try(system("convert tmp/4vy2u1335899939.ps tmp/4vy2u1335899939.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 17.220 0.830 19.616