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Type 'q()' to quit R. > par9 = 'ATTLES separate' > par8 = 'ATTLES connected' > par7 = 'all' > par6 = 'all' > par5 = 'all' > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '0' > par9 <- 'ATTLES separate' > 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] 27 41 28 37 30 38 35 30 35 40 31 39 35 38 32 33 39 35 33 35 35 28 39 32 37 [26] 36 33 33 35 35 36 32 32 32 40 32 32 34 38 31 31 34 37 30 36 33 38 38 32 38 [51] 36 33 37 32 33 33 38 35 35 37 28 27 31 37 36 38 38 33 37 32 28 35 27 40 32 [76] 33 37 35 39 34 37 34 36 36 32 37 39 38 32 32 33 27 31 32 33 39 34 31 34 31 [101] 38 35 31 42 33 38 34 23 39 38 34 31 32 32 34 35 37 41 34 34 31 36 41 31 30 [126] 31 30 35 35 31 33 37 34 33 35 33 31 37 36 29 33 22 34 32 37 31 27 36 33 31 [151] 34 38 36 39 39 39 29 35 33 37 34 33 31 32 35 30 32 27 22 33 32 31 36 27 30 [176] 35 32 33 34 20 30 26 37 39 37 32 31 21 28 32 31 38 34 31 31 34 34 30 30 28 [201] 29 36 27 32 32 34 35 32 32 36 34 38 33 27 35 25 30 36 34 25 44 32 39 33 38 [226] 32 33 34 35 36 38 30 34 36 37 37 36 33 33 35 30 33 35 35 37 33 28 36 26 35 [251] 32 32 30 30 36 34 33 36 17 33 23 33 34 38 35 33 29 39 32 26 35 33 31 23 32 [276] 25 29 33 34 37 34 32 36 33 35 32 33 37 28 32 35 36 38 31 36 31 40 32 34 33 [301] 39 41 36 34 31 36 39 28 39 31 39 35 42 42 39 34 35 37 30 30 37 42 39 40 31 [326] 38 35 32 34 36 31 37 36 27 32 35 39 32 39 38 39 36 34 37 36 42 30 33 38 28 [351] 37 38 34 28 46 33 37 36 40 32 33 31 36 35 37 33 36 28 31 41 33 47 38 40 39 [376] 29 35 40 42 27 29 33 31 40 34 34 39 22 30 40 35 31 37 32 37 35 32 31 37 37 [401] 37 35 35 49 37 36 37 30 36 30 36 37 31 42 39 32 37 32 48 38 36 28 39 36 39 [426] 38 37 35 31 37 30 36 35 36 32 33 35 31 34 38 36 25 35 36 41 36 35 40 39 21 [451] 39 38 37 40 31 39 36 27 37 32 37 35 42 40 29 28 40 39 29 31 32 39 33 38 33 [476] 36 35 39 32 41 38 39 35 36 35 35 32 35 32 35 33 33 31 35 37 38 23 34 34 41 [501] 34 37 37 31 24 30 36 31 39 34 40 32 46 34 31 40 36 34 42 32 37 36 33 35 34 [526] 35 39 31 36 38 38 41 34 38 41 35 38 27 32 27 38 37 31 38 39 34 33 25 32 28 [551] 33 38 33 37 34 34 31 37 30 29 34 30 37 39 40 38 25 38 32 35 30 27 46 31 30 [576] 31 29 37 31 32 35 30 40 36 38 41 39 45 37 40 35 30 29 36 33 34 33 37 36 32 [601] 39 29 33 34 25 34 40 35 34 36 34 40 37 42 25 35 27 34 32 38 30 31 40 33 35 [626] 30 34 36 36 39 37 24 29 24 31 38 31 38 37 36 35 35 33 38 32 26 31 35 34 44 [651] 35 29 33 38 42 36 33 34 28 35 42 26 36 29 39 37 36 35 42 34 29 33 31 30 44 [676] 28 33 36 37 34 37 31 26 31 29 34 27 36 33 25 37 32 37 30 33 35 43 30 32 30 [701] 29 32 31 32 35 30 40 36 32 31 34 36 37 37 40 35 30 39 24 33 38 40 32 32 37 [726] 29 40 28 25 33 37 36 34 33 35 38 29 48 30 29 31 30 31 32 39 32 29 30 39 34 [751] 32 30 30 29 36 23 40 33 24 36 33 41 33 25 35 30 27 32 22 32 24 31 31 34 33 [776] 33 29 28 35 38 37 30 30 40 29 32 31 31 35 20 35 37 29 30 29 33 21 31 29 36 [801] 32 30 36 35 32 31 30 36 36 39 36 34 36 35 34 29 27 33 36 34 33 37 29 31 41 [826] 33 34 37 33 39 30 36 30 31 29 45 26 33 33 36 33 34 36 32 36 33 32 35 27 35 [851] 30 32 40 32 30 32 35 34 34 31 38 29 32 31 38 36 31 31 29 42 35 40 30 33 29 [876] 36 31 30 33 34 30 31 42 35 29 34 31 35 30 36 31 37 31 35 43 31 32 32 29 21 [901] 37 39 36 39 22 39 34 26 38 42 32 33 35 30 33 29 28 35 45 33 32 32 34 31 26 [926] 28 33 30 37 25 30 29 34 36 37 31 31 26 34 32 39 34 46 30 38 35 26 36 33 28 [951] 30 39 40 32 32 35 31 33 35 39 32 36 37 39 39 29 26 32 29 33 32 34 36 29 40 [976] 35 37 34 29 29 26 28 31 37 35 21 34 39 33 31 29 35 42 > 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]) 17 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 1 2 5 5 5 6 12 13 20 23 45 61 84 93 91 82 94 86 77 54 56 32 13 17 2 3 45 46 47 48 49 3 4 1 2 1 > colnames(x) [1] "endo" "A1" "A2" "A3" "A4" "A5" "A6" "A7" "A8" "A9" [11] "A10" > colnames(x)[par1] [1] "endo" > x[,par1] [1] 27 41 28 37 30 38 35 30 35 40 31 39 35 38 32 33 39 35 33 35 35 28 39 32 37 [26] 36 33 33 35 35 36 32 32 32 40 32 32 34 38 31 31 34 37 30 36 33 38 38 32 38 [51] 36 33 37 32 33 33 38 35 35 37 28 27 31 37 36 38 38 33 37 32 28 35 27 40 32 [76] 33 37 35 39 34 37 34 36 36 32 37 39 38 32 32 33 27 31 32 33 39 34 31 34 31 [101] 38 35 31 42 33 38 34 23 39 38 34 31 32 32 34 35 37 41 34 34 31 36 41 31 30 [126] 31 30 35 35 31 33 37 34 33 35 33 31 37 36 29 33 22 34 32 37 31 27 36 33 31 [151] 34 38 36 39 39 39 29 35 33 37 34 33 31 32 35 30 32 27 22 33 32 31 36 27 30 [176] 35 32 33 34 20 30 26 37 39 37 32 31 21 28 32 31 38 34 31 31 34 34 30 30 28 [201] 29 36 27 32 32 34 35 32 32 36 34 38 33 27 35 25 30 36 34 25 44 32 39 33 38 [226] 32 33 34 35 36 38 30 34 36 37 37 36 33 33 35 30 33 35 35 37 33 28 36 26 35 [251] 32 32 30 30 36 34 33 36 17 33 23 33 34 38 35 33 29 39 32 26 35 33 31 23 32 [276] 25 29 33 34 37 34 32 36 33 35 32 33 37 28 32 35 36 38 31 36 31 40 32 34 33 [301] 39 41 36 34 31 36 39 28 39 31 39 35 42 42 39 34 35 37 30 30 37 42 39 40 31 [326] 38 35 32 34 36 31 37 36 27 32 35 39 32 39 38 39 36 34 37 36 42 30 33 38 28 [351] 37 38 34 28 46 33 37 36 40 32 33 31 36 35 37 33 36 28 31 41 33 47 38 40 39 [376] 29 35 40 42 27 29 33 31 40 34 34 39 22 30 40 35 31 37 32 37 35 32 31 37 37 [401] 37 35 35 49 37 36 37 30 36 30 36 37 31 42 39 32 37 32 48 38 36 28 39 36 39 [426] 38 37 35 31 37 30 36 35 36 32 33 35 31 34 38 36 25 35 36 41 36 35 40 39 21 [451] 39 38 37 40 31 39 36 27 37 32 37 35 42 40 29 28 40 39 29 31 32 39 33 38 33 [476] 36 35 39 32 41 38 39 35 36 35 35 32 35 32 35 33 33 31 35 37 38 23 34 34 41 [501] 34 37 37 31 24 30 36 31 39 34 40 32 46 34 31 40 36 34 42 32 37 36 33 35 34 [526] 35 39 31 36 38 38 41 34 38 41 35 38 27 32 27 38 37 31 38 39 34 33 25 32 28 [551] 33 38 33 37 34 34 31 37 30 29 34 30 37 39 40 38 25 38 32 35 30 27 46 31 30 [576] 31 29 37 31 32 35 30 40 36 38 41 39 45 37 40 35 30 29 36 33 34 33 37 36 32 [601] 39 29 33 34 25 34 40 35 34 36 34 40 37 42 25 35 27 34 32 38 30 31 40 33 35 [626] 30 34 36 36 39 37 24 29 24 31 38 31 38 37 36 35 35 33 38 32 26 31 35 34 44 [651] 35 29 33 38 42 36 33 34 28 35 42 26 36 29 39 37 36 35 42 34 29 33 31 30 44 [676] 28 33 36 37 34 37 31 26 31 29 34 27 36 33 25 37 32 37 30 33 35 43 30 32 30 [701] 29 32 31 32 35 30 40 36 32 31 34 36 37 37 40 35 30 39 24 33 38 40 32 32 37 [726] 29 40 28 25 33 37 36 34 33 35 38 29 48 30 29 31 30 31 32 39 32 29 30 39 34 [751] 32 30 30 29 36 23 40 33 24 36 33 41 33 25 35 30 27 32 22 32 24 31 31 34 33 [776] 33 29 28 35 38 37 30 30 40 29 32 31 31 35 20 35 37 29 30 29 33 21 31 29 36 [801] 32 30 36 35 32 31 30 36 36 39 36 34 36 35 34 29 27 33 36 34 33 37 29 31 41 [826] 33 34 37 33 39 30 36 30 31 29 45 26 33 33 36 33 34 36 32 36 33 32 35 27 35 [851] 30 32 40 32 30 32 35 34 34 31 38 29 32 31 38 36 31 31 29 42 35 40 30 33 29 [876] 36 31 30 33 34 30 31 42 35 29 34 31 35 30 36 31 37 31 35 43 31 32 32 29 21 [901] 37 39 36 39 22 39 34 26 38 42 32 33 35 30 33 29 28 35 45 33 32 32 34 31 26 [926] 28 33 30 37 25 30 29 34 36 37 31 31 26 34 32 39 34 46 30 38 35 26 36 33 28 [951] 30 39 40 32 32 35 31 33 35 39 32 36 37 39 39 29 26 32 29 33 32 34 36 29 40 [976] 35 37 34 29 29 26 28 31 37 35 21 34 39 33 31 29 35 42 > 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/1tk261335802816.tab") + } + } > m Conditional inference tree with 15 terminal nodes Response: endo Inputs: A1, A2, A3, A4, A5, A6, A7, A8, A9, A10 Number of observations: 993 1) A8 <= 2; criterion = 1, statistic = 102.572 2) A6 <= 3; criterion = 0.999, statistic = 14.997 3) A10 <= 2; criterion = 0.981, statistic = 9.624 4)* weights = 18 3) A10 > 2 5)* weights = 20 2) A6 > 3 6)* weights = 46 1) A8 > 2 7) A1 <= 4; criterion = 1, statistic = 65.238 8) A6 <= 3; criterion = 1, statistic = 42.496 9) A1 <= 2; criterion = 0.996, statistic = 12.434 10)* weights = 33 9) A1 > 2 11)* weights = 150 8) A6 > 3 12) A3 <= 4; criterion = 0.999, statistic = 15.807 13) A9 <= 2; criterion = 0.963, statistic = 8.395 14) A10 <= 2; criterion = 0.998, statistic = 13.977 15)* weights = 15 14) A10 > 2 16) A5 <= 4; criterion = 0.986, statistic = 10.26 17)* weights = 144 16) A5 > 4 18)* weights = 33 13) A9 > 2 19)* weights = 251 12) A3 > 4 20) A10 <= 3; criterion = 0.977, statistic = 9.288 21)* weights = 23 20) A10 > 3 22)* weights = 87 7) A1 > 4 23) A10 <= 4; criterion = 0.999, statistic = 16.132 24)* weights = 102 23) A10 > 4 25) A6 <= 4; criterion = 0.994, statistic = 11.626 26) A9 <= 3; criterion = 0.998, statistic = 13.769 27)* weights = 30 26) A9 > 3 28)* weights = 10 25) A6 > 4 29)* weights = 31 > postscript(file="/var/wessaorg/rcomp/tmp/2v28f1335802816.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/33a5z1335802816.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 27 33.10417 -6.10416667 2 41 39.32258 1.67741935 3 28 33.10417 -5.10416667 4 37 31.73913 5.26086957 5 30 31.73913 -1.73913043 6 38 34.29880 3.70119522 7 35 32.70000 2.30000000 8 30 31.73913 -1.73913043 9 35 35.16667 -0.16666667 10 40 33.10417 6.89583333 11 31 31.73913 -0.73913043 12 39 35.42424 3.57575758 13 35 33.10417 1.89583333 14 38 34.29880 3.70119522 15 32 36.17241 -4.17241379 16 33 33.10417 -0.10416667 17 39 35.42424 3.57575758 18 35 34.29880 0.70119522 19 33 32.70000 0.30000000 20 35 33.10417 1.89583333 21 35 35.16667 -0.16666667 22 28 33.10417 -5.10416667 23 39 33.10417 5.89583333 24 32 33.10417 -1.10416667 25 37 33.10417 3.89583333 26 36 34.29880 1.70119522 27 33 33.10417 -0.10416667 28 33 33.10417 -0.10416667 29 35 34.29880 0.70119522 30 35 31.73913 3.26086957 31 36 33.56522 2.43478261 32 32 32.70000 -0.70000000 33 32 36.17241 -4.17241379 34 32 35.16667 -3.16666667 35 40 34.29880 5.70119522 36 32 32.70000 -0.70000000 37 32 34.29880 -2.29880478 38 34 35.16667 -1.16666667 39 38 32.70000 5.30000000 40 31 34.29880 -3.29880478 41 31 30.35000 0.65000000 42 34 32.70000 1.30000000 43 37 35.03333 1.96666667 44 30 33.10417 -3.10416667 45 36 34.29880 1.70119522 46 33 33.10417 -0.10416667 47 38 35.16667 2.83333333 48 38 36.17241 1.82758621 49 32 33.10417 -1.10416667 50 38 39.32258 -1.32258065 51 36 35.03333 0.96666667 52 33 29.75758 3.24242424 53 37 33.10417 3.89583333 54 32 35.16667 -3.16666667 55 33 35.16667 -2.16666667 56 33 29.86667 3.13333333 57 38 39.50000 -1.50000000 58 35 35.03333 -0.03333333 59 35 36.17241 -1.17241379 60 37 34.29880 2.70119522 61 28 33.10417 -5.10416667 62 27 32.70000 -5.70000000 63 31 33.56522 -2.56521739 64 37 34.29880 2.70119522 65 36 33.10417 2.89583333 66 38 34.29880 3.70119522 67 38 33.10417 4.89583333 68 33 34.29880 -1.29880478 69 37 34.29880 2.70119522 70 32 35.16667 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3.83333333 905 22 32.70000 -10.70000000 906 39 33.10417 5.89583333 907 34 35.42424 -1.42424242 908 26 32.70000 -6.70000000 909 38 34.29880 3.70119522 910 42 35.16667 6.83333333 911 32 34.29880 -2.29880478 912 33 34.29880 -1.29880478 913 35 35.16667 -0.16666667 914 30 32.70000 -2.70000000 915 33 32.70000 0.30000000 916 29 32.70000 -3.70000000 917 28 33.10417 -5.10416667 918 35 32.70000 2.30000000 919 45 34.29880 10.70119522 920 33 34.29880 -1.29880478 921 32 35.42424 -3.42424242 922 32 34.29880 -2.29880478 923 34 26.00000 8.00000000 924 31 34.29880 -3.29880478 925 26 32.70000 -6.70000000 926 28 31.73913 -3.73913043 927 33 36.17241 -3.17241379 928 30 34.29880 -4.29880478 929 37 34.29880 2.70119522 930 25 34.29880 -9.29880478 931 30 34.29880 -4.29880478 932 29 29.75758 -0.75757576 933 34 34.29880 -0.29880478 934 36 35.16667 0.83333333 935 37 36.17241 0.82758621 936 31 29.75758 1.24242424 937 31 34.29880 -3.29880478 938 26 36.17241 -10.17241379 939 34 34.29880 -0.29880478 940 32 32.70000 -0.70000000 941 39 32.70000 6.30000000 942 34 32.70000 1.30000000 943 46 35.16667 10.83333333 944 30 33.10417 -3.10416667 945 38 35.42424 2.57575758 946 35 35.42424 -0.42424242 947 26 31.73913 -5.73913043 948 36 34.29880 1.70119522 949 33 33.10417 -0.10416667 950 28 29.75758 -1.75757576 951 30 36.17241 -6.17241379 952 39 34.29880 4.70119522 953 40 34.29880 5.70119522 954 32 33.10417 -1.10416667 955 32 32.70000 -0.70000000 956 35 34.29880 0.70119522 957 31 34.29880 -3.29880478 958 33 34.29880 -1.29880478 959 35 34.29880 0.70119522 960 39 33.10417 5.89583333 961 32 30.35000 1.65000000 962 36 36.17241 -0.17241379 963 37 34.29880 2.70119522 964 39 35.16667 3.83333333 965 39 34.29880 4.70119522 966 29 32.70000 -3.70000000 967 26 34.29880 -8.29880478 968 32 34.29880 -2.29880478 969 29 33.10417 -4.10416667 970 33 34.29880 -1.29880478 971 32 34.29880 -2.29880478 972 34 32.70000 1.30000000 973 36 34.29880 1.70119522 974 29 29.75758 -0.75757576 975 40 34.29880 5.70119522 976 35 34.29880 0.70119522 977 37 32.70000 4.30000000 978 34 32.70000 1.30000000 979 29 32.70000 -3.70000000 980 29 33.10417 -4.10416667 981 26 26.00000 0.00000000 982 28 33.10417 -5.10416667 983 31 30.35000 0.65000000 984 37 32.70000 4.30000000 985 35 35.16667 -0.16666667 986 21 26.00000 -5.00000000 987 34 32.70000 1.30000000 988 39 39.32258 -0.32258065 989 33 32.70000 0.30000000 990 31 31.73913 -0.73913043 991 29 34.29880 -5.29880478 992 35 33.10417 1.89583333 993 42 39.50000 2.50000000 > 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/4dkma1335802816.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/59faf1335802816.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/67xxa1335802816.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/75nfr1335802816.tab") + } > > try(system("convert tmp/2v28f1335802816.ps tmp/2v28f1335802816.png",intern=TRUE)) character(0) > try(system("convert tmp/33a5z1335802816.ps tmp/33a5z1335802816.png",intern=TRUE)) character(0) > try(system("convert tmp/4dkma1335802816.ps tmp/4dkma1335802816.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 23.237 0.415 23.767