R version 2.8.0 (2008-10-20) Copyright (C) 2008 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. 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,0.0523775174 + ,-0.0328847346 + ,0.0131292719 + ,0.0350404489 + ,0.0004433607 + ,0.0885041253 + ,0.0595552648 + ,0.0582277708 + ,-0.1271063631 + ,0.0681769863 + ,-0.0162178014 + ,0.0227588167 + ,0.0523775174 + ,-0.0328847346 + ,0.0131292719 + ,0.0379810835 + ,0.0004433607 + ,0.0885041253 + ,0.0595552648 + ,0.0582277708 + ,-0.0520908486 + ,-0.0127524528 + ,-0.0162178014 + ,0.0227588167 + ,0.0523775174 + ,-0.0328847346 + ,0.0681769863 + ,0.0379810835 + ,0.0004433607 + ,0.0885041253 + ,0.0595552648 + ,0.0666010623 + ,-0.0822608913 + ,-0.0127524528 + ,-0.0162178014 + ,0.0227588167 + ,0.0523775174 + ,-0.0520908486 + ,0.0681769863 + ,0.0379810835 + ,0.0004433607 + ,0.0885041253 + ,0.0677173945 + ,0.0221140649 + ,-0.0822608913 + ,-0.0127524528 + ,-0.0162178014 + ,0.0227588167 + ,0.0666010623 + ,-0.0520908486 + ,0.0681769863 + ,0.0379810835 + ,0.0004433607 + ,0.1251127483 + ,0.0317810643 + ,0.0221140649 + ,-0.0822608913 + ,-0.0127524528 + ,-0.0162178014 + ,0.0677173945 + ,0.0666010623 + ,-0.0520908486 + ,0.0681769863 + ,0.0379810835 + ,-0.0218349286 + ,-0.0773298837 + ,0.0317810643 + ,0.0221140649 + ,-0.0822608913 + ,-0.0127524528 + ,0.1251127483 + ,0.0677173945 + ,0.0666010623 + ,-0.0520908486 + ,0.0681769863 + ,0.0178312442 + ,0.0027974224 + ,-0.0773298837 + ,0.0317810643 + ,0.0221140649 + ,-0.0822608913 + ,-0.0218349286 + ,0.1251127483 + ,0.0677173945 + ,0.0666010623 + ,-0.0520908486 + ,0.0199663138 + ,-0.0684060589 + ,0.0027974224 + ,-0.0773298837 + ,0.0317810643 + ,0.0221140649 + ,0.0178312442 + ,-0.0218349286 + ,0.1251127483 + ,0.0677173945 + ,0.0666010623 + ,0.088436301 + ,-0.0326538799 + ,-0.0684060589 + ,0.0027974224 + ,-0.0773298837 + ,0.0317810643 + ,0.0199663138 + ,0.0178312442 + ,-0.0218349286 + ,0.1251127483 + ,0.0677173945 + ,0.0646054028 + ,-0.0125972204 + ,-0.0326538799 + ,-0.0684060589 + ,0.0027974224 + ,-0.0773298837 + ,0.088436301 + ,0.0199663138 + ,0.0178312442 + ,-0.0218349286 + ,0.1251127483 + ,0.1233912353 + ,0.048660559 + ,-0.0125972204 + ,-0.0326538799 + ,-0.0684060589 + ,0.0027974224 + ,0.0646054028 + ,0.088436301 + ,0.0199663138 + ,0.0178312442 + ,-0.0218349286 + ,0.0673308704 + ,-0.0147704378 + ,0.048660559 + ,-0.0125972204 + ,-0.0326538799 + ,-0.0684060589 + ,0.1233912353 + ,0.0646054028 + ,0.088436301 + ,0.0199663138 + ,0.0178312442 + ,0.0232412696 + ,-0.0812692141 + ,-0.0147704378 + ,0.048660559 + ,-0.0125972204 + ,-0.0326538799 + ,0.0673308704 + ,0.1233912353 + ,0.0646054028 + ,0.088436301 + ,0.0199663138 + ,-0.1570633927 + ,-0.1445904237 + ,-0.0812692141 + ,-0.0147704378 + ,0.048660559 + ,-0.0125972204 + ,0.0232412696 + ,0.0673308704 + ,0.1233912353 + ,0.0646054028 + ,0.088436301 + ,-0.134923147 + ,0.0047470083 + ,-0.1445904237 + ,-0.0812692141 + ,-0.0147704378 + ,0.048660559 + ,-0.1570633927 + ,0.0232412696 + ,0.0673308704 + ,0.1233912353 + ,0.0646054028 + ,-0.2920959272 + ,-0.0281878491 + ,0.0047470083 + ,-0.1445904237 + ,-0.0812692141 + ,-0.0147704378 + ,-0.134923147 + ,-0.1570633927 + ,0.0232412696 + ,0.0673308704 + ,0.1233912353 + ,-0.3111517877 + ,-0.2985135366 + ,-0.0281878491 + ,0.0047470083 + ,-0.1445904237 + ,-0.0812692141 + ,-0.2920959272 + ,-0.134923147 + ,-0.1570633927 + ,0.0232412696 + ,0.0673308704 + ,-0.2363887781 + ,-0.0871197547 + ,-0.2985135366 + ,-0.0281878491 + ,0.0047470083 + ,-0.1445904237 + ,-0.3111517877 + ,-0.2920959272 + ,-0.134923147 + ,-0.1570633927 + ,0.0232412696 + ,0.0334524402 + ,-0.0782493685 + ,-0.0871197547 + ,-0.2985135366 + ,-0.0281878491 + ,0.0047470083 + ,-0.2363887781 + ,-0.3111517877 + ,-0.2920959272 + ,-0.134923147 + ,-0.1570633927) + ,dim=c(11 + ,103) + ,dimnames=list(c('PCYt' + ,'PCXt1' + ,'PCXt2' + ,'PCXt3' + ,'PCXt4' + ,'PCXt5' + ,'PCYt1' + ,'PCYt2' + ,'PCYt3' + ,'PCYt4' + ,'PCYt5') + ,1:103)) > y <- array(NA,dim=c(11,103),dimnames=list(c('PCYt','PCXt1','PCXt2','PCXt3','PCXt4','PCXt5','PCYt1','PCYt2','PCYt3','PCYt4','PCYt5'),1:103)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'no' > par3 = '3' > par2 = 'none' > par1 = '1' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Dr. Ian E. Holliday > #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > 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 Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric 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 <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "PCYt" > x[,par1] [1] -0.0326433382 0.0440720340 0.0882361169 -0.0355066885 0.0278273388 [6] -0.2004308914 -0.0263424279 0.0655051718 -0.0709357514 -0.0129185590 [11] 0.1183957540 -0.0330932173 -0.0860908693 0.0210698391 0.0149456700 [16] -0.1900347570 -0.1242436027 0.0062305498 0.0255507001 0.0268806628 [21] 0.1773155966 0.0660542373 -0.0139591255 -0.0373949697 0.0366137196 [26] 0.0193504490 0.0837801497 -0.0369814175 -0.1113117010 0.1156912701 [31] 0.0961044085 0.0671607829 -0.0791409595 -0.1831923744 0.0242315729 [36] 0.0682600023 0.0345855796 0.0463590447 -0.0931151599 0.0760673553 [41] -0.0110651198 0.0284509336 0.0368261882 -0.0058804482 0.0680750192 [46] 0.0157914001 0.1121766425 -0.0437664455 0.0603266530 0.1028673441 [51] 0.0259509727 0.1348695746 -0.0967153180 -0.1035936648 0.0950389075 [56] 0.0359719068 0.1520609453 -0.0022505636 -0.0277954311 0.0653827593 [61] 0.0443888626 0.1010961169 -0.0029750276 -0.0752062699 -0.0525843352 [66] 0.0229004195 0.1009621422 -0.0289088246 0.0208474516 0.0788578228 [71] 0.0549314322 -0.0321652782 0.0646729207 -0.0010757027 -0.1458885691 [76] -0.0677816324 -0.0098770369 0.0501991563 -0.1271063631 0.0582277708 [81] 0.0595552648 0.0885041253 0.0004433607 0.0379810835 0.0681769863 [86] -0.0520908486 0.0666010623 0.0677173945 0.1251127483 -0.0218349286 [91] 0.0178312442 0.0199663138 0.0884363010 0.0646054028 0.1233912353 [96] 0.0673308704 0.0232412696 -0.1570633927 -0.1349231470 -0.2920959272 [101] -0.3111517877 -0.2363887781 0.0334524402 > 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]) -0.3111517877 -0.2920959272 -0.2363887781 -0.2004308914 -0.190034757 1 1 1 1 1 -0.1831923744 -0.1570633927 -0.1458885691 -0.134923147 -0.1271063631 1 1 1 1 1 -0.1242436027 -0.111311701 -0.1035936648 -0.096715318 -0.0931151599 1 1 1 1 1 -0.0860908693 -0.0791409595 -0.0752062699 -0.0709357514 -0.0677816324 1 1 1 1 1 -0.0525843352 -0.0520908486 -0.0437664455 -0.0373949697 -0.0369814175 1 1 1 1 1 -0.0355066885 -0.0330932173 -0.0326433382 -0.0321652782 -0.0289088246 1 1 1 1 1 -0.0277954311 -0.0263424279 -0.0218349286 -0.0139591255 -0.012918559 1 1 1 1 1 -0.0110651198 -0.0098770369 -0.0058804482 -0.0029750276 -0.0022505636 1 1 1 1 1 -0.0010757027 0.0004433607 0.0062305498 0.01494567 0.0157914001 1 1 1 1 1 0.0178312442 0.019350449 0.0199663138 0.0208474516 0.0210698391 1 1 1 1 1 0.0229004195 0.0232412696 0.0242315729 0.0255507001 0.0259509727 1 1 1 1 1 0.0268806628 0.0278273388 0.0284509336 0.0334524402 0.0345855796 1 1 1 1 1 0.0359719068 0.0366137196 0.0368261882 0.0379810835 0.044072034 1 1 1 1 1 0.0443888626 0.0463590447 0.0501991563 0.0549314322 0.0582277708 1 1 1 1 1 0.0595552648 0.060326653 0.0646054028 0.0646729207 0.0653827593 1 1 1 1 1 0.0655051718 0.0660542373 0.0666010623 0.0671607829 0.0673308704 1 1 1 1 1 0.0677173945 0.0680750192 0.0681769863 0.0682600023 0.0760673553 1 1 1 1 1 0.0788578228 0.0837801497 0.0882361169 0.088436301 0.0885041253 1 1 1 1 1 0.0950389075 0.0961044085 0.1009621422 0.1010961169 0.1028673441 1 1 1 1 1 0.1121766425 0.1156912701 0.118395754 0.1233912353 0.1251127483 1 1 1 1 1 0.1348695746 0.1520609453 0.1773155966 1 1 1 > colnames(x) [1] "PCYt" "PCXt1" "PCXt2" "PCXt3" "PCXt4" "PCXt5" "PCYt1" "PCYt2" "PCYt3" [10] "PCYt4" "PCYt5" > colnames(x)[par1] [1] "PCYt" > x[,par1] [1] -0.0326433382 0.0440720340 0.0882361169 -0.0355066885 0.0278273388 [6] -0.2004308914 -0.0263424279 0.0655051718 -0.0709357514 -0.0129185590 [11] 0.1183957540 -0.0330932173 -0.0860908693 0.0210698391 0.0149456700 [16] -0.1900347570 -0.1242436027 0.0062305498 0.0255507001 0.0268806628 [21] 0.1773155966 0.0660542373 -0.0139591255 -0.0373949697 0.0366137196 [26] 0.0193504490 0.0837801497 -0.0369814175 -0.1113117010 0.1156912701 [31] 0.0961044085 0.0671607829 -0.0791409595 -0.1831923744 0.0242315729 [36] 0.0682600023 0.0345855796 0.0463590447 -0.0931151599 0.0760673553 [41] -0.0110651198 0.0284509336 0.0368261882 -0.0058804482 0.0680750192 [46] 0.0157914001 0.1121766425 -0.0437664455 0.0603266530 0.1028673441 [51] 0.0259509727 0.1348695746 -0.0967153180 -0.1035936648 0.0950389075 [56] 0.0359719068 0.1520609453 -0.0022505636 -0.0277954311 0.0653827593 [61] 0.0443888626 0.1010961169 -0.0029750276 -0.0752062699 -0.0525843352 [66] 0.0229004195 0.1009621422 -0.0289088246 0.0208474516 0.0788578228 [71] 0.0549314322 -0.0321652782 0.0646729207 -0.0010757027 -0.1458885691 [76] -0.0677816324 -0.0098770369 0.0501991563 -0.1271063631 0.0582277708 [81] 0.0595552648 0.0885041253 0.0004433607 0.0379810835 0.0681769863 [86] -0.0520908486 0.0666010623 0.0677173945 0.1251127483 -0.0218349286 [91] 0.0178312442 0.0199663138 0.0884363010 0.0646054028 0.1233912353 [96] 0.0673308704 0.0232412696 -0.1570633927 -0.1349231470 -0.2920959272 [101] -0.3111517877 -0.2363887781 0.0334524402 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/html/freestat/rcomp/tmp/1glzf1291888016.tab") + } + } > m Conditional inference tree with 2 terminal nodes Response: PCYt Inputs: PCXt1, PCXt2, PCXt3, PCXt4, PCXt5, PCYt1, PCYt2, PCYt3, PCYt4, PCYt5 Number of observations: 103 1) PCXt1 <= -0.08711975; criterion = 1, statistic = 17.328 2)* weights = 7 1) PCXt1 > -0.08711975 3)* weights = 96 > postscript(file="/var/www/html/freestat/rcomp/tmp/2glzf1291888016.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/html/freestat/rcomp/tmp/3qcyi1291888016.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 -0.0326433382 0.01399795 -0.0466412893 2 0.0440720340 0.01399795 0.0300740829 3 0.0882361169 0.01399795 0.0742381658 4 -0.0355066885 0.01399795 -0.0495046396 5 0.0278273388 0.01399795 0.0138293877 6 -0.2004308914 0.01399795 -0.2144288425 7 -0.0263424279 0.01399795 -0.0403403790 8 0.0655051718 0.01399795 0.0515072207 9 -0.0709357514 0.01399795 -0.0849337025 10 -0.0129185590 0.01399795 -0.0269165101 11 0.1183957540 0.01399795 0.1043978029 12 -0.0330932173 0.01399795 -0.0470911684 13 -0.0860908693 0.01399795 -0.1000888204 14 0.0210698391 0.01399795 0.0070718880 15 0.0149456700 0.01399795 0.0009477189 16 -0.1900347570 -0.13111650 -0.0589182561 17 -0.1242436027 0.01399795 -0.1382415538 18 0.0062305498 0.01399795 -0.0077674013 19 0.0255507001 0.01399795 0.0115527490 20 0.0268806628 0.01399795 0.0128827117 21 0.1773155966 0.01399795 0.1633176455 22 0.0660542373 0.01399795 0.0520562862 23 -0.0139591255 0.01399795 -0.0279570766 24 -0.0373949697 0.01399795 -0.0513929208 25 0.0366137196 -0.13111650 0.1677302205 26 0.0193504490 -0.13111650 0.1504669499 27 0.0837801497 0.01399795 0.0697821986 28 -0.0369814175 0.01399795 -0.0509793686 29 -0.1113117010 0.01399795 -0.1253096521 30 0.1156912701 0.01399795 0.1016933190 31 0.0961044085 0.01399795 0.0821064574 32 0.0671607829 0.01399795 0.0531628318 33 -0.0791409595 -0.13111650 0.0519755414 34 -0.1831923744 0.01399795 -0.1971903255 35 0.0242315729 0.01399795 0.0102336218 36 0.0682600023 0.01399795 0.0542620512 37 0.0345855796 0.01399795 0.0205876285 38 0.0463590447 0.01399795 0.0323610936 39 -0.0931151599 0.01399795 -0.1071131110 40 0.0760673553 0.01399795 0.0620694042 41 -0.0110651198 0.01399795 -0.0250630709 42 0.0284509336 0.01399795 0.0144529825 43 0.0368261882 0.01399795 0.0228282371 44 -0.0058804482 0.01399795 -0.0198783993 45 0.0680750192 0.01399795 0.0540770681 46 0.0157914001 0.01399795 0.0017934490 47 0.1121766425 0.01399795 0.0981786914 48 -0.0437664455 0.01399795 -0.0577643966 49 0.0603266530 0.01399795 0.0463287019 50 0.1028673441 0.01399795 0.0888693930 51 0.0259509727 0.01399795 0.0119530216 52 0.1348695746 0.01399795 0.1208716235 53 -0.0967153180 0.01399795 -0.1107132691 54 -0.1035936648 0.01399795 -0.1175916159 55 0.0950389075 0.01399795 0.0810409564 56 0.0359719068 0.01399795 0.0219739557 57 0.1520609453 0.01399795 0.1380629942 58 -0.0022505636 0.01399795 -0.0162485147 59 -0.0277954311 0.01399795 -0.0417933822 60 0.0653827593 0.01399795 0.0513848082 61 0.0443888626 0.01399795 0.0303909115 62 0.1010961169 0.01399795 0.0870981658 63 -0.0029750276 0.01399795 -0.0169729787 64 -0.0752062699 0.01399795 -0.0892042210 65 -0.0525843352 0.01399795 -0.0665822863 66 0.0229004195 0.01399795 0.0089024684 67 0.1009621422 0.01399795 0.0869641911 68 -0.0289088246 0.01399795 -0.0429067757 69 0.0208474516 0.01399795 0.0068495005 70 0.0788578228 0.01399795 0.0648598717 71 0.0549314322 0.01399795 0.0409334811 72 -0.0321652782 0.01399795 -0.0461632293 73 0.0646729207 0.01399795 0.0506749696 74 -0.0010757027 0.01399795 -0.0150736538 75 -0.1458885691 0.01399795 -0.1598865202 76 -0.0677816324 0.01399795 -0.0817795835 77 -0.0098770369 0.01399795 -0.0238749880 78 0.0501991563 0.01399795 0.0362012052 79 -0.1271063631 0.01399795 -0.1411043142 80 0.0582277708 0.01399795 0.0442298197 81 0.0595552648 0.01399795 0.0455573137 82 0.0885041253 0.01399795 0.0745061742 83 0.0004433607 0.01399795 -0.0135545904 84 0.0379810835 0.01399795 0.0239831324 85 0.0681769863 0.01399795 0.0541790352 86 -0.0520908486 0.01399795 -0.0660887997 87 0.0666010623 0.01399795 0.0526031112 88 0.0677173945 0.01399795 0.0537194434 89 0.1251127483 0.01399795 0.1111147972 90 -0.0218349286 0.01399795 -0.0358328797 91 0.0178312442 0.01399795 0.0038332931 92 0.0199663138 0.01399795 0.0059683627 93 0.0884363010 0.01399795 0.0744383499 94 0.0646054028 0.01399795 0.0506074517 95 0.1233912353 0.01399795 0.1093932842 96 0.0673308704 0.01399795 0.0533329193 97 0.0232412696 0.01399795 0.0092433185 98 -0.1570633927 -0.13111650 -0.0259468918 99 -0.1349231470 0.01399795 -0.1489210981 100 -0.2920959272 0.01399795 -0.3060938783 101 -0.3111517877 -0.13111650 -0.1800352868 102 -0.2363887781 -0.13111650 -0.1052722772 103 0.0334524402 0.01399795 0.0194544891 > 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/html/freestat/rcomp/tmp/41mxl1291888016.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/html/freestat/rcomp/tmp/5m4w91291888016.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/html/freestat/rcomp/tmp/6q4cw1291888016.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/html/freestat/rcomp/tmp/7jwui1291888016.tab") + } > > try(system("convert tmp/2glzf1291888016.ps tmp/2glzf1291888016.png",intern=TRUE)) character(0) > try(system("convert tmp/3qcyi1291888016.ps tmp/3qcyi1291888016.png",intern=TRUE)) character(0) > try(system("convert tmp/41mxl1291888016.ps tmp/41mxl1291888016.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.402 0.763 4.588