R version 2.9.0 (2009-04-17) Copyright (C) 2009 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. 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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+ ,69863 + ,0 + ,1 + ,286963 + ,7419 + ,21 + ,63255 + ,1 + ,1 + ,269753 + ,1164 + ,15 + ,57320 + ,1 + ,1 + ,448243 + ,3310 + ,32 + ,75230 + ,1 + ,1 + ,165404 + ,1920 + ,11 + ,79420 + ,0 + ,1 + ,204325 + ,965 + ,2 + ,73490 + ,0 + ,0 + ,407159 + ,3256 + ,23 + ,35250 + ,0 + ,1 + ,290476 + ,1135 + ,20 + ,62285 + ,1 + ,0 + ,275311 + ,1270 + ,24 + ,69206 + ,0 + ,0 + ,246541 + ,661 + ,1 + ,65920 + ,0 + ,0 + ,253468 + ,1013 + ,1 + ,69770 + ,0 + ,0 + ,240897 + ,2844 + ,74 + ,72683 + ,0 + ,1 + ,-83265 + ,11528 + ,68 + ,-14545 + ,1 + ,1 + ,-42143 + ,6526 + ,20 + ,55830 + ,0 + ,1 + ,272713 + ,2264 + ,20 + ,55174 + ,0 + ,1 + ,215362 + ,5109 + ,82 + ,67038 + ,1 + ,1 + ,42754 + ,3999 + ,21 + ,51252 + ,0 + ,1 + ,306275 + ,35624 + ,244 + ,157278 + ,0 + ,1 + ,253537 + ,9252 + ,32 + ,79510 + ,0 + ,1 + ,372631 + ,15236 + ,86 + ,77440 + ,0 + ,1 + ,-7170 + ,18073 + ,69 + ,27284 + ,0 + ,1) + ,dim=c(6 + ,430) + ,dimnames=list(c('Wealth' + ,'Costs' + ,'Orders' + ,'Dividends' + ,'Group' + ,'Gender ') + ,1:430)) > y <- array(NA,dim=c(6,430),dimnames=list(c('Wealth','Costs','Orders','Dividends','Group','Gender '),1:430)) > 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 = '6' > 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] "Wealth" > x[,par1] [1] 4321023 4111912 223193 1491348 1629616 1398893 1926517 983660 1443586 [10] 1073089 984885 1405225 227132 929118 1071292 638830 856956 992426 [19] 444477 857217 711969 702380 358589 297978 585715 657954 209458 [28] 786690 439798 688779 574339 741409 597793 644190 377934 640273 [37] 697458 550608 207393 301607 345783 501749 379983 387475 377305 [46] 370837 430866 469107 194493 530670 518365 491303 527021 233773 [55] 405972 652925 446211 341340 387699 493408 146494 414462 364304 [64] 355178 357760 261216 397144 374943 424898 202055 378525 310768 [73] 325738 394510 247060 368078 236761 312378 339836 347385 426280 [82] 352850 301881 377516 357312 458343 354228 308636 386212 393343 [91] 378509 452469 364839 358649 376641 429112 330546 403560 317892 [100] 307528 235133 299243 314073 368186 269661 125390 510834 321896 [109] 249898 408881 158492 292154 289513 378049 343466 332743 442882 [118] 214215 315688 375195 334280 355864 480382 353058 217193 315380 [127] 314533 318056 315380 314353 369448 315380 312846 312075 315009 [136] 318903 314887 314913 315380 325506 315380 298568 315834 329784 [145] 312878 315380 314987 325249 315877 291650 305959 315380 297765 [154] 315245 315380 315380 315236 336425 315380 315380 315380 315380 [163] 306268 302187 314882 315380 382712 341570 315380 315380 312412 [172] 315380 309596 315380 315547 313267 316176 315380 315380 359335 [181] 330068 314289 297413 314806 333210 352108 313332 291787 315380 [190] 318745 315380 315366 315380 315688 315380 409642 315380 315380 [199] 269587 315380 315380 315380 300962 325479 316155 318574 315380 [208] 343613 306948 315380 315380 330059 288985 304485 315380 315688 [217] 317736 315380 322331 296656 315380 315354 312161 315576 314922 [226] 314551 315380 312339 315380 298700 321376 315380 303230 315380 [235] 315487 315380 315793 315380 315380 315380 312887 315380 315637 [244] 324385 315380 315380 308989 315380 315380 296702 315380 307322 [253] 304376 253588 315380 309560 298466 315380 315380 315380 315380 [262] 343929 331955 315380 315380 315380 381180 315380 331420 315380 [271] 315380 315380 310201 315380 320016 320398 315380 291841 310670 [280] 315380 315380 313491 315380 331323 315380 319210 318098 315380 [289] 292754 315380 325176 365959 315380 302409 340968 315380 315380 [298] 315380 315380 313164 301164 315380 315380 344425 315394 315380 [307] 316647 309836 315380 315380 346611 315380 322031 315656 339445 [316] 314964 297141 315372 315380 315380 315380 315380 315380 315380 [325] 312502 315380 315380 315380 315380 315380 315380 315380 313729 [334] 315388 315371 296139 315380 313880 317698 295580 315380 315380 [343] 315380 308256 315380 303677 315380 315380 319369 318690 314049 [352] 325699 314210 315380 315380 322378 315380 315380 315380 315398 [361] 315380 315380 308336 316386 315380 315380 315380 315380 315553 [370] 315380 323361 336639 307424 315380 315380 295370 322340 319864 [379] 315380 315380 317291 280398 315380 317330 238125 327071 309038 [388] 314210 307930 322327 292136 263276 367655 283910 283587 243650 [397] 438493 296261 230621 304252 333505 296919 278990 276898 327007 [406] 317046 304555 298096 231861 309422 286963 269753 448243 165404 [415] 204325 407159 290476 275311 246541 253468 240897 -83265 -42143 [424] 272713 215362 42754 306275 253537 372631 -7170 > 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]) -83265 -42143 -7170 42754 125390 146494 158492 165404 194493 202055 1 1 1 1 1 1 1 1 1 1 204325 207393 209458 214215 215362 217193 223193 227132 230621 231861 1 1 1 1 1 1 1 1 1 1 233773 235133 236761 238125 240897 243650 246541 247060 249898 253468 1 1 1 1 1 1 1 1 1 1 253537 253588 261216 263276 269587 269661 269753 272713 275311 276898 1 1 1 1 1 1 1 1 1 1 278990 280398 283587 283910 286963 288985 289513 290476 291650 291787 1 1 1 1 1 1 1 1 1 1 291841 292136 292154 292754 295370 295580 296139 296261 296656 296702 1 1 1 1 1 1 1 1 1 1 296919 297141 297413 297765 297978 298096 298466 298568 298700 299243 1 1 1 1 1 1 1 1 1 1 300962 301164 301607 301881 302187 302409 303230 303677 304252 304376 1 1 1 1 1 1 1 1 1 1 304485 304555 305959 306268 306275 306948 307322 307424 307528 307930 1 1 1 1 1 1 1 1 1 1 308256 308336 308636 308989 309038 309422 309560 309596 309836 310201 1 1 1 1 1 1 1 1 1 1 310670 310768 312075 312161 312339 312378 312412 312502 312846 312878 1 1 1 1 1 1 1 1 1 1 312887 313164 313267 313332 313491 313729 313880 314049 314073 314210 1 1 1 1 1 1 1 1 1 2 314289 314353 314533 314551 314806 314882 314887 314913 314922 314964 1 1 1 1 1 1 1 1 1 1 314987 315009 315236 315245 315354 315366 315371 315372 315380 315388 1 1 1 1 1 1 1 1 117 1 315394 315398 315487 315547 315553 315576 315637 315656 315688 315793 1 1 1 1 1 1 1 1 3 1 315834 315877 316155 316176 316386 316647 317046 317291 317330 317698 1 1 1 1 1 1 1 1 1 1 317736 317892 318056 318098 318574 318690 318745 318903 319210 319369 1 1 1 1 1 1 1 1 1 1 319864 320016 320398 321376 321896 322031 322327 322331 322340 322378 1 1 1 1 1 1 1 1 1 1 323361 324385 325176 325249 325479 325506 325699 325738 327007 327071 1 1 1 1 1 1 1 1 1 1 329784 330059 330068 330546 331323 331420 331955 332743 333210 333505 1 1 1 1 1 1 1 1 1 1 334280 336425 336639 339445 339836 340968 341340 341570 343466 343613 1 1 1 1 1 1 1 1 1 1 343929 344425 345783 346611 347385 352108 352850 353058 354228 355178 1 1 1 1 1 1 1 1 1 1 355864 357312 357760 358589 358649 359335 364304 364839 365959 367655 1 1 1 1 1 1 1 1 1 1 368078 368186 369448 370837 372631 374943 375195 376641 377305 377516 1 1 1 1 1 1 1 1 1 1 377934 378049 378509 378525 379983 381180 382712 386212 387475 387699 1 1 1 1 1 1 1 1 1 1 393343 394510 397144 403560 405972 407159 408881 409642 414462 424898 1 1 1 1 1 1 1 1 1 1 426280 429112 430866 438493 439798 442882 444477 446211 448243 452469 1 1 1 1 1 1 1 1 1 1 458343 469107 480382 491303 493408 501749 510834 518365 527021 530670 1 1 1 1 1 1 1 1 1 1 550608 574339 585715 597793 638830 640273 644190 652925 657954 688779 1 1 1 1 1 1 1 1 1 1 697458 702380 711969 741409 786690 856956 857217 929118 983660 984885 1 1 1 1 1 1 1 1 1 1 992426 1071292 1073089 1398893 1405225 1443586 1491348 1629616 1926517 4111912 1 1 1 1 1 1 1 1 1 1 4321023 1 > colnames(x) [1] "Wealth" "Costs" "Orders" "Dividends" "Group" "Gender." > colnames(x)[par1] [1] "Wealth" > x[,par1] [1] 4321023 4111912 223193 1491348 1629616 1398893 1926517 983660 1443586 [10] 1073089 984885 1405225 227132 929118 1071292 638830 856956 992426 [19] 444477 857217 711969 702380 358589 297978 585715 657954 209458 [28] 786690 439798 688779 574339 741409 597793 644190 377934 640273 [37] 697458 550608 207393 301607 345783 501749 379983 387475 377305 [46] 370837 430866 469107 194493 530670 518365 491303 527021 233773 [55] 405972 652925 446211 341340 387699 493408 146494 414462 364304 [64] 355178 357760 261216 397144 374943 424898 202055 378525 310768 [73] 325738 394510 247060 368078 236761 312378 339836 347385 426280 [82] 352850 301881 377516 357312 458343 354228 308636 386212 393343 [91] 378509 452469 364839 358649 376641 429112 330546 403560 317892 [100] 307528 235133 299243 314073 368186 269661 125390 510834 321896 [109] 249898 408881 158492 292154 289513 378049 343466 332743 442882 [118] 214215 315688 375195 334280 355864 480382 353058 217193 315380 [127] 314533 318056 315380 314353 369448 315380 312846 312075 315009 [136] 318903 314887 314913 315380 325506 315380 298568 315834 329784 [145] 312878 315380 314987 325249 315877 291650 305959 315380 297765 [154] 315245 315380 315380 315236 336425 315380 315380 315380 315380 [163] 306268 302187 314882 315380 382712 341570 315380 315380 312412 [172] 315380 309596 315380 315547 313267 316176 315380 315380 359335 [181] 330068 314289 297413 314806 333210 352108 313332 291787 315380 [190] 318745 315380 315366 315380 315688 315380 409642 315380 315380 [199] 269587 315380 315380 315380 300962 325479 316155 318574 315380 [208] 343613 306948 315380 315380 330059 288985 304485 315380 315688 [217] 317736 315380 322331 296656 315380 315354 312161 315576 314922 [226] 314551 315380 312339 315380 298700 321376 315380 303230 315380 [235] 315487 315380 315793 315380 315380 315380 312887 315380 315637 [244] 324385 315380 315380 308989 315380 315380 296702 315380 307322 [253] 304376 253588 315380 309560 298466 315380 315380 315380 315380 [262] 343929 331955 315380 315380 315380 381180 315380 331420 315380 [271] 315380 315380 310201 315380 320016 320398 315380 291841 310670 [280] 315380 315380 313491 315380 331323 315380 319210 318098 315380 [289] 292754 315380 325176 365959 315380 302409 340968 315380 315380 [298] 315380 315380 313164 301164 315380 315380 344425 315394 315380 [307] 316647 309836 315380 315380 346611 315380 322031 315656 339445 [316] 314964 297141 315372 315380 315380 315380 315380 315380 315380 [325] 312502 315380 315380 315380 315380 315380 315380 315380 313729 [334] 315388 315371 296139 315380 313880 317698 295580 315380 315380 [343] 315380 308256 315380 303677 315380 315380 319369 318690 314049 [352] 325699 314210 315380 315380 322378 315380 315380 315380 315398 [361] 315380 315380 308336 316386 315380 315380 315380 315380 315553 [370] 315380 323361 336639 307424 315380 315380 295370 322340 319864 [379] 315380 315380 317291 280398 315380 317330 238125 327071 309038 [388] 314210 307930 322327 292136 263276 367655 283910 283587 243650 [397] 438493 296261 230621 304252 333505 296919 278990 276898 327007 [406] 317046 304555 298096 231861 309422 286963 269753 448243 165404 [415] 204325 407159 290476 275311 246541 253468 240897 -83265 -42143 [424] 272713 215362 42754 306275 253537 372631 -7170 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/rcomp/tmp/14qtn1292932635.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Wealth Inputs: Costs, Orders, Dividends, Group, Gender. Number of observations: 430 1) Orders <= 251; criterion = 1, statistic = 177.617 2) Costs <= 11920; criterion = 1, statistic = 128.117 3) Dividends <= 86111; criterion = 1, statistic = 97.872 4)* weights = 342 3) Dividends > 86111 5)* weights = 58 2) Costs > 11920 6)* weights = 19 1) Orders > 251 7)* weights = 11 > postscript(file="/var/www/html/rcomp/tmp/24qtn1292932635.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/rcomp/tmp/3xha71292932635.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 4321023 1587931.9 2733091.0909 2 4111912 1587931.9 2523980.0909 3 223193 1587931.9 -1364738.9091 4 1491348 1587931.9 -96583.9091 5 1629616 686454.7 943161.2632 6 1398893 1587931.9 -189038.9091 7 1926517 1587931.9 338585.0909 8 983660 686454.7 297205.2632 9 1443586 686454.7 757131.2632 10 1073089 686454.7 386634.2632 11 984885 686454.7 298430.2632 12 1405225 1587931.9 -182706.9091 13 227132 686454.7 -459322.7368 14 929118 1587931.9 -658813.9091 15 1071292 436863.3 634428.7414 16 638830 436863.3 201966.7414 17 856956 1587931.9 -730975.9091 18 992426 686454.7 305971.2632 19 444477 1587931.9 -1143454.9091 20 857217 686454.7 170762.2632 21 711969 686454.7 25514.2632 22 702380 686454.7 15925.2632 23 358589 1587931.9 -1229342.9091 24 297978 436863.3 -138885.2586 25 585715 686454.7 -100739.7368 26 657954 686454.7 -28500.7368 27 209458 311459.0 -102001.0088 28 786690 311459.0 475230.9912 29 439798 686454.7 -246656.7368 30 688779 436863.3 251915.7414 31 574339 436863.3 137475.7414 32 741409 311459.0 429949.9912 33 597793 436863.3 160929.7414 34 644190 436863.3 207326.7414 35 377934 686454.7 -308520.7368 36 640273 436863.3 203409.7414 37 697458 436863.3 260594.7414 38 550608 436863.3 113744.7414 39 207393 311459.0 -104066.0088 40 301607 436863.3 -135256.2586 41 345783 686454.7 -340671.7368 42 501749 436863.3 64885.7414 43 379983 436863.3 -56880.2586 44 387475 311459.0 76015.9912 45 377305 436863.3 -59558.2586 46 370837 436863.3 -66026.2586 47 430866 436863.3 -5997.2586 48 469107 436863.3 32243.7414 49 194493 311459.0 -116966.0088 50 530670 436863.3 93806.7414 51 518365 436863.3 81501.7414 52 491303 436863.3 54439.7414 53 527021 436863.3 90157.7414 54 233773 436863.3 -203090.2586 55 405972 311459.0 94512.9912 56 652925 311459.0 341465.9912 57 446211 436863.3 9347.7414 58 341340 436863.3 -95523.2586 59 387699 436863.3 -49164.2586 60 493408 436863.3 56544.7414 61 146494 311459.0 -164965.0088 62 414462 436863.3 -22401.2586 63 364304 311459.0 52844.9912 64 355178 311459.0 43718.9912 65 357760 686454.7 -328694.7368 66 261216 311459.0 -50243.0088 67 397144 436863.3 -39719.2586 68 374943 436863.3 -61920.2586 69 424898 436863.3 -11965.2586 70 202055 311459.0 -109404.0088 71 378525 311459.0 67065.9912 72 310768 436863.3 -126095.2586 73 325738 311459.0 14278.9912 74 394510 436863.3 -42353.2586 75 247060 311459.0 -64399.0088 76 368078 436863.3 -68785.2586 77 236761 311459.0 -74698.0088 78 312378 311459.0 918.9912 79 339836 436863.3 -97027.2586 80 347385 311459.0 35925.9912 81 426280 436863.3 -10583.2586 82 352850 436863.3 -84013.2586 83 301881 311459.0 -9578.0088 84 377516 436863.3 -59347.2586 85 357312 311459.0 45852.9912 86 458343 436863.3 21479.7414 87 354228 436863.3 -82635.2586 88 308636 436863.3 -128227.2586 89 386212 436863.3 -50651.2586 90 393343 436863.3 -43520.2586 91 378509 311459.0 67049.9912 92 452469 311459.0 141009.9912 93 364839 436863.3 -72024.2586 94 358649 311459.0 47189.9912 95 376641 311459.0 65181.9912 96 429112 436863.3 -7751.2586 97 330546 311459.0 19086.9912 98 403560 436863.3 -33303.2586 99 317892 311459.0 6432.9912 100 307528 311459.0 -3931.0088 101 235133 436863.3 -201730.2586 102 299243 436863.3 -137620.2586 103 314073 436863.3 -122790.2586 104 368186 311459.0 56726.9912 105 269661 311459.0 -41798.0088 106 125390 311459.0 -186069.0088 107 510834 436863.3 73970.7414 108 321896 436863.3 -114967.2586 109 249898 311459.0 -61561.0088 110 408881 311459.0 97421.9912 111 158492 311459.0 -152967.0088 112 292154 311459.0 -19305.0088 113 289513 311459.0 -21946.0088 114 378049 311459.0 66589.9912 115 343466 311459.0 32006.9912 116 332743 311459.0 21283.9912 117 442882 436863.3 6018.7414 118 214215 311459.0 -97244.0088 119 315688 311459.0 4228.9912 120 375195 436863.3 -61668.2586 121 334280 311459.0 22820.9912 122 355864 436863.3 -80999.2586 123 480382 436863.3 43518.7414 124 353058 311459.0 41598.9912 125 217193 311459.0 -94266.0088 126 315380 311459.0 3920.9912 127 314533 311459.0 3073.9912 128 318056 311459.0 6596.9912 129 315380 311459.0 3920.9912 130 314353 311459.0 2893.9912 131 369448 311459.0 57988.9912 132 315380 311459.0 3920.9912 133 312846 311459.0 1386.9912 134 312075 311459.0 615.9912 135 315009 311459.0 3549.9912 136 318903 311459.0 7443.9912 137 314887 311459.0 3427.9912 138 314913 311459.0 3453.9912 139 315380 311459.0 3920.9912 140 325506 311459.0 14046.9912 141 315380 311459.0 3920.9912 142 298568 311459.0 -12891.0088 143 315834 311459.0 4374.9912 144 329784 311459.0 18324.9912 145 312878 311459.0 1418.9912 146 315380 311459.0 3920.9912 147 314987 311459.0 3527.9912 148 325249 311459.0 13789.9912 149 315877 311459.0 4417.9912 150 291650 311459.0 -19809.0088 151 305959 311459.0 -5500.0088 152 315380 311459.0 3920.9912 153 297765 311459.0 -13694.0088 154 315245 311459.0 3785.9912 155 315380 311459.0 3920.9912 156 315380 311459.0 3920.9912 157 315236 311459.0 3776.9912 158 336425 311459.0 24965.9912 159 315380 311459.0 3920.9912 160 315380 311459.0 3920.9912 161 315380 311459.0 3920.9912 162 315380 311459.0 3920.9912 163 306268 311459.0 -5191.0088 164 302187 311459.0 -9272.0088 165 314882 311459.0 3422.9912 166 315380 311459.0 3920.9912 167 382712 311459.0 71252.9912 168 341570 311459.0 30110.9912 169 315380 311459.0 3920.9912 170 315380 311459.0 3920.9912 171 312412 311459.0 952.9912 172 315380 311459.0 3920.9912 173 309596 311459.0 -1863.0088 174 315380 311459.0 3920.9912 175 315547 311459.0 4087.9912 176 313267 311459.0 1807.9912 177 316176 311459.0 4716.9912 178 315380 311459.0 3920.9912 179 315380 311459.0 3920.9912 180 359335 311459.0 47875.9912 181 330068 311459.0 18608.9912 182 314289 311459.0 2829.9912 183 297413 311459.0 -14046.0088 184 314806 311459.0 3346.9912 185 333210 311459.0 21750.9912 186 352108 311459.0 40648.9912 187 313332 311459.0 1872.9912 188 291787 311459.0 -19672.0088 189 315380 311459.0 3920.9912 190 318745 311459.0 7285.9912 191 315380 311459.0 3920.9912 192 315366 311459.0 3906.9912 193 315380 311459.0 3920.9912 194 315688 311459.0 4228.9912 195 315380 311459.0 3920.9912 196 409642 436863.3 -27221.2586 197 315380 311459.0 3920.9912 198 315380 311459.0 3920.9912 199 269587 311459.0 -41872.0088 200 315380 311459.0 3920.9912 201 315380 311459.0 3920.9912 202 315380 311459.0 3920.9912 203 300962 311459.0 -10497.0088 204 325479 436863.3 -111384.2586 205 316155 311459.0 4695.9912 206 318574 311459.0 7114.9912 207 315380 311459.0 3920.9912 208 343613 311459.0 32153.9912 209 306948 311459.0 -4511.0088 210 315380 311459.0 3920.9912 211 315380 311459.0 3920.9912 212 330059 311459.0 18599.9912 213 288985 311459.0 -22474.0088 214 304485 311459.0 -6974.0088 215 315380 311459.0 3920.9912 216 315688 311459.0 4228.9912 217 317736 311459.0 6276.9912 218 315380 311459.0 3920.9912 219 322331 311459.0 10871.9912 220 296656 311459.0 -14803.0088 221 315380 311459.0 3920.9912 222 315354 311459.0 3894.9912 223 312161 311459.0 701.9912 224 315576 311459.0 4116.9912 225 314922 311459.0 3462.9912 226 314551 311459.0 3091.9912 227 315380 311459.0 3920.9912 228 312339 311459.0 879.9912 229 315380 311459.0 3920.9912 230 298700 311459.0 -12759.0088 231 321376 311459.0 9916.9912 232 315380 311459.0 3920.9912 233 303230 311459.0 -8229.0088 234 315380 311459.0 3920.9912 235 315487 311459.0 4027.9912 236 315380 311459.0 3920.9912 237 315793 311459.0 4333.9912 238 315380 311459.0 3920.9912 239 315380 311459.0 3920.9912 240 315380 311459.0 3920.9912 241 312887 311459.0 1427.9912 242 315380 311459.0 3920.9912 243 315637 311459.0 4177.9912 244 324385 311459.0 12925.9912 245 315380 311459.0 3920.9912 246 315380 311459.0 3920.9912 247 308989 311459.0 -2470.0088 248 315380 311459.0 3920.9912 249 315380 311459.0 3920.9912 250 296702 311459.0 -14757.0088 251 315380 311459.0 3920.9912 252 307322 311459.0 -4137.0088 253 304376 311459.0 -7083.0088 254 253588 311459.0 -57871.0088 255 315380 311459.0 3920.9912 256 309560 311459.0 -1899.0088 257 298466 311459.0 -12993.0088 258 315380 311459.0 3920.9912 259 315380 311459.0 3920.9912 260 315380 311459.0 3920.9912 261 315380 311459.0 3920.9912 262 343929 311459.0 32469.9912 263 331955 311459.0 20495.9912 264 315380 311459.0 3920.9912 265 315380 311459.0 3920.9912 266 315380 311459.0 3920.9912 267 381180 311459.0 69720.9912 268 315380 311459.0 3920.9912 269 331420 311459.0 19960.9912 270 315380 311459.0 3920.9912 271 315380 311459.0 3920.9912 272 315380 311459.0 3920.9912 273 310201 311459.0 -1258.0088 274 315380 311459.0 3920.9912 275 320016 311459.0 8556.9912 276 320398 311459.0 8938.9912 277 315380 311459.0 3920.9912 278 291841 311459.0 -19618.0088 279 310670 311459.0 -789.0088 280 315380 311459.0 3920.9912 281 315380 311459.0 3920.9912 282 313491 311459.0 2031.9912 283 315380 311459.0 3920.9912 284 331323 311459.0 19863.9912 285 315380 311459.0 3920.9912 286 319210 311459.0 7750.9912 287 318098 311459.0 6638.9912 288 315380 311459.0 3920.9912 289 292754 311459.0 -18705.0088 290 315380 311459.0 3920.9912 291 325176 311459.0 13716.9912 292 365959 311459.0 54499.9912 293 315380 311459.0 3920.9912 294 302409 311459.0 -9050.0088 295 340968 311459.0 29508.9912 296 315380 311459.0 3920.9912 297 315380 311459.0 3920.9912 298 315380 311459.0 3920.9912 299 315380 311459.0 3920.9912 300 313164 311459.0 1704.9912 301 301164 311459.0 -10295.0088 302 315380 311459.0 3920.9912 303 315380 311459.0 3920.9912 304 344425 311459.0 32965.9912 305 315394 311459.0 3934.9912 306 315380 311459.0 3920.9912 307 316647 311459.0 5187.9912 308 309836 311459.0 -1623.0088 309 315380 311459.0 3920.9912 310 315380 311459.0 3920.9912 311 346611 436863.3 -90252.2586 312 315380 311459.0 3920.9912 313 322031 311459.0 10571.9912 314 315656 311459.0 4196.9912 315 339445 311459.0 27985.9912 316 314964 311459.0 3504.9912 317 297141 311459.0 -14318.0088 318 315372 311459.0 3912.9912 319 315380 311459.0 3920.9912 320 315380 311459.0 3920.9912 321 315380 311459.0 3920.9912 322 315380 311459.0 3920.9912 323 315380 311459.0 3920.9912 324 315380 311459.0 3920.9912 325 312502 311459.0 1042.9912 326 315380 311459.0 3920.9912 327 315380 311459.0 3920.9912 328 315380 311459.0 3920.9912 329 315380 311459.0 3920.9912 330 315380 311459.0 3920.9912 331 315380 311459.0 3920.9912 332 315380 311459.0 3920.9912 333 313729 311459.0 2269.9912 334 315388 311459.0 3928.9912 335 315371 311459.0 3911.9912 336 296139 311459.0 -15320.0088 337 315380 311459.0 3920.9912 338 313880 311459.0 2420.9912 339 317698 311459.0 6238.9912 340 295580 311459.0 -15879.0088 341 315380 311459.0 3920.9912 342 315380 311459.0 3920.9912 343 315380 311459.0 3920.9912 344 308256 311459.0 -3203.0088 345 315380 311459.0 3920.9912 346 303677 311459.0 -7782.0088 347 315380 311459.0 3920.9912 348 315380 311459.0 3920.9912 349 319369 311459.0 7909.9912 350 318690 311459.0 7230.9912 351 314049 311459.0 2589.9912 352 325699 311459.0 14239.9912 353 314210 311459.0 2750.9912 354 315380 311459.0 3920.9912 355 315380 311459.0 3920.9912 356 322378 311459.0 10918.9912 357 315380 311459.0 3920.9912 358 315380 311459.0 3920.9912 359 315380 311459.0 3920.9912 360 315398 311459.0 3938.9912 361 315380 311459.0 3920.9912 362 315380 311459.0 3920.9912 363 308336 311459.0 -3123.0088 364 316386 311459.0 4926.9912 365 315380 311459.0 3920.9912 366 315380 311459.0 3920.9912 367 315380 311459.0 3920.9912 368 315380 311459.0 3920.9912 369 315553 311459.0 4093.9912 370 315380 311459.0 3920.9912 371 323361 311459.0 11901.9912 372 336639 311459.0 25179.9912 373 307424 311459.0 -4035.0088 374 315380 311459.0 3920.9912 375 315380 311459.0 3920.9912 376 295370 311459.0 -16089.0088 377 322340 311459.0 10880.9912 378 319864 311459.0 8404.9912 379 315380 311459.0 3920.9912 380 315380 311459.0 3920.9912 381 317291 311459.0 5831.9912 382 280398 311459.0 -31061.0088 383 315380 311459.0 3920.9912 384 317330 311459.0 5870.9912 385 238125 311459.0 -73334.0088 386 327071 311459.0 15611.9912 387 309038 311459.0 -2421.0088 388 314210 311459.0 2750.9912 389 307930 311459.0 -3529.0088 390 322327 311459.0 10867.9912 391 292136 311459.0 -19323.0088 392 263276 311459.0 -48183.0088 393 367655 311459.0 56195.9912 394 283910 311459.0 -27549.0088 395 283587 311459.0 -27872.0088 396 243650 311459.0 -67809.0088 397 438493 436863.3 1629.7414 398 296261 311459.0 -15198.0088 399 230621 311459.0 -80838.0088 400 304252 311459.0 -7207.0088 401 333505 311459.0 22045.9912 402 296919 311459.0 -14540.0088 403 278990 311459.0 -32469.0088 404 276898 311459.0 -34561.0088 405 327007 311459.0 15547.9912 406 317046 311459.0 5586.9912 407 304555 311459.0 -6904.0088 408 298096 311459.0 -13363.0088 409 231861 311459.0 -79598.0088 410 309422 311459.0 -2037.0088 411 286963 311459.0 -24496.0088 412 269753 311459.0 -41706.0088 413 448243 311459.0 136783.9912 414 165404 311459.0 -146055.0088 415 204325 311459.0 -107134.0088 416 407159 311459.0 95699.9912 417 290476 311459.0 -20983.0088 418 275311 311459.0 -36148.0088 419 246541 311459.0 -64918.0088 420 253468 311459.0 -57991.0088 421 240897 311459.0 -70562.0088 422 -83265 311459.0 -394724.0088 423 -42143 311459.0 -353602.0088 424 272713 311459.0 -38746.0088 425 215362 311459.0 -96097.0088 426 42754 311459.0 -268705.0088 427 306275 686454.7 -380179.7368 428 253537 311459.0 -57922.0088 429 372631 686454.7 -313823.7368 430 -7170 686454.7 -693624.7368 > 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/rcomp/tmp/4p9ra1292932635.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/rcomp/tmp/5t9qy1292932635.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/rcomp/tmp/6wao41292932635.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/rcomp/tmp/7u4ve1292932635.tab") + } > > try(system("convert tmp/24qtn1292932635.ps tmp/24qtn1292932635.png",intern=TRUE)) character(0) > try(system("convert tmp/3xha71292932635.ps tmp/3xha71292932635.png",intern=TRUE)) character(0) > try(system("convert tmp/4p9ra1292932635.ps tmp/4p9ra1292932635.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.761 0.792 19.059