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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+ ,1310296 + ,1179875840 + ,6659840 + ,-7170 + ,1247037 + ,493103732 + ,1882596) + ,dim=c(4 + ,431) + ,dimnames=list(c('Wealth' + ,'C_O' + ,'C_D' + ,'O_D') + ,1:431)) > y <- array(NA,dim=c(4,431),dimnames=list(c('Wealth','C_O','C_D','O_D'),1:431)) > 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] "Wealth" > x[,par1] [1] 6282154 4321023 4111912 223193 1491348 1629616 1398893 1926517 983660 [10] 1443586 1073089 984885 1405225 227132 929118 1071292 638830 856956 [19] 992426 444477 857217 711969 702380 358589 297978 585715 657954 [28] 209458 786690 439798 688779 574339 741409 597793 644190 377934 [37] 640273 697458 550608 207393 301607 345783 501749 379983 387475 [46] 377305 370837 430866 469107 194493 530670 518365 491303 527021 [55] 233773 405972 652925 446211 341340 387699 493408 146494 414462 [64] 364304 355178 357760 261216 397144 374943 424898 202055 378525 [73] 310768 325738 394510 247060 368078 236761 312378 339836 347385 [82] 426280 352850 301881 377516 357312 458343 354228 308636 386212 [91] 393343 378509 452469 364839 358649 376641 429112 330546 403560 [100] 317892 307528 235133 299243 314073 368186 269661 125390 510834 [109] 321896 249898 408881 158492 292154 289513 378049 343466 332743 [118] 442882 214215 315688 375195 334280 355864 480382 353058 217193 [127] 315380 314533 318056 315380 314353 369448 315380 312846 312075 [136] 315009 318903 314887 314913 315380 325506 315380 298568 315834 [145] 329784 312878 315380 314987 325249 315877 291650 305959 315380 [154] 297765 315245 315380 315380 315236 336425 315380 315380 315380 [163] 315380 306268 302187 314882 315380 382712 341570 315380 315380 [172] 312412 315380 309596 315380 315547 313267 316176 315380 315380 [181] 359335 330068 314289 297413 314806 333210 352108 313332 291787 [190] 315380 318745 315380 315366 315380 315688 315380 409642 315380 [199] 315380 269587 315380 315380 315380 300962 325479 316155 318574 [208] 315380 343613 306948 315380 315380 330059 288985 304485 315380 [217] 315688 317736 315380 322331 296656 315380 315354 312161 315576 [226] 314922 314551 315380 312339 315380 298700 321376 315380 303230 [235] 315380 315487 315380 315793 315380 315380 315380 312887 315380 [244] 315637 324385 315380 315380 308989 315380 315380 296702 315380 [253] 307322 304376 253588 315380 309560 298466 315380 315380 315380 [262] 315380 343929 331955 315380 315380 315380 381180 315380 331420 [271] 315380 315380 315380 310201 315380 320016 320398 315380 291841 [280] 310670 315380 315380 313491 315380 331323 315380 319210 318098 [289] 315380 292754 315380 325176 365959 315380 302409 340968 315380 [298] 315380 315380 315380 313164 301164 315380 315380 344425 315394 [307] 315380 316647 309836 315380 315380 346611 315380 322031 315656 [316] 339445 314964 297141 315372 315380 315380 315380 315380 315380 [325] 315380 312502 315380 315380 315380 315380 315380 315380 315380 [334] 313729 315388 315371 296139 315380 313880 317698 295580 315380 [343] 315380 315380 308256 315380 303677 315380 315380 319369 318690 [352] 314049 325699 314210 315380 315380 322378 315380 315380 315380 [361] 315398 315380 315380 308336 316386 315380 315380 315380 315380 [370] 315553 315380 323361 336639 307424 315380 315380 295370 322340 [379] 319864 315380 315380 317291 280398 315380 317330 238125 327071 [388] 309038 314210 307930 322327 292136 263276 367655 283910 283587 [397] 243650 438493 296261 230621 304252 333505 296919 278990 276898 [406] 327007 317046 304555 298096 231861 309422 286963 269753 448243 [415] 165404 204325 407159 290476 275311 246541 253468 240897 -83265 [424] -42143 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 6282154 1 1 > colnames(x) [1] "Wealth" "C_O" "C_D" "O_D" > colnames(x)[par1] [1] "Wealth" > x[,par1] [1] 6282154 4321023 4111912 223193 1491348 1629616 1398893 1926517 983660 [10] 1443586 1073089 984885 1405225 227132 929118 1071292 638830 856956 [19] 992426 444477 857217 711969 702380 358589 297978 585715 657954 [28] 209458 786690 439798 688779 574339 741409 597793 644190 377934 [37] 640273 697458 550608 207393 301607 345783 501749 379983 387475 [46] 377305 370837 430866 469107 194493 530670 518365 491303 527021 [55] 233773 405972 652925 446211 341340 387699 493408 146494 414462 [64] 364304 355178 357760 261216 397144 374943 424898 202055 378525 [73] 310768 325738 394510 247060 368078 236761 312378 339836 347385 [82] 426280 352850 301881 377516 357312 458343 354228 308636 386212 [91] 393343 378509 452469 364839 358649 376641 429112 330546 403560 [100] 317892 307528 235133 299243 314073 368186 269661 125390 510834 [109] 321896 249898 408881 158492 292154 289513 378049 343466 332743 [118] 442882 214215 315688 375195 334280 355864 480382 353058 217193 [127] 315380 314533 318056 315380 314353 369448 315380 312846 312075 [136] 315009 318903 314887 314913 315380 325506 315380 298568 315834 [145] 329784 312878 315380 314987 325249 315877 291650 305959 315380 [154] 297765 315245 315380 315380 315236 336425 315380 315380 315380 [163] 315380 306268 302187 314882 315380 382712 341570 315380 315380 [172] 312412 315380 309596 315380 315547 313267 316176 315380 315380 [181] 359335 330068 314289 297413 314806 333210 352108 313332 291787 [190] 315380 318745 315380 315366 315380 315688 315380 409642 315380 [199] 315380 269587 315380 315380 315380 300962 325479 316155 318574 [208] 315380 343613 306948 315380 315380 330059 288985 304485 315380 [217] 315688 317736 315380 322331 296656 315380 315354 312161 315576 [226] 314922 314551 315380 312339 315380 298700 321376 315380 303230 [235] 315380 315487 315380 315793 315380 315380 315380 312887 315380 [244] 315637 324385 315380 315380 308989 315380 315380 296702 315380 [253] 307322 304376 253588 315380 309560 298466 315380 315380 315380 [262] 315380 343929 331955 315380 315380 315380 381180 315380 331420 [271] 315380 315380 315380 310201 315380 320016 320398 315380 291841 [280] 310670 315380 315380 313491 315380 331323 315380 319210 318098 [289] 315380 292754 315380 325176 365959 315380 302409 340968 315380 [298] 315380 315380 315380 313164 301164 315380 315380 344425 315394 [307] 315380 316647 309836 315380 315380 346611 315380 322031 315656 [316] 339445 314964 297141 315372 315380 315380 315380 315380 315380 [325] 315380 312502 315380 315380 315380 315380 315380 315380 315380 [334] 313729 315388 315371 296139 315380 313880 317698 295580 315380 [343] 315380 315380 308256 315380 303677 315380 315380 319369 318690 [352] 314049 325699 314210 315380 315380 322378 315380 315380 315380 [361] 315398 315380 315380 308336 316386 315380 315380 315380 315380 [370] 315553 315380 323361 336639 307424 315380 315380 295370 322340 [379] 319864 315380 315380 317291 280398 315380 317330 238125 327071 [388] 309038 314210 307930 322327 292136 263276 367655 283910 283587 [397] 243650 438493 296261 230621 304252 333505 296919 278990 276898 [406] 327007 317046 304555 298096 231861 309422 286963 269753 448243 [415] 165404 204325 407159 290476 275311 246541 253468 240897 -83265 [424] -42143 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/1b3r31292960354.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Wealth Inputs: C_O, C_D, O_D Number of observations: 431 1) C_D <= 2365728850; criterion = 1, statistic = 310.911 2) C_D <= 493103732; criterion = 1, statistic = 100.636 3) O_D <= 2075269; criterion = 1, statistic = 15.874 4)* weights = 330 3) O_D > 2075269 5)* weights = 49 2) C_D > 493103732 6)* weights = 42 1) C_D > 2365728850 7)* weights = 10 > postscript(file="/var/www/html/rcomp/tmp/2b3r31292960354.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/3mv861292960354.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 6282154 2339778.3 3.942376e+06 2 4321023 2339778.3 1.981245e+06 3 4111912 2339778.3 1.772134e+06 4 223193 314478.5 -9.128546e+04 5 1491348 2339778.3 -8.484303e+05 6 1629616 2339778.3 -7.101623e+05 7 1398893 2339778.3 -9.408853e+05 8 1926517 2339778.3 -4.132613e+05 9 983660 553455.8 4.302042e+05 10 1443586 553455.8 8.901302e+05 11 1073089 2339778.3 -1.266689e+06 12 984885 553455.8 4.314292e+05 13 1405225 314478.5 1.090747e+06 14 227132 553455.8 -3.263238e+05 15 929118 553455.8 3.756622e+05 16 1071292 553455.8 5.178362e+05 17 638830 553455.8 8.537419e+04 18 856956 2339778.3 -1.482822e+06 19 992426 553455.8 4.389702e+05 20 444477 553455.8 -1.089788e+05 21 857217 553455.8 3.037612e+05 22 711969 553455.8 1.585132e+05 23 702380 553455.8 1.489242e+05 24 358589 553455.8 -1.948668e+05 25 297978 553455.8 -2.554778e+05 26 585715 553455.8 3.225919e+04 27 657954 553455.8 1.044982e+05 28 209458 372005.7 -1.625477e+05 29 786690 314478.5 4.722115e+05 30 439798 553455.8 -1.136578e+05 31 688779 372005.7 3.167733e+05 32 574339 553455.8 2.088319e+04 33 741409 553455.8 1.879532e+05 34 597793 372005.7 2.257873e+05 35 644190 553455.8 9.073419e+04 36 377934 553455.8 -1.755218e+05 37 640273 553455.8 8.681719e+04 38 697458 553455.8 1.440022e+05 39 550608 372005.7 1.786023e+05 40 207393 372005.7 -1.646127e+05 41 301607 553455.8 -2.518488e+05 42 345783 553455.8 -2.076728e+05 43 501749 372005.7 1.297433e+05 44 379983 553455.8 -1.734728e+05 45 387475 372005.7 1.546935e+04 46 377305 372005.7 5.299347e+03 47 370837 553455.8 -1.826188e+05 48 430866 553455.8 -1.225898e+05 49 469107 553455.8 -8.434881e+04 50 194493 372005.7 -1.775127e+05 51 530670 372005.7 1.586643e+05 52 518365 553455.8 -3.509081e+04 53 491303 553455.8 -6.215281e+04 54 527021 372005.7 1.550153e+05 55 233773 553455.8 -3.196828e+05 56 405972 372005.7 3.396635e+04 57 652925 314478.5 3.384465e+05 58 446211 372005.7 7.420535e+04 59 341340 372005.7 -3.066565e+04 60 387699 553455.8 -1.657568e+05 61 493408 553455.8 -6.004781e+04 62 146494 314478.5 -1.679845e+05 63 414462 372005.7 4.245635e+04 64 364304 553455.8 -1.891518e+05 65 355178 372005.7 -1.682765e+04 66 357760 553455.8 -1.956958e+05 67 261216 314478.5 -5.326246e+04 68 397144 372005.7 2.513835e+04 69 374943 372005.7 2.937347e+03 70 424898 372005.7 5.289235e+04 71 202055 314478.5 -1.124235e+05 72 378525 314478.5 6.404654e+04 73 310768 372005.7 -6.123765e+04 74 325738 314478.5 1.125954e+04 75 394510 372005.7 2.250435e+04 76 247060 314478.5 -6.741846e+04 77 368078 372005.7 -3.927653e+03 78 236761 314478.5 -7.771746e+04 79 312378 372005.7 -5.962765e+04 80 339836 314478.5 2.535754e+04 81 347385 314478.5 3.290654e+04 82 426280 372005.7 5.427435e+04 83 352850 372005.7 -1.915565e+04 84 301881 314478.5 -1.259746e+04 85 377516 314478.5 6.303754e+04 86 357312 372005.7 -1.469365e+04 87 458343 372005.7 8.633735e+04 88 354228 314478.5 3.974954e+04 89 308636 372005.7 -6.336965e+04 90 386212 314478.5 7.173354e+04 91 393343 372005.7 2.133735e+04 92 378509 372005.7 6.503347e+03 93 452469 372005.7 8.046335e+04 94 364839 553455.8 -1.886168e+05 95 358649 314478.5 4.417054e+04 96 376641 314478.5 6.216254e+04 97 429112 314478.5 1.146335e+05 98 330546 314478.5 1.606754e+04 99 403560 372005.7 3.155435e+04 100 317892 314478.5 3.413542e+03 101 307528 372005.7 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304252 314478.5 -1.022646e+04 402 333505 314478.5 1.902654e+04 403 296919 314478.5 -1.755946e+04 404 278990 314478.5 -3.548846e+04 405 276898 314478.5 -3.758046e+04 406 327007 314478.5 1.252854e+04 407 317046 314478.5 2.567542e+03 408 304555 314478.5 -9.923458e+03 409 298096 314478.5 -1.638246e+04 410 231861 314478.5 -8.261746e+04 411 309422 372005.7 -6.258365e+04 412 286963 314478.5 -2.751546e+04 413 269753 314478.5 -4.472546e+04 414 448243 372005.7 7.623735e+04 415 165404 314478.5 -1.490745e+05 416 204325 314478.5 -1.101535e+05 417 407159 314478.5 9.268054e+04 418 290476 314478.5 -2.400246e+04 419 275311 314478.5 -3.916746e+04 420 246541 314478.5 -6.793746e+04 421 253468 314478.5 -6.101046e+04 422 240897 372005.7 -1.311087e+05 423 -83265 314478.5 -3.977435e+05 424 -42143 314478.5 -3.566215e+05 425 272713 314478.5 -4.176546e+04 426 215362 372005.7 -1.566437e+05 427 42754 314478.5 -2.717245e+05 428 306275 2339778.3 -2.033503e+06 429 253537 553455.8 -2.999188e+05 430 372631 553455.8 -1.808248e+05 431 -7170 314478.5 -3.216485e+05 > 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/4fm8r1292960354.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/50m6f1292960354.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/63nm21292960354.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/7wemn1292960354.tab") + } > > try(system("convert tmp/2b3r31292960354.ps tmp/2b3r31292960354.png",intern=TRUE)) character(0) > try(system("convert tmp/3mv861292960354.ps tmp/3mv861292960354.png",intern=TRUE)) character(0) > try(system("convert tmp/4fm8r1292960354.ps tmp/4fm8r1292960354.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.061 0.841 41.663