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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+ ,1164 + ,15 + ,57320 + ,269753 + ,3310 + ,32 + ,75230 + ,448243 + ,1920 + ,11 + ,79420 + ,165404 + ,965 + ,2 + ,73490 + ,204325 + ,3256 + ,23 + ,35250 + ,407159 + ,1135 + ,20 + ,62285 + ,290476 + ,1270 + ,24 + ,69206 + ,275311 + ,661 + ,1 + ,65920 + ,246541 + ,1013 + ,1 + ,69770 + ,253468 + ,2844 + ,74 + ,72683 + ,240897 + ,11528 + ,68 + ,-14545 + ,-83265 + ,6526 + ,20 + ,55830 + ,-42143 + ,2264 + ,20 + ,55174 + ,272713 + ,5109 + ,82 + ,67038 + ,215362 + ,3999 + ,21 + ,51252 + ,42754 + ,35624 + ,244 + ,157278 + ,306275 + ,9252 + ,32 + ,79510 + ,253537 + ,15236 + ,86 + ,77440 + ,372631 + ,18073 + ,69 + ,27284 + ,-7170) + ,dim=c(4 + ,431) + ,dimnames=list(c('Costs' + ,'Orders' + ,'Dividends' + ,'Wealth') + ,1:431)) > y <- array(NA,dim=c(4,431),dimnames=list(c('Costs','Orders','Dividends','Wealth'),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 = '4' > #'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] "Costs" "Orders" "Dividends" "Wealth" > 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/1k0ng1292939468.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: Wealth Inputs: Costs, Orders, Dividends Number of observations: 431 1) Costs <= 27664; criterion = 1, statistic = 271.943 2) Costs <= 11920; criterion = 1, statistic = 130.808 3) Dividends <= 86111; criterion = 1, statistic = 97.872 4)* weights = 342 3) Dividends > 86111 5)* weights = 58 2) Costs > 11920 6)* weights = 19 1) Costs > 27664 7)* weights = 12 > postscript(file="/var/www/html/rcomp/tmp/2da4j1292939468.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/3da4j1292939468.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 1991179.1 4290974.9167 2 4321023 1991179.1 2329843.9167 3 4111912 1991179.1 2120732.9167 4 223193 1991179.1 -1767986.0833 5 1491348 1991179.1 -499831.0833 6 1629616 1991179.1 -361563.0833 7 1398893 1991179.1 -592286.0833 8 1926517 1991179.1 -64662.0833 9 983660 1991179.1 -1007519.0833 10 1443586 678836.6 764749.3684 11 1073089 678836.6 394252.3684 12 984885 678836.6 306048.3684 13 1405225 678836.6 726388.3684 14 227132 1991179.1 -1764047.0833 15 929118 678836.6 250281.3684 16 1071292 436863.3 634428.7414 17 638830 436863.3 201966.7414 18 856956 678836.6 178119.3684 19 992426 1991179.1 -998753.0833 20 444477 678836.6 -234359.6316 21 857217 678836.6 178380.3684 22 711969 678836.6 33132.3684 23 702380 678836.6 23543.3684 24 358589 678836.6 -320247.6316 25 297978 436863.3 -138885.2586 26 585715 678836.6 -93121.6316 27 657954 678836.6 -20882.6316 28 209458 311459.0 -102001.0088 29 786690 311459.0 475230.9912 30 439798 678836.6 -239038.6316 31 688779 436863.3 251915.7414 32 574339 436863.3 137475.7414 33 741409 311459.0 429949.9912 34 597793 436863.3 160929.7414 35 644190 436863.3 207326.7414 36 377934 678836.6 -300902.6316 37 640273 436863.3 203409.7414 38 697458 436863.3 260594.7414 39 550608 436863.3 113744.7414 40 207393 311459.0 -104066.0088 41 301607 436863.3 -135256.2586 42 345783 678836.6 -333053.6316 43 501749 436863.3 64885.7414 44 379983 436863.3 -56880.2586 45 387475 311459.0 76015.9912 46 377305 436863.3 -59558.2586 47 370837 436863.3 -66026.2586 48 430866 436863.3 -5997.2586 49 469107 436863.3 32243.7414 50 194493 311459.0 -116966.0088 51 530670 436863.3 93806.7414 52 518365 436863.3 81501.7414 53 491303 436863.3 54439.7414 54 527021 436863.3 90157.7414 55 233773 436863.3 -203090.2586 56 405972 311459.0 94512.9912 57 652925 311459.0 341465.9912 58 446211 436863.3 9347.7414 59 341340 436863.3 -95523.2586 60 387699 436863.3 -49164.2586 61 493408 436863.3 56544.7414 62 146494 311459.0 -164965.0088 63 414462 436863.3 -22401.2586 64 364304 311459.0 52844.9912 65 355178 311459.0 43718.9912 66 357760 678836.6 -321076.6316 67 261216 311459.0 -50243.0088 68 397144 436863.3 -39719.2586 69 374943 436863.3 -61920.2586 70 424898 436863.3 -11965.2586 71 202055 311459.0 -109404.0088 72 378525 311459.0 67065.9912 73 310768 436863.3 -126095.2586 74 325738 311459.0 14278.9912 75 394510 436863.3 -42353.2586 76 247060 311459.0 -64399.0088 77 368078 436863.3 -68785.2586 78 236761 311459.0 -74698.0088 79 312378 311459.0 918.9912 80 339836 436863.3 -97027.2586 81 347385 311459.0 35925.9912 82 426280 436863.3 -10583.2586 83 352850 436863.3 -84013.2586 84 301881 311459.0 -9578.0088 85 377516 436863.3 -59347.2586 86 357312 311459.0 45852.9912 87 458343 436863.3 21479.7414 88 354228 436863.3 -82635.2586 89 308636 436863.3 -128227.2586 90 386212 436863.3 -50651.2586 91 393343 436863.3 -43520.2586 92 378509 311459.0 67049.9912 93 452469 311459.0 141009.9912 94 364839 436863.3 -72024.2586 95 358649 311459.0 47189.9912 96 376641 311459.0 65181.9912 97 429112 436863.3 -7751.2586 98 330546 311459.0 19086.9912 99 403560 436863.3 -33303.2586 100 317892 311459.0 6432.9912 101 307528 311459.0 -3931.0088 102 235133 436863.3 -201730.2586 103 299243 436863.3 -137620.2586 104 314073 436863.3 -122790.2586 105 368186 311459.0 56726.9912 106 269661 311459.0 -41798.0088 107 125390 311459.0 -186069.0088 108 510834 436863.3 73970.7414 109 321896 436863.3 -114967.2586 110 249898 311459.0 -61561.0088 111 408881 311459.0 97421.9912 112 158492 311459.0 -152967.0088 113 292154 311459.0 -19305.0088 114 289513 311459.0 -21946.0088 115 378049 311459.0 66589.9912 116 343466 311459.0 32006.9912 117 332743 311459.0 21283.9912 118 442882 436863.3 6018.7414 119 214215 311459.0 -97244.0088 120 315688 311459.0 4228.9912 121 375195 436863.3 -61668.2586 122 334280 311459.0 22820.9912 123 355864 436863.3 -80999.2586 124 480382 436863.3 43518.7414 125 353058 311459.0 41598.9912 126 217193 311459.0 -94266.0088 127 315380 311459.0 3920.9912 128 314533 311459.0 3073.9912 129 318056 311459.0 6596.9912 130 315380 311459.0 3920.9912 131 314353 311459.0 2893.9912 132 369448 311459.0 57988.9912 133 315380 311459.0 3920.9912 134 312846 311459.0 1386.9912 135 312075 311459.0 615.9912 136 315009 311459.0 3549.9912 137 318903 311459.0 7443.9912 138 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301164 311459.0 -10295.0088 303 315380 311459.0 3920.9912 304 315380 311459.0 3920.9912 305 344425 311459.0 32965.9912 306 315394 311459.0 3934.9912 307 315380 311459.0 3920.9912 308 316647 311459.0 5187.9912 309 309836 311459.0 -1623.0088 310 315380 311459.0 3920.9912 311 315380 311459.0 3920.9912 312 346611 436863.3 -90252.2586 313 315380 311459.0 3920.9912 314 322031 311459.0 10571.9912 315 315656 311459.0 4196.9912 316 339445 311459.0 27985.9912 317 314964 311459.0 3504.9912 318 297141 311459.0 -14318.0088 319 315372 311459.0 3912.9912 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 315380 311459.0 3920.9912 326 312502 311459.0 1042.9912 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 315380 311459.0 3920.9912 334 313729 311459.0 2269.9912 335 315388 311459.0 3928.9912 336 315371 311459.0 3911.9912 337 296139 311459.0 -15320.0088 338 315380 311459.0 3920.9912 339 313880 311459.0 2420.9912 340 317698 311459.0 6238.9912 341 295580 311459.0 -15879.0088 342 315380 311459.0 3920.9912 343 315380 311459.0 3920.9912 344 315380 311459.0 3920.9912 345 308256 311459.0 -3203.0088 346 315380 311459.0 3920.9912 347 303677 311459.0 -7782.0088 348 315380 311459.0 3920.9912 349 315380 311459.0 3920.9912 350 319369 311459.0 7909.9912 351 318690 311459.0 7230.9912 352 314049 311459.0 2589.9912 353 325699 311459.0 14239.9912 354 314210 311459.0 2750.9912 355 315380 311459.0 3920.9912 356 315380 311459.0 3920.9912 357 322378 311459.0 10918.9912 358 315380 311459.0 3920.9912 359 315380 311459.0 3920.9912 360 315380 311459.0 3920.9912 361 315398 311459.0 3938.9912 362 315380 311459.0 3920.9912 363 315380 311459.0 3920.9912 364 308336 311459.0 -3123.0088 365 316386 311459.0 4926.9912 366 315380 311459.0 3920.9912 367 315380 311459.0 3920.9912 368 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-686006.6316 > 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/451l41292939468.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/5r12s1292939468.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/6uk1g1292939468.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/7xlh41292939468.tab") + } > > try(system("convert tmp/2da4j1292939468.ps tmp/2da4j1292939468.png",intern=TRUE)) character(0) > try(system("convert tmp/3da4j1292939468.ps tmp/3da4j1292939468.png",intern=TRUE)) character(0) > try(system("convert tmp/451l41292939468.ps tmp/451l41292939468.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.965 0.759 25.876