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Type 'q()' to quit R. > x <- array(list(0.04374 + ,0.426 + ,0.02182 + ,0.0313 + ,0.02971 + ,0.06545 + ,0.06134 + ,0.626 + ,0.03134 + ,0.04518 + ,0.04368 + ,0.09403 + ,0.05233 + ,0.482 + ,0.02757 + ,0.03858 + ,0.0359 + ,0.0827 + ,0.05492 + ,0.517 + ,0.02924 + ,0.04005 + ,0.03772 + ,0.08771 + ,0.06425 + ,0.584 + ,0.0349 + ,0.04825 + ,0.04465 + ,0.1047 + ,0.04701 + ,0.456 + ,0.02328 + ,0.03526 + ,0.03243 + ,0.06985 + ,0.01608 + ,0.14 + ,0.00779 + ,0.00937 + ,0.01351 + ,0.02337 + ,0.01567 + ,0.134 + ,0.00829 + ,0.00946 + ,0.01256 + ,0.02487 + ,0.02093 + ,0.191 + ,0.01073 + ,0.01277 + ,0.01717 + ,0.03218 + ,0.02838 + ,0.255 + ,0.01441 + ,0.01725 + ,0.02444 + ,0.04324 + ,0.02143 + ,0.197 + ,0.01079 + ,0.01342 + ,0.01892 + ,0.03237 + ,0.02752 + ,0.249 + ,0.01424 + ,0.01641 + ,0.02214 + ,0.04272 + ,0.01259 + ,0.112 + ,0.00656 + ,0.00717 + ,0.0114 + ,0.01968 + ,0.01642 + ,0.154 + ,0.00728 + ,0.00932 + ,0.01797 + ,0.02184 + ,0.01828 + ,0.158 + ,0.01064 + ,0.00972 + ,0.01246 + ,0.03191 + ,0.01503 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root > #To cite this work: Wessa P., 2012, Recursive Partitioning (Regression Trees) (v1.0.3) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, 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) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object is masked from 'package:survival': untangle.specials The following objects 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] "MDVP.Shimmer" > x[,par1] [1] 0.04374 0.06134 0.05233 0.05492 0.06425 0.04701 0.01608 0.01567 0.02093 [10] 0.02838 0.02143 0.02752 0.01259 0.01642 0.01828 0.01503 0.02047 0.03327 [19] 0.05517 0.03995 0.03810 0.04137 0.04351 0.04192 0.01659 0.03767 0.01966 [28] 0.01919 0.01718 0.01791 0.01098 0.01015 0.01263 0.00954 0.00958 0.01194 [37] 0.02126 0.01851 0.01444 0.01663 0.01495 0.01463 0.01752 0.01760 0.01419 [46] 0.01494 0.01608 0.01152 0.01613 0.01681 0.02184 0.02033 0.02297 0.02498 [55] 0.02719 0.03209 0.03715 0.02293 0.02645 0.03225 0.01861 0.01906 0.01643 [64] 0.01644 0.01457 0.01745 0.03198 0.03111 0.05384 0.05428 0.03485 0.04978 [73] 0.01706 0.02448 0.02442 0.02215 0.03999 0.02199 0.03202 0.03121 0.04024 [82] 0.03156 0.02427 0.02223 0.04795 0.03852 0.03759 0.06511 0.06727 0.04313 [91] 0.06640 0.07959 0.04190 0.05925 0.03716 0.03272 0.03381 0.03886 0.04689 [100] 0.06734 0.09178 0.06170 0.09419 0.01131 0.01030 0.01346 0.01064 0.01450 [109] 0.01024 0.03044 0.02286 0.01761 0.02378 0.01680 0.02105 0.01843 0.01458 [118] 0.01725 0.01279 0.01299 0.02008 0.01169 0.04479 0.02503 0.02343 0.02362 [127] 0.02791 0.02857 0.01033 0.01022 0.01412 0.01516 0.01201 0.01043 0.04932 [136] 0.04128 0.04879 0.05279 0.05643 0.03026 0.03273 0.06725 0.03527 0.01997 [145] 0.02662 0.02536 0.08143 0.06050 0.07118 0.07170 0.05830 0.11908 0.08684 [154] 0.02534 0.02682 0.03087 0.02293 0.04912 0.02852 0.03235 0.04009 0.03273 [163] 0.03658 0.01756 0.02814 0.02448 0.01242 0.02030 0.02177 0.02018 0.01897 [172] 0.01358 0.01484 0.01472 0.01657 0.01503 0.01725 0.01469 0.01574 0.01450 [181] 0.02551 0.01831 0.02145 0.01909 0.01795 0.01564 0.01660 0.01300 0.01185 [190] 0.02574 0.04087 0.02751 0.02308 0.02296 0.01884 > 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]) 0.00954 0.00958 0.01015 0.01022 0.01024 0.0103 0.01033 0.01043 0.01064 0.01098 1 1 1 1 1 1 1 1 1 1 0.01131 0.01152 0.01169 0.01185 0.01194 0.01201 0.01242 0.01259 0.01263 0.01279 1 1 1 1 1 1 1 1 1 1 0.01299 0.013 0.01346 0.01358 0.01412 0.01419 0.01444 0.0145 0.01457 0.01458 1 1 1 1 1 1 1 2 1 1 0.01463 0.01469 0.01472 0.01484 0.01494 0.01495 0.01503 0.01516 0.01564 0.01567 1 1 1 1 1 1 2 1 1 1 0.01574 0.01608 0.01613 0.01642 0.01643 0.01644 0.01657 0.01659 0.0166 0.01663 1 2 1 1 1 1 1 1 1 1 0.0168 0.01681 0.01706 0.01718 0.01725 0.01745 0.01752 0.01756 0.0176 0.01761 1 1 1 1 2 1 1 1 1 1 0.01791 0.01795 0.01828 0.01831 0.01843 0.01851 0.01861 0.01884 0.01897 0.01906 1 1 1 1 1 1 1 1 1 1 0.01909 0.01919 0.01966 0.01997 0.02008 0.02018 0.0203 0.02033 0.02047 0.02093 1 1 1 1 1 1 1 1 1 1 0.02105 0.02126 0.02143 0.02145 0.02177 0.02184 0.02199 0.02215 0.02223 0.02286 1 1 1 1 1 1 1 1 1 1 0.02293 0.02296 0.02297 0.02308 0.02343 0.02362 0.02378 0.02427 0.02442 0.02448 2 1 1 1 1 1 1 1 1 2 0.02498 0.02503 0.02534 0.02536 0.02551 0.02574 0.02645 0.02662 0.02682 0.02719 1 1 1 1 1 1 1 1 1 1 0.02751 0.02752 0.02791 0.02814 0.02838 0.02852 0.02857 0.03026 0.03044 0.03087 1 1 1 1 1 1 1 1 1 1 0.03111 0.03121 0.03156 0.03198 0.03202 0.03209 0.03225 0.03235 0.03272 0.03273 1 1 1 1 1 1 1 1 1 2 0.03327 0.03381 0.03485 0.03527 0.03658 0.03715 0.03716 0.03759 0.03767 0.0381 1 1 1 1 1 1 1 1 1 1 0.03852 0.03886 0.03995 0.03999 0.04009 0.04024 0.04087 0.04128 0.04137 0.0419 1 1 1 1 1 1 1 1 1 1 0.04192 0.04313 0.04351 0.04374 0.04479 0.04689 0.04701 0.04795 0.04879 0.04912 1 1 1 1 1 1 1 1 1 1 0.04932 0.04978 0.05233 0.05279 0.05384 0.05428 0.05492 0.05517 0.05643 0.0583 1 1 1 1 1 1 1 1 1 1 0.05925 0.0605 0.06134 0.0617 0.06425 0.06511 0.0664 0.06725 0.06727 0.06734 1 1 1 1 1 1 1 1 1 1 0.07118 0.0717 0.07959 0.08143 0.08684 0.09178 0.09419 0.11908 1 1 1 1 1 1 1 1 > colnames(x) [1] "MDVP.Shimmer" "MDVP.Shimmer.dB." "Shimmer.APQ3" "Shimmer.APQ5" [5] "MDVP.APQ" "Shimmer.DDA" > colnames(x)[par1] [1] "MDVP.Shimmer" > x[,par1] [1] 0.04374 0.06134 0.05233 0.05492 0.06425 0.04701 0.01608 0.01567 0.02093 [10] 0.02838 0.02143 0.02752 0.01259 0.01642 0.01828 0.01503 0.02047 0.03327 [19] 0.05517 0.03995 0.03810 0.04137 0.04351 0.04192 0.01659 0.03767 0.01966 [28] 0.01919 0.01718 0.01791 0.01098 0.01015 0.01263 0.00954 0.00958 0.01194 [37] 0.02126 0.01851 0.01444 0.01663 0.01495 0.01463 0.01752 0.01760 0.01419 [46] 0.01494 0.01608 0.01152 0.01613 0.01681 0.02184 0.02033 0.02297 0.02498 [55] 0.02719 0.03209 0.03715 0.02293 0.02645 0.03225 0.01861 0.01906 0.01643 [64] 0.01644 0.01457 0.01745 0.03198 0.03111 0.05384 0.05428 0.03485 0.04978 [73] 0.01706 0.02448 0.02442 0.02215 0.03999 0.02199 0.03202 0.03121 0.04024 [82] 0.03156 0.02427 0.02223 0.04795 0.03852 0.03759 0.06511 0.06727 0.04313 [91] 0.06640 0.07959 0.04190 0.05925 0.03716 0.03272 0.03381 0.03886 0.04689 [100] 0.06734 0.09178 0.06170 0.09419 0.01131 0.01030 0.01346 0.01064 0.01450 [109] 0.01024 0.03044 0.02286 0.01761 0.02378 0.01680 0.02105 0.01843 0.01458 [118] 0.01725 0.01279 0.01299 0.02008 0.01169 0.04479 0.02503 0.02343 0.02362 [127] 0.02791 0.02857 0.01033 0.01022 0.01412 0.01516 0.01201 0.01043 0.04932 [136] 0.04128 0.04879 0.05279 0.05643 0.03026 0.03273 0.06725 0.03527 0.01997 [145] 0.02662 0.02536 0.08143 0.06050 0.07118 0.07170 0.05830 0.11908 0.08684 [154] 0.02534 0.02682 0.03087 0.02293 0.04912 0.02852 0.03235 0.04009 0.03273 [163] 0.03658 0.01756 0.02814 0.02448 0.01242 0.02030 0.02177 0.02018 0.01897 [172] 0.01358 0.01484 0.01472 0.01657 0.01503 0.01725 0.01469 0.01574 0.01450 [181] 0.02551 0.01831 0.02145 0.01909 0.01795 0.01564 0.01660 0.01300 0.01185 [190] 0.02574 0.04087 0.02751 0.02308 0.02296 0.01884 > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > if (par2 != 'none') { + m <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data = x) + if (par4=='yes') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'10-Fold Cross Validation',3+2*par3,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + a<-table.element(a,'Prediction (training)',par3+1,TRUE) + a<-table.element(a,'Prediction (testing)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Actual',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,paste('C',jjj,sep=''),1,TRUE) + a<-table.element(a,'CV',1,TRUE) + a<-table.row.end(a) + for (i in 1:10) { + ind <- sample(2, nrow(x), replace=T, prob=c(0.9,0.1)) + m.ct <- ctree(as.formula(paste('as.factor(',colnames(x)[par1],') ~ .',sep='')),data =x[ind==1,]) + if (i==1) { + m.ct.i.pred <- predict(m.ct, newdata=x[ind==1,]) + m.ct.i.actu <- x[ind==1,par1] + m.ct.x.pred <- predict(m.ct, newdata=x[ind==2,]) + m.ct.x.actu <- x[ind==2,par1] + } else { + m.ct.i.pred <- c(m.ct.i.pred,predict(m.ct, newdata=x[ind==1,])) + m.ct.i.actu <- c(m.ct.i.actu,x[ind==1,par1]) + m.ct.x.pred <- c(m.ct.x.pred,predict(m.ct, newdata=x[ind==2,])) + m.ct.x.actu <- c(m.ct.x.actu,x[ind==2,par1]) + } + } + print(m.ct.i.tab <- table(m.ct.i.actu,m.ct.i.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.i.tab[i,i] / sum(m.ct.i.tab[i,])) + numer <- numer + m.ct.i.tab[i,i] + } + print(m.ct.i.cp <- numer / sum(m.ct.i.tab)) + print(m.ct.x.tab <- table(m.ct.x.actu,m.ct.x.pred)) + numer <- 0 + for (i in 1:par3) { + print(m.ct.x.tab[i,i] / sum(m.ct.x.tab[i,])) + numer <- numer + m.ct.x.tab[i,i] + } + print(m.ct.x.cp <- numer / sum(m.ct.x.tab)) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (jjj in 1:par3) a<-table.element(a,m.ct.i.tab[i,jjj]) + a<-table.element(a,round(m.ct.i.tab[i,i]/sum(m.ct.i.tab[i,]),4)) + for (jjj in 1:par3) a<-table.element(a,m.ct.x.tab[i,jjj]) + a<-table.element(a,round(m.ct.x.tab[i,i]/sum(m.ct.x.tab[i,]),4)) + a<-table.row.end(a) + } + a<-table.row.start(a) + a<-table.element(a,'Overall',1,TRUE) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.i.cp,4)) + for (jjj in 1:par3) a<-table.element(a,'-') + a<-table.element(a,round(m.ct.x.cp,4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/12ru11386166376.tab") + } + } > m Conditional inference tree with 16 terminal nodes Response: MDVP.Shimmer Inputs: MDVP.Shimmer.dB., Shimmer.APQ3, Shimmer.APQ5, MDVP.APQ, Shimmer.DDA Number of observations: 195 1) Shimmer.DDA <= 0.06406; criterion = 1, statistic = 189.228 2) Shimmer.APQ5 <= 0.01365; criterion = 1, statistic = 144.666 3) MDVP.Shimmer.dB. <= 0.148; criterion = 1, statistic = 89.949 4) MDVP.Shimmer.dB. <= 0.124; criterion = 1, statistic = 44.87 5) MDVP.Shimmer.dB. <= 0.099; criterion = 1, statistic = 17.744 6)* weights = 10 5) MDVP.Shimmer.dB. > 0.099 7)* weights = 11 4) MDVP.Shimmer.dB. > 0.124 8) Shimmer.DDA <= 0.0233; criterion = 1, statistic = 16.465 9)* weights = 18 8) Shimmer.DDA > 0.0233 10)* weights = 10 3) MDVP.Shimmer.dB. > 0.148 11) Shimmer.DDA <= 0.03191; criterion = 1, statistic = 38.183 12) MDVP.Shimmer.dB. <= 0.164; criterion = 0.999, statistic = 13.356 13)* weights = 17 12) MDVP.Shimmer.dB. > 0.164 14)* weights = 12 11) Shimmer.DDA > 0.03191 15) Shimmer.DDA <= 0.03557; criterion = 0.999, statistic = 14.194 16)* weights = 10 15) Shimmer.DDA > 0.03557 17)* weights = 11 2) Shimmer.APQ5 > 0.01365 18) Shimmer.APQ5 <= 0.01859; criterion = 1, statistic = 44.97 19) MDVP.Shimmer.dB. <= 0.265; criterion = 1, statistic = 25.949 20) Shimmer.DDA <= 0.04231; criterion = 1, statistic = 15.935 21)* weights = 13 20) Shimmer.DDA > 0.04231 22)* weights = 9 19) MDVP.Shimmer.dB. > 0.265 23)* weights = 11 18) Shimmer.APQ5 > 0.01859 24) Shimmer.APQ5 <= 0.01992; criterion = 0.996, statistic = 11.281 25)* weights = 8 24) Shimmer.APQ5 > 0.01992 26)* weights = 12 1) Shimmer.DDA > 0.06406 27) MDVP.Shimmer.dB. <= 0.542; criterion = 1, statistic = 39.056 28) MDVP.Shimmer.dB. <= 0.456; criterion = 1, statistic = 19.715 29)* weights = 16 28) MDVP.Shimmer.dB. > 0.456 30)* weights = 8 27) MDVP.Shimmer.dB. > 0.542 31)* weights = 19 > postscript(file="/var/wessaorg/rcomp/tmp/2m39m1386166376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > plot(m) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/3rrkc1386166376.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 0.04374 0.04440313 -6.631250e-04 2 0.06134 0.07339474 -1.205474e-02 3 0.05233 0.05369250 -1.362500e-03 4 0.05492 0.05369250 1.227500e-03 5 0.06425 0.07339474 -9.144737e-03 6 0.04701 0.04440313 2.606875e-03 7 0.01608 0.01591100 1.690000e-04 8 0.01567 0.01591100 -2.410000e-04 9 0.02093 0.02058700 3.430000e-04 10 0.02838 0.02736222 1.017778e-03 11 0.02143 0.02058700 8.430000e-04 12 0.02752 0.02736222 1.577778e-04 13 0.01259 0.01217727 4.127273e-04 14 0.01642 0.01727176 -8.517647e-04 15 0.01828 0.01727176 1.008235e-03 16 0.01503 0.01446611 5.638889e-04 17 0.02047 0.01892500 1.545000e-03 18 0.03327 0.03331375 -4.375000e-05 19 0.05517 0.05369250 1.477500e-03 20 0.03995 0.03796167 1.988333e-03 21 0.03810 0.03796167 1.383333e-04 22 0.04137 0.03796167 3.408333e-03 23 0.04351 0.04440313 -8.931250e-04 24 0.04192 0.04440313 -2.483125e-03 25 0.01659 0.01727176 -6.817647e-04 26 0.03767 0.03796167 -2.916667e-04 27 0.01966 0.01892500 7.350000e-04 28 0.01919 0.01892500 2.650000e-04 29 0.01718 0.01727176 -9.176471e-05 30 0.01791 0.01892500 -1.015000e-03 31 0.01098 0.01024100 7.390000e-04 32 0.01015 0.01024100 -9.100000e-05 33 0.01263 0.01217727 4.527273e-04 34 0.00954 0.01024100 -7.010000e-04 35 0.00958 0.01024100 -6.610000e-04 36 0.01194 0.01217727 -2.372727e-04 37 0.02126 0.02058700 6.730000e-04 38 0.01851 0.01892500 -4.150000e-04 39 0.01444 0.01446611 -2.611111e-05 40 0.01663 0.01727176 -6.417647e-04 41 0.01495 0.01446611 4.838889e-04 42 0.01463 0.01446611 1.638889e-04 43 0.01752 0.01727176 2.482353e-04 44 0.01760 0.01727176 3.282353e-04 45 0.01419 0.01446611 -2.761111e-04 46 0.01494 0.01591100 -9.710000e-04 47 0.01608 0.01591100 1.690000e-04 48 0.01152 0.01217727 -6.572727e-04 49 0.01613 0.01591100 2.190000e-04 50 0.01681 0.01727176 -4.617647e-04 51 0.02184 0.02284727 -1.007273e-03 52 0.02033 0.02058700 -2.570000e-04 53 0.02297 0.02284727 1.227273e-04 54 0.02498 0.02284727 2.132727e-03 55 0.02719 0.02736222 -1.722222e-04 56 0.03209 0.03331375 -1.223750e-03 57 0.03715 0.03796167 -8.116667e-04 58 0.02293 0.02442615 -1.496154e-03 59 0.02645 0.02442615 2.023846e-03 60 0.03225 0.03331375 -1.063750e-03 61 0.01861 0.01892500 -3.150000e-04 62 0.01906 0.01892500 1.350000e-04 63 0.01643 0.01591100 5.190000e-04 64 0.01644 0.01591100 5.290000e-04 65 0.01457 0.01446611 1.038889e-04 66 0.01745 0.01727176 1.782353e-04 67 0.03198 0.03083364 1.146364e-03 68 0.03111 0.03083364 2.763636e-04 69 0.05384 0.05369250 1.475000e-04 70 0.05428 0.05369250 5.875000e-04 71 0.03485 0.03331375 1.536250e-03 72 0.04978 0.05369250 -3.912500e-03 73 0.01706 0.01727176 -2.117647e-04 74 0.02448 0.02442615 5.384615e-05 75 0.02442 0.02284727 1.572727e-03 76 0.02215 0.02284727 -6.972727e-04 77 0.03999 0.04440313 -4.413125e-03 78 0.02199 0.02284727 -8.572727e-04 79 0.03202 0.03331375 -1.293750e-03 80 0.03121 0.03083364 3.763636e-04 81 0.04024 0.04440313 -4.163125e-03 82 0.03156 0.03083364 7.263636e-04 83 0.02427 0.02442615 -1.561538e-04 84 0.02223 0.02284727 -6.172727e-04 85 0.04795 0.04440313 3.546875e-03 86 0.03852 0.03796167 5.583333e-04 87 0.03759 0.03796167 -3.716667e-04 88 0.06511 0.07339474 -8.284737e-03 89 0.06727 0.07339474 -6.124737e-03 90 0.04313 0.04440313 -1.273125e-03 91 0.06640 0.07339474 -6.994737e-03 92 0.07959 0.07339474 6.195263e-03 93 0.04190 0.04440313 -2.503125e-03 94 0.05925 0.07339474 -1.414474e-02 95 0.03716 0.03796167 -8.016667e-04 96 0.03272 0.03331375 -5.937500e-04 97 0.03381 0.03796167 -4.151667e-03 98 0.03886 0.03796167 8.983333e-04 99 0.04689 0.04440313 2.486875e-03 100 0.06734 0.07339474 -6.054737e-03 101 0.09178 0.07339474 1.838526e-02 102 0.06170 0.07339474 -1.169474e-02 103 0.09419 0.07339474 2.079526e-02 104 0.01131 0.01217727 -8.672727e-04 105 0.01030 0.01024100 5.900000e-05 106 0.01346 0.01446611 -1.006111e-03 107 0.01064 0.01024100 3.990000e-04 108 0.01450 0.01446611 3.388889e-05 109 0.01024 0.01024100 -1.000000e-06 110 0.03044 0.03083364 -3.936364e-04 111 0.02286 0.02442615 -1.566154e-03 112 0.01761 0.01727176 3.382353e-04 113 0.02378 0.02442615 -6.461538e-04 114 0.01680 0.01727176 -4.717647e-04 115 0.02105 0.02058700 4.630000e-04 116 0.01843 0.01892500 -4.950000e-04 117 0.01458 0.01446611 1.138889e-04 118 0.01725 0.01892500 -1.675000e-03 119 0.01279 0.01446611 -1.676111e-03 120 0.01299 0.01217727 8.127273e-04 121 0.02008 0.01892500 1.155000e-03 122 0.01169 0.01217727 -4.872727e-04 123 0.04479 0.04440313 3.868750e-04 124 0.02503 0.02442615 6.038462e-04 125 0.02343 0.02442615 -9.961538e-04 126 0.02362 0.02442615 -8.061538e-04 127 0.02791 0.02736222 5.477778e-04 128 0.02857 0.02736222 1.207778e-03 129 0.01033 0.01024100 8.900000e-05 130 0.01022 0.01024100 -2.100000e-05 131 0.01412 0.01446611 -3.461111e-04 132 0.01516 0.01446611 6.938889e-04 133 0.01201 0.01217727 -1.672727e-04 134 0.01043 0.01024100 1.890000e-04 135 0.04932 0.04440313 4.916875e-03 136 0.04128 0.04440313 -3.123125e-03 137 0.04879 0.04440313 4.386875e-03 138 0.05279 0.05369250 -9.025000e-04 139 0.05643 0.05369250 2.737500e-03 140 0.03026 0.03083364 -5.736364e-04 141 0.03273 0.03331375 -5.837500e-04 142 0.06725 0.07339474 -6.144737e-03 143 0.03527 0.03796167 -2.691667e-03 144 0.01997 0.02058700 -6.170000e-04 145 0.02662 0.02736222 -7.422222e-04 146 0.02536 0.02442615 9.338462e-04 147 0.08143 0.07339474 8.035263e-03 148 0.06050 0.07339474 -1.289474e-02 149 0.07118 0.07339474 -2.214737e-03 150 0.07170 0.07339474 -1.694737e-03 151 0.05830 0.07339474 -1.509474e-02 152 0.11908 0.07339474 4.568526e-02 153 0.08684 0.07339474 1.344526e-02 154 0.02534 0.02442615 9.138462e-04 155 0.02682 0.02736222 -5.422222e-04 156 0.03087 0.03083364 3.636364e-05 157 0.02293 0.02284727 8.272727e-05 158 0.04912 0.04440313 4.716875e-03 159 0.02852 0.03083364 -2.313636e-03 160 0.03235 0.03083364 1.516364e-03 161 0.04009 0.03796167 2.128333e-03 162 0.03273 0.03083364 1.896364e-03 163 0.03658 0.03331375 3.266250e-03 164 0.01756 0.01727176 2.882353e-04 165 0.02814 0.03083364 -2.693636e-03 166 0.02448 0.02442615 5.384615e-05 167 0.01242 0.01217727 2.427273e-04 168 0.02030 0.02058700 -2.870000e-04 169 0.02177 0.02284727 -1.077273e-03 170 0.02018 0.02058700 -4.070000e-04 171 0.01897 0.02058700 -1.617000e-03 172 0.01358 0.01446611 -8.861111e-04 173 0.01484 0.01446611 3.738889e-04 174 0.01472 0.01446611 2.538889e-04 175 0.01657 0.01591100 6.590000e-04 176 0.01503 0.01591100 -8.810000e-04 177 0.01725 0.01727176 -2.176471e-05 178 0.01469 0.01446611 2.238889e-04 179 0.01574 0.01591100 -1.710000e-04 180 0.01450 0.01446611 3.388889e-05 181 0.02551 0.02442615 1.083846e-03 182 0.01831 0.01727176 1.038235e-03 183 0.02145 0.02058700 8.630000e-04 184 0.01909 0.01892500 1.650000e-04 185 0.01795 0.01727176 6.782353e-04 186 0.01564 0.01446611 1.173889e-03 187 0.01660 0.01727176 -6.717647e-04 188 0.01300 0.01217727 8.227273e-04 189 0.01185 0.01217727 -3.272727e-04 190 0.02574 0.02736222 -1.622222e-03 191 0.04087 0.04440313 -3.533125e-03 192 0.02751 0.02736222 1.477778e-04 193 0.02308 0.02284727 2.327273e-04 194 0.02296 0.02284727 1.127273e-04 195 0.01884 0.01892500 -8.500000e-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/wessaorg/rcomp/tmp/4ybsa1386166376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > if(par2=='none') { + op <- par(mfrow=c(2,2)) + plot(density(result$Actuals),main='Kernel Density Plot of Actuals') + plot(density(result$Residuals),main='Kernel Density Plot of Residuals') + plot(result$Forecasts,result$Actuals,main='Actuals versus Predictions',xlab='Predictions',ylab='Actuals') + plot(density(result$Forecasts),main='Kernel Density Plot of Predictions') + par(op) + } > if(par2!='none') { + plot(myt,main='Confusion Matrix',xlab='Actual',ylab='Predicted') + } > dev.off() null device 1 > if (par2 == 'none') { + detcoef <- cor(result$Forecasts,result$Actuals) + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Goodness of Fit',2,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'Correlation',1,TRUE) + a<-table.element(a,round(detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'R-squared',1,TRUE) + a<-table.element(a,round(detcoef*detcoef,4)) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'RMSE',1,TRUE) + a<-table.element(a,round(sqrt(mean((result$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/56u5a1386166376.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$Actuals)) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,result$Actuals[i]) + a<-table.element(a,result$Forecasts[i]) + a<-table.element(a,result$Residuals[i]) + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/61qr51386166376.tab") + } > if (par2 != 'none') { + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Confusion Matrix (predicted in columns / actuals in rows)',par3+1,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'',1,TRUE) + for (i in 1:par3) { + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + } + a<-table.row.end(a) + for (i in 1:par3) { + a<-table.row.start(a) + a<-table.element(a,paste('C',i,sep=''),1,TRUE) + for (j in 1:par3) { + a<-table.element(a,myt[i,j]) + } + a<-table.row.end(a) + } + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/7ber31386166376.tab") + } > > try(system("convert tmp/2m39m1386166376.ps tmp/2m39m1386166376.png",intern=TRUE)) character(0) > try(system("convert tmp/3rrkc1386166376.ps tmp/3rrkc1386166376.png",intern=TRUE)) character(0) > try(system("convert tmp/4ybsa1386166376.ps tmp/4ybsa1386166376.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.846 1.447 11.190