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Type 'q()' to quit R. > x <- array(list(119.992 + ,157.302 + ,74.997 + ,0.00007 + ,0.00554 + ,122.4 + ,148.65 + ,113.819 + ,0.00008 + ,0.00696 + ,116.682 + ,131.111 + ,111.555 + ,0.00009 + ,0.00781 + ,116.676 + ,137.871 + ,111.366 + ,0.00009 + ,0.00698 + ,116.014 + ,141.781 + ,110.655 + ,0.00011 + ,0.00908 + ,120.552 + ,131.162 + ,113.787 + ,0.00008 + ,0.0075 + ,120.267 + ,137.244 + ,114.82 + ,0.00003 + ,0.00202 + ,107.332 + ,113.84 + ,104.315 + ,0.00003 + ,0.00182 + ,95.73 + ,132.068 + ,91.754 + ,0.00006 + ,0.00332 + ,95.056 + ,120.103 + ,91.226 + ,0.00006 + ,0.00332 + ,88.333 + ,112.24 + ,84.072 + ,0.00006 + ,0.0033 + ,91.904 + ,115.871 + ,86.292 + ,0.00006 + ,0.00336 + ,136.926 + ,159.866 + ,131.276 + ,0.00002 + ,0.00153 + ,139.173 + ,179.139 + ,76.556 + ,0.00003 + ,0.00208 + ,152.845 + ,163.305 + ,75.836 + ,0.00002 + ,0.00149 + ,142.167 + ,217.455 + ,83.159 + ,0.00003 + ,0.00203 + ,144.188 + ,349.259 + ,82.764 + ,0.00004 + ,0.00292 + ,168.778 + ,232.181 + ,75.603 + ,0.00004 + 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,102.137 + ,0.00001 + ,0.00153 + ,237.323 + ,243.709 + ,229.256 + ,0.00001 + ,0.00159 + ,260.105 + ,264.919 + ,237.303 + ,0.00001 + ,0.00186 + ,197.569 + ,217.627 + ,90.794 + ,0.00004 + ,0.00448 + ,240.301 + ,245.135 + ,219.783 + ,0.00002 + ,0.00283 + ,244.99 + ,272.21 + ,239.17 + ,0.00002 + ,0.00237 + ,112.547 + ,133.374 + ,105.715 + ,0.00003 + ,0.0019 + ,110.739 + ,113.597 + ,100.139 + ,0.00003 + ,0.002 + ,113.715 + ,116.443 + ,96.913 + ,0.00003 + ,0.00203 + ,117.004 + ,144.466 + ,99.923 + ,0.00003 + ,0.00218 + ,115.38 + ,123.109 + ,108.634 + ,0.00003 + ,0.00199 + ,116.388 + ,129.038 + ,108.97 + ,0.00003 + ,0.00213 + ,151.737 + ,190.204 + ,129.859 + ,0.00002 + ,0.00162 + ,148.79 + ,158.359 + ,138.99 + ,0.00002 + ,0.00186 + ,148.143 + ,155.982 + ,135.041 + ,0.00003 + ,0.00231 + ,150.44 + ,163.441 + ,144.736 + ,0.00003 + ,0.00233 + ,148.462 + ,161.078 + ,141.998 + ,0.00003 + ,0.00235 + ,149.818 + ,163.417 + ,144.786 + ,0.00002 + ,0.00198 + ,117.226 + ,123.925 + ,106.656 + ,0.00004 + ,0.0027 + ,116.848 + ,217.552 + ,99.503 + ,0.00005 + ,0.00346 + ,116.286 + ,177.291 + ,96.983 + ,0.00003 + ,0.00192 + ,116.556 + ,592.03 + ,86.228 + ,0.00004 + ,0.00263 + ,116.342 + ,581.289 + ,94.246 + ,0.00002 + ,0.00148 + ,114.563 + ,119.167 + ,86.647 + ,0.00003 + ,0.00184 + ,201.774 + ,262.707 + ,78.228 + ,0.00003 + ,0.00396 + ,174.188 + ,230.978 + ,94.261 + ,0.00003 + ,0.00259 + ,209.516 + ,253.017 + ,89.488 + ,0.00003 + ,0.00292 + ,174.688 + ,240.005 + ,74.287 + ,0.00008 + ,0.00564 + ,198.764 + ,396.961 + ,74.904 + ,0.00004 + ,0.0039 + ,214.289 + ,260.277 + ,77.973 + ,0.00003 + ,0.00317) + ,dim=c(5 + ,195) + ,dimnames=list(c('MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:PPQ') + ,1:195)) > y <- array(NA,dim=c(5,195),dimnames=list(c('MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(Abs)','MDVP:PPQ'),1:195)) > 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 = 'equal' > par1 = '1' > par4 <- 'no' > par3 <- '3' > par2 <- 'equal' > par1 <- '1' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: 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.Fo.Hz." > x[,par1] [1] 119.992 122.400 116.682 116.676 116.014 120.552 120.267 107.332 95.730 [10] 95.056 88.333 91.904 136.926 139.173 152.845 142.167 144.188 168.778 [19] 153.046 156.405 153.848 153.880 167.930 173.917 163.656 104.400 171.041 [28] 146.845 155.358 162.568 197.076 199.228 198.383 202.266 203.184 201.464 [37] 177.876 176.170 180.198 187.733 186.163 184.055 237.226 241.404 243.439 [46] 242.852 245.510 252.455 122.188 122.964 124.445 126.344 128.001 129.336 [55] 108.807 109.860 110.417 117.274 116.879 114.847 209.144 223.365 222.236 [64] 228.832 229.401 228.969 140.341 136.969 143.533 148.090 142.729 136.358 [73] 120.080 112.014 110.793 110.707 112.876 110.568 95.385 100.770 96.106 [82] 95.605 100.960 98.804 176.858 180.978 178.222 176.281 173.898 179.711 [91] 166.605 151.955 148.272 152.125 157.821 157.447 159.116 125.036 125.791 [100] 126.512 125.641 128.451 139.224 150.258 154.003 149.689 155.078 151.884 [109] 151.989 193.030 200.714 208.519 204.664 210.141 206.327 151.872 158.219 [118] 170.756 178.285 217.116 128.940 176.824 138.190 182.018 156.239 145.174 [127] 138.145 166.888 119.031 120.078 120.289 120.256 119.056 118.747 106.516 [136] 110.453 113.400 113.166 112.239 116.150 170.368 208.083 198.458 202.805 [145] 202.544 223.361 169.774 183.520 188.620 202.632 186.695 192.818 198.116 [154] 121.345 119.100 117.870 122.336 117.963 126.144 127.930 114.238 115.322 [163] 114.554 112.150 102.273 236.200 237.323 260.105 197.569 240.301 244.990 [172] 112.547 110.739 113.715 117.004 115.380 116.388 151.737 148.790 148.143 [181] 150.440 148.462 149.818 117.226 116.848 116.286 116.556 116.342 114.563 [190] 201.774 174.188 209.516 174.688 198.764 214.289 > 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]) C1 C2 C3 92 76 27 > colnames(x) [1] "MDVP.Fo.Hz." "MDVP.Fhi.Hz." "MDVP.Flo.Hz." "MDVP.Jitter.Abs." [5] "MDVP.PPQ" > colnames(x)[par1] [1] "MDVP.Fo.Hz." > x[,par1] [1] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C2 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 [26] C1 C2 C2 C2 C2 C2 C2 C2 C2 C3 C2 C2 C2 C2 C2 C2 C2 C3 C3 C3 C3 C3 C3 C1 C1 [51] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C3 C3 C3 C3 C3 C3 C1 C1 C1 C2 C1 C1 C1 C1 C1 [76] C1 C1 C1 C1 C1 C1 C1 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C1 C1 [101] C1 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C3 C3 C3 C3 C2 C2 C2 C2 C3 C1 C2 C1 C2 C2 [126] C1 C1 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C2 C3 C2 C2 C2 C3 C2 C2 C2 C2 [151] C2 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C3 C3 C3 C2 C3 C3 C1 C1 C1 C1 [176] C1 C1 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C2 C2 C3 C2 C2 C3 Levels: C1 C2 C3 > 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/16l9x1386100354.tab") + } + } > m Conditional inference tree with 5 terminal nodes Response: as.factor(MDVP.Fo.Hz.) Inputs: MDVP.Fhi.Hz., MDVP.Flo.Hz., MDVP.Jitter.Abs., MDVP.PPQ Number of observations: 195 1) MDVP.Flo.Hz. <= 197.079; criterion = 1, statistic = 61.01 2) MDVP.Jitter.Abs. <= 2e-05; criterion = 1, statistic = 26.871 3)* weights = 42 2) MDVP.Jitter.Abs. > 2e-05 4) MDVP.Fhi.Hz. <= 159.774; criterion = 1, statistic = 18.782 5) MDVP.Flo.Hz. <= 114.82; criterion = 0.975, statistic = 7.437 6)* weights = 65 5) MDVP.Flo.Hz. > 114.82 7)* weights = 7 4) MDVP.Fhi.Hz. > 159.774 8)* weights = 68 1) MDVP.Flo.Hz. > 197.079 9)* weights = 13 > postscript(file="/var/wessaorg/rcomp/tmp/2le6n1386100354.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/3hjqs1386100354.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) + } > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } [,1] [,2] [1,] 1 1 [2,] 1 1 [3,] 1 1 [4,] 1 1 [5,] 1 1 [6,] 1 1 [7,] 1 1 [8,] 1 1 [9,] 1 1 [10,] 1 1 [11,] 1 1 [12,] 1 1 [13,] 1 2 [14,] 1 2 [15,] 2 2 [16,] 1 2 [17,] 1 2 [18,] 2 2 [19,] 2 2 [20,] 2 2 [21,] 2 2 [22,] 2 2 [23,] 2 2 [24,] 2 2 [25,] 2 2 [26,] 1 2 [27,] 2 2 [28,] 2 2 [29,] 2 2 [30,] 2 2 [31,] 2 2 [32,] 2 2 [33,] 2 2 [34,] 2 2 [35,] 3 2 [36,] 2 2 [37,] 2 2 [38,] 2 2 [39,] 2 2 [40,] 2 2 [41,] 2 2 [42,] 2 2 [43,] 3 3 [44,] 3 3 [45,] 3 3 [46,] 3 3 [47,] 3 3 [48,] 3 2 [49,] 1 1 [50,] 1 1 [51,] 1 1 [52,] 1 1 [53,] 1 1 [54,] 1 1 [55,] 1 1 [56,] 1 1 [57,] 1 1 [58,] 1 1 [59,] 1 1 [60,] 1 2 [61,] 3 2 [62,] 3 2 [63,] 3 3 [64,] 3 3 [65,] 3 3 [66,] 3 2 [67,] 1 1 [68,] 1 2 [69,] 1 2 [70,] 2 2 [71,] 1 2 [72,] 1 2 [73,] 1 1 [74,] 1 2 [75,] 1 1 [76,] 1 1 [77,] 1 1 [78,] 1 1 [79,] 1 1 [80,] 1 1 [81,] 1 1 [82,] 1 1 [83,] 1 1 [84,] 1 1 [85,] 2 2 [86,] 2 2 [87,] 2 2 [88,] 2 2 [89,] 2 2 [90,] 2 2 [91,] 2 2 [92,] 2 2 [93,] 2 2 [94,] 2 2 [95,] 2 2 [96,] 2 2 [97,] 2 2 [98,] 1 1 [99,] 1 1 [100,] 1 1 [101,] 1 1 [102,] 1 1 [103,] 1 2 [104,] 2 2 [105,] 2 2 [106,] 2 2 [107,] 2 2 [108,] 2 2 [109,] 2 2 [110,] 2 2 [111,] 2 2 [112,] 3 3 [113,] 3 2 [114,] 3 2 [115,] 3 2 [116,] 2 2 [117,] 2 2 [118,] 2 2 [119,] 2 2 [120,] 3 2 [121,] 1 2 [122,] 2 2 [123,] 1 2 [124,] 2 2 [125,] 2 2 [126,] 1 2 [127,] 1 2 [128,] 2 2 [129,] 1 1 [130,] 1 2 [131,] 1 1 [132,] 1 1 [133,] 1 1 [134,] 1 1 [135,] 1 1 [136,] 1 1 [137,] 1 1 [138,] 1 1 [139,] 1 1 [140,] 1 1 [141,] 2 2 [142,] 3 2 [143,] 2 2 [144,] 2 2 [145,] 2 2 [146,] 3 2 [147,] 2 2 [148,] 2 2 [149,] 2 2 [150,] 2 2 [151,] 2 2 [152,] 2 2 [153,] 2 2 [154,] 1 1 [155,] 1 1 [156,] 1 1 [157,] 1 1 [158,] 1 1 [159,] 1 1 [160,] 1 1 [161,] 1 1 [162,] 1 1 [163,] 1 1 [164,] 1 1 [165,] 1 1 [166,] 3 2 [167,] 3 3 [168,] 3 3 [169,] 2 2 [170,] 3 3 [171,] 3 3 [172,] 1 1 [173,] 1 1 [174,] 1 1 [175,] 1 1 [176,] 1 1 [177,] 1 1 [178,] 2 2 [179,] 2 2 [180,] 2 1 [181,] 2 2 [182,] 2 2 [183,] 2 2 [184,] 1 1 [185,] 1 2 [186,] 1 2 [187,] 1 2 [188,] 1 2 [189,] 1 1 [190,] 2 2 [191,] 2 2 [192,] 3 2 [193,] 2 2 [194,] 2 2 [195,] 3 2 C1 C2 C3 C1 71 21 0 C2 1 75 0 C3 0 14 13 > postscript(file="/var/wessaorg/rcomp/tmp/4zo1w1386100354.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/5zja41386100354.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/6z5bh1386100354.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/7omdn1386100354.tab") + } > > try(system("convert tmp/2le6n1386100354.ps tmp/2le6n1386100354.png",intern=TRUE)) character(0) > try(system("convert tmp/3hjqs1386100354.ps tmp/3hjqs1386100354.png",intern=TRUE)) character(0) > try(system("convert tmp/4zo1w1386100354.ps tmp/4zo1w1386100354.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.978 1.102 7.056