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 = '2' > par2 = 'equal' > 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] "Costs" > x[,par1] [1] 162556 29790 87550 84738 54660 42634 40949 45187 37704 16275 [11] 25830 12679 18014 43556 24811 6575 7123 21950 37597 17821 [21] 12988 22330 13326 16189 7146 15824 27664 11920 8568 14416 [31] 3369 11819 6984 4519 2220 18562 10327 5336 2365 4069 [41] 8636 13718 4525 6869 4628 3689 4891 7489 4901 2284 [51] 3160 4150 7285 1134 4658 2384 3748 5371 1285 9327 [61] 5565 1528 3122 7561 2675 13253 880 2053 1424 4036 [71] 3045 5119 1431 554 1975 1765 1012 810 1280 666 [81] 1380 4677 876 814 514 5692 3642 540 2099 567 [91] 2001 2949 2253 6533 1889 3055 272 1414 2564 1383 [101] 1261 975 3366 576 1686 746 3192 2045 5702 1932 [111] 936 3437 5131 2397 1389 1503 402 2239 2234 837 [121] 10579 875 1585 1659 2647 3294 0 94 422 0 [131] 34 1558 0 43 645 316 115 5 897 0 [141] 389 0 1002 36 460 309 0 9 271 14 [151] 520 1766 0 458 20 0 0 98 405 0 [161] 0 0 0 483 454 47 0 757 4655 0 [171] 0 36 0 203 0 126 400 71 0 0 [181] 972 531 2461 378 23 638 2300 149 226 0 [191] 275 0 141 0 28 0 4980 0 0 472 [201] 0 0 0 203 496 10 63 0 1136 265 [211] 0 0 267 474 534 0 15 397 0 1866 [221] 288 0 3 468 20 278 61 0 192 0 [231] 317 738 0 368 0 2 0 53 0 0 [241] 0 94 0 24 2332 0 0 131 0 0 [251] 206 0 167 622 2328 0 365 364 0 0 [261] 0 0 226 307 0 0 0 188 0 138 [271] 0 0 0 125 0 282 335 0 1324 176 [281] 0 0 249 0 333 0 601 30 0 249 [291] 0 165 453 0 53 382 0 0 0 0 [301] 30 290 0 0 366 2 0 209 384 0 [311] 0 365 0 49 3 133 32 368 1 0 [321] 0 0 0 0 0 22 0 0 0 0 [331] 0 0 0 96 1 314 844 0 26 125 [341] 304 0 0 0 621 0 119 0 0 1595 [351] 312 60 587 135 0 0 514 0 0 0 [361] 1 0 0 1763 180 0 0 0 0 218 [371] 0 448 227 174 0 0 121 607 2212 0 [381] 0 530 571 0 78 2489 131 923 72 572 [391] 397 450 622 694 3425 562 4917 1442 529 2126 [401] 1061 776 611 1526 592 1182 621 989 438 726 [411] 1303 7419 1164 3310 1920 965 3256 1135 1270 661 [421] 1013 2844 11528 6526 2264 5109 3999 35624 9252 15236 [431] 18073 > 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 428 3 > colnames(x) [1] "Costs" "Orders" "Dividends" "Wealth" > colnames(x)[par1] [1] "Costs" > x[,par1] [1] C2 C1 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [26] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [51] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [76] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [101] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [126] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [151] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [176] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [201] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [226] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [251] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [276] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [301] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [326] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [351] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [376] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [401] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 [426] C1 C1 C1 C1 C1 C1 Levels: C1 C2 > 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/1w73p1292938108.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: as.factor(Costs) Inputs: Orders, Dividends, Wealth Number of observations: 431 1) Wealth <= 1405225; criterion = 1, statistic = 161.509 2) Orders <= 251; criterion = 1, statistic = 51.857 3)* weights = 417 2) Orders > 251 4)* weights = 7 1) Wealth > 1405225 5)* weights = 7 > postscript(file="/var/www/html/rcomp/tmp/2w73p1292938108.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/3w73p1292938108.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,] 2 1 [2,] 1 1 [3,] 2 1 [4,] 2 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 1 [14,] 1 1 [15,] 1 1 [16,] 1 1 [17,] 1 1 [18,] 1 1 [19,] 1 1 [20,] 1 1 [21,] 1 1 [22,] 1 1 [23,] 1 1 [24,] 1 1 [25,] 1 1 [26,] 1 1 [27,] 1 1 [28,] 1 1 [29,] 1 1 [30,] 1 1 [31,] 1 1 [32,] 1 1 [33,] 1 1 [34,] 1 1 [35,] 1 1 [36,] 1 1 [37,] 1 1 [38,] 1 1 [39,] 1 1 [40,] 1 1 [41,] 1 1 [42,] 1 1 [43,] 1 1 [44,] 1 1 [45,] 1 1 [46,] 1 1 [47,] 1 1 [48,] 1 1 [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 1 [61,] 1 1 [62,] 1 1 [63,] 1 1 [64,] 1 1 [65,] 1 1 [66,] 1 1 [67,] 1 1 [68,] 1 1 [69,] 1 1 [70,] 1 1 [71,] 1 1 [72,] 1 1 [73,] 1 1 [74,] 1 1 [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,] 1 1 [86,] 1 1 [87,] 1 1 [88,] 1 1 [89,] 1 1 [90,] 1 1 [91,] 1 1 [92,] 1 1 [93,] 1 1 [94,] 1 1 [95,] 1 1 [96,] 1 1 [97,] 1 1 [98,] 1 1 [99,] 1 1 [100,] 1 1 [101,] 1 1 [102,] 1 1 [103,] 1 1 [104,] 1 1 [105,] 1 1 [106,] 1 1 [107,] 1 1 [108,] 1 1 [109,] 1 1 [110,] 1 1 [111,] 1 1 [112,] 1 1 [113,] 1 1 [114,] 1 1 [115,] 1 1 [116,] 1 1 [117,] 1 1 [118,] 1 1 [119,] 1 1 [120,] 1 1 [121,] 1 1 [122,] 1 1 [123,] 1 1 [124,] 1 1 [125,] 1 1 [126,] 1 1 [127,] 1 1 [128,] 1 1 [129,] 1 1 [130,] 1 1 [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,] 1 1 [142,] 1 1 [143,] 1 1 [144,] 1 1 [145,] 1 1 [146,] 1 1 [147,] 1 1 [148,] 1 1 [149,] 1 1 [150,] 1 1 [151,] 1 1 [152,] 1 1 [153,] 1 1 [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,] 1 1 [167,] 1 1 [168,] 1 1 [169,] 1 1 [170,] 1 1 [171,] 1 1 [172,] 1 1 [173,] 1 1 [174,] 1 1 [175,] 1 1 [176,] 1 1 [177,] 1 1 [178,] 1 1 [179,] 1 1 [180,] 1 1 [181,] 1 1 [182,] 1 1 [183,] 1 1 [184,] 1 1 [185,] 1 1 [186,] 1 1 [187,] 1 1 [188,] 1 1 [189,] 1 1 [190,] 1 1 [191,] 1 1 [192,] 1 1 [193,] 1 1 [194,] 1 1 [195,] 1 1 [196,] 1 1 [197,] 1 1 [198,] 1 1 [199,] 1 1 [200,] 1 1 [201,] 1 1 [202,] 1 1 [203,] 1 1 [204,] 1 1 [205,] 1 1 [206,] 1 1 [207,] 1 1 [208,] 1 1 [209,] 1 1 [210,] 1 1 [211,] 1 1 [212,] 1 1 [213,] 1 1 [214,] 1 1 [215,] 1 1 [216,] 1 1 [217,] 1 1 [218,] 1 1 [219,] 1 1 [220,] 1 1 [221,] 1 1 [222,] 1 1 [223,] 1 1 [224,] 1 1 [225,] 1 1 [226,] 1 1 [227,] 1 1 [228,] 1 1 [229,] 1 1 [230,] 1 1 [231,] 1 1 [232,] 1 1 [233,] 1 1 [234,] 1 1 [235,] 1 1 [236,] 1 1 [237,] 1 1 [238,] 1 1 [239,] 1 1 [240,] 1 1 [241,] 1 1 [242,] 1 1 [243,] 1 1 [244,] 1 1 [245,] 1 1 [246,] 1 1 [247,] 1 1 [248,] 1 1 [249,] 1 1 [250,] 1 1 [251,] 1 1 [252,] 1 1 [253,] 1 1 [254,] 1 1 [255,] 1 1 [256,] 1 1 [257,] 1 1 [258,] 1 1 [259,] 1 1 [260,] 1 1 [261,] 1 1 [262,] 1 1 [263,] 1 1 [264,] 1 1 [265,] 1 1 [266,] 1 1 [267,] 1 1 [268,] 1 1 [269,] 1 1 [270,] 1 1 [271,] 1 1 [272,] 1 1 [273,] 1 1 [274,] 1 1 [275,] 1 1 [276,] 1 1 [277,] 1 1 [278,] 1 1 [279,] 1 1 [280,] 1 1 [281,] 1 1 [282,] 1 1 [283,] 1 1 [284,] 1 1 [285,] 1 1 [286,] 1 1 [287,] 1 1 [288,] 1 1 [289,] 1 1 [290,] 1 1 [291,] 1 1 [292,] 1 1 [293,] 1 1 [294,] 1 1 [295,] 1 1 [296,] 1 1 [297,] 1 1 [298,] 1 1 [299,] 1 1 [300,] 1 1 [301,] 1 1 [302,] 1 1 [303,] 1 1 [304,] 1 1 [305,] 1 1 [306,] 1 1 [307,] 1 1 [308,] 1 1 [309,] 1 1 [310,] 1 1 [311,] 1 1 [312,] 1 1 [313,] 1 1 [314,] 1 1 [315,] 1 1 [316,] 1 1 [317,] 1 1 [318,] 1 1 [319,] 1 1 [320,] 1 1 [321,] 1 1 [322,] 1 1 [323,] 1 1 [324,] 1 1 [325,] 1 1 [326,] 1 1 [327,] 1 1 [328,] 1 1 [329,] 1 1 [330,] 1 1 [331,] 1 1 [332,] 1 1 [333,] 1 1 [334,] 1 1 [335,] 1 1 [336,] 1 1 [337,] 1 1 [338,] 1 1 [339,] 1 1 [340,] 1 1 [341,] 1 1 [342,] 1 1 [343,] 1 1 [344,] 1 1 [345,] 1 1 [346,] 1 1 [347,] 1 1 [348,] 1 1 [349,] 1 1 [350,] 1 1 [351,] 1 1 [352,] 1 1 [353,] 1 1 [354,] 1 1 [355,] 1 1 [356,] 1 1 [357,] 1 1 [358,] 1 1 [359,] 1 1 [360,] 1 1 [361,] 1 1 [362,] 1 1 [363,] 1 1 [364,] 1 1 [365,] 1 1 [366,] 1 1 [367,] 1 1 [368,] 1 1 [369,] 1 1 [370,] 1 1 [371,] 1 1 [372,] 1 1 [373,] 1 1 [374,] 1 1 [375,] 1 1 [376,] 1 1 [377,] 1 1 [378,] 1 1 [379,] 1 1 [380,] 1 1 [381,] 1 1 [382,] 1 1 [383,] 1 1 [384,] 1 1 [385,] 1 1 [386,] 1 1 [387,] 1 1 [388,] 1 1 [389,] 1 1 [390,] 1 1 [391,] 1 1 [392,] 1 1 [393,] 1 1 [394,] 1 1 [395,] 1 1 [396,] 1 1 [397,] 1 1 [398,] 1 1 [399,] 1 1 [400,] 1 1 [401,] 1 1 [402,] 1 1 [403,] 1 1 [404,] 1 1 [405,] 1 1 [406,] 1 1 [407,] 1 1 [408,] 1 1 [409,] 1 1 [410,] 1 1 [411,] 1 1 [412,] 1 1 [413,] 1 1 [414,] 1 1 [415,] 1 1 [416,] 1 1 [417,] 1 1 [418,] 1 1 [419,] 1 1 [420,] 1 1 [421,] 1 1 [422,] 1 1 [423,] 1 1 [424,] 1 1 [425,] 1 1 [426,] 1 1 [427,] 1 1 [428,] 1 1 [429,] 1 1 [430,] 1 1 [431,] 1 1 C1 C2 C1 428 0 C2 3 0 > postscript(file="/var/www/html/rcomp/tmp/4py3b1292938108.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/538011292938108.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/6dz041292938108.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/7zzys1292938108.tab") + } > > try(system("convert tmp/2w73p1292938108.ps tmp/2w73p1292938108.png",intern=TRUE)) character(0) > try(system("convert tmp/3w73p1292938108.ps tmp/3w73p1292938108.png",intern=TRUE)) character(0) > try(system("convert tmp/4py3b1292938108.ps tmp/4py3b1292938108.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.615 0.471 5.540