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Type 'q()' to quit R. > x <- array(list(159261 + ,0 + ,48 + ,19 + ,20465 + ,23975 + ,189672 + ,1 + ,53 + ,20 + ,33629 + ,85634 + ,7215 + ,0 + ,0 + ,0 + ,1423 + ,1929 + ,129098 + ,0 + ,51 + ,27 + ,25629 + ,36294 + ,230632 + ,0 + ,76 + ,31 + ,54002 + ,72255 + ,515038 + ,1 + ,136 + ,36 + ,151036 + ,189748 + ,180745 + ,1 + ,62 + ,23 + ,33287 + ,61834 + ,185559 + ,0 + ,83 + ,30 + ,31172 + ,68167 + ,154581 + ,0 + ,55 + ,30 + ,28113 + ,38462 + ,298001 + ,1 + ,67 + ,26 + ,57803 + ,101219 + ,121844 + ,2 + ,50 + ,24 + ,49830 + ,43270 + ,184039 + ,0 + ,77 + ,30 + ,52143 + ,76183 + ,100324 + ,0 + ,46 + ,22 + ,21055 + ,31476 + ,217742 + ,4 + ,79 + ,28 + ,47007 + ,62157 + ,168265 + ,4 + ,56 + ,18 + ,28735 + ,46261 + ,154647 + ,3 + ,54 + ,22 + ,59147 + ,50063 + ,142018 + ,0 + ,81 + ,33 + ,78950 + ,64483 + ,79030 + ,5 + ,6 + ,15 + ,13497 + ,2341 + ,167047 + ,0 + ,74 + ,34 + ,46154 + ,48149 + ,27997 + ,0 + ,13 + ,18 + ,53249 + ,12743 + ,73019 + ,0 + ,22 + ,15 + ,10726 + ,18743 + ,241082 + ,0 + ,99 + 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,8 + ,71 + ,36 + ,74990 + ,110600 + ,138708 + ,2 + ,93 + ,25 + ,29653 + ,52235 + ,114408 + ,0 + ,59 + ,24 + ,64622 + ,53986 + ,31970 + ,0 + ,5 + ,21 + ,4157 + ,4105 + ,225558 + ,3 + ,53 + ,19 + ,29245 + ,59331 + ,137011 + ,1 + ,40 + ,12 + ,50008 + ,47796 + ,113612 + ,2 + ,72 + ,30 + ,52338 + ,38302 + ,108641 + ,1 + ,51 + ,21 + ,13310 + ,14063 + ,162203 + ,0 + ,81 + ,34 + ,92901 + ,54414 + ,100098 + ,2 + ,27 + ,32 + ,10956 + ,9903 + ,174768 + ,1 + ,94 + ,28 + ,34241 + ,53987 + ,158459 + ,0 + ,71 + ,28 + ,75043 + ,88937 + ,80934 + ,0 + ,20 + ,21 + ,21152 + ,21928 + ,84971 + ,0 + ,34 + ,31 + ,42249 + ,29487 + ,80545 + ,0 + ,54 + ,26 + ,42005 + ,35334 + ,287191 + ,0 + ,49 + ,29 + ,41152 + ,57596 + ,62974 + ,1 + ,26 + ,23 + ,14399 + ,29750 + ,134091 + ,0 + ,48 + ,25 + ,28263 + ,41029 + ,75555 + ,0 + ,35 + ,22 + ,17215 + ,12416 + ,162154 + ,0 + ,32 + ,26 + ,48140 + ,51158 + ,226638 + ,0 + ,55 + ,33 + ,62897 + ,79935 + ,115019 + ,0 + ,58 + ,24 + ,22883 + ,26552 + ,108749 + ,7 + ,44 + ,24 + 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+ ,31032 + ,50848 + ,136540 + ,0 + ,59 + ,21 + ,32683 + ,39443 + ,76656 + ,0 + ,36 + ,21 + ,34545 + ,27023 + ,3616 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,183065 + ,0 + ,40 + ,23 + ,27525 + ,61022 + ,144677 + ,0 + ,68 + ,33 + ,66856 + ,63528 + ,159104 + ,2 + ,28 + ,30 + ,28549 + ,34835 + ,113273 + ,0 + ,36 + ,23 + ,38610 + ,37172 + ,43410 + ,0 + ,7 + ,1 + ,2781 + ,13 + ,175774 + ,1 + ,70 + ,29 + ,41211 + ,62548 + ,95401 + ,0 + ,30 + ,18 + ,22698 + ,31334 + ,134837 + ,8 + ,69 + ,33 + ,41194 + ,20839 + ,60493 + ,3 + ,3 + ,12 + ,32689 + ,5084 + ,19764 + ,1 + ,10 + ,2 + ,5752 + ,9927 + ,164062 + ,3 + ,46 + ,21 + ,26757 + ,53229 + ,132696 + ,0 + ,34 + ,28 + ,22527 + ,29877 + ,155367 + ,0 + ,54 + ,29 + ,44810 + ,37310 + ,11796 + ,0 + ,1 + ,2 + ,0 + ,0 + ,10674 + ,0 + ,0 + ,0 + ,0 + ,0 + ,142261 + ,0 + ,39 + ,18 + ,100674 + ,50067 + ,6836 + ,0 + ,0 + ,1 + ,0 + ,0 + ,162563 + ,6 + ,48 + ,21 + ,57786 + ,47708 + ,5118 + ,0 + ,5 + ,0 + ,0 + ,0 + ,40248 + ,1 + ,8 + ,4 + ,5444 + ,6012 + ,0 + ,0 + ,0 + ,0 + ,0 + ,0 + ,122641 + ,0 + ,38 + ,25 + ,28470 + ,27749 + ,88837 + ,0 + ,21 + ,26 + ,61849 + ,47555 + ,7131 + ,1 + ,0 + ,0 + ,0 + ,0 + ,9056 + ,0 + ,0 + ,4 + ,2179 + ,1336 + ,76611 + ,1 + ,15 + ,17 + ,8019 + ,11017 + ,132697 + ,0 + ,50 + ,21 + ,39644 + ,55184 + ,100681 + ,1 + ,17 + ,22 + ,23494 + ,43485) + ,dim=c(6 + ,144) + ,dimnames=list(c('time' + ,'shared' + ,'blogs' + ,'reviews' + ,'CWcharacters' + ,'Cwseconds') + ,1:144)) > y <- array(NA,dim=c(6,144),dimnames=list(c('time','shared','blogs','reviews','CWcharacters','Cwseconds'),1:144)) > 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 = 'quantiles' > 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 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] "time" > x[,par1] [1] 159261 189672 7215 129098 230632 515038 180745 185559 154581 298001 [11] 121844 184039 100324 217742 168265 154647 142018 79030 167047 27997 [21] 73019 241082 195820 142001 145433 183744 202232 199532 354924 192399 [31] 182286 181590 133801 233686 219428 0 223044 100129 136733 249965 [41] 242379 145794 96404 195891 117156 157787 81293 237435 233155 160344 [51] 48188 161922 307432 235223 195583 146061 208834 93764 151985 190545 [61] 148922 132856 129561 112718 160930 99184 192535 138708 114408 31970 [71] 225558 137011 113612 108641 162203 100098 174768 158459 80934 84971 [81] 80545 287191 62974 134091 75555 162154 226638 115019 108749 155537 [91] 153133 165618 151517 133686 61342 245196 195576 19349 225371 152796 [101] 59117 91762 136769 114798 85338 27676 153535 122417 0 91529 [111] 107205 144664 136540 76656 3616 0 183065 144677 159104 113273 [121] 43410 175774 95401 134837 60493 19764 164062 132696 155367 11796 [131] 10674 142261 6836 162563 5118 40248 0 122641 88837 7131 [141] 9056 76611 132697 100681 > 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]) [ 0,112718) [112718,161922) [161922,515038] 48 48 48 > colnames(x) [1] "time" "shared" "blogs" "reviews" "CWcharacters" [6] "Cwseconds" > colnames(x)[par1] [1] "time" > x[,par1] [1] [112718,161922) [161922,515038] [ 0,112718) [112718,161922) [5] [161922,515038] [161922,515038] [161922,515038] [161922,515038] [9] [112718,161922) [161922,515038] [112718,161922) [161922,515038] [13] [ 0,112718) [161922,515038] [161922,515038] [112718,161922) [17] [112718,161922) [ 0,112718) [161922,515038] [ 0,112718) [21] [ 0,112718) [161922,515038] [161922,515038] [112718,161922) [25] [112718,161922) [161922,515038] [161922,515038] [161922,515038] [29] [161922,515038] [161922,515038] [161922,515038] [161922,515038] [33] [112718,161922) [161922,515038] [161922,515038] [ 0,112718) [37] [161922,515038] [ 0,112718) [112718,161922) [161922,515038] [41] [161922,515038] [112718,161922) [ 0,112718) [161922,515038] [45] [112718,161922) [112718,161922) [ 0,112718) [161922,515038] [49] [161922,515038] [112718,161922) [ 0,112718) [161922,515038] [53] [161922,515038] [161922,515038] [161922,515038] [112718,161922) [57] [161922,515038] [ 0,112718) [112718,161922) [161922,515038] [61] [112718,161922) [112718,161922) [112718,161922) [112718,161922) [65] [112718,161922) [ 0,112718) [161922,515038] [112718,161922) [69] [112718,161922) [ 0,112718) [161922,515038] [112718,161922) [73] [112718,161922) [ 0,112718) [161922,515038] [ 0,112718) [77] [161922,515038] [112718,161922) [ 0,112718) [ 0,112718) [81] [ 0,112718) [161922,515038] [ 0,112718) [112718,161922) [85] [ 0,112718) [161922,515038] [161922,515038] [112718,161922) [89] [ 0,112718) [112718,161922) [112718,161922) [161922,515038] [93] [112718,161922) [112718,161922) [ 0,112718) [161922,515038] [97] [161922,515038] [ 0,112718) [161922,515038] [112718,161922) [101] [ 0,112718) [ 0,112718) [112718,161922) [112718,161922) [105] [ 0,112718) [ 0,112718) [112718,161922) [112718,161922) [109] [ 0,112718) [ 0,112718) [ 0,112718) [112718,161922) [113] [112718,161922) [ 0,112718) [ 0,112718) [ 0,112718) [117] [161922,515038] [112718,161922) [112718,161922) [112718,161922) [121] [ 0,112718) [161922,515038] [ 0,112718) [112718,161922) [125] [ 0,112718) [ 0,112718) [161922,515038] [112718,161922) [129] [112718,161922) [ 0,112718) [ 0,112718) [112718,161922) [133] [ 0,112718) [161922,515038] [ 0,112718) [ 0,112718) [137] [ 0,112718) [112718,161922) [ 0,112718) [ 0,112718) [141] [ 0,112718) [ 0,112718) [112718,161922) [ 0,112718) Levels: [ 0,112718) [112718,161922) [161922,515038] > if (par2 == 'none') { + m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x) + } > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/14vhp1324479942.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: as.factor(time) Inputs: shared, blogs, reviews, CWcharacters, Cwseconds Number of observations: 144 1) blogs <= 26; criterion = 1, statistic = 79.512 2)* weights = 34 1) blogs > 26 3) Cwseconds <= 35381; criterion = 1, statistic = 33.973 4)* weights = 26 3) Cwseconds > 35381 5) Cwseconds <= 55516; criterion = 0.999, statistic = 17.267 6)* weights = 49 5) Cwseconds > 55516 7)* weights = 35 > postscript(file="/var/www/rcomp/tmp/2sk8m1324479942.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/rcomp/tmp/3awek1324479942.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,] 3 3 [3,] 1 1 [4,] 2 2 [5,] 3 3 [6,] 3 3 [7,] 3 3 [8,] 3 3 [9,] 2 2 [10,] 3 3 [11,] 2 2 [12,] 3 3 [13,] 1 1 [14,] 3 3 [15,] 3 2 [16,] 2 2 [17,] 2 3 [18,] 1 1 [19,] 3 2 [20,] 1 1 [21,] 1 1 [22,] 3 3 [23,] 3 1 [24,] 2 1 [25,] 2 2 [26,] 3 2 [27,] 3 3 [28,] 3 3 [29,] 3 3 [30,] 3 3 [31,] 3 2 [32,] 3 3 [33,] 2 2 [34,] 3 1 [35,] 3 3 [36,] 1 1 [37,] 3 2 [38,] 1 1 [39,] 2 2 [40,] 3 3 [41,] 3 3 [42,] 2 2 [43,] 1 1 [44,] 3 3 [45,] 2 2 [46,] 2 2 [47,] 1 1 [48,] 3 2 [49,] 3 3 [50,] 2 2 [51,] 1 1 [52,] 3 2 [53,] 3 3 [54,] 3 2 [55,] 3 3 [56,] 2 2 [57,] 3 2 [58,] 1 1 [59,] 2 3 [60,] 3 1 [61,] 2 2 [62,] 2 2 [63,] 2 2 [64,] 2 2 [65,] 2 2 [66,] 1 2 [67,] 3 3 [68,] 2 2 [69,] 2 2 [70,] 1 1 [71,] 3 3 [72,] 2 2 [73,] 2 2 [74,] 1 1 [75,] 3 2 [76,] 1 1 [77,] 3 2 [78,] 2 3 [79,] 1 1 [80,] 1 1 [81,] 1 1 [82,] 3 3 [83,] 1 1 [84,] 2 2 [85,] 1 1 [86,] 3 2 [87,] 3 3 [88,] 2 1 [89,] 1 1 [90,] 2 2 [91,] 2 3 [92,] 3 3 [93,] 2 2 [94,] 2 2 [95,] 1 1 [96,] 3 3 [97,] 3 3 [98,] 1 1 [99,] 3 2 [100,] 2 2 [101,] 1 1 [102,] 1 1 [103,] 2 1 [104,] 2 1 [105,] 1 1 [106,] 1 1 [107,] 2 2 [108,] 2 1 [109,] 1 1 [110,] 1 1 [111,] 1 1 [112,] 2 2 [113,] 2 2 [114,] 1 1 [115,] 1 1 [116,] 1 1 [117,] 3 3 [118,] 2 3 [119,] 2 1 [120,] 2 2 [121,] 1 1 [122,] 3 3 [123,] 1 1 [124,] 2 1 [125,] 1 1 [126,] 1 1 [127,] 3 2 [128,] 2 1 [129,] 2 2 [130,] 1 1 [131,] 1 1 [132,] 2 2 [133,] 1 1 [134,] 3 2 [135,] 1 1 [136,] 1 1 [137,] 1 1 [138,] 2 1 [139,] 1 1 [140,] 1 1 [141,] 1 1 [142,] 1 1 [143,] 2 2 [144,] 1 1 [ 0,112718) [112718,161922) [161922,515038] [ 0,112718) 47 1 0 [112718,161922) 10 33 5 [161922,515038] 3 15 30 > postscript(file="/var/www/rcomp/tmp/4oako1324479942.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/rcomp/tmp/5j9sw1324479942.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/rcomp/tmp/65i7z1324479942.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/rcomp/tmp/7b7ny1324479942.tab") + } > > try(system("convert tmp/2sk8m1324479942.ps tmp/2sk8m1324479942.png",intern=TRUE)) character(0) > try(system("convert tmp/3awek1324479942.ps tmp/3awek1324479942.png",intern=TRUE)) character(0) > try(system("convert tmp/4oako1324479942.ps tmp/4oako1324479942.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.230 0.080 2.314