R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) 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. Type 'q()' to quit R. > x <- array(list(1 + ,1 + ,41 + ,38 + ,13 + ,12 + ,14 + ,1 + ,1 + ,39 + ,32 + ,16 + ,11 + ,18 + ,1 + ,1 + ,30 + ,35 + ,19 + ,15 + ,11 + ,1 + ,0 + ,31 + ,33 + ,15 + ,6 + ,12 + ,1 + ,1 + ,34 + ,37 + ,14 + ,13 + ,16 + ,1 + ,1 + ,35 + ,29 + ,13 + ,10 + ,18 + ,1 + ,1 + ,39 + ,31 + ,19 + ,12 + ,14 + ,1 + ,1 + ,34 + ,36 + ,15 + ,14 + ,14 + ,1 + ,1 + ,36 + ,35 + ,14 + ,12 + ,15 + ,1 + ,1 + ,37 + ,38 + ,15 + ,9 + ,15 + ,1 + ,0 + ,38 + ,31 + ,16 + ,10 + ,17 + ,1 + ,1 + ,36 + ,34 + ,16 + ,12 + ,19 + ,1 + ,0 + ,38 + ,35 + ,16 + ,12 + ,10 + ,1 + ,1 + ,39 + ,38 + ,16 + ,11 + ,16 + ,1 + ,1 + ,33 + ,37 + ,17 + ,15 + ,18 + ,1 + ,0 + ,32 + ,33 + ,15 + ,12 + ,14 + ,1 + ,0 + ,36 + ,32 + ,15 + ,10 + ,14 + ,1 + ,1 + ,38 + ,38 + ,20 + ,12 + ,17 + ,1 + ,0 + ,39 + ,38 + ,18 + ,11 + ,14 + ,1 + ,1 + ,32 + ,32 + ,16 + ,12 + ,16 + ,1 + ,0 + ,32 + ,33 + ,16 + ,11 + ,18 + ,1 + ,1 + ,31 + ,31 + ,16 + ,12 + ,11 + ,1 + ,1 + ,39 + ,38 + ,19 + ,13 + ,14 + ,1 + ,1 + ,37 + ,39 + ,16 + ,11 + ,12 + ,1 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,0 + ,35 + ,32 + ,15 + ,11 + ,14 + ,0 + ,1 + ,33 + ,34 + ,14 + ,12 + ,15 + ,0 + ,0 + ,37 + ,36 + ,11 + ,9 + ,11 + ,0 + ,0 + ,38 + ,31 + ,16 + ,12 + ,15 + ,0 + ,1 + ,34 + ,35 + ,15 + ,10 + ,14 + ,0 + ,0 + ,27 + ,29 + ,12 + ,9 + ,13 + ,0 + ,1 + ,16 + ,22 + ,6 + ,6 + ,12 + ,0 + ,0 + ,40 + ,41 + ,16 + ,10 + ,16 + ,0 + ,0 + ,36 + ,36 + ,10 + ,9 + ,16 + ,0 + ,1 + ,42 + ,42 + ,15 + ,13 + ,9 + ,0 + ,1 + ,30 + ,33 + ,14 + ,12 + ,14) + ,dim=c(7 + ,288) + ,dimnames=list(c('Pop' + ,'Gender' + ,'Connected' + ,'Separate' + ,'Learning' + ,'Software' + ,'Happiness') + ,1:288)) > y <- array(NA,dim=c(7,288),dimnames=list(c('Pop','Gender','Connected','Separate','Learning','Software','Happiness'),1:288)) > 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 = 'quantiles' > par1 = '5' > 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, 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(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] "Learning" > x[,par1] [1] 13 16 19 15 14 13 19 15 14 15 16 16 16 16 17 15 15 20 18 16 16 16 19 16 17 [26] 17 16 15 16 14 15 12 14 16 14 10 10 14 16 16 16 14 20 14 14 11 14 15 16 14 [51] 16 14 12 16 9 14 16 16 15 16 12 16 16 14 16 17 18 18 12 16 10 14 18 18 16 [76] 17 16 16 13 16 16 16 15 15 16 14 16 16 15 12 17 16 15 13 16 16 16 16 14 16 [101] 16 20 15 16 13 17 16 16 12 16 16 17 12 18 14 14 13 16 13 16 13 16 15 16 15 [126] 17 15 12 16 10 16 12 14 15 13 15 11 12 11 16 15 17 16 10 18 13 16 13 10 15 [151] 16 16 14 10 13 15 16 12 13 12 17 15 10 14 11 13 16 12 16 12 9 12 15 12 12 [176] 14 12 16 11 19 15 8 16 17 12 11 11 14 16 12 16 13 15 16 16 14 16 14 11 12 [201] 15 15 16 16 11 15 12 12 15 15 16 14 17 14 13 15 13 14 15 12 8 14 14 11 12 [226] 13 10 16 18 13 11 4 13 16 10 12 12 10 13 12 14 10 12 12 11 10 12 16 12 14 [251] 16 14 13 4 15 11 11 14 15 14 13 11 15 11 13 13 16 13 16 16 12 7 16 5 16 [276] 4 12 15 14 11 16 15 12 6 16 10 15 14 > 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]) [ 4,16) [16,20] 179 109 > colnames(x) [1] "Pop" "Gender" "Connected" "Separate" "Learning" "Software" [7] "Happiness" > colnames(x)[par1] [1] "Learning" > x[,par1] [1] [ 4,16) [16,20] [16,20] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [ 4,16) [10] [ 4,16) [16,20] [16,20] [16,20] [16,20] [16,20] [ 4,16) [ 4,16) [16,20] [19] [16,20] [16,20] [16,20] [16,20] [16,20] [16,20] [16,20] [16,20] [16,20] [28] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [ 4,16) [37] [ 4,16) [ 4,16) [16,20] [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [46] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [16,20] [55] [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [16,20] [16,20] [64] [ 4,16) [16,20] [16,20] [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [73] [16,20] [16,20] [16,20] [16,20] [16,20] [16,20] [ 4,16) [16,20] [16,20] [82] [16,20] [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [16,20] [ 4,16) [ 4,16) [91] [16,20] [16,20] [ 4,16) [ 4,16) [16,20] [16,20] [16,20] [16,20] [ 4,16) [100] [16,20] [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [16,20] [16,20] [16,20] [109] [ 4,16) [16,20] [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [118] [16,20] [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [16,20] [127] [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [136] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [16,20] [ 4,16) [145] [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [154] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [163] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [172] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [181] [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [190] [ 4,16) [16,20] [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [16,20] [ 4,16) [199] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [ 4,16) [ 4,16) [208] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [217] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [226] [ 4,16) [ 4,16) [16,20] [16,20] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [235] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [244] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [ 4,16) [16,20] [ 4,16) [253] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [262] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [16,20] [271] [ 4,16) [ 4,16) [16,20] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [ 4,16) [280] [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) [16,20] [ 4,16) [ 4,16) [ 4,16) Levels: [ 4,16) [16,20] > 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/1zr5s1355161079.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: as.factor(Learning) Inputs: Pop, Gender, Connected, Separate, Software, Happiness Number of observations: 288 1) Software <= 10; criterion = 1, statistic = 58.801 2) Pop <= 0; criterion = 0.993, statistic = 10.441 3)* weights = 79 2) Pop > 0 4)* weights = 52 1) Software > 10 5) Software <= 13; criterion = 0.995, statistic = 11.099 6)* weights = 136 5) Software > 13 7)* weights = 21 > postscript(file="/var/wessaorg/rcomp/tmp/29s0w1355161079.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/3qe2l1355161079.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 2 [2,] 2 2 [3,] 2 2 [4,] 1 1 [5,] 1 2 [6,] 1 1 [7,] 2 2 [8,] 1 2 [9,] 1 2 [10,] 1 1 [11,] 2 1 [12,] 2 2 [13,] 2 2 [14,] 2 2 [15,] 2 2 [16,] 1 2 [17,] 1 1 [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,] 2 2 [27,] 2 1 [28,] 1 2 [29,] 2 2 [30,] 1 1 [31,] 1 2 [32,] 1 1 [33,] 1 1 [34,] 2 2 [35,] 1 2 [36,] 1 1 [37,] 1 1 [38,] 1 2 [39,] 2 1 [40,] 2 1 [41,] 2 1 [42,] 1 2 [43,] 2 2 [44,] 1 1 [45,] 1 1 [46,] 1 2 [47,] 1 2 [48,] 1 2 [49,] 2 2 [50,] 1 2 [51,] 2 2 [52,] 1 1 [53,] 1 2 [54,] 2 2 [55,] 1 1 [56,] 1 1 [57,] 2 2 [58,] 2 2 [59,] 1 2 [60,] 2 1 [61,] 1 1 [62,] 2 2 [63,] 2 2 [64,] 1 2 [65,] 2 2 [66,] 2 2 [67,] 2 2 [68,] 2 2 [69,] 1 2 [70,] 2 2 [71,] 1 1 [72,] 1 2 [73,] 2 2 [74,] 2 2 [75,] 2 2 [76,] 2 1 [77,] 2 2 [78,] 2 2 [79,] 1 2 [80,] 2 2 [81,] 2 2 [82,] 2 2 [83,] 1 2 [84,] 1 2 [85,] 2 1 [86,] 1 1 [87,] 2 2 [88,] 2 2 [89,] 1 2 [90,] 1 1 [91,] 2 2 [92,] 2 2 [93,] 1 2 [94,] 1 2 [95,] 2 1 [96,] 2 2 [97,] 2 1 [98,] 2 2 [99,] 1 1 [100,] 2 2 [101,] 2 2 [102,] 2 2 [103,] 1 2 [104,] 2 2 [105,] 1 2 [106,] 2 2 [107,] 2 2 [108,] 2 2 [109,] 1 1 [110,] 2 2 [111,] 2 2 [112,] 2 2 [113,] 1 1 [114,] 2 2 [115,] 1 2 [116,] 1 1 [117,] 1 2 [118,] 2 2 [119,] 1 2 [120,] 2 2 [121,] 1 2 [122,] 2 2 [123,] 1 2 [124,] 2 2 [125,] 1 1 [126,] 2 2 [127,] 1 1 [128,] 1 2 [129,] 2 1 [130,] 1 2 [131,] 2 1 [132,] 1 2 [133,] 1 2 [134,] 1 2 [135,] 1 1 [136,] 1 2 [137,] 1 1 [138,] 1 1 [139,] 1 1 [140,] 2 1 [141,] 1 1 [142,] 2 2 [143,] 2 2 [144,] 1 1 [145,] 2 2 [146,] 1 2 [147,] 2 2 [148,] 1 1 [149,] 1 1 [150,] 1 2 [151,] 2 1 [152,] 2 1 [153,] 1 1 [154,] 1 1 [155,] 1 1 [156,] 1 1 [157,] 2 2 [158,] 1 1 [159,] 1 1 [160,] 1 1 [161,] 2 2 [162,] 1 1 [163,] 1 1 [164,] 1 2 [165,] 1 2 [166,] 1 2 [167,] 2 2 [168,] 1 1 [169,] 2 2 [170,] 1 1 [171,] 1 1 [172,] 1 2 [173,] 1 2 [174,] 1 1 [175,] 1 1 [176,] 1 1 [177,] 1 1 [178,] 2 2 [179,] 1 1 [180,] 2 2 [181,] 1 2 [182,] 1 2 [183,] 2 2 [184,] 2 2 [185,] 1 1 [186,] 1 1 [187,] 1 1 [188,] 1 2 [189,] 2 2 [190,] 1 1 [191,] 2 2 [192,] 1 1 [193,] 1 2 [194,] 2 1 [195,] 2 2 [196,] 1 1 [197,] 2 2 [198,] 1 1 [199,] 1 1 [200,] 1 2 [201,] 1 1 [202,] 1 2 [203,] 2 2 [204,] 2 2 [205,] 1 1 [206,] 1 1 [207,] 1 2 [208,] 1 2 [209,] 1 2 [210,] 1 1 [211,] 2 2 [212,] 1 1 [213,] 2 2 [214,] 1 1 [215,] 1 1 [216,] 1 2 [217,] 1 2 [218,] 1 1 [219,] 1 2 [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,] 2 1 [229,] 2 2 [230,] 1 1 [231,] 1 1 [232,] 1 1 [233,] 1 2 [234,] 2 2 [235,] 1 1 [236,] 1 1 [237,] 1 2 [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,] 2 1 [249,] 1 1 [250,] 1 2 [251,] 2 2 [252,] 1 1 [253,] 1 2 [254,] 1 1 [255,] 1 2 [256,] 1 2 [257,] 1 1 [258,] 1 1 [259,] 1 2 [260,] 1 1 [261,] 1 2 [262,] 1 1 [263,] 1 1 [264,] 1 1 [265,] 1 1 [266,] 1 1 [267,] 2 1 [268,] 1 1 [269,] 2 2 [270,] 2 2 [271,] 1 1 [272,] 1 1 [273,] 2 2 [274,] 1 1 [275,] 2 1 [276,] 1 1 [277,] 1 1 [278,] 1 2 [279,] 1 2 [280,] 1 1 [281,] 2 2 [282,] 1 1 [283,] 1 1 [284,] 1 1 [285,] 2 1 [286,] 1 1 [287,] 1 2 [288,] 1 2 [ 4,16) [16,20] [ 4,16) 110 69 [16,20] 21 88 > postscript(file="/var/wessaorg/rcomp/tmp/44ipv1355161079.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/wessaorg/rcomp/tmp/5nuv21355161079.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/61ly71355161079.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/7inga1355161079.tab") + } > > try(system("convert tmp/29s0w1355161079.ps tmp/29s0w1355161079.png",intern=TRUE)) character(0) > try(system("convert tmp/3qe2l1355161079.ps tmp/3qe2l1355161079.png",intern=TRUE)) character(0) > try(system("convert tmp/44ipv1355161079.ps tmp/44ipv1355161079.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.269 0.536 5.790