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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,'Zonneschijn' + ,'windsnelheid' + ,'dampspanning' + ,'Luchtdruk' + ,'vochtigheid ') + ,1:240)) > y <- array(NA,dim=c(7,240),dimnames=list(c('temperatuur','neerslag','Zonneschijn','windsnelheid','dampspanning','Luchtdruk','vochtigheid '),1:240)) > for (i in 1:dim(x)[1]) + { + for (j in 1:dim(x)[2]) + { + y[i,j] <- as.numeric(x[i,j]) + } + } > par4 = 'yes' > par3 = '2' > 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 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] "temperatuur" > x[,par1] [1] 5.2 7.9 8.7 8.9 15.3 15.4 18.1 19.7 13.0 12.6 6.2 3.5 3.4 0.0 9.5 [16] 8.9 10.4 13.2 18.9 19.0 16.3 10.6 5.8 3.6 2.6 5.0 7.3 9.2 15.7 16.8 [31] 18.4 18.1 14.6 7.8 7.6 3.8 5.6 2.2 6.8 11.8 14.9 16.7 16.7 15.9 13.6 [46] 9.2 2.8 2.5 4.8 2.8 7.8 9.0 12.9 16.4 21.8 17.8 13.5 10.0 10.4 5.5 [61] 4.0 6.8 5.7 9.1 13.6 15.0 20.9 20.4 14.0 13.7 7.1 0.8 2.1 1.3 3.9 [76] 10.7 11.1 16.4 17.1 17.3 12.9 10.9 5.3 0.7 -0.2 6.5 8.6 8.5 13.3 16.2 [91] 17.5 21.2 14.8 10.3 7.3 5.1 4.4 6.2 7.7 9.3 15.6 16.3 16.6 17.4 15.3 [106] 9.7 3.7 4.6 5.4 3.1 7.9 10.1 15.0 15.6 19.7 18.1 17.7 10.7 6.2 4.2 [121] 4.0 5.9 7.1 10.5 15.1 16.8 15.3 18.4 16.1 11.3 7.9 5.6 3.4 4.8 6.5 [136] 8.5 15.1 15.7 18.7 19.2 12.9 14.4 6.2 3.3 4.6 7.2 7.8 9.9 13.6 17.1 [151] 17.8 18.6 14.7 10.5 8.6 4.4 2.3 2.8 8.8 10.7 13.9 19.3 19.5 20.4 15.3 [166] 7.9 8.3 4.5 3.2 5.0 6.6 11.1 12.8 16.3 17.4 18.9 15.8 11.7 6.4 2.9 [181] 4.7 2.4 7.2 10.7 13.4 18.5 18.3 16.8 16.6 14.1 6.1 3.5 1.7 2.3 4.5 [196] 9.3 14.2 17.3 23.0 16.3 18.4 14.2 9.1 5.9 7.2 6.8 8.0 14.3 14.6 17.5 [211] 17.2 17.2 14.1 10.5 6.8 4.1 6.5 6.1 6.3 9.3 16.4 16.1 18.0 17.6 14.0 [226] 10.5 6.9 2.8 0.7 3.6 6.7 12.5 14.4 16.5 18.7 19.4 15.8 11.3 9.7 2.9 > 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]) [-0.2,10.6) [10.6,23.0] 123 117 > colnames(x) [1] "temperatuur" "neerslag" "Zonneschijn" "windsnelheid" "dampspanning" [6] "Luchtdruk" "vochtigheid." > colnames(x)[par1] [1] "temperatuur" > x[,par1] [1] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [7] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [13] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [19] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [25] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [31] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [37] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [43] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [49] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [55] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [61] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [67] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [73] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [79] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [85] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [91] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [97] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [103] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [109] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [115] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [121] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [127] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [133] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [139] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [145] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [151] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [157] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [163] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [169] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [175] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [181] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [187] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [193] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [199] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [205] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [211] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [217] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [223] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [229] [-0.2,10.6) [-0.2,10.6) [-0.2,10.6) [10.6,23.0] [10.6,23.0] [10.6,23.0] [235] [10.6,23.0] [10.6,23.0] [10.6,23.0] [10.6,23.0] [-0.2,10.6) [-0.2,10.6) Levels: [-0.2,10.6) [10.6,23.0] > 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/1q4jz1293603418.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 1 1050 69 2 45 1024 [1] 0.9383378 [1] 0.9579046 [1] 0.9478976 m.ct.x.pred m.ct.x.actu 1 2 1 103 8 2 9 92 [1] 0.927928 [1] 0.910891 [1] 0.9198113 > m Conditional inference tree with 4 terminal nodes Response: as.factor(temperatuur) Inputs: neerslag, Zonneschijn, windsnelheid, dampspanning, Luchtdruk, vochtigheid. Number of observations: 240 1) dampspanning <= 10.5; criterion = 1, statistic = 169.614 2) vochtigheid. <= 74; criterion = 1, statistic = 39.085 3)* weights = 19 2) vochtigheid. > 74 4)* weights = 110 1) dampspanning > 10.5 5) vochtigheid. <= 84; criterion = 0.999, statistic = 14.958 6)* weights = 100 5) vochtigheid. > 84 7)* weights = 11 > postscript(file="/var/www/html/rcomp/tmp/2q4jz1293603418.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/31d0k1293603418.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 2 [5,] 2 2 [6,] 2 2 [7,] 2 2 [8,] 2 2 [9,] 2 2 [10,] 2 2 [11,] 1 1 [12,] 1 1 [13,] 1 1 [14,] 1 1 [15,] 1 1 [16,] 1 2 [17,] 1 2 [18,] 2 2 [19,] 2 2 [20,] 2 2 [21,] 2 2 [22,] 2 2 [23,] 1 1 [24,] 1 1 [25,] 1 1 [26,] 1 1 [27,] 1 1 [28,] 1 1 [29,] 2 2 [30,] 2 2 [31,] 2 2 [32,] 2 2 [33,] 2 2 [34,] 1 1 [35,] 1 1 [36,] 1 1 [37,] 1 1 [38,] 1 1 [39,] 1 2 [40,] 2 2 [41,] 2 2 [42,] 2 2 [43,] 2 2 [44,] 2 2 [45,] 2 2 [46,] 1 1 [47,] 1 1 [48,] 1 1 [49,] 1 1 [50,] 1 1 [51,] 1 1 [52,] 1 1 [53,] 2 2 [54,] 2 2 [55,] 2 2 [56,] 2 2 [57,] 2 2 [58,] 1 1 [59,] 1 2 [60,] 1 1 [61,] 1 1 [62,] 1 1 [63,] 1 1 [64,] 1 1 [65,] 2 2 [66,] 2 2 [67,] 2 2 [68,] 2 2 [69,] 2 2 [70,] 2 2 [71,] 1 1 [72,] 1 1 [73,] 1 1 [74,] 1 1 [75,] 1 1 [76,] 2 2 [77,] 2 2 [78,] 2 2 [79,] 2 2 [80,] 2 2 [81,] 2 2 [82,] 2 2 [83,] 1 1 [84,] 1 1 [85,] 1 1 [86,] 1 1 [87,] 1 1 [88,] 1 2 [89,] 2 2 [90,] 2 2 [91,] 2 2 [92,] 2 2 [93,] 2 2 [94,] 1 1 [95,] 1 1 [96,] 1 1 [97,] 1 1 [98,] 1 1 [99,] 1 1 [100,] 1 1 [101,] 2 2 [102,] 2 2 [103,] 2 2 [104,] 2 2 [105,] 2 2 [106,] 1 2 [107,] 1 1 [108,] 1 1 [109,] 1 1 [110,] 1 1 [111,] 1 1 [112,] 1 1 [113,] 2 2 [114,] 2 2 [115,] 2 2 [116,] 2 2 [117,] 2 2 [118,] 2 2 [119,] 1 1 [120,] 1 1 [121,] 1 1 [122,] 1 1 [123,] 1 1 [124,] 1 1 [125,] 2 2 [126,] 2 2 [127,] 2 2 [128,] 2 2 [129,] 2 2 [130,] 2 2 [131,] 1 1 [132,] 1 1 [133,] 1 1 [134,] 1 1 [135,] 1 1 [136,] 1 1 [137,] 2 2 [138,] 2 2 [139,] 2 2 [140,] 2 2 [141,] 2 2 [142,] 2 2 [143,] 1 1 [144,] 1 1 [145,] 1 1 [146,] 1 1 [147,] 1 1 [148,] 1 2 [149,] 2 2 [150,] 2 2 [151,] 2 2 [152,] 2 2 [153,] 2 2 [154,] 1 2 [155,] 1 1 [156,] 1 1 [157,] 1 1 [158,] 1 1 [159,] 1 2 [160,] 2 2 [161,] 2 2 [162,] 2 2 [163,] 2 2 [164,] 2 2 [165,] 2 2 [166,] 1 1 [167,] 1 1 [168,] 1 1 [169,] 1 1 [170,] 1 1 [171,] 1 1 [172,] 2 2 [173,] 2 2 [174,] 2 2 [175,] 2 2 [176,] 2 2 [177,] 2 2 [178,] 2 2 [179,] 1 1 [180,] 1 1 [181,] 1 1 [182,] 1 1 [183,] 1 1 [184,] 2 2 [185,] 2 2 [186,] 2 2 [187,] 2 2 [188,] 2 2 [189,] 2 2 [190,] 2 2 [191,] 1 1 [192,] 1 1 [193,] 1 1 [194,] 1 1 [195,] 1 1 [196,] 1 1 [197,] 2 2 [198,] 2 2 [199,] 2 2 [200,] 2 2 [201,] 2 2 [202,] 2 2 [203,] 1 1 [204,] 1 1 [205,] 1 1 [206,] 1 1 [207,] 1 1 [208,] 2 2 [209,] 2 2 [210,] 2 2 [211,] 2 2 [212,] 2 2 [213,] 2 2 [214,] 1 2 [215,] 1 1 [216,] 1 1 [217,] 1 1 [218,] 1 2 [219,] 1 1 [220,] 1 2 [221,] 2 2 [222,] 2 2 [223,] 2 2 [224,] 2 2 [225,] 2 2 [226,] 1 1 [227,] 1 1 [228,] 1 1 [229,] 1 1 [230,] 1 1 [231,] 1 1 [232,] 2 2 [233,] 2 2 [234,] 2 2 [235,] 2 2 [236,] 2 2 [237,] 2 2 [238,] 2 2 [239,] 1 1 [240,] 1 1 [-0.2,10.6) [10.6,23.0] [-0.2,10.6) 110 13 [10.6,23.0] 0 117 > postscript(file="/var/www/html/rcomp/tmp/4mf2i1293603419.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/5pfjo1293603419.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/6byzt1293603419.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/7l7yw1293603419.tab") + } > > try(system("convert tmp/2q4jz1293603418.ps tmp/2q4jz1293603418.png",intern=TRUE)) character(0) > try(system("convert tmp/31d0k1293603418.ps tmp/31d0k1293603418.png",intern=TRUE)) character(0) > try(system("convert tmp/4mf2i1293603419.ps tmp/4mf2i1293603419.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 3.412 0.524 12.778