R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing 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 + ,119.992 + ,157.302 + ,74.997 + ,0.00784 + ,0.00007 + ,0.0037 + ,0.00554 + ,1 + ,122.4 + ,148.65 + ,113.819 + ,0.00968 + ,0.00008 + ,0.00465 + ,0.00696 + ,1 + ,116.682 + ,131.111 + ,111.555 + ,0.0105 + ,0.00009 + ,0.00544 + ,0.00781 + ,1 + ,116.676 + ,137.871 + ,111.366 + ,0.00997 + ,0.00009 + ,0.00502 + ,0.00698 + ,1 + ,116.014 + ,141.781 + ,110.655 + ,0.01284 + ,0.00011 + ,0.00655 + ,0.00908 + ,1 + ,120.552 + ,131.162 + ,113.787 + ,0.00968 + ,0.00008 + ,0.00463 + ,0.0075 + ,1 + ,120.267 + ,137.244 + ,114.82 + ,0.00333 + ,0.00003 + ,0.00155 + ,0.00202 + ,1 + ,107.332 + ,113.84 + ,104.315 + ,0.0029 + ,0.00003 + ,0.00144 + ,0.00182 + ,1 + ,95.73 + ,132.068 + ,91.754 + ,0.00551 + ,0.00006 + ,0.00293 + ,0.00332 + ,1 + ,95.056 + ,120.103 + ,91.226 + ,0.00532 + ,0.00006 + ,0.00268 + ,0.00332 + ,1 + ,88.333 + ,112.24 + ,84.072 + ,0.00505 + ,0.00006 + ,0.00254 + ,0.0033 + ,1 + ,91.904 + ,115.871 + ,86.292 + ,0.0054 + ,0.00006 + ,0.00281 + ,0.00336 + 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+ ,0.00346 + ,0 + ,116.286 + ,177.291 + ,96.983 + ,0.00314 + ,0.00003 + ,0.00134 + ,0.00192 + ,0 + ,116.556 + ,592.03 + ,86.228 + ,0.00496 + ,0.00004 + ,0.00254 + ,0.00263 + ,0 + ,116.342 + ,581.289 + ,94.246 + ,0.00267 + ,0.00002 + ,0.00115 + ,0.00148 + ,0 + ,114.563 + ,119.167 + ,86.647 + ,0.00327 + ,0.00003 + ,0.00146 + ,0.00184 + ,0 + ,201.774 + ,262.707 + ,78.228 + ,0.00694 + ,0.00003 + ,0.00412 + ,0.00396 + ,0 + ,174.188 + ,230.978 + ,94.261 + ,0.00459 + ,0.00003 + ,0.00263 + ,0.00259 + ,0 + ,209.516 + ,253.017 + ,89.488 + ,0.00564 + ,0.00003 + ,0.00331 + ,0.00292 + ,0 + ,174.688 + ,240.005 + ,74.287 + ,0.0136 + ,0.00008 + ,0.00624 + ,0.00564 + ,0 + ,198.764 + ,396.961 + ,74.904 + ,0.0074 + ,0.00004 + ,0.0037 + ,0.0039 + ,0 + ,214.289 + ,260.277 + ,77.973 + ,0.00567 + ,0.00003 + ,0.00295 + ,0.00317) + ,dim=c(8 + ,195) + ,dimnames=list(c('status' + ,'MDVP:Fo(Hz)' + ,'MDVP:Fhi(Hz)' + ,'MDVP:Flo(Hz)' + ,'MDVP:Jitter(%)' + ,'MDVP:Jitter(Abs)' + ,'MDVP:RAP' + ,'MDVP:PPQ') + ,1:195)) > y <- array(NA,dim=c(8,195),dimnames=list(c('status','MDVP:Fo(Hz)','MDVP:Fhi(Hz)','MDVP:Flo(Hz)','MDVP:Jitter(%)','MDVP:Jitter(Abs)','MDVP:RAP','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 = 'none' > par1 = '1' > par4 <- 'no' > par3 <- '3' > par2 <- 'none' > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_regression_trees.wasp/ > #Source of accompanying publication: > # > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Hmisc library by Frank E Harrell Jr Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview') to see overall documentation. NOTE:Hmisc no longer redefines [.factor to drop unused levels when subsetting. To get the old behavior of Hmisc type dropUnusedLevels(). Attaching package: 'Hmisc' The following object is masked from 'package:survival': untangle.specials The following objects are masked from 'package:base': format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "status" > x[,par1] [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 [38] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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 1 48 147 > colnames(x) [1] "status" "MDVP.Fo.Hz." "MDVP.Fhi.Hz." "MDVP.Flo.Hz." [5] "MDVP.Jitter..." "MDVP.Jitter.Abs." "MDVP.RAP" "MDVP.PPQ" > colnames(x)[par1] [1] "status" > x[,par1] [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 1 [38] 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 [186] 0 0 0 0 0 0 0 0 0 0 > 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/1oq2c1386682106.tab") + } + } > m Conditional inference tree with 3 terminal nodes Response: status Inputs: MDVP.Fo.Hz., MDVP.Fhi.Hz., MDVP.Flo.Hz., MDVP.Jitter..., MDVP.Jitter.Abs., MDVP.RAP, MDVP.PPQ Number of observations: 195 1) MDVP.Fo.Hz. <= 193.03; criterion = 1, statistic = 28.537 2)* weights = 154 1) MDVP.Fo.Hz. > 193.03 3) MDVP.Jitter.Abs. <= 1e-05; criterion = 0.981, statistic = 8.953 4)* weights = 22 3) MDVP.Jitter.Abs. > 1e-05 5)* weights = 19 > postscript(file="/var/wessaorg/rcomp/tmp/22er61386682106.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/306431386682106.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) + } Actuals Forecasts Residuals 1 1 0.87012987 0.12987013 2 1 0.87012987 0.12987013 3 1 0.87012987 0.12987013 4 1 0.87012987 0.12987013 5 1 0.87012987 0.12987013 6 1 0.87012987 0.12987013 7 1 0.87012987 0.12987013 8 1 0.87012987 0.12987013 9 1 0.87012987 0.12987013 10 1 0.87012987 0.12987013 11 1 0.87012987 0.12987013 12 1 0.87012987 0.12987013 13 1 0.87012987 0.12987013 14 1 0.87012987 0.12987013 15 1 0.87012987 0.12987013 16 1 0.87012987 0.12987013 17 1 0.87012987 0.12987013 18 1 0.87012987 0.12987013 19 1 0.87012987 0.12987013 20 1 0.87012987 0.12987013 21 1 0.87012987 0.12987013 22 1 0.87012987 0.12987013 23 1 0.87012987 0.12987013 24 1 0.87012987 0.12987013 25 1 0.87012987 0.12987013 26 1 0.87012987 0.12987013 27 1 0.87012987 0.12987013 28 1 0.87012987 0.12987013 29 1 0.87012987 0.12987013 30 1 0.87012987 0.12987013 31 0 0.04545455 -0.04545455 32 0 0.04545455 -0.04545455 33 0 0.04545455 -0.04545455 34 0 0.04545455 -0.04545455 35 0 0.04545455 -0.04545455 36 0 0.04545455 -0.04545455 37 1 0.87012987 0.12987013 38 1 0.87012987 0.12987013 39 1 0.87012987 0.12987013 40 1 0.87012987 0.12987013 41 1 0.87012987 0.12987013 42 1 0.87012987 0.12987013 43 0 0.04545455 -0.04545455 44 0 0.04545455 -0.04545455 45 0 0.04545455 -0.04545455 46 0 0.04545455 -0.04545455 47 0 0.04545455 -0.04545455 48 0 0.04545455 -0.04545455 49 0 0.87012987 -0.87012987 50 0 0.87012987 -0.87012987 51 0 0.87012987 -0.87012987 52 0 0.87012987 -0.87012987 53 0 0.87012987 -0.87012987 54 0 0.87012987 -0.87012987 55 1 0.87012987 0.12987013 56 1 0.87012987 0.12987013 57 1 0.87012987 0.12987013 58 1 0.87012987 0.12987013 59 1 0.87012987 0.12987013 60 1 0.87012987 0.12987013 61 0 0.04545455 -0.04545455 62 0 0.04545455 -0.04545455 63 0 0.04545455 -0.04545455 64 0 0.04545455 -0.04545455 65 0 0.04545455 -0.04545455 66 0 0.04545455 -0.04545455 67 1 0.87012987 0.12987013 68 1 0.87012987 0.12987013 69 1 0.87012987 0.12987013 70 1 0.87012987 0.12987013 71 1 0.87012987 0.12987013 72 1 0.87012987 0.12987013 73 1 0.87012987 0.12987013 74 1 0.87012987 0.12987013 75 1 0.87012987 0.12987013 76 1 0.87012987 0.12987013 77 1 0.87012987 0.12987013 78 1 0.87012987 0.12987013 79 1 0.87012987 0.12987013 80 1 0.87012987 0.12987013 81 1 0.87012987 0.12987013 82 1 0.87012987 0.12987013 83 1 0.87012987 0.12987013 84 1 0.87012987 0.12987013 85 1 0.87012987 0.12987013 86 1 0.87012987 0.12987013 87 1 0.87012987 0.12987013 88 1 0.87012987 0.12987013 89 1 0.87012987 0.12987013 90 1 0.87012987 0.12987013 91 1 0.87012987 0.12987013 92 1 0.87012987 0.12987013 93 1 0.87012987 0.12987013 94 1 0.87012987 0.12987013 95 1 0.87012987 0.12987013 96 1 0.87012987 0.12987013 97 1 0.87012987 0.12987013 98 1 0.87012987 0.12987013 99 1 0.87012987 0.12987013 100 1 0.87012987 0.12987013 101 1 0.87012987 0.12987013 102 1 0.87012987 0.12987013 103 1 0.87012987 0.12987013 104 1 0.87012987 0.12987013 105 1 0.87012987 0.12987013 106 1 0.87012987 0.12987013 107 1 0.87012987 0.12987013 108 1 0.87012987 0.12987013 109 1 0.87012987 0.12987013 110 1 0.87012987 0.12987013 111 1 0.63157895 0.36842105 112 1 0.63157895 0.36842105 113 1 0.63157895 0.36842105 114 1 0.63157895 0.36842105 115 1 0.63157895 0.36842105 116 1 0.87012987 0.12987013 117 1 0.87012987 0.12987013 118 1 0.87012987 0.12987013 119 1 0.87012987 0.12987013 120 1 0.63157895 0.36842105 121 1 0.87012987 0.12987013 122 1 0.87012987 0.12987013 123 1 0.87012987 0.12987013 124 1 0.87012987 0.12987013 125 1 0.87012987 0.12987013 126 1 0.87012987 0.12987013 127 1 0.87012987 0.12987013 128 1 0.87012987 0.12987013 129 1 0.87012987 0.12987013 130 1 0.87012987 0.12987013 131 1 0.87012987 0.12987013 132 1 0.87012987 0.12987013 133 1 0.87012987 0.12987013 134 1 0.87012987 0.12987013 135 1 0.87012987 0.12987013 136 1 0.87012987 0.12987013 137 1 0.87012987 0.12987013 138 1 0.87012987 0.12987013 139 1 0.87012987 0.12987013 140 1 0.87012987 0.12987013 141 1 0.87012987 0.12987013 142 1 0.63157895 0.36842105 143 1 0.63157895 0.36842105 144 1 0.63157895 0.36842105 145 1 0.04545455 0.95454545 146 1 0.63157895 0.36842105 147 1 0.87012987 0.12987013 148 1 0.87012987 0.12987013 149 1 0.87012987 0.12987013 150 1 0.63157895 0.36842105 151 1 0.87012987 0.12987013 152 1 0.87012987 0.12987013 153 1 0.63157895 0.36842105 154 1 0.87012987 0.12987013 155 1 0.87012987 0.12987013 156 1 0.87012987 0.12987013 157 1 0.87012987 0.12987013 158 1 0.87012987 0.12987013 159 1 0.87012987 0.12987013 160 1 0.87012987 0.12987013 161 1 0.87012987 0.12987013 162 1 0.87012987 0.12987013 163 1 0.87012987 0.12987013 164 1 0.87012987 0.12987013 165 1 0.87012987 0.12987013 166 0 0.04545455 -0.04545455 167 0 0.04545455 -0.04545455 168 0 0.04545455 -0.04545455 169 0 0.63157895 -0.63157895 170 0 0.63157895 -0.63157895 171 0 0.63157895 -0.63157895 172 0 0.87012987 -0.87012987 173 0 0.87012987 -0.87012987 174 0 0.87012987 -0.87012987 175 0 0.87012987 -0.87012987 176 0 0.87012987 -0.87012987 177 0 0.87012987 -0.87012987 178 1 0.87012987 0.12987013 179 1 0.87012987 0.12987013 180 1 0.87012987 0.12987013 181 1 0.87012987 0.12987013 182 1 0.87012987 0.12987013 183 1 0.87012987 0.12987013 184 0 0.87012987 -0.87012987 185 0 0.87012987 -0.87012987 186 0 0.87012987 -0.87012987 187 0 0.87012987 -0.87012987 188 0 0.87012987 -0.87012987 189 0 0.87012987 -0.87012987 190 0 0.63157895 -0.63157895 191 0 0.87012987 -0.87012987 192 0 0.63157895 -0.63157895 193 0 0.87012987 -0.87012987 194 0 0.63157895 -0.63157895 195 0 0.63157895 -0.63157895 > if (par2 != 'none') { + print(cbind(as.factor(x[,par1]),predict(m))) + myt <- table(as.factor(x[,par1]),predict(m)) + print(myt) + } > postscript(file="/var/wessaorg/rcomp/tmp/4fhel1386682107.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/5e9ea1386682107.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/6g8x61386682107.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/7ntd41386682107.tab") + } > > try(system("convert tmp/22er61386682106.ps tmp/22er61386682106.png",intern=TRUE)) character(0) > try(system("convert tmp/306431386682106.ps tmp/306431386682106.png",intern=TRUE)) character(0) > try(system("convert tmp/4fhel1386682107.ps tmp/4fhel1386682107.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.492 1.539 10.016