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Type 'q()' to quit R. > x <- array(list(104.37 + ,1 + ,1 + ,167.16 + ,101.56 + ,100.93 + ,104.89 + ,2 + ,2 + ,179.84 + ,102.13 + ,101.18 + ,105.15 + ,3 + ,3 + ,174.44 + ,102.39 + ,101.11 + ,105.72 + ,4 + ,4 + ,180.35 + ,102.42 + ,102.42 + ,106.38 + ,5 + ,5 + ,193.17 + ,103.87 + ,102.37 + ,106.40 + ,6 + ,6 + ,195.16 + ,104.44 + ,101.95 + ,106.47 + ,7 + ,7 + ,202.43 + ,104.97 + ,102.20 + ,106.59 + ,8 + ,8 + ,189.91 + ,105.17 + ,103.35 + ,106.76 + ,9 + ,9 + ,195.98 + ,105.35 + ,103.65 + ,107.35 + ,10 + ,10 + ,212.09 + ,104.65 + ,102.06 + ,107.81 + ,11 + ,11 + ,205.81 + ,106.62 + ,102.66 + ,108.03 + ,12 + ,12 + ,204.31 + ,107.05 + ,102.32 + ,109.08 + ,1 + ,13 + ,196.07 + ,112.30 + ,102.21 + ,109.86 + ,2 + ,14 + ,199.98 + ,114.70 + ,102.33 + ,110.29 + ,3 + ,15 + ,199.1 + ,115.40 + ,104.41 + ,110.34 + ,4 + ,16 + ,198.31 + ,115.64 + ,104.33 + ,110.59 + ,5 + ,17 + ,195.72 + ,115.66 + ,105.27 + ,110.64 + ,6 + ,18 + ,223.04 + ,114.50 + ,105.34 + ,110.83 + ,7 + ,19 + ,238.41 + ,115.14 + ,104.88 + ,111.51 + ,8 + ,20 + ,259.73 + ,115.41 + ,105.49 + ,113.32 + ,9 + ,21 + ,326.54 + ,119.32 + ,105.90 + ,115.89 + ,10 + ,22 + ,335.15 + ,124.77 + ,105.39 + ,116.51 + ,11 + ,23 + ,321.81 + ,130.96 + ,104.40 + ,117.44 + ,12 + ,24 + ,368.62 + ,141.02 + ,106.19 + ,118.25 + ,1 + ,25 + ,369.59 + ,150.60 + ,106.54 + ,118.65 + ,2 + ,26 + ,425 + ,151.10 + ,108.26 + ,118.52 + ,3 + ,27 + ,439.72 + ,157.19 + ,106.95 + ,119.07 + ,4 + ,28 + ,362.23 + ,157.28 + ,108.32 + ,119.12 + ,5 + ,29 + ,328.76 + ,156.54 + ,108.35 + ,119.28 + ,6 + ,30 + ,348.55 + ,159.62 + ,109.29 + ,119.30 + ,7 + ,31 + ,328.18 + ,163.77 + ,109.46 + ,119.44 + ,8 + ,32 + ,329.34 + ,165.08 + ,109.50 + ,119.57 + ,9 + ,33 + ,295.55 + ,164.75 + ,109.84 + ,119.93 + ,10 + ,34 + ,237.38 + ,163.93 + ,108.73 + ,120.03 + ,11 + ,35 + ,226.85 + ,157.51 + ,109.38 + ,119.66 + ,12 + ,36 + ,220.14 + ,153.36 + ,109.97 + ,119.46 + ,1 + ,37 + ,239.36 + ,156.83 + ,111.10 + ,119.48 + ,2 + ,38 + ,224.69 + ,154.98 + ,110.53 + ,119.56 + ,3 + ,39 + ,230.98 + ,155.02 + ,110.23 + ,119.43 + ,4 + ,40 + ,233.47 + ,153.34 + ,109.41 + ,119.57 + ,5 + ,41 + ,256.7 + ,153.19 + ,108.94 + ,119.59 + ,6 + ,42 + ,253.41 + ,152.80 + ,109.81 + ,119.50 + ,7 + ,43 + ,224.95 + ,152.97 + ,109.20 + ,119.54 + ,8 + ,44 + ,210.37 + ,152.96 + ,109.45 + ,119.56 + ,9 + ,45 + ,191.09 + ,152.35 + ,110.61 + ,119.61 + ,10 + ,46 + ,198.85 + ,151.88 + ,109.44 + ,119.64 + ,11 + ,47 + ,211.04 + ,150.27 + ,109.77 + ,119.60 + ,12 + ,48 + ,206.25 + ,148.80 + ,108.04 + ,119.71 + ,1 + ,49 + ,201.19 + ,149.28 + ,109.65 + ,119.72 + ,2 + ,50 + ,194.37 + ,148.64 + ,111.69 + ,119.66 + ,3 + ,51 + ,191.08 + ,150.36 + ,111.65 + ,119.76 + ,4 + ,52 + ,192.87 + ,149.69 + ,112.04 + ,119.80 + ,5 + ,53 + ,181.61 + ,152.94 + ,111.42 + ,119.88 + ,6 + ,54 + ,157.67 + ,155.18 + ,112.25 + ,119.78 + ,7 + ,55 + ,196.14 + ,156.32 + ,111.46 + ,120.08 + ,8 + ,56 + ,246.35 + ,156.25 + ,111.62 + ,120.22 + ,9 + ,57 + ,271.9 + ,155.52 + ,111.77) + ,dim=c(6 + ,57) + ,dimnames=list(c('Brood' + ,'Maand' + ,'Trend' + ,'Tarwe' + ,'Meel' + ,'Water') + ,1:57)) > y <- array(NA,dim=c(6,57),dimnames=list(c('Brood','Maand','Trend','Tarwe','Meel','Water'),1:57)) > 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 = '4' > par2 = 'quantiles' > par1 = '1' > 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] "Brood" > x[,par1] [1] 104.37 104.89 105.15 105.72 106.38 106.40 106.47 106.59 106.76 107.35 [11] 107.81 108.03 109.08 109.86 110.29 110.34 110.59 110.64 110.83 111.51 [21] 113.32 115.89 116.51 117.44 118.25 118.65 118.52 119.07 119.12 119.28 [31] 119.30 119.44 119.57 119.93 120.03 119.66 119.46 119.48 119.56 119.43 [41] 119.57 119.59 119.50 119.54 119.56 119.61 119.64 119.60 119.71 119.72 [51] 119.66 119.76 119.80 119.88 119.78 120.08 120.22 > 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]) [104,110) [110,119) [119,120) [120,120] 15 14 14 14 > colnames(x) [1] "Brood" "Maand" "Trend" "Tarwe" "Meel" "Water" > colnames(x)[par1] [1] "Brood" > x[,par1] [1] [104,110) [104,110) [104,110) [104,110) [104,110) [104,110) [104,110) [8] [104,110) [104,110) [104,110) [104,110) [104,110) [104,110) [104,110) [15] [104,110) [110,119) [110,119) [110,119) [110,119) [110,119) [110,119) [22] [110,119) [110,119) [110,119) [110,119) [110,119) [110,119) [110,119) [29] [110,119) [119,120) [119,120) [119,120) [119,120) [120,120] [120,120] [36] [120,120] [119,120) [119,120) [119,120) [119,120) [119,120) [119,120) [43] [119,120) [119,120) [119,120) [120,120] [120,120] [119,120) [120,120] [50] [120,120] [120,120] [120,120] [120,120] [120,120] [120,120] [120,120] [57] [120,120] Levels: [104,110) [110,119) [119,120) [120,120] > 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/1w7qj1293212051.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 3 4 1 122 5 0 0 2 0 124 0 0 3 0 3 116 3 4 0 0 30 95 [1] 0.96063 [1] 1 [1] 0.9508197 [1] 0.76 [1] 0.9176707 m.ct.x.pred m.ct.x.actu 1 2 3 4 1 14 9 0 0 2 0 15 1 0 3 0 1 13 4 4 0 0 5 10 [1] 0.6086957 [1] 0.9375 [1] 0.7222222 [1] 0.6666667 [1] 0.7222222 > m Conditional inference tree with 4 terminal nodes Response: as.factor(Brood) Inputs: Maand, Trend, Tarwe, Meel, Water Number of observations: 57 1) Water <= 103.65; criterion = 1, statistic = 50.862 2)* weights = 14 1) Water > 103.65 3) Trend <= 29; criterion = 1, statistic = 32.916 4)* weights = 15 3) Trend > 29 5) Trend <= 45; criterion = 0.988, statistic = 9.196 6)* weights = 16 5) Trend > 45 7)* weights = 12 > postscript(file="/var/www/html/rcomp/tmp/2w7qj1293212051.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/36y741293212051.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 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 2 [16,] 2 2 [17,] 2 2 [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 2 [28,] 2 2 [29,] 2 2 [30,] 3 3 [31,] 3 3 [32,] 3 3 [33,] 3 3 [34,] 4 3 [35,] 4 3 [36,] 4 3 [37,] 3 3 [38,] 3 3 [39,] 3 3 [40,] 3 3 [41,] 3 3 [42,] 3 3 [43,] 3 3 [44,] 3 3 [45,] 3 3 [46,] 4 4 [47,] 4 4 [48,] 3 4 [49,] 4 4 [50,] 4 4 [51,] 4 4 [52,] 4 4 [53,] 4 4 [54,] 4 4 [55,] 4 4 [56,] 4 4 [57,] 4 4 [104,110) [110,119) [119,120) [120,120] [104,110) 14 1 0 0 [110,119) 0 14 0 0 [119,120) 0 0 13 1 [120,120] 0 0 3 11 > postscript(file="/var/www/html/rcomp/tmp/4z8p71293212051.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$Residuals)^2)),4)) + a<-table.row.end(a) + a<-table.end(a) + table.save(a,file="/var/www/html/rcomp/tmp/5k8nd1293212051.tab") + a<-table.start() + a<-table.row.start(a) + a<-table.element(a,'Actuals, Predictions, and Residuals',4,TRUE) + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'#',header=TRUE) + a<-table.element(a,'Actuals',header=TRUE) + a<-table.element(a,'Forecasts',header=TRUE) + a<-table.element(a,'Residuals',header=TRUE) + a<-table.row.end(a) + for (i in 1:length(result$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/6orl11293212051.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/7yilm1293212051.tab") + } > > try(system("convert tmp/2w7qj1293212051.ps tmp/2w7qj1293212051.png",intern=TRUE)) character(0) > try(system("convert tmp/36y741293212051.ps tmp/36y741293212051.png",intern=TRUE)) character(0) > try(system("convert tmp/4z8p71293212051.ps tmp/4z8p71293212051.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.661 0.486 9.761