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Type 'q()' to quit R. > x <- array(list(1.8 + ,0.8 + ,2.9 + ,1.8 + ,2.3 + ,0.8 + ,2.6 + ,1.7 + ,-0.1 + ,2.9 + ,1.7 + ,2.2 + ,1 + ,2.2 + ,1.4 + ,-1.5 + ,2.9 + ,1.6 + ,2.1 + ,0.6 + ,2.3 + ,1.2 + ,-4.4 + ,1.4 + ,1.8 + ,2.4 + ,0.9 + ,2.4 + ,1 + ,-4.2 + ,1.1 + ,1.6 + ,2.5 + ,0.6 + ,2.1 + ,1.7 + ,3.5 + ,1.9 + ,1.5 + ,2.4 + ,0.6 + ,1.9 + ,2.4 + ,10 + ,2.8 + ,1.5 + ,2.3 + ,0.4 + ,2.2 + ,2 + ,8.6 + ,1.4 + ,1.3 + ,2.1 + ,0.3 + ,1.9 + ,2.1 + ,9.5 + ,0.7 + ,1.4 + ,2.3 + ,0 + ,2.3 + ,2 + ,9.9 + ,-0.8 + ,1.4 + ,2.2 + ,0.3 + ,2.1 + ,1.8 + ,10.4 + ,-3.1 + ,1.3 + ,2.1 + ,0.1 + ,2.2 + ,2.7 + ,16 + ,0.1 + ,1.3 + ,2 + ,0 + ,2.3 + ,2.3 + ,12.7 + ,1 + ,1.2 + ,2.1 + ,0 + ,1.9 + ,1.9 + ,10.2 + ,1.9 + ,1.1 + ,2.1 + ,0 + ,1.7 + ,2 + ,8.9 + ,-0.5 + ,1.4 + ,2.5 + ,-0.2 + ,2.5 + ,2.3 + ,12.6 + ,1.5 + ,1.2 + ,2.2 + ,-0.3 + ,2.1 + ,2.8 + ,13.6 + ,3.9 + ,1.5 + ,2.3 + ,0.1 + ,2.4 + ,2.4 + ,14.8 + ,1.9 + ,1.1 + ,2.3 + ,0.1 + ,1.5 + ,2.3 + ,9.5 + ,2.6 + ,1.3 + ,2.2 + ,0.4 + ,1.9 + ,2.7 + ,13.7 + ,1.7 + ,1.5 + ,2.2 + ,0.4 + ,2.1 + ,2.7 + ,17 + ,1.4 + ,1.1 + ,1.6 + ,-0.5 + ,2.2 + ,2.9 + ,14.7 + ,2.8 + ,1.4 + ,1.8 + ,0.5 + ,2 + ,3 + ,17.4 + ,0.5 + ,1.3 + ,1.7 + ,0.4 + ,2 + ,2.2 + ,9 + ,1 + ,1.5 + ,1.9 + ,0.7 + ,2.2 + ,2.3 + ,9.1 + ,1.5 + ,1.6 + ,1.8 + ,0.8 + ,2.3 + ,2.8 + ,12.2 + ,1.8 + ,1.7 + ,1.9 + ,0.8 + ,2.3 + ,2.8 + ,15.9 + ,2.7 + ,1.1 + ,1.5 + ,0 + ,2 + ,2.8 + ,12.9 + ,3 + ,1.6 + ,1 + ,1.1 + ,2.2 + ,2.2 + ,10.9 + ,-0.3 + ,1.3 + ,0.8 + ,0.9 + ,1.9 + ,2.6 + ,10.6 + ,1.1 + ,1.7 + ,1.1 + ,1.1 + ,2.3 + ,2.8 + ,13.2 + ,1.7 + ,1.6 + ,1.5 + ,1 + ,2.2 + ,2.5 + ,9.6 + ,1.6 + ,1.7 + ,1.7 + ,1.1 + ,2.3 + ,2.4 + ,6.4 + ,3 + ,1.9 + ,2.3 + ,1.5 + ,2.1 + ,2.3 + ,5.8 + ,3.3 + ,1.8 + ,2.4 + ,1 + ,2.4 + ,1.9 + ,-1 + ,6.7 + ,1.9 + ,3 + ,1 + ,2.3 + ,1.7 + ,-0.2 + ,5.6 + ,1.6 + ,3 + ,0.9 + ,1.9 + ,2 + ,2.7 + ,6 + ,1.5 + ,3.2 + ,0.8 + ,1.6 + ,2.1 + ,3.6 + ,4.8 + ,1.6 + ,3.2 + ,0.8 + ,1.8 + ,1.7 + ,-0.9 + ,5.9 + ,1.6 + ,3.2 + ,0.8 + ,1.8 + ,1.8 + ,0.3 + ,4.3 + ,1.7 + ,3.5 + ,0.8 + ,2 + ,1.8 + ,-1.1 + ,3.7 + ,2 + ,4 + ,0.9 + ,2.3 + ,1.8 + ,-2.5 + ,5.6 + ,2 + ,4.3 + ,0.8 + ,2.2 + ,1.3 + ,-3.4 + ,1.7 + ,1.9 + ,4.1 + ,0.7 + ,2.2 + ,1.3 + ,-3.5 + ,3.2 + ,1.7 + ,4 + ,0.6 + ,2 + ,1.3 + ,-3.9 + ,3.6 + ,1.8 + ,4.1 + ,0.6 + ,2 + ,1.2 + ,-4.6 + ,1.7 + ,1.9 + ,4.2 + ,1 + ,1.9 + ,1.4 + ,-0.1 + ,0.5 + ,1.7 + ,4.5 + ,1 + ,1.5 + ,2.2 + ,4.3 + ,2.1 + ,2 + ,5.6 + ,1 + ,1.6 + ,2.9 + ,10.2 + ,1.5 + ,2.1 + ,6.5 + ,1.1 + ,1.5 + ,3.1 + ,8.7 + ,2.7 + ,2.4 + ,7.6 + ,1.1 + ,2 + ,3.5 + ,13.3 + ,1.4 + ,2.5 + ,8.5 + ,1.4 + ,1.5 + ,3.6 + ,15 + ,1.2 + ,2.5 + ,8.7 + ,1.2 + ,1.5 + ,4.4 + ,20.7 + ,2.3 + ,2.6 + ,8.3 + ,1.2 + ,1.9 + ,4.1 + ,20.7 + ,1.6 + ,2.2 + ,8.3 + ,1.3 + ,1.1 + ,5.1 + ,26.4 + ,4.7 + ,2.5 + ,8.5 + ,1.4 + ,1.5 + ,5.8 + ,31.2 + ,3.5 + ,2.8 + ,8.7 + ,1.4 + ,2.1 + ,5.9 + ,31.4 + ,4.4 + ,2.8 + ,8.7 + ,1.1 + ,2.3 + ,5.4 + ,26.6 + ,3.9 + ,2.9 + ,8.5 + ,1.1 + ,2.6 + ,5.5 + ,26.6 + ,3.5 + ,3 + ,7.9 + ,1.3 + ,2.9 + ,4.8 + ,19.2 + ,3 + ,3.1 + ,7 + ,1.5 + ,3.2 + ,3.2 + ,6.5 + ,1.6 + ,2.9 + ,5.8 + ,1.5 + ,3.2) + ,dim=c(7 + ,61) + ,dimnames=list(c('HICP' + ,'ED' + ,'NBL' + ,'IT' + ,'BL' + ,'NEI' + ,'D') + ,1:61)) > y <- array(NA,dim=c(7,61),dimnames=list(c('HICP','ED','NBL','IT','BL','NEI','D'),1:61)) > 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 = '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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "HICP" > x[,par1] [1] 1.8 1.7 1.4 1.2 1.0 1.7 2.4 2.0 2.1 2.0 1.8 2.7 2.3 1.9 2.0 2.3 2.8 2.4 2.3 [20] 2.7 2.7 2.9 3.0 2.2 2.3 2.8 2.8 2.8 2.2 2.6 2.8 2.5 2.4 2.3 1.9 1.7 2.0 2.1 [39] 1.7 1.8 1.8 1.8 1.3 1.3 1.3 1.2 1.4 2.2 2.9 3.1 3.5 3.6 4.4 4.1 5.1 5.8 5.9 [58] 5.4 5.5 4.8 3.2 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) [1.0,2.4) [2.4,5.9] 33 28 > colnames(x) [1] "HICP" "ED" "NBL" "IT" "BL" "NEI" "D" > colnames(x)[par1] [1] "HICP" > x[,par1] [1] [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [2.4,5.9] [8] [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [2.4,5.9] [1.0,2.4) [1.0,2.4) [15] [1.0,2.4) [1.0,2.4) [2.4,5.9] [2.4,5.9] [1.0,2.4) [2.4,5.9] [2.4,5.9] [22] [2.4,5.9] [2.4,5.9] [1.0,2.4) [1.0,2.4) [2.4,5.9] [2.4,5.9] [2.4,5.9] [29] [1.0,2.4) [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [1.0,2.4) [1.0,2.4) [36] [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [43] [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [1.0,2.4) [2.4,5.9] [50] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [57] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] [2.4,5.9] Levels: [1.0,2.4) [2.4,5.9] > 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/1oz5a1293192517.tab") + } + } > m Conditional inference tree with 4 terminal nodes Response: as.factor(HICP) Inputs: ED, NBL, IT, BL, NEI, D Number of observations: 61 1) ED <= 12.7; criterion = 1, statistic = 30.955 2) IT <= 1.6; criterion = 0.981, statistic = 8.69 3)* weights = 20 2) IT > 1.6 4) ED <= 5.8; criterion = 0.999, statistic = 15.095 5)* weights = 14 4) ED > 5.8 6)* weights = 7 1) ED > 12.7 7)* weights = 20 > postscript(file="/var/www/html/rcomp/tmp/2oz5a1293192517.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/3oz5a1293192517.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,] 2 1 [8,] 1 1 [9,] 1 1 [10,] 1 1 [11,] 1 1 [12,] 2 2 [13,] 1 1 [14,] 1 1 [15,] 1 1 [16,] 1 1 [17,] 2 2 [18,] 2 2 [19,] 1 1 [20,] 2 2 [21,] 2 2 [22,] 2 2 [23,] 2 2 [24,] 1 1 [25,] 1 1 [26,] 2 2 [27,] 2 2 [28,] 2 2 [29,] 1 1 [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 1 [40,] 1 1 [41,] 1 1 [42,] 1 1 [43,] 1 1 [44,] 1 1 [45,] 1 1 [46,] 1 1 [47,] 1 1 [48,] 1 1 [49,] 2 2 [50,] 2 2 [51,] 2 2 [52,] 2 2 [53,] 2 2 [54,] 2 2 [55,] 2 2 [56,] 2 2 [57,] 2 2 [58,] 2 2 [59,] 2 2 [60,] 2 2 [61,] 2 2 [1.0,2.4) [2.4,5.9] [1.0,2.4) 33 0 [2.4,5.9] 1 27 > postscript(file="/var/www/html/rcomp/tmp/4si4y1293192517.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/5d0341293192517.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/6hj1a1293192517.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/7rs0v1293192517.tab") + } > > try(system("convert tmp/2oz5a1293192517.ps tmp/2oz5a1293192517.png",intern=TRUE)) character(0) > try(system("convert tmp/3oz5a1293192517.ps tmp/3oz5a1293192517.png",intern=TRUE)) character(0) > try(system("convert tmp/4si4y1293192517.ps tmp/4si4y1293192517.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.095 0.491 6.402