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Type 'q()' to quit R. > x <- array(list(0 + ,13 + ,26 + ,9 + ,15 + ,25 + ,25 + ,0 + ,16 + ,20 + ,9 + ,15 + ,25 + ,24 + ,0 + ,19 + ,21 + ,9 + ,14 + ,19 + ,21 + ,1 + ,15 + ,31 + ,14 + ,10 + ,18 + ,23 + ,0 + ,14 + ,21 + ,8 + ,10 + ,18 + ,17 + ,0 + ,13 + ,18 + ,8 + ,12 + ,22 + ,19 + ,0 + ,19 + ,26 + ,11 + ,18 + ,29 + ,18 + ,0 + ,15 + ,22 + ,10 + ,12 + ,26 + ,27 + ,0 + ,14 + ,22 + ,9 + ,14 + ,25 + ,23 + ,0 + ,15 + ,29 + ,15 + ,18 + ,23 + ,23 + ,1 + ,16 + ,15 + ,14 + ,9 + ,23 + ,29 + ,0 + ,16 + ,16 + ,11 + ,11 + ,23 + ,21 + ,1 + ,16 + ,24 + ,14 + ,11 + ,24 + ,26 + ,0 + ,17 + ,17 + ,6 + ,17 + ,30 + ,25 + ,1 + ,15 + ,19 + ,20 + ,8 + ,19 + ,25 + ,1 + ,15 + ,22 + ,9 + ,16 + ,24 + ,23 + ,0 + ,20 + ,31 + ,10 + ,21 + ,32 + ,26 + ,1 + ,18 + ,28 + ,8 + ,24 + ,30 + ,20 + ,0 + ,16 + ,38 + ,11 + ,21 + ,29 + ,29 + ,1 + ,16 + ,26 + ,14 + ,14 + ,17 + ,24 + ,0 + ,19 + ,25 + ,11 + ,7 + ,25 + ,23 + ,0 + ,16 + ,25 + ,16 + ,18 + ,26 + ,24 + ,1 + ,17 + ,29 + ,14 + ,18 + ,26 + ,30 + ,0 + ,17 + ,28 + ,11 + ,13 + 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+ ,19 + ,27 + ,0 + ,16 + ,11 + ,6 + ,6 + ,15 + ,16 + ,1 + ,14 + ,19 + ,10 + ,12 + ,17 + ,21 + ,0 + ,15 + ,30 + ,9 + ,15 + ,27 + ,26 + ,1 + ,13 + ,21 + ,12 + ,13 + ,19 + ,22 + ,1 + ,15 + ,20 + ,11 + ,17 + ,21 + ,23 + ,0 + ,11 + ,22 + ,14 + ,14 + ,25 + ,19 + ,0 + ,12 + ,30 + ,12 + ,16 + ,19 + ,18 + ,0 + ,16 + ,28 + ,8 + ,16 + ,18 + ,24 + ,1 + ,15 + ,23 + ,14 + ,11 + ,20 + ,29 + ,0 + ,17 + ,23 + ,8 + ,11 + ,15 + ,22 + ,1 + ,16 + ,21 + ,11 + ,16 + ,20 + ,24 + ,0 + ,10 + ,30 + ,12 + ,15 + ,29 + ,22 + ,0 + ,18 + ,22 + ,9 + ,14 + ,19 + ,12 + ,1 + ,13 + ,32 + ,16 + ,9 + ,29 + ,26 + ,0 + ,15 + ,22 + ,11 + ,13 + ,24 + ,18 + ,1 + ,16 + ,15 + ,11 + ,11 + ,23 + ,22 + ,0 + ,16 + ,21 + ,12 + ,14 + ,22 + ,24 + ,0 + ,14 + ,27 + ,15 + ,11 + ,23 + ,21 + ,0 + ,10 + ,22 + ,13 + ,12 + ,22 + ,15 + ,0 + ,17 + ,9 + ,6 + ,8 + ,29 + ,23 + ,0 + ,13 + ,29 + ,11 + ,7 + ,26 + ,22 + ,0 + ,15 + ,20 + ,7 + ,11 + ,26 + ,22 + ,0 + ,16 + ,16 + ,8 + ,13 + ,21 + ,24 + ,0 + ,12 + ,16 + ,8 + ,9 + ,18 + ,23 + ,0 + ,13 + ,16 + ,9 + ,12 + ,10 + ,13) + ,dim=c(7 + ,148) + ,dimnames=list(c('Gender' + ,'Learning' + ,'Concern' + ,'Doubts' + ,'Expectations' + ,'Standards' + ,'Organization') + ,1:148)) > y <- array(NA,dim=c(7,148),dimnames=list(c('Gender','Learning','Concern','Doubts','Expectations','Standards','Organization'),1:148)) > 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 = '5' > #'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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: > #Technical description: > library(party) Loading required package: survival Loading required package: splines Loading required package: grid Loading required package: modeltools Loading required package: stats4 Loading required package: coin Loading required package: mvtnorm Loading required package: zoo Attaching package: 'zoo' The following object(s) are masked from package:base : as.Date.numeric Loading required package: sandwich Loading required package: strucchange Loading required package: vcd Loading required package: MASS Loading required package: colorspace > library(Hmisc) Attaching package: 'Hmisc' The following object(s) are masked from package:survival : untangle.specials The following object(s) are masked from package:base : format.pval, round.POSIXt, trunc.POSIXt, units > par1 <- as.numeric(par1) > par3 <- as.numeric(par3) > x <- data.frame(t(y)) > is.data.frame(x) [1] TRUE > x <- x[!is.na(x[,par1]),] > k <- length(x[1,]) > n <- length(x[,1]) > colnames(x)[par1] [1] "Expectations" > x[,par1] [1] 15 15 14 10 10 12 18 12 14 18 9 11 11 17 8 16 21 24 21 14 7 18 18 13 11 [26] 13 13 18 14 12 9 12 5 10 11 11 12 12 15 12 16 14 17 13 10 17 12 13 13 11 [51] 13 12 12 12 9 7 17 12 12 9 9 13 10 11 12 10 13 6 7 13 11 18 9 9 11 [76] 11 15 8 11 14 14 12 12 8 11 10 17 16 13 15 11 12 16 20 16 11 15 15 12 9 [101] 24 15 18 17 12 15 11 11 15 12 14 11 20 11 12 17 12 11 10 11 12 9 8 6 12 [126] 15 13 17 14 16 16 11 11 16 15 14 9 13 11 14 11 12 8 7 11 13 9 12 > if (par2 == 'kmeans') { + cl <- kmeans(x[,par1], par3) + print(cl) + clm <- matrix(cbind(cl$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]) [ 5,13) [13,24] 82 66 > colnames(x) [1] "Gender" "Learning" "Concern" "Doubts" "Expectations" [6] "Standards" "Organization" > colnames(x)[par1] [1] "Expectations" > x[,par1] [1] [13,24] [13,24] [13,24] [ 5,13) [ 5,13) [ 5,13) [13,24] [ 5,13) [13,24] [10] [13,24] [ 5,13) [ 5,13) [ 5,13) [13,24] [ 5,13) [13,24] [13,24] [13,24] [19] [13,24] [13,24] [ 5,13) [13,24] [13,24] [13,24] [ 5,13) [13,24] [13,24] [28] [13,24] [13,24] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [37] [ 5,13) [ 5,13) [13,24] [ 5,13) [13,24] [13,24] [13,24] [13,24] [ 5,13) [46] [13,24] [ 5,13) [13,24] [13,24] [ 5,13) [13,24] [ 5,13) [ 5,13) [ 5,13) [55] [ 5,13) [ 5,13) [13,24] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [13,24] [ 5,13) [64] [ 5,13) [ 5,13) [ 5,13) [13,24] [ 5,13) [ 5,13) [13,24] [ 5,13) [13,24] [73] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [13,24] [ 5,13) [ 5,13) [13,24] [13,24] [82] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [13,24] [13,24] [13,24] [13,24] [91] [ 5,13) [ 5,13) [13,24] [13,24] [13,24] [ 5,13) [13,24] [13,24] [ 5,13) [100] [ 5,13) [13,24] [13,24] [13,24] [13,24] [ 5,13) [13,24] [ 5,13) [ 5,13) [109] [13,24] [ 5,13) [13,24] [ 5,13) [13,24] [ 5,13) [ 5,13) [13,24] [ 5,13) [118] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [ 5,13) [13,24] [127] [13,24] [13,24] [13,24] [13,24] [13,24] [ 5,13) [ 5,13) [13,24] [13,24] [136] [13,24] [ 5,13) [13,24] [ 5,13) [13,24] [ 5,13) [ 5,13) [ 5,13) [ 5,13) [145] [ 5,13) [13,24] [ 5,13) [ 5,13) Levels: [ 5,13) [13,24] > 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/1j2dp1292540368.tab") + } + } m.ct.i.pred m.ct.i.actu 1 2 1 434 301 2 154 436 [1] 0.5904762 [1] 0.738983 [1] 0.6566038 m.ct.x.pred m.ct.x.actu 1 2 1 34 51 2 29 41 [1] 0.4 [1] 0.5857143 [1] 0.483871 > m Conditional inference tree with 2 terminal nodes Response: as.factor(Expectations) Inputs: Gender, Learning, Concern, Doubts, Standards, Organization Number of observations: 148 1) Concern <= 20; criterion = 0.987, statistic = 9.372 2)* weights = 70 1) Concern > 20 3)* weights = 78 > postscript(file="/var/www/html/rcomp/tmp/2j2dp1292540368.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/3j2dp1292540368.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,] 2 2 [2,] 2 1 [3,] 2 2 [4,] 1 2 [5,] 1 2 [6,] 1 1 [7,] 2 2 [8,] 1 2 [9,] 2 2 [10,] 2 2 [11,] 1 1 [12,] 1 1 [13,] 1 2 [14,] 2 1 [15,] 1 1 [16,] 2 2 [17,] 2 2 [18,] 2 2 [19,] 2 2 [20,] 2 2 [21,] 1 2 [22,] 2 2 [23,] 2 2 [24,] 2 2 [25,] 1 1 [26,] 2 1 [27,] 2 2 [28,] 2 2 [29,] 2 2 [30,] 1 2 [31,] 1 1 [32,] 1 1 [33,] 1 2 [34,] 1 1 [35,] 1 1 [36,] 1 2 [37,] 1 1 [38,] 1 2 [39,] 2 1 [40,] 1 2 [41,] 2 2 [42,] 2 1 [43,] 2 1 [44,] 2 1 [45,] 1 1 [46,] 2 2 [47,] 1 2 [48,] 2 1 [49,] 2 2 [50,] 1 1 [51,] 2 2 [52,] 1 2 [53,] 1 1 [54,] 1 1 [55,] 1 2 [56,] 1 2 [57,] 2 1 [58,] 1 1 [59,] 1 2 [60,] 1 2 [61,] 1 2 [62,] 2 2 [63,] 1 1 [64,] 1 2 [65,] 1 1 [66,] 1 1 [67,] 2 1 [68,] 1 1 [69,] 1 1 [70,] 2 2 [71,] 1 1 [72,] 2 2 [73,] 1 2 [74,] 1 2 [75,] 1 1 [76,] 1 1 [77,] 2 2 [78,] 1 1 [79,] 1 1 [80,] 2 1 [81,] 2 2 [82,] 1 1 [83,] 1 1 [84,] 1 1 [85,] 1 1 [86,] 1 1 [87,] 2 2 [88,] 2 1 [89,] 2 1 [90,] 2 1 [91,] 1 2 [92,] 1 1 [93,] 2 2 [94,] 2 2 [95,] 2 1 [96,] 1 1 [97,] 2 2 [98,] 2 1 [99,] 1 1 [100,] 1 2 [101,] 2 2 [102,] 2 2 [103,] 2 2 [104,] 2 2 [105,] 1 1 [106,] 2 1 [107,] 1 1 [108,] 1 2 [109,] 2 2 [110,] 1 1 [111,] 2 2 [112,] 1 2 [113,] 2 1 [114,] 1 1 [115,] 1 1 [116,] 2 2 [117,] 1 2 [118,] 1 1 [119,] 1 1 [120,] 1 1 [121,] 1 1 [122,] 1 1 [123,] 1 2 [124,] 1 1 [125,] 1 1 [126,] 2 2 [127,] 2 2 [128,] 2 1 [129,] 2 2 [130,] 2 2 [131,] 2 2 [132,] 1 2 [133,] 1 2 [134,] 2 2 [135,] 2 2 [136,] 2 2 [137,] 1 2 [138,] 2 2 [139,] 1 1 [140,] 2 2 [141,] 1 2 [142,] 1 2 [143,] 1 1 [144,] 1 2 [145,] 1 1 [146,] 2 1 [147,] 1 1 [148,] 1 1 [ 5,13) [13,24] [ 5,13) 50 32 [13,24] 20 46 > postscript(file="/var/www/html/rcomp/tmp/4izm61292540368.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/53z3u1292540368.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/6601i1292540368.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/7z9031292540368.tab") + } > > try(system("convert tmp/2j2dp1292540368.ps tmp/2j2dp1292540368.png",intern=TRUE)) character(0) > try(system("convert tmp/3j2dp1292540368.ps tmp/3j2dp1292540368.png",intern=TRUE)) character(0) > try(system("convert tmp/4izm61292540368.ps tmp/4izm61292540368.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.780 0.491 5.936