R version 2.13.0 (2011-04-13) Copyright (C) 2011 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i486-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. 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array(NA,dim=c(15,144),dimnames=list(c('Pageviews','Time','Logins','CompendiumViews','CompendiumViews(PRonly)','Shared','Blogs','Reviews','Submits','Submits(+120)','Characters','CW:Revisions','CW:seconds','CW:Hyperlinks','CW:blogs'),1:144)) > 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 = '2' > #'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 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] "Time" > x[,par1] [1] 127476 130358 7215 112861 210171 393802 117604 126029 99729 256310 [11] 113066 156212 69952 152673 125841 125769 123467 56232 108244 22762 [21] 48554 178697 139115 93773 132796 113933 144781 140711 283337 158146 [31] 123344 157640 91279 189374 167915 0 175403 92342 100023 178277 [41] 145062 110980 86039 119514 95535 109894 61554 156520 159121 129362 [51] 48188 91198 229864 180317 150640 104416 159645 60368 100056 137214 [61] 99630 84557 91199 83419 101723 94982 129700 110708 81518 31970 [71] 192268 87611 77890 83261 116290 55254 116173 111488 60138 73422 [81] 67751 213351 51185 97181 42311 115801 183637 68161 76441 103613 [91] 98707 126527 136781 105863 38775 179984 164808 19349 143902 108660 [101] 43803 47062 110845 92517 58660 27676 98550 43284 0 66016 [111] 57359 96933 70369 65494 3616 0 143931 109894 122973 84336 [121] 43410 136250 79015 92937 57586 19764 105757 96410 113402 11796 [131] 7627 121085 6836 139563 5118 40248 0 95079 80750 7131 [141] 4194 60378 96971 83484 > 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 3616 4194 5118 6836 7131 7215 7627 11796 19349 19764 4 1 1 1 1 1 1 1 1 1 1 22762 27676 31970 38775 40248 42311 43284 43410 43803 47062 48188 1 1 1 1 1 1 1 1 1 1 1 48554 51185 55254 56232 57359 57586 58660 60138 60368 60378 61554 1 1 1 1 1 1 1 1 1 1 1 65494 66016 67751 68161 69952 70369 73422 76441 77890 79015 80750 1 1 1 1 1 1 1 1 1 1 1 81518 83261 83419 83484 84336 84557 86039 87611 91198 91199 91279 1 1 1 1 1 1 1 1 1 1 1 92342 92517 92937 93773 94982 95079 95535 96410 96933 96971 97181 1 1 1 1 1 1 1 1 1 1 1 98550 98707 99630 99729 100023 100056 101723 103613 104416 105757 105863 1 1 1 1 1 1 1 1 1 1 1 108244 108660 109894 110708 110845 110980 111488 112861 113066 113402 113933 1 1 2 1 1 1 1 1 1 1 1 115801 116173 116290 117604 119514 121085 122973 123344 123467 125769 125841 1 1 1 1 1 1 1 1 1 1 1 126029 126527 127476 129362 129700 130358 132796 136250 136781 137214 139115 1 1 1 1 1 1 1 1 1 1 1 139563 140711 143902 143931 144781 145062 150640 152673 156212 156520 157640 1 1 1 1 1 1 1 1 1 1 1 158146 159121 159645 164808 167915 175403 178277 178697 179984 180317 183637 1 1 1 1 1 1 1 1 1 1 1 189374 192268 210171 213351 229864 256310 283337 393802 1 1 1 1 1 1 1 1 > colnames(x) [1] "Pageviews" "Time" [3] "Logins" "CompendiumViews" [5] "CompendiumViews.PRonly." "Shared" [7] "Blogs" "Reviews" [9] "Submits" "Submits..120." [11] "Characters" "CW.Revisions" [13] "CW.seconds" "CW.Hyperlinks" [15] "CW.blogs" > colnames(x)[par1] [1] "Time" > x[,par1] [1] 127476 130358 7215 112861 210171 393802 117604 126029 99729 256310 [11] 113066 156212 69952 152673 125841 125769 123467 56232 108244 22762 [21] 48554 178697 139115 93773 132796 113933 144781 140711 283337 158146 [31] 123344 157640 91279 189374 167915 0 175403 92342 100023 178277 [41] 145062 110980 86039 119514 95535 109894 61554 156520 159121 129362 [51] 48188 91198 229864 180317 150640 104416 159645 60368 100056 137214 [61] 99630 84557 91199 83419 101723 94982 129700 110708 81518 31970 [71] 192268 87611 77890 83261 116290 55254 116173 111488 60138 73422 [81] 67751 213351 51185 97181 42311 115801 183637 68161 76441 103613 [91] 98707 126527 136781 105863 38775 179984 164808 19349 143902 108660 [101] 43803 47062 110845 92517 58660 27676 98550 43284 0 66016 [111] 57359 96933 70369 65494 3616 0 143931 109894 122973 84336 [121] 43410 136250 79015 92937 57586 19764 105757 96410 113402 11796 [131] 7627 121085 6836 139563 5118 40248 0 95079 80750 7131 [141] 4194 60378 96971 83484 > 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/1c37j1323873214.tab") + } + } > m Conditional inference tree with 8 terminal nodes Response: Time Inputs: Pageviews, Logins, CompendiumViews, CompendiumViews.PRonly., Shared, Blogs, Reviews, Submits, Submits..120., Characters, CW.Revisions, CW.seconds, CW.Hyperlinks, CW.blogs Number of observations: 144 1) Pageviews <= 1069; criterion = 1, statistic = 111.367 2) CW.seconds <= 18741; criterion = 1, statistic = 47.555 3) Pageviews <= 285; criterion = 1, statistic = 24.184 4)* weights = 14 3) Pageviews > 285 5)* weights = 14 2) CW.seconds > 18741 6) CW.seconds <= 31334; criterion = 1, statistic = 17.547 7)* weights = 16 6) CW.seconds > 31334 8)* weights = 17 1) Pageviews > 1069 9) Pageviews <= 2190; criterion = 1, statistic = 46.314 10) CW.seconds <= 49562; criterion = 1, statistic = 25.307 11) CompendiumViews <= 532; criterion = 0.975, statistic = 9.771 12)* weights = 32 11) CompendiumViews > 532 13)* weights = 13 10) CW.seconds > 49562 14)* weights = 28 9) Pageviews > 2190 15)* weights = 10 > postscript(file="/var/wessaorg/rcomp/tmp/2k1201323873214.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/3eptu1323873214.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 127476 106450.312 21025.6875 2 130358 148556.393 -18198.3929 3 7215 6617.571 597.4286 4 112861 124982.231 -12121.2308 5 210171 210949.400 -778.4000 6 393802 210949.400 182852.6000 7 117604 148556.393 -30952.3929 8 126029 148556.393 -22527.3929 9 99729 106450.312 -6721.3125 10 256310 210949.400 45360.6000 11 113066 124982.231 -11916.2308 12 156212 148556.393 7655.6071 13 69952 106450.312 -36498.3125 14 152673 210949.400 -58276.4000 15 125841 106450.312 19390.6875 16 125769 106450.312 19318.6875 17 123467 148556.393 -25089.3929 18 56232 44404.000 11828.0000 19 108244 124982.231 -16738.2308 20 22762 44404.000 -21642.0000 21 48554 44404.000 4150.0000 22 178697 148556.393 30140.6071 23 139115 106450.312 32664.6875 24 93773 106450.312 -12677.3125 25 132796 148556.393 -15760.3929 26 113933 106450.312 7482.6875 27 144781 148556.393 -3775.3929 28 140711 148556.393 -7845.3929 29 283337 210949.400 72387.6000 30 158146 148556.393 9589.6071 31 123344 106450.312 16893.6875 32 157640 148556.393 9083.6071 33 91279 124982.231 -33703.2308 34 189374 210949.400 -21575.4000 35 167915 148556.393 19358.6071 36 0 6617.571 -6617.5714 37 175403 124982.231 50420.7692 38 92342 106450.312 -14108.3125 39 100023 106450.312 -6427.3125 40 178277 148556.393 29720.6071 41 145062 124982.231 20079.7692 42 110980 106450.312 4529.6875 43 86039 93575.059 -7536.0588 44 119514 148556.393 -29042.3929 45 95535 93575.059 1959.9412 46 109894 93575.059 16318.9412 47 61554 65591.750 -4037.7500 48 156520 124982.231 31537.7692 49 159121 148556.393 10564.6071 50 129362 124982.231 4379.7692 51 48188 44404.000 3784.0000 52 91198 106450.312 -15252.3125 53 229864 148556.393 81307.6071 54 180317 148556.393 31760.6071 55 150640 148556.393 2083.6071 56 104416 106450.312 -2034.3125 57 159645 106450.312 53194.6875 58 60368 65591.750 -5223.7500 59 100056 93575.059 6480.9412 60 137214 210949.400 -73735.4000 61 99630 106450.312 -6820.3125 62 84557 124982.231 -40425.2308 63 91199 106450.312 -15251.3125 64 83419 93575.059 -10156.0588 65 101723 93575.059 8147.9412 66 94982 106450.312 -11468.3125 67 129700 148556.393 -18856.3929 68 110708 210949.400 -100241.4000 69 81518 93575.059 -12057.0588 70 31970 44404.000 -12434.0000 71 192268 210949.400 -18681.4000 72 87611 93575.059 -5964.0588 73 77890 93575.059 -15685.0588 74 83261 106450.312 -23189.3125 75 116290 124982.231 -8692.2308 76 55254 44404.000 10850.0000 77 116173 106450.312 9722.6875 78 111488 148556.393 -37068.3929 79 60138 65591.750 -5453.7500 80 73422 106450.312 -33028.3125 81 67751 65591.750 2159.2500 82 213351 148556.393 64794.6071 83 51185 65591.750 -14406.7500 84 97181 106450.312 -9269.3125 85 42311 44404.000 -2093.0000 86 115801 106450.312 9350.6875 87 183637 210949.400 -27312.4000 88 68161 65591.750 2569.2500 89 76441 65591.750 10849.2500 90 103613 106450.312 -2837.3125 91 98707 148556.393 -49849.3929 92 126527 148556.393 -22029.3929 93 136781 148556.393 -11775.3929 94 105863 106450.312 -587.3125 95 38775 65591.750 -26816.7500 96 179984 148556.393 31427.6071 97 164808 148556.393 16251.6071 98 19349 6617.571 12731.4286 99 143902 124982.231 18919.7692 100 108660 124982.231 -16322.2308 101 43803 44404.000 -601.0000 102 47062 65591.750 -18529.7500 103 110845 106450.312 4394.6875 104 92517 93575.059 -1058.0588 105 58660 65591.750 -6931.7500 106 27676 44404.000 -16728.0000 107 98550 93575.059 4974.9412 108 43284 44404.000 -1120.0000 109 0 6617.571 -6617.5714 110 66016 65591.750 424.2500 111 57359 65591.750 -8232.7500 112 96933 93575.059 3357.9412 113 70369 93575.059 -23206.0588 114 65494 65591.750 -97.7500 115 3616 6617.571 -3001.5714 116 0 6617.571 -6617.5714 117 143931 93575.059 50355.9412 118 109894 148556.393 -38662.3929 119 122973 106450.312 16522.6875 120 84336 93575.059 -9239.0588 121 43410 44404.000 -994.0000 122 136250 148556.393 -12306.3929 123 79015 65591.750 13423.2500 124 92937 106450.312 -13513.3125 125 57586 44404.000 13182.0000 126 19764 6617.571 13146.4286 127 105757 106450.312 -693.3125 128 96410 65591.750 30818.2500 129 113402 106450.312 6951.6875 130 11796 6617.571 5178.4286 131 7627 6617.571 1009.4286 132 121085 106450.312 14634.6875 133 6836 6617.571 218.4286 134 139563 124982.231 14580.7692 135 5118 6617.571 -1499.5714 136 40248 44404.000 -4156.0000 137 0 6617.571 -6617.5714 138 95079 65591.750 29487.2500 139 80750 106450.312 -25700.3125 140 7131 6617.571 513.4286 141 4194 6617.571 -2423.5714 142 60378 44404.000 15974.0000 143 96971 93575.059 3395.9412 144 83484 93575.059 -10091.0588 > 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/434ri1323873214.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/5um2s1323873214.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/6maht1323873214.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/7nxf51323873214.tab") + } > > try(system("convert tmp/2k1201323873214.ps tmp/2k1201323873214.png",intern=TRUE)) character(0) > try(system("convert tmp/3eptu1323873214.ps tmp/3eptu1323873214.png",intern=TRUE)) character(0) > try(system("convert tmp/434ri1323873214.ps tmp/434ri1323873214.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.337 0.281 4.632