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)

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Type 'q()' to quit R.

> x <- array(list(158258
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+ ,100681
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+ ,17
+ ,22
+ ,23494)
+ ,dim=c(5
+ ,144)
+ ,dimnames=list(c('time'
+ ,'logins'
+ ,'bloggedcomputations'
+ ,'reviewedcompendiums'
+ ,'compendiumcharacters')
+ ,1:144))
>  y <- array(NA,dim=c(5,144),dimnames=list(c('time','logins','bloggedcomputations','reviewedcompendiums','compendiumcharacters'),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 = '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
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] 158258 186930   7215 129098 230587 508313 180745 185559 154581 290658
 [11] 121844 184039 100324 209427 167592 154593 142018  77855 167047  27997
 [21]  70824 241082 195820 141899 145433 180241 202232 190230 354924 192399
 [31] 182286 181590 133801 233686 219428      0 223044 100129 136733 249965
 [41] 242379 145794  96404 195891 115335 157787  81293 224049 223789 160344
 [51]  48188 152206 294283 235223 195583 145942 208834  93764 151985 190545
 [61] 148922 132856 124234 112718 160930  99184 182022 138708 114408  31970
 [71] 225558 137011 113612 108641 162203 100098 174768 158459  80934  84971
 [81]  80545 287191  62974 130982  75555 162154 224670 115019 105038 155537
 [91] 153133 165577 151517 133686  58128 245196 195576  19349 225371 152796
[101]  59117  91762 127987 113552  85338  27676 147984 122417      0  91529
[111] 107205 144664 136540  76656   3616      0 183065 144636 152826 113273
[121]  43410 175774  95401 118893  60493  19764 164062 132696 155088  11796
[131]  10674 142261   6836 154206   5118  40248      0 122641  88837   7131
[141]   9056  76611 132697 100681
> 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])

[     0,141899) [141899,508313] 
             72              72 
> colnames(x)
[1] "time"                 "logins"               "bloggedcomputations" 
[4] "reviewedcompendiums"  "compendiumcharacters"
> colnames(x)[par1]
[1] "time"
> x[,par1]
  [1] [141899,508313] [141899,508313] [     0,141899) [     0,141899)
  [5] [141899,508313] [141899,508313] [141899,508313] [141899,508313]
  [9] [141899,508313] [141899,508313] [     0,141899) [141899,508313]
 [13] [     0,141899) [141899,508313] [141899,508313] [141899,508313]
 [17] [141899,508313] [     0,141899) [141899,508313] [     0,141899)
 [21] [     0,141899) [141899,508313] [141899,508313] [141899,508313]
 [25] [141899,508313] [141899,508313] [141899,508313] [141899,508313]
 [29] [141899,508313] [141899,508313] [141899,508313] [141899,508313]
 [33] [     0,141899) [141899,508313] [141899,508313] [     0,141899)
 [37] [141899,508313] [     0,141899) [     0,141899) [141899,508313]
 [41] [141899,508313] [141899,508313] [     0,141899) [141899,508313]
 [45] [     0,141899) [141899,508313] [     0,141899) [141899,508313]
 [49] [141899,508313] [141899,508313] [     0,141899) [141899,508313]
 [53] [141899,508313] [141899,508313] [141899,508313] [141899,508313]
 [57] [141899,508313] [     0,141899) [141899,508313] [141899,508313]
 [61] [141899,508313] [     0,141899) [     0,141899) [     0,141899)
 [65] [141899,508313] [     0,141899) [141899,508313] [     0,141899)
 [69] [     0,141899) [     0,141899) [141899,508313] [     0,141899)
 [73] [     0,141899) [     0,141899) [141899,508313] [     0,141899)
 [77] [141899,508313] [141899,508313] [     0,141899) [     0,141899)
 [81] [     0,141899) [141899,508313] [     0,141899) [     0,141899)
 [85] [     0,141899) [141899,508313] [141899,508313] [     0,141899)
 [89] [     0,141899) [141899,508313] [141899,508313] [141899,508313]
 [93] [141899,508313] [     0,141899) [     0,141899) [141899,508313]
 [97] [141899,508313] [     0,141899) [141899,508313] [141899,508313]
[101] [     0,141899) [     0,141899) [     0,141899) [     0,141899)
[105] [     0,141899) [     0,141899) [141899,508313] [     0,141899)
[109] [     0,141899) [     0,141899) [     0,141899) [141899,508313]
[113] [     0,141899) [     0,141899) [     0,141899) [     0,141899)
[117] [141899,508313] [141899,508313] [141899,508313] [     0,141899)
[121] [     0,141899) [141899,508313] [     0,141899) [     0,141899)
[125] [     0,141899) [     0,141899) [141899,508313] [     0,141899)
[129] [141899,508313] [     0,141899) [     0,141899) [141899,508313]
[133] [     0,141899) [141899,508313] [     0,141899) [     0,141899)
[137] [     0,141899) [     0,141899) [     0,141899) [     0,141899)
[141] [     0,141899) [     0,141899) [     0,141899) [     0,141899)
Levels: [     0,141899) [141899,508313]
> 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/1g63v1324484889.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(time) 
Inputs:  logins, bloggedcomputations, reviewedcompendiums, compendiumcharacters 
Number of observations:  144 

1) bloggedcomputations <= 36; criterion = 1, statistic = 54.707
  2) logins <= 47; criterion = 0.998, statistic = 11.718
    3)*  weights = 40 
  2) logins > 47
    4)*  weights = 10 
1) bloggedcomputations > 36
  5) reviewedcompendiums <= 27; criterion = 0.967, statistic = 6.97
    6)*  weights = 59 
  5) reviewedcompendiums > 27
    7)*  weights = 35 
> postscript(file="/var/wessaorg/rcomp/tmp/2cb7c1324484889.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/3rn2d1324484889.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    2
  [3,]    1    1
  [4,]    1    2
  [5,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    2
 [10,]    2    2
 [11,]    1    2
 [12,]    2    2
 [13,]    1    2
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    1    1
 [19,]    2    2
 [20,]    1    1
 [21,]    1    1
 [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,]    2    2
 [31,]    2    2
 [32,]    2    2
 [33,]    1    2
 [34,]    2    2
 [35,]    2    2
 [36,]    1    1
 [37,]    2    2
 [38,]    1    2
 [39,]    1    1
 [40,]    2    2
 [41,]    2    2
 [42,]    2    2
 [43,]    1    1
 [44,]    2    2
 [45,]    1    2
 [46,]    2    2
 [47,]    1    1
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    1    1
 [52,]    2    2
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    2    2
 [58,]    1    1
 [59,]    2    2
 [60,]    2    1
 [61,]    2    2
 [62,]    1    2
 [63,]    1    2
 [64,]    1    2
 [65,]    2    1
 [66,]    1    2
 [67,]    2    2
 [68,]    1    2
 [69,]    1    2
 [70,]    1    1
 [71,]    2    2
 [72,]    1    2
 [73,]    1    2
 [74,]    1    2
 [75,]    2    2
 [76,]    1    1
 [77,]    2    2
 [78,]    2    2
 [79,]    1    1
 [80,]    1    1
 [81,]    1    2
 [82,]    2    2
 [83,]    1    1
 [84,]    1    2
 [85,]    1    1
 [86,]    2    1
 [87,]    2    2
 [88,]    1    2
 [89,]    1    2
 [90,]    2    2
 [91,]    2    2
 [92,]    2    2
 [93,]    2    2
 [94,]    1    1
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    1    1
 [99,]    2    2
[100,]    2    2
[101,]    1    1
[102,]    1    2
[103,]    1    2
[104,]    1    1
[105,]    1    1
[106,]    1    1
[107,]    2    2
[108,]    1    1
[109,]    1    1
[110,]    1    2
[111,]    1    1
[112,]    2    2
[113,]    1    2
[114,]    1    1
[115,]    1    1
[116,]    1    1
[117,]    2    2
[118,]    2    2
[119,]    2    1
[120,]    1    1
[121,]    1    1
[122,]    2    2
[123,]    1    1
[124,]    1    2
[125,]    1    1
[126,]    1    1
[127,]    2    2
[128,]    1    1
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    2    2
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    1    2
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    2
[144,]    1    1
                 
                  [     0,141899) [141899,508313]
  [     0,141899)              46              26
  [141899,508313]               4              68
> postscript(file="/var/wessaorg/rcomp/tmp/4ls471324484889.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/5y9h51324484889.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/6oyve1324484889.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/7e30n1324484889.tab") 
+ }
> 
> try(system("convert tmp/2cb7c1324484889.ps tmp/2cb7c1324484889.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rn2d1324484889.ps tmp/3rn2d1324484889.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ls471324484889.ps tmp/4ls471324484889.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.703   0.266   2.994