R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

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+ ,1141
+ ,104838
+ ,49
+ ,1
+ ,46
+ ,16
+ ,63
+ ,29236
+ ,5930
+ ,44
+ ,42
+ ,680
+ ,62215
+ ,27
+ ,0
+ ,24
+ ,10
+ ,40
+ ,19546
+ ,3710
+ ,26
+ ,26
+ ,1090
+ ,69304
+ ,30
+ ,6
+ ,40
+ ,19
+ ,69
+ ,22818
+ ,705
+ ,21
+ ,21
+ ,616
+ ,53117
+ ,22
+ ,3
+ ,3
+ ,12
+ ,48
+ ,32689
+ ,443
+ ,4
+ ,4
+ ,285
+ ,19764
+ ,12
+ ,1
+ ,10
+ ,2
+ ,8
+ ,5752
+ ,2416
+ ,10
+ ,10
+ ,1145
+ ,86680
+ ,31
+ ,2
+ ,37
+ ,14
+ ,52
+ ,22197
+ ,7747
+ ,43
+ ,43
+ ,733
+ ,84105
+ ,20
+ ,0
+ ,17
+ ,17
+ ,66
+ ,20055
+ ,5432
+ ,34
+ ,34
+ ,888
+ ,77945
+ ,20
+ ,0
+ ,28
+ ,19
+ ,76
+ ,25272
+ ,4913
+ ,32
+ ,31
+ ,849
+ ,89113
+ ,39
+ ,0
+ ,19
+ ,14
+ ,43
+ ,82206
+ ,2650
+ ,20
+ ,19
+ ,1182
+ ,91005
+ ,29
+ ,3
+ ,29
+ ,11
+ ,39
+ ,32073
+ ,2370
+ ,34
+ ,34
+ ,528
+ ,40248
+ ,16
+ ,1
+ ,8
+ ,4
+ ,14
+ ,5444
+ ,775
+ ,6
+ ,6
+ ,642
+ ,64187
+ ,27
+ ,0
+ ,10
+ ,16
+ ,61
+ ,20154
+ ,5576
+ ,12
+ ,11
+ ,947
+ ,50857
+ ,21
+ ,0
+ ,15
+ ,20
+ ,71
+ ,36944
+ ,1352
+ ,24
+ ,24
+ ,819
+ ,56613
+ ,19
+ ,1
+ ,15
+ ,12
+ ,44
+ ,8019
+ ,3080
+ ,16
+ ,16
+ ,757
+ ,62792
+ ,35
+ ,0
+ ,28
+ ,15
+ ,60
+ ,30884
+ ,10205
+ ,72
+ ,72
+ ,894
+ ,72535
+ ,14
+ ,0
+ ,17
+ ,16
+ ,64
+ ,19540
+ ,6095
+ ,27
+ ,21)
+ ,dim=c(11
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'shared_compendiums'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p1'
+ ,'totsize'
+ ,'totrevisions'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(11,289),dimnames=list(c('pageviews','time_in_rfc','logins','shared_compendiums','blogged_computations','compendiums_reviewed','feedback_messages_p1','totsize','totrevisions','tothyperlinks','totblogs'),1:289))
>  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'
> 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, 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)
Hmisc library by Frank E Harrell Jr

Type library(help='Hmisc'), ?Overview, or ?Hmisc.Overview')
to see overall documentation.

NOTE:Hmisc no longer redefines [.factor to drop unused levels when
subsetting.  To get the old behavior of Hmisc type dropUnusedLevels().


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] "pageviews"
> x[,par1]
  [1] 1418  869 1530 2172  901  463 3201  371 1192 1583 1439 1764 1495 1373 2187
 [16] 1491 4041 1706 2152 1036 1882 1929 2242 1220 1289 2515 2147 2352 1638 1222
 [31] 1812 1677 1579 1731  807 2452  829 1940 2662  186 1499  865 1793 2527 2747
 [46] 1324 2702 1383 1179 2099 4308  918 1831 3373 1713 1438  496 2253  744 1161
 [61] 2352 2144 4691 1112 2694 1973 1769 3148 2474 2084 1954 1226 1389 1496 2269
 [76] 1833 1268 1943  893 1762 1403 1425 1857 1840 1502 1441 1420 1416 2970 1317
 [91] 1644  870 1654 1054  937 3004 2008 2547 1885 1626 1468 2445 1964 1381 1369
[106] 1659 2888 1290 2845 1982 1904 1391  602 1743 1559 2014 2143 2146  874 1590
[121] 1590 1210 2072 1281 1401  834 1105 1272 1944  391  761 1605  530 1988 1386
[136] 2395  387 1742  620  449  800 1684 1050 2699 1606 1502 1204 1138  568 1459
[151] 2158 1111 1421 2833 1955 2922 1002 1060  956 2186 3604 1035 1417 3261 1587
[166] 1424 1701 1249  946 1926 3352 1641 2035 2312 1369 1577 2201  961 1900 1254
[181] 1335 1597  207 1645 2429  151  474  141 1639  872 1318 1018 1383 1314 1335
[196] 1403  910  616 1407  771  766  473 1376 1232 1521  572 1059 1544 1230 1206
[211] 1205 1255  613  721 1109  740 1126  728  689  592  995 1613 2048  705  301
[226] 1803  799  861 1186 1451  628 1161 1463  742  979  675 1241  676 1049  620
[241] 1081 1688  736  617  812 1051 1656  705  945  554 1597  982  222 1212 1143
[256]  435  532  882  608  459  578  826  509  717  637  857  830  652  707  954
[271] 1461  672  778 1141  680 1090  616  285 1145  733  888  849 1182  528  642
[286]  947  819  757  894
> 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])

[ 141,1369) [1369,4691] 
        145         144 
> colnames(x)
 [1] "pageviews"            "time_in_rfc"          "logins"              
 [4] "shared_compendiums"   "blogged_computations" "compendiums_reviewed"
 [7] "feedback_messages_p1" "totsize"              "totrevisions"        
[10] "tothyperlinks"        "totblogs"            
> colnames(x)[par1]
[1] "pageviews"
> x[,par1]
  [1] [1369,4691] [ 141,1369) [1369,4691] [1369,4691] [ 141,1369) [ 141,1369)
  [7] [1369,4691] [ 141,1369) [ 141,1369) [1369,4691] [1369,4691] [1369,4691]
 [13] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691]
 [19] [1369,4691] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
 [25] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
 [31] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691]
 [37] [ 141,1369) [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [ 141,1369)
 [43] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [1369,4691]
 [49] [ 141,1369) [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [1369,4691]
 [55] [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369)
 [61] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [1369,4691]
 [67] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
 [73] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691]
 [79] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691]
 [85] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
 [91] [1369,4691] [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369) [1369,4691]
 [97] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691]
[103] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
[109] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691]
[115] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691]
[121] [1369,4691] [ 141,1369) [1369,4691] [ 141,1369) [1369,4691] [ 141,1369)
[127] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369) [1369,4691]
[133] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691]
[139] [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [1369,4691]
[145] [1369,4691] [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691]
[151] [1369,4691] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [1369,4691]
[157] [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691] [1369,4691] [ 141,1369)
[163] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691] [ 141,1369)
[169] [ 141,1369) [1369,4691] [1369,4691] [1369,4691] [1369,4691] [1369,4691]
[175] [1369,4691] [1369,4691] [1369,4691] [ 141,1369) [1369,4691] [ 141,1369)
[181] [ 141,1369) [1369,4691] [ 141,1369) [1369,4691] [1369,4691] [ 141,1369)
[187] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369)
[193] [1369,4691] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369)
[199] [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369)
[205] [1369,4691] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369)
[211] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[217] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691]
[223] [1369,4691] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369)
[229] [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369)
[235] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[241] [ 141,1369) [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[247] [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369) [1369,4691] [ 141,1369)
[253] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[259] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[265] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[271] [1369,4691] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[277] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[283] [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369) [ 141,1369)
[289] [ 141,1369)
Levels: [ 141,1369) [1369,4691]
> 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/1y53t1354801563.tab") 
+ }
+ }
> m

	 Conditional inference tree with 5 terminal nodes

Response:  as.factor(pageviews) 
Inputs:  time_in_rfc, logins, shared_compendiums, blogged_computations, compendiums_reviewed, feedback_messages_p1, totsize, totrevisions, tothyperlinks, totblogs 
Number of observations:  289 

1) time_in_rfc <= 130585; criterion = 1, statistic = 154.086
  2) logins <= 53; criterion = 1, statistic = 35.07
    3) blogged_computations <= 47; criterion = 0.999, statistic = 14.999
      4)*  weights = 120 
    3) blogged_computations > 47
      5)*  weights = 8 
  2) logins > 53
    6)*  weights = 25 
1) time_in_rfc > 130585
  7) blogged_computations <= 56; criterion = 1, statistic = 18.21
    8)*  weights = 33 
  7) blogged_computations > 56
    9)*  weights = 103 
> postscript(file="/var/wessaorg/rcomp/tmp/2umcg1354801563.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/30wk01354801563.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,]    1    2
  [3,]    2    2
  [4,]    2    2
  [5,]    1    1
  [6,]    1    1
  [7,]    2    2
  [8,]    1    1
  [9,]    1    2
 [10,]    2    2
 [11,]    2    2
 [12,]    2    2
 [13,]    2    2
 [14,]    2    2
 [15,]    2    2
 [16,]    2    2
 [17,]    2    2
 [18,]    2    2
 [19,]    2    2
 [20,]    1    2
 [21,]    2    2
 [22,]    2    2
 [23,]    2    2
 [24,]    1    2
 [25,]    1    2
 [26,]    2    2
 [27,]    2    2
 [28,]    2    2
 [29,]    2    2
 [30,]    1    1
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    1    1
 [36,]    2    2
 [37,]    1    1
 [38,]    2    2
 [39,]    2    2
 [40,]    1    1
 [41,]    2    2
 [42,]    1    1
 [43,]    2    2
 [44,]    2    2
 [45,]    2    2
 [46,]    1    2
 [47,]    2    2
 [48,]    2    2
 [49,]    1    1
 [50,]    2    2
 [51,]    2    2
 [52,]    1    1
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    1    1
 [58,]    2    2
 [59,]    1    1
 [60,]    1    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    1    2
 [73,]    2    2
 [74,]    2    2
 [75,]    2    2
 [76,]    2    2
 [77,]    1    1
 [78,]    2    2
 [79,]    1    1
 [80,]    2    2
 [81,]    2    2
 [82,]    2    2
 [83,]    2    2
 [84,]    2    2
 [85,]    2    2
 [86,]    2    2
 [87,]    2    2
 [88,]    2    1
 [89,]    2    2
 [90,]    1    2
 [91,]    2    2
 [92,]    1    1
 [93,]    2    1
 [94,]    1    1
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    2
[100,]    2    2
[101,]    2    2
[102,]    2    2
[103,]    2    2
[104,]    2    2
[105,]    2    2
[106,]    2    2
[107,]    2    2
[108,]    1    1
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    2    2
[113,]    1    1
[114,]    2    2
[115,]    2    2
[116,]    2    2
[117,]    2    2
[118,]    2    2
[119,]    1    1
[120,]    2    2
[121,]    2    2
[122,]    1    2
[123,]    2    2
[124,]    1    2
[125,]    2    2
[126,]    1    1
[127,]    1    1
[128,]    1    1
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    2    2
[135,]    2    2
[136,]    2    2
[137,]    1    1
[138,]    2    2
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    2    2
[143,]    1    1
[144,]    2    2
[145,]    2    2
[146,]    2    1
[147,]    1    2
[148,]    1    1
[149,]    1    1
[150,]    2    2
[151,]    2    2
[152,]    1    2
[153,]    2    2
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    1    2
[158,]    1    1
[159,]    1    1
[160,]    2    2
[161,]    2    2
[162,]    1    1
[163,]    2    2
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    1    2
[169,]    1    2
[170,]    2    2
[171,]    2    2
[172,]    2    2
[173,]    2    2
[174,]    2    2
[175,]    2    1
[176,]    2    2
[177,]    2    2
[178,]    1    1
[179,]    2    2
[180,]    1    2
[181,]    1    2
[182,]    2    2
[183,]    1    1
[184,]    2    2
[185,]    2    2
[186,]    1    1
[187,]    1    1
[188,]    1    1
[189,]    2    2
[190,]    1    1
[191,]    1    2
[192,]    1    1
[193,]    2    1
[194,]    1    1
[195,]    1    2
[196,]    2    1
[197,]    1    1
[198,]    1    1
[199,]    2    2
[200,]    1    1
[201,]    1    2
[202,]    1    1
[203,]    2    2
[204,]    1    2
[205,]    2    1
[206,]    1    1
[207,]    1    1
[208,]    2    2
[209,]    1    2
[210,]    1    1
[211,]    1    1
[212,]    1    1
[213,]    1    1
[214,]    1    1
[215,]    1    1
[216,]    1    1
[217,]    1    1
[218,]    1    1
[219,]    1    1
[220,]    1    1
[221,]    1    1
[222,]    2    2
[223,]    2    2
[224,]    1    1
[225,]    1    1
[226,]    2    2
[227,]    1    1
[228,]    1    1
[229,]    1    1
[230,]    2    1
[231,]    1    1
[232,]    1    1
[233,]    2    2
[234,]    1    1
[235,]    1    1
[236,]    1    1
[237,]    1    2
[238,]    1    1
[239,]    1    1
[240,]    1    1
[241,]    1    1
[242,]    2    2
[243,]    1    1
[244,]    1    1
[245,]    1    1
[246,]    1    1
[247,]    2    2
[248,]    1    1
[249,]    1    1
[250,]    1    1
[251,]    2    2
[252,]    1    1
[253,]    1    1
[254,]    1    2
[255,]    1    1
[256,]    1    1
[257,]    1    1
[258,]    1    1
[259,]    1    1
[260,]    1    1
[261,]    1    1
[262,]    1    1
[263,]    1    1
[264,]    1    1
[265,]    1    1
[266,]    1    1
[267,]    1    1
[268,]    1    1
[269,]    1    1
[270,]    1    1
[271,]    2    2
[272,]    1    1
[273,]    1    1
[274,]    1    1
[275,]    1    1
[276,]    1    1
[277,]    1    1
[278,]    1    1
[279,]    1    1
[280,]    1    1
[281,]    1    1
[282,]    1    1
[283,]    1    1
[284,]    1    1
[285,]    1    1
[286,]    1    1
[287,]    1    1
[288,]    1    1
[289,]    1    1
             
              [ 141,1369) [1369,4691]
  [ 141,1369)         120          25
  [1369,4691]           8         136
> postscript(file="/var/wessaorg/rcomp/tmp/4ffhp1354801563.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/5z1t71354801563.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/65uuy1354801563.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/726ej1354801563.tab") 
+ }
> 
> try(system("convert tmp/2umcg1354801563.ps tmp/2umcg1354801563.png",intern=TRUE))
character(0)
> try(system("convert tmp/30wk01354801563.ps tmp/30wk01354801563.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ffhp1354801563.ps tmp/4ffhp1354801563.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  7.083   0.532   7.587