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.
Type 'q()' to quit R.

> x <- array(list(79
+ ,30
+ ,94
+ ,112285
+ ,146283
+ ,58
+ ,28
+ ,103
+ ,84786
+ ,98364
+ ,60
+ ,38
+ ,93
+ ,83123
+ ,86146
+ ,108
+ ,30
+ ,103
+ ,101193
+ ,96933
+ ,49
+ ,22
+ ,51
+ ,38361
+ ,79234
+ ,0
+ ,26
+ ,70
+ ,68504
+ ,42551
+ ,121
+ ,25
+ ,91
+ ,119182
+ ,195663
+ ,1
+ ,18
+ ,22
+ ,22807
+ ,6853
+ ,43
+ ,26
+ ,93
+ ,116174
+ ,95757
+ ,69
+ ,25
+ ,60
+ ,57635
+ ,85584
+ ,78
+ ,38
+ ,123
+ ,66198
+ ,143983
+ ,86
+ ,44
+ ,148
+ ,71701
+ ,75851
+ ,44
+ ,30
+ ,90
+ ,57793
+ ,59238
+ ,104
+ ,40
+ ,124
+ ,80444
+ ,93163
+ ,63
+ ,34
+ ,70
+ ,53855
+ ,96037
+ ,158
+ ,47
+ ,168
+ ,97668
+ ,151511
+ ,102
+ ,30
+ ,115
+ ,133824
+ ,136368
+ ,77
+ ,31
+ ,71
+ ,101481
+ ,112642
+ ,82
+ ,23
+ ,66
+ ,99645
+ ,94728
+ ,115
+ ,36
+ ,134
+ ,114789
+ ,105499
+ ,101
+ ,36
+ ,117
+ ,99052
+ ,121527
+ ,80
+ ,30
+ ,108
+ ,67654
+ ,127766
+ ,50
+ ,25
+ ,84
+ ,65553
+ ,98958
+ ,83
+ ,39
+ ,156
+ ,97500
+ ,77900
+ ,123
+ ,34
+ ,120
+ ,69112
+ ,85646
+ ,73
+ ,31
+ ,114
+ ,82753
+ ,98579
+ ,81
+ ,31
+ ,94
+ ,85323
+ ,130767
+ ,105
+ ,33
+ ,120
+ ,72654
+ ,131741
+ ,47
+ ,25
+ ,81
+ ,30727
+ ,53907
+ ,105
+ ,33
+ ,110
+ ,77873
+ ,178812
+ ,94
+ ,35
+ ,133
+ ,117478
+ ,146761
+ ,44
+ ,42
+ ,122
+ ,74007
+ ,82036
+ ,114
+ ,43
+ ,158
+ ,90183
+ ,163253
+ ,38
+ ,30
+ ,109
+ ,61542
+ ,27032
+ ,107
+ ,33
+ ,124
+ ,101494
+ ,171975
+ ,71
+ ,32
+ ,92
+ ,55813
+ ,86572
+ ,84
+ ,36
+ ,126
+ ,79215
+ ,159676
+ ,59
+ ,28
+ ,70
+ ,55461
+ ,85371
+ ,33
+ ,14
+ ,37
+ ,31081
+ ,58391
+ ,96
+ ,32
+ ,120
+ ,83122
+ ,136815
+ ,106
+ ,30
+ ,93
+ ,70106
+ ,120642
+ ,56
+ ,35
+ ,95
+ ,60578
+ ,69107
+ ,59
+ ,28
+ ,90
+ ,79892
+ ,108016
+ ,39
+ ,28
+ ,80
+ ,49810
+ ,46341
+ ,34
+ ,39
+ ,31
+ ,71570
+ ,78348
+ ,76
+ ,34
+ ,110
+ ,100708
+ ,79336
+ ,20
+ ,26
+ ,66
+ ,33032
+ ,56968
+ ,91
+ ,39
+ ,138
+ ,82875
+ ,93176
+ ,115
+ ,39
+ ,133
+ ,139077
+ ,161632
+ ,85
+ ,33
+ ,113
+ ,71595
+ ,87850
+ ,76
+ ,28
+ ,100
+ ,72260
+ ,127969
+ ,8
+ ,4
+ ,7
+ ,5950
+ ,15049
+ ,79
+ ,39
+ ,140
+ ,115762
+ ,155135
+ ,21
+ ,18
+ ,61
+ ,32551
+ ,25109
+ ,30
+ ,14
+ ,41
+ ,31701
+ ,45824
+ ,76
+ ,29
+ ,96
+ ,80670
+ ,102996
+ ,101
+ ,44
+ ,164
+ ,143558
+ ,160604
+ ,92
+ ,28
+ ,102
+ ,120733
+ ,162647
+ ,123
+ ,35
+ ,124
+ ,105195
+ ,174141
+ ,75
+ ,28
+ ,99
+ ,73107
+ ,60622
+ ,128
+ ,38
+ ,129
+ ,132068
+ ,179566
+ ,105
+ ,23
+ ,62
+ ,149193
+ ,184301
+ ,55
+ ,36
+ ,73
+ ,46821
+ ,75661
+ ,56
+ ,32
+ ,114
+ ,87011
+ ,96144
+ ,41
+ ,29
+ ,99
+ ,95260
+ ,129847
+ ,72
+ ,25
+ ,70
+ ,55183
+ ,117286
+ ,67
+ ,27
+ ,104
+ ,106671
+ ,71180
+ ,75
+ ,36
+ ,116
+ ,73511
+ ,109377
+ ,114
+ ,28
+ ,91
+ ,92945
+ ,85298
+ ,118
+ ,23
+ ,74
+ ,78664
+ ,73631
+ ,77
+ ,40
+ ,138
+ ,70054
+ ,86767
+ ,22
+ ,23
+ ,67
+ ,22618
+ ,23824
+ ,66
+ ,40
+ ,151
+ ,74011
+ ,93487
+ ,69
+ ,28
+ ,72
+ ,83737
+ ,82981
+ ,105
+ ,34
+ ,120
+ ,69094
+ ,73815
+ ,116
+ ,33
+ ,115
+ ,93133
+ ,94552
+ ,88
+ ,28
+ ,105
+ ,95536
+ ,132190
+ ,73
+ ,34
+ ,104
+ ,225920
+ ,128754
+ ,99
+ ,30
+ ,108
+ ,62133
+ ,66363
+ ,62
+ ,33
+ ,98
+ ,61370
+ ,67808
+ ,53
+ ,22
+ ,69
+ ,43836
+ ,61724
+ ,118
+ ,38
+ ,111
+ ,106117
+ ,131722
+ ,30
+ ,26
+ ,99
+ ,38692
+ ,68580
+ ,100
+ ,35
+ ,71
+ ,84651
+ ,106175
+ ,49
+ ,8
+ ,27
+ ,56622
+ ,55792
+ ,24
+ ,24
+ ,69
+ ,15986
+ ,25157
+ ,67
+ ,29
+ ,107
+ ,95364
+ ,76669
+ ,57
+ ,29
+ ,107
+ ,89691
+ ,105805
+ ,75
+ ,45
+ ,93
+ ,67267
+ ,129484
+ ,135
+ ,37
+ ,129
+ ,126846
+ ,72413
+ ,68
+ ,33
+ ,69
+ ,41140
+ ,87831
+ ,124
+ ,33
+ ,118
+ ,102860
+ ,96971
+ ,33
+ ,25
+ ,73
+ ,51715
+ ,71299
+ ,98
+ ,32
+ ,119
+ ,55801
+ ,77494
+ ,58
+ ,29
+ ,104
+ ,111813
+ ,120336
+ ,68
+ ,28
+ ,107
+ ,120293
+ ,93913
+ ,81
+ ,28
+ ,99
+ ,138599
+ ,136048
+ ,131
+ ,31
+ ,90
+ ,161647
+ ,181248
+ ,110
+ ,52
+ ,197
+ ,115929
+ ,146123
+ ,130
+ ,24
+ ,85
+ ,162901
+ ,186646
+ ,93
+ ,41
+ ,139
+ ,109825
+ ,102255
+ ,118
+ ,33
+ ,106
+ ,129838
+ ,168237
+ ,39
+ ,32
+ ,50
+ ,37510
+ ,64219
+ ,13
+ ,19
+ ,64
+ ,43750
+ ,19630
+ ,74
+ ,20
+ ,31
+ ,40652
+ ,76825
+ ,81
+ ,31
+ ,63
+ ,87771
+ ,115338
+ ,109
+ ,31
+ ,92
+ ,85872
+ ,109427
+ ,151
+ ,32
+ ,106
+ ,89275
+ ,118168
+ ,28
+ ,23
+ ,69
+ ,192565
+ ,153197
+ ,83
+ ,30
+ ,93
+ ,140867
+ ,68370
+ ,54
+ ,31
+ ,114
+ ,120662
+ ,146304
+ ,133
+ ,42
+ ,110
+ ,101338
+ ,103950
+ ,12
+ ,1
+ ,0
+ ,1168
+ ,5841
+ ,106
+ ,32
+ ,83
+ ,65567
+ ,84396
+ ,23
+ ,11
+ ,30
+ ,25162
+ ,24610
+ ,71
+ ,36
+ ,98
+ ,40735
+ ,55515
+ ,116
+ ,31
+ ,82
+ ,91413
+ ,209056
+ ,4
+ ,0
+ ,0
+ ,855
+ ,6622
+ ,62
+ ,24
+ ,60
+ ,97068
+ ,115814
+ ,18
+ ,8
+ ,9
+ ,14116
+ ,13155
+ ,98
+ ,33
+ ,115
+ ,76643
+ ,142775
+ ,64
+ ,40
+ ,140
+ ,110681
+ ,68847
+ ,32
+ ,38
+ ,120
+ ,92696
+ ,20112
+ ,25
+ ,24
+ ,66
+ ,94785
+ ,61023
+ ,16
+ ,8
+ ,21
+ ,8773
+ ,13983
+ ,48
+ ,35
+ ,124
+ ,83209
+ ,65176
+ ,100
+ ,43
+ ,152
+ ,93815
+ ,132432
+ ,46
+ ,43
+ ,139
+ ,86687
+ ,112494
+ ,129
+ ,41
+ ,144
+ ,105547
+ ,170875
+ ,130
+ ,38
+ ,120
+ ,103487
+ ,180759
+ ,136
+ ,45
+ ,160
+ ,213688
+ ,214921
+ ,59
+ ,31
+ ,114
+ ,71220
+ ,100226
+ ,32
+ ,28
+ ,78
+ ,56926
+ ,54454
+ ,63
+ ,31
+ ,119
+ ,91721
+ ,78876
+ ,95
+ ,40
+ ,141
+ ,115168
+ ,170745
+ ,14
+ ,30
+ ,101
+ ,111194
+ ,6940
+ ,113
+ ,37
+ ,133
+ ,135777
+ ,122037
+ ,47
+ ,30
+ ,83
+ ,51513
+ ,53782
+ ,92
+ ,35
+ ,116
+ ,74163
+ ,127748
+ ,70
+ ,32
+ ,90
+ ,51633
+ ,86839
+ ,19
+ ,27
+ ,36
+ ,75345
+ ,44830
+ ,91
+ ,31
+ ,97
+ ,98952
+ ,103300
+ ,111
+ ,31
+ ,98
+ ,102372
+ ,112283
+ ,41
+ ,21
+ ,78
+ ,37238
+ ,10901
+ ,120
+ ,39
+ ,117
+ ,103772
+ ,120691
+ ,135
+ ,41
+ ,148
+ ,123969
+ ,58106
+ ,87
+ ,32
+ ,105
+ ,135400
+ ,122422
+ ,131
+ ,39
+ ,132
+ ,130115
+ ,139296
+ ,4
+ ,0
+ ,0
+ ,6023
+ ,7953
+ ,47
+ ,30
+ ,73
+ ,64466
+ ,89455
+ ,109
+ ,37
+ ,86
+ ,54990
+ ,147866
+ ,7
+ ,0
+ ,0
+ ,1644
+ ,4245
+ ,12
+ ,5
+ ,13
+ ,6179
+ ,21509
+ ,0
+ ,1
+ ,4
+ ,3926
+ ,7670
+ ,37
+ ,32
+ ,48
+ ,34777
+ ,14336
+ ,46
+ ,24
+ ,46
+ ,73224
+ ,53608)
+ ,dim=c(5
+ ,156)
+ ,dimnames=list(c('blogged_computations'
+ ,'compendiums_reviewed'
+ ,'feedback_messages_p120'
+ ,'totsize'
+ ,'totseconds')
+ ,1:156))
>  y <- array(NA,dim=c(5,156),dimnames=list(c('blogged_computations','compendiums_reviewed','feedback_messages_p120','totsize','totseconds'),1:156))
>  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 = '3'
> 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] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  93  60 123 148  90 124  70 168 115  71
 [19]  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124  92
 [37] 126  70  37 120  93  95  90  80  31 110  66 138 133 113 100   7 140  61
 [55]  41  96 164 102 124  99 129  62  73 114  99  70 104 116  91  74 138  67
 [73] 151  72 120 115 105 104 108  98  69 111  99  71  27  69 107 107  93 129
 [91]  69 118  73 119 104 107  99  90 197  85 139 106  50  64  31  63  92 106
[109]  69  93 114 110   0  83  30  98  82   0  60   9 115 140 120  66  21 124
[127] 152 139 144 120 160 114  78 119 141 101 133  83 116  90  36  97  98  78
[145] 117 148 105 132   0  73  86   0  13   4  48  46
> 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   4   7   9  13  21  22  27  30  31  36  37  41  46  48  50  51  60  61  62 
  4   1   1   1   1   1   1   1   1   2   1   1   1   1   1   1   1   2   1   1 
 63  64  66  67  69  70  71  72  73  74  78  80  81  82  83  84  85  86  90  91 
  1   1   3   1   4   4   2   1   3   1   2   1   1   1   2   1   1   1   4   2 
 92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 
  2   5   2   1   1   1   3   4   1   1   1   2   3   2   2   3   2   1   3   1 
113 114 115 116 117 118 119 120 122 123 124 126 129 132 133 134 138 139 140 141 
  1   4   3   2   2   1   2   6   1   1   4   1   2   1   3   1   2   2   2   1 
144 148 151 152 156 158 160 164 168 197 
  1   2   1   1   1   1   1   1   1   1 
> colnames(x)
[1] "blogged_computations"   "compendiums_reviewed"   "feedback_messages_p120"
[4] "totsize"                "totseconds"            
> colnames(x)[par1]
[1] "feedback_messages_p120"
> x[,par1]
  [1]  94 103  93 103  51  70  91  22  93  60 123 148  90 124  70 168 115  71
 [19]  66 134 117 108  84 156 120 114  94 120  81 110 133 122 158 109 124  92
 [37] 126  70  37 120  93  95  90  80  31 110  66 138 133 113 100   7 140  61
 [55]  41  96 164 102 124  99 129  62  73 114  99  70 104 116  91  74 138  67
 [73] 151  72 120 115 105 104 108  98  69 111  99  71  27  69 107 107  93 129
 [91]  69 118  73 119 104 107  99  90 197  85 139 106  50  64  31  63  92 106
[109]  69  93 114 110   0  83  30  98  82   0  60   9 115 140 120  66  21 124
[127] 152 139 144 120 160 114  78 119 141 101 133  83 116  90  36  97  98  78
[145] 117 148 105 132   0  73  86   0  13   4  48  46
> 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/1e9q01324174502.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

Response:  feedback_messages_p120 
Inputs:  blogged_computations, compendiums_reviewed, totsize, totseconds 
Number of observations:  156 

1) compendiums_reviewed <= 27; criterion = 1, statistic = 118.093
  2) compendiums_reviewed <= 18; criterion = 1, statistic = 32.8
    3)*  weights = 15 
  2) compendiums_reviewed > 18
    4)*  weights = 27 
1) compendiums_reviewed > 27
  5) compendiums_reviewed <= 39; criterion = 1, statistic = 49.449
    6) blogged_computations <= 71; criterion = 1, statistic = 17.717
      7) totsize <= 83123; criterion = 0.998, statistic = 12.22
        8)*  weights = 21 
      7) totsize > 83123
        9)*  weights = 13 
    6) blogged_computations > 71
      10) compendiums_reviewed <= 32; criterion = 1, statistic = 24.641
        11)*  weights = 28 
      10) compendiums_reviewed > 32
        12)*  weights = 33 
  5) compendiums_reviewed > 39
    13)*  weights = 19 
> postscript(file="/var/wessaorg/rcomp/tmp/2apko1324174502.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/3u3wa1324174502.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        94  97.85714  -3.85714286
2       103 107.00000  -4.00000000
3        93  80.66667  12.33333333
4       103  97.85714   5.14285714
5        51  69.77778 -18.77777778
6        70  69.77778   0.22222222
7        91  69.77778  21.22222222
8        22  18.13333   3.86666667
9        93  69.77778  23.22222222
10       60  69.77778  -9.77777778
11      123 119.96970   3.03030303
12      148 144.00000   4.00000000
13       90  80.66667   9.33333333
14      124 144.00000 -20.00000000
15       70  80.66667 -10.66666667
16      168 144.00000  24.00000000
17      115  97.85714  17.14285714
18       71  97.85714 -26.85714286
19       66  69.77778  -3.77777778
20      134 119.96970  14.03030303
21      117 119.96970  -2.96969697
22      108  97.85714  10.14285714
23       84  69.77778  14.22222222
24      156 119.96970  36.03030303
25      120 119.96970   0.03030303
26      114  97.85714  16.14285714
27       94  97.85714  -3.85714286
28      120 119.96970   0.03030303
29       81  69.77778  11.22222222
30      110 119.96970  -9.96969697
31      133 119.96970  13.03030303
32      122 144.00000 -22.00000000
33      158 144.00000  14.00000000
34      109  80.66667  28.33333333
35      124 119.96970   4.03030303
36       92  80.66667  11.33333333
37      126 119.96970   6.03030303
38       70  80.66667 -10.66666667
39       37  18.13333  18.86666667
40      120  97.85714  22.14285714
41       93  97.85714  -4.85714286
42       95  80.66667  14.33333333
43       90  80.66667   9.33333333
44       80  80.66667  -0.66666667
45       31  80.66667 -49.66666667
46      110 119.96970  -9.96969697
47       66  69.77778  -3.77777778
48      138 119.96970  18.03030303
49      133 119.96970  13.03030303
50      113 119.96970  -6.96969697
51      100  97.85714   2.14285714
52        7  18.13333 -11.13333333
53      140 119.96970  20.03030303
54       61  18.13333  42.86666667
55       41  18.13333  22.86666667
56       96  97.85714  -1.85714286
57      164 144.00000  20.00000000
58      102  97.85714   4.14285714
59      124 119.96970   4.03030303
60       99  97.85714   1.14285714
61      129 119.96970   9.03030303
62       62  69.77778  -7.77777778
63       73  80.66667  -7.66666667
64      114 107.00000   7.00000000
65       99 107.00000  -8.00000000
66       70  69.77778   0.22222222
67      104  69.77778  34.22222222
68      116 119.96970  -3.96969697
69       91  97.85714  -6.85714286
70       74  69.77778   4.22222222
71      138 144.00000  -6.00000000
72       67  69.77778  -2.77777778
73      151 144.00000   7.00000000
74       72 107.00000 -35.00000000
75      120 119.96970   0.03030303
76      115 119.96970  -4.96969697
77      105  97.85714   7.14285714
78      104 119.96970 -15.96969697
79      108  97.85714  10.14285714
80       98  80.66667  17.33333333
81       69  69.77778  -0.77777778
82      111 119.96970  -8.96969697
83       99  69.77778  29.22222222
84       71 119.96970 -48.96969697
85       27  18.13333   8.86666667
86       69  69.77778  -0.77777778
87      107 107.00000   0.00000000
88      107 107.00000   0.00000000
89       93 144.00000 -51.00000000
90      129 119.96970   9.03030303
91       69  80.66667 -11.66666667
92      118 119.96970  -1.96969697
93       73  69.77778   3.22222222
94      119  97.85714  21.14285714
95      104 107.00000  -3.00000000
96      107 107.00000   0.00000000
97       99  97.85714   1.14285714
98       90  97.85714  -7.85714286
99      197 144.00000  53.00000000
100      85  69.77778  15.22222222
101     139 144.00000  -5.00000000
102     106 119.96970 -13.96969697
103      50  80.66667 -30.66666667
104      64  69.77778  -5.77777778
105      31  69.77778 -38.77777778
106      63  97.85714 -34.85714286
107      92  97.85714  -5.85714286
108     106  97.85714   8.14285714
109      69  69.77778  -0.77777778
110      93  97.85714  -4.85714286
111     114 107.00000   7.00000000
112     110 144.00000 -34.00000000
113       0  18.13333 -18.13333333
114      83  97.85714 -14.85714286
115      30  18.13333  11.86666667
116      98  80.66667  17.33333333
117      82  97.85714 -15.85714286
118       0  18.13333 -18.13333333
119      60  69.77778  -9.77777778
120       9  18.13333  -9.13333333
121     115 119.96970  -4.96969697
122     140 144.00000  -4.00000000
123     120 107.00000  13.00000000
124      66  69.77778  -3.77777778
125      21  18.13333   2.86666667
126     124 107.00000  17.00000000
127     152 144.00000   8.00000000
128     139 144.00000  -5.00000000
129     144 144.00000   0.00000000
130     120 119.96970   0.03030303
131     160 144.00000  16.00000000
132     114  80.66667  33.33333333
133      78  80.66667  -2.66666667
134     119 107.00000  12.00000000
135     141 144.00000  -3.00000000
136     101 107.00000  -6.00000000
137     133 119.96970  13.03030303
138      83  80.66667   2.33333333
139     116 119.96970  -3.96969697
140      90  80.66667   9.33333333
141      36  69.77778 -33.77777778
142      97  97.85714  -0.85714286
143      98  97.85714   0.14285714
144      78  69.77778   8.22222222
145     117 119.96970  -2.96969697
146     148 144.00000   4.00000000
147     105  97.85714   7.14285714
148     132 119.96970  12.03030303
149       0  18.13333 -18.13333333
150      73  80.66667  -7.66666667
151      86 119.96970 -33.96969697
152       0  18.13333 -18.13333333
153      13  18.13333  -5.13333333
154       4  18.13333 -14.13333333
155      48  80.66667 -32.66666667
156      46  69.77778 -23.77777778
> 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/48tbk1324174502.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/53y7w1324174502.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/68wgk1324174502.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/7abmr1324174502.tab") 
+ }
> 
> try(system("convert tmp/2apko1324174502.ps tmp/2apko1324174502.png",intern=TRUE))
character(0)
> try(system("convert tmp/3u3wa1324174502.ps tmp/3u3wa1324174502.png",intern=TRUE))
character(0)
> try(system("convert tmp/48tbk1324174502.ps tmp/48tbk1324174502.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.489   0.270   4.889