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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+ ,8100
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+ ,31
+ ,22
+ ,45588
+ ,9653
+ ,62991
+ ,41
+ ,39
+ ,21
+ ,45097
+ ,8914
+ ,49363
+ ,54
+ ,54
+ ,1
+ ,3895
+ ,786
+ ,9604
+ ,14
+ ,14
+ ,18
+ ,28394
+ ,6700
+ ,24552
+ ,25
+ ,24
+ ,17
+ ,18632
+ ,5788
+ ,31493
+ ,25
+ ,24
+ ,4
+ ,2325
+ ,593
+ ,3439
+ ,8
+ ,8
+ ,10
+ ,25139
+ ,4506
+ ,19555
+ ,26
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+ ,27975
+ ,6382
+ ,21228
+ ,20
+ ,19
+ ,16
+ ,14483
+ ,5621
+ ,23177
+ ,11
+ ,11
+ ,9
+ ,13127
+ ,3997
+ ,22094
+ ,14
+ ,14
+ ,16
+ ,5839
+ ,520
+ ,2342
+ ,3
+ ,1
+ ,17
+ ,24069
+ ,8891
+ ,38798
+ ,40
+ ,39
+ ,7
+ ,3738
+ ,999
+ ,3255
+ ,5
+ ,5
+ ,15
+ ,18625
+ ,7067
+ ,24261
+ ,38
+ ,37
+ ,14
+ ,36341
+ ,4639
+ ,18511
+ ,32
+ ,32
+ ,14
+ ,24548
+ ,5654
+ ,40798
+ ,41
+ ,38
+ ,18
+ ,21792
+ ,6928
+ ,28893
+ ,46
+ ,47
+ ,12
+ ,26263
+ ,1514
+ ,21425
+ ,47
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+ ,16
+ ,23686
+ ,9238
+ ,50276
+ ,37
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+ ,8204
+ ,37643
+ ,51
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+ ,5926
+ ,30377
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+ ,16
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+ ,1
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+ ,705
+ ,14335
+ ,21
+ ,21
+ ,12
+ ,32689
+ ,443
+ ,5084
+ ,4
+ ,4
+ ,2
+ ,5752
+ ,2416
+ ,9927
+ ,10
+ ,10
+ ,14
+ ,22197
+ ,7747
+ ,43527
+ ,43
+ ,43
+ ,17
+ ,20055
+ ,5432
+ ,27184
+ ,34
+ ,34
+ ,19
+ ,25272
+ ,4913
+ ,21610
+ ,32
+ ,31
+ ,14
+ ,82206
+ ,2650
+ ,20484
+ ,20
+ ,19
+ ,11
+ ,32073
+ ,2370
+ ,20156
+ ,34
+ ,34
+ ,4
+ ,5444
+ ,775
+ ,6012
+ ,6
+ ,6
+ ,16
+ ,20154
+ ,5576
+ ,18475
+ ,12
+ ,11
+ ,20
+ ,36944
+ ,1352
+ ,12645
+ ,24
+ ,24
+ ,12
+ ,8019
+ ,3080
+ ,11017
+ ,16
+ ,16
+ ,15
+ ,30884
+ ,10205
+ ,37623
+ ,72
+ ,72
+ ,16
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
>  y <- array(NA,dim=c(6,289),dimnames=list(c('compendiums_reviewed','totsize','totrevisions','totseconds','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'
> #'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] "compendiums_reviewed"
> x[,par1]
  [1] 30 28 38 30 22 26 25 18 11 26 25 38 44 30 40 34 47 30 31 23 36 36 30 25 39
 [26] 34 31 31 33 25 33 35 42 43 30 33 13 32 36  0 28 14 17 32 30 35 20 28 28 39
 [51] 34 26 39 39 33 28  4 39 18 14 29 44 21 16 28 35 28 38 23 36 32 29 25 27 36
 [76] 28 23 40 23 40 28 34 33 28 34 30 33 22 38 26 35  8 24 29 20 29 45 37 33 33
[101] 25 32 29 28 28 31 52 21 24 41 33 32 19 20 31 31 32 18 23 17 20 12 17 30 31
[126] 10 13 22 42  1  9 32 11 25 36 31  0 24 13  8 13 19 18 33 40 22 38 24  8 35
[151] 43 43 14 41 38 45 31 13 28 31 40 30 16 37 30 35 32 27 20 18 31 31 21 39 41
[176] 13 32 18 39 14  7 17  0 30 37  0  5  1 16 32 24 17 11 24 22 12 19 13 17 15
[201] 16 24 15 17 18 20 16 16 18 22  8 17 18 16 23 22 13 13 16 16 20 22 17 18 17
[226] 12  7 17 14 23 17 14 15 17 21 18 18 17 17 16 15 21 16 14 15 17 15 15 10  6
[251] 22 21  1 18 17  4 10 16 16  9 16 17  7 15 14 14 18 12 16 21 19 16  1 16 10
[276] 19 12  2 14 17 19 14 11  4 16 20 12 15 16
> 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])

[ 0,23) [23,52] 
    148     141 
> colnames(x)
[1] "compendiums_reviewed" "totsize"              "totrevisions"        
[4] "totseconds"           "tothyperlinks"        "totblogs"            
> colnames(x)[par1]
[1] "compendiums_reviewed"
> x[,par1]
  [1] [23,52] [23,52] [23,52] [23,52] [ 0,23) [23,52] [23,52] [ 0,23) [ 0,23)
 [10] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [19] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [28] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [37] [ 0,23) [23,52] [23,52] [ 0,23) [23,52] [ 0,23) [ 0,23) [23,52] [23,52]
 [46] [23,52] [ 0,23) [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [55] [23,52] [23,52] [ 0,23) [23,52] [ 0,23) [ 0,23) [23,52] [23,52] [ 0,23)
 [64] [ 0,23) [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [73] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52]
 [82] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [ 0,23) [23,52] [23,52]
 [91] [23,52] [ 0,23) [23,52] [23,52] [ 0,23) [23,52] [23,52] [23,52] [23,52]
[100] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [23,52] [ 0,23)
[109] [23,52] [23,52] [23,52] [23,52] [ 0,23) [ 0,23) [23,52] [23,52] [23,52]
[118] [ 0,23) [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [23,52] [23,52] [ 0,23)
[127] [ 0,23) [ 0,23) [23,52] [ 0,23) [ 0,23) [23,52] [ 0,23) [23,52] [23,52]
[136] [23,52] [ 0,23) [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [23,52]
[145] [23,52] [ 0,23) [23,52] [23,52] [ 0,23) [23,52] [23,52] [23,52] [ 0,23)
[154] [23,52] [23,52] [23,52] [23,52] [ 0,23) [23,52] [23,52] [23,52] [23,52]
[163] [ 0,23) [23,52] [23,52] [23,52] [23,52] [23,52] [ 0,23) [ 0,23) [23,52]
[172] [23,52] [ 0,23) [23,52] [23,52] [ 0,23) [23,52] [ 0,23) [23,52] [ 0,23)
[181] [ 0,23) [ 0,23) [ 0,23) [23,52] [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[190] [23,52] [23,52] [ 0,23) [ 0,23) [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[199] [ 0,23) [ 0,23) [ 0,23) [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[208] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [23,52] [ 0,23)
[217] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[226] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [23,52] [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[235] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[244] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[253] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[262] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[271] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[280] [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23) [ 0,23)
[289] [ 0,23)
Levels: [ 0,23) [23,52]
> 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/18jsz1324322203.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  as.factor(compendiums_reviewed) 
Inputs:  totsize, totrevisions, totseconds, tothyperlinks, totblogs 
Number of observations:  289 

1) totsize <= 51009; criterion = 1, statistic = 155.636
  2) totsize <= 30594; criterion = 0.959, statistic = 6.952
    3)*  weights = 94 
  2) totsize > 30594
    4)*  weights = 58 
1) totsize > 51009
  5) totblogs <= 78; criterion = 0.998, statistic = 12.895
    6)*  weights = 37 
  5) totblogs > 78
    7)*  weights = 100 
> postscript(file="/var/wessaorg/rcomp/tmp/2vggx1324322203.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/3igxa1324322203.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,]    2    2
  [4,]    2    2
  [5,]    1    1
  [6,]    2    2
  [7,]    2    2
  [8,]    1    1
  [9,]    1    1
 [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,]    2    2
 [21,]    2    2
 [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    1
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    2    2
 [36,]    2    2
 [37,]    1    1
 [38,]    2    2
 [39,]    2    2
 [40,]    1    1
 [41,]    2    2
 [42,]    1    1
 [43,]    1    1
 [44,]    2    2
 [45,]    2    2
 [46,]    2    2
 [47,]    1    1
 [48,]    2    2
 [49,]    2    1
 [50,]    2    2
 [51,]    2    2
 [52,]    2    1
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    1    1
 [58,]    2    2
 [59,]    1    1
 [60,]    1    1
 [61,]    2    2
 [62,]    2    2
 [63,]    1    2
 [64,]    1    1
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    1
 [71,]    2    2
 [72,]    2    2
 [73,]    2    2
 [74,]    2    2
 [75,]    2    2
 [76,]    2    2
 [77,]    2    2
 [78,]    2    2
 [79,]    2    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,]    1    1
 [89,]    2    2
 [90,]    2    1
 [91,]    2    2
 [92,]    1    2
 [93,]    2    1
 [94,]    2    2
 [95,]    1    1
 [96,]    2    2
 [97,]    2    2
 [98,]    2    2
 [99,]    2    1
[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    1
[113,]    1    1
[114,]    1    1
[115,]    2    2
[116,]    2    2
[117,]    2    2
[118,]    1    1
[119,]    2    2
[120,]    1    1
[121,]    1    1
[122,]    1    1
[123,]    1    1
[124,]    2    2
[125,]    2    2
[126,]    1    1
[127,]    1    1
[128,]    1    2
[129,]    2    2
[130,]    1    1
[131,]    1    1
[132,]    2    2
[133,]    1    1
[134,]    2    1
[135,]    2    1
[136,]    2    2
[137,]    1    1
[138,]    2    2
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    2
[143,]    1    1
[144,]    2    2
[145,]    2    2
[146,]    1    1
[147,]    2    2
[148,]    2    2
[149,]    1    1
[150,]    2    2
[151,]    2    2
[152,]    2    2
[153,]    1    1
[154,]    2    2
[155,]    2    2
[156,]    2    2
[157,]    2    2
[158,]    1    1
[159,]    2    2
[160,]    2    2
[161,]    2    2
[162,]    2    2
[163,]    1    1
[164,]    2    2
[165,]    2    2
[166,]    2    2
[167,]    2    2
[168,]    2    2
[169,]    1    1
[170,]    1    2
[171,]    2    2
[172,]    2    2
[173,]    1    1
[174,]    2    2
[175,]    2    2
[176,]    1    1
[177,]    2    2
[178,]    1    1
[179,]    2    2
[180,]    1    1
[181,]    1    1
[182,]    1    1
[183,]    1    1
[184,]    2    2
[185,]    2    2
[186,]    1    1
[187,]    1    1
[188,]    1    1
[189,]    1    1
[190,]    2    1
[191,]    2    2
[192,]    1    1
[193,]    1    1
[194,]    2    1
[195,]    1    2
[196,]    1    1
[197,]    1    1
[198,]    1    2
[199,]    1    2
[200,]    1    1
[201,]    1    1
[202,]    2    1
[203,]    1    1
[204,]    1    1
[205,]    1    1
[206,]    1    1
[207,]    1    1
[208,]    1    1
[209,]    1    1
[210,]    1    1
[211,]    1    1
[212,]    1    1
[213,]    1    1
[214,]    1    1
[215,]    2    1
[216,]    1    1
[217,]    1    1
[218,]    1    1
[219,]    1    1
[220,]    1    1
[221,]    1    1
[222,]    1    2
[223,]    1    1
[224,]    1    1
[225,]    1    1
[226,]    1    1
[227,]    1    1
[228,]    1    1
[229,]    1    1
[230,]    2    2
[231,]    1    1
[232,]    1    1
[233,]    1    2
[234,]    1    1
[235,]    1    1
[236,]    1    1
[237,]    1    1
[238,]    1    1
[239,]    1    1
[240,]    1    1
[241,]    1    1
[242,]    1    1
[243,]    1    1
[244,]    1    1
[245,]    1    1
[246,]    1    1
[247,]    1    1
[248,]    1    1
[249,]    1    1
[250,]    1    1
[251,]    1    1
[252,]    1    1
[253,]    1    1
[254,]    1    1
[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,]    1    1
[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    2
[283,]    1    1
[284,]    1    1
[285,]    1    1
[286,]    1    1
[287,]    1    1
[288,]    1    1
[289,]    1    1
         
          [ 0,23) [23,52]
  [ 0,23)     137      11
  [23,52]      15     126
> postscript(file="/var/wessaorg/rcomp/tmp/4gg5q1324322203.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/5b05a1324322203.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/6bf5j1324322203.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/7ev111324322203.tab") 
+ }
> 
> try(system("convert tmp/2vggx1324322203.ps tmp/2vggx1324322203.png",intern=TRUE))
character(0)
> try(system("convert tmp/3igxa1324322203.ps tmp/3igxa1324322203.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gg5q1324322203.ps tmp/4gg5q1324322203.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.456   0.260   3.735