R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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+ ,1
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+ ,64187
+ ,27
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+ ,1
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+ ,35
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+ ,0
+ ,28
+ ,15
+ ,894
+ ,72535
+ ,14
+ ,329
+ ,0
+ ,17
+ ,16)
+ ,dim=c(7
+ ,289)
+ ,dimnames=list(c('pageviews'
+ ,'timeRFC'
+ ,'logins'
+ ,'compendiumviews'
+ ,'sharedcompendiums'
+ ,'bloggedcomputation'
+ ,'compendiumsreviewed')
+ ,1:289))
>  y <- array(NA,dim=c(7,289),dimnames=list(c('pageviews','timeRFC','logins','compendiumviews','sharedcompendiums','bloggedcomputation','compendiumsreviewed'),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 = 'yes'
> par3 = '7'
> 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] "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, 689) [ 689, 937) [ 937,1212) [1212,1438) [1438,1701) [1701,2147) 
         42          41          41          42          41          41 
[2147,4691] 
         41 
> colnames(x)
[1] "pageviews"           "timeRFC"             "logins"             
[4] "compendiumviews"     "sharedcompendiums"   "bloggedcomputation" 
[7] "compendiumsreviewed"
> colnames(x)[par1]
[1] "pageviews"
> x[,par1]
  [1] [1212,1438) [ 689, 937) [1438,1701) [2147,4691] [ 689, 937) [ 141, 689)
  [7] [2147,4691] [ 141, 689) [ 937,1212) [1438,1701) [1438,1701) [1701,2147)
 [13] [1438,1701) [1212,1438) [2147,4691] [1438,1701) [2147,4691] [1701,2147)
 [19] [2147,4691] [ 937,1212) [1701,2147) [1701,2147) [2147,4691] [1212,1438)
 [25] [1212,1438) [2147,4691] [2147,4691] [2147,4691] [1438,1701) [1212,1438)
 [31] [1701,2147) [1438,1701) [1438,1701) [1701,2147) [ 689, 937) [2147,4691]
 [37] [ 689, 937) [1701,2147) [2147,4691] [ 141, 689) [1438,1701) [ 689, 937)
 [43] [1701,2147) [2147,4691] [2147,4691] [1212,1438) [2147,4691] [1212,1438)
 [49] [ 937,1212) [1701,2147) [2147,4691] [ 689, 937) [1701,2147) [2147,4691]
 [55] [1701,2147) [1438,1701) [ 141, 689) [2147,4691] [ 689, 937) [ 937,1212)
 [61] [2147,4691] [1701,2147) [2147,4691] [ 937,1212) [2147,4691] [1701,2147)
 [67] [1701,2147) [2147,4691] [2147,4691] [1701,2147) [1701,2147) [1212,1438)
 [73] [1212,1438) [1438,1701) [2147,4691] [1701,2147) [1212,1438) [1701,2147)
 [79] [ 689, 937) [1701,2147) [1212,1438) [1212,1438) [1701,2147) [1701,2147)
 [85] [1438,1701) [1438,1701) [1212,1438) [1212,1438) [2147,4691] [1212,1438)
 [91] [1438,1701) [ 689, 937) [1438,1701) [ 937,1212) [ 937,1212) [2147,4691]
 [97] [1701,2147) [2147,4691] [1701,2147) [1438,1701) [1438,1701) [2147,4691]
[103] [1701,2147) [1212,1438) [1212,1438) [1438,1701) [2147,4691] [1212,1438)
[109] [2147,4691] [1701,2147) [1701,2147) [1212,1438) [ 141, 689) [1701,2147)
[115] [1438,1701) [1701,2147) [1701,2147) [1701,2147) [ 689, 937) [1438,1701)
[121] [1438,1701) [ 937,1212) [1701,2147) [1212,1438) [1212,1438) [ 689, 937)
[127] [ 937,1212) [1212,1438) [1701,2147) [ 141, 689) [ 689, 937) [1438,1701)
[133] [ 141, 689) [1701,2147) [1212,1438) [2147,4691] [ 141, 689) [1701,2147)
[139] [ 141, 689) [ 141, 689) [ 689, 937) [1438,1701) [ 937,1212) [2147,4691]
[145] [1438,1701) [1438,1701) [ 937,1212) [ 937,1212) [ 141, 689) [1438,1701)
[151] [2147,4691] [ 937,1212) [1212,1438) [2147,4691] [1701,2147) [2147,4691]
[157] [ 937,1212) [ 937,1212) [ 937,1212) [2147,4691] [2147,4691] [ 937,1212)
[163] [1212,1438) [2147,4691] [1438,1701) [1212,1438) [1701,2147) [1212,1438)
[169] [ 937,1212) [1701,2147) [2147,4691] [1438,1701) [1701,2147) [2147,4691]
[175] [1212,1438) [1438,1701) [2147,4691] [ 937,1212) [1701,2147) [1212,1438)
[181] [1212,1438) [1438,1701) [ 141, 689) [1438,1701) [2147,4691] [ 141, 689)
[187] [ 141, 689) [ 141, 689) [1438,1701) [ 689, 937) [1212,1438) [ 937,1212)
[193] [1212,1438) [1212,1438) [1212,1438) [1212,1438) [ 689, 937) [ 141, 689)
[199] [1212,1438) [ 689, 937) [ 689, 937) [ 141, 689) [1212,1438) [1212,1438)
[205] [1438,1701) [ 141, 689) [ 937,1212) [1438,1701) [1212,1438) [ 937,1212)
[211] [ 937,1212) [1212,1438) [ 141, 689) [ 689, 937) [ 937,1212) [ 689, 937)
[217] [ 937,1212) [ 689, 937) [ 689, 937) [ 141, 689) [ 937,1212) [1438,1701)
[223] [1701,2147) [ 689, 937) [ 141, 689) [1701,2147) [ 689, 937) [ 689, 937)
[229] [ 937,1212) [1438,1701) [ 141, 689) [ 937,1212) [1438,1701) [ 689, 937)
[235] [ 937,1212) [ 141, 689) [1212,1438) [ 141, 689) [ 937,1212) [ 141, 689)
[241] [ 937,1212) [1438,1701) [ 689, 937) [ 141, 689) [ 689, 937) [ 937,1212)
[247] [1438,1701) [ 689, 937) [ 937,1212) [ 141, 689) [1438,1701) [ 937,1212)
[253] [ 141, 689) [1212,1438) [ 937,1212) [ 141, 689) [ 141, 689) [ 689, 937)
[259] [ 141, 689) [ 141, 689) [ 141, 689) [ 689, 937) [ 141, 689) [ 689, 937)
[265] [ 141, 689) [ 689, 937) [ 689, 937) [ 141, 689) [ 689, 937) [ 937,1212)
[271] [1438,1701) [ 141, 689) [ 689, 937) [ 937,1212) [ 141, 689) [ 937,1212)
[277] [ 141, 689) [ 141, 689) [ 937,1212) [ 689, 937) [ 689, 937) [ 689, 937)
[283] [ 937,1212) [ 141, 689) [ 141, 689) [ 937,1212) [ 689, 937) [ 689, 937)
[289] [ 689, 937)
7 Levels: [ 141, 689) [ 689, 937) [ 937,1212) [1212,1438) ... [2147,4691]
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/www/rcomp/tmp/1lv0o1323792540.tab") 
+ }
+ }
           m.ct.i.pred
m.ct.i.actu   1   2   3   4   5   6   7
          1 341  44   0   0   0   0   0
          2  76 254  37   7   1   0   0
          3  13  40 248  68  12   0   0
          4   0  10  45 257  48  17   0
          5   0   0   3 223  54  80   7
          6   0   0   0  15   4 321  47
          7   0   0   0   0   0  16 352
[1] 0.8857143
[1] 0.6773333
[1] 0.6509186
[1] 0.6816976
[1] 0.1471390
[1] 0.8294574
[1] 0.9565217
[1] 0.6920455
           m.ct.x.pred
m.ct.x.actu  1  2  3  4  5  6  7
          1 29  6  0  0  0  0  0
          2 11 17  5  1  1  0  0
          3  2  4 15  6  2  0  0
          4  0  1  9 16 14  3  0
          5  0  0  1 30  6  6  0
          6  0  0  0  1  0 18  4
          7  0  0  0  0  0  4 38
[1] 0.8285714
[1] 0.4857143
[1] 0.5172414
[1] 0.372093
[1] 0.1395349
[1] 0.7826087
[1] 0.9047619
[1] 0.556
> m

	 Conditional inference tree with 12 terminal nodes

Response:  as.factor(pageviews) 
Inputs:  timeRFC, logins, compendiumviews, sharedcompendiums, bloggedcomputation, compendiumsreviewed 
Number of observations:  289 

1) compendiumviews <= 819; criterion = 1, statistic = 247.716
  2) compendiumviews <= 232; criterion = 1, statistic = 216.365
    3) timeRFC <= 49862; criterion = 0.997, statistic = 14.952
      4)*  weights = 28 
    3) timeRFC > 49862
      5)*  weights = 24 
  2) compendiumviews > 232
    6) compendiumviews <= 565; criterion = 1, statistic = 159.586
      7) compendiumviews <= 304; criterion = 1, statistic = 100.16
        8) bloggedcomputation <= 16; criterion = 0.965, statistic = 12.451
          9)*  weights = 8 
        8) bloggedcomputation > 16
          10)*  weights = 25 
      7) compendiumviews > 304
        11) compendiumviews <= 400; criterion = 1, statistic = 53.95
          12) logins <= 53; criterion = 0.999, statistic = 19.282
            13)*  weights = 35 
          12) logins > 53
            14)*  weights = 7 
        11) compendiumviews > 400
          15) compendiumviews <= 489; criterion = 0.953, statistic = 13.799
            16)*  weights = 41 
          15) compendiumviews > 489
            17)*  weights = 30 
    6) compendiumviews > 565
      18) logins <= 84; criterion = 1, statistic = 26.573
        19)*  weights = 45 
      18) logins > 84
        20)*  weights = 7 
1) compendiumviews > 819
  21) timeRFC <= 201940; criterion = 0.972, statistic = 7.954
    22)*  weights = 8 
  21) timeRFC > 201940
    23)*  weights = 31 
> postscript(file="/var/www/rcomp/tmp/2afed1323792540.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/www/rcomp/tmp/36mtr1323792540.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,]    4    4
  [2,]    2    2
  [3,]    5    5
  [4,]    7    7
  [5,]    2    2
  [6,]    1    1
  [7,]    7    7
  [8,]    1    1
  [9,]    3    4
 [10,]    5    6
 [11,]    5    5
 [12,]    6    6
 [13,]    5    4
 [14,]    4    5
 [15,]    7    7
 [16,]    5    5
 [17,]    7    7
 [18,]    6    6
 [19,]    7    7
 [20,]    3    3
 [21,]    6    6
 [22,]    6    6
 [23,]    7    7
 [24,]    4    3
 [25,]    4    4
 [26,]    7    7
 [27,]    7    7
 [28,]    7    7
 [29,]    5    5
 [30,]    4    4
 [31,]    6    6
 [32,]    5    6
 [33,]    5    5
 [34,]    6    6
 [35,]    2    2
 [36,]    7    7
 [37,]    2    2
 [38,]    6    6
 [39,]    7    7
 [40,]    1    1
 [41,]    5    5
 [42,]    2    2
 [43,]    6    5
 [44,]    7    7
 [45,]    7    7
 [46,]    4    5
 [47,]    7    7
 [48,]    4    5
 [49,]    3    3
 [50,]    6    7
 [51,]    7    7
 [52,]    2    3
 [53,]    6    5
 [54,]    7    7
 [55,]    6    6
 [56,]    5    4
 [57,]    1    1
 [58,]    7    7
 [59,]    2    2
 [60,]    3    4
 [61,]    7    7
 [62,]    6    6
 [63,]    7    7
 [64,]    3    3
 [65,]    7    7
 [66,]    6    7
 [67,]    6    6
 [68,]    7    7
 [69,]    7    7
 [70,]    6    7
 [71,]    6    6
 [72,]    4    5
 [73,]    4    3
 [74,]    5    4
 [75,]    7    7
 [76,]    6    6
 [77,]    4    4
 [78,]    6    6
 [79,]    2    3
 [80,]    6    6
 [81,]    4    6
 [82,]    4    4
 [83,]    6    6
 [84,]    6    6
 [85,]    5    4
 [86,]    5    4
 [87,]    4    5
 [88,]    4    5
 [89,]    7    7
 [90,]    4    6
 [91,]    5    4
 [92,]    2    4
 [93,]    5    6
 [94,]    3    2
 [95,]    3    2
 [96,]    7    7
 [97,]    6    6
 [98,]    7    7
 [99,]    6    6
[100,]    5    5
[101,]    5    4
[102,]    7    7
[103,]    6    6
[104,]    4    4
[105,]    4    4
[106,]    5    6
[107,]    7    7
[108,]    4    3
[109,]    7    7
[110,]    6    7
[111,]    6    6
[112,]    4    4
[113,]    1    1
[114,]    6    6
[115,]    5    7
[116,]    6    7
[117,]    6    6
[118,]    6    6
[119,]    2    2
[120,]    5    5
[121,]    5    5
[122,]    3    4
[123,]    6    6
[124,]    4    4
[125,]    4    4
[126,]    2    2
[127,]    3    3
[128,]    4    3
[129,]    6    6
[130,]    1    1
[131,]    2    1
[132,]    5    6
[133,]    1    1
[134,]    6    6
[135,]    4    4
[136,]    7    7
[137,]    1    1
[138,]    6    6
[139,]    1    1
[140,]    1    1
[141,]    2    2
[142,]    5    5
[143,]    3    3
[144,]    7    7
[145,]    5    5
[146,]    5    4
[147,]    3    4
[148,]    3    4
[149,]    1    2
[150,]    5    5
[151,]    7    7
[152,]    3    3
[153,]    4    4
[154,]    7    7
[155,]    6    6
[156,]    7    7
[157,]    3    3
[158,]    3    3
[159,]    3    3
[160,]    7    7
[161,]    7    7
[162,]    3    3
[163,]    4    4
[164,]    7    7
[165,]    5    6
[166,]    4    4
[167,]    6    6
[168,]    4    4
[169,]    3    1
[170,]    6    6
[171,]    7    7
[172,]    5    5
[173,]    6    6
[174,]    7    6
[175,]    4    4
[176,]    5    6
[177,]    7    7
[178,]    3    3
[179,]    6    6
[180,]    4    4
[181,]    4    4
[182,]    5    5
[183,]    1    1
[184,]    5    5
[185,]    7    7
[186,]    1    1
[187,]    1    2
[188,]    1    1
[189,]    5    5
[190,]    2    2
[191,]    4    4
[192,]    3    4
[193,]    4    4
[194,]    4    2
[195,]    4    5
[196,]    4    4
[197,]    2    3
[198,]    1    1
[199,]    4    5
[200,]    2    2
[201,]    2    1
[202,]    1    1
[203,]    4    4
[204,]    4    4
[205,]    5    5
[206,]    1    1
[207,]    3    2
[208,]    5    6
[209,]    4    4
[210,]    3    4
[211,]    3    3
[212,]    4    3
[213,]    1    1
[214,]    2    2
[215,]    3    3
[216,]    2    1
[217,]    3    3
[218,]    2    1
[219,]    2    1
[220,]    1    1
[221,]    3    3
[222,]    5    4
[223,]    6    6
[224,]    2    1
[225,]    1    1
[226,]    6    6
[227,]    2    2
[228,]    2    2
[229,]    3    3
[230,]    5    4
[231,]    1    1
[232,]    3    3
[233,]    5    5
[234,]    2    1
[235,]    3    3
[236,]    1    1
[237,]    4    4
[238,]    1    1
[239,]    3    3
[240,]    1    1
[241,]    3    3
[242,]    5    4
[243,]    2    1
[244,]    1    1
[245,]    2    2
[246,]    3    3
[247,]    5    5
[248,]    2    2
[249,]    3    3
[250,]    1    1
[251,]    5    5
[252,]    3    2
[253,]    1    1
[254,]    4    4
[255,]    3    4
[256,]    1    1
[257,]    1    1
[258,]    2    2
[259,]    1    1
[260,]    1    1
[261,]    1    1
[262,]    2    1
[263,]    1    1
[264,]    2    1
[265,]    1    1
[266,]    2    2
[267,]    2    2
[268,]    1    1
[269,]    2    2
[270,]    3    3
[271,]    5    4
[272,]    1    1
[273,]    2    2
[274,]    3    3
[275,]    1    1
[276,]    3    3
[277,]    1    1
[278,]    1    1
[279,]    3    3
[280,]    2    2
[281,]    2    2
[282,]    2    2
[283,]    3    4
[284,]    1    1
[285,]    1    2
[286,]    3    1
[287,]    2    2
[288,]    2    1
[289,]    2    3
             
              [ 141, 689) [ 689, 937) [ 937,1212) [1212,1438) [1438,1701)
  [ 141, 689)          39           3           0           0           0
  [ 689, 937)          11          25           4           1           0
  [ 937,1212)           2           4          26           9           0
  [1212,1438)           0           1           5          26           8
  [1438,1701)           0           0           0          12          20
  [1701,2147)           0           0           0           0           2
  [2147,4691]           0           0           0           0           0
             
              [1701,2147) [2147,4691]
  [ 141, 689)           0           0
  [ 689, 937)           0           0
  [ 937,1212)           0           0
  [1212,1438)           2           0
  [1438,1701)           8           1
  [1701,2147)          34           5
  [2147,4691]           1          40
> postscript(file="/var/www/rcomp/tmp/4om311323792540.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/www/rcomp/tmp/50n6k1323792540.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/www/rcomp/tmp/6pbvg1323792540.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/www/rcomp/tmp/7j62t1323792540.tab") 
+ }
> 
> try(system("convert tmp/2afed1323792540.ps tmp/2afed1323792540.png",intern=TRUE))
character(0)
> try(system("convert tmp/36mtr1323792540.ps tmp/36mtr1323792540.png",intern=TRUE))
character(0)
> try(system("convert tmp/4om311323792540.ps tmp/4om311323792540.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.020   0.050   4.184