R version 2.12.1 (2010-12-16)
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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(11.73
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+ ,1.5
+ ,-7.8
+ ,8955.2
+ ,50.6
+ ,7.204
+ ,2172.5
+ ,2427.5
+ ,25.09
+ ,1.5
+ ,-7.8
+ ,9060.8
+ ,50.6
+ ,7.198
+ ,2173
+ ,2387.5
+ ,27.23
+ ,1.5
+ ,-7.8
+ ,8950.74
+ ,50.6
+ ,7.685
+ ,2237.5
+ ,2427.5
+ ,26.5
+ ,1.5
+ ,-7.8
+ ,8784.46
+ ,50.6
+ ,7.795
+ ,2241
+ ,2435.5
+ ,25.67
+ ,1.5
+ ,-7.8
+ ,8590.57
+ ,50.6
+ ,7.46
+ ,2197.5
+ ,2442.5
+ ,25.42
+ ,1.5
+ ,-7.8
+ ,8763.41
+ ,50.6
+ ,7.274
+ ,2174
+ ,2417.5
+ ,26.28
+ ,1.5
+ ,-7.8
+ ,8793.12
+ ,50.6
+ ,7.33
+ ,2217.5
+ ,2402.5
+ ,27.66
+ ,1.5
+ ,-7.8
+ ,8737.66
+ ,50.6
+ ,7.655
+ ,2155
+ ,2333.5
+ ,28.16
+ ,1.5
+ ,-7.8
+ ,8535.67
+ ,50.6
+ ,7.767
+ ,2194
+ ,2392.5
+ ,27.73
+ ,1.5
+ ,-7.8
+ ,8616.55
+ ,50.6
+ ,7.84
+ ,2187.5
+ ,2382.5
+ ,26.28
+ ,1.5
+ ,-7.8
+ ,8518.57
+ ,50.6
+ ,7.424
+ ,2107.5
+ ,2312.5
+ ,26.39
+ ,1.5
+ ,-7.8
+ ,8668.86
+ ,50.6
+ ,7.54
+ ,2095
+ ,2326.5
+ ,25.7
+ ,1.5
+ ,-7.8
+ ,8864.16
+ ,50.6
+ ,7.351
+ ,2067.5
+ ,2236.5
+ ,24.33
+ ,1.5
+ ,-7.8
+ ,8721.24
+ ,50.6
+ ,6.735
+ ,2031
+ ,2132.5
+ ,24.72
+ ,1.5
+ ,-7.8
+ ,8741.16
+ ,50.6
+ ,6.777
+ ,1957.5
+ ,2027.5
+ ,25.3
+ ,1.5
+ ,-7.8
+ ,8560.26
+ ,50.6
+ ,6.679
+ ,1867.5
+ ,1902.5
+ ,25.61
+ ,1.5
+ ,-7.8
+ ,8374.13
+ ,50.6
+ ,7.34
+ ,1995.5
+ ,2015
+ ,24.01
+ ,1.5
+ ,-7.8
+ ,8609.95
+ ,50.6
+ ,6.978
+ ,1960
+ ,2005
+ ,24.45
+ ,1.5
+ ,-7.8
+ ,8615.65
+ ,50.6
+ ,6.92
+ ,1926.5
+ ,2012.5
+ ,23.75
+ ,1.5
+ ,-7.8
+ ,8701.23
+ ,50.6
+ ,6.628
+ ,1872.5
+ ,1992.5
+ ,21.98
+ ,1.5
+ ,-7.8
+ ,8700.29
+ ,50.6
+ ,6.385
+ ,1877.5
+ ,1936.5
+ ,23.51
+ ,1.5
+ ,-9.4
+ ,8545.48
+ ,51.6
+ ,5.984
+ ,1876
+ ,1912.5
+ ,24.25
+ ,1.5
+ ,-9.4
+ ,8456.12
+ ,51.6
+ ,6.268
+ ,1831
+ ,1872.5
+ ,24.45
+ ,1.5
+ ,-9.4
+ ,8382.98
+ ,51.6
+ ,6.596
+ ,1862.5
+ ,1932.5
+ ,24.37
+ ,1.5
+ ,-9.4
+ ,8522.02
+ ,51.6
+ ,6.395
+ ,1892.5
+ ,1952.5
+ ,25.87
+ ,1.5
+ ,-9.4
+ ,8605.62
+ ,51.6
+ ,6.715
+ ,1947.5
+ ,1991.5
+ ,26.46
+ ,1.5
+ ,-9.4
+ ,8773.68
+ ,51.6
+ ,6.804
+ ,1908.5
+ ,1972.5
+ ,27.71
+ ,1.5
+ ,-9.4
+ ,8738.9
+ ,51.6
+ ,6.929
+ ,1967.5
+ ,2032.5
+ ,26.99
+ ,1.5
+ ,-9.4
+ ,8823.25
+ ,51.6
+ ,6.846
+ ,1912.5
+ ,2000.5
+ ,27.5
+ ,1.5
+ ,-9.4
+ ,8747.96
+ ,51.6
+ ,6.992
+ ,1922.5
+ ,2028.5
+ ,26.89
+ ,1.5
+ ,-9.4
+ ,8879.6
+ ,51.6
+ ,6.774
+ ,1897.5
+ ,1992.5
+ ,27.19
+ ,1.5
+ ,-9.4
+ ,8741.91
+ ,51.6
+ ,6.75
+ ,1869.5
+ ,1932.5
+ ,26.53
+ ,1.5
+ ,-9.4
+ ,8772.54
+ ,51.6
+ ,6.485
+ ,1851
+ ,1902.5
+ ,27.03
+ ,1.5
+ ,-9.4
+ ,8682.15
+ ,51.6
+ ,6.27
+ ,1762.5
+ ,1807.5
+ ,26.78
+ ,1.5
+ ,-9.4
+ ,8678.89
+ ,51.6
+ ,6.47
+ ,1803.5
+ ,1867.5
+ ,27.49
+ ,1.5
+ ,-9.4
+ ,8843.98
+ ,51.6
+ ,6.78
+ ,1873.5
+ ,1987.5
+ ,26.49
+ ,1.5
+ ,-9.4
+ ,8762.31
+ ,51.6
+ ,6.71
+ ,1868.5
+ ,1997.5
+ ,26.05
+ ,1.5
+ ,-9.4
+ ,8748.47
+ ,51.6
+ ,6.141
+ ,1862.5
+ ,1943.5
+ ,27.51
+ ,1.5
+ ,-9.4
+ ,8926.54
+ ,51.6
+ ,6.72
+ ,1919
+ ,1987.5
+ ,28
+ ,1.5
+ ,-9.4
+ ,9050.47
+ ,51.6
+ ,6.68
+ ,1947
+ ,2023.5
+ ,26.57
+ ,1.5
+ ,-9.4
+ ,8988.39
+ ,51.6
+ ,6.371
+ ,1962.5
+ ,2077.5
+ ,24.76
+ ,1.5
+ ,-9.4
+ ,8835.52
+ ,51.6
+ ,6.097
+ ,1922.5
+ ,2007.5
+ ,25.53
+ ,1.5
+ ,-10.4
+ ,8640.42
+ ,50.8
+ ,6.27
+ ,1942.5
+ ,2022.5
+ ,27.08
+ ,1.5
+ ,-10.4
+ ,8801.4
+ ,50.8
+ ,6.447
+ ,1946
+ ,2032.5
+ ,26.66
+ ,1.5
+ ,-10.4
+ ,8767.09
+ ,50.8
+ ,6.37
+ ,1951.5
+ ,2038.5
+ ,26.63
+ ,1.5
+ ,-10.4
+ ,8655.51
+ ,50.8
+ ,6.446
+ ,1937.5
+ ,2012.5
+ ,27.61
+ ,1.5
+ ,-10.4
+ ,8755.44
+ ,50.8
+ ,6.54
+ ,1986.5
+ ,2032.5
+ ,26.72
+ ,1.5
+ ,-10.4
+ ,8500.8
+ ,50.8
+ ,6.374
+ ,1946.5
+ ,1986
+ ,26.96
+ ,1.5
+ ,-10.4
+ ,8514.47
+ ,50.8
+ ,6.33
+ ,1877.5
+ ,1940
+ ,27.73
+ ,1.5
+ ,-10.4
+ ,8603.7
+ ,50.8
+ ,6.63
+ ,1907.5
+ ,1997.5
+ ,26.96
+ ,1.5
+ ,-10.4
+ ,8541.93
+ ,50.8
+ ,6.498
+ ,1947.5
+ ,2017.5
+ ,27.33
+ ,1.5
+ ,-10.4
+ ,8463.16
+ ,50.8
+ ,6.485
+ ,1937.5
+ ,2022
+ ,27.1
+ ,1.5
+ ,-10.4
+ ,8479.63
+ ,50.8
+ ,6.36
+ ,1936
+ ,2016.5
+ ,26.58
+ ,1.5
+ ,-10.4
+ ,8374.91
+ ,50.8)
+ ,dim=c(8
+ ,191)
+ ,dimnames=list(c('koersnyrstar'
+ ,'Zinkprijsdagervoor'
+ ,'Loodprijsdagervoor'
+ ,'NYSEeindkoersvorigedag'
+ ,'RenteopLTleningenin%'
+ ,'Conjunctuurenquete'
+ ,'Nikkei'
+ ,'PMI(purchasingmanagersindex)
')
+ ,1:191))
>  y <- array(NA,dim=c(8,191),dimnames=list(c('koersnyrstar','Zinkprijsdagervoor','Loodprijsdagervoor','NYSEeindkoersvorigedag','RenteopLTleningenin%','Conjunctuurenquete','Nikkei','PMI(purchasingmanagersindex)
'),1:191))
>  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 = 'equal'
> 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] "koersnyrstar"
> x[,par1]
  [1] 11.730 11.750 11.390 11.540  9.620  9.820  9.940  9.900  9.800  9.860
 [11] 10.500 10.330 10.160  9.910  9.960 10.030  9.550  9.510  9.800 10.080
 [21] 10.200 10.230 10.200 10.070 10.010 10.050  9.920 10.030 10.180 10.100
 [31] 10.160 10.150 10.130 10.090 10.180 10.060  9.650  9.740  9.530  9.500
 [41]  9.000  9.150  9.320  9.620  9.590  9.370  9.350  9.320  9.490  9.520
 [51]  9.590  9.350  9.200  9.570  9.780  9.790  9.570  9.530  9.650  9.360
 [61]  9.400  9.320  9.310  9.190  9.390  9.280  9.280  9.310  9.280  9.310
 [71]  9.350  9.190  9.070  8.960  8.690  8.580  8.560  8.470  8.460  8.750
 [81]  8.950  9.330  9.510  9.561  9.940  9.900  9.275  9.560  9.779  9.746
 [91]  9.991  9.980 10.195 10.310 10.250  9.871 10.060  9.894  9.590  9.640
[101]  9.890  9.530  9.388  9.160  9.418  9.570  9.857  9.877  9.760  9.760
[111]  9.695  9.475  9.262  9.097  8.550  8.160  7.532  7.325  6.749  7.130
[121]  6.995  7.346  7.730  7.837  7.514  7.580  6.830  6.617  6.715  6.630
[131]  6.891  7.002  7.090  7.360  7.477  7.826  7.790  7.578  7.204  7.198
[141]  7.685  7.795  7.460  7.274  7.330  7.655  7.767  7.840  7.424  7.540
[151]  7.351  6.735  6.777  6.679  7.340  6.978  6.920  6.628  6.385  5.984
[161]  6.268  6.596  6.395  6.715  6.804  6.929  6.846  6.992  6.774  6.750
[171]  6.485  6.270  6.470  6.780  6.710  6.141  6.720  6.680  6.371  6.097
[181]  6.270  6.447  6.370  6.446  6.540  6.374  6.330  6.630  6.498  6.485
[191]  6.360
> 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])

 C1  C2 
 83 108 
> colnames(x)
[1] "koersnyrstar"                  "Zinkprijsdagervoor"           
[3] "Loodprijsdagervoor"            "NYSEeindkoersvorigedag"       
[5] "RenteopLTleningenin."          "Conjunctuurenquete"           
[7] "Nikkei"                        "PMI.purchasingmanagersindex.."
> colnames(x)[par1]
[1] "koersnyrstar"
> x[,par1]
  [1] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2
 [26] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2
 [51] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1
 [76] C1 C1 C1 C1 C1 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2
[101] C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C2 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1
[126] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1
[151] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1
[176] C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1 C1
Levels: C1 C2
> 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/1dbpf1323799166.tab") 
+ }
+ }
> m

	 Conditional inference tree with 3 terminal nodes

Response:  as.factor(koersnyrstar) 
Inputs:  Zinkprijsdagervoor, Loodprijsdagervoor, NYSEeindkoersvorigedag, RenteopLTleningenin., Conjunctuurenquete, Nikkei, PMI.purchasingmanagersindex.. 
Number of observations:  191 

1) NYSEeindkoersvorigedag <= 31.4; criterion = 1, statistic = 147.08
  2)*  weights = 77 
1) NYSEeindkoersvorigedag > 31.4
  3) PMI.purchasingmanagersindex.. <= 53.5; criterion = 0.998, statistic = 13.595
    4)*  weights = 23 
  3) PMI.purchasingmanagersindex.. > 53.5
    5)*  weights = 91 
> postscript(file="/var/www/rcomp/tmp/2w0nw1323799166.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/3jaf51323799166.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,]    2    2
  [6,]    2    2
  [7,]    2    2
  [8,]    2    2
  [9,]    2    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,]    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    2
 [31,]    2    2
 [32,]    2    2
 [33,]    2    2
 [34,]    2    2
 [35,]    2    2
 [36,]    2    2
 [37,]    2    2
 [38,]    2    2
 [39,]    2    2
 [40,]    2    2
 [41,]    2    2
 [42,]    2    2
 [43,]    2    2
 [44,]    2    2
 [45,]    2    2
 [46,]    2    2
 [47,]    2    2
 [48,]    2    2
 [49,]    2    2
 [50,]    2    2
 [51,]    2    2
 [52,]    2    2
 [53,]    2    2
 [54,]    2    2
 [55,]    2    2
 [56,]    2    2
 [57,]    2    2
 [58,]    2    2
 [59,]    2    2
 [60,]    2    2
 [61,]    2    2
 [62,]    2    2
 [63,]    2    2
 [64,]    2    2
 [65,]    2    2
 [66,]    2    2
 [67,]    2    2
 [68,]    2    2
 [69,]    2    2
 [70,]    2    2
 [71,]    2    2
 [72,]    2    2
 [73,]    2    2
 [74,]    2    2
 [75,]    1    2
 [76,]    1    2
 [77,]    1    2
 [78,]    1    2
 [79,]    1    2
 [80,]    1    2
 [81,]    2    2
 [82,]    2    2
 [83,]    2    2
 [84,]    2    2
 [85,]    2    2
 [86,]    2    2
 [87,]    2    2
 [88,]    2    2
 [89,]    2    2
 [90,]    2    2
 [91,]    2    2
 [92,]    2    2
 [93,]    2    2
 [94,]    2    2
 [95,]    2    2
 [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,]    2    2
[109,]    2    2
[110,]    2    2
[111,]    2    2
[112,]    2    2
[113,]    2    2
[114,]    2    2
[115,]    1    1
[116,]    1    1
[117,]    1    1
[118,]    1    1
[119,]    1    1
[120,]    1    1
[121,]    1    1
[122,]    1    1
[123,]    1    1
[124,]    1    1
[125,]    1    1
[126,]    1    1
[127,]    1    1
[128,]    1    1
[129,]    1    1
[130,]    1    1
[131,]    1    1
[132,]    1    1
[133,]    1    1
[134,]    1    1
[135,]    1    1
[136,]    1    1
[137,]    1    1
[138,]    1    1
[139,]    1    1
[140,]    1    1
[141,]    1    1
[142,]    1    1
[143,]    1    1
[144,]    1    1
[145,]    1    1
[146,]    1    1
[147,]    1    1
[148,]    1    1
[149,]    1    1
[150,]    1    1
[151,]    1    1
[152,]    1    1
[153,]    1    1
[154,]    1    1
[155,]    1    1
[156,]    1    1
[157,]    1    1
[158,]    1    1
[159,]    1    1
[160,]    1    1
[161,]    1    1
[162,]    1    1
[163,]    1    1
[164,]    1    1
[165,]    1    1
[166,]    1    1
[167,]    1    1
[168,]    1    1
[169,]    1    1
[170,]    1    1
[171,]    1    1
[172,]    1    1
[173,]    1    1
[174,]    1    1
[175,]    1    1
[176,]    1    1
[177,]    1    1
[178,]    1    1
[179,]    1    1
[180,]    1    1
[181,]    1    1
[182,]    1    1
[183,]    1    1
[184,]    1    1
[185,]    1    1
[186,]    1    1
[187,]    1    1
[188,]    1    1
[189,]    1    1
[190,]    1    1
[191,]    1    1
    
      C1  C2
  C1  77   6
  C2   0 108
> postscript(file="/var/www/rcomp/tmp/46u7w1323799166.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/5vdn81323799166.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/6sjbm1323799166.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/7j3dt1323799166.tab") 
+ }
> 
> try(system("convert tmp/2w0nw1323799166.ps tmp/2w0nw1323799166.png",intern=TRUE))
character(0)
> try(system("convert tmp/3jaf51323799166.ps tmp/3jaf51323799166.png",intern=TRUE))
character(0)
> try(system("convert tmp/46u7w1323799166.ps tmp/46u7w1323799166.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.552   0.268   3.957