R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
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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(3.66356
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+ ,131
+ ,4.34381
+ ,6.65801
+ ,5.5
+ ,1.1
+ ,0.9
+ ,138
+ ,24
+ ,199
+ ,2.3979
+ ,7.10003
+ ,-4
+ ,5.8
+ ,-0.4
+ ,78.4
+ ,11
+ ,399
+ ,2.63906
+ ,7.65681
+ ,-0.2
+ ,2.4
+ ,0
+ ,41
+ ,19
+ ,157
+ ,2.56495
+ ,4.70953
+ ,-2.8
+ ,1.8
+ ,1.4
+ ,198.9
+ ,3
+ ,537
+ ,3.3322
+ ,5.60212
+ ,3.8
+ ,3.9
+ ,-0.2
+ ,85
+ ,5
+ ,187
+ ,2.56495
+ ,7.85127
+ ,0.2
+ ,2.8
+ ,0.1
+ ,78
+ ,17
+ ,112
+ ,3.68888
+ ,7.89655
+ ,12.8
+ ,6.8
+ ,-0.6
+ ,227.7
+ ,15
+ ,583
+ ,2.3979
+ ,7.20638
+ ,-0.8
+ ,1.6
+ ,1.7
+ ,192.6
+ ,22
+ ,469
+ ,2.3979
+ ,4.89035
+ ,-10.8
+ ,1.5
+ ,0.2
+ ,57
+ ,3
+ ,460
+ ,4.68213
+ ,7.56735
+ ,-10.2
+ ,0.6
+ ,2.4
+ ,110.8
+ ,10
+ ,465
+ ,3.04452
+ ,7.61135
+ ,0.8
+ ,3.1
+ ,-0.1
+ ,65.6
+ ,19
+ ,429
+ ,3.04452
+ ,6.66823
+ ,4.1
+ ,1.4
+ ,0.2
+ ,139.8
+ ,24
+ ,572
+ ,2.19722
+ ,5.0689
+ ,4.3
+ ,4.9
+ ,0.1
+ ,82
+ ,2
+ ,212
+ ,3.17805
+ ,7.46107
+ ,-2.8
+ ,1.4
+ ,-0.1
+ ,243
+ ,20
+ ,102
+ ,4.48864
+ ,7.68018
+ ,-0.4
+ ,0.8
+ ,2.6
+ ,147
+ ,21
+ ,49
+ ,2.30259
+ ,7.80344
+ ,2.4
+ ,3.9
+ ,0.2
+ ,14.3
+ ,20
+ ,574
+ ,3.29584
+ ,8.26256
+ ,3.4
+ ,0.9
+ ,0.3
+ ,228
+ ,16
+ ,109
+ ,3.17805
+ ,7.6695
+ ,12.1
+ ,2.8
+ ,-3.2
+ ,256
+ ,13
+ ,203
+ ,4.06044
+ ,5.33272
+ ,-2.5
+ ,2
+ ,1.3
+ ,80.7
+ ,3
+ ,532
+ ,4.15888
+ ,6.30992
+ ,-4.5
+ ,2
+ ,0.3
+ ,90
+ ,24
+ ,100
+ ,3.09104
+ ,6.67456
+ ,2.2
+ ,7.1
+ ,0.1
+ ,37.9
+ ,6
+ ,557
+ ,4.41884
+ ,8.09316
+ ,-4.7
+ ,1.4
+ ,1.2
+ ,276.8
+ ,18
+ ,445
+ ,2.89037
+ ,6.56667
+ ,0.1
+ ,2.1
+ ,-0.2
+ ,247.4
+ ,6
+ ,543
+ ,1.09861
+ ,5.83481
+ ,-5.8
+ ,7.7
+ ,0.1
+ ,47
+ ,7
+ ,90
+ ,3.43399
+ ,7.794
+ ,3.3
+ ,4.3
+ ,0.8
+ ,217
+ ,17
+ ,105
+ ,3.52636
+ ,7.76089
+ ,2
+ ,1.7
+ ,1.1
+ ,234
+ ,15
+ ,121
+ ,3.13549
+ ,7.67276
+ ,1.3
+ ,4.2
+ ,0.2
+ ,169
+ ,10
+ ,61
+ ,2.19722
+ ,7.979
+ ,1.4
+ ,5
+ ,-0.1
+ ,40.7
+ ,9
+ ,480
+ ,3.13549
+ ,7.78406
+ ,6.1
+ ,1.9
+ ,0.4
+ ,171
+ ,21
+ ,210
+ ,2.07944
+ ,5.95842
+ ,-3.1
+ ,4.2
+ ,-0.1
+ ,52.5
+ ,2
+ ,426
+ ,3.63759
+ ,4.54329
+ ,-11.5
+ ,1.7
+ ,3.7
+ ,90.4
+ ,4
+ ,465
+ ,3.73767
+ ,5.62762
+ ,-6.6
+ ,1
+ ,-0.1
+ ,200
+ ,2
+ ,445
+ ,2.48491
+ ,5.66643
+ ,-3.8
+ ,2.1
+ ,0.6
+ ,73
+ ,3
+ ,77
+ ,1.94591
+ ,7.54539
+ ,6.5
+ ,9.4
+ ,-0.9
+ ,250
+ ,11
+ ,160
+ ,1.94591
+ ,6.23832
+ ,-0.4
+ ,3.3
+ ,0.3
+ ,215.1
+ ,24
+ ,470
+ ,2.77259
+ ,4.56435
+ ,-6.3
+ ,2
+ ,2.3
+ ,223
+ ,3
+ ,148
+ ,2.70805
+ ,6.58203
+ ,2.2
+ ,1.8
+ ,0.1
+ ,64.2
+ ,6
+ ,549
+ ,1.79176
+ ,5.31321
+ ,-4.9
+ ,4.2
+ ,0.3
+ ,353
+ ,2
+ ,117
+ ,2.30259
+ ,5.61677
+ ,-1.3
+ ,2.8
+ ,-0.1
+ ,65.2
+ ,1
+ ,486
+ ,4.11087
+ ,7.7111
+ ,-5.1
+ ,0.7
+ ,0.3
+ ,60
+ ,10
+ ,99
+ ,3.4012
+ ,6.2519
+ ,0.1
+ ,1
+ ,0.2
+ ,87
+ ,24
+ ,111
+ ,3.68888
+ ,7.85516
+ ,6.5
+ ,5.2
+ ,-0.2
+ ,69
+ ,19
+ ,196
+ ,4.17439
+ ,8.24512
+ ,8.6
+ ,1.6
+ ,-1
+ ,258.8
+ ,15
+ ,530)
+ ,dim=c(8
+ ,500)
+ ,dimnames=list(c('PM10'
+ ,'Cars'
+ ,'Temp'
+ ,'Windspeed'
+ ,'Tempdiff'
+ ,'Winddir'
+ ,'hour'
+ ,'day')
+ ,1:500))
>  y <- array(NA,dim=c(8,500),dimnames=list(c('PM10','Cars','Temp','Windspeed','Tempdiff','Winddir','hour','day'),1:500))
>  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 = ''
> par2 = 'none'
> 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

Attaching package: 'zoo'


	The following object(s) are masked from package:base :

	 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)

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] "PM10"
> x[,par1]
  [1] 3.66356 3.04452 3.71357 2.94444 4.06044 3.68888 3.33220 3.36730 2.07944
 [10] 1.94591 3.33220 3.21888 1.09861 5.38450 3.93183 3.36730 2.83321 3.04452
 [19] 2.99573 4.21951 3.04452 3.76120 3.58352 2.48491 2.48491 3.33220 3.13549
 [28] 2.07944 3.68888 2.99573 3.98898 3.04452 3.55535 1.79176 3.04452 2.56495
 [37] 4.36945 3.46574 4.60517 3.09104 4.15888 1.79176 2.48491 3.21888 3.73767
 [46] 4.24850 3.87120 4.00733 2.94444 3.09104 1.94591 3.52636 3.87120 2.99573
 [55] 4.07754 3.21888 3.49651 3.25810 2.99573 2.70805 3.09104 4.06044 3.61092
 [64] 2.56495 1.60944 5.08760 2.30259 3.25810 3.66356 3.04452 3.40120 3.04452
 [73] 3.40120 3.55535 3.97029 4.72739 4.30407 2.56495 3.78419 4.95583 3.66356
 [82] 5.26786 3.46574 2.07944 3.36730 3.29584 2.19722 3.43399 3.25810 2.83321
 [91] 3.63759 2.70805 2.99573 4.00733 3.43399 0.69315 3.52636 2.63906 1.94591
[100] 3.71357 2.83321 3.43399 3.29584 3.17805 4.81218 4.63473 3.04452 3.87120
[109] 3.68888 4.33073 4.61512 4.43082 3.40120 2.48491 3.29584 2.94444 3.36730
[118] 2.89037 4.90527 2.63906 3.29584 3.71357 4.39445 3.82864 2.56495 3.33220
[127] 3.13549 0.69315 4.00733 3.25810 3.25810 4.84419 2.63906 1.38629 3.25810
[136] 2.30259 3.09104 2.94444 3.40120 2.99573 3.82864 2.30259 4.29046 4.41884
[145] 3.36730 2.30259 3.98898 4.12713 3.68888 2.30259 3.29584 3.13549 2.07944
[154] 5.25227 3.78419 3.97029 2.99573 2.56495 3.21888 1.38629 3.13549 4.06044
[163] 4.27667 3.49651 3.21888 4.07754 4.20469 4.18965 2.48491 1.09861 2.48491
[172] 2.48491 0.69315 4.34381 2.63906 3.29584 4.17439 3.71357 4.02535 4.85203
[181] 4.64439 3.63759 3.40120 1.79176 4.11087 3.36730 4.44265 2.77259 3.71357
[190] 5.17048 3.09104 2.70805 4.11087 4.79579 2.39790 2.99573 3.13549 3.43399
[199] 3.40120 3.09104 4.45435 4.18965 3.73767 4.20469 2.56495 2.56495 4.85981
[208] 5.25227 2.63906 3.13549 1.94591 3.40120 4.72739 3.36730 3.25810 3.73767
[217] 1.79176 4.06044 3.91202 0.69315 1.60944 4.46591 3.17805 2.89037 4.17439
[226] 3.73767 4.00733 3.04452 2.63906 4.11087 4.47734 3.17805 3.33220 0.69315
[235] 4.58497 0.69315 3.40120 2.07944 4.55388 3.21888 2.77259 3.29584 3.33220
[244] 4.02535 4.38203 2.99573 3.55535 3.25810 4.47734 4.06044 3.21888 4.46591
[253] 3.13549 2.30259 3.61092 3.13549 3.89182 3.21888 3.17805 3.73767 2.39790
[262] 3.68888 4.26268 4.94164 3.29584 2.07944 2.19722 2.07944 4.54329 2.07944
[271] 4.36945 3.97029 3.98898 2.99573 3.52636 2.99573 5.39363 3.63759 2.30259
[280] 4.92725 2.94444 2.70805 2.07944 3.40120 5.05625 4.93447 2.56495 3.04452
[289] 1.38629 2.77259 3.91202 4.35671 3.40120 4.02535 3.73767 2.94444 4.09434
[298] 3.04452 3.17805 2.56495 2.63906 3.09104 3.40120 2.19722 3.76120 2.77259
[307] 3.40120 3.61092 3.36730 0.69315 2.94444 4.17439 2.83321 3.04452 3.25810
[316] 3.21888 4.23411 4.81218 4.39445 2.63906 3.25810 1.94591 2.94444 2.56495
[325] 3.21888 2.77259 4.61512 1.60944 2.07944 3.87120 3.17805 3.58352 4.68213
[334] 2.56495 2.30259 3.55535 3.46574 2.48491 4.21951 2.30259 2.70805 2.99573
[343] 2.48491 3.13549 4.15888 1.79176 3.29584 2.83321 3.52636 3.25810 3.98898
[352] 1.60944 3.49651 3.17805 4.67283 3.43399 2.07944 3.29584 2.30259 2.07944
[361] 0.69315 3.13549 3.82864 3.87120 2.99573 3.25810 3.33220 3.49651 1.94591
[370] 4.57471 3.91202 3.80666 3.93183 2.48491 3.13549 2.94444 3.40120 1.94591
[379] 3.17805 3.21888 2.63906 3.33220 2.70805 3.78419 3.78419 1.60944 3.36730
[388] 3.04452 4.45435 1.94591 2.63906 3.33220 3.43399 3.89182 2.19722 4.59512
[397] 3.49651 2.83321 2.19722 2.89037 3.40120 2.99573 3.73767 2.94444 3.58352
[406] 3.29584 4.87520 4.33073 3.29584 3.78419 3.61092 2.83321 3.76120 3.58352
[415] 3.17805 3.52636 4.21951 3.93183 2.30259 3.25810 2.70805 3.55535 1.94591
[424] 4.36945 4.70048 2.48491 1.60944 3.78419 1.94591 3.09104 3.21888 2.48491
[433] 2.19722 4.93447 4.04305 4.51086 1.94591 3.09104 4.60517 3.63759 3.46574
[442] 3.87120 5.15329 3.04452 3.49651 2.56495 3.85015 2.48491 2.07944 2.99573
[451] 1.38629 3.17805 2.19722 3.55535 3.87120 3.40120 3.04452 4.34381 2.39790
[460] 2.63906 2.56495 3.33220 2.56495 3.68888 2.39790 2.39790 4.68213 3.04452
[469] 3.04452 2.19722 3.17805 4.48864 2.30259 3.29584 3.17805 4.06044 4.15888
[478] 3.09104 4.41884 2.89037 1.09861 3.43399 3.52636 3.13549 2.19722 3.13549
[487] 2.07944 3.63759 3.73767 2.48491 1.94591 1.94591 2.77259 2.70805 1.79176
[496] 2.30259 4.11087 3.40120 3.68888 4.17439
> 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])

0.69315 1.09861 1.38629 1.60944 1.79176 1.94591 2.07944 2.19722 2.30259  2.3979 
      8       3       4       6       6      13      14       9      13       5 
2.48491 2.56495 2.63906 2.70805 2.77259 2.83321 2.89037 2.94444 2.99573 3.04452 
     14      14      11       8       6       7       4      10      15      17 
3.09104 3.13549 3.17805 3.21888  3.2581 3.29584  3.3322  3.3673  3.4012 3.43399 
     10      13      12      12      13      13      10       9      16       7 
3.46574 3.49651 3.52636 3.55535 3.58352 3.61092 3.63759 3.66356 3.68888 3.71357 
      4       6       6       6       4       4       5       3       7       5 
3.73767  3.7612 3.78419 3.80666 3.82864 3.85015  3.8712 3.89182 3.91202 3.93183 
      8       3       6       1       3       1       7       2       3       3 
3.97029 3.98898 4.00733 4.02535 4.04305 4.06044 4.07754 4.09434 4.11087 4.12713 
      3       4       4       3       1       6       2       1       4       1 
4.15888 4.17439 4.18965 4.20469 4.21951 4.23411  4.2485 4.26268 4.27667 4.29046 
      3       4       2       2       3       1       1       1       1       1 
4.30407 4.33073 4.34381 4.35671 4.36945 4.38203 4.39445 4.41884 4.43082 4.44265 
      1       2       2       1       3       1       2       2       1       1 
4.45435 4.46591 4.47734 4.48864 4.51086 4.54329 4.55388 4.57471 4.58497 4.59512 
      2       2       2       1       1       1       1       1       1       1 
4.60517 4.61512 4.63473 4.64439 4.67283 4.68213 4.70048 4.72739 4.79579 4.81218 
      2       2       1       1       1       2       1       2       1       2 
4.84419 4.85203 4.85981  4.8752 4.90527 4.92725 4.93447 4.94164 4.95583 5.05625 
      1       1       1       1       1       1       2       1       1       1 
 5.0876 5.15329 5.17048 5.25227 5.26786  5.3845 5.39363 
      1       1       1       2       1       1       1 
> colnames(x)
[1] "PM10"      "Cars"      "Temp"      "Windspeed" "Tempdiff"  "Winddir"  
[7] "hour"      "day"      
> colnames(x)[par1]
[1] "PM10"
> x[,par1]
  [1] 3.66356 3.04452 3.71357 2.94444 4.06044 3.68888 3.33220 3.36730 2.07944
 [10] 1.94591 3.33220 3.21888 1.09861 5.38450 3.93183 3.36730 2.83321 3.04452
 [19] 2.99573 4.21951 3.04452 3.76120 3.58352 2.48491 2.48491 3.33220 3.13549
 [28] 2.07944 3.68888 2.99573 3.98898 3.04452 3.55535 1.79176 3.04452 2.56495
 [37] 4.36945 3.46574 4.60517 3.09104 4.15888 1.79176 2.48491 3.21888 3.73767
 [46] 4.24850 3.87120 4.00733 2.94444 3.09104 1.94591 3.52636 3.87120 2.99573
 [55] 4.07754 3.21888 3.49651 3.25810 2.99573 2.70805 3.09104 4.06044 3.61092
 [64] 2.56495 1.60944 5.08760 2.30259 3.25810 3.66356 3.04452 3.40120 3.04452
 [73] 3.40120 3.55535 3.97029 4.72739 4.30407 2.56495 3.78419 4.95583 3.66356
 [82] 5.26786 3.46574 2.07944 3.36730 3.29584 2.19722 3.43399 3.25810 2.83321
 [91] 3.63759 2.70805 2.99573 4.00733 3.43399 0.69315 3.52636 2.63906 1.94591
[100] 3.71357 2.83321 3.43399 3.29584 3.17805 4.81218 4.63473 3.04452 3.87120
[109] 3.68888 4.33073 4.61512 4.43082 3.40120 2.48491 3.29584 2.94444 3.36730
[118] 2.89037 4.90527 2.63906 3.29584 3.71357 4.39445 3.82864 2.56495 3.33220
[127] 3.13549 0.69315 4.00733 3.25810 3.25810 4.84419 2.63906 1.38629 3.25810
[136] 2.30259 3.09104 2.94444 3.40120 2.99573 3.82864 2.30259 4.29046 4.41884
[145] 3.36730 2.30259 3.98898 4.12713 3.68888 2.30259 3.29584 3.13549 2.07944
[154] 5.25227 3.78419 3.97029 2.99573 2.56495 3.21888 1.38629 3.13549 4.06044
[163] 4.27667 3.49651 3.21888 4.07754 4.20469 4.18965 2.48491 1.09861 2.48491
[172] 2.48491 0.69315 4.34381 2.63906 3.29584 4.17439 3.71357 4.02535 4.85203
[181] 4.64439 3.63759 3.40120 1.79176 4.11087 3.36730 4.44265 2.77259 3.71357
[190] 5.17048 3.09104 2.70805 4.11087 4.79579 2.39790 2.99573 3.13549 3.43399
[199] 3.40120 3.09104 4.45435 4.18965 3.73767 4.20469 2.56495 2.56495 4.85981
[208] 5.25227 2.63906 3.13549 1.94591 3.40120 4.72739 3.36730 3.25810 3.73767
[217] 1.79176 4.06044 3.91202 0.69315 1.60944 4.46591 3.17805 2.89037 4.17439
[226] 3.73767 4.00733 3.04452 2.63906 4.11087 4.47734 3.17805 3.33220 0.69315
[235] 4.58497 0.69315 3.40120 2.07944 4.55388 3.21888 2.77259 3.29584 3.33220
[244] 4.02535 4.38203 2.99573 3.55535 3.25810 4.47734 4.06044 3.21888 4.46591
[253] 3.13549 2.30259 3.61092 3.13549 3.89182 3.21888 3.17805 3.73767 2.39790
[262] 3.68888 4.26268 4.94164 3.29584 2.07944 2.19722 2.07944 4.54329 2.07944
[271] 4.36945 3.97029 3.98898 2.99573 3.52636 2.99573 5.39363 3.63759 2.30259
[280] 4.92725 2.94444 2.70805 2.07944 3.40120 5.05625 4.93447 2.56495 3.04452
[289] 1.38629 2.77259 3.91202 4.35671 3.40120 4.02535 3.73767 2.94444 4.09434
[298] 3.04452 3.17805 2.56495 2.63906 3.09104 3.40120 2.19722 3.76120 2.77259
[307] 3.40120 3.61092 3.36730 0.69315 2.94444 4.17439 2.83321 3.04452 3.25810
[316] 3.21888 4.23411 4.81218 4.39445 2.63906 3.25810 1.94591 2.94444 2.56495
[325] 3.21888 2.77259 4.61512 1.60944 2.07944 3.87120 3.17805 3.58352 4.68213
[334] 2.56495 2.30259 3.55535 3.46574 2.48491 4.21951 2.30259 2.70805 2.99573
[343] 2.48491 3.13549 4.15888 1.79176 3.29584 2.83321 3.52636 3.25810 3.98898
[352] 1.60944 3.49651 3.17805 4.67283 3.43399 2.07944 3.29584 2.30259 2.07944
[361] 0.69315 3.13549 3.82864 3.87120 2.99573 3.25810 3.33220 3.49651 1.94591
[370] 4.57471 3.91202 3.80666 3.93183 2.48491 3.13549 2.94444 3.40120 1.94591
[379] 3.17805 3.21888 2.63906 3.33220 2.70805 3.78419 3.78419 1.60944 3.36730
[388] 3.04452 4.45435 1.94591 2.63906 3.33220 3.43399 3.89182 2.19722 4.59512
[397] 3.49651 2.83321 2.19722 2.89037 3.40120 2.99573 3.73767 2.94444 3.58352
[406] 3.29584 4.87520 4.33073 3.29584 3.78419 3.61092 2.83321 3.76120 3.58352
[415] 3.17805 3.52636 4.21951 3.93183 2.30259 3.25810 2.70805 3.55535 1.94591
[424] 4.36945 4.70048 2.48491 1.60944 3.78419 1.94591 3.09104 3.21888 2.48491
[433] 2.19722 4.93447 4.04305 4.51086 1.94591 3.09104 4.60517 3.63759 3.46574
[442] 3.87120 5.15329 3.04452 3.49651 2.56495 3.85015 2.48491 2.07944 2.99573
[451] 1.38629 3.17805 2.19722 3.55535 3.87120 3.40120 3.04452 4.34381 2.39790
[460] 2.63906 2.56495 3.33220 2.56495 3.68888 2.39790 2.39790 4.68213 3.04452
[469] 3.04452 2.19722 3.17805 4.48864 2.30259 3.29584 3.17805 4.06044 4.15888
[478] 3.09104 4.41884 2.89037 1.09861 3.43399 3.52636 3.13549 2.19722 3.13549
[487] 2.07944 3.63759 3.73767 2.48491 1.94591 1.94591 2.77259 2.70805 1.79176
[496] 2.30259 4.11087 3.40120 3.68888 4.17439
> if (par2 == 'none') {
+ m <- ctree(as.formula(paste(colnames(x)[par1],' ~ .',sep='')),data = x)
+ }
> 
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/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/html/freestat/rcomp/tmp/1ta3i1291992932.tab") 
+ }
+ }
> m

	 Conditional inference tree with 4 terminal nodes

Response:  PM10 
Inputs:  Cars, Temp, Windspeed, Tempdiff, Winddir, hour, day 
Number of observations:  500 

1) Cars <= 7.0775; criterion = 1, statistic = 63.102
  2) Windspeed <= 1.5; criterion = 1, statistic = 37.441
    3)*  weights = 49 
  2) Windspeed > 1.5
    4) Windspeed <= 4.4; criterion = 0.985, statistic = 9.456
      5)*  weights = 118 
    4) Windspeed > 4.4
      6)*  weights = 26 
1) Cars > 7.0775
  7)*  weights = 307 
> postscript(file="/var/www/html/freestat/rcomp/tmp/2ta3i1291992932.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/html/freestat/rcomp/tmp/3m1331291992932.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   3.66356  3.523635  0.139925277
2   3.04452  3.523635 -0.479114723
3   3.71357  2.767723  0.945847119
4   2.94444  3.523635 -0.579194723
5   4.06044  3.523635  0.536805277
6   3.68888  3.523635  0.165245277
7   3.33220  3.523635 -0.191434723
8   3.36730  3.523635 -0.156334723
9   2.07944  2.767723 -0.688282881
10  1.94591  3.523635 -1.577724723
11  3.33220  2.767723  0.564477119
12  3.21888  3.523635 -0.304754723
13  1.09861  2.101027 -1.002416923
14  5.38450  3.523635  1.860865277
15  3.93183  3.523635  0.408195277
16  3.36730  3.520219 -0.152918571
17  2.83321  3.523635 -0.690424723
18  3.04452  3.523635 -0.479114723
19  2.99573  2.767723  0.228007119
20  4.21951  3.523635  0.695875277
21  3.04452  3.523635 -0.479114723
22  3.76120  3.523635  0.237565277
23  3.58352  3.523635  0.059885277
24  2.48491  2.767723 -0.282812881
25  2.48491  3.523635 -1.038724723
26  3.33220  3.523635 -0.191434723
27  3.13549  3.523635 -0.388144723
28  2.07944  3.523635 -1.444194723
29  3.68888  3.523635  0.165245277
30  2.99573  2.767723  0.228007119
31  3.98898  3.523635  0.465345277
32  3.04452  2.101027  0.943493077
33  3.55535  3.523635  0.031715277
34  1.79176  3.523635 -1.731874723
35  3.04452  2.767723  0.276797119
36  2.56495  3.523635 -0.958684723
37  4.36945  3.523635  0.845815277
38  3.46574  3.523635 -0.057894723
39  4.60517  3.523635  1.081535277
40  3.09104  3.523635 -0.432594723
41  4.15888  3.523635  0.635245277
42  1.79176  3.523635 -1.731874723
43  2.48491  3.520219 -1.035308571
44  3.21888  3.523635 -0.304754723
45  3.73767  2.767723  0.969947119
46  4.24850  2.767723  1.480777119
47  3.87120  3.520219  0.350981429
48  4.00733  3.523635  0.483695277
49  2.94444  2.767723  0.176717119
50  3.09104  2.767723  0.323317119
51  1.94591  2.767723 -0.821812881
52  3.52636  3.523635  0.002725277
53  3.87120  3.523635  0.347565277
54  2.99573  3.523635 -0.527904723
55  4.07754  3.523635  0.553905277
56  3.21888  3.523635 -0.304754723
57  3.49651  3.523635 -0.027124723
58  3.25810  2.767723  0.490377119
59  2.99573  3.523635 -0.527904723
60  2.70805  2.767723 -0.059672881
61  3.09104  3.520219 -0.429178571
62  4.06044  3.523635  0.536805277
63  3.61092  3.523635  0.087285277
64  2.56495  2.767723 -0.202772881
65  1.60944  2.101027 -0.491586923
66  5.08760  3.523635  1.563965277
67  2.30259  3.523635 -1.221044723
68  3.25810  3.523635 -0.265534723
69  3.66356  2.767723  0.895837119
70  3.04452  3.523635 -0.479114723
71  3.40120  3.523635 -0.122434723
72  3.04452  3.520219 -0.475698571
73  3.40120  3.523635 -0.122434723
74  3.55535  3.520219  0.035131429
75  3.97029  3.523635  0.446655277
76  4.72739  3.523635  1.203755277
77  4.30407  3.523635  0.780435277
78  2.56495  3.523635 -0.958684723
79  3.78419  3.523635  0.260555277
80  4.95583  3.520219  1.435611429
81  3.66356  3.523635  0.139925277
82  5.26786  3.523635  1.744225277
83  3.46574  2.767723  0.698017119
84  2.07944  2.767723 -0.688282881
85  3.36730  3.523635 -0.156334723
86  3.29584  3.523635 -0.227794723
87  2.19722  3.523635 -1.326414723
88  3.43399  3.523635 -0.089644723
89  3.25810  3.520219 -0.262118571
90  2.83321  3.523635 -0.690424723
91  3.63759  3.523635  0.113955277
92  2.70805  3.523635 -0.815584723
93  2.99573  3.523635 -0.527904723
94  4.00733  3.523635  0.483695277
95  3.43399  2.767723  0.666267119
96  0.69315  2.767723 -2.074572881
97  3.52636  3.523635  0.002725277
98  2.63906  3.523635 -0.884574723
99  1.94591  3.523635 -1.577724723
100 3.71357  3.523635  0.189935277
101 2.83321  2.767723  0.065487119
102 3.43399  3.523635 -0.089644723
103 3.29584  3.523635 -0.227794723
104 3.17805  3.523635 -0.345584723
105 4.81218  3.523635  1.288545277
106 4.63473  3.523635  1.111095277
107 3.04452  2.767723  0.276797119
108 3.87120  3.520219  0.350981429
109 3.68888  3.523635  0.165245277
110 4.33073  3.523635  0.807095277
111 4.61512  3.523635  1.091485277
112 4.43082  3.523635  0.907185277
113 3.40120  2.767723  0.633477119
114 2.48491  3.523635 -1.038724723
115 3.29584  3.523635 -0.227794723
116 2.94444  3.523635 -0.579194723
117 3.36730  3.523635 -0.156334723
118 2.89037  3.523635 -0.633264723
119 4.90527  3.523635  1.381635277
120 2.63906  2.767723 -0.128662881
121 3.29584  3.523635 -0.227794723
122 3.71357  2.767723  0.945847119
123 4.39445  3.523635  0.870815277
124 3.82864  3.523635  0.305005277
125 2.56495  2.767723 -0.202772881
126 3.33220  3.523635 -0.191434723
127 3.13549  2.767723  0.367767119
128 0.69315  2.101027 -1.407876923
129 4.00733  3.523635  0.483695277
130 3.25810  3.523635 -0.265534723
131 3.25810  2.767723  0.490377119
132 4.84419  3.523635  1.320555277
133 2.63906  2.767723 -0.128662881
134 1.38629  2.101027 -0.714736923
135 3.25810  2.767723  0.490377119
136 2.30259  3.523635 -1.221044723
137 3.09104  2.767723  0.323317119
138 2.94444  3.523635 -0.579194723
139 3.40120  2.767723  0.633477119
140 2.99573  2.101027  0.894703077
141 3.82864  3.523635  0.305005277
142 2.30259  3.523635 -1.221044723
143 4.29046  3.523635  0.766825277
144 4.41884  3.523635  0.895205277
145 3.36730  3.523635 -0.156334723
146 2.30259  2.101027  0.201563077
147 3.98898  3.523635  0.465345277
148 4.12713  2.767723  1.359407119
149 3.68888  3.520219  0.168661429
150 2.30259  2.101027  0.201563077
151 3.29584  3.523635 -0.227794723
152 3.13549  2.767723  0.367767119
153 2.07944  2.767723 -0.688282881
154 5.25227  3.523635  1.728635277
155 3.78419  3.523635  0.260555277
156 3.97029  3.520219  0.450071429
157 2.99573  2.767723  0.228007119
158 2.56495  3.520219 -0.955268571
159 3.21888  2.767723  0.451157119
160 1.38629  3.523635 -2.137344723
161 3.13549  3.523635 -0.388144723
162 4.06044  3.523635  0.536805277
163 4.27667  3.523635  0.753035277
164 3.49651  3.523635 -0.027124723
165 3.21888  3.523635 -0.304754723
166 4.07754  3.523635  0.553905277
167 4.20469  3.523635  0.681055277
168 4.18965  3.520219  0.669431429
169 2.48491  2.767723 -0.282812881
170 1.09861  2.767723 -1.669112881
171 2.48491  3.523635 -1.038724723
172 2.48491  3.523635 -1.038724723
173 0.69315  3.523635 -2.830484723
174 4.34381  3.523635  0.820175277
175 2.63906  2.767723 -0.128662881
176 3.29584  3.523635 -0.227794723
177 4.17439  3.523635  0.650755277
178 3.71357  3.523635  0.189935277
179 4.02535  3.523635  0.501715277
180 4.85203  3.523635  1.328395277
181 4.64439  3.520219  1.124171429
182 3.63759  3.523635  0.113955277
183 3.40120  3.523635 -0.122434723
184 1.79176  2.767723 -0.975962881
185 4.11087  3.523635  0.587235277
186 3.36730  3.523635 -0.156334723
187 4.44265  3.523635  0.919015277
188 2.77259  3.523635 -0.751044723
189 3.71357  2.767723  0.945847119
190 5.17048  3.523635  1.646845277
191 3.09104  3.523635 -0.432594723
192 2.70805  3.523635 -0.815584723
193 4.11087  3.523635  0.587235277
194 4.79579  3.523635  1.272155277
195 2.39790  3.520219 -1.122318571
196 2.99573  2.767723  0.228007119
197 3.13549  3.523635 -0.388144723
198 3.43399  3.523635 -0.089644723
199 3.40120  3.523635 -0.122434723
200 3.09104  3.523635 -0.432594723
201 4.45435  3.523635  0.930715277
202 4.18965  3.523635  0.666015277
203 3.73767  3.523635  0.214035277
204 4.20469  3.520219  0.684471429
205 2.56495  2.767723 -0.202772881
206 2.56495  3.523635 -0.958684723
207 4.85981  3.523635  1.336175277
208 5.25227  3.523635  1.728635277
209 2.63906  2.767723 -0.128662881
210 3.13549  2.767723  0.367767119
211 1.94591  2.767723 -0.821812881
212 3.40120  3.520219 -0.119018571
213 4.72739  3.523635  1.203755277
214 3.36730  2.767723  0.599577119
215 3.25810  3.523635 -0.265534723
216 3.73767  3.523635  0.214035277
217 1.79176  3.523635 -1.731874723
218 4.06044  3.523635  0.536805277
219 3.91202  3.523635  0.388385277
220 0.69315  2.767723 -2.074572881
221 1.60944  2.767723 -1.158282881
222 4.46591  3.523635  0.942275277
223 3.17805  3.523635 -0.345584723
224 2.89037  3.520219 -0.629848571
225 4.17439  3.523635  0.650755277
226 3.73767  3.523635  0.214035277
227 4.00733  3.523635  0.483695277
228 3.04452  3.523635 -0.479114723
229 2.63906  3.523635 -0.884574723
230 4.11087  3.523635  0.587235277
231 4.47734  3.523635  0.953705277
232 3.17805  3.523635 -0.345584723
233 3.33220  3.523635 -0.191434723
234 0.69315  2.767723 -2.074572881
235 4.58497  3.523635  1.061335277
236 0.69315  2.101027 -1.407876923
237 3.40120  3.523635 -0.122434723
238 2.07944  3.523635 -1.444194723
239 4.55388  3.523635  1.030245277
240 3.21888  2.767723  0.451157119
241 2.77259  2.101027  0.671563077
242 3.29584  2.767723  0.528117119
243 3.33220  3.523635 -0.191434723
244 4.02535  3.523635  0.501715277
245 4.38203  3.523635  0.858395277
246 2.99573  3.520219 -0.524488571
247 3.55535  3.523635  0.031715277
248 3.25810  3.523635 -0.265534723
249 4.47734  3.523635  0.953705277
250 4.06044  3.520219  0.540221429
251 3.21888  3.523635 -0.304754723
252 4.46591  3.520219  0.945691429
253 3.13549  2.767723  0.367767119
254 2.30259  2.101027  0.201563077
255 3.61092  3.520219  0.090701429
256 3.13549  3.520219 -0.384728571
257 3.89182  3.523635  0.368185277
258 3.21888  3.523635 -0.304754723
259 3.17805  2.767723  0.410327119
260 3.73767  3.523635  0.214035277
261 2.39790  3.523635 -1.125734723
262 3.68888  3.523635  0.165245277
263 4.26268  3.523635  0.739045277
264 4.94164  3.523635  1.418005277
265 3.29584  3.523635 -0.227794723
266 2.07944  2.767723 -0.688282881
267 2.19722  3.523635 -1.326414723
268 2.07944  3.520219 -1.440778571
269 4.54329  3.523635  1.019655277
270 2.07944  3.523635 -1.444194723
271 4.36945  3.523635  0.845815277
272 3.97029  3.523635  0.446655277
273 3.98898  3.520219  0.468761429
274 2.99573  3.523635 -0.527904723
275 3.52636  2.767723  0.758637119
276 2.99573  3.523635 -0.527904723
277 5.39363  3.523635  1.869995277
278 3.63759  3.523635  0.113955277
279 2.30259  2.767723 -0.465132881
280 4.92725  3.523635  1.403615277
281 2.94444  3.523635 -0.579194723
282 2.70805  3.523635 -0.815584723
283 2.07944  3.523635 -1.444194723
284 3.40120  3.523635 -0.122434723
285 5.05625  3.523635  1.532615277
286 4.93447  3.523635  1.410835277
287 2.56495  3.523635 -0.958684723
288 3.04452  3.523635 -0.479114723
289 1.38629  2.767723 -1.381432881
290 2.77259  3.523635 -0.751044723
291 3.91202  3.523635  0.388385277
292 4.35671  3.523635  0.833075277
293 3.40120  3.523635 -0.122434723
294 4.02535  3.523635  0.501715277
295 3.73767  3.523635  0.214035277
296 2.94444  3.523635 -0.579194723
297 4.09434  3.520219  0.574121429
298 3.04452  3.520219 -0.475698571
299 3.17805  3.523635 -0.345584723
300 2.56495  3.523635 -0.958684723
301 2.63906  2.101027  0.538033077
302 3.09104  3.523635 -0.432594723
303 3.40120  3.523635 -0.122434723
304 2.19722  2.767723 -0.570502881
305 3.76120  3.520219  0.240981429
306 2.77259  2.767723  0.004867119
307 3.40120  2.767723  0.633477119
308 3.61092  2.767723  0.843197119
309 3.36730  2.767723  0.599577119
310 0.69315  2.101027 -1.407876923
311 2.94444  2.767723  0.176717119
312 4.17439  3.523635  0.650755277
313 2.83321  3.523635 -0.690424723
314 3.04452  2.101027  0.943493077
315 3.25810  3.523635 -0.265534723
316 3.21888  3.523635 -0.304754723
317 4.23411  3.523635  0.710475277
318 4.81218  3.523635  1.288545277
319 4.39445  3.523635  0.870815277
320 2.63906  2.767723 -0.128662881
321 3.25810  3.523635 -0.265534723
322 1.94591  3.520219 -1.574308571
323 2.94444  3.523635 -0.579194723
324 2.56495  3.523635 -0.958684723
325 3.21888  2.767723  0.451157119
326 2.77259  3.523635 -0.751044723
327 4.61512  3.523635  1.091485277
328 1.60944  2.101027 -0.491586923
329 2.07944  2.101027 -0.021586923
330 3.87120  3.520219  0.350981429
331 3.17805  3.523635 -0.345584723
332 3.58352  3.523635  0.059885277
333 4.68213  3.523635  1.158495277
334 2.56495  3.523635 -0.958684723
335 2.30259  2.767723 -0.465132881
336 3.55535  3.523635  0.031715277
337 3.46574  3.520219 -0.054478571
338 2.48491  3.523635 -1.038724723
339 4.21951  3.520219  0.699291429
340 2.30259  3.523635 -1.221044723
341 2.70805  3.520219 -0.812168571
342 2.99573  3.523635 -0.527904723
343 2.48491  2.767723 -0.282812881
344 3.13549  3.523635 -0.388144723
345 4.15888  3.523635  0.635245277
346 1.79176  2.767723 -0.975962881
347 3.29584  3.523635 -0.227794723
348 2.83321  3.520219 -0.687008571
349 3.52636  2.101027  1.425333077
350 3.25810  2.767723  0.490377119
351 3.98898  2.767723  1.221257119
352 1.60944  2.767723 -1.158282881
353 3.49651  3.523635 -0.027124723
354 3.17805  3.523635 -0.345584723
355 4.67283  3.523635  1.149195277
356 3.43399  2.767723  0.666267119
357 2.07944  3.523635 -1.444194723
358 3.29584  3.523635 -0.227794723
359 2.30259  3.523635 -1.221044723
360 2.07944  2.767723 -0.688282881
361 0.69315  2.101027 -1.407876923
362 3.13549  3.523635 -0.388144723
363 3.82864  3.523635  0.305005277
364 3.87120  3.523635  0.347565277
365 2.99573  2.767723  0.228007119
366 3.25810  3.523635 -0.265534723
367 3.33220  3.520219 -0.188018571
368 3.49651  3.523635 -0.027124723
369 1.94591  2.767723 -0.821812881
370 4.57471  3.523635  1.051075277
371 3.91202  3.523635  0.388385277
372 3.80666  2.767723  1.038937119
373 3.93183  3.520219  0.411611429
374 2.48491  2.767723 -0.282812881
375 3.13549  3.523635 -0.388144723
376 2.94444  2.767723  0.176717119
377 3.40120  3.523635 -0.122434723
378 1.94591  2.767723 -0.821812881
379 3.17805  2.767723  0.410327119
380 3.21888  3.523635 -0.304754723
381 2.63906  2.101027  0.538033077
382 3.33220  3.523635 -0.191434723
383 2.70805  2.767723 -0.059672881
384 3.78419  3.520219  0.263971429
385 3.78419  3.523635  0.260555277
386 1.60944  3.523635 -1.914194723
387 3.36730  3.523635 -0.156334723
388 3.04452  2.101027  0.943493077
389 4.45435  3.523635  0.930715277
390 1.94591  2.767723 -0.821812881
391 2.63906  2.101027  0.538033077
392 3.33220  3.523635 -0.191434723
393 3.43399  2.767723  0.666267119
394 3.89182  3.523635  0.368185277
395 2.19722  3.523635 -1.326414723
396 4.59512  3.523635  1.071485277
397 3.49651  3.520219 -0.023708571
398 2.83321  3.523635 -0.690424723
399 2.19722  2.767723 -0.570502881
400 2.89037  2.767723  0.122647119
401 3.40120  3.520219 -0.119018571
402 2.99573  2.767723  0.228007119
403 3.73767  3.523635  0.214035277
404 2.94444  2.767723  0.176717119
405 3.58352  3.523635  0.059885277
406 3.29584  3.523635 -0.227794723
407 4.87520  3.523635  1.351565277
408 4.33073  3.523635  0.807095277
409 3.29584  3.520219 -0.224378571
410 3.78419  3.523635  0.260555277
411 3.61092  2.767723  0.843197119
412 2.83321  2.767723  0.065487119
413 3.76120  3.520219  0.240981429
414 3.58352  2.767723  0.815797119
415 3.17805  2.767723  0.410327119
416 3.52636  3.520219  0.006141429
417 4.21951  3.523635  0.695875277
418 3.93183  3.523635  0.408195277
419 2.30259  2.767723 -0.465132881
420 3.25810  2.767723  0.490377119
421 2.70805  2.767723 -0.059672881
422 3.55535  2.767723  0.787627119
423 1.94591  2.767723 -0.821812881
424 4.36945  3.520219  0.849231429
425 4.70048  3.523635  1.176845277
426 2.48491  2.101027  0.383883077
427 1.60944  3.523635 -1.914194723
428 3.78419  3.523635  0.260555277
429 1.94591  2.767723 -0.821812881
430 3.09104  3.523635 -0.432594723
431 3.21888  3.523635 -0.304754723
432 2.48491  3.523635 -1.038724723
433 2.19722  2.767723 -0.570502881
434 4.93447  3.520219  1.414251429
435 4.04305  3.523635  0.519415277
436 4.51086  3.523635  0.987225277
437 1.94591  2.101027 -0.155116923
438 3.09104  3.523635 -0.432594723
439 4.60517  3.523635  1.081535277
440 3.63759  3.523635  0.113955277
441 3.46574  3.523635 -0.057894723
442 3.87120  3.523635  0.347565277
443 5.15329  3.523635  1.629655277
444 3.04452  3.523635 -0.479114723
445 3.49651  3.523635 -0.027124723
446 2.56495  2.767723 -0.202772881
447 3.85015  3.523635  0.326515277
448 2.48491  2.767723 -0.282812881
449 2.07944  2.767723 -0.688282881
450 2.99573  3.523635 -0.527904723
451 1.38629  2.767723 -1.381432881
452 3.17805  3.523635 -0.345584723
453 2.19722  2.767723 -0.570502881
454 3.55535  3.523635  0.031715277
455 3.87120  3.523635  0.347565277
456 3.40120  3.523635 -0.122434723
457 3.04452  3.523635 -0.479114723
458 4.34381  3.520219  0.823591429
459 2.39790  3.523635 -1.125734723
460 2.63906  3.523635 -0.884574723
461 2.56495  2.767723 -0.202772881
462 3.33220  2.767723  0.564477119
463 2.56495  3.523635 -0.958684723
464 3.68888  3.523635  0.165245277
465 2.39790  3.523635 -1.125734723
466 2.39790  3.520219 -1.122318571
467 4.68213  3.523635  1.158495277
468 3.04452  3.523635 -0.479114723
469 3.04452  3.520219 -0.475698571
470 2.19722  2.101027  0.096193077
471 3.17805  3.523635 -0.345584723
472 4.48864  3.523635  0.965005277
473 2.30259  3.523635 -1.221044723
474 3.29584  3.523635 -0.227794723
475 3.17805  3.523635 -0.345584723
476 4.06044  2.767723  1.292717119
477 4.15888  2.767723  1.391157119
478 3.09104  2.101027  0.990013077
479 4.41884  3.523635  0.895205277
480 2.89037  2.767723  0.122647119
481 1.09861  2.101027 -1.002416923
482 3.43399  3.523635 -0.089644723
483 3.52636  3.523635  0.002725277
484 3.13549  3.523635 -0.388144723
485 2.19722  3.523635 -1.326414723
486 3.13549  3.523635 -0.388144723
487 2.07944  2.767723 -0.688282881
488 3.63759  2.767723  0.869867119
489 3.73767  3.520219  0.217451429
490 2.48491  2.767723 -0.282812881
491 1.94591  3.523635 -1.577724723
492 1.94591  2.767723 -0.821812881
493 2.77259  2.767723  0.004867119
494 2.70805  2.767723 -0.059672881
495 1.79176  2.767723 -0.975962881
496 2.30259  2.767723 -0.465132881
497 4.11087  3.523635  0.587235277
498 3.40120  3.520219 -0.119018571
499 3.68888  3.523635  0.165245277
500 4.17439  3.523635  0.650755277
> if (par2 != 'none') {
+ print(cbind(as.factor(x[,par1]),predict(m)))
+ myt <- table(as.factor(x[,par1]),predict(m))
+ print(myt)
+ }
> postscript(file="/var/www/html/freestat/rcomp/tmp/4wa261291992932.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/html/freestat/rcomp/tmp/5ib1u1291992932.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/html/freestat/rcomp/tmp/6lbh01291992932.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/html/freestat/rcomp/tmp/7elhl1291992932.tab") 
+ }
> 
> try(system("convert tmp/2ta3i1291992932.ps tmp/2ta3i1291992932.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m1331291992932.ps tmp/3m1331291992932.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wa261291992932.ps tmp/4wa261291992932.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
 16.359   0.878  16.812