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

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

  Natural language support but running in an English locale

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- array(list(40399
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+ ,dim=c(10
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+ ,dimnames=list(c('OPENVAC'
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+ ,'X4')
+ ,1:125))
>  y <- array(NA,dim=c(10,125),dimnames=list(c('OPENVAC','Y1','Y2','Y3','Y4','NWWZ','X1','X2','X3','X4'),1:125))
>  for (i in 1:dim(x)[1])
+  {
+  	for (j in 1:dim(x)[2])
+  	{
+  		y[i,j] <- as.numeric(x[i,j])
+  	}
+  }
> par4 = 'no'
> par3 = '3'
> par2 = 'none'
> par1 = '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] "OPENVAC"
> x[,par1]
  [1] 40399 36763 37903 35532 35533 32110 33374 35462 33508 36080 34560 38737
 [13] 38144 37594 36424 36843 37246 38661 40454 44928 48441 48140 45998 47369
 [25] 49554 47510 44873 45344 42413 36912 43452 42142 44382 43636 44167 44423
 [37] 42868 43908 42013 38846 35087 33026 34646 37135 37985 43121 43722 43630
 [49] 42234 39351 39327 35704 30466 28155 29257 29998 32529 34787 33855 34556
 [61] 31348 30805 28353 24514 21106 21346 23335 24379 26290 30084 29429 30632
 [73] 27349 27264 27474 24482 21453 18788 19282 19713 21917 23812 23785 24696
 [85] 24562 23580 24939 23899 21454 19761 19815 20780 23462 25005 24725 26198
 [97] 27543 26471 26558 25317 22896 22248 23406 25073 27691 30599 31948 32946
[109] 34012 32936 32974 30951 29812 29010 31068 32447 34844 35676 35387 36488
[121] 35652 33488 32914 29781 27951
> 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])

18788 19282 19713 19761 19815 20780 21106 21346 21453 21454 21917 22248 22896 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
23335 23406 23462 23580 23785 23812 23899 24379 24482 24514 24562 24696 24725 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
24939 25005 25073 25317 26198 26290 26471 26558 27264 27349 27474 27543 27691 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
27951 28155 28353 29010 29257 29429 29781 29812 29998 30084 30466 30599 30632 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
30805 30951 31068 31348 31948 32110 32447 32529 32914 32936 32946 32974 33026 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
33374 33488 33508 33855 34012 34556 34560 34646 34787 34844 35087 35387 35462 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
35532 35533 35652 35676 35704 36080 36424 36488 36763 36843 36912 37135 37246 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
37594 37903 37985 38144 38661 38737 38846 39327 39351 40399 40454 42013 42142 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
42234 42413 42868 43121 43452 43630 43636 43722 43908 44167 44382 44423 44873 
    1     1     1     1     1     1     1     1     1     1     1     1     1 
44928 45344 45998 47369 47510 48140 48441 49554 
    1     1     1     1     1     1     1     1 
> colnames(x)
 [1] "OPENVAC" "Y1"      "Y2"      "Y3"      "Y4"      "NWWZ"    "X1"     
 [8] "X2"      "X3"      "X4"     
> colnames(x)[par1]
[1] "OPENVAC"
> x[,par1]
  [1] 40399 36763 37903 35532 35533 32110 33374 35462 33508 36080 34560 38737
 [13] 38144 37594 36424 36843 37246 38661 40454 44928 48441 48140 45998 47369
 [25] 49554 47510 44873 45344 42413 36912 43452 42142 44382 43636 44167 44423
 [37] 42868 43908 42013 38846 35087 33026 34646 37135 37985 43121 43722 43630
 [49] 42234 39351 39327 35704 30466 28155 29257 29998 32529 34787 33855 34556
 [61] 31348 30805 28353 24514 21106 21346 23335 24379 26290 30084 29429 30632
 [73] 27349 27264 27474 24482 21453 18788 19282 19713 21917 23812 23785 24696
 [85] 24562 23580 24939 23899 21454 19761 19815 20780 23462 25005 24725 26198
 [97] 27543 26471 26558 25317 22896 22248 23406 25073 27691 30599 31948 32946
[109] 34012 32936 32974 30951 29812 29010 31068 32447 34844 35676 35387 36488
[121] 35652 33488 32914 29781 27951
> 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/1hvnl1291972502.tab") 
+ }
+ }
> m

	 Conditional inference tree with 7 terminal nodes

Response:  OPENVAC 
Inputs:  Y1, Y2, Y3, Y4, NWWZ, X1, X2, X3, X4 
Number of observations:  125 

1) Y1 <= 31348; criterion = 1, statistic = 114.635
  2) Y1 <= 25317; criterion = 1, statistic = 40.859
    3) Y1 <= 21454; criterion = 0.997, statistic = 13.091
      4)*  weights = 10 
    3) Y1 > 21454
      5)*  weights = 20 
  2) Y1 > 25317
    6)*  weights = 25 
1) Y1 > 31348
  7) Y1 <= 40399; criterion = 1, statistic = 53.627
    8) Y1 <= 36080; criterion = 0.999, statistic = 14.943
      9)*  weights = 28 
    8) Y1 > 36080
      10)*  weights = 17 
  7) Y1 > 40399
    11) Y1 <= 44423; criterion = 0.985, statistic = 9.934
      12)*  weights = 16 
    11) Y1 > 44423
      13)*  weights = 9 
> postscript(file="/var/www/html/freestat/rcomp/tmp/2hvnl1291972502.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/3sm561291972502.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     40399  42347.56 -1948.562500
2     36763  37993.65 -1230.647059
3     37903  37993.65   -90.647059
4     35532  37993.65 -2461.647059
5     35533  33985.00  1548.000000
6     32110  33985.00 -1875.000000
7     33374  33985.00  -611.000000
8     35462  33985.00  1477.000000
9     33508  33985.00  -477.000000
10    36080  33985.00  2095.000000
11    34560  33985.00   575.000000
12    38737  33985.00  4752.000000
13    38144  37993.65   150.352941
14    37594  37993.65  -399.647059
15    36424  37993.65 -1569.647059
16    36843  37993.65 -1150.647059
17    37246  37993.65  -747.647059
18    38661  37993.65   667.352941
19    40454  37993.65  2460.352941
20    44928  42347.56  2580.437500
21    48441  46626.89  1814.111111
22    48140  46626.89  1513.111111
23    45998  46626.89  -628.888889
24    47369  46626.89   742.111111
25    49554  46626.89  2927.111111
26    47510  46626.89   883.111111
27    44873  46626.89 -1753.888889
28    45344  46626.89 -1282.888889
29    42413  46626.89 -4213.888889
30    36912  42347.56 -5435.562500
31    43452  37993.65  5458.352941
32    42142  42347.56  -205.562500
33    44382  42347.56  2034.437500
34    43636  42347.56  1288.437500
35    44167  42347.56  1819.437500
36    44423  42347.56  2075.437500
37    42868  42347.56   520.437500
38    43908  42347.56  1560.437500
39    42013  42347.56  -334.562500
40    38846  42347.56 -3501.562500
41    35087  37993.65 -2906.647059
42    33026  33985.00  -959.000000
43    34646  33985.00   661.000000
44    37135  33985.00  3150.000000
45    37985  37993.65    -8.647059
46    43121  37993.65  5127.352941
47    43722  42347.56  1374.437500
48    43630  42347.56  1282.437500
49    42234  42347.56  -113.562500
50    39351  42347.56 -2996.562500
51    39327  37993.65  1333.352941
52    35704  37993.65 -2289.647059
53    30466  33985.00 -3519.000000
54    28155  28761.96  -606.960000
55    29257  28761.96   495.040000
56    29998  28761.96  1236.040000
57    32529  28761.96  3767.040000
58    34787  33985.00   802.000000
59    33855  33985.00  -130.000000
60    34556  33985.00   571.000000
61    31348  33985.00 -2637.000000
62    30805  28761.96  2043.040000
63    28353  28761.96  -408.960000
64    24514  28761.96 -4247.960000
65    21106  24059.85 -2953.850000
66    21346  20819.90   526.100000
67    23335  20819.90  2515.100000
68    24379  24059.85   319.150000
69    26290  24059.85  2230.150000
70    30084  28761.96  1322.040000
71    29429  28761.96   667.040000
72    30632  28761.96  1870.040000
73    27349  28761.96 -1412.960000
74    27264  28761.96 -1497.960000
75    27474  28761.96 -1287.960000
76    24482  28761.96 -4279.960000
77    21453  24059.85 -2606.850000
78    18788  20819.90 -2031.900000
79    19282  20819.90 -1537.900000
80    19713  20819.90 -1106.900000
81    21917  20819.90  1097.100000
82    23812  24059.85  -247.850000
83    23785  24059.85  -274.850000
84    24696  24059.85   636.150000
85    24562  24059.85   502.150000
86    23580  24059.85  -479.850000
87    24939  24059.85   879.150000
88    23899  24059.85  -160.850000
89    21454  24059.85 -2605.850000
90    19761  20819.90 -1058.900000
91    19815  20819.90 -1004.900000
92    20780  20819.90   -39.900000
93    23462  20819.90  2642.100000
94    25005  24059.85   945.150000
95    24725  24059.85   665.150000
96    26198  24059.85  2138.150000
97    27543  28761.96 -1218.960000
98    26471  28761.96 -2290.960000
99    26558  28761.96 -2203.960000
100   25317  28761.96 -3444.960000
101   22896  24059.85 -1163.850000
102   22248  24059.85 -1811.850000
103   23406  24059.85  -653.850000
104   25073  24059.85  1013.150000
105   27691  24059.85  3631.150000
106   30599  28761.96  1837.040000
107   31948  28761.96  3186.040000
108   32946  33985.00 -1039.000000
109   34012  33985.00    27.000000
110   32936  33985.00 -1049.000000
111   32974  33985.00 -1011.000000
112   30951  33985.00 -3034.000000
113   29812  28761.96  1050.040000
114   29010  28761.96   248.040000
115   31068  28761.96  2306.040000
116   32447  28761.96  3685.040000
117   34844  33985.00   859.000000
118   35676  33985.00  1691.000000
119   35387  33985.00  1402.000000
120   36488  33985.00  2503.000000
121   35652  37993.65 -2341.647059
122   33488  33985.00  -497.000000
123   32914  33985.00 -1071.000000
124   29781  33985.00 -4204.000000
125   27951  28761.96  -810.960000
> 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/43em91291972502.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/56w2x1291972502.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/6rx131291972502.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/7vfhr1291972502.tab") 
+ }
> 
> try(system("convert tmp/2hvnl1291972502.ps tmp/2hvnl1291972502.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sm561291972502.ps tmp/3sm561291972502.png",intern=TRUE))
character(0)
> try(system("convert tmp/43em91291972502.ps tmp/43em91291972502.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  4.995   0.797   6.066