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)

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.

R is a collaborative project with many contributors.
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(0.01244
+ ,0.00149
+ ,0.01848
+ ,0.00338
+ ,0.00099
+ ,-0.01826
+ ,-0.01025
+ ,0.04860
+ ,0.04399
+ ,-0.03429
+ ,0.00779
+ ,0.01150
+ ,0.01244
+ ,0.00149
+ ,0.01848
+ ,0.00338
+ ,0.00099
+ ,-0.03086
+ ,-0.01025
+ ,0.04860
+ ,0.04399
+ ,-0.03429
+ ,-0.00793
+ ,0.01150
+ ,0.01244
+ ,0.00149
+ ,0.01848
+ ,0.00338
+ ,0.04033
+ ,-0.03086
+ ,-0.01025
+ ,0.04860
+ ,0.04399
+ ,-0.01514
+ ,-0.00793
+ ,0.01150
+ ,0.01244
+ ,0.00149
+ ,0.01848
+ ,-0.02352
+ ,0.04033
+ ,-0.03086
+ ,-0.01025
+ ,0.04860
+ ,0.01778
+ ,-0.01514
+ ,-0.00793
+ ,0.01150
+ ,0.01244
+ ,0.00149
+ ,0.00573
+ ,-0.02352
+ ,0.04033
+ ,-0.03086
+ ,-0.01025
+ ,0.00634
+ ,0.01778
+ ,-0.01514
+ ,-0.00793
+ ,0.01150
+ ,0.01244
+ ,0.01805
+ ,0.00573
+ ,-0.02352
+ ,0.04033
+ ,-0.03086
+ ,0.00770
+ ,0.00634
+ ,0.01778
+ ,-0.01514
+ ,-0.00793
+ ,0.01150
+ ,-0.01887
+ ,0.01805
+ ,0.00573
+ ,-0.02352
+ ,0.04033
+ ,0.00692
+ ,0.00770
+ ,0.00634
+ ,0.01778
+ ,-0.01514
+ ,-0.00793
+ ,0.04363
+ ,-0.01887
+ ,0.01805
+ ,0.00573
+ ,-0.02352
+ ,0.00029
+ ,0.00692
+ ,0.00770
+ ,0.00634
+ ,0.01778
+ ,-0.01514
+ ,0.02875
+ ,0.04363
+ ,-0.01887
+ ,0.01805
+ ,0.00573
+ ,0.02487
+ ,0.00029
+ ,0.00692
+ ,0.00770
+ ,0.00634
+ ,0.01778
+ ,-0.00393
+ ,0.02875
+ ,0.04363
+ ,-0.01887
+ ,0.01805
+ ,0.01708
+ ,0.02487
+ ,0.00029
+ ,0.00692
+ ,0.00770
+ ,0.00634
+ ,0.05280
+ ,-0.00393
+ ,0.02875
+ ,0.04363
+ ,-0.01887
+ ,0.02540
+ ,0.01708
+ ,0.02487
+ ,0.00029
+ ,0.00692
+ ,0.00770
+ ,-0.00351
+ ,0.05280
+ ,-0.00393
+ ,0.02875
+ ,0.04363
+ ,0.02935
+ ,0.02540
+ ,0.01708
+ ,0.02487
+ ,0.00029
+ ,0.00692
+ ,0.05407
+ ,-0.00351
+ ,0.05280
+ ,-0.00393
+ ,0.02875
+ ,0.02615
+ ,0.02935
+ ,0.02540
+ ,0.01708
+ ,0.02487
+ ,0.00029
+ ,-0.01299
+ ,0.05407
+ ,-0.00351
+ ,0.05280
+ ,-0.00393
+ ,0.00424
+ ,0.02615
+ ,0.02935
+ ,0.02540
+ ,0.01708
+ ,0.02487
+ ,0.00747
+ ,-0.01299
+ ,0.05407
+ ,-0.00351
+ ,0.05280
+ ,-0.00032
+ ,0.00424
+ ,0.02615
+ ,0.02935
+ ,0.02540
+ ,0.01708
+ ,-0.03288
+ ,0.00747
+ ,-0.01299
+ ,0.05407
+ ,-0.00351
+ ,-0.02353
+ ,-0.00032
+ ,0.00424
+ ,0.02615
+ ,0.02935
+ ,0.02540
+ ,-0.05013
+ ,-0.03288
+ ,0.00747
+ ,-0.01299
+ ,0.05407
+ ,0.01387
+ ,-0.02353
+ ,-0.00032
+ ,0.00424
+ ,0.02615
+ ,0.02935
+ ,0.03715
+ ,-0.05013
+ ,-0.03288
+ ,0.00747
+ ,-0.01299
+ ,0.01286
+ ,0.01387
+ ,-0.02353
+ ,-0.00032
+ ,0.00424
+ ,0.02615
+ ,0.00205
+ ,0.03715
+ ,-0.05013
+ ,-0.03288
+ ,0.00747
+ ,-0.00609
+ ,0.01286
+ ,0.01387
+ ,-0.02353
+ ,-0.00032
+ ,0.00424
+ ,0.02912
+ ,0.00205
+ ,0.03715
+ ,-0.05013
+ ,-0.03288
+ ,0.00635
+ ,-0.00609
+ ,0.01286
+ ,0.01387
+ ,-0.02353
+ ,-0.00032
+ ,-0.00832
+ ,0.02912
+ ,0.00205
+ ,0.03715
+ ,-0.05013
+ ,0.02049
+ ,0.00635
+ ,-0.00609
+ ,0.01286
+ ,0.01387
+ ,-0.02353
+ ,0.02908
+ ,-0.00832
+ ,0.02912
+ ,0.00205
+ ,0.03715
+ ,0.00332
+ ,0.02049
+ ,0.00635
+ ,-0.00609
+ ,0.01286
+ ,0.01387
+ ,-0.00942
+ ,0.02908
+ ,-0.00832
+ ,0.02912
+ ,0.00205
+ ,0.00409
+ ,0.00332
+ ,0.02049
+ ,0.00635
+ ,-0.00609
+ ,0.01286
+ ,0.04381
+ ,-0.00942
+ ,0.02908
+ ,-0.00832
+ ,0.02912
+ ,0.02753
+ ,0.00409
+ ,0.00332
+ ,0.02049
+ ,0.00635
+ ,-0.00609
+ ,0.00603
+ ,0.04381
+ ,-0.00942
+ ,0.02908
+ ,-0.00832
+ ,0.01205
+ ,0.02753
+ ,0.00409
+ ,0.00332
+ ,0.02049
+ ,0.00635
+ ,0.02253
+ ,0.00603
+ ,0.04381
+ ,-0.00942
+ ,0.02908
+ ,0.01773
+ ,0.01205
+ ,0.02753
+ ,0.00409
+ ,0.00332
+ ,0.02049
+ ,0.05789
+ ,0.02253
+ ,0.00603
+ ,0.04381
+ ,-0.00942
+ ,-0.00897
+ ,0.01773
+ ,0.01205
+ ,0.02753
+ ,0.00409
+ ,0.00332
+ ,-0.03783
+ ,0.05789
+ ,0.02253
+ ,0.00603
+ ,0.04381
+ ,-0.01226
+ ,-0.00897
+ ,0.01773
+ ,0.01205
+ ,0.02753
+ ,0.00409
+ ,-0.03176
+ ,-0.03783
+ ,0.05789
+ ,0.02253
+ ,0.00603
+ ,0.00644
+ ,-0.01226
+ ,-0.00897
+ ,0.01773
+ ,0.01205
+ ,0.02753
+ ,-0.00572
+ ,-0.03176
+ ,-0.03783
+ ,0.05789
+ ,0.02253
+ ,-0.00059
+ ,0.00644
+ ,-0.01226
+ ,-0.00897
+ ,0.01773
+ ,0.01205
+ ,0.01040
+ ,-0.00572
+ ,-0.03176
+ ,-0.03783
+ ,0.05789
+ ,0.01707
+ ,-0.00059
+ ,0.00644
+ ,-0.01226
+ ,-0.00897
+ ,0.01773
+ ,0.03662
+ ,0.01040
+ ,-0.00572
+ ,-0.03176
+ ,-0.03783
+ ,-0.00104
+ ,0.01707
+ ,-0.00059
+ ,0.00644
+ ,-0.01226
+ ,-0.00897
+ ,0.03771
+ ,0.03662
+ ,0.01040
+ ,-0.00572
+ ,-0.03176
+ ,0.01272
+ ,-0.00104
+ ,0.01707
+ ,-0.00059
+ ,0.00644
+ ,-0.01226
+ ,0.05981
+ ,0.03771
+ ,0.03662
+ ,0.01040
+ ,-0.00572
+ ,0.01859
+ ,0.01272
+ ,-0.00104
+ ,0.01707
+ ,-0.00059
+ ,0.00644
+ ,-0.03204
+ ,0.05981
+ ,0.03771
+ ,0.03662
+ ,0.01040
+ ,0.03238
+ ,0.01859
+ ,0.01272
+ ,-0.00104
+ ,0.01707
+ ,-0.00059
+ ,0.02837
+ ,-0.03204
+ ,0.05981
+ ,0.03771
+ ,0.03662
+ ,0.03132
+ ,0.03238
+ ,0.01859
+ ,0.01272
+ ,-0.00104
+ ,0.01707
+ ,0.05003
+ ,0.02837
+ ,-0.03204
+ ,0.05981
+ ,0.03771
+ ,0.01412
+ ,0.03132
+ ,0.03238
+ ,0.01859
+ ,0.01272
+ ,-0.00104
+ ,0.04980
+ ,0.05003
+ ,0.02837
+ ,-0.03204
+ ,0.05981
+ ,0.00588
+ ,0.01412
+ ,0.03132
+ ,0.03238
+ ,0.01859
+ ,0.01272
+ ,-0.02299
+ ,0.04980
+ ,0.05003
+ ,0.02837
+ ,-0.03204
+ ,0.05686
+ ,0.00588
+ ,0.01412
+ ,0.03132
+ ,0.03238
+ ,0.01859
+ ,0.04030
+ ,-0.02299
+ ,0.04980
+ ,0.05003
+ ,0.02837
+ ,0.05681
+ ,0.05686
+ ,0.00588
+ ,0.01412
+ ,0.03132
+ ,0.03238
+ ,0.03176
+ ,0.04030
+ ,-0.02299
+ ,0.04980
+ ,0.05003
+ ,-0.04078
+ ,0.05681
+ ,0.05686
+ ,0.00588
+ ,0.01412
+ ,0.03132
+ ,-0.00135
+ ,0.03176
+ ,0.04030
+ ,-0.02299
+ ,0.04980
+ ,0.02507
+ ,-0.04078
+ ,0.05681
+ ,0.05686
+ ,0.00588
+ ,0.01412
+ ,-0.02473
+ ,-0.00135
+ ,0.03176
+ ,0.04030
+ ,-0.02299
+ ,0.00600
+ ,0.02507
+ ,-0.04078
+ ,0.05681
+ ,0.05686
+ ,0.00588
+ ,-0.00171
+ ,-0.02473
+ ,-0.00135
+ ,0.03176
+ ,0.04030
+ ,0.00249
+ ,0.00600
+ ,0.02507
+ ,-0.04078
+ ,0.05681
+ ,0.05686
+ ,-0.01575
+ ,-0.00171
+ ,-0.02473
+ ,-0.00135
+ ,0.03176
+ ,0.01885
+ ,0.00249
+ ,0.00600
+ ,0.02507
+ ,-0.04078
+ ,0.05681
+ ,-0.02624
+ ,-0.01575
+ ,-0.00171
+ ,-0.02473
+ ,-0.00135
+ ,0.00125
+ ,0.01885
+ ,0.00249
+ ,0.00600
+ ,0.02507
+ ,-0.04078
+ ,0.06724
+ ,-0.02624
+ ,-0.01575
+ ,-0.00171
+ ,-0.02473
+ ,0.00695
+ ,0.00125
+ ,0.01885
+ ,0.00249
+ ,0.00600
+ ,0.02507
+ ,-0.01362
+ ,0.06724
+ ,-0.02624
+ ,-0.01575
+ ,-0.00171
+ ,-0.01563
+ ,0.00695
+ ,0.00125
+ ,0.01885
+ ,0.00249
+ ,0.00600
+ ,-0.00422
+ ,-0.01362
+ ,0.06724
+ ,-0.02624
+ ,-0.01575
+ ,0.00814
+ ,-0.01563
+ ,0.00695
+ ,0.00125
+ ,0.01885
+ ,0.00249
+ ,0.00754
+ ,-0.00422
+ ,-0.01362
+ ,0.06724
+ ,-0.02624
+ ,0.02368
+ ,0.00814
+ ,-0.01563
+ ,0.00695
+ ,0.00125
+ ,0.01885
+ ,0.00087
+ ,0.00754
+ ,-0.00422
+ ,-0.01362
+ ,0.06724
+ ,0.04099
+ ,0.02368
+ ,0.00814
+ ,-0.01563
+ ,0.00695
+ ,0.00125
+ ,0.02715
+ ,0.00087
+ ,0.00754
+ ,-0.00422
+ ,-0.01362
+ ,0.00731
+ ,0.04099
+ ,0.02368
+ ,0.00814
+ ,-0.01563
+ ,0.00695
+ ,0.02976
+ ,0.02715
+ ,0.00087
+ ,0.00754
+ ,-0.00422
+ ,-0.01730
+ ,0.00731
+ ,0.04099
+ ,0.02368
+ ,0.00814
+ ,-0.01563
+ ,0.07946
+ ,0.02976
+ ,0.02715
+ ,0.00087
+ ,0.00754
+ ,-0.00183
+ ,-0.01730
+ ,0.00731
+ ,0.04099
+ ,0.02368
+ ,0.00814
+ ,0.01909
+ ,0.07946
+ ,0.02976
+ ,0.02715
+ ,0.00087
+ ,-0.03830
+ ,-0.00183
+ ,-0.01730
+ ,0.00731
+ ,0.04099
+ ,0.02368
+ ,-0.02483
+ ,0.01909
+ ,0.07946
+ ,0.02976
+ ,0.02715
+ ,-0.01249
+ ,-0.03830
+ ,-0.00183
+ ,-0.01730
+ ,0.00731
+ ,0.04099
+ ,-0.01870
+ ,-0.02483
+ ,0.01909
+ ,0.07946
+ ,0.02976
+ ,0.01229
+ ,-0.01249
+ ,-0.03830
+ ,-0.00183
+ ,-0.01730
+ ,0.00731
+ ,0.09682
+ ,-0.01870
+ ,-0.02483
+ ,0.01909
+ ,0.07946
+ ,-0.01747
+ ,0.01229
+ ,-0.01249
+ ,-0.03830
+ ,-0.00183
+ ,-0.01730
+ ,0.03823
+ ,0.09682
+ ,-0.01870
+ ,-0.02483
+ ,0.01909
+ ,-0.02645
+ ,-0.01747
+ ,0.01229
+ ,-0.01249
+ ,-0.03830
+ ,-0.00183
+ ,0.09571
+ ,0.03823
+ ,0.09682
+ ,-0.01870
+ ,-0.02483
+ ,0.04038
+ ,-0.02645
+ ,-0.01747
+ ,0.01229
+ ,-0.01249
+ ,-0.03830
+ ,-0.04663
+ ,0.09571
+ ,0.03823
+ ,0.09682
+ ,-0.01870
+ ,0.02925
+ ,0.04038
+ ,-0.02645
+ ,-0.01747
+ ,0.01229
+ ,-0.01249
+ ,-0.01359
+ ,-0.04663
+ ,0.09571
+ ,0.03823
+ ,0.09682
+ ,0.02270
+ ,0.02925
+ ,0.04038
+ ,-0.02645
+ ,-0.01747
+ ,0.01229
+ ,0.05114
+ ,-0.01359
+ ,-0.04663
+ ,0.09571
+ ,0.03823
+ ,-0.00460
+ ,0.02270
+ ,0.02925
+ ,0.04038
+ ,-0.02645
+ ,-0.01747
+ ,-0.04275
+ ,0.05114
+ ,-0.01359
+ ,-0.04663
+ ,0.09571
+ ,-0.01894
+ ,-0.00460
+ ,0.02270
+ ,0.02925
+ ,0.04038
+ ,-0.02645
+ ,0.05739
+ ,-0.04275
+ ,0.05114
+ ,-0.01359
+ ,-0.04663
+ ,-0.00966
+ ,-0.01894
+ ,-0.00460
+ ,0.02270
+ ,0.02925
+ ,0.04038
+ ,0.01186
+ ,0.05739
+ ,-0.04275
+ ,0.05114
+ ,-0.01359
+ ,0.00392
+ ,-0.00966
+ ,-0.01894
+ ,-0.00460
+ ,0.02270
+ ,0.02925
+ ,0.01066
+ ,0.01186
+ ,0.05739
+ ,-0.04275
+ ,0.05114
+ ,-0.03105
+ ,0.00392
+ ,-0.00966
+ ,-0.01894
+ ,-0.00460
+ ,0.02270
+ ,-0.07387
+ ,0.01066
+ ,0.01186
+ ,0.05739
+ ,-0.04275
+ ,-0.02790
+ ,-0.03105
+ ,0.00392
+ ,-0.00966
+ ,-0.01894
+ ,-0.00460
+ ,-0.04131
+ ,-0.07387
+ ,0.01066
+ ,0.01186
+ ,0.05739
+ ,-0.09625
+ ,-0.02790
+ ,-0.03105
+ ,0.00392
+ ,-0.00966
+ ,-0.01894
+ ,-0.17889
+ ,-0.04131
+ ,-0.07387
+ ,0.01066
+ ,0.01186
+ ,-0.05388
+ ,-0.09625
+ ,-0.02790
+ ,-0.03105
+ ,0.00392
+ ,-0.00966
+ ,-0.12781
+ ,-0.17889
+ ,-0.04131
+ ,-0.07387
+ ,0.01066
+ ,-0.05034
+ ,-0.05388
+ ,-0.09625
+ ,-0.02790
+ ,-0.03105
+ ,0.00392
+ ,-0.26933
+ ,-0.12781
+ ,-0.17889
+ ,-0.04131
+ ,-0.07387
+ ,-0.02846
+ ,-0.05034
+ ,-0.05388
+ ,-0.09625
+ ,-0.02790
+ ,-0.03105
+ ,-0.05095
+ ,-0.26933
+ ,-0.12781
+ ,-0.17889
+ ,-0.04131
+ ,-0.01454
+ ,-0.02846
+ ,-0.05034
+ ,-0.05388
+ ,-0.09625
+ ,-0.02790
+ ,-0.01074
+ ,-0.05095
+ ,-0.26933
+ ,-0.12781
+ ,-0.17889
+ ,0.01284
+ ,-0.01454
+ ,-0.02846
+ ,-0.05034
+ ,-0.05388
+ ,-0.09625
+ ,0.08172
+ ,-0.01074
+ ,-0.05095
+ ,-0.26933
+ ,-0.12781
+ ,0.03762
+ ,0.01284
+ ,-0.01454
+ ,-0.02846
+ ,-0.05034
+ ,-0.05388
+ ,0.11870
+ ,0.08172
+ ,-0.01074
+ ,-0.05095
+ ,-0.26933
+ ,0.01973
+ ,0.03762
+ ,0.01284
+ ,-0.01454
+ ,-0.02846
+ ,-0.05034
+ ,0.08475
+ ,0.11870
+ ,0.08172
+ ,-0.01074
+ ,-0.05095
+ ,0.03178
+ ,0.01973
+ ,0.03762
+ ,0.01284
+ ,-0.01454
+ ,-0.02846
+ ,0.04663
+ ,0.08475
+ ,0.11870
+ ,0.08172
+ ,-0.01074
+ ,0.01329
+ ,0.03178
+ ,0.01973
+ ,0.03762
+ ,0.01284
+ ,-0.01454
+ ,-0.04415
+ ,0.04663
+ ,0.08475
+ ,0.11870
+ ,0.08172
+ ,0.05094
+ ,0.01329
+ ,0.03178
+ ,0.01973
+ ,0.03762
+ ,0.01284
+ ,0.00970
+ ,-0.04415
+ ,0.04663
+ ,0.08475
+ ,0.11870
+ ,-0.00804
+ ,0.05094
+ ,0.01329
+ ,0.03178
+ ,0.01973
+ ,0.03762
+ ,-0.03341
+ ,0.00970
+ ,-0.04415
+ ,0.04663
+ ,0.08475
+ ,0.01116
+ ,-0.00804
+ ,0.05094
+ ,0.01329
+ ,0.03178
+ ,0.01973
+ ,0.04031
+ ,-0.03341
+ ,0.00970
+ ,-0.04415
+ ,0.04663
+ ,0.01128
+ ,0.01116
+ ,-0.00804
+ ,0.05094
+ ,0.01329
+ ,0.03178
+ ,0.01938
+ ,0.04031
+ ,-0.03341
+ ,0.00970
+ ,-0.04415
+ ,0.02227
+ ,0.01128
+ ,0.01116
+ ,-0.00804
+ ,0.05094
+ ,0.01329
+ ,0.05928
+ ,0.01938
+ ,0.04031
+ ,-0.03341
+ ,0.00970
+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,0.01116
+ ,-0.00804
+ ,0.05094
+ ,0.02343
+ ,0.05928
+ ,0.01938
+ ,0.04031
+ ,-0.03341
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,0.01116
+ ,-0.00804
+ ,-0.04536
+ ,0.02343
+ ,0.05928
+ ,0.01938
+ ,0.04031
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,0.01116
+ ,0.03355
+ ,-0.04536
+ ,0.02343
+ ,0.05928
+ ,0.01938
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,0.01128
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,0.02343
+ ,0.05928
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,0.02227
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,0.02343
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,0.01494
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,-0.04536
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,-0.02514
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.03355
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,0.02975
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.05659
+ ,0.01545
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.05216
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,-0.06579
+ ,0.01140
+ ,0.01545
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,-0.04459
+ ,0.06617
+ ,-0.00903
+ ,0.07584
+ ,-0.02422
+ ,-0.04267
+ ,0.00238
+ ,0.01140
+ ,0.01545
+ ,0.02985
+ ,0.03171
+ ,-0.02212
+ ,0.04485
+ ,0.06617
+ ,-0.00903
+ ,0.07584
+ ,-0.02422)
+ ,dim=c(11
+ ,95)
+ ,dimnames=list(c('PCYt'
+ ,'PCYt1'
+ ,'PCYt2'
+ ,'PCYt3'
+ ,'PCYt4'
+ ,'PCYt5'
+ ,'PCXt1'
+ ,'PCXt2'
+ ,'PCXt3'
+ ,'PCXt4'
+ ,'PCXt5')
+ ,1:95))
>  y <- array(NA,dim=c(11,95),dimnames=list(c('PCYt','PCYt1','PCYt2','PCYt3','PCYt4','PCYt5','PCXt1','PCXt2','PCXt3','PCXt4','PCXt5'),1:95))
>  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
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] "PCYt"
> x[,par1]
 [1]  0.01244  0.01150 -0.00793 -0.01514  0.01778  0.00634  0.00770  0.00692
 [9]  0.00029  0.02487  0.01708  0.02540  0.02935  0.02615  0.00424 -0.00032
[17] -0.02353  0.01387  0.01286 -0.00609  0.00635  0.02049  0.00332  0.00409
[25]  0.02753  0.01205  0.01773 -0.00897 -0.01226  0.00644 -0.00059  0.01707
[33] -0.00104  0.01272  0.01859  0.03238  0.03132  0.01412  0.00588  0.05686
[41]  0.05681 -0.04078  0.02507  0.00600  0.00249  0.01885  0.00125  0.00695
[49] -0.01563  0.00814  0.02368  0.04099  0.00731 -0.01730 -0.00183 -0.03830
[57] -0.01249  0.01229 -0.01747 -0.02645  0.04038  0.02925  0.02270 -0.00460
[65] -0.01894 -0.00966  0.00392 -0.03105 -0.02790 -0.09625 -0.05388 -0.05034
[73] -0.02846 -0.01454  0.01284  0.03762  0.01973  0.03178  0.01329  0.05094
[81] -0.00804  0.01116  0.01128  0.02227  0.01494 -0.02514  0.02975  0.05216
[89] -0.04459 -0.02212  0.03171  0.02985  0.01545  0.01140  0.00238
> 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])

-0.09625 -0.05388 -0.05034 -0.04459 -0.04078  -0.0383 -0.03105 -0.02846 
       1        1        1        1        1        1        1        1 
 -0.0279 -0.02645 -0.02514 -0.02353 -0.02212 -0.01894 -0.01747  -0.0173 
       1        1        1        1        1        1        1        1 
-0.01563 -0.01514 -0.01454 -0.01249 -0.01226 -0.00966 -0.00897 -0.00804 
       1        1        1        1        1        1        1        1 
-0.00793 -0.00609  -0.0046 -0.00183 -0.00104 -0.00059 -0.00032  0.00029 
       1        1        1        1        1        1        1        1 
 0.00125  0.00238  0.00249  0.00332  0.00392  0.00409  0.00424  0.00588 
       1        1        1        1        1        1        1        1 
   0.006  0.00634  0.00635  0.00644  0.00692  0.00695  0.00731   0.0077 
       1        1        1        1        1        1        1        1 
 0.00814  0.01116  0.01128   0.0114   0.0115  0.01205  0.01229  0.01244 
       1        1        1        1        1        1        1        1 
 0.01272  0.01284  0.01286  0.01329  0.01387  0.01412  0.01494  0.01545 
       1        1        1        1        1        1        1        1 
 0.01707  0.01708  0.01773  0.01778  0.01859  0.01885  0.01973  0.02049 
       1        1        1        1        1        1        1        1 
 0.02227   0.0227  0.02368  0.02487  0.02507   0.0254  0.02615  0.02753 
       1        1        1        1        1        1        1        1 
 0.02925  0.02935  0.02975  0.02985  0.03132  0.03171  0.03178  0.03238 
       1        1        1        1        1        1        1        1 
 0.03762  0.04038  0.04099  0.05094  0.05216  0.05681  0.05686 
       1        1        1        1        1        1        1 
> colnames(x)
 [1] "PCYt"  "PCYt1" "PCYt2" "PCYt3" "PCYt4" "PCYt5" "PCXt1" "PCXt2" "PCXt3"
[10] "PCXt4" "PCXt5"
> colnames(x)[par1]
[1] "PCYt"
> x[,par1]
 [1]  0.01244  0.01150 -0.00793 -0.01514  0.01778  0.00634  0.00770  0.00692
 [9]  0.00029  0.02487  0.01708  0.02540  0.02935  0.02615  0.00424 -0.00032
[17] -0.02353  0.01387  0.01286 -0.00609  0.00635  0.02049  0.00332  0.00409
[25]  0.02753  0.01205  0.01773 -0.00897 -0.01226  0.00644 -0.00059  0.01707
[33] -0.00104  0.01272  0.01859  0.03238  0.03132  0.01412  0.00588  0.05686
[41]  0.05681 -0.04078  0.02507  0.00600  0.00249  0.01885  0.00125  0.00695
[49] -0.01563  0.00814  0.02368  0.04099  0.00731 -0.01730 -0.00183 -0.03830
[57] -0.01249  0.01229 -0.01747 -0.02645  0.04038  0.02925  0.02270 -0.00460
[65] -0.01894 -0.00966  0.00392 -0.03105 -0.02790 -0.09625 -0.05388 -0.05034
[73] -0.02846 -0.01454  0.01284  0.03762  0.01973  0.03178  0.01329  0.05094
[81] -0.00804  0.01116  0.01128  0.02227  0.01494 -0.02514  0.02975  0.05216
[89] -0.04459 -0.02212  0.03171  0.02985  0.01545  0.01140  0.00238
> 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/12mst1293049346.tab") 
+ }
+ }
> m

	 Conditional inference tree with 2 terminal nodes

Response:  PCYt 
Inputs:  PCYt1, PCYt2, PCYt3, PCYt4, PCYt5, PCXt1, PCXt2, PCXt3, PCXt4, PCXt5 
Number of observations:  95 

1) PCXt1 <= -0.05013; criterion = 1, statistic = 29.437
  2)*  weights = 7 
1) PCXt1 > -0.05013
  3)*  weights = 88 
> postscript(file="/var/www/rcomp/tmp/22mst1293049346.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/32mst1293049346.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   0.01244  0.009713977  0.002726023
2   0.01150  0.009713977  0.001786023
3  -0.00793  0.009713977 -0.017643977
4  -0.01514  0.009713977 -0.024853977
5   0.01778  0.009713977  0.008066023
6   0.00634  0.009713977 -0.003373977
7   0.00770  0.009713977 -0.002013977
8   0.00692  0.009713977 -0.002793977
9   0.00029  0.009713977 -0.009423977
10  0.02487  0.009713977  0.015156023
11  0.01708  0.009713977  0.007366023
12  0.02540  0.009713977  0.015686023
13  0.02935  0.009713977  0.019636023
14  0.02615  0.009713977  0.016436023
15  0.00424  0.009713977 -0.005473977
16 -0.00032  0.009713977 -0.010033977
17 -0.02353 -0.046871429  0.023341429
18  0.01387  0.009713977  0.004156023
19  0.01286  0.009713977  0.003146023
20 -0.00609  0.009713977 -0.015803977
21  0.00635  0.009713977 -0.003363977
22  0.02049  0.009713977  0.010776023
23  0.00332  0.009713977 -0.006393977
24  0.00409  0.009713977 -0.005623977
25  0.02753  0.009713977  0.017816023
26  0.01205  0.009713977  0.002336023
27  0.01773  0.009713977  0.008016023
28 -0.00897  0.009713977 -0.018683977
29 -0.01226  0.009713977 -0.021973977
30  0.00644  0.009713977 -0.003273977
31 -0.00059  0.009713977 -0.010303977
32  0.01707  0.009713977  0.007356023
33 -0.00104  0.009713977 -0.010753977
34  0.01272  0.009713977  0.003006023
35  0.01859  0.009713977  0.008876023
36  0.03238  0.009713977  0.022666023
37  0.03132  0.009713977  0.021606023
38  0.01412  0.009713977  0.004406023
39  0.00588  0.009713977 -0.003833977
40  0.05686  0.009713977  0.047146023
41  0.05681  0.009713977  0.047096023
42 -0.04078  0.009713977 -0.050493977
43  0.02507  0.009713977  0.015356023
44  0.00600  0.009713977 -0.003713977
45  0.00249  0.009713977 -0.007223977
46  0.01885  0.009713977  0.009136023
47  0.00125  0.009713977 -0.008463977
48  0.00695  0.009713977 -0.002763977
49 -0.01563  0.009713977 -0.025343977
50  0.00814  0.009713977 -0.001573977
51  0.02368  0.009713977  0.013966023
52  0.04099  0.009713977  0.031276023
53  0.00731  0.009713977 -0.002403977
54 -0.01730  0.009713977 -0.027013977
55 -0.00183  0.009713977 -0.011543977
56 -0.03830  0.009713977 -0.048013977
57 -0.01249  0.009713977 -0.022203977
58  0.01229  0.009713977  0.002576023
59 -0.01747  0.009713977 -0.027183977
60 -0.02645  0.009713977 -0.036163977
61  0.04038  0.009713977  0.030666023
62  0.02925  0.009713977  0.019536023
63  0.02270  0.009713977  0.012986023
64 -0.00460  0.009713977 -0.014313977
65 -0.01894  0.009713977 -0.028653977
66 -0.00966  0.009713977 -0.019373977
67  0.00392  0.009713977 -0.005793977
68 -0.03105 -0.046871429  0.015821429
69 -0.02790  0.009713977 -0.037613977
70 -0.09625 -0.046871429 -0.049378571
71 -0.05388 -0.046871429 -0.007008571
72 -0.05034 -0.046871429 -0.003468571
73 -0.02846 -0.046871429  0.018411429
74 -0.01454  0.009713977 -0.024253977
75  0.01284  0.009713977  0.003126023
76  0.03762  0.009713977  0.027906023
77  0.01973  0.009713977  0.010016023
78  0.03178  0.009713977  0.022066023
79  0.01329  0.009713977  0.003576023
80  0.05094  0.009713977  0.041226023
81 -0.00804  0.009713977 -0.017753977
82  0.01116  0.009713977  0.001446023
83  0.01128  0.009713977  0.001566023
84  0.02227  0.009713977  0.012556023
85  0.01494  0.009713977  0.005226023
86 -0.02514  0.009713977 -0.034853977
87  0.02975  0.009713977  0.020036023
88  0.05216  0.009713977  0.042446023
89 -0.04459 -0.046871429  0.002281429
90 -0.02212  0.009713977 -0.031833977
91  0.03171  0.009713977  0.021996023
92  0.02985  0.009713977  0.020136023
93  0.01545  0.009713977  0.005736023
94  0.01140  0.009713977  0.001686023
95  0.00238  0.009713977 -0.007333977
> 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/rcomp/tmp/4cvrw1293049346.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/5w8wu1293049346.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/61eoq1293049346.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/7nxmw1293049346.tab") 
+ }
> 
> try(system("convert tmp/22mst1293049346.ps tmp/22mst1293049346.png",intern=TRUE))
character(0)
> try(system("convert tmp/32mst1293049346.ps tmp/32mst1293049346.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cvrw1293049346.ps tmp/4cvrw1293049346.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.570   0.390   2.942