R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-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.
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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(1
+ ,110.115
+ ,110.661
+ ,100.294
+ ,107.711
+ ,1
+ ,114.151
+ ,115.626
+ ,109.764
+ ,113.241
+ ,1
+ ,122.563
+ ,121.828
+ ,120.711
+ ,121.998
+ ,1
+ ,136.114
+ ,137.409
+ ,131.551
+ ,135.136
+ ,1
+ ,159.863
+ ,171.274
+ ,145.023
+ ,157.641
+ ,1
+ ,210.638
+ ,238.258
+ ,177.164
+ ,205.875
+ ,1
+ ,219.929
+ ,251.688
+ ,190.269
+ ,216.347
+ ,1
+ ,255.015
+ ,296.637
+ ,219.996
+ ,251.435
+ ,1
+ ,242.351
+ ,296.679
+ ,218.186
+ ,243.588
+ ,1
+ ,220.855
+ ,256.573
+ ,191.582
+ ,217.678
+ ,1
+ ,215.9
+ ,244.361
+ ,204.484
+ ,216.346
+ ,1
+ ,239.951
+ ,274.37
+ ,219.318
+ ,239.488
+ ,2
+ ,110.439
+ ,113.149
+ ,100.578
+ ,108.313
+ ,2
+ ,114.069
+ ,116.697
+ ,108.502
+ ,113.015
+ ,2
+ ,124.143
+ ,122.603
+ ,121.37
+ ,123.239
+ ,2
+ ,136.177
+ ,138.866
+ ,131.422
+ ,135.336
+ ,2
+ ,164.488
+ ,177.848
+ ,148.287
+ ,162.182
+ ,2
+ ,212.831
+ ,241.42
+ ,174.947
+ ,207.085
+ ,2
+ ,222.144
+ ,255.734
+ ,196.606
+ ,219.825
+ ,2
+ ,253.493
+ ,299.882
+ ,223.847
+ ,252.091
+ ,2
+ ,238.172
+ ,285.712
+ ,206.917
+ ,236.447
+ ,2
+ ,220.127
+ ,257.917
+ ,195.727
+ ,218.478
+ ,2
+ ,217.141
+ ,255.116
+ ,208.73
+ ,220.221
+ ,2
+ ,240.436
+ ,275.671
+ ,213.558
+ ,238.741
+ ,3
+ ,100
+ ,100
+ ,100
+ ,100
+ ,3
+ ,111.054
+ ,113.853
+ ,97.9592
+ ,108.124
+ ,3
+ ,114.798
+ ,119.368
+ ,109.211
+ ,113.998
+ ,3
+ ,126.574
+ ,123.803
+ ,120.473
+ ,124.666
+ ,3
+ ,136.883
+ ,135.802
+ ,131.112
+ ,135.284
+ ,3
+ ,172.288
+ ,185.538
+ ,147.732
+ ,167.86
+ ,3
+ ,214.227
+ ,242.44
+ ,179.407
+ ,209.204
+ ,3
+ ,224.73
+ ,257.646
+ ,197.796
+ ,221.956
+ ,3
+ ,255.976
+ ,292.588
+ ,227.227
+ ,252.946
+ ,3
+ ,226.723
+ ,270.085
+ ,197.833
+ ,224.906
+ ,3
+ ,215.471
+ ,253.316
+ ,194.766
+ ,214.815
+ ,3
+ ,219.459
+ ,256.46
+ ,210.264
+ ,222.182
+ ,3
+ ,241.588
+ ,270.831
+ ,214.026
+ ,238.5
+ ,4
+ ,102.815
+ ,101.542
+ ,100.254
+ ,102
+ ,4
+ ,112.319
+ ,115.143
+ ,100.107
+ ,109.615
+ ,4
+ ,114.537
+ ,120.264
+ ,113.097
+ ,114.936
+ ,4
+ ,128.069
+ ,127.692
+ ,122.204
+ ,126.54
+ ,4
+ ,139.095
+ ,139.408
+ ,131.193
+ ,137.144
+ ,4
+ ,181.098
+ ,193.704
+ ,151.23
+ ,175.245
+ ,4
+ ,216.573
+ ,248.809
+ ,181.625
+ ,212.246
+ ,4
+ ,228.912
+ ,263.016
+ ,205.874
+ ,227.184
+ ,4
+ ,255.878
+ ,292.523
+ ,226.757
+ ,252.773
+ ,4
+ ,225.84
+ ,261.006
+ ,194.438
+ ,221.934
+ ,4
+ ,214.691
+ ,257.496
+ ,194.576
+ ,215.143
+ ,4
+ ,222.898
+ ,258.249
+ ,214.211
+ ,225.455
+ ,4
+ ,241.512
+ ,275.141
+ ,225.59
+ ,242.116
+ ,5
+ ,104.301
+ ,102.179
+ ,102.839
+ ,103.65
+ ,5
+ ,113.607
+ ,116.923
+ ,102.865
+ ,111.34
+ ,5
+ ,114.118
+ ,118.74
+ ,112.18
+ ,114.245
+ ,5
+ ,128.101
+ ,128.336
+ ,124.943
+ ,127.336
+ ,5
+ ,141.551
+ ,142.191
+ ,136.448
+ ,140.349
+ ,5
+ ,186.026
+ ,203.366
+ ,150.278
+ ,179.32
+ ,5
+ ,217.504
+ ,254.991
+ ,188.871
+ ,215.466
+ ,5
+ ,231.613
+ ,265.367
+ ,206.229
+ ,229.247
+ ,5
+ ,254.149
+ ,290.063
+ ,223.928
+ ,250.677
+ ,5
+ ,225.751
+ ,266.44
+ ,202.508
+ ,224.903
+ ,5
+ ,216.2
+ ,264.861
+ ,198.563
+ ,218.381
+ ,5
+ ,225.478
+ ,256.327
+ ,214.169
+ ,226.42
+ ,5
+ ,243.05
+ ,277.59
+ ,227.637
+ ,243.923
+ ,6
+ ,104.964
+ ,105.494
+ ,104.726
+ ,104.974
+ ,6
+ ,112.716
+ ,116.638
+ ,102.719
+ ,110.717
+ ,6
+ ,113.814
+ ,116.522
+ ,114.855
+ ,114.437
+ ,6
+ ,128.752
+ ,128.718
+ ,125.276
+ ,127.871
+ ,6
+ ,144.647
+ ,146.027
+ ,138.433
+ ,143.264
+ ,6
+ ,191.144
+ ,213.692
+ ,154.789
+ ,184.979
+ ,6
+ ,219.151
+ ,255.458
+ ,189.866
+ ,216.693
+ ,6
+ ,235.936
+ ,271.406
+ ,208.473
+ ,233.33
+ ,6
+ ,252.408
+ ,296.831
+ ,220.682
+ ,250.105
+ ,6
+ ,226.192
+ ,267.075
+ ,196.651
+ ,223.798
+ ,6
+ ,219.85
+ ,257.795
+ ,201.679
+ ,219.962
+ ,6
+ ,228.098
+ ,259.192
+ ,213.656
+ ,228.287
+ ,6
+ ,246.469
+ ,276.357
+ ,229
+ ,245.813
+ ,7
+ ,104.83
+ ,106.14
+ ,103.387
+ ,104.641
+ ,7
+ ,113.126
+ ,116.227
+ ,103.921
+ ,111.217
+ ,7
+ ,115.232
+ ,116.967
+ ,114.53
+ ,115.286
+ ,7
+ ,129.991
+ ,130.539
+ ,130.192
+ ,130.115
+ ,7
+ ,147.403
+ ,145.695
+ ,136.323
+ ,144.381
+ ,7
+ ,196.021
+ ,220.819
+ ,153.029
+ ,188.482
+ ,7
+ ,220.494
+ ,261.125
+ ,192.114
+ ,219.019
+ ,7
+ ,239.005
+ ,278.478
+ ,211.102
+ ,236.987
+ ,7
+ ,252.503
+ ,296.742
+ ,227.654
+ ,251.788
+ ,7
+ ,220.037
+ ,263.672
+ ,191.446
+ ,218.529
+ ,7
+ ,220.182
+ ,251.318
+ ,201.506
+ ,218.933
+ ,7
+ ,230.729
+ ,260.776
+ ,219.028
+ ,231.349
+ ,7
+ ,248.64
+ ,279.389
+ ,226.841
+ ,247.143
+ ,8
+ ,105.878
+ ,106.371
+ ,101.746
+ ,104.902
+ ,8
+ ,112.818
+ ,115.942
+ ,105.751
+ ,111.452
+ ,8
+ ,115.945
+ ,118.061
+ ,115.328
+ ,116.071
+ ,8
+ ,133.236
+ ,132.864
+ ,131.595
+ ,132.773
+ ,8
+ ,148.778
+ ,148.469
+ ,137.453
+ ,145.881
+ ,8
+ ,200.338
+ ,225.005
+ ,157.658
+ ,192.86
+ ,8
+ ,220.484
+ ,258.58
+ ,189.665
+ ,217.924
+ ,8
+ ,242.293
+ ,284.415
+ ,211.503
+ ,240.027
+ ,8
+ ,253.733
+ ,296.479
+ ,218.398
+ ,250.212
+ ,8
+ ,220.406
+ ,259.121
+ ,190.056
+ ,217.521
+ ,8
+ ,220.283
+ ,243.526
+ ,204.453
+ ,218.36
+ ,8
+ ,230.535
+ ,261.166
+ ,217.602
+ ,231.015
+ ,8
+ ,251.147
+ ,274.787
+ ,221.488
+ ,246.381
+ ,9
+ ,107.542
+ ,107.249
+ ,100.371
+ ,105.695
+ ,9
+ ,112.565
+ ,116.42
+ ,106.746
+ ,111.611
+ ,9
+ ,117.543
+ ,118.711
+ ,117.973
+ ,117.807
+ ,9
+ ,134.689
+ ,134.529
+ ,133.091
+ ,134.265
+ ,9
+ ,149.123
+ ,152.221
+ ,137.072
+ ,146.497
+ ,9
+ ,202.319
+ ,229.096
+ ,161.039
+ ,195.475
+ ,9
+ ,220.269
+ ,257.981
+ ,191.006
+ ,217.978
+ ,9
+ ,248.077
+ ,287.685
+ ,218.055
+ ,245.433
+ ,9
+ ,252.299
+ ,295.557
+ ,213.639
+ ,248.073
+ ,9
+ ,223.551
+ ,262.711
+ ,190.322
+ ,219.971
+ ,9
+ ,216.675
+ ,247.503
+ ,206.552
+ ,217.72
+ ,9
+ ,229.735
+ ,265.351
+ ,220.635
+ ,232.241
+ ,10
+ ,107.954
+ ,109.481
+ ,101.337
+ ,106.489
+ ,10
+ ,112.698
+ ,113.365
+ ,108.454
+ ,111.717
+ ,10
+ ,118.205
+ ,119.223
+ ,117.863
+ ,118.255
+ ,10
+ ,135.058
+ ,135.166
+ ,133.167
+ ,134.596
+ ,10
+ ,150.925
+ ,157.061
+ ,139.485
+ ,148.857
+ ,10
+ ,204.148
+ ,233.982
+ ,165.599
+ ,198.4
+ ,10
+ ,222.524
+ ,257.756
+ ,186.398
+ ,218.186
+ ,10
+ ,248.956
+ ,287.97
+ ,221.076
+ ,246.641
+ ,10
+ ,248.838
+ ,288.037
+ ,212.71
+ ,244.468
+ ,10
+ ,223.373
+ ,265.838
+ ,203.701
+ ,223.841
+ ,10
+ ,217.808
+ ,256.9
+ ,205.642
+ ,219.934
+ ,10
+ ,233.148
+ ,261.627
+ ,222.011
+ ,233.688
+ ,11
+ ,108.09
+ ,111.951
+ ,102.307
+ ,107.146
+ ,11
+ ,113.701
+ ,112.709
+ ,107.724
+ ,112.062
+ ,11
+ ,119.899
+ ,119.196
+ ,116.582
+ ,118.969
+ ,11
+ ,135.615
+ ,133.458
+ ,131.858
+ ,134.38
+ ,11
+ ,152.195
+ ,160.782
+ ,142.049
+ ,150.78
+ ,11
+ ,205.288
+ ,234.529
+ ,171.248
+ ,200.598
+ ,11
+ ,221.905
+ ,257.984
+ ,189.577
+ ,218.54
+ ,11
+ ,252.358
+ ,290.44
+ ,226.743
+ ,250.328
+ ,11
+ ,247.559
+ ,287.377
+ ,217.355
+ ,244.727
+ ,11
+ ,224.678
+ ,265.766
+ ,200.524
+ ,223.764
+ ,11
+ ,217.66
+ ,261.806
+ ,205.679
+ ,220.842
+ ,11
+ ,235.221
+ ,266.932
+ ,224.948
+ ,236.667
+ ,12
+ ,109.19
+ ,111.972
+ ,101.794
+ ,107.695
+ ,12
+ ,113.844
+ ,115.609
+ ,108.936
+ ,112.842
+ ,12
+ ,121.35
+ ,120.729
+ ,117.645
+ ,120.333
+ ,12
+ ,136.088
+ ,135.621
+ ,132.5
+ ,135.121
+ ,12
+ ,155.762
+ ,164.581
+ ,141.315
+ ,153.293
+ ,12
+ ,206.439
+ ,238.753
+ ,172.249
+ ,202.121
+ ,12
+ ,222.286
+ ,252.604
+ ,190.244
+ ,217.886
+ ,12
+ ,254.122
+ ,292.298
+ ,223.179
+ ,250.849
+ ,12
+ ,245.331
+ ,290.101
+ ,217.786
+ ,244.034
+ ,12
+ ,223.629
+ ,269.162
+ ,200.524
+ ,223.664
+ ,12
+ ,217.951
+ ,260.758
+ ,204.583
+ ,220.584
+ ,12
+ ,237.46
+ ,268.695
+ ,225.566
+ ,238.439)
+ ,dim=c(5
+ ,150)
+ ,dimnames=list(c('month'
+ ,'MSF'
+ ,'SSF'
+ ,'NS'
+ ,'TOT')
+ ,1:150))
> y <- array(NA,dim=c(5,150),dimnames=list(c('month','MSF','SSF','NS','TOT'),1:150))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '5'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
TOT month MSF SSF NS t
1 107.711 1 110.115 110.661 100.2940 1
2 113.241 1 114.151 115.626 109.7640 2
3 121.998 1 122.563 121.828 120.7110 3
4 135.136 1 136.114 137.409 131.5510 4
5 157.641 1 159.863 171.274 145.0230 5
6 205.875 1 210.638 238.258 177.1640 6
7 216.347 1 219.929 251.688 190.2690 7
8 251.435 1 255.015 296.637 219.9960 8
9 243.588 1 242.351 296.679 218.1860 9
10 217.678 1 220.855 256.573 191.5820 10
11 216.346 1 215.900 244.361 204.4840 11
12 239.488 1 239.951 274.370 219.3180 12
13 108.313 2 110.439 113.149 100.5780 13
14 113.015 2 114.069 116.697 108.5020 14
15 123.239 2 124.143 122.603 121.3700 15
16 135.336 2 136.177 138.866 131.4220 16
17 162.182 2 164.488 177.848 148.2870 17
18 207.085 2 212.831 241.420 174.9470 18
19 219.825 2 222.144 255.734 196.6060 19
20 252.091 2 253.493 299.882 223.8470 20
21 236.447 2 238.172 285.712 206.9170 21
22 218.478 2 220.127 257.917 195.7270 22
23 220.221 2 217.141 255.116 208.7300 23
24 238.741 2 240.436 275.671 213.5580 24
25 100.000 3 100.000 100.000 100.0000 25
26 108.124 3 111.054 113.853 97.9592 26
27 113.998 3 114.798 119.368 109.2110 27
28 124.666 3 126.574 123.803 120.4730 28
29 135.284 3 136.883 135.802 131.1120 29
30 167.860 3 172.288 185.538 147.7320 30
31 209.204 3 214.227 242.440 179.4070 31
32 221.956 3 224.730 257.646 197.7960 32
33 252.946 3 255.976 292.588 227.2270 33
34 224.906 3 226.723 270.085 197.8330 34
35 214.815 3 215.471 253.316 194.7660 35
36 222.182 3 219.459 256.460 210.2640 36
37 238.500 3 241.588 270.831 214.0260 37
38 102.000 4 102.815 101.542 100.2540 38
39 109.615 4 112.319 115.143 100.1070 39
40 114.936 4 114.537 120.264 113.0970 40
41 126.540 4 128.069 127.692 122.2040 41
42 137.144 4 139.095 139.408 131.1930 42
43 175.245 4 181.098 193.704 151.2300 43
44 212.246 4 216.573 248.809 181.6250 44
45 227.184 4 228.912 263.016 205.8740 45
46 252.773 4 255.878 292.523 226.7570 46
47 221.934 4 225.840 261.006 194.4380 47
48 215.143 4 214.691 257.496 194.5760 48
49 225.455 4 222.898 258.249 214.2110 49
50 242.116 4 241.512 275.141 225.5900 50
51 103.650 5 104.301 102.179 102.8390 51
52 111.340 5 113.607 116.923 102.8650 52
53 114.245 5 114.118 118.740 112.1800 53
54 127.336 5 128.101 128.336 124.9430 54
55 140.349 5 141.551 142.191 136.4480 55
56 179.320 5 186.026 203.366 150.2780 56
57 215.466 5 217.504 254.991 188.8710 57
58 229.247 5 231.613 265.367 206.2290 58
59 250.677 5 254.149 290.063 223.9280 59
60 224.903 5 225.751 266.440 202.5080 60
61 218.381 5 216.200 264.861 198.5630 61
62 226.420 5 225.478 256.327 214.1690 62
63 243.923 5 243.050 277.590 227.6370 63
64 104.974 6 104.964 105.494 104.7260 64
65 110.717 6 112.716 116.638 102.7190 65
66 114.437 6 113.814 116.522 114.8550 66
67 127.871 6 128.752 128.718 125.2760 67
68 143.264 6 144.647 146.027 138.4330 68
69 184.979 6 191.144 213.692 154.7890 69
70 216.693 6 219.151 255.458 189.8660 70
71 233.330 6 235.936 271.406 208.4730 71
72 250.105 6 252.408 296.831 220.6820 72
73 223.798 6 226.192 267.075 196.6510 73
74 219.962 6 219.850 257.795 201.6790 74
75 228.287 6 228.098 259.192 213.6560 75
76 245.813 6 246.469 276.357 229.0000 76
77 104.641 7 104.830 106.140 103.3870 77
78 111.217 7 113.126 116.227 103.9210 78
79 115.286 7 115.232 116.967 114.5300 79
80 130.115 7 129.991 130.539 130.1920 80
81 144.381 7 147.403 145.695 136.3230 81
82 188.482 7 196.021 220.819 153.0290 82
83 219.019 7 220.494 261.125 192.1140 83
84 236.987 7 239.005 278.478 211.1020 84
85 251.788 7 252.503 296.742 227.6540 85
86 218.529 7 220.037 263.672 191.4460 86
87 218.933 7 220.182 251.318 201.5060 87
88 231.349 7 230.729 260.776 219.0280 88
89 247.143 7 248.640 279.389 226.8410 89
90 104.902 8 105.878 106.371 101.7460 90
91 111.452 8 112.818 115.942 105.7510 91
92 116.071 8 115.945 118.061 115.3280 92
93 132.773 8 133.236 132.864 131.5950 93
94 145.881 8 148.778 148.469 137.4530 94
95 192.860 8 200.338 225.005 157.6580 95
96 217.924 8 220.484 258.580 189.6650 96
97 240.027 8 242.293 284.415 211.5030 97
98 250.212 8 253.733 296.479 218.3980 98
99 217.521 8 220.406 259.121 190.0560 99
100 218.360 8 220.283 243.526 204.4530 100
101 231.015 8 230.535 261.166 217.6020 101
102 246.381 8 251.147 274.787 221.4880 102
103 105.695 9 107.542 107.249 100.3710 103
104 111.611 9 112.565 116.420 106.7460 104
105 117.807 9 117.543 118.711 117.9730 105
106 134.265 9 134.689 134.529 133.0910 106
107 146.497 9 149.123 152.221 137.0720 107
108 195.475 9 202.319 229.096 161.0390 108
109 217.978 9 220.269 257.981 191.0060 109
110 245.433 9 248.077 287.685 218.0550 110
111 248.073 9 252.299 295.557 213.6390 111
112 219.971 9 223.551 262.711 190.3220 112
113 217.720 9 216.675 247.503 206.5520 113
114 232.241 9 229.735 265.351 220.6350 114
115 106.489 10 107.954 109.481 101.3370 115
116 111.717 10 112.698 113.365 108.4540 116
117 118.255 10 118.205 119.223 117.8630 117
118 134.596 10 135.058 135.166 133.1670 118
119 148.857 10 150.925 157.061 139.4850 119
120 198.400 10 204.148 233.982 165.5990 120
121 218.186 10 222.524 257.756 186.3980 121
122 246.641 10 248.956 287.970 221.0760 122
123 244.468 10 248.838 288.037 212.7100 123
124 223.841 10 223.373 265.838 203.7010 124
125 219.934 10 217.808 256.900 205.6420 125
126 233.688 10 233.148 261.627 222.0110 126
127 107.146 11 108.090 111.951 102.3070 127
128 112.062 11 113.701 112.709 107.7240 128
129 118.969 11 119.899 119.196 116.5820 129
130 134.380 11 135.615 133.458 131.8580 130
131 150.780 11 152.195 160.782 142.0490 131
132 200.598 11 205.288 234.529 171.2480 132
133 218.540 11 221.905 257.984 189.5770 133
134 250.328 11 252.358 290.440 226.7430 134
135 244.727 11 247.559 287.377 217.3550 135
136 223.764 11 224.678 265.766 200.5240 136
137 220.842 11 217.660 261.806 205.6790 137
138 236.667 11 235.221 266.932 224.9480 138
139 107.695 12 109.190 111.972 101.7940 139
140 112.842 12 113.844 115.609 108.9360 140
141 120.333 12 121.350 120.729 117.6450 141
142 135.121 12 136.088 135.621 132.5000 142
143 153.293 12 155.762 164.581 141.3150 143
144 202.121 12 206.439 238.753 172.2490 144
145 217.886 12 222.286 252.604 190.2440 145
146 250.849 12 254.122 292.298 223.1790 146
147 244.034 12 245.331 290.101 217.7860 147
148 223.664 12 223.629 269.162 200.5240 148
149 220.584 12 217.951 260.758 204.5830 149
150 238.439 12 237.460 268.695 225.5660 150
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month MSF SSF NS t
0.89447 -0.21178 0.60445 0.14077 0.24872 0.01658
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.78116 -0.08407 -0.01273 0.10554 0.61824
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.894472 0.282350 3.168 0.00187 **
month -0.211784 0.162420 -1.304 0.19434
MSF 0.604451 0.004484 134.793 < 2e-16 ***
SSF 0.140768 0.002930 48.050 < 2e-16 ***
NS 0.248721 0.002760 90.116 < 2e-16 ***
t 0.016582 0.012831 1.292 0.19832
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2049 on 144 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.028e+06 on 5 and 144 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.3085898 0.61717956 0.691410218
[2,] 0.4963622 0.99272442 0.503637791
[3,] 0.3677031 0.73540616 0.632296918
[4,] 0.8860712 0.22785759 0.113928797
[5,] 0.8244296 0.35114076 0.175570380
[6,] 0.7491493 0.50170130 0.250850651
[7,] 0.6631614 0.67367712 0.336838560
[8,] 0.5722663 0.85546743 0.427733717
[9,] 0.4985206 0.99704128 0.501479362
[10,] 0.4658412 0.93168244 0.534158779
[11,] 0.5310610 0.93787806 0.468939030
[12,] 0.4555242 0.91104847 0.544475767
[13,] 0.3762839 0.75256779 0.623716107
[14,] 0.4463768 0.89275358 0.553623208
[15,] 0.5318598 0.93628033 0.468140167
[16,] 0.8984822 0.20303569 0.101517847
[17,] 0.8697562 0.26048768 0.130243840
[18,] 0.8317710 0.33645792 0.168228959
[19,] 0.7880933 0.42381347 0.211906734
[20,] 0.7420735 0.51585307 0.257926535
[21,] 0.6922340 0.61553198 0.307765989
[22,] 0.6402949 0.71941015 0.359705077
[23,] 0.6007612 0.79847769 0.399238844
[24,] 0.6384408 0.72311843 0.361559213
[25,] 0.7749791 0.45004175 0.225020876
[26,] 0.7591696 0.48166083 0.240830413
[27,] 0.8188917 0.36221660 0.181108298
[28,] 0.8412819 0.31743630 0.158718149
[29,] 0.8304047 0.33919051 0.169595254
[30,] 0.7930641 0.41387183 0.206935915
[31,] 0.7530186 0.49396276 0.246981381
[32,] 0.7087442 0.58251155 0.291255777
[33,] 0.6599797 0.68004053 0.340020267
[34,] 0.6110070 0.77798603 0.388993016
[35,] 0.5766125 0.84677501 0.423387503
[36,] 0.6816852 0.63662968 0.318314841
[37,] 0.6962125 0.60757491 0.303787455
[38,] 0.7458464 0.50830715 0.254153574
[39,] 0.8963650 0.20726996 0.103634981
[40,] 0.8753484 0.24930311 0.124651557
[41,] 0.8796796 0.24064089 0.120320445
[42,] 0.9267352 0.14652954 0.073264771
[43,] 0.9078326 0.18433477 0.092167387
[44,] 0.8869709 0.22605811 0.113029055
[45,] 0.8625811 0.27483777 0.137418887
[46,] 0.8336132 0.33277359 0.166386793
[47,] 0.8054843 0.38903138 0.194515689
[48,] 0.7826550 0.43468993 0.217344965
[49,] 0.8594265 0.28114709 0.140573546
[50,] 0.8576354 0.28472927 0.142364635
[51,] 0.8812851 0.23742975 0.118714876
[52,] 0.8899205 0.22015898 0.110079491
[53,] 0.8889627 0.22207450 0.111037250
[54,] 0.8732702 0.25345968 0.126729840
[55,] 0.9380737 0.12385262 0.061926311
[56,] 0.9226232 0.15475353 0.077376764
[57,] 0.9060281 0.18794390 0.093971948
[58,] 0.8847995 0.23040107 0.115200536
[59,] 0.8605241 0.27895184 0.139475922
[60,] 0.8393736 0.32125276 0.160626381
[61,] 0.8167515 0.36649704 0.183248518
[62,] 0.8463408 0.30731849 0.153659243
[63,] 0.8264030 0.34719399 0.173596996
[64,] 0.7985723 0.40285535 0.201427673
[65,] 0.8175981 0.36480379 0.182401893
[66,] 0.8239660 0.35206795 0.176033976
[67,] 0.8004968 0.39900650 0.199503249
[68,] 0.7853775 0.42924495 0.214622476
[69,] 0.7521865 0.49562692 0.247813459
[70,] 0.7177925 0.56441504 0.282207520
[71,] 0.6767226 0.64655470 0.323277352
[72,] 0.6361892 0.72762163 0.363810816
[73,] 0.6154369 0.76912624 0.384563120
[74,] 0.5794848 0.84103034 0.420515171
[75,] 0.7465564 0.50688717 0.253443587
[76,] 0.7125134 0.57497323 0.287486615
[77,] 0.6735884 0.65282313 0.326411563
[78,] 0.6289091 0.74218185 0.371090924
[79,] 0.8117313 0.37653738 0.188268692
[80,] 0.7933162 0.41336762 0.206683808
[81,] 0.8286244 0.34275127 0.171375635
[82,] 0.7973953 0.40520946 0.202604730
[83,] 0.7650540 0.46989198 0.234945988
[84,] 0.7260181 0.54796378 0.273981891
[85,] 0.6954235 0.60915300 0.304576500
[86,] 0.6861818 0.62763634 0.313818170
[87,] 0.6641590 0.67168204 0.335841021
[88,] 0.7450210 0.50995792 0.254978961
[89,] 0.7645035 0.47099293 0.235496467
[90,] 0.7461073 0.50778544 0.253892722
[91,] 0.7732731 0.45345386 0.226726930
[92,] 0.9909559 0.01808813 0.009044066
[93,] 0.9872559 0.02548816 0.012744081
[94,] 0.9824940 0.03501200 0.017505998
[95,] 0.9760275 0.04794499 0.023972496
[96,] 0.9692518 0.06149637 0.030748186
[97,] 0.9586628 0.08267442 0.041337210
[98,] 0.9484681 0.10306383 0.051531913
[99,] 0.9355992 0.12880162 0.064400808
[100,] 0.9235586 0.15288277 0.076441383
[101,] 0.9460835 0.10783292 0.053916460
[102,] 0.9360923 0.12781544 0.063907722
[103,] 0.9255077 0.14898466 0.074492332
[104,] 0.9728547 0.05429064 0.027145320
[105,] 0.9927690 0.01446200 0.007230998
[106,] 0.9947094 0.01058112 0.005290559
[107,] 0.9919000 0.01620007 0.008100034
[108,] 0.9878144 0.02437125 0.012185624
[109,] 0.9816724 0.03665516 0.018327582
[110,] 0.9765018 0.04699631 0.023498154
[111,] 0.9663974 0.06720522 0.033602612
[112,] 0.9535280 0.09294399 0.046471997
[113,] 0.9797620 0.04047603 0.020238015
[114,] 0.9752516 0.04949675 0.024748375
[115,] 0.9663835 0.06723290 0.033616450
[116,] 0.9594953 0.08100946 0.040504728
[117,] 0.9419874 0.11602510 0.058012551
[118,] 0.9529946 0.09401074 0.047005368
[119,] 0.9322302 0.13553959 0.067769793
[120,] 0.9051052 0.18978952 0.094894761
[121,] 0.8713411 0.25731773 0.128658866
[122,] 0.8266076 0.34678471 0.173392357
[123,] 0.7663175 0.46736491 0.233682453
[124,] 0.7525593 0.49488149 0.247440744
[125,] 0.8569833 0.28603348 0.143016741
[126,] 0.8011377 0.39772455 0.198862275
[127,] 0.7347679 0.53046413 0.265232065
[128,] 0.8541972 0.29160558 0.145802789
[129,] 0.8671019 0.26579626 0.132898132
[130,] 0.7846536 0.43069276 0.215346382
[131,] 0.6708836 0.65823272 0.329116358
[132,] 0.5404662 0.91906765 0.459533825
[133,] 0.4014713 0.80294266 0.598528670
> postscript(file="/var/fisher/rcomp/tmp/1cm1x1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2chzv1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3w7ok1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4lyg31353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51fee1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 150
Frequency = 1
1 2 3 4 5
-7.018288e-02 -5.063008e-02 9.353331e-03 5.040818e-02 6.582702e-02
6 7 8 9 10
1.688715e-01 -1.416712e-01 8.518855e-04 2.363121e-01 -4.343622e-01
11 12 13 14 15
-2.778191e-01 3.961009e-01 -7.209019e-02 -5.113909e-02 3.512140e-02
16 17 18 19 20
5.211654e-02 8.680763e-02 1.724176e-01 -1.354195e-01 1.750129e-01
21 22 23 24 25
-1.924174e-02 -4.016585e-01 2.898277e-01 6.182372e-01 -6.769632e-02
26 27 28 29 30
-8.435704e-02 -6.489774e-02 4.310273e-02 7.801365e-02 1.018410e-01
31 32 33 34 35
1.909472e-01 -1.364354e-01 -2.885288e-01 -1.844911e-01 -3.674159e-01
36 37 38 39 40
2.752013e-01 2.420491e-01 -5.324461e-02 -7.755970e-02 -6.557152e-02
41 42 43 44 45
3.168595e-02 6.943206e-02 1.383078e-01 3.629098e-01 -2.051210e-01
46 47 48 49 50
-2.800221e-01 -5.040955e-01 -1.128774e-01 2.321800e-01 4.172907e-01
51 52 53 54 55
-3.784990e-02 -7.141065e-02 -6.447674e-02 3.266429e-02 8.733574e-02
56 57 58 59 60
1.074717e-01 3.439266e-01 -1.977643e-01 -2.847830e-01 -2.571896e-01
61 62 63 64 65
1.808185e-01 -8.507911e-02 4.369933e-01 -5.436265e-02 -8.319069e-02
66 67 68 69 70
-4.560542e-02 3.379078e-02 9.347830e-02 9.355834e-02 2.584013e-01
71 72 73 74 75
-1.398149e-01 4.641496e-02 -2.651615e-01 -2.285506e-01 -8.122725e-02
76 77 78 79 80
9.115758e-02 -6.804380e-02 -7.590042e-02 -3.930316e-02 4.604708e-02
81 82 83 84 85
1.123685e-01 7.936361e-02 4.119862e-01 8.945055e-03 -5.333872e-02
86 87 88 89 90
-4.391638e-02 -5.072208e-01 -1.724215e-01 2.152940e-01 -6.865367e-02
91 92 93 94 95
-7.354766e-02 -4.153491e-02 6.258299e-02 1.059238e-01 1.035847e-01
96 97 98 99 100
2.866252e-01 1.222522e-01 -3.741071e-02 -2.923782e-01 -7.811610e-01
101 102 103 104 105
-9.316017e-02 -8.662493e-02 -6.684235e-02 -8.016421e-02 -2.459196e-02
106 107 108 109 110
6.607152e-02 7.620933e-02 1.005802e-01 2.175897e-01 -6.160268e-02
111 112 113 114 115
4.446652e-05 -3.186737e-01 -3.259804e-01 2.691366e-01 -6.353219e-02
116 117 118 119 120
-3.652026e-02 -8.649371e-03 7.826019e-02 7.830940e-02 1.108814e-01
121 122 123 124 125
2.531338e-01 -1.636162e-01 -2.105065e-01 -9.609559e-02 1.195148e-01
126 127 128 129 130
-1.520699e-01 -6.489127e-02 -1.107114e-02 1.662577e-02 1.043922e-01
131 132 133 134 135
8.494054e-02 1.505867e-01 1.713141e-01 -2.773541e-01 -2.280106e-01
136 137 138 139 140
-1.487799e-01 4.299644e-01 1.094369e-01 -4.334654e-02 -1.438222e-02
141 142 143 144 145
3.618239e-02 1.081294e-01 1.024484e-01 1.470915e-01 -1.087410e-01
146 147 148 149 150
-1.849096e-01 -5.214185e-02 -7.995676e-02 4.289958e-01 1.389900e-01
> postscript(file="/var/fisher/rcomp/tmp/6vmcu1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 150
Frequency = 1
lag(myerror, k = 1) myerror
0 -7.018288e-02 NA
1 -5.063008e-02 -7.018288e-02
2 9.353331e-03 -5.063008e-02
3 5.040818e-02 9.353331e-03
4 6.582702e-02 5.040818e-02
5 1.688715e-01 6.582702e-02
6 -1.416712e-01 1.688715e-01
7 8.518855e-04 -1.416712e-01
8 2.363121e-01 8.518855e-04
9 -4.343622e-01 2.363121e-01
10 -2.778191e-01 -4.343622e-01
11 3.961009e-01 -2.778191e-01
12 -7.209019e-02 3.961009e-01
13 -5.113909e-02 -7.209019e-02
14 3.512140e-02 -5.113909e-02
15 5.211654e-02 3.512140e-02
16 8.680763e-02 5.211654e-02
17 1.724176e-01 8.680763e-02
18 -1.354195e-01 1.724176e-01
19 1.750129e-01 -1.354195e-01
20 -1.924174e-02 1.750129e-01
21 -4.016585e-01 -1.924174e-02
22 2.898277e-01 -4.016585e-01
23 6.182372e-01 2.898277e-01
24 -6.769632e-02 6.182372e-01
25 -8.435704e-02 -6.769632e-02
26 -6.489774e-02 -8.435704e-02
27 4.310273e-02 -6.489774e-02
28 7.801365e-02 4.310273e-02
29 1.018410e-01 7.801365e-02
30 1.909472e-01 1.018410e-01
31 -1.364354e-01 1.909472e-01
32 -2.885288e-01 -1.364354e-01
33 -1.844911e-01 -2.885288e-01
34 -3.674159e-01 -1.844911e-01
35 2.752013e-01 -3.674159e-01
36 2.420491e-01 2.752013e-01
37 -5.324461e-02 2.420491e-01
38 -7.755970e-02 -5.324461e-02
39 -6.557152e-02 -7.755970e-02
40 3.168595e-02 -6.557152e-02
41 6.943206e-02 3.168595e-02
42 1.383078e-01 6.943206e-02
43 3.629098e-01 1.383078e-01
44 -2.051210e-01 3.629098e-01
45 -2.800221e-01 -2.051210e-01
46 -5.040955e-01 -2.800221e-01
47 -1.128774e-01 -5.040955e-01
48 2.321800e-01 -1.128774e-01
49 4.172907e-01 2.321800e-01
50 -3.784990e-02 4.172907e-01
51 -7.141065e-02 -3.784990e-02
52 -6.447674e-02 -7.141065e-02
53 3.266429e-02 -6.447674e-02
54 8.733574e-02 3.266429e-02
55 1.074717e-01 8.733574e-02
56 3.439266e-01 1.074717e-01
57 -1.977643e-01 3.439266e-01
58 -2.847830e-01 -1.977643e-01
59 -2.571896e-01 -2.847830e-01
60 1.808185e-01 -2.571896e-01
61 -8.507911e-02 1.808185e-01
62 4.369933e-01 -8.507911e-02
63 -5.436265e-02 4.369933e-01
64 -8.319069e-02 -5.436265e-02
65 -4.560542e-02 -8.319069e-02
66 3.379078e-02 -4.560542e-02
67 9.347830e-02 3.379078e-02
68 9.355834e-02 9.347830e-02
69 2.584013e-01 9.355834e-02
70 -1.398149e-01 2.584013e-01
71 4.641496e-02 -1.398149e-01
72 -2.651615e-01 4.641496e-02
73 -2.285506e-01 -2.651615e-01
74 -8.122725e-02 -2.285506e-01
75 9.115758e-02 -8.122725e-02
76 -6.804380e-02 9.115758e-02
77 -7.590042e-02 -6.804380e-02
78 -3.930316e-02 -7.590042e-02
79 4.604708e-02 -3.930316e-02
80 1.123685e-01 4.604708e-02
81 7.936361e-02 1.123685e-01
82 4.119862e-01 7.936361e-02
83 8.945055e-03 4.119862e-01
84 -5.333872e-02 8.945055e-03
85 -4.391638e-02 -5.333872e-02
86 -5.072208e-01 -4.391638e-02
87 -1.724215e-01 -5.072208e-01
88 2.152940e-01 -1.724215e-01
89 -6.865367e-02 2.152940e-01
90 -7.354766e-02 -6.865367e-02
91 -4.153491e-02 -7.354766e-02
92 6.258299e-02 -4.153491e-02
93 1.059238e-01 6.258299e-02
94 1.035847e-01 1.059238e-01
95 2.866252e-01 1.035847e-01
96 1.222522e-01 2.866252e-01
97 -3.741071e-02 1.222522e-01
98 -2.923782e-01 -3.741071e-02
99 -7.811610e-01 -2.923782e-01
100 -9.316017e-02 -7.811610e-01
101 -8.662493e-02 -9.316017e-02
102 -6.684235e-02 -8.662493e-02
103 -8.016421e-02 -6.684235e-02
104 -2.459196e-02 -8.016421e-02
105 6.607152e-02 -2.459196e-02
106 7.620933e-02 6.607152e-02
107 1.005802e-01 7.620933e-02
108 2.175897e-01 1.005802e-01
109 -6.160268e-02 2.175897e-01
110 4.446652e-05 -6.160268e-02
111 -3.186737e-01 4.446652e-05
112 -3.259804e-01 -3.186737e-01
113 2.691366e-01 -3.259804e-01
114 -6.353219e-02 2.691366e-01
115 -3.652026e-02 -6.353219e-02
116 -8.649371e-03 -3.652026e-02
117 7.826019e-02 -8.649371e-03
118 7.830940e-02 7.826019e-02
119 1.108814e-01 7.830940e-02
120 2.531338e-01 1.108814e-01
121 -1.636162e-01 2.531338e-01
122 -2.105065e-01 -1.636162e-01
123 -9.609559e-02 -2.105065e-01
124 1.195148e-01 -9.609559e-02
125 -1.520699e-01 1.195148e-01
126 -6.489127e-02 -1.520699e-01
127 -1.107114e-02 -6.489127e-02
128 1.662577e-02 -1.107114e-02
129 1.043922e-01 1.662577e-02
130 8.494054e-02 1.043922e-01
131 1.505867e-01 8.494054e-02
132 1.713141e-01 1.505867e-01
133 -2.773541e-01 1.713141e-01
134 -2.280106e-01 -2.773541e-01
135 -1.487799e-01 -2.280106e-01
136 4.299644e-01 -1.487799e-01
137 1.094369e-01 4.299644e-01
138 -4.334654e-02 1.094369e-01
139 -1.438222e-02 -4.334654e-02
140 3.618239e-02 -1.438222e-02
141 1.081294e-01 3.618239e-02
142 1.024484e-01 1.081294e-01
143 1.470915e-01 1.024484e-01
144 -1.087410e-01 1.470915e-01
145 -1.849096e-01 -1.087410e-01
146 -5.214185e-02 -1.849096e-01
147 -7.995676e-02 -5.214185e-02
148 4.289958e-01 -7.995676e-02
149 1.389900e-01 4.289958e-01
150 NA 1.389900e-01
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5.063008e-02 -7.018288e-02
[2,] 9.353331e-03 -5.063008e-02
[3,] 5.040818e-02 9.353331e-03
[4,] 6.582702e-02 5.040818e-02
[5,] 1.688715e-01 6.582702e-02
[6,] -1.416712e-01 1.688715e-01
[7,] 8.518855e-04 -1.416712e-01
[8,] 2.363121e-01 8.518855e-04
[9,] -4.343622e-01 2.363121e-01
[10,] -2.778191e-01 -4.343622e-01
[11,] 3.961009e-01 -2.778191e-01
[12,] -7.209019e-02 3.961009e-01
[13,] -5.113909e-02 -7.209019e-02
[14,] 3.512140e-02 -5.113909e-02
[15,] 5.211654e-02 3.512140e-02
[16,] 8.680763e-02 5.211654e-02
[17,] 1.724176e-01 8.680763e-02
[18,] -1.354195e-01 1.724176e-01
[19,] 1.750129e-01 -1.354195e-01
[20,] -1.924174e-02 1.750129e-01
[21,] -4.016585e-01 -1.924174e-02
[22,] 2.898277e-01 -4.016585e-01
[23,] 6.182372e-01 2.898277e-01
[24,] -6.769632e-02 6.182372e-01
[25,] -8.435704e-02 -6.769632e-02
[26,] -6.489774e-02 -8.435704e-02
[27,] 4.310273e-02 -6.489774e-02
[28,] 7.801365e-02 4.310273e-02
[29,] 1.018410e-01 7.801365e-02
[30,] 1.909472e-01 1.018410e-01
[31,] -1.364354e-01 1.909472e-01
[32,] -2.885288e-01 -1.364354e-01
[33,] -1.844911e-01 -2.885288e-01
[34,] -3.674159e-01 -1.844911e-01
[35,] 2.752013e-01 -3.674159e-01
[36,] 2.420491e-01 2.752013e-01
[37,] -5.324461e-02 2.420491e-01
[38,] -7.755970e-02 -5.324461e-02
[39,] -6.557152e-02 -7.755970e-02
[40,] 3.168595e-02 -6.557152e-02
[41,] 6.943206e-02 3.168595e-02
[42,] 1.383078e-01 6.943206e-02
[43,] 3.629098e-01 1.383078e-01
[44,] -2.051210e-01 3.629098e-01
[45,] -2.800221e-01 -2.051210e-01
[46,] -5.040955e-01 -2.800221e-01
[47,] -1.128774e-01 -5.040955e-01
[48,] 2.321800e-01 -1.128774e-01
[49,] 4.172907e-01 2.321800e-01
[50,] -3.784990e-02 4.172907e-01
[51,] -7.141065e-02 -3.784990e-02
[52,] -6.447674e-02 -7.141065e-02
[53,] 3.266429e-02 -6.447674e-02
[54,] 8.733574e-02 3.266429e-02
[55,] 1.074717e-01 8.733574e-02
[56,] 3.439266e-01 1.074717e-01
[57,] -1.977643e-01 3.439266e-01
[58,] -2.847830e-01 -1.977643e-01
[59,] -2.571896e-01 -2.847830e-01
[60,] 1.808185e-01 -2.571896e-01
[61,] -8.507911e-02 1.808185e-01
[62,] 4.369933e-01 -8.507911e-02
[63,] -5.436265e-02 4.369933e-01
[64,] -8.319069e-02 -5.436265e-02
[65,] -4.560542e-02 -8.319069e-02
[66,] 3.379078e-02 -4.560542e-02
[67,] 9.347830e-02 3.379078e-02
[68,] 9.355834e-02 9.347830e-02
[69,] 2.584013e-01 9.355834e-02
[70,] -1.398149e-01 2.584013e-01
[71,] 4.641496e-02 -1.398149e-01
[72,] -2.651615e-01 4.641496e-02
[73,] -2.285506e-01 -2.651615e-01
[74,] -8.122725e-02 -2.285506e-01
[75,] 9.115758e-02 -8.122725e-02
[76,] -6.804380e-02 9.115758e-02
[77,] -7.590042e-02 -6.804380e-02
[78,] -3.930316e-02 -7.590042e-02
[79,] 4.604708e-02 -3.930316e-02
[80,] 1.123685e-01 4.604708e-02
[81,] 7.936361e-02 1.123685e-01
[82,] 4.119862e-01 7.936361e-02
[83,] 8.945055e-03 4.119862e-01
[84,] -5.333872e-02 8.945055e-03
[85,] -4.391638e-02 -5.333872e-02
[86,] -5.072208e-01 -4.391638e-02
[87,] -1.724215e-01 -5.072208e-01
[88,] 2.152940e-01 -1.724215e-01
[89,] -6.865367e-02 2.152940e-01
[90,] -7.354766e-02 -6.865367e-02
[91,] -4.153491e-02 -7.354766e-02
[92,] 6.258299e-02 -4.153491e-02
[93,] 1.059238e-01 6.258299e-02
[94,] 1.035847e-01 1.059238e-01
[95,] 2.866252e-01 1.035847e-01
[96,] 1.222522e-01 2.866252e-01
[97,] -3.741071e-02 1.222522e-01
[98,] -2.923782e-01 -3.741071e-02
[99,] -7.811610e-01 -2.923782e-01
[100,] -9.316017e-02 -7.811610e-01
[101,] -8.662493e-02 -9.316017e-02
[102,] -6.684235e-02 -8.662493e-02
[103,] -8.016421e-02 -6.684235e-02
[104,] -2.459196e-02 -8.016421e-02
[105,] 6.607152e-02 -2.459196e-02
[106,] 7.620933e-02 6.607152e-02
[107,] 1.005802e-01 7.620933e-02
[108,] 2.175897e-01 1.005802e-01
[109,] -6.160268e-02 2.175897e-01
[110,] 4.446652e-05 -6.160268e-02
[111,] -3.186737e-01 4.446652e-05
[112,] -3.259804e-01 -3.186737e-01
[113,] 2.691366e-01 -3.259804e-01
[114,] -6.353219e-02 2.691366e-01
[115,] -3.652026e-02 -6.353219e-02
[116,] -8.649371e-03 -3.652026e-02
[117,] 7.826019e-02 -8.649371e-03
[118,] 7.830940e-02 7.826019e-02
[119,] 1.108814e-01 7.830940e-02
[120,] 2.531338e-01 1.108814e-01
[121,] -1.636162e-01 2.531338e-01
[122,] -2.105065e-01 -1.636162e-01
[123,] -9.609559e-02 -2.105065e-01
[124,] 1.195148e-01 -9.609559e-02
[125,] -1.520699e-01 1.195148e-01
[126,] -6.489127e-02 -1.520699e-01
[127,] -1.107114e-02 -6.489127e-02
[128,] 1.662577e-02 -1.107114e-02
[129,] 1.043922e-01 1.662577e-02
[130,] 8.494054e-02 1.043922e-01
[131,] 1.505867e-01 8.494054e-02
[132,] 1.713141e-01 1.505867e-01
[133,] -2.773541e-01 1.713141e-01
[134,] -2.280106e-01 -2.773541e-01
[135,] -1.487799e-01 -2.280106e-01
[136,] 4.299644e-01 -1.487799e-01
[137,] 1.094369e-01 4.299644e-01
[138,] -4.334654e-02 1.094369e-01
[139,] -1.438222e-02 -4.334654e-02
[140,] 3.618239e-02 -1.438222e-02
[141,] 1.081294e-01 3.618239e-02
[142,] 1.024484e-01 1.081294e-01
[143,] 1.470915e-01 1.024484e-01
[144,] -1.087410e-01 1.470915e-01
[145,] -1.849096e-01 -1.087410e-01
[146,] -5.214185e-02 -1.849096e-01
[147,] -7.995676e-02 -5.214185e-02
[148,] 4.289958e-01 -7.995676e-02
[149,] 1.389900e-01 4.289958e-01
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5.063008e-02 -7.018288e-02
2 9.353331e-03 -5.063008e-02
3 5.040818e-02 9.353331e-03
4 6.582702e-02 5.040818e-02
5 1.688715e-01 6.582702e-02
6 -1.416712e-01 1.688715e-01
7 8.518855e-04 -1.416712e-01
8 2.363121e-01 8.518855e-04
9 -4.343622e-01 2.363121e-01
10 -2.778191e-01 -4.343622e-01
11 3.961009e-01 -2.778191e-01
12 -7.209019e-02 3.961009e-01
13 -5.113909e-02 -7.209019e-02
14 3.512140e-02 -5.113909e-02
15 5.211654e-02 3.512140e-02
16 8.680763e-02 5.211654e-02
17 1.724176e-01 8.680763e-02
18 -1.354195e-01 1.724176e-01
19 1.750129e-01 -1.354195e-01
20 -1.924174e-02 1.750129e-01
21 -4.016585e-01 -1.924174e-02
22 2.898277e-01 -4.016585e-01
23 6.182372e-01 2.898277e-01
24 -6.769632e-02 6.182372e-01
25 -8.435704e-02 -6.769632e-02
26 -6.489774e-02 -8.435704e-02
27 4.310273e-02 -6.489774e-02
28 7.801365e-02 4.310273e-02
29 1.018410e-01 7.801365e-02
30 1.909472e-01 1.018410e-01
31 -1.364354e-01 1.909472e-01
32 -2.885288e-01 -1.364354e-01
33 -1.844911e-01 -2.885288e-01
34 -3.674159e-01 -1.844911e-01
35 2.752013e-01 -3.674159e-01
36 2.420491e-01 2.752013e-01
37 -5.324461e-02 2.420491e-01
38 -7.755970e-02 -5.324461e-02
39 -6.557152e-02 -7.755970e-02
40 3.168595e-02 -6.557152e-02
41 6.943206e-02 3.168595e-02
42 1.383078e-01 6.943206e-02
43 3.629098e-01 1.383078e-01
44 -2.051210e-01 3.629098e-01
45 -2.800221e-01 -2.051210e-01
46 -5.040955e-01 -2.800221e-01
47 -1.128774e-01 -5.040955e-01
48 2.321800e-01 -1.128774e-01
49 4.172907e-01 2.321800e-01
50 -3.784990e-02 4.172907e-01
51 -7.141065e-02 -3.784990e-02
52 -6.447674e-02 -7.141065e-02
53 3.266429e-02 -6.447674e-02
54 8.733574e-02 3.266429e-02
55 1.074717e-01 8.733574e-02
56 3.439266e-01 1.074717e-01
57 -1.977643e-01 3.439266e-01
58 -2.847830e-01 -1.977643e-01
59 -2.571896e-01 -2.847830e-01
60 1.808185e-01 -2.571896e-01
61 -8.507911e-02 1.808185e-01
62 4.369933e-01 -8.507911e-02
63 -5.436265e-02 4.369933e-01
64 -8.319069e-02 -5.436265e-02
65 -4.560542e-02 -8.319069e-02
66 3.379078e-02 -4.560542e-02
67 9.347830e-02 3.379078e-02
68 9.355834e-02 9.347830e-02
69 2.584013e-01 9.355834e-02
70 -1.398149e-01 2.584013e-01
71 4.641496e-02 -1.398149e-01
72 -2.651615e-01 4.641496e-02
73 -2.285506e-01 -2.651615e-01
74 -8.122725e-02 -2.285506e-01
75 9.115758e-02 -8.122725e-02
76 -6.804380e-02 9.115758e-02
77 -7.590042e-02 -6.804380e-02
78 -3.930316e-02 -7.590042e-02
79 4.604708e-02 -3.930316e-02
80 1.123685e-01 4.604708e-02
81 7.936361e-02 1.123685e-01
82 4.119862e-01 7.936361e-02
83 8.945055e-03 4.119862e-01
84 -5.333872e-02 8.945055e-03
85 -4.391638e-02 -5.333872e-02
86 -5.072208e-01 -4.391638e-02
87 -1.724215e-01 -5.072208e-01
88 2.152940e-01 -1.724215e-01
89 -6.865367e-02 2.152940e-01
90 -7.354766e-02 -6.865367e-02
91 -4.153491e-02 -7.354766e-02
92 6.258299e-02 -4.153491e-02
93 1.059238e-01 6.258299e-02
94 1.035847e-01 1.059238e-01
95 2.866252e-01 1.035847e-01
96 1.222522e-01 2.866252e-01
97 -3.741071e-02 1.222522e-01
98 -2.923782e-01 -3.741071e-02
99 -7.811610e-01 -2.923782e-01
100 -9.316017e-02 -7.811610e-01
101 -8.662493e-02 -9.316017e-02
102 -6.684235e-02 -8.662493e-02
103 -8.016421e-02 -6.684235e-02
104 -2.459196e-02 -8.016421e-02
105 6.607152e-02 -2.459196e-02
106 7.620933e-02 6.607152e-02
107 1.005802e-01 7.620933e-02
108 2.175897e-01 1.005802e-01
109 -6.160268e-02 2.175897e-01
110 4.446652e-05 -6.160268e-02
111 -3.186737e-01 4.446652e-05
112 -3.259804e-01 -3.186737e-01
113 2.691366e-01 -3.259804e-01
114 -6.353219e-02 2.691366e-01
115 -3.652026e-02 -6.353219e-02
116 -8.649371e-03 -3.652026e-02
117 7.826019e-02 -8.649371e-03
118 7.830940e-02 7.826019e-02
119 1.108814e-01 7.830940e-02
120 2.531338e-01 1.108814e-01
121 -1.636162e-01 2.531338e-01
122 -2.105065e-01 -1.636162e-01
123 -9.609559e-02 -2.105065e-01
124 1.195148e-01 -9.609559e-02
125 -1.520699e-01 1.195148e-01
126 -6.489127e-02 -1.520699e-01
127 -1.107114e-02 -6.489127e-02
128 1.662577e-02 -1.107114e-02
129 1.043922e-01 1.662577e-02
130 8.494054e-02 1.043922e-01
131 1.505867e-01 8.494054e-02
132 1.713141e-01 1.505867e-01
133 -2.773541e-01 1.713141e-01
134 -2.280106e-01 -2.773541e-01
135 -1.487799e-01 -2.280106e-01
136 4.299644e-01 -1.487799e-01
137 1.094369e-01 4.299644e-01
138 -4.334654e-02 1.094369e-01
139 -1.438222e-02 -4.334654e-02
140 3.618239e-02 -1.438222e-02
141 1.081294e-01 3.618239e-02
142 1.024484e-01 1.081294e-01
143 1.470915e-01 1.024484e-01
144 -1.087410e-01 1.470915e-01
145 -1.849096e-01 -1.087410e-01
146 -5.214185e-02 -1.849096e-01
147 -7.995676e-02 -5.214185e-02
148 4.289958e-01 -7.995676e-02
149 1.389900e-01 4.289958e-01
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/75cqt1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8kdqj1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9daqo1353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/103l541353429186.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11c2jf1353429186.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12klfu1353429186.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13tqum1353429186.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14luce1353429186.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15l6jv1353429186.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/163gzy1353429186.tab")
+ }
>
> try(system("convert tmp/1cm1x1353429186.ps tmp/1cm1x1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/2chzv1353429186.ps tmp/2chzv1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w7ok1353429186.ps tmp/3w7ok1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lyg31353429186.ps tmp/4lyg31353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/51fee1353429186.ps tmp/51fee1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vmcu1353429186.ps tmp/6vmcu1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/75cqt1353429186.ps tmp/75cqt1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/8kdqj1353429186.ps tmp/8kdqj1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/9daqo1353429186.ps tmp/9daqo1353429186.png",intern=TRUE))
character(0)
> try(system("convert tmp/103l541353429186.ps tmp/103l541353429186.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.843 1.390 9.233