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.
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(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 = '4'
> 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
NS month MSF SSF TOT t
1 100.2940 1 110.115 110.661 107.711 1
2 109.7640 1 114.151 115.626 113.241 2
3 120.7110 1 122.563 121.828 121.998 3
4 131.5510 1 136.114 137.409 135.136 4
5 145.0230 1 159.863 171.274 157.641 5
6 177.1640 1 210.638 238.258 205.875 6
7 190.2690 1 219.929 251.688 216.347 7
8 219.9960 1 255.015 296.637 251.435 8
9 218.1860 1 242.351 296.679 243.588 9
10 191.5820 1 220.855 256.573 217.678 10
11 204.4840 1 215.900 244.361 216.346 11
12 219.3180 1 239.951 274.370 239.488 12
13 100.5780 2 110.439 113.149 108.313 13
14 108.5020 2 114.069 116.697 113.015 14
15 121.3700 2 124.143 122.603 123.239 15
16 131.4220 2 136.177 138.866 135.336 16
17 148.2870 2 164.488 177.848 162.182 17
18 174.9470 2 212.831 241.420 207.085 18
19 196.6060 2 222.144 255.734 219.825 19
20 223.8470 2 253.493 299.882 252.091 20
21 206.9170 2 238.172 285.712 236.447 21
22 195.7270 2 220.127 257.917 218.478 22
23 208.7300 2 217.141 255.116 220.221 23
24 213.5580 2 240.436 275.671 238.741 24
25 100.0000 3 100.000 100.000 100.000 25
26 97.9592 3 111.054 113.853 108.124 26
27 109.2110 3 114.798 119.368 113.998 27
28 120.4730 3 126.574 123.803 124.666 28
29 131.1120 3 136.883 135.802 135.284 29
30 147.7320 3 172.288 185.538 167.860 30
31 179.4070 3 214.227 242.440 209.204 31
32 197.7960 3 224.730 257.646 221.956 32
33 227.2270 3 255.976 292.588 252.946 33
34 197.8330 3 226.723 270.085 224.906 34
35 194.7660 3 215.471 253.316 214.815 35
36 210.2640 3 219.459 256.460 222.182 36
37 214.0260 3 241.588 270.831 238.500 37
38 100.2540 4 102.815 101.542 102.000 38
39 100.1070 4 112.319 115.143 109.615 39
40 113.0970 4 114.537 120.264 114.936 40
41 122.2040 4 128.069 127.692 126.540 41
42 131.1930 4 139.095 139.408 137.144 42
43 151.2300 4 181.098 193.704 175.245 43
44 181.6250 4 216.573 248.809 212.246 44
45 205.8740 4 228.912 263.016 227.184 45
46 226.7570 4 255.878 292.523 252.773 46
47 194.4380 4 225.840 261.006 221.934 47
48 194.5760 4 214.691 257.496 215.143 48
49 214.2110 4 222.898 258.249 225.455 49
50 225.5900 4 241.512 275.141 242.116 50
51 102.8390 5 104.301 102.179 103.650 51
52 102.8650 5 113.607 116.923 111.340 52
53 112.1800 5 114.118 118.740 114.245 53
54 124.9430 5 128.101 128.336 127.336 54
55 136.4480 5 141.551 142.191 140.349 55
56 150.2780 5 186.026 203.366 179.320 56
57 188.8710 5 217.504 254.991 215.466 57
58 206.2290 5 231.613 265.367 229.247 58
59 223.9280 5 254.149 290.063 250.677 59
60 202.5080 5 225.751 266.440 224.903 60
61 198.5630 5 216.200 264.861 218.381 61
62 214.1690 5 225.478 256.327 226.420 62
63 227.6370 5 243.050 277.590 243.923 63
64 104.7260 6 104.964 105.494 104.974 64
65 102.7190 6 112.716 116.638 110.717 65
66 114.8550 6 113.814 116.522 114.437 66
67 125.2760 6 128.752 128.718 127.871 67
68 138.4330 6 144.647 146.027 143.264 68
69 154.7890 6 191.144 213.692 184.979 69
70 189.8660 6 219.151 255.458 216.693 70
71 208.4730 6 235.936 271.406 233.330 71
72 220.6820 6 252.408 296.831 250.105 72
73 196.6510 6 226.192 267.075 223.798 73
74 201.6790 6 219.850 257.795 219.962 74
75 213.6560 6 228.098 259.192 228.287 75
76 229.0000 6 246.469 276.357 245.813 76
77 103.3870 7 104.830 106.140 104.641 77
78 103.9210 7 113.126 116.227 111.217 78
79 114.5300 7 115.232 116.967 115.286 79
80 130.1920 7 129.991 130.539 130.115 80
81 136.3230 7 147.403 145.695 144.381 81
82 153.0290 7 196.021 220.819 188.482 82
83 192.1140 7 220.494 261.125 219.019 83
84 211.1020 7 239.005 278.478 236.987 84
85 227.6540 7 252.503 296.742 251.788 85
86 191.4460 7 220.037 263.672 218.529 86
87 201.5060 7 220.182 251.318 218.933 87
88 219.0280 7 230.729 260.776 231.349 88
89 226.8410 7 248.640 279.389 247.143 89
90 101.7460 8 105.878 106.371 104.902 90
91 105.7510 8 112.818 115.942 111.452 91
92 115.3280 8 115.945 118.061 116.071 92
93 131.5950 8 133.236 132.864 132.773 93
94 137.4530 8 148.778 148.469 145.881 94
95 157.6580 8 200.338 225.005 192.860 95
96 189.6650 8 220.484 258.580 217.924 96
97 211.5030 8 242.293 284.415 240.027 97
98 218.3980 8 253.733 296.479 250.212 98
99 190.0560 8 220.406 259.121 217.521 99
100 204.4530 8 220.283 243.526 218.360 100
101 217.6020 8 230.535 261.166 231.015 101
102 221.4880 8 251.147 274.787 246.381 102
103 100.3710 9 107.542 107.249 105.695 103
104 106.7460 9 112.565 116.420 111.611 104
105 117.9730 9 117.543 118.711 117.807 105
106 133.0910 9 134.689 134.529 134.265 106
107 137.0720 9 149.123 152.221 146.497 107
108 161.0390 9 202.319 229.096 195.475 108
109 191.0060 9 220.269 257.981 217.978 109
110 218.0550 9 248.077 287.685 245.433 110
111 213.6390 9 252.299 295.557 248.073 111
112 190.3220 9 223.551 262.711 219.971 112
113 206.5520 9 216.675 247.503 217.720 113
114 220.6350 9 229.735 265.351 232.241 114
115 101.3370 10 107.954 109.481 106.489 115
116 108.4540 10 112.698 113.365 111.717 116
117 117.8630 10 118.205 119.223 118.255 117
118 133.1670 10 135.058 135.166 134.596 118
119 139.4850 10 150.925 157.061 148.857 119
120 165.5990 10 204.148 233.982 198.400 120
121 186.3980 10 222.524 257.756 218.186 121
122 221.0760 10 248.956 287.970 246.641 122
123 212.7100 10 248.838 288.037 244.468 123
124 203.7010 10 223.373 265.838 223.841 124
125 205.6420 10 217.808 256.900 219.934 125
126 222.0110 10 233.148 261.627 233.688 126
127 102.3070 11 108.090 111.951 107.146 127
128 107.7240 11 113.701 112.709 112.062 128
129 116.5820 11 119.899 119.196 118.969 129
130 131.8580 11 135.615 133.458 134.380 130
131 142.0490 11 152.195 160.782 150.780 131
132 171.2480 11 205.288 234.529 200.598 132
133 189.5770 11 221.905 257.984 218.540 133
134 226.7430 11 252.358 290.440 250.328 134
135 217.3550 11 247.559 287.377 244.727 135
136 200.5240 11 224.678 265.766 223.764 136
137 205.6790 11 217.660 261.806 220.842 137
138 224.9480 11 235.221 266.932 236.667 138
139 101.7940 12 109.190 111.972 107.695 139
140 108.9360 12 113.844 115.609 112.842 140
141 117.6450 12 121.350 120.729 120.333 141
142 132.5000 12 136.088 135.621 135.121 142
143 141.3150 12 155.762 164.581 153.293 143
144 172.2490 12 206.439 238.753 202.121 144
145 190.2440 12 222.286 252.604 217.886 145
146 223.1790 12 254.122 292.298 250.849 146
147 217.7860 12 245.331 290.101 244.034 147
148 200.5240 12 223.629 269.162 223.664 148
149 204.5830 12 217.951 260.758 220.584 149
150 225.5660 12 237.460 268.695 238.439 150
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) month MSF SSF TOT t
-2.44716 0.19359 -2.37440 -0.55780 3.95052 -0.01461
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.52194 -0.49615 0.06524 0.35976 3.05581
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.44716 1.14582 -2.136 0.0344 *
month 0.19359 0.65092 0.297 0.7666
MSF -2.37440 0.03831 -61.976 <2e-16 ***
SSF -0.55780 0.01270 -43.914 <2e-16 ***
TOT 3.95052 0.04384 90.116 <2e-16 ***
t -0.01461 0.05142 -0.284 0.7767
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.8165 on 144 degrees of freedom
Multiple R-squared: 0.9997, Adjusted R-squared: 0.9997
F-statistic: 9.042e+04 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,] 0.3313223 0.66264462 0.66867769
[2,] 0.4182199 0.83643975 0.58178012
[3,] 0.3006539 0.60130786 0.69934607
[4,] 0.8097671 0.38046577 0.19023289
[5,] 0.7243530 0.55129408 0.27564704
[6,] 0.6296931 0.74061376 0.37030688
[7,] 0.5321433 0.93571338 0.46785669
[8,] 0.4337542 0.86750844 0.56624578
[9,] 0.3640568 0.72811359 0.63594321
[10,] 0.3593408 0.71868156 0.64065922
[11,] 0.4530115 0.90602305 0.54698847
[12,] 0.3767791 0.75355826 0.62322087
[13,] 0.3021054 0.60421080 0.69789460
[14,] 0.3393050 0.67860992 0.66069504
[15,] 0.3759825 0.75196492 0.62401754
[16,] 0.8323344 0.33533121 0.16766561
[17,] 0.7955924 0.40881527 0.20440763
[18,] 0.7492080 0.50158402 0.25079201
[19,] 0.6942933 0.61141331 0.30570665
[20,] 0.6394786 0.72104278 0.36052139
[21,] 0.5830529 0.83389417 0.41694708
[22,] 0.5357919 0.92841616 0.46420808
[23,] 0.5014643 0.99707133 0.49853567
[24,] 0.5553548 0.88929045 0.44464522
[25,] 0.7579360 0.48412798 0.24206399
[26,] 0.7314898 0.53702031 0.26851016
[27,] 0.7846169 0.43076623 0.21538312
[28,] 0.7868266 0.42634678 0.21317339
[29,] 0.7836852 0.43262967 0.21631483
[30,] 0.7414054 0.51718925 0.25859463
[31,] 0.6987480 0.60250394 0.30125197
[32,] 0.6516966 0.69660684 0.34830342
[33,] 0.5992704 0.80145929 0.40072964
[34,] 0.5477558 0.90448832 0.45224416
[35,] 0.5309069 0.93818612 0.46909306
[36,] 0.6592456 0.68150888 0.34075444
[37,] 0.6994987 0.60100252 0.30050126
[38,] 0.7757495 0.44850092 0.22425046
[39,] 0.8905016 0.21899682 0.10949841
[40,] 0.8686399 0.26272012 0.13136006
[41,] 0.8598897 0.28022056 0.14011028
[42,] 0.9028406 0.19431875 0.09715937
[43,] 0.8801722 0.23965569 0.11982784
[44,] 0.8558063 0.28838741 0.14419371
[45,] 0.8281075 0.34378497 0.17189249
[46,] 0.7943867 0.41122655 0.20561327
[47,] 0.7610624 0.47787518 0.23893759
[48,] 0.7577445 0.48451105 0.24225553
[49,] 0.8361134 0.32777322 0.16388661
[50,] 0.8422281 0.31554385 0.15777192
[51,] 0.8769813 0.24603745 0.12301872
[52,] 0.8858058 0.22838847 0.11419424
[53,] 0.8846560 0.23068803 0.11534402
[54,] 0.8727637 0.25447270 0.12723635
[55,] 0.9294127 0.14117459 0.07058730
[56,] 0.9136429 0.17271424 0.08635712
[57,] 0.8953712 0.20925764 0.10462882
[58,] 0.8739433 0.25211347 0.12605674
[59,] 0.8475449 0.30491014 0.15245507
[60,] 0.8233611 0.35327789 0.17663895
[61,] 0.8139751 0.37204977 0.18602489
[62,] 0.8412111 0.31757782 0.15878891
[63,] 0.8252378 0.34952436 0.17476218
[64,] 0.7947206 0.41055884 0.20527942
[65,] 0.8030823 0.39383543 0.19691771
[66,] 0.8097453 0.38050934 0.19025467
[67,] 0.7866259 0.42674814 0.21337407
[68,] 0.7668762 0.46624767 0.23312383
[69,] 0.7357604 0.52847913 0.26423957
[70,] 0.7005899 0.59882011 0.29941005
[71,] 0.6604415 0.67911691 0.33955845
[72,] 0.6159592 0.76808152 0.38404076
[73,] 0.5956154 0.80876914 0.40438457
[74,] 0.5855133 0.82897340 0.41448670
[75,] 0.7347300 0.53053996 0.26526998
[76,] 0.6956248 0.60875045 0.30437522
[77,] 0.6553207 0.68935863 0.34467931
[78,] 0.6091077 0.78178462 0.39089231
[79,] 0.7942482 0.41150359 0.20575179
[80,] 0.7826907 0.43461852 0.21730926
[81,] 0.8157933 0.36841334 0.18420667
[82,] 0.7859423 0.42811539 0.21405769
[83,] 0.7542177 0.49156456 0.24578228
[84,] 0.7170332 0.56593359 0.28296679
[85,] 0.6792348 0.64153038 0.32076519
[86,] 0.6701028 0.65979450 0.32989725
[87,] 0.6749467 0.65010661 0.32505331
[88,] 0.7438036 0.51239276 0.25619638
[89,] 0.7442986 0.51140284 0.25570142
[90,] 0.7169258 0.56614838 0.28307419
[91,] 0.7283693 0.54326143 0.27163071
[92,] 0.9868146 0.02637088 0.01318544
[93,] 0.9820999 0.03580016 0.01790008
[94,] 0.9752465 0.04950709 0.02475354
[95,] 0.9669276 0.06614488 0.03307244
[96,] 0.9588461 0.08230777 0.04115388
[97,] 0.9462521 0.10749571 0.05374786
[98,] 0.9320458 0.13590844 0.06795422
[99,] 0.9184277 0.16314453 0.08157226
[100,] 0.9166720 0.16665608 0.08332804
[101,] 0.9370293 0.12594145 0.06297073
[102,] 0.9223125 0.15537503 0.07768751
[103,] 0.9118845 0.17623099 0.08811549
[104,] 0.9498832 0.10023359 0.05011679
[105,] 0.9852410 0.02951809 0.01475904
[106,] 0.9878513 0.02429750 0.01214875
[107,] 0.9825181 0.03496381 0.01748191
[108,] 0.9751748 0.04965031 0.02482516
[109,] 0.9647059 0.07058829 0.03529414
[110,] 0.9542003 0.09159938 0.04579969
[111,] 0.9379817 0.12403658 0.06201829
[112,] 0.9242247 0.15155067 0.07577534
[113,] 0.9724966 0.05500689 0.02750344
[114,] 0.9647642 0.07047155 0.03523578
[115,] 0.9512511 0.09749772 0.04874886
[116,] 0.9423945 0.11521103 0.05760552
[117,] 0.9190478 0.16190444 0.08095222
[118,] 0.9317385 0.13652300 0.06826150
[119,] 0.9081819 0.18363629 0.09181815
[120,] 0.8756545 0.24869091 0.12434546
[121,] 0.8371131 0.32577379 0.16288690
[122,] 0.7848496 0.43030081 0.21515041
[123,] 0.7175297 0.56494064 0.28247032
[124,] 0.7359097 0.52818066 0.26409033
[125,] 0.8647735 0.27045305 0.13522653
[126,] 0.8170782 0.36584359 0.18292180
[127,] 0.7545760 0.49084808 0.24542404
[128,] 0.8347023 0.33059537 0.16529768
[129,] 0.8436455 0.31270892 0.15635446
[130,] 0.7534499 0.49310010 0.24655005
[131,] 0.6377966 0.72440673 0.36220337
[132,] 0.5178976 0.96420487 0.48210244
[133,] 0.3951685 0.79033705 0.60483147
> postscript(file="/var/fisher/rcomp/tmp/1hupg1353428236.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/2ri9r1353428236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/33boo1353428236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4pdi71353428236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/54k9o1353428236.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 6
0.23151543 0.22230165 0.02214346 -0.15860170 -0.29893540 -0.76889080
7 8 9 10 11 12
0.53266918 0.03917012 -0.80250458 1.55489869 1.15660360 -1.57201079
13 14 15 16 17 18
0.27615845 0.23756943 -0.05585243 -0.13365783 -0.34392327 -0.81344708
19 20 21 22 23 24
0.62765824 -0.52341347 0.08091950 1.54234376 -0.97816984 -2.52193982
25 26 27 28 29 30
0.39970584 0.25331658 0.28038566 -0.15235164 -0.27463241 -0.52380617
31 32 33 34 35 36
-0.84462038 0.60217602 1.30232097 0.68499274 1.42681170 -0.94123288
37 38 39 40 41 42
-1.06992994 0.29309559 0.23042970 0.33722358 -0.10925909 -0.28164105
43 44 45 46 47 48
-0.73057020 -1.52478103 0.94834071 1.24317362 1.86646074 0.41696004
49 50 51 52 53 54
-0.76445348 -1.57100454 0.23977718 0.22125281 0.30143594 -0.08331141
55 56 57 58 59 60
-0.30781510 -0.69407918 -1.34422568 0.87442379 1.21330091 1.02347156
61 62 63 64 65 66
-0.70031326 0.43153036 -1.64835636 0.31597631 0.25822199 0.25528192
67 68 69 70 71 72
-0.10866381 -0.35135752 -0.63070100 -1.02901956 0.61789441 -0.13530302
73 74 75 76 77 78
0.92947371 0.89145024 0.35829708 -0.32515743 0.33102817 0.22557269
79 80 81 82 83 84
0.18777526 -0.10363796 -0.51905939 -0.67762315 -1.62370824 0.02798900
85 86 87 88 89 90
0.36025304 0.02348951 1.95534487 0.76076906 -0.89591572 0.27253017
91 92 93 94 95 96
0.23327167 0.18415737 -0.20294222 -0.50634456 -0.76240232 -1.19386478
97 98 99 100 101 102
-0.46554989 0.10045426 0.94959464 3.05580971 0.40751093 0.14339074
103 104 105 106 107 108
0.20188277 0.26240231 0.12427875 -0.22601735 -0.41247868 -0.73001097
109 110 111 112 113 114
-0.91445009 0.28384305 -0.13120360 1.00317272 1.33101393 -0.97160895
115 116 117 118 119 120
0.23617689 0.14512012 0.08364267 -0.24441820 -0.36254335 -0.67535317
121 122 123 124 125 126
-1.13363085 0.76043570 0.75072485 0.39699010 -0.41186690 0.69633945
127 128 129 130 131 132
0.29311272 0.04955210 -0.02909958 -0.34853095 -0.32258881 -0.71591501
133 134 135 136 137 138
-0.71394498 1.29909484 0.94927875 0.56436669 -1.59504781 -0.27225892
139 140 141 142 143 144
0.21658449 0.11904754 -0.07249724 -0.32250849 -0.41392635 -0.66073341
145 146 147 148 149 150
0.42214244 0.88346053 0.32900743 0.34469897 -1.58368378 -0.37314523
> postscript(file="/var/fisher/rcomp/tmp/6cqs91353428236.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 0.23151543 NA
1 0.22230165 0.23151543
2 0.02214346 0.22230165
3 -0.15860170 0.02214346
4 -0.29893540 -0.15860170
5 -0.76889080 -0.29893540
6 0.53266918 -0.76889080
7 0.03917012 0.53266918
8 -0.80250458 0.03917012
9 1.55489869 -0.80250458
10 1.15660360 1.55489869
11 -1.57201079 1.15660360
12 0.27615845 -1.57201079
13 0.23756943 0.27615845
14 -0.05585243 0.23756943
15 -0.13365783 -0.05585243
16 -0.34392327 -0.13365783
17 -0.81344708 -0.34392327
18 0.62765824 -0.81344708
19 -0.52341347 0.62765824
20 0.08091950 -0.52341347
21 1.54234376 0.08091950
22 -0.97816984 1.54234376
23 -2.52193982 -0.97816984
24 0.39970584 -2.52193982
25 0.25331658 0.39970584
26 0.28038566 0.25331658
27 -0.15235164 0.28038566
28 -0.27463241 -0.15235164
29 -0.52380617 -0.27463241
30 -0.84462038 -0.52380617
31 0.60217602 -0.84462038
32 1.30232097 0.60217602
33 0.68499274 1.30232097
34 1.42681170 0.68499274
35 -0.94123288 1.42681170
36 -1.06992994 -0.94123288
37 0.29309559 -1.06992994
38 0.23042970 0.29309559
39 0.33722358 0.23042970
40 -0.10925909 0.33722358
41 -0.28164105 -0.10925909
42 -0.73057020 -0.28164105
43 -1.52478103 -0.73057020
44 0.94834071 -1.52478103
45 1.24317362 0.94834071
46 1.86646074 1.24317362
47 0.41696004 1.86646074
48 -0.76445348 0.41696004
49 -1.57100454 -0.76445348
50 0.23977718 -1.57100454
51 0.22125281 0.23977718
52 0.30143594 0.22125281
53 -0.08331141 0.30143594
54 -0.30781510 -0.08331141
55 -0.69407918 -0.30781510
56 -1.34422568 -0.69407918
57 0.87442379 -1.34422568
58 1.21330091 0.87442379
59 1.02347156 1.21330091
60 -0.70031326 1.02347156
61 0.43153036 -0.70031326
62 -1.64835636 0.43153036
63 0.31597631 -1.64835636
64 0.25822199 0.31597631
65 0.25528192 0.25822199
66 -0.10866381 0.25528192
67 -0.35135752 -0.10866381
68 -0.63070100 -0.35135752
69 -1.02901956 -0.63070100
70 0.61789441 -1.02901956
71 -0.13530302 0.61789441
72 0.92947371 -0.13530302
73 0.89145024 0.92947371
74 0.35829708 0.89145024
75 -0.32515743 0.35829708
76 0.33102817 -0.32515743
77 0.22557269 0.33102817
78 0.18777526 0.22557269
79 -0.10363796 0.18777526
80 -0.51905939 -0.10363796
81 -0.67762315 -0.51905939
82 -1.62370824 -0.67762315
83 0.02798900 -1.62370824
84 0.36025304 0.02798900
85 0.02348951 0.36025304
86 1.95534487 0.02348951
87 0.76076906 1.95534487
88 -0.89591572 0.76076906
89 0.27253017 -0.89591572
90 0.23327167 0.27253017
91 0.18415737 0.23327167
92 -0.20294222 0.18415737
93 -0.50634456 -0.20294222
94 -0.76240232 -0.50634456
95 -1.19386478 -0.76240232
96 -0.46554989 -1.19386478
97 0.10045426 -0.46554989
98 0.94959464 0.10045426
99 3.05580971 0.94959464
100 0.40751093 3.05580971
101 0.14339074 0.40751093
102 0.20188277 0.14339074
103 0.26240231 0.20188277
104 0.12427875 0.26240231
105 -0.22601735 0.12427875
106 -0.41247868 -0.22601735
107 -0.73001097 -0.41247868
108 -0.91445009 -0.73001097
109 0.28384305 -0.91445009
110 -0.13120360 0.28384305
111 1.00317272 -0.13120360
112 1.33101393 1.00317272
113 -0.97160895 1.33101393
114 0.23617689 -0.97160895
115 0.14512012 0.23617689
116 0.08364267 0.14512012
117 -0.24441820 0.08364267
118 -0.36254335 -0.24441820
119 -0.67535317 -0.36254335
120 -1.13363085 -0.67535317
121 0.76043570 -1.13363085
122 0.75072485 0.76043570
123 0.39699010 0.75072485
124 -0.41186690 0.39699010
125 0.69633945 -0.41186690
126 0.29311272 0.69633945
127 0.04955210 0.29311272
128 -0.02909958 0.04955210
129 -0.34853095 -0.02909958
130 -0.32258881 -0.34853095
131 -0.71591501 -0.32258881
132 -0.71394498 -0.71591501
133 1.29909484 -0.71394498
134 0.94927875 1.29909484
135 0.56436669 0.94927875
136 -1.59504781 0.56436669
137 -0.27225892 -1.59504781
138 0.21658449 -0.27225892
139 0.11904754 0.21658449
140 -0.07249724 0.11904754
141 -0.32250849 -0.07249724
142 -0.41392635 -0.32250849
143 -0.66073341 -0.41392635
144 0.42214244 -0.66073341
145 0.88346053 0.42214244
146 0.32900743 0.88346053
147 0.34469897 0.32900743
148 -1.58368378 0.34469897
149 -0.37314523 -1.58368378
150 NA -0.37314523
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.22230165 0.23151543
[2,] 0.02214346 0.22230165
[3,] -0.15860170 0.02214346
[4,] -0.29893540 -0.15860170
[5,] -0.76889080 -0.29893540
[6,] 0.53266918 -0.76889080
[7,] 0.03917012 0.53266918
[8,] -0.80250458 0.03917012
[9,] 1.55489869 -0.80250458
[10,] 1.15660360 1.55489869
[11,] -1.57201079 1.15660360
[12,] 0.27615845 -1.57201079
[13,] 0.23756943 0.27615845
[14,] -0.05585243 0.23756943
[15,] -0.13365783 -0.05585243
[16,] -0.34392327 -0.13365783
[17,] -0.81344708 -0.34392327
[18,] 0.62765824 -0.81344708
[19,] -0.52341347 0.62765824
[20,] 0.08091950 -0.52341347
[21,] 1.54234376 0.08091950
[22,] -0.97816984 1.54234376
[23,] -2.52193982 -0.97816984
[24,] 0.39970584 -2.52193982
[25,] 0.25331658 0.39970584
[26,] 0.28038566 0.25331658
[27,] -0.15235164 0.28038566
[28,] -0.27463241 -0.15235164
[29,] -0.52380617 -0.27463241
[30,] -0.84462038 -0.52380617
[31,] 0.60217602 -0.84462038
[32,] 1.30232097 0.60217602
[33,] 0.68499274 1.30232097
[34,] 1.42681170 0.68499274
[35,] -0.94123288 1.42681170
[36,] -1.06992994 -0.94123288
[37,] 0.29309559 -1.06992994
[38,] 0.23042970 0.29309559
[39,] 0.33722358 0.23042970
[40,] -0.10925909 0.33722358
[41,] -0.28164105 -0.10925909
[42,] -0.73057020 -0.28164105
[43,] -1.52478103 -0.73057020
[44,] 0.94834071 -1.52478103
[45,] 1.24317362 0.94834071
[46,] 1.86646074 1.24317362
[47,] 0.41696004 1.86646074
[48,] -0.76445348 0.41696004
[49,] -1.57100454 -0.76445348
[50,] 0.23977718 -1.57100454
[51,] 0.22125281 0.23977718
[52,] 0.30143594 0.22125281
[53,] -0.08331141 0.30143594
[54,] -0.30781510 -0.08331141
[55,] -0.69407918 -0.30781510
[56,] -1.34422568 -0.69407918
[57,] 0.87442379 -1.34422568
[58,] 1.21330091 0.87442379
[59,] 1.02347156 1.21330091
[60,] -0.70031326 1.02347156
[61,] 0.43153036 -0.70031326
[62,] -1.64835636 0.43153036
[63,] 0.31597631 -1.64835636
[64,] 0.25822199 0.31597631
[65,] 0.25528192 0.25822199
[66,] -0.10866381 0.25528192
[67,] -0.35135752 -0.10866381
[68,] -0.63070100 -0.35135752
[69,] -1.02901956 -0.63070100
[70,] 0.61789441 -1.02901956
[71,] -0.13530302 0.61789441
[72,] 0.92947371 -0.13530302
[73,] 0.89145024 0.92947371
[74,] 0.35829708 0.89145024
[75,] -0.32515743 0.35829708
[76,] 0.33102817 -0.32515743
[77,] 0.22557269 0.33102817
[78,] 0.18777526 0.22557269
[79,] -0.10363796 0.18777526
[80,] -0.51905939 -0.10363796
[81,] -0.67762315 -0.51905939
[82,] -1.62370824 -0.67762315
[83,] 0.02798900 -1.62370824
[84,] 0.36025304 0.02798900
[85,] 0.02348951 0.36025304
[86,] 1.95534487 0.02348951
[87,] 0.76076906 1.95534487
[88,] -0.89591572 0.76076906
[89,] 0.27253017 -0.89591572
[90,] 0.23327167 0.27253017
[91,] 0.18415737 0.23327167
[92,] -0.20294222 0.18415737
[93,] -0.50634456 -0.20294222
[94,] -0.76240232 -0.50634456
[95,] -1.19386478 -0.76240232
[96,] -0.46554989 -1.19386478
[97,] 0.10045426 -0.46554989
[98,] 0.94959464 0.10045426
[99,] 3.05580971 0.94959464
[100,] 0.40751093 3.05580971
[101,] 0.14339074 0.40751093
[102,] 0.20188277 0.14339074
[103,] 0.26240231 0.20188277
[104,] 0.12427875 0.26240231
[105,] -0.22601735 0.12427875
[106,] -0.41247868 -0.22601735
[107,] -0.73001097 -0.41247868
[108,] -0.91445009 -0.73001097
[109,] 0.28384305 -0.91445009
[110,] -0.13120360 0.28384305
[111,] 1.00317272 -0.13120360
[112,] 1.33101393 1.00317272
[113,] -0.97160895 1.33101393
[114,] 0.23617689 -0.97160895
[115,] 0.14512012 0.23617689
[116,] 0.08364267 0.14512012
[117,] -0.24441820 0.08364267
[118,] -0.36254335 -0.24441820
[119,] -0.67535317 -0.36254335
[120,] -1.13363085 -0.67535317
[121,] 0.76043570 -1.13363085
[122,] 0.75072485 0.76043570
[123,] 0.39699010 0.75072485
[124,] -0.41186690 0.39699010
[125,] 0.69633945 -0.41186690
[126,] 0.29311272 0.69633945
[127,] 0.04955210 0.29311272
[128,] -0.02909958 0.04955210
[129,] -0.34853095 -0.02909958
[130,] -0.32258881 -0.34853095
[131,] -0.71591501 -0.32258881
[132,] -0.71394498 -0.71591501
[133,] 1.29909484 -0.71394498
[134,] 0.94927875 1.29909484
[135,] 0.56436669 0.94927875
[136,] -1.59504781 0.56436669
[137,] -0.27225892 -1.59504781
[138,] 0.21658449 -0.27225892
[139,] 0.11904754 0.21658449
[140,] -0.07249724 0.11904754
[141,] -0.32250849 -0.07249724
[142,] -0.41392635 -0.32250849
[143,] -0.66073341 -0.41392635
[144,] 0.42214244 -0.66073341
[145,] 0.88346053 0.42214244
[146,] 0.32900743 0.88346053
[147,] 0.34469897 0.32900743
[148,] -1.58368378 0.34469897
[149,] -0.37314523 -1.58368378
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.22230165 0.23151543
2 0.02214346 0.22230165
3 -0.15860170 0.02214346
4 -0.29893540 -0.15860170
5 -0.76889080 -0.29893540
6 0.53266918 -0.76889080
7 0.03917012 0.53266918
8 -0.80250458 0.03917012
9 1.55489869 -0.80250458
10 1.15660360 1.55489869
11 -1.57201079 1.15660360
12 0.27615845 -1.57201079
13 0.23756943 0.27615845
14 -0.05585243 0.23756943
15 -0.13365783 -0.05585243
16 -0.34392327 -0.13365783
17 -0.81344708 -0.34392327
18 0.62765824 -0.81344708
19 -0.52341347 0.62765824
20 0.08091950 -0.52341347
21 1.54234376 0.08091950
22 -0.97816984 1.54234376
23 -2.52193982 -0.97816984
24 0.39970584 -2.52193982
25 0.25331658 0.39970584
26 0.28038566 0.25331658
27 -0.15235164 0.28038566
28 -0.27463241 -0.15235164
29 -0.52380617 -0.27463241
30 -0.84462038 -0.52380617
31 0.60217602 -0.84462038
32 1.30232097 0.60217602
33 0.68499274 1.30232097
34 1.42681170 0.68499274
35 -0.94123288 1.42681170
36 -1.06992994 -0.94123288
37 0.29309559 -1.06992994
38 0.23042970 0.29309559
39 0.33722358 0.23042970
40 -0.10925909 0.33722358
41 -0.28164105 -0.10925909
42 -0.73057020 -0.28164105
43 -1.52478103 -0.73057020
44 0.94834071 -1.52478103
45 1.24317362 0.94834071
46 1.86646074 1.24317362
47 0.41696004 1.86646074
48 -0.76445348 0.41696004
49 -1.57100454 -0.76445348
50 0.23977718 -1.57100454
51 0.22125281 0.23977718
52 0.30143594 0.22125281
53 -0.08331141 0.30143594
54 -0.30781510 -0.08331141
55 -0.69407918 -0.30781510
56 -1.34422568 -0.69407918
57 0.87442379 -1.34422568
58 1.21330091 0.87442379
59 1.02347156 1.21330091
60 -0.70031326 1.02347156
61 0.43153036 -0.70031326
62 -1.64835636 0.43153036
63 0.31597631 -1.64835636
64 0.25822199 0.31597631
65 0.25528192 0.25822199
66 -0.10866381 0.25528192
67 -0.35135752 -0.10866381
68 -0.63070100 -0.35135752
69 -1.02901956 -0.63070100
70 0.61789441 -1.02901956
71 -0.13530302 0.61789441
72 0.92947371 -0.13530302
73 0.89145024 0.92947371
74 0.35829708 0.89145024
75 -0.32515743 0.35829708
76 0.33102817 -0.32515743
77 0.22557269 0.33102817
78 0.18777526 0.22557269
79 -0.10363796 0.18777526
80 -0.51905939 -0.10363796
81 -0.67762315 -0.51905939
82 -1.62370824 -0.67762315
83 0.02798900 -1.62370824
84 0.36025304 0.02798900
85 0.02348951 0.36025304
86 1.95534487 0.02348951
87 0.76076906 1.95534487
88 -0.89591572 0.76076906
89 0.27253017 -0.89591572
90 0.23327167 0.27253017
91 0.18415737 0.23327167
92 -0.20294222 0.18415737
93 -0.50634456 -0.20294222
94 -0.76240232 -0.50634456
95 -1.19386478 -0.76240232
96 -0.46554989 -1.19386478
97 0.10045426 -0.46554989
98 0.94959464 0.10045426
99 3.05580971 0.94959464
100 0.40751093 3.05580971
101 0.14339074 0.40751093
102 0.20188277 0.14339074
103 0.26240231 0.20188277
104 0.12427875 0.26240231
105 -0.22601735 0.12427875
106 -0.41247868 -0.22601735
107 -0.73001097 -0.41247868
108 -0.91445009 -0.73001097
109 0.28384305 -0.91445009
110 -0.13120360 0.28384305
111 1.00317272 -0.13120360
112 1.33101393 1.00317272
113 -0.97160895 1.33101393
114 0.23617689 -0.97160895
115 0.14512012 0.23617689
116 0.08364267 0.14512012
117 -0.24441820 0.08364267
118 -0.36254335 -0.24441820
119 -0.67535317 -0.36254335
120 -1.13363085 -0.67535317
121 0.76043570 -1.13363085
122 0.75072485 0.76043570
123 0.39699010 0.75072485
124 -0.41186690 0.39699010
125 0.69633945 -0.41186690
126 0.29311272 0.69633945
127 0.04955210 0.29311272
128 -0.02909958 0.04955210
129 -0.34853095 -0.02909958
130 -0.32258881 -0.34853095
131 -0.71591501 -0.32258881
132 -0.71394498 -0.71591501
133 1.29909484 -0.71394498
134 0.94927875 1.29909484
135 0.56436669 0.94927875
136 -1.59504781 0.56436669
137 -0.27225892 -1.59504781
138 0.21658449 -0.27225892
139 0.11904754 0.21658449
140 -0.07249724 0.11904754
141 -0.32250849 -0.07249724
142 -0.41392635 -0.32250849
143 -0.66073341 -0.41392635
144 0.42214244 -0.66073341
145 0.88346053 0.42214244
146 0.32900743 0.88346053
147 0.34469897 0.32900743
148 -1.58368378 0.34469897
149 -0.37314523 -1.58368378
> 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/7nc261353428236.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/89als1353428236.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/9qsvu1353428236.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/106rx01353428236.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11abv91353428236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12lszt1353428236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13nees1353428236.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14o0981353428236.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/15bvtj1353428236.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/16saur1353428236.tab")
+ }
>
> try(system("convert tmp/1hupg1353428236.ps tmp/1hupg1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ri9r1353428236.ps tmp/2ri9r1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/33boo1353428236.ps tmp/33boo1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/4pdi71353428236.ps tmp/4pdi71353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/54k9o1353428236.ps tmp/54k9o1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cqs91353428236.ps tmp/6cqs91353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nc261353428236.ps tmp/7nc261353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/89als1353428236.ps tmp/89als1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/9qsvu1353428236.ps tmp/9qsvu1353428236.png",intern=TRUE))
character(0)
> try(system("convert tmp/106rx01353428236.ps tmp/106rx01353428236.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.607 1.348 8.972