R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(97
+ ,197426
+ ,39
+ ,178377
+ ,490
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+ ,173
+ ,250931
+ ,2563
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+ ,898
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+ ,176225
+ ,139
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+ ,1212
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+ ,790
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+ ,116
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+ ,738
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+ ,114
+ ,164263
+ ,845
+ ,248
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+ ,155
+ ,189944
+ ,1369
+ ,161
+ ,165933
+ ,127
+ ,147581
+ ,1830
+ ,155
+ ,165904
+ ,107
+ ,127667
+ ,711
+ ,142
+ ,160902
+ ,126
+ ,106330
+ ,992
+ ,145
+ ,160141
+ ,161
+ ,175721
+ ,1272
+ ,159
+ ,156349
+ ,185
+ ,169216
+ ,852
+ ,153
+ ,154771
+ ,63
+ ,18284
+ ,575
+ ,130
+ ,154451
+ ,121
+ ,134969
+ ,1101
+ ,177
+ ,151911
+ ,150
+ ,191889
+ ,1410
+ ,181
+ ,151715
+ ,160
+ ,197765
+ ,1352
+ ,140
+ ,150491
+ ,132
+ ,194679
+ ,1208
+ ,196
+ ,150047
+ ,147
+ ,75767
+ ,739
+ ,140
+ ,149959
+ ,176
+ ,195894
+ ,926
+ ,175
+ ,149695
+ ,88
+ ,191179
+ ,865
+ ,155
+ ,147172
+ ,82
+ ,178303
+ ,677
+ ,147
+ ,146975
+ ,75
+ ,135599
+ ,971
+ ,177
+ ,146760
+ ,128
+ ,195791
+ ,1574
+ ,159
+ ,144551
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+ ,81716
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+ ,724
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+ ,1118
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+ ,141
+ ,189723
+ ,1293
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+ ,118906
+ ,103
+ ,102509
+ ,636
+ ,187
+ ,117440
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+ ,157384
+ ,1031
+ ,148
+ ,116066
+ ,55
+ ,24469
+ ,524
+ ,208
+ ,114948
+ ,176
+ ,165354
+ ,1775
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+ ,114799
+ ,66
+ ,153242
+ ,669
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+ ,114360
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+ ,79367
+ ,2089
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+ ,113344
+ ,155
+ ,230054
+ ,1230
+ ,160
+ ,112431
+ ,123
+ ,140303
+ ,847
+ ,187
+ ,112302
+ ,145
+ ,198299
+ ,906
+ ,146
+ ,112098
+ ,134
+ ,106194
+ ,1154
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+ ,110529
+ ,164
+ ,232241
+ ,1251
+ ,143
+ ,110459
+ ,75
+ ,113854
+ ,510
+ ,115
+ ,109432
+ ,148
+ ,116938
+ ,698
+ ,151
+ ,108535
+ ,85
+ ,118845
+ ,1586
+ ,144
+ ,108146
+ ,140
+ ,100125
+ ,1001
+ ,118
+ ,105079
+ ,70
+ ,99776
+ ,710
+ ,98
+ ,104978
+ ,117
+ ,139292
+ ,906
+ ,142
+ ,104767
+ ,103
+ ,124527
+ ,1030
+ ,151
+ ,104581
+ ,116
+ ,116136
+ ,1092
+ ,171
+ ,104128
+ ,50
+ ,122975
+ ,511
+ ,156
+ ,103925
+ ,152
+ ,164808
+ ,1319
+ ,171
+ ,103297
+ ,139
+ ,101345
+ ,1186
+ ,142
+ ,103129
+ ,114
+ ,158376
+ ,1201
+ ,148
+ ,103037
+ ,110
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+ ,96
+ ,102812
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+ ,136323
+ ,703
+ ,151
+ ,102153
+ ,98
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+ ,862
+ ,173
+ ,102070
+ ,94
+ ,86480
+ ,1031
+ ,151
+ ,101629
+ ,112
+ ,188355
+ ,1348
+ ,77
+ ,101382
+ ,81
+ ,127097
+ ,866
+ ,135
+ ,101047
+ ,169
+ ,135848
+ ,1079
+ ,71
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+ ,764
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+ ,816
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+ ,8
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+ ,795
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+ ,87
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+ ,641
+ ,73
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+ ,83
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+ ,64
+ ,43750
+ ,13
+ ,19630
+ ,214
+ ,99
+ ,38692
+ ,30
+ ,68580
+ ,657
+ ,78
+ ,37238
+ ,41
+ ,10901
+ ,716
+ ,110
+ ,37110
+ ,67
+ ,64057
+ ,665)
+ ,dim=c(5
+ ,137)
+ ,dimnames=list(c('FbackMess'
+ ,'CompendiumCharacters'
+ ,'BloggedComputations'
+ ,'CompendiumSeconds'
+ ,'CourseViews')
+ ,1:137))
> y <- array(NA,dim=c(5,137),dimnames=list(c('FbackMess','CompendiumCharacters','BloggedComputations','CompendiumSeconds','CourseViews'),1:137))
> 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 = '2'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
CompendiumCharacters FbackMess BloggedComputations CompendiumSeconds
1 197426 97 39 178377
2 187326 146 173 250931
3 184923 116 165 226168
4 183500 113 181 211381
5 176225 75 139 214738
6 169707 228 166 210012
7 169265 138 116 163073
8 167949 153 114 164263
9 165986 248 155 189944
10 165933 161 127 147581
11 165904 155 107 127667
12 160902 142 126 106330
13 160141 145 161 175721
14 156349 159 185 169216
15 154771 153 63 18284
16 154451 130 121 134969
17 151911 177 150 191889
18 151715 181 160 197765
19 150491 140 132 194679
20 150047 196 147 75767
21 149959 140 176 195894
22 149695 175 88 191179
23 147172 155 82 178303
24 146975 147 75 135599
25 146760 177 128 195791
26 144551 159 165 81716
27 144408 132 88 115466
28 144244 94 62 88229
29 143592 140 93 113963
30 140824 130 96 186099
31 140358 176 121 117495
32 140015 153 146 145758
33 139165 179 143 184531
34 136588 197 135 134163
35 135356 163 76 91502
36 134238 170 152 191469
37 134047 145 163 231257
38 131072 129 154 114268
39 128692 57 48 100187
40 127654 144 168 105590
41 126817 92 143 94333
42 126372 144 156 165278
43 125818 95 103 111669
44 125386 126 128 134218
45 125081 97 121 135213
46 124089 144 96 130332
47 123534 137 76 100922
48 120192 155 161 197680
49 119442 163 141 189723
50 118906 227 103 102509
51 117440 187 137 157384
52 116066 148 55 24469
53 114948 208 176 165354
54 114799 136 66 153242
55 114360 134 101 79367
56 113344 149 155 230054
57 112431 160 123 140303
58 112302 187 145 198299
59 112098 146 134 106194
60 110529 102 164 232241
61 110459 143 75 113854
62 109432 115 148 116938
63 108535 151 85 118845
64 108146 144 140 100125
65 105079 118 70 99776
66 104978 98 117 139292
67 104767 142 103 124527
68 104581 151 116 116136
69 104128 171 50 122975
70 103925 156 152 164808
71 103297 171 139 101345
72 103129 142 114 158376
73 103037 148 110 150773
74 102812 96 120 136323
75 102153 151 98 80716
76 102070 173 94 86480
77 101629 151 112 188355
78 101382 77 81 127097
79 101047 135 169 135848
80 100350 71 62 75882
81 100087 133 102 129711
82 100046 139 133 128602
83 96125 201 107 106314
84 95893 141 90 81180
85 95676 158 99 160792
86 93879 126 152 170492
87 93487 119 84 133252
88 93473 65 57 121850
89 92622 128 126 134097
90 92280 147 118 147341
91 92059 90 101 91313
92 89626 169 85 134904
93 89506 150 118 160501
94 89256 156 129 104864
95 88977 179 85 111563
96 86652 149 50 114198
97 84601 94 85 105406
98 83515 154 158 96785
99 83248 103 146 106020
100 83243 148 150 153990
101 82317 84 77 111848
102 81897 144 131 89770
103 81625 203 132 94853
104 81351 160 107 102204
105 79756 152 80 122531
106 79089 147 114 169351
107 79011 111 97 80238
108 76173 89 8 47552
109 72128 87 163 145707
110 71571 121 102 75881
111 71154 146 137 80906
112 70168 100 79 104470
113 69867 127 83 100826
114 69652 153 56 33750
115 69446 87 87 113713
116 68946 129 164 174586
117 68788 113 57 72591
118 67150 124 110 114651
119 66485 92 104 110896
120 66089 112 65 61394
121 65594 102 48 92795
122 64593 115 60 72558
123 64520 148 68 54518
124 59938 135 149 82390
125 59900 97 104 96252
126 57224 59 86 80684
127 56750 101 89 115750
128 56622 27 49 55792
129 55918 112 74 83963
130 52789 89 37 15673
131 48029 40 120 88634
132 45724 130 87 74151
133 43929 73 83 100792
134 43750 64 13 19630
135 38692 99 30 68580
136 37238 78 41 10901
137 37110 110 67 64057
CourseViews t
1 490 1
2 2563 2
3 1538 3
4 898 4
5 1212 5
6 790 6
7 738 7
8 845 8
9 1369 9
10 1830 10
11 711 11
12 992 12
13 1272 13
14 852 14
15 575 15
16 1101 16
17 1410 17
18 1352 18
19 1208 19
20 739 20
21 926 21
22 865 22
23 677 23
24 971 24
25 1574 25
26 1051 26
27 763 27
28 724 28
29 652 29
30 504 30
31 893 31
32 1034 32
33 1111 33
34 692 34
35 740 35
36 1716 36
37 884 37
38 925 38
39 723 39
40 732 40
41 637 41
42 1266 42
43 527 43
44 811 44
45 1390 45
46 1613 46
47 459 47
48 1118 48
49 1293 49
50 636 50
51 1031 51
52 524 52
53 1775 53
54 669 54
55 2089 55
56 1230 56
57 847 57
58 906 58
59 1154 59
60 1251 60
61 510 61
62 698 62
63 1586 63
64 1001 64
65 710 65
66 906 66
67 1030 67
68 1092 68
69 511 69
70 1319 70
71 1186 71
72 1201 72
73 1443 73
74 703 74
75 862 75
76 1031 76
77 1348 77
78 866 78
79 1079 79
80 695 80
81 1229 81
82 1288 82
83 764 83
84 919 84
85 691 85
86 1099 86
87 766 87
88 1150 88
89 1566 89
90 668 90
91 910 91
92 894 92
93 1351 93
94 1187 94
95 784 95
96 758 96
97 816 97
98 1370 98
99 785 99
100 763 100
101 569 101
102 781 102
103 743 103
104 900 104
105 575 105
106 981 106
107 784 107
108 179 108
109 542 109
110 746 110
111 767 111
112 695 112
113 1186 113
114 456 114
115 724 115
116 1145 116
117 785 117
118 905 118
119 661 119
120 507 120
121 632 121
122 790 122
123 488 123
124 1128 124
125 1257 125
126 800 126
127 846 127
128 437 128
129 795 129
130 309 130
131 833 131
132 641 132
133 415 133
134 214 134
135 657 135
136 716 136
137 665 137
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) FbackMess BloggedComputations
1.692e+05 -1.797e+01 -2.052e+01
CompendiumSeconds CourseViews t
3.780e-02 5.041e-01 -8.967e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-9950.2 -3974.2 216.4 3112.7 24709.0
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.692e+05 3.018e+03 56.061 < 2e-16 ***
FbackMess -1.797e+01 1.415e+01 -1.270 0.20623
BloggedComputations -2.052e+01 1.655e+01 -1.240 0.21734
CompendiumSeconds 3.780e-02 1.383e-02 2.734 0.00713 **
CourseViews 5.041e-01 1.551e+00 0.325 0.74570
t -8.967e+02 1.491e+01 -60.126 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5363 on 131 degrees of freedom
Multiple R-squared: 0.9792, Adjusted R-squared: 0.9784
F-statistic: 1233 on 5 and 131 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,] 0.5660923 0.8678153310 0.4339076655
[2,] 0.4436308 0.8872615592 0.5563692204
[3,] 0.5418167 0.9163665197 0.4581832598
[4,] 0.4398056 0.8796111993 0.5601944003
[5,] 0.6564066 0.6871868127 0.3435934063
[6,] 0.6264786 0.7470428424 0.3735214212
[7,] 0.5326707 0.9346586746 0.4673293373
[8,] 0.4538518 0.9077036086 0.5461481957
[9,] 0.3773567 0.7547133168 0.6226433416
[10,] 0.3423289 0.6846577123 0.6576711439
[11,] 0.2655358 0.5310716565 0.7344641717
[12,] 0.6137492 0.7725015620 0.3862507810
[13,] 0.6363254 0.7273492677 0.3636746339
[14,] 0.5940284 0.8119432710 0.4059716355
[15,] 0.5219702 0.9560596530 0.4780298265
[16,] 0.4908147 0.9816293064 0.5091853468
[17,] 0.5231074 0.9537851252 0.4768925626
[18,] 0.7657981 0.4684038415 0.2342019207
[19,] 0.7582230 0.4835539970 0.2417769985
[20,] 0.7416345 0.5167309984 0.2583654992
[21,] 0.7904639 0.4190721899 0.2095360949
[22,] 0.7503031 0.4993937967 0.2496968984
[23,] 0.8311246 0.3377507485 0.1688753742
[24,] 0.8689691 0.2620618825 0.1310309412
[25,] 0.8894938 0.2210123566 0.1105061783
[26,] 0.9118537 0.1762926588 0.0881463294
[27,] 0.9047389 0.1905222636 0.0952611318
[28,] 0.8839354 0.2321292310 0.1160646155
[29,] 0.8566498 0.2867003517 0.1433501758
[30,] 0.8335060 0.3329880027 0.1664940013
[31,] 0.8251586 0.3496827193 0.1748413596
[32,] 0.8057318 0.3885364515 0.1942682257
[33,] 0.7660621 0.4678758448 0.2339379224
[34,] 0.7290861 0.5418278595 0.2709139298
[35,] 0.6835518 0.6328963487 0.3164481744
[36,] 0.6502079 0.6995842137 0.3497921069
[37,] 0.6047501 0.7904997158 0.3952498579
[38,] 0.5772938 0.8454123624 0.4227061812
[39,] 0.5798654 0.8402692006 0.4201346003
[40,] 0.5495250 0.9009499678 0.4504749839
[41,] 0.5231421 0.9537157887 0.4768578944
[42,] 0.6052569 0.7894861686 0.3947430843
[43,] 0.6057577 0.7884845200 0.3942422600
[44,] 0.6004099 0.7991802873 0.3995901436
[45,] 0.6337733 0.7324534590 0.3662267295
[46,] 0.6049093 0.7901813472 0.3950906736
[47,] 0.5806972 0.8386056490 0.4193028245
[48,] 0.5832223 0.8335554369 0.4167777185
[49,] 0.6026845 0.7946310592 0.3973155296
[50,] 0.6525808 0.6948383103 0.3474191551
[51,] 0.6874450 0.6251099654 0.3125549827
[52,] 0.6978775 0.6042450764 0.3021225382
[53,] 0.7169388 0.5661223932 0.2830611966
[54,] 0.7490888 0.5018223449 0.2509111724
[55,] 0.7520085 0.4959830661 0.2479915331
[56,] 0.7987807 0.4024386956 0.2012193478
[57,] 0.8133877 0.3732245265 0.1866122633
[58,] 0.8344341 0.3311317707 0.1655658853
[59,] 0.8682228 0.2635543317 0.1317771658
[60,] 0.9054714 0.1890571925 0.0945285963
[61,] 0.9388074 0.1223851035 0.0611925517
[62,] 0.9624418 0.0751163423 0.0375581712
[63,] 0.9822136 0.0355728446 0.0177864223
[64,] 0.9881544 0.0236911698 0.0118455849
[65,] 0.9917402 0.0165196978 0.0082598489
[66,] 0.9942249 0.0115502918 0.0057751459
[67,] 0.9971563 0.0056873298 0.0028436649
[68,] 0.9986184 0.0027632454 0.0013816227
[69,] 0.9989055 0.0021890020 0.0010945010
[70,] 0.9988617 0.0022765446 0.0011382723
[71,] 0.9993438 0.0013124328 0.0006562164
[72,] 0.9993151 0.0013697951 0.0006848975
[73,] 0.9993648 0.0012703743 0.0006351872
[74,] 0.9994816 0.0010368824 0.0005184412
[75,] 0.9997476 0.0005048111 0.0002524056
[76,] 0.9997880 0.0004239650 0.0002119825
[77,] 0.9998129 0.0003741317 0.0001870659
[78,] 0.9998297 0.0003405071 0.0001702535
[79,] 0.9998257 0.0003486859 0.0001743429
[80,] 0.9997341 0.0005318637 0.0002659319
[81,] 0.9996838 0.0006323267 0.0003161633
[82,] 0.9996893 0.0006213285 0.0003106643
[83,] 0.9996083 0.0007833006 0.0003916503
[84,] 0.9996309 0.0007381233 0.0003690616
[85,] 0.9995866 0.0008268579 0.0004134289
[86,] 0.9995509 0.0008982906 0.0004491453
[87,] 0.9995024 0.0009951354 0.0004975677
[88,] 0.9994966 0.0010068826 0.0005034413
[89,] 0.9994950 0.0010099960 0.0005049980
[90,] 0.9995987 0.0008026361 0.0004013180
[91,] 0.9995317 0.0009366771 0.0004683385
[92,] 0.9994056 0.0011888877 0.0005944439
[93,] 0.9992834 0.0014331404 0.0007165702
[94,] 0.9991002 0.0017995861 0.0008997930
[95,] 0.9988512 0.0022975850 0.0011487925
[96,] 0.9983939 0.0032121215 0.0016060607
[97,] 0.9976169 0.0047662600 0.0023831300
[98,] 0.9966521 0.0066958286 0.0033479143
[99,] 0.9949313 0.0101373985 0.0050686992
[100,] 0.9927077 0.0145845273 0.0072922637
[101,] 0.9926233 0.0147534587 0.0073767294
[102,] 0.9936931 0.0126138608 0.0063069304
[103,] 0.9954247 0.0091506663 0.0045753332
[104,] 0.9974107 0.0051785399 0.0025892700
[105,] 0.9982195 0.0035609368 0.0017804684
[106,] 0.9992524 0.0014951906 0.0007475953
[107,] 0.9995264 0.0009471703 0.0004735851
[108,] 0.9993825 0.0012349434 0.0006174717
[109,] 0.9994237 0.0011526952 0.0005763476
[110,] 0.9994948 0.0010104961 0.0005052480
[111,] 0.9996298 0.0007404395 0.0003702197
[112,] 0.9997353 0.0005294304 0.0002647152
[113,] 0.9996971 0.0006058160 0.0003029080
[114,] 0.9995368 0.0009263059 0.0004631530
[115,] 0.9988984 0.0022031260 0.0011015630
[116,] 0.9981813 0.0036373251 0.0018186626
[117,] 0.9940740 0.0118520681 0.0059260340
[118,] 0.9919508 0.0160984071 0.0080492036
[119,] 0.9790466 0.0419068903 0.0209534451
[120,] 0.9314691 0.1370618004 0.0685309002
> postscript(file="/var/wessaorg/rcomp/tmp/1mjfv1324669260.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/wessaorg/rcomp/tmp/2yrb51324669260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/35uky1324669260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4xolh1324669260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5i4ff1324669260.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 137
Frequency = 1
1 2 3 4 5 6
24709.00854 15348.06440 14591.13054 15220.72240 7012.53982 5086.66385
7 8 9 10 11 12
4698.32040 4408.68598 4656.24865 4730.59545 6396.96507 3112.65287
13 14 15 16 17 18
1256.25409 -437.41578 2115.25412 -1207.30876 -3718.14152 -2933.25745
19 20 21 22 23 24
-4382.72126 2115.49010 -2122.40261 -2456.99643 -3984.41253 -2106.15890
25 26 27 28 29 30
-2377.07326 1321.74305 -1120.11840 -554.67259 216.42112 -4425.14848
31 32 33 34 35 36
-257.65353 -743.87817 -1795.78673 -1201.58163 -1770.09883 -4577.06414
37 38 39 40 41 42
-5179.59018 -3328.71876 -7646.78128 -3971.16945 -4885.65304 -6231.31168
43 44 45 46 47 48
-5457.79376 -4918.48874 -5321.15889 -5012.46225 -3513.48311 -7880.98447
49 50 51 52 53 54
-7788.25527 -3428.95970 -6293.06752 -3874.01605 -6490.41154 -8278.24492
55 56 57 58 59 60
-5061.88038 -9066.51568 -5956.01581 -6473.59585 -3387.06748 -9048.14861
61 62 63 64 65 66
-4461.91374 -3809.20925 -4974.64548 -2461.90344 -6375.75663 -6567.79180
67 68 69 70 71 72
-4882.83332 -3457.74255 -3974.20397 -3446.10125 -708.59576 -3177.33861
73 74 75 76 77 78
-2181.46446 -1320.05392 1476.64771 2300.65674 -1280.39415 -38.33062
79 80 81 82 83 84
2933.12284 2247.50252 2512.37424 4124.08237 2787.42527 2896.77931
85 86 87 88 89 90
1172.36364 211.87209 771.18888 366.73523 2287.78374 2971.95741
91 92 93 94 95 96
4270.14309 2185.95629 2100.22850 5266.14965 5344.51692 2572.43108
97 98 99 100 101 102
1450.65983 3884.07094 3296.70212 3277.19146 2290.59146 5681.27391
103 104 105 106 107 108
7214.01781 6193.90437 4193.37548 3056.30864 6346.85189 3724.70067
109 110 111 112 113 114
-172.78526 2063.11664 3509.70339 549.25367 1602.55111 5101.08251
115 116 117 118 119 120
2083.80403 2301.96968 4594.69457 3488.12083 3286.47308 5295.31765
121 122 123 124 125 126
3918.55798 4979.40515 7394.53103 3761.15820 2424.60999 411.82280
127 128 129 130 131 132
302.35225 1392.83561 2380.96350 1802.46238 -4260.81476 -4084.17753
133 134 135 136 137
-6982.17688 -4693.17173 -9950.18133 -8508.80854 -8615.05228
> postscript(file="/var/wessaorg/rcomp/tmp/6egpb1324669260.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 = 137
Frequency = 1
lag(myerror, k = 1) myerror
0 24709.00854 NA
1 15348.06440 24709.00854
2 14591.13054 15348.06440
3 15220.72240 14591.13054
4 7012.53982 15220.72240
5 5086.66385 7012.53982
6 4698.32040 5086.66385
7 4408.68598 4698.32040
8 4656.24865 4408.68598
9 4730.59545 4656.24865
10 6396.96507 4730.59545
11 3112.65287 6396.96507
12 1256.25409 3112.65287
13 -437.41578 1256.25409
14 2115.25412 -437.41578
15 -1207.30876 2115.25412
16 -3718.14152 -1207.30876
17 -2933.25745 -3718.14152
18 -4382.72126 -2933.25745
19 2115.49010 -4382.72126
20 -2122.40261 2115.49010
21 -2456.99643 -2122.40261
22 -3984.41253 -2456.99643
23 -2106.15890 -3984.41253
24 -2377.07326 -2106.15890
25 1321.74305 -2377.07326
26 -1120.11840 1321.74305
27 -554.67259 -1120.11840
28 216.42112 -554.67259
29 -4425.14848 216.42112
30 -257.65353 -4425.14848
31 -743.87817 -257.65353
32 -1795.78673 -743.87817
33 -1201.58163 -1795.78673
34 -1770.09883 -1201.58163
35 -4577.06414 -1770.09883
36 -5179.59018 -4577.06414
37 -3328.71876 -5179.59018
38 -7646.78128 -3328.71876
39 -3971.16945 -7646.78128
40 -4885.65304 -3971.16945
41 -6231.31168 -4885.65304
42 -5457.79376 -6231.31168
43 -4918.48874 -5457.79376
44 -5321.15889 -4918.48874
45 -5012.46225 -5321.15889
46 -3513.48311 -5012.46225
47 -7880.98447 -3513.48311
48 -7788.25527 -7880.98447
49 -3428.95970 -7788.25527
50 -6293.06752 -3428.95970
51 -3874.01605 -6293.06752
52 -6490.41154 -3874.01605
53 -8278.24492 -6490.41154
54 -5061.88038 -8278.24492
55 -9066.51568 -5061.88038
56 -5956.01581 -9066.51568
57 -6473.59585 -5956.01581
58 -3387.06748 -6473.59585
59 -9048.14861 -3387.06748
60 -4461.91374 -9048.14861
61 -3809.20925 -4461.91374
62 -4974.64548 -3809.20925
63 -2461.90344 -4974.64548
64 -6375.75663 -2461.90344
65 -6567.79180 -6375.75663
66 -4882.83332 -6567.79180
67 -3457.74255 -4882.83332
68 -3974.20397 -3457.74255
69 -3446.10125 -3974.20397
70 -708.59576 -3446.10125
71 -3177.33861 -708.59576
72 -2181.46446 -3177.33861
73 -1320.05392 -2181.46446
74 1476.64771 -1320.05392
75 2300.65674 1476.64771
76 -1280.39415 2300.65674
77 -38.33062 -1280.39415
78 2933.12284 -38.33062
79 2247.50252 2933.12284
80 2512.37424 2247.50252
81 4124.08237 2512.37424
82 2787.42527 4124.08237
83 2896.77931 2787.42527
84 1172.36364 2896.77931
85 211.87209 1172.36364
86 771.18888 211.87209
87 366.73523 771.18888
88 2287.78374 366.73523
89 2971.95741 2287.78374
90 4270.14309 2971.95741
91 2185.95629 4270.14309
92 2100.22850 2185.95629
93 5266.14965 2100.22850
94 5344.51692 5266.14965
95 2572.43108 5344.51692
96 1450.65983 2572.43108
97 3884.07094 1450.65983
98 3296.70212 3884.07094
99 3277.19146 3296.70212
100 2290.59146 3277.19146
101 5681.27391 2290.59146
102 7214.01781 5681.27391
103 6193.90437 7214.01781
104 4193.37548 6193.90437
105 3056.30864 4193.37548
106 6346.85189 3056.30864
107 3724.70067 6346.85189
108 -172.78526 3724.70067
109 2063.11664 -172.78526
110 3509.70339 2063.11664
111 549.25367 3509.70339
112 1602.55111 549.25367
113 5101.08251 1602.55111
114 2083.80403 5101.08251
115 2301.96968 2083.80403
116 4594.69457 2301.96968
117 3488.12083 4594.69457
118 3286.47308 3488.12083
119 5295.31765 3286.47308
120 3918.55798 5295.31765
121 4979.40515 3918.55798
122 7394.53103 4979.40515
123 3761.15820 7394.53103
124 2424.60999 3761.15820
125 411.82280 2424.60999
126 302.35225 411.82280
127 1392.83561 302.35225
128 2380.96350 1392.83561
129 1802.46238 2380.96350
130 -4260.81476 1802.46238
131 -4084.17753 -4260.81476
132 -6982.17688 -4084.17753
133 -4693.17173 -6982.17688
134 -9950.18133 -4693.17173
135 -8508.80854 -9950.18133
136 -8615.05228 -8508.80854
137 NA -8615.05228
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 15348.06440 24709.00854
[2,] 14591.13054 15348.06440
[3,] 15220.72240 14591.13054
[4,] 7012.53982 15220.72240
[5,] 5086.66385 7012.53982
[6,] 4698.32040 5086.66385
[7,] 4408.68598 4698.32040
[8,] 4656.24865 4408.68598
[9,] 4730.59545 4656.24865
[10,] 6396.96507 4730.59545
[11,] 3112.65287 6396.96507
[12,] 1256.25409 3112.65287
[13,] -437.41578 1256.25409
[14,] 2115.25412 -437.41578
[15,] -1207.30876 2115.25412
[16,] -3718.14152 -1207.30876
[17,] -2933.25745 -3718.14152
[18,] -4382.72126 -2933.25745
[19,] 2115.49010 -4382.72126
[20,] -2122.40261 2115.49010
[21,] -2456.99643 -2122.40261
[22,] -3984.41253 -2456.99643
[23,] -2106.15890 -3984.41253
[24,] -2377.07326 -2106.15890
[25,] 1321.74305 -2377.07326
[26,] -1120.11840 1321.74305
[27,] -554.67259 -1120.11840
[28,] 216.42112 -554.67259
[29,] -4425.14848 216.42112
[30,] -257.65353 -4425.14848
[31,] -743.87817 -257.65353
[32,] -1795.78673 -743.87817
[33,] -1201.58163 -1795.78673
[34,] -1770.09883 -1201.58163
[35,] -4577.06414 -1770.09883
[36,] -5179.59018 -4577.06414
[37,] -3328.71876 -5179.59018
[38,] -7646.78128 -3328.71876
[39,] -3971.16945 -7646.78128
[40,] -4885.65304 -3971.16945
[41,] -6231.31168 -4885.65304
[42,] -5457.79376 -6231.31168
[43,] -4918.48874 -5457.79376
[44,] -5321.15889 -4918.48874
[45,] -5012.46225 -5321.15889
[46,] -3513.48311 -5012.46225
[47,] -7880.98447 -3513.48311
[48,] -7788.25527 -7880.98447
[49,] -3428.95970 -7788.25527
[50,] -6293.06752 -3428.95970
[51,] -3874.01605 -6293.06752
[52,] -6490.41154 -3874.01605
[53,] -8278.24492 -6490.41154
[54,] -5061.88038 -8278.24492
[55,] -9066.51568 -5061.88038
[56,] -5956.01581 -9066.51568
[57,] -6473.59585 -5956.01581
[58,] -3387.06748 -6473.59585
[59,] -9048.14861 -3387.06748
[60,] -4461.91374 -9048.14861
[61,] -3809.20925 -4461.91374
[62,] -4974.64548 -3809.20925
[63,] -2461.90344 -4974.64548
[64,] -6375.75663 -2461.90344
[65,] -6567.79180 -6375.75663
[66,] -4882.83332 -6567.79180
[67,] -3457.74255 -4882.83332
[68,] -3974.20397 -3457.74255
[69,] -3446.10125 -3974.20397
[70,] -708.59576 -3446.10125
[71,] -3177.33861 -708.59576
[72,] -2181.46446 -3177.33861
[73,] -1320.05392 -2181.46446
[74,] 1476.64771 -1320.05392
[75,] 2300.65674 1476.64771
[76,] -1280.39415 2300.65674
[77,] -38.33062 -1280.39415
[78,] 2933.12284 -38.33062
[79,] 2247.50252 2933.12284
[80,] 2512.37424 2247.50252
[81,] 4124.08237 2512.37424
[82,] 2787.42527 4124.08237
[83,] 2896.77931 2787.42527
[84,] 1172.36364 2896.77931
[85,] 211.87209 1172.36364
[86,] 771.18888 211.87209
[87,] 366.73523 771.18888
[88,] 2287.78374 366.73523
[89,] 2971.95741 2287.78374
[90,] 4270.14309 2971.95741
[91,] 2185.95629 4270.14309
[92,] 2100.22850 2185.95629
[93,] 5266.14965 2100.22850
[94,] 5344.51692 5266.14965
[95,] 2572.43108 5344.51692
[96,] 1450.65983 2572.43108
[97,] 3884.07094 1450.65983
[98,] 3296.70212 3884.07094
[99,] 3277.19146 3296.70212
[100,] 2290.59146 3277.19146
[101,] 5681.27391 2290.59146
[102,] 7214.01781 5681.27391
[103,] 6193.90437 7214.01781
[104,] 4193.37548 6193.90437
[105,] 3056.30864 4193.37548
[106,] 6346.85189 3056.30864
[107,] 3724.70067 6346.85189
[108,] -172.78526 3724.70067
[109,] 2063.11664 -172.78526
[110,] 3509.70339 2063.11664
[111,] 549.25367 3509.70339
[112,] 1602.55111 549.25367
[113,] 5101.08251 1602.55111
[114,] 2083.80403 5101.08251
[115,] 2301.96968 2083.80403
[116,] 4594.69457 2301.96968
[117,] 3488.12083 4594.69457
[118,] 3286.47308 3488.12083
[119,] 5295.31765 3286.47308
[120,] 3918.55798 5295.31765
[121,] 4979.40515 3918.55798
[122,] 7394.53103 4979.40515
[123,] 3761.15820 7394.53103
[124,] 2424.60999 3761.15820
[125,] 411.82280 2424.60999
[126,] 302.35225 411.82280
[127,] 1392.83561 302.35225
[128,] 2380.96350 1392.83561
[129,] 1802.46238 2380.96350
[130,] -4260.81476 1802.46238
[131,] -4084.17753 -4260.81476
[132,] -6982.17688 -4084.17753
[133,] -4693.17173 -6982.17688
[134,] -9950.18133 -4693.17173
[135,] -8508.80854 -9950.18133
[136,] -8615.05228 -8508.80854
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 15348.06440 24709.00854
2 14591.13054 15348.06440
3 15220.72240 14591.13054
4 7012.53982 15220.72240
5 5086.66385 7012.53982
6 4698.32040 5086.66385
7 4408.68598 4698.32040
8 4656.24865 4408.68598
9 4730.59545 4656.24865
10 6396.96507 4730.59545
11 3112.65287 6396.96507
12 1256.25409 3112.65287
13 -437.41578 1256.25409
14 2115.25412 -437.41578
15 -1207.30876 2115.25412
16 -3718.14152 -1207.30876
17 -2933.25745 -3718.14152
18 -4382.72126 -2933.25745
19 2115.49010 -4382.72126
20 -2122.40261 2115.49010
21 -2456.99643 -2122.40261
22 -3984.41253 -2456.99643
23 -2106.15890 -3984.41253
24 -2377.07326 -2106.15890
25 1321.74305 -2377.07326
26 -1120.11840 1321.74305
27 -554.67259 -1120.11840
28 216.42112 -554.67259
29 -4425.14848 216.42112
30 -257.65353 -4425.14848
31 -743.87817 -257.65353
32 -1795.78673 -743.87817
33 -1201.58163 -1795.78673
34 -1770.09883 -1201.58163
35 -4577.06414 -1770.09883
36 -5179.59018 -4577.06414
37 -3328.71876 -5179.59018
38 -7646.78128 -3328.71876
39 -3971.16945 -7646.78128
40 -4885.65304 -3971.16945
41 -6231.31168 -4885.65304
42 -5457.79376 -6231.31168
43 -4918.48874 -5457.79376
44 -5321.15889 -4918.48874
45 -5012.46225 -5321.15889
46 -3513.48311 -5012.46225
47 -7880.98447 -3513.48311
48 -7788.25527 -7880.98447
49 -3428.95970 -7788.25527
50 -6293.06752 -3428.95970
51 -3874.01605 -6293.06752
52 -6490.41154 -3874.01605
53 -8278.24492 -6490.41154
54 -5061.88038 -8278.24492
55 -9066.51568 -5061.88038
56 -5956.01581 -9066.51568
57 -6473.59585 -5956.01581
58 -3387.06748 -6473.59585
59 -9048.14861 -3387.06748
60 -4461.91374 -9048.14861
61 -3809.20925 -4461.91374
62 -4974.64548 -3809.20925
63 -2461.90344 -4974.64548
64 -6375.75663 -2461.90344
65 -6567.79180 -6375.75663
66 -4882.83332 -6567.79180
67 -3457.74255 -4882.83332
68 -3974.20397 -3457.74255
69 -3446.10125 -3974.20397
70 -708.59576 -3446.10125
71 -3177.33861 -708.59576
72 -2181.46446 -3177.33861
73 -1320.05392 -2181.46446
74 1476.64771 -1320.05392
75 2300.65674 1476.64771
76 -1280.39415 2300.65674
77 -38.33062 -1280.39415
78 2933.12284 -38.33062
79 2247.50252 2933.12284
80 2512.37424 2247.50252
81 4124.08237 2512.37424
82 2787.42527 4124.08237
83 2896.77931 2787.42527
84 1172.36364 2896.77931
85 211.87209 1172.36364
86 771.18888 211.87209
87 366.73523 771.18888
88 2287.78374 366.73523
89 2971.95741 2287.78374
90 4270.14309 2971.95741
91 2185.95629 4270.14309
92 2100.22850 2185.95629
93 5266.14965 2100.22850
94 5344.51692 5266.14965
95 2572.43108 5344.51692
96 1450.65983 2572.43108
97 3884.07094 1450.65983
98 3296.70212 3884.07094
99 3277.19146 3296.70212
100 2290.59146 3277.19146
101 5681.27391 2290.59146
102 7214.01781 5681.27391
103 6193.90437 7214.01781
104 4193.37548 6193.90437
105 3056.30864 4193.37548
106 6346.85189 3056.30864
107 3724.70067 6346.85189
108 -172.78526 3724.70067
109 2063.11664 -172.78526
110 3509.70339 2063.11664
111 549.25367 3509.70339
112 1602.55111 549.25367
113 5101.08251 1602.55111
114 2083.80403 5101.08251
115 2301.96968 2083.80403
116 4594.69457 2301.96968
117 3488.12083 4594.69457
118 3286.47308 3488.12083
119 5295.31765 3286.47308
120 3918.55798 5295.31765
121 4979.40515 3918.55798
122 7394.53103 4979.40515
123 3761.15820 7394.53103
124 2424.60999 3761.15820
125 411.82280 2424.60999
126 302.35225 411.82280
127 1392.83561 302.35225
128 2380.96350 1392.83561
129 1802.46238 2380.96350
130 -4260.81476 1802.46238
131 -4084.17753 -4260.81476
132 -6982.17688 -4084.17753
133 -4693.17173 -6982.17688
134 -9950.18133 -4693.17173
135 -8508.80854 -9950.18133
136 -8615.05228 -8508.80854
> 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/wessaorg/rcomp/tmp/7d9zc1324669260.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/wessaorg/rcomp/tmp/8h8031324669260.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/wessaorg/rcomp/tmp/9sgyq1324669260.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/wessaorg/rcomp/tmp/1079wc1324669260.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/114gb21324669260.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/125wkn1324669260.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/131z3g1324669260.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14gsru1324669260.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/wessaorg/rcomp/tmp/152q6j1324669260.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/wessaorg/rcomp/tmp/16z7ns1324669260.tab")
+ }
>
> try(system("convert tmp/1mjfv1324669260.ps tmp/1mjfv1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yrb51324669260.ps tmp/2yrb51324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/35uky1324669260.ps tmp/35uky1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/4xolh1324669260.ps tmp/4xolh1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i4ff1324669260.ps tmp/5i4ff1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/6egpb1324669260.ps tmp/6egpb1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/7d9zc1324669260.ps tmp/7d9zc1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/8h8031324669260.ps tmp/8h8031324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/9sgyq1324669260.ps tmp/9sgyq1324669260.png",intern=TRUE))
character(0)
> try(system("convert tmp/1079wc1324669260.ps tmp/1079wc1324669260.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.361 0.590 4.959