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.
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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(119830
+ ,64507
+ ,21673
+ ,206010
+ ,116068
+ ,61865
+ ,20179
+ ,198112
+ ,114976
+ ,60844
+ ,18699
+ ,194519
+ ,110296
+ ,57604
+ ,17805
+ ,185705
+ ,107832
+ ,55672
+ ,16669
+ ,180173
+ ,105624
+ ,53636
+ ,16882
+ ,176142
+ ,114858
+ ,63487
+ ,25056
+ ,203401
+ ,119598
+ ,71468
+ ,30836
+ ,221902
+ ,106675
+ ,63200
+ ,27503
+ ,197378
+ ,103315
+ ,58166
+ ,23520
+ ,185001
+ ,100826
+ ,54664
+ ,20866
+ ,176356
+ ,103574
+ ,55860
+ ,21015
+ ,180449
+ ,104708
+ ,56190
+ ,19246
+ ,180144
+ ,101817
+ ,54300
+ ,17549
+ ,173666
+ ,97898
+ ,51362
+ ,16428
+ ,165688
+ ,95559
+ ,49802
+ ,16209
+ ,161570
+ ,92822
+ ,48088
+ ,15235
+ ,156145
+ ,90848
+ ,46696
+ ,16186
+ ,153730
+ ,101141
+ ,56586
+ ,24971
+ ,182698
+ ,105841
+ ,64148
+ ,30776
+ ,200765
+ ,93647
+ ,56449
+ ,26416
+ ,176512
+ ,90923
+ ,52538
+ ,23157
+ ,166618
+ ,89130
+ ,49359
+ ,20155
+ ,158644
+ ,90212
+ ,49583
+ ,19790
+ ,159585
+ ,93196
+ ,51050
+ ,18849
+ ,163095
+ ,91861
+ ,49610
+ ,17573
+ ,159044
+ ,90593
+ ,48321
+ ,16597
+ ,155511
+ ,89895
+ ,47692
+ ,16158
+ ,153745
+ ,88819
+ ,46243
+ ,15507
+ ,150569
+ ,87924
+ ,46248
+ ,16433
+ ,150605
+ ,96906
+ ,56381
+ ,26325
+ ,179612
+ ,101217
+ ,62329
+ ,31144
+ ,194690
+ ,98709
+ ,60673
+ ,30535
+ ,189917
+ ,98139
+ ,58393
+ ,27596
+ ,184128
+ ,95529
+ ,55742
+ ,24064
+ ,175335
+ ,98577
+ ,57135
+ ,23854
+ ,179566
+ ,100772
+ ,57961
+ ,22407
+ ,181140
+ ,100180
+ ,56571
+ ,21125
+ ,177876
+ ,99200
+ ,55615
+ ,20226
+ ,175041
+ ,96251
+ ,53494
+ ,19547
+ ,169292
+ ,94514
+ ,52623
+ ,18933
+ ,166070
+ ,93780
+ ,52820
+ ,20372
+ ,166972
+ ,105192
+ ,66825
+ ,34331
+ ,206348
+ ,107682
+ ,70695
+ ,37329
+ ,215706
+ ,99687
+ ,65660
+ ,36761
+ ,202108
+ ,99436
+ ,63238
+ ,32737
+ ,195411
+ ,102049
+ ,61741
+ ,29321
+ ,193111
+ ,102673
+ ,63642
+ ,28883
+ ,195198
+ ,105813
+ ,65521
+ ,27436
+ ,198770
+ ,105056
+ ,64006
+ ,25101
+ ,194163
+ ,103916
+ ,62728
+ ,23776
+ ,190420
+ ,103513
+ ,62438
+ ,23782
+ ,189733
+ ,101893
+ ,61109
+ ,23027
+ ,186029
+ ,102503
+ ,63422
+ ,25606
+ ,191531
+ ,113149
+ ,78094
+ ,41328
+ ,232571
+ ,116696
+ ,82030
+ ,44751
+ ,243477
+ ,108500
+ ,75892
+ ,42855
+ ,227247
+ ,107800
+ ,72431
+ ,37628
+ ,217859
+ ,105941
+ ,69194
+ ,33544
+ ,208679
+ ,108742
+ ,71171
+ ,33275
+ ,213188
+ ,111680
+ ,72545
+ ,32009
+ ,216234
+ ,111270
+ ,71503
+ ,30813
+ ,213586
+ ,110698
+ ,69624
+ ,29143
+ ,209465
+ ,108517
+ ,67407
+ ,28121
+ ,204045
+ ,107127
+ ,66103
+ ,27007
+ ,200237
+ ,107088
+ ,67466
+ ,29112
+ ,203666
+ ,116321
+ ,81088
+ ,44067
+ ,241476
+ ,125045
+ ,86781
+ ,48481
+ ,260307
+ ,116779
+ ,79964
+ ,46581
+ ,243324
+ ,122887
+ ,80407
+ ,41166
+ ,244460
+ ,120162
+ ,76589
+ ,36824
+ ,233575
+ ,123198
+ ,78083
+ ,35936
+ ,237217
+ ,123610
+ ,78000
+ ,33633
+ ,235243
+ ,122293
+ ,76431
+ ,31630
+ ,230354
+ ,121289
+ ,75461
+ ,30434
+ ,227184
+ ,119393
+ ,73739
+ ,28546
+ ,221678
+ ,117494
+ ,71988
+ ,27660
+ ,217142
+ ,116693
+ ,72929
+ ,29830
+ ,219452
+ ,125062
+ ,85785
+ ,45599
+ ,256446
+ ,127281
+ ,89261
+ ,49303
+ ,265845
+ ,120195
+ ,84012
+ ,44417
+ ,248624
+ ,119804
+ ,80924
+ ,40386
+ ,241114
+ ,117113
+ ,76588
+ ,35544
+ ,229245
+ ,119240
+ ,77546
+ ,35019
+ ,231805
+ ,115823
+ ,73054
+ ,30400
+ ,219277
+ ,116281
+ ,73430
+ ,29602
+ ,219313
+ ,113816
+ ,71093
+ ,27701
+ ,212610
+ ,114632
+ ,72202
+ ,27937
+ ,214771
+ ,112987
+ ,70872
+ ,27283
+ ,211142
+ ,111633
+ ,70452
+ ,29372
+ ,211457
+ ,116721
+ ,80506
+ ,42821
+ ,240048
+ ,114850
+ ,80400
+ ,45386
+ ,240636
+ ,112797
+ ,77613
+ ,40170
+ ,230580
+ ,105368
+ ,69056
+ ,34371
+ ,208795
+ ,102524
+ ,65321
+ ,30077
+ ,197922
+ ,101327
+ ,64018
+ ,29251
+ ,194596
+ ,102612
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+ ,27202
+ ,194581
+ ,98873
+ ,61099
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+ ,185686
+ ,95993
+ ,58329
+ ,23784
+ ,178106
+ ,93244
+ ,56396
+ ,22968
+ ,172608
+ ,90403
+ ,54656
+ ,22243
+ ,167302
+ ,88539
+ ,55259
+ ,24255
+ ,168053
+ ,98106
+ ,66912
+ ,37282
+ ,202300
+ ,96963
+ ,66631
+ ,38794
+ ,202388
+ ,90781
+ ,59907
+ ,31828
+ ,182516
+ ,89253
+ ,56274
+ ,27949
+ ,173476
+ ,87794
+ ,54045
+ ,24605
+ ,166444
+ ,89810
+ ,55792
+ ,25695
+ ,171297
+ ,90864
+ ,55499
+ ,23338
+ ,169701
+ ,89025
+ ,53216
+ ,21941
+ ,164182
+ ,87621
+ ,52259
+ ,22034
+ ,161914
+ ,87718
+ ,51257
+ ,20637
+ ,159612
+ ,83433
+ ,48150
+ ,19418
+ ,151001
+ ,84535
+ ,51125
+ ,22454
+ ,158114
+ ,92223
+ ,61046
+ ,33261
+ ,186530
+ ,91052
+ ,61022
+ ,34995
+ ,187069
+ ,88456
+ ,56742
+ ,29132
+ ,174330
+ ,88706
+ ,54485
+ ,26171
+ ,169362
+ ,89137
+ ,53862
+ ,23828
+ ,166827
+ ,94066
+ ,58228
+ ,25743
+ ,178037
+ ,99258
+ ,61951
+ ,25204
+ ,186413
+ ,100673
+ ,62874
+ ,25679
+ ,189226
+ ,102269
+ ,64013
+ ,25281
+ ,191563
+ ,100833
+ ,62937
+ ,25136
+ ,188906
+ ,99314
+ ,61897
+ ,24794
+ ,186005
+ ,101764
+ ,65267
+ ,28278
+ ,195309
+ ,108242
+ ,75228
+ ,40062
+ ,223532
+ ,108148
+ ,76161
+ ,42590
+ ,226899
+ ,104761
+ ,71480
+ ,37885
+ ,214126
+ ,103772
+ ,69070
+ ,34061
+ ,206903
+ ,103737
+ ,68293
+ ,32412
+ ,204442
+ ,111043
+ ,74685
+ ,34647
+ ,220375
+ ,109906
+ ,72664
+ ,31750
+ ,214320
+ ,109335
+ ,71965
+ ,31288
+ ,212588
+ ,107247
+ ,69238
+ ,29331
+ ,205816
+ ,105690
+ ,67738
+ ,28768
+ ,202196
+ ,102755
+ ,65187
+ ,27780
+ ,195722
+ ,102280
+ ,66170
+ ,30113
+ ,198563
+ ,110590
+ ,77309
+ ,41240
+ ,229139
+ ,109122
+ ,77134
+ ,43271
+ ,229527
+ ,102803
+ ,70957
+ ,38108
+ ,211868
+ ,101424
+ ,67749
+ ,34382
+ ,203555
+ ,99138
+ ,65081
+ ,31551
+ ,195770
+ ,101284
+ ,66600
+ ,31950
+ ,199834
+ ,104260
+ ,68384
+ ,30445
+ ,203089
+ ,102526
+ ,66677
+ ,29277
+ ,198480
+ ,100001
+ ,64507
+ ,28176
+ ,192684
+ ,97562
+ ,62526
+ ,27739
+ ,187827
+ ,95539
+ ,60570
+ ,26305
+ ,182414
+ ,93831
+ ,60663
+ ,28016
+ ,182510
+ ,101031
+ ,72923
+ ,37570
+ ,211524
+ ,98744
+ ,72952
+ ,39755
+ ,211451
+ ,95847
+ ,68503
+ ,35790
+ ,200140
+ ,94278
+ ,65289
+ ,32001
+ ,191568)
+ ,dim=c(4
+ ,154)
+ ,dimnames=list(c('x1'
+ ,'x2'
+ ,'x3'
+ ,'totaal')
+ ,1:154))
> y <- array(NA,dim=c(4,154),dimnames=list(c('x1','x2','x3','totaal'),1:154))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '4'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> 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
totaal x1 x2 x3
1 206010 119830 64507 21673
2 198112 116068 61865 20179
3 194519 114976 60844 18699
4 185705 110296 57604 17805
5 180173 107832 55672 16669
6 176142 105624 53636 16882
7 203401 114858 63487 25056
8 221902 119598 71468 30836
9 197378 106675 63200 27503
10 185001 103315 58166 23520
11 176356 100826 54664 20866
12 180449 103574 55860 21015
13 180144 104708 56190 19246
14 173666 101817 54300 17549
15 165688 97898 51362 16428
16 161570 95559 49802 16209
17 156145 92822 48088 15235
18 153730 90848 46696 16186
19 182698 101141 56586 24971
20 200765 105841 64148 30776
21 176512 93647 56449 26416
22 166618 90923 52538 23157
23 158644 89130 49359 20155
24 159585 90212 49583 19790
25 163095 93196 51050 18849
26 159044 91861 49610 17573
27 155511 90593 48321 16597
28 153745 89895 47692 16158
29 150569 88819 46243 15507
30 150605 87924 46248 16433
31 179612 96906 56381 26325
32 194690 101217 62329 31144
33 189917 98709 60673 30535
34 184128 98139 58393 27596
35 175335 95529 55742 24064
36 179566 98577 57135 23854
37 181140 100772 57961 22407
38 177876 100180 56571 21125
39 175041 99200 55615 20226
40 169292 96251 53494 19547
41 166070 94514 52623 18933
42 166972 93780 52820 20372
43 206348 105192 66825 34331
44 215706 107682 70695 37329
45 202108 99687 65660 36761
46 195411 99436 63238 32737
47 193111 102049 61741 29321
48 195198 102673 63642 28883
49 198770 105813 65521 27436
50 194163 105056 64006 25101
51 190420 103916 62728 23776
52 189733 103513 62438 23782
53 186029 101893 61109 23027
54 191531 102503 63422 25606
55 232571 113149 78094 41328
56 243477 116696 82030 44751
57 227247 108500 75892 42855
58 217859 107800 72431 37628
59 208679 105941 69194 33544
60 213188 108742 71171 33275
61 216234 111680 72545 32009
62 213586 111270 71503 30813
63 209465 110698 69624 29143
64 204045 108517 67407 28121
65 200237 107127 66103 27007
66 203666 107088 67466 29112
67 241476 116321 81088 44067
68 260307 125045 86781 48481
69 243324 116779 79964 46581
70 244460 122887 80407 41166
71 233575 120162 76589 36824
72 237217 123198 78083 35936
73 235243 123610 78000 33633
74 230354 122293 76431 31630
75 227184 121289 75461 30434
76 221678 119393 73739 28546
77 217142 117494 71988 27660
78 219452 116693 72929 29830
79 256446 125062 85785 45599
80 265845 127281 89261 49303
81 248624 120195 84012 44417
82 241114 119804 80924 40386
83 229245 117113 76588 35544
84 231805 119240 77546 35019
85 219277 115823 73054 30400
86 219313 116281 73430 29602
87 212610 113816 71093 27701
88 214771 114632 72202 27937
89 211142 112987 70872 27283
90 211457 111633 70452 29372
91 240048 116721 80506 42821
92 240636 114850 80400 45386
93 230580 112797 77613 40170
94 208795 105368 69056 34371
95 197922 102524 65321 30077
96 194596 101327 64018 29251
97 194581 102612 64767 27202
98 185686 98873 61099 25714
99 178106 95993 58329 23784
100 172608 93244 56396 22968
101 167302 90403 54656 22243
102 168053 88539 55259 24255
103 202300 98106 66912 37282
104 202388 96963 66631 38794
105 182516 90781 59907 31828
106 173476 89253 56274 27949
107 166444 87794 54045 24605
108 171297 89810 55792 25695
109 169701 90864 55499 23338
110 164182 89025 53216 21941
111 161914 87621 52259 22034
112 159612 87718 51257 20637
113 151001 83433 48150 19418
114 158114 84535 51125 22454
115 186530 92223 61046 33261
116 187069 91052 61022 34995
117 174330 88456 56742 29132
118 169362 88706 54485 26171
119 166827 89137 53862 23828
120 178037 94066 58228 25743
121 186413 99258 61951 25204
122 189226 100673 62874 25679
123 191563 102269 64013 25281
124 188906 100833 62937 25136
125 186005 99314 61897 24794
126 195309 101764 65267 28278
127 223532 108242 75228 40062
128 226899 108148 76161 42590
129 214126 104761 71480 37885
130 206903 103772 69070 34061
131 204442 103737 68293 32412
132 220375 111043 74685 34647
133 214320 109906 72664 31750
134 212588 109335 71965 31288
135 205816 107247 69238 29331
136 202196 105690 67738 28768
137 195722 102755 65187 27780
138 198563 102280 66170 30113
139 229139 110590 77309 41240
140 229527 109122 77134 43271
141 211868 102803 70957 38108
142 203555 101424 67749 34382
143 195770 99138 65081 31551
144 199834 101284 66600 31950
145 203089 104260 68384 30445
146 198480 102526 66677 29277
147 192684 100001 64507 28176
148 187827 97562 62526 27739
149 182414 95539 60570 26305
150 182510 93831 60663 28016
151 211524 101031 72923 37570
152 211451 98744 72952 39755
153 200140 95847 68503 35790
154 191568 94278 65289 32001
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) x1 x2 x3
1.689e-11 1.000e+00 1.000e+00 1.000e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.115e-10 -1.033e-12 1.150e-13 2.181e-12 8.885e-12
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.689e-11 8.727e-12 1.936e+00 0.0548 .
x1 1.000e+00 2.303e-16 4.342e+15 <2e-16 ***
x2 1.000e+00 4.381e-16 2.283e+15 <2e-16 ***
x3 1.000e+00 3.469e-16 2.883e+15 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 9.582e-12 on 150 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.607e+32 on 3 and 150 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,] 3.430052e-02 6.860104e-02 9.656995e-01
[2,] 1.510820e-02 3.021639e-02 9.848918e-01
[3,] 6.723217e-03 1.344643e-02 9.932768e-01
[4,] 4.254712e-05 8.509424e-05 9.999575e-01
[5,] 1.466198e-02 2.932397e-02 9.853380e-01
[6,] 9.998130e-01 3.740877e-04 1.870438e-04
[7,] 2.258231e-03 4.516463e-03 9.977418e-01
[8,] 1.000000e+00 6.611006e-11 3.305503e-11
[9,] 5.641303e-05 1.128261e-04 9.999436e-01
[10,] 3.259769e-04 6.519538e-04 9.996740e-01
[11,] 1.479634e-01 2.959269e-01 8.520366e-01
[12,] 1.022756e-03 2.045513e-03 9.989772e-01
[13,] 2.671023e-01 5.342045e-01 7.328977e-01
[14,] 2.092450e-12 4.184899e-12 1.000000e+00
[15,] 1.812708e-06 3.625415e-06 9.999982e-01
[16,] 6.963263e-02 1.392653e-01 9.303674e-01
[17,] 9.448697e-06 1.889739e-05 9.999906e-01
[18,] 9.998447e-01 3.105402e-04 1.552701e-04
[19,] 5.823922e-01 8.352156e-01 4.176078e-01
[20,] 1.376685e-08 2.753371e-08 1.000000e+00
[21,] 1.868035e-04 3.736071e-04 9.998132e-01
[22,] 9.967827e-01 6.434593e-03 3.217296e-03
[23,] 3.602623e-05 7.205245e-05 9.999640e-01
[24,] 9.710615e-01 5.787695e-02 2.893848e-02
[25,] 9.999999e-01 2.943775e-07 1.471887e-07
[26,] 1.036969e-09 2.073938e-09 1.000000e+00
[27,] 8.419683e-13 1.683937e-12 1.000000e+00
[28,] 1.082331e-11 2.164662e-11 1.000000e+00
[29,] 9.999998e-01 3.241197e-07 1.620599e-07
[30,] 1.000000e+00 1.013015e-11 5.065073e-12
[31,] 6.950048e-01 6.099905e-01 3.049952e-01
[32,] 3.664875e-03 7.329749e-03 9.963351e-01
[33,] 3.541754e-14 7.083507e-14 1.000000e+00
[34,] 7.977353e-46 1.595471e-45 1.000000e+00
[35,] 2.688924e-14 5.377847e-14 1.000000e+00
[36,] 1.376140e-07 2.752279e-07 9.999999e-01
[37,] 7.415402e-01 5.169197e-01 2.584598e-01
[38,] 2.646144e-04 5.292288e-04 9.997354e-01
[39,] 8.814317e-01 2.371367e-01 1.185683e-01
[40,] 3.921892e-09 7.843783e-09 1.000000e+00
[41,] 9.126154e-23 1.825231e-22 1.000000e+00
[42,] 6.290972e-14 1.258194e-13 1.000000e+00
[43,] 2.717968e-34 5.435936e-34 1.000000e+00
[44,] 9.916936e-01 1.661275e-02 8.306377e-03
[45,] 1.000000e+00 7.146666e-15 3.573333e-15
[46,] 9.753210e-03 1.950642e-02 9.902468e-01
[47,] 9.385556e-01 1.228887e-01 6.144437e-02
[48,] 1.000000e+00 1.154610e-32 5.773051e-33
[49,] 9.998514e-01 2.971864e-04 1.485932e-04
[50,] 9.999999e-01 2.118223e-07 1.059112e-07
[51,] 1.302059e-12 2.604118e-12 1.000000e+00
[52,] 4.033881e-20 8.067761e-20 1.000000e+00
[53,] 1.302277e-28 2.604554e-28 1.000000e+00
[54,] 2.478165e-03 4.956330e-03 9.975218e-01
[55,] 9.762813e-01 4.743744e-02 2.371872e-02
[56,] 8.328813e-06 1.665763e-05 9.999917e-01
[57,] 8.047204e-33 1.609441e-32 1.000000e+00
[58,] 9.848242e-07 1.969648e-06 9.999990e-01
[59,] 1.000000e+00 7.809262e-09 3.904631e-09
[60,] 5.532973e-09 1.106595e-08 1.000000e+00
[61,] 1.000000e+00 9.518787e-12 4.759394e-12
[62,] 1.000000e+00 9.149131e-50 4.574565e-50
[63,] 2.637652e-08 5.275304e-08 1.000000e+00
[64,] 2.077360e-35 4.154720e-35 1.000000e+00
[65,] 1.196916e-05 2.393833e-05 9.999880e-01
[66,] 9.640125e-01 7.197495e-02 3.598747e-02
[67,] 5.436511e-01 9.126977e-01 4.563489e-01
[68,] 3.550965e-27 7.101931e-27 1.000000e+00
[69,] 9.999998e-01 3.283518e-07 1.641759e-07
[70,] 1.000000e+00 2.170933e-32 1.085466e-32
[71,] 3.029968e-03 6.059935e-03 9.969700e-01
[72,] 2.987557e-22 5.975115e-22 1.000000e+00
[73,] 9.998872e-01 2.256098e-04 1.128049e-04
[74,] 9.995167e-01 9.666606e-04 4.833303e-04
[75,] 9.999977e-01 4.675499e-06 2.337750e-06
[76,] 1.000000e+00 1.296906e-46 6.484532e-47
[77,] 9.999991e-01 1.858958e-06 9.294789e-07
[78,] 9.810569e-02 1.962114e-01 9.018943e-01
[79,] 1.000000e+00 6.505186e-25 3.252593e-25
[80,] 9.999629e-01 7.412641e-05 3.706320e-05
[81,] 1.056366e-26 2.112733e-26 1.000000e+00
[82,] 5.636073e-01 8.727853e-01 4.363927e-01
[83,] 3.939348e-30 7.878696e-30 1.000000e+00
[84,] 1.157451e-24 2.314901e-24 1.000000e+00
[85,] 9.013182e-22 1.802636e-21 1.000000e+00
[86,] 2.757461e-04 5.514921e-04 9.997243e-01
[87,] 7.816541e-44 1.563308e-43 1.000000e+00
[88,] 9.992578e-01 1.484431e-03 7.422153e-04
[89,] 9.986729e-01 2.654146e-03 1.327073e-03
[90,] 1.000000e+00 9.027432e-15 4.513716e-15
[91,] 1.421425e-05 2.842851e-05 9.999858e-01
[92,] 1.000000e+00 4.213407e-18 2.106703e-18
[93,] 1.000000e+00 1.039899e-28 5.199494e-29
[94,] 3.375904e-09 6.751808e-09 1.000000e+00
[95,] 1.000000e+00 4.888279e-10 2.444139e-10
[96,] 2.111430e-04 4.222860e-04 9.997889e-01
[97,] 6.875042e-07 1.375008e-06 9.999993e-01
[98,] 2.692821e-68 5.385642e-68 1.000000e+00
[99,] 1.000000e+00 1.021640e-21 5.108200e-22
[100,] 1.000000e+00 1.039801e-11 5.199007e-12
[101,] 9.752523e-01 4.949531e-02 2.474765e-02
[102,] 6.252774e-57 1.250555e-56 1.000000e+00
[103,] 3.237026e-15 6.474051e-15 1.000000e+00
[104,] 1.000000e+00 4.188874e-08 2.094437e-08
[105,] 1.000000e+00 9.599067e-09 4.799534e-09
[106,] 3.417810e-13 6.835619e-13 1.000000e+00
[107,] 3.638660e-42 7.277321e-42 1.000000e+00
[108,] 2.228457e-12 4.456914e-12 1.000000e+00
[109,] 9.999999e-01 2.356573e-07 1.178286e-07
[110,] 4.469500e-01 8.939000e-01 5.530500e-01
[111,] 9.890917e-01 2.181666e-02 1.090833e-02
[112,] 8.391433e-04 1.678287e-03 9.991609e-01
[113,] 9.984439e-01 3.112263e-03 1.556131e-03
[114,] 6.656082e-12 1.331216e-11 1.000000e+00
[115,] 9.853236e-01 2.935289e-02 1.467644e-02
[116,] 1.872277e-01 3.744554e-01 8.127723e-01
[117,] 9.999997e-01 5.672822e-07 2.836411e-07
[118,] 2.887112e-15 5.774224e-15 1.000000e+00
[119,] 3.364493e-19 6.728987e-19 1.000000e+00
[120,] 1.974741e-01 3.949482e-01 8.025259e-01
[121,] 1.000000e+00 2.505349e-13 1.252675e-13
[122,] 5.052023e-01 9.895953e-01 4.947977e-01
[123,] 1.000000e+00 1.844845e-24 9.224227e-25
[124,] 4.498009e-20 8.996017e-20 1.000000e+00
[125,] 9.999992e-01 1.612185e-06 8.060923e-07
[126,] 1.000000e+00 4.172802e-08 2.086401e-08
[127,] 1.083526e-01 2.167051e-01 8.916474e-01
[128,] 4.918149e-02 9.836297e-02 9.508185e-01
[129,] 9.999784e-01 4.322208e-05 2.161104e-05
[130,] 1.306575e-14 2.613151e-14 1.000000e+00
[131,] 5.071849e-44 1.014370e-43 1.000000e+00
[132,] 6.857755e-01 6.284489e-01 3.142245e-01
[133,] 9.458444e-01 1.083111e-01 5.415556e-02
[134,] 4.256088e-05 8.512177e-05 9.999574e-01
[135,] 9.886940e-01 2.261199e-02 1.130599e-02
[136,] 1.976837e-09 3.953674e-09 1.000000e+00
[137,] 1.617406e-32 3.234812e-32 1.000000e+00
[138,] 3.408851e-01 6.817702e-01 6.591149e-01
[139,] 9.993781e-01 1.243895e-03 6.219475e-04
[140,] 9.998231e-01 3.538432e-04 1.769216e-04
[141,] 8.827367e-01 2.345266e-01 1.172633e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1uesy1321807544.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ca401321807544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3mtl41321807544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4qd911321807544.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5eov51321807544.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 = 154
Frequency = 1
1 2 3 4 5
-1.114563e-10 8.578828e-12 7.148461e-12 7.133197e-13 6.523008e-12
6 7 8 9 10
7.494735e-12 6.266874e-12 8.884864e-12 2.967950e-12 3.605763e-12
11 12 13 14 15
6.945342e-12 6.190240e-12 6.226970e-12 6.052846e-12 3.776151e-12
16 17 18 19 20
2.619264e-12 1.808018e-13 -1.202467e-13 2.343650e-12 2.329332e-12
21 22 23 24 25
2.049147e-12 1.510903e-12 -2.469125e-12 -7.843616e-13 1.867909e-14
26 27 28 29 30
-5.582273e-13 -2.742854e-13 -2.715165e-13 -2.920327e-13 -1.825872e-14
31 32 33 34 35
3.875784e-12 1.798389e-12 5.377623e-13 2.481486e-13 3.937285e-12
36 37 38 39 40
2.481810e-12 3.054932e-12 2.410425e-12 3.975902e-12 2.648459e-12
41 42 43 44 45
2.203847e-12 4.183074e-12 2.045821e-12 1.967316e-12 -2.093866e-12
46 47 48 49 50
-2.546414e-13 1.680581e-12 1.573384e-13 5.486040e-13 3.197545e-15
51 52 53 54 55
3.272580e-13 1.272370e-12 1.312184e-12 -2.917071e-13 -3.896203e-13
56 57 58 59 60
-1.559063e-13 -7.655288e-13 -1.882954e-13 8.752608e-13 5.232823e-13
61 62 63 64 65
1.567607e-12 3.713847e-13 7.382354e-13 9.922841e-13 9.784806e-13
66 67 68 69 70
3.993509e-13 -3.329261e-13 4.305942e-12 -8.056304e-13 9.005498e-13
71 72 73 74 75
2.341314e-12 4.816493e-12 3.537183e-12 5.266877e-12 7.040296e-12
76 77 78 79 80
4.483447e-12 5.457145e-12 4.186983e-12 -3.639968e-15 7.699384e-13
81 82 83 84 85
-8.209154e-13 -1.415559e-12 4.469327e-12 1.916649e-12 4.697145e-12
86 87 88 89 90
4.405809e-12 1.398174e-12 4.193191e-12 7.186671e-14 4.976723e-14
91 92 93 94 95
-1.088612e-12 1.624096e-12 -3.937772e-12 9.648071e-13 -5.817341e-13
96 97 98 99 100
-5.693372e-13 -6.514325e-13 -1.994303e-13 1.729054e-12 1.783412e-12
101 102 103 104 105
5.487048e-13 1.940554e-14 -1.738315e-12 -4.361407e-12 -1.021642e-12
106 107 108 109 110
3.191361e-14 -1.262377e-12 1.410086e-12 -3.856353e-13 -4.104460e-12
111 112 113 114 115
-3.279596e-12 -3.416984e-12 -3.976797e-12 -5.665331e-12 -1.412070e-12
116 117 118 119 120
-1.133726e-12 -2.353165e-14 4.039752e-14 2.501982e-13 1.120220e-12
121 122 123 124 125
-1.772810e-12 -1.339212e-12 -1.036857e-12 -1.102125e-12 -6.288718e-13
126 127 128 129 130
-1.301884e-12 1.239201e-12 -1.578248e-12 2.111578e-12 -5.462170e-13
131 132 133 134 135
-2.769776e-13 7.566191e-15 1.799939e-13 -9.992997e-13 -1.462507e-12
136 137 138 139 140
-5.880915e-13 -1.217251e-12 -1.312101e-12 2.661359e-12 -1.984175e-12
141 142 143 144 145
-2.184381e-12 -6.178345e-13 -2.894103e-12 -2.186026e-12 -1.440561e-12
146 147 148 149 150
-1.541195e-12 -2.607644e-12 -1.882488e-12 -1.240302e-12 -1.330273e-12
151 152 153 154
-4.169426e-12 -9.154652e-12 -4.787662e-12 -3.817614e-12
> postscript(file="/var/wessaorg/rcomp/tmp/6ph4c1321807544.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 = 154
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.114563e-10 NA
1 8.578828e-12 -1.114563e-10
2 7.148461e-12 8.578828e-12
3 7.133197e-13 7.148461e-12
4 6.523008e-12 7.133197e-13
5 7.494735e-12 6.523008e-12
6 6.266874e-12 7.494735e-12
7 8.884864e-12 6.266874e-12
8 2.967950e-12 8.884864e-12
9 3.605763e-12 2.967950e-12
10 6.945342e-12 3.605763e-12
11 6.190240e-12 6.945342e-12
12 6.226970e-12 6.190240e-12
13 6.052846e-12 6.226970e-12
14 3.776151e-12 6.052846e-12
15 2.619264e-12 3.776151e-12
16 1.808018e-13 2.619264e-12
17 -1.202467e-13 1.808018e-13
18 2.343650e-12 -1.202467e-13
19 2.329332e-12 2.343650e-12
20 2.049147e-12 2.329332e-12
21 1.510903e-12 2.049147e-12
22 -2.469125e-12 1.510903e-12
23 -7.843616e-13 -2.469125e-12
24 1.867909e-14 -7.843616e-13
25 -5.582273e-13 1.867909e-14
26 -2.742854e-13 -5.582273e-13
27 -2.715165e-13 -2.742854e-13
28 -2.920327e-13 -2.715165e-13
29 -1.825872e-14 -2.920327e-13
30 3.875784e-12 -1.825872e-14
31 1.798389e-12 3.875784e-12
32 5.377623e-13 1.798389e-12
33 2.481486e-13 5.377623e-13
34 3.937285e-12 2.481486e-13
35 2.481810e-12 3.937285e-12
36 3.054932e-12 2.481810e-12
37 2.410425e-12 3.054932e-12
38 3.975902e-12 2.410425e-12
39 2.648459e-12 3.975902e-12
40 2.203847e-12 2.648459e-12
41 4.183074e-12 2.203847e-12
42 2.045821e-12 4.183074e-12
43 1.967316e-12 2.045821e-12
44 -2.093866e-12 1.967316e-12
45 -2.546414e-13 -2.093866e-12
46 1.680581e-12 -2.546414e-13
47 1.573384e-13 1.680581e-12
48 5.486040e-13 1.573384e-13
49 3.197545e-15 5.486040e-13
50 3.272580e-13 3.197545e-15
51 1.272370e-12 3.272580e-13
52 1.312184e-12 1.272370e-12
53 -2.917071e-13 1.312184e-12
54 -3.896203e-13 -2.917071e-13
55 -1.559063e-13 -3.896203e-13
56 -7.655288e-13 -1.559063e-13
57 -1.882954e-13 -7.655288e-13
58 8.752608e-13 -1.882954e-13
59 5.232823e-13 8.752608e-13
60 1.567607e-12 5.232823e-13
61 3.713847e-13 1.567607e-12
62 7.382354e-13 3.713847e-13
63 9.922841e-13 7.382354e-13
64 9.784806e-13 9.922841e-13
65 3.993509e-13 9.784806e-13
66 -3.329261e-13 3.993509e-13
67 4.305942e-12 -3.329261e-13
68 -8.056304e-13 4.305942e-12
69 9.005498e-13 -8.056304e-13
70 2.341314e-12 9.005498e-13
71 4.816493e-12 2.341314e-12
72 3.537183e-12 4.816493e-12
73 5.266877e-12 3.537183e-12
74 7.040296e-12 5.266877e-12
75 4.483447e-12 7.040296e-12
76 5.457145e-12 4.483447e-12
77 4.186983e-12 5.457145e-12
78 -3.639968e-15 4.186983e-12
79 7.699384e-13 -3.639968e-15
80 -8.209154e-13 7.699384e-13
81 -1.415559e-12 -8.209154e-13
82 4.469327e-12 -1.415559e-12
83 1.916649e-12 4.469327e-12
84 4.697145e-12 1.916649e-12
85 4.405809e-12 4.697145e-12
86 1.398174e-12 4.405809e-12
87 4.193191e-12 1.398174e-12
88 7.186671e-14 4.193191e-12
89 4.976723e-14 7.186671e-14
90 -1.088612e-12 4.976723e-14
91 1.624096e-12 -1.088612e-12
92 -3.937772e-12 1.624096e-12
93 9.648071e-13 -3.937772e-12
94 -5.817341e-13 9.648071e-13
95 -5.693372e-13 -5.817341e-13
96 -6.514325e-13 -5.693372e-13
97 -1.994303e-13 -6.514325e-13
98 1.729054e-12 -1.994303e-13
99 1.783412e-12 1.729054e-12
100 5.487048e-13 1.783412e-12
101 1.940554e-14 5.487048e-13
102 -1.738315e-12 1.940554e-14
103 -4.361407e-12 -1.738315e-12
104 -1.021642e-12 -4.361407e-12
105 3.191361e-14 -1.021642e-12
106 -1.262377e-12 3.191361e-14
107 1.410086e-12 -1.262377e-12
108 -3.856353e-13 1.410086e-12
109 -4.104460e-12 -3.856353e-13
110 -3.279596e-12 -4.104460e-12
111 -3.416984e-12 -3.279596e-12
112 -3.976797e-12 -3.416984e-12
113 -5.665331e-12 -3.976797e-12
114 -1.412070e-12 -5.665331e-12
115 -1.133726e-12 -1.412070e-12
116 -2.353165e-14 -1.133726e-12
117 4.039752e-14 -2.353165e-14
118 2.501982e-13 4.039752e-14
119 1.120220e-12 2.501982e-13
120 -1.772810e-12 1.120220e-12
121 -1.339212e-12 -1.772810e-12
122 -1.036857e-12 -1.339212e-12
123 -1.102125e-12 -1.036857e-12
124 -6.288718e-13 -1.102125e-12
125 -1.301884e-12 -6.288718e-13
126 1.239201e-12 -1.301884e-12
127 -1.578248e-12 1.239201e-12
128 2.111578e-12 -1.578248e-12
129 -5.462170e-13 2.111578e-12
130 -2.769776e-13 -5.462170e-13
131 7.566191e-15 -2.769776e-13
132 1.799939e-13 7.566191e-15
133 -9.992997e-13 1.799939e-13
134 -1.462507e-12 -9.992997e-13
135 -5.880915e-13 -1.462507e-12
136 -1.217251e-12 -5.880915e-13
137 -1.312101e-12 -1.217251e-12
138 2.661359e-12 -1.312101e-12
139 -1.984175e-12 2.661359e-12
140 -2.184381e-12 -1.984175e-12
141 -6.178345e-13 -2.184381e-12
142 -2.894103e-12 -6.178345e-13
143 -2.186026e-12 -2.894103e-12
144 -1.440561e-12 -2.186026e-12
145 -1.541195e-12 -1.440561e-12
146 -2.607644e-12 -1.541195e-12
147 -1.882488e-12 -2.607644e-12
148 -1.240302e-12 -1.882488e-12
149 -1.330273e-12 -1.240302e-12
150 -4.169426e-12 -1.330273e-12
151 -9.154652e-12 -4.169426e-12
152 -4.787662e-12 -9.154652e-12
153 -3.817614e-12 -4.787662e-12
154 NA -3.817614e-12
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 8.578828e-12 -1.114563e-10
[2,] 7.148461e-12 8.578828e-12
[3,] 7.133197e-13 7.148461e-12
[4,] 6.523008e-12 7.133197e-13
[5,] 7.494735e-12 6.523008e-12
[6,] 6.266874e-12 7.494735e-12
[7,] 8.884864e-12 6.266874e-12
[8,] 2.967950e-12 8.884864e-12
[9,] 3.605763e-12 2.967950e-12
[10,] 6.945342e-12 3.605763e-12
[11,] 6.190240e-12 6.945342e-12
[12,] 6.226970e-12 6.190240e-12
[13,] 6.052846e-12 6.226970e-12
[14,] 3.776151e-12 6.052846e-12
[15,] 2.619264e-12 3.776151e-12
[16,] 1.808018e-13 2.619264e-12
[17,] -1.202467e-13 1.808018e-13
[18,] 2.343650e-12 -1.202467e-13
[19,] 2.329332e-12 2.343650e-12
[20,] 2.049147e-12 2.329332e-12
[21,] 1.510903e-12 2.049147e-12
[22,] -2.469125e-12 1.510903e-12
[23,] -7.843616e-13 -2.469125e-12
[24,] 1.867909e-14 -7.843616e-13
[25,] -5.582273e-13 1.867909e-14
[26,] -2.742854e-13 -5.582273e-13
[27,] -2.715165e-13 -2.742854e-13
[28,] -2.920327e-13 -2.715165e-13
[29,] -1.825872e-14 -2.920327e-13
[30,] 3.875784e-12 -1.825872e-14
[31,] 1.798389e-12 3.875784e-12
[32,] 5.377623e-13 1.798389e-12
[33,] 2.481486e-13 5.377623e-13
[34,] 3.937285e-12 2.481486e-13
[35,] 2.481810e-12 3.937285e-12
[36,] 3.054932e-12 2.481810e-12
[37,] 2.410425e-12 3.054932e-12
[38,] 3.975902e-12 2.410425e-12
[39,] 2.648459e-12 3.975902e-12
[40,] 2.203847e-12 2.648459e-12
[41,] 4.183074e-12 2.203847e-12
[42,] 2.045821e-12 4.183074e-12
[43,] 1.967316e-12 2.045821e-12
[44,] -2.093866e-12 1.967316e-12
[45,] -2.546414e-13 -2.093866e-12
[46,] 1.680581e-12 -2.546414e-13
[47,] 1.573384e-13 1.680581e-12
[48,] 5.486040e-13 1.573384e-13
[49,] 3.197545e-15 5.486040e-13
[50,] 3.272580e-13 3.197545e-15
[51,] 1.272370e-12 3.272580e-13
[52,] 1.312184e-12 1.272370e-12
[53,] -2.917071e-13 1.312184e-12
[54,] -3.896203e-13 -2.917071e-13
[55,] -1.559063e-13 -3.896203e-13
[56,] -7.655288e-13 -1.559063e-13
[57,] -1.882954e-13 -7.655288e-13
[58,] 8.752608e-13 -1.882954e-13
[59,] 5.232823e-13 8.752608e-13
[60,] 1.567607e-12 5.232823e-13
[61,] 3.713847e-13 1.567607e-12
[62,] 7.382354e-13 3.713847e-13
[63,] 9.922841e-13 7.382354e-13
[64,] 9.784806e-13 9.922841e-13
[65,] 3.993509e-13 9.784806e-13
[66,] -3.329261e-13 3.993509e-13
[67,] 4.305942e-12 -3.329261e-13
[68,] -8.056304e-13 4.305942e-12
[69,] 9.005498e-13 -8.056304e-13
[70,] 2.341314e-12 9.005498e-13
[71,] 4.816493e-12 2.341314e-12
[72,] 3.537183e-12 4.816493e-12
[73,] 5.266877e-12 3.537183e-12
[74,] 7.040296e-12 5.266877e-12
[75,] 4.483447e-12 7.040296e-12
[76,] 5.457145e-12 4.483447e-12
[77,] 4.186983e-12 5.457145e-12
[78,] -3.639968e-15 4.186983e-12
[79,] 7.699384e-13 -3.639968e-15
[80,] -8.209154e-13 7.699384e-13
[81,] -1.415559e-12 -8.209154e-13
[82,] 4.469327e-12 -1.415559e-12
[83,] 1.916649e-12 4.469327e-12
[84,] 4.697145e-12 1.916649e-12
[85,] 4.405809e-12 4.697145e-12
[86,] 1.398174e-12 4.405809e-12
[87,] 4.193191e-12 1.398174e-12
[88,] 7.186671e-14 4.193191e-12
[89,] 4.976723e-14 7.186671e-14
[90,] -1.088612e-12 4.976723e-14
[91,] 1.624096e-12 -1.088612e-12
[92,] -3.937772e-12 1.624096e-12
[93,] 9.648071e-13 -3.937772e-12
[94,] -5.817341e-13 9.648071e-13
[95,] -5.693372e-13 -5.817341e-13
[96,] -6.514325e-13 -5.693372e-13
[97,] -1.994303e-13 -6.514325e-13
[98,] 1.729054e-12 -1.994303e-13
[99,] 1.783412e-12 1.729054e-12
[100,] 5.487048e-13 1.783412e-12
[101,] 1.940554e-14 5.487048e-13
[102,] -1.738315e-12 1.940554e-14
[103,] -4.361407e-12 -1.738315e-12
[104,] -1.021642e-12 -4.361407e-12
[105,] 3.191361e-14 -1.021642e-12
[106,] -1.262377e-12 3.191361e-14
[107,] 1.410086e-12 -1.262377e-12
[108,] -3.856353e-13 1.410086e-12
[109,] -4.104460e-12 -3.856353e-13
[110,] -3.279596e-12 -4.104460e-12
[111,] -3.416984e-12 -3.279596e-12
[112,] -3.976797e-12 -3.416984e-12
[113,] -5.665331e-12 -3.976797e-12
[114,] -1.412070e-12 -5.665331e-12
[115,] -1.133726e-12 -1.412070e-12
[116,] -2.353165e-14 -1.133726e-12
[117,] 4.039752e-14 -2.353165e-14
[118,] 2.501982e-13 4.039752e-14
[119,] 1.120220e-12 2.501982e-13
[120,] -1.772810e-12 1.120220e-12
[121,] -1.339212e-12 -1.772810e-12
[122,] -1.036857e-12 -1.339212e-12
[123,] -1.102125e-12 -1.036857e-12
[124,] -6.288718e-13 -1.102125e-12
[125,] -1.301884e-12 -6.288718e-13
[126,] 1.239201e-12 -1.301884e-12
[127,] -1.578248e-12 1.239201e-12
[128,] 2.111578e-12 -1.578248e-12
[129,] -5.462170e-13 2.111578e-12
[130,] -2.769776e-13 -5.462170e-13
[131,] 7.566191e-15 -2.769776e-13
[132,] 1.799939e-13 7.566191e-15
[133,] -9.992997e-13 1.799939e-13
[134,] -1.462507e-12 -9.992997e-13
[135,] -5.880915e-13 -1.462507e-12
[136,] -1.217251e-12 -5.880915e-13
[137,] -1.312101e-12 -1.217251e-12
[138,] 2.661359e-12 -1.312101e-12
[139,] -1.984175e-12 2.661359e-12
[140,] -2.184381e-12 -1.984175e-12
[141,] -6.178345e-13 -2.184381e-12
[142,] -2.894103e-12 -6.178345e-13
[143,] -2.186026e-12 -2.894103e-12
[144,] -1.440561e-12 -2.186026e-12
[145,] -1.541195e-12 -1.440561e-12
[146,] -2.607644e-12 -1.541195e-12
[147,] -1.882488e-12 -2.607644e-12
[148,] -1.240302e-12 -1.882488e-12
[149,] -1.330273e-12 -1.240302e-12
[150,] -4.169426e-12 -1.330273e-12
[151,] -9.154652e-12 -4.169426e-12
[152,] -4.787662e-12 -9.154652e-12
[153,] -3.817614e-12 -4.787662e-12
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 8.578828e-12 -1.114563e-10
2 7.148461e-12 8.578828e-12
3 7.133197e-13 7.148461e-12
4 6.523008e-12 7.133197e-13
5 7.494735e-12 6.523008e-12
6 6.266874e-12 7.494735e-12
7 8.884864e-12 6.266874e-12
8 2.967950e-12 8.884864e-12
9 3.605763e-12 2.967950e-12
10 6.945342e-12 3.605763e-12
11 6.190240e-12 6.945342e-12
12 6.226970e-12 6.190240e-12
13 6.052846e-12 6.226970e-12
14 3.776151e-12 6.052846e-12
15 2.619264e-12 3.776151e-12
16 1.808018e-13 2.619264e-12
17 -1.202467e-13 1.808018e-13
18 2.343650e-12 -1.202467e-13
19 2.329332e-12 2.343650e-12
20 2.049147e-12 2.329332e-12
21 1.510903e-12 2.049147e-12
22 -2.469125e-12 1.510903e-12
23 -7.843616e-13 -2.469125e-12
24 1.867909e-14 -7.843616e-13
25 -5.582273e-13 1.867909e-14
26 -2.742854e-13 -5.582273e-13
27 -2.715165e-13 -2.742854e-13
28 -2.920327e-13 -2.715165e-13
29 -1.825872e-14 -2.920327e-13
30 3.875784e-12 -1.825872e-14
31 1.798389e-12 3.875784e-12
32 5.377623e-13 1.798389e-12
33 2.481486e-13 5.377623e-13
34 3.937285e-12 2.481486e-13
35 2.481810e-12 3.937285e-12
36 3.054932e-12 2.481810e-12
37 2.410425e-12 3.054932e-12
38 3.975902e-12 2.410425e-12
39 2.648459e-12 3.975902e-12
40 2.203847e-12 2.648459e-12
41 4.183074e-12 2.203847e-12
42 2.045821e-12 4.183074e-12
43 1.967316e-12 2.045821e-12
44 -2.093866e-12 1.967316e-12
45 -2.546414e-13 -2.093866e-12
46 1.680581e-12 -2.546414e-13
47 1.573384e-13 1.680581e-12
48 5.486040e-13 1.573384e-13
49 3.197545e-15 5.486040e-13
50 3.272580e-13 3.197545e-15
51 1.272370e-12 3.272580e-13
52 1.312184e-12 1.272370e-12
53 -2.917071e-13 1.312184e-12
54 -3.896203e-13 -2.917071e-13
55 -1.559063e-13 -3.896203e-13
56 -7.655288e-13 -1.559063e-13
57 -1.882954e-13 -7.655288e-13
58 8.752608e-13 -1.882954e-13
59 5.232823e-13 8.752608e-13
60 1.567607e-12 5.232823e-13
61 3.713847e-13 1.567607e-12
62 7.382354e-13 3.713847e-13
63 9.922841e-13 7.382354e-13
64 9.784806e-13 9.922841e-13
65 3.993509e-13 9.784806e-13
66 -3.329261e-13 3.993509e-13
67 4.305942e-12 -3.329261e-13
68 -8.056304e-13 4.305942e-12
69 9.005498e-13 -8.056304e-13
70 2.341314e-12 9.005498e-13
71 4.816493e-12 2.341314e-12
72 3.537183e-12 4.816493e-12
73 5.266877e-12 3.537183e-12
74 7.040296e-12 5.266877e-12
75 4.483447e-12 7.040296e-12
76 5.457145e-12 4.483447e-12
77 4.186983e-12 5.457145e-12
78 -3.639968e-15 4.186983e-12
79 7.699384e-13 -3.639968e-15
80 -8.209154e-13 7.699384e-13
81 -1.415559e-12 -8.209154e-13
82 4.469327e-12 -1.415559e-12
83 1.916649e-12 4.469327e-12
84 4.697145e-12 1.916649e-12
85 4.405809e-12 4.697145e-12
86 1.398174e-12 4.405809e-12
87 4.193191e-12 1.398174e-12
88 7.186671e-14 4.193191e-12
89 4.976723e-14 7.186671e-14
90 -1.088612e-12 4.976723e-14
91 1.624096e-12 -1.088612e-12
92 -3.937772e-12 1.624096e-12
93 9.648071e-13 -3.937772e-12
94 -5.817341e-13 9.648071e-13
95 -5.693372e-13 -5.817341e-13
96 -6.514325e-13 -5.693372e-13
97 -1.994303e-13 -6.514325e-13
98 1.729054e-12 -1.994303e-13
99 1.783412e-12 1.729054e-12
100 5.487048e-13 1.783412e-12
101 1.940554e-14 5.487048e-13
102 -1.738315e-12 1.940554e-14
103 -4.361407e-12 -1.738315e-12
104 -1.021642e-12 -4.361407e-12
105 3.191361e-14 -1.021642e-12
106 -1.262377e-12 3.191361e-14
107 1.410086e-12 -1.262377e-12
108 -3.856353e-13 1.410086e-12
109 -4.104460e-12 -3.856353e-13
110 -3.279596e-12 -4.104460e-12
111 -3.416984e-12 -3.279596e-12
112 -3.976797e-12 -3.416984e-12
113 -5.665331e-12 -3.976797e-12
114 -1.412070e-12 -5.665331e-12
115 -1.133726e-12 -1.412070e-12
116 -2.353165e-14 -1.133726e-12
117 4.039752e-14 -2.353165e-14
118 2.501982e-13 4.039752e-14
119 1.120220e-12 2.501982e-13
120 -1.772810e-12 1.120220e-12
121 -1.339212e-12 -1.772810e-12
122 -1.036857e-12 -1.339212e-12
123 -1.102125e-12 -1.036857e-12
124 -6.288718e-13 -1.102125e-12
125 -1.301884e-12 -6.288718e-13
126 1.239201e-12 -1.301884e-12
127 -1.578248e-12 1.239201e-12
128 2.111578e-12 -1.578248e-12
129 -5.462170e-13 2.111578e-12
130 -2.769776e-13 -5.462170e-13
131 7.566191e-15 -2.769776e-13
132 1.799939e-13 7.566191e-15
133 -9.992997e-13 1.799939e-13
134 -1.462507e-12 -9.992997e-13
135 -5.880915e-13 -1.462507e-12
136 -1.217251e-12 -5.880915e-13
137 -1.312101e-12 -1.217251e-12
138 2.661359e-12 -1.312101e-12
139 -1.984175e-12 2.661359e-12
140 -2.184381e-12 -1.984175e-12
141 -6.178345e-13 -2.184381e-12
142 -2.894103e-12 -6.178345e-13
143 -2.186026e-12 -2.894103e-12
144 -1.440561e-12 -2.186026e-12
145 -1.541195e-12 -1.440561e-12
146 -2.607644e-12 -1.541195e-12
147 -1.882488e-12 -2.607644e-12
148 -1.240302e-12 -1.882488e-12
149 -1.330273e-12 -1.240302e-12
150 -4.169426e-12 -1.330273e-12
151 -9.154652e-12 -4.169426e-12
152 -4.787662e-12 -9.154652e-12
153 -3.817614e-12 -4.787662e-12
> 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/7tgzq1321807545.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8uyvo1321807545.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9cnnp1321807545.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10yrpk1321807545.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11lbob1321807545.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/12oyc31321807545.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/13n78c1321807545.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/14pcoj1321807545.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/15soan1321807545.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/16m5cz1321807545.tab")
+ }
>
> try(system("convert tmp/1uesy1321807544.ps tmp/1uesy1321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ca401321807544.ps tmp/2ca401321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mtl41321807544.ps tmp/3mtl41321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qd911321807544.ps tmp/4qd911321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/5eov51321807544.ps tmp/5eov51321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ph4c1321807544.ps tmp/6ph4c1321807544.png",intern=TRUE))
character(0)
> try(system("convert tmp/7tgzq1321807545.ps tmp/7tgzq1321807545.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uyvo1321807545.ps tmp/8uyvo1321807545.png",intern=TRUE))
character(0)
> try(system("convert tmp/9cnnp1321807545.ps tmp/9cnnp1321807545.png",intern=TRUE))
character(0)
> try(system("convert tmp/10yrpk1321807545.ps tmp/10yrpk1321807545.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.388 0.518 4.984