R version 2.12.1 (2010-12-16)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(1536
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+ ,0
+ ,0
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+ ,40
+ ,1
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+ ,45
+ ,8019
+ ,3080
+ ,11017
+ ,16
+ ,16
+ ,970
+ ,96971
+ ,40
+ ,233
+ ,49
+ ,0
+ ,40
+ ,18
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+ ,60
+ ,34542
+ ,11409
+ ,46741
+ ,84
+ ,84
+ ,964
+ ,83484
+ ,16
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+ ,47
+ ,0
+ ,17
+ ,19
+ ,76
+ ,48
+ ,21157
+ ,6769
+ ,39869
+ ,28
+ ,22)
+ ,dim=c(15
+ ,144)
+ ,dimnames=list(c('Pageviews'
+ ,'Time'
+ ,'Logins'
+ ,'CompendiumViews'
+ ,'CompendiumViews(PRonly)'
+ ,'Shared'
+ ,'Blogs'
+ ,'Reviews'
+ ,'Submits'
+ ,'Submits(+120)'
+ ,'Characters'
+ ,'CW:Revisions'
+ ,'CW:seconds'
+ ,'CW:Hyperlinks'
+ ,'CW:blogs')
+ ,1:144))
> y <- array(NA,dim=c(15,144),dimnames=list(c('Pageviews','Time','Logins','CompendiumViews','CompendiumViews(PRonly)','Shared','Blogs','Reviews','Submits','Submits(+120)','Characters','CW:Revisions','CW:seconds','CW:Hyperlinks','CW:blogs'),1:144))
> 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 = '2'
> #'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
Time Pageviews Logins CompendiumViews CompendiumViews(PRonly) Shared
1 127476 1536 78 490 107 0
2 130358 1134 46 329 68 1
3 7215 192 18 72 1 0
4 112861 2032 84 584 146 0
5 210171 3231 125 1077 124 0
6 393802 5777 215 1578 267 1
7 117604 1322 50 442 83 1
8 126029 1181 48 319 48 0
9 99729 1462 37 406 87 0
10 256310 2568 86 818 129 1
11 113066 1810 69 568 146 2
12 156212 1789 59 551 94 0
13 69952 1334 85 494 57 0
14 152673 2415 84 818 240 4
15 125841 1156 44 331 40 4
16 125769 1374 67 419 81 3
17 123467 1504 50 364 85 0
18 56232 999 47 284 62 5
19 108244 2190 77 667 126 0
20 22762 633 20 188 44 0
21 48554 838 49 286 37 0
22 178697 2167 81 633 94 0
23 139115 1452 58 514 127 0
24 93773 1790 45 532 159 1
25 132796 1718 76 540 41 1
26 113933 1179 22 428 153 0
27 144781 1688 138 539 86 0
28 140711 1101 75 266 55 0
29 283337 2259 102 745 73 0
30 158146 1768 36 733 79 0
31 123344 1300 39 394 71 0
32 157640 1432 38 482 111 2
33 91279 1791 88 567 71 4
34 189374 2475 102 746 243 0
35 167915 1930 42 626 66 1
36 0 1 1 0 0 0
37 175403 1782 54 835 58 0
38 92342 1505 46 464 131 3
39 100023 1820 41 418 258 9
40 178277 1648 49 607 56 0
41 145062 1668 56 539 90 2
42 110980 1366 47 519 57 0
43 86039 864 25 309 35 2
44 119514 1602 62 609 50 1
45 95535 1023 41 321 46 2
46 109894 963 73 246 32 2
47 61554 629 26 180 45 1
48 156520 1568 77 544 96 0
49 159121 1715 75 544 104 1
50 129362 2093 51 758 150 4
51 48188 658 28 205 37 0
52 91198 1199 54 309 49 0
53 229864 2059 64 709 83 0
54 180317 1592 67 542 67 0
55 150640 1447 48 526 39 1
56 104416 1342 44 418 68 5
57 159645 1527 55 409 58 0
58 60368 670 17 189 59 0
59 100056 859 55 293 30 0
60 137214 2329 73 781 54 10
61 99630 1326 47 383 65 6
62 84557 1567 62 572 81 0
63 91199 1081 45 308 81 11
64 83419 897 29 288 45 3
65 101723 855 25 285 52 0
66 94982 1229 37 391 36 0
67 129700 1939 60 446 80 8
68 110708 2293 57 690 137 2
69 81518 820 32 208 45 0
70 31970 340 15 101 40 0
71 192268 2443 102 858 126 3
72 87611 993 52 293 74 1
73 77890 1038 53 349 48 2
74 83261 1380 58 411 82 1
75 116290 2186 51 561 86 0
76 55254 1069 31 289 60 2
77 116173 1763 50 492 99 1
78 111488 1995 78 669 63 0
79 60138 816 23 253 76 0
80 73422 1121 66 366 92 0
81 67751 808 56 192 45 0
82 213351 1690 51 616 57 0
83 51185 751 24 221 44 0
84 97181 1309 32 438 132 0
85 42311 685 37 229 43 0
86 115801 1326 42 388 67 0
87 183637 2224 182 536 82 0
88 68161 923 84 220 71 0
89 76441 967 46 313 44 4
90 103613 1099 40 422 68 0
91 98707 1300 33 452 54 3
92 126527 1872 66 556 86 1
93 136781 1091 52 366 59 0
94 105863 1106 51 406 74 0
95 38775 632 30 254 18 0
96 179984 1901 89 606 156 0
97 164808 1580 49 479 87 0
98 19349 223 12 67 15 0
99 143902 1698 83 578 104 1
100 108660 1420 52 581 49 0
101 43803 552 24 240 11 0
102 47062 708 19 219 37 0
103 110845 1079 44 349 80 0
104 92517 957 52 241 66 1
105 58660 584 35 136 27 0
106 27676 596 22 194 59 0
107 98550 980 32 222 113 0
108 43284 576 22 151 24 0
109 0 0 0 0 0 0
110 66016 880 26 239 54 0
111 57359 750 48 240 43 0
112 96933 999 35 323 45 0
113 70369 931 47 302 55 0
114 65494 782 55 267 66 0
115 3616 78 5 14 5 0
116 0 0 0 0 0 0
117 143931 874 37 287 67 0
118 109894 1262 65 442 67 0
119 122973 1711 81 490 115 1
120 84336 749 32 243 51 0
121 43410 778 19 292 63 0
122 136250 1373 58 410 84 1
123 79015 806 33 217 35 0
124 92937 1448 42 422 57 8
125 57586 684 37 160 29 3
126 19764 285 12 75 19 1
127 105757 1336 42 412 51 2
128 96410 841 23 293 51 0
129 113402 1283 35 417 96 0
130 11796 256 9 79 22 0
131 7627 81 9 25 7 0
132 121085 1214 49 431 34 0
133 6836 41 3 11 5 0
134 139563 1629 41 564 43 4
135 5118 42 3 6 1 0
136 40248 528 16 183 34 1
137 0 0 0 0 0 0
138 95079 889 41 295 49 0
139 80750 1197 31 228 44 0
140 7131 81 4 27 0 1
141 4194 61 11 14 4 0
142 60378 849 20 240 40 1
143 96971 970 40 233 49 0
144 83484 964 16 347 47 0
Blogs Reviews Submits Submits(+120) Characters CW:Revisions CW:seconds
1 20 17 66 59 18158 5636 22622
2 38 17 68 50 30461 9079 73570
3 0 0 0 0 1423 603 1929
4 49 22 68 51 25629 8874 36294
5 74 30 120 112 48758 17988 62378
6 104 31 120 118 129230 21325 167760
7 37 19 72 59 27376 8325 52443
8 53 25 96 90 26706 7117 57283
9 42 30 109 50 26505 7996 36614
10 62 26 104 79 49801 14218 93268
11 50 20 54 49 46580 6321 35439
12 65 25 98 74 48352 19690 72405
13 28 15 49 32 13899 5659 24044
14 48 22 88 82 39342 11370 55909
15 42 12 45 43 27465 4778 44689
16 47 19 74 65 55211 5954 49319
17 71 28 112 111 74098 22924 62075
18 0 12 45 36 13497 70 2341
19 50 28 110 89 38338 14369 40551
20 12 13 39 28 52505 3706 11621
21 16 14 55 35 10663 3147 18741
22 76 27 102 78 74484 16801 84202
23 29 25 96 67 28895 2162 15334
24 38 30 86 61 32827 4721 28024
25 50 20 74 55 36188 5290 53306
26 33 17 64 49 28173 6446 37918
27 45 22 82 77 54926 14711 54819
28 59 28 100 71 38900 13311 89058
29 49 25 95 82 88530 13577 103354
30 40 16 63 53 35482 14634 70239
31 40 23 87 71 26730 6931 33045
32 51 20 65 58 29806 9992 63852
33 41 11 43 25 41799 6185 30905
34 73 20 80 59 54289 3445 24242
35 43 21 84 77 36805 12327 78907
36 0 0 0 0 0 0 0
37 46 27 105 75 33146 9898 36005
38 44 14 51 39 23333 8022 31972
39 31 29 98 83 47686 10765 35853
40 71 31 124 123 77783 22717 115301
41 61 19 75 67 36042 10090 47689
42 28 30 120 105 34541 12385 34223
43 21 23 84 76 75620 8513 43431
44 42 20 78 54 60610 5508 52220
45 44 22 87 82 55041 9628 33863
46 34 19 70 57 32087 11872 46879
47 15 32 97 57 16356 4186 23228
48 46 18 72 72 40161 10877 42827
49 43 26 104 94 55459 17066 65765
50 47 25 93 72 36679 9175 38167
51 12 22 82 39 22346 2102 14812
52 42 19 73 60 27377 10807 32615
53 56 24 87 84 50273 13662 82188
54 41 26 95 69 32104 9224 51763
55 48 27 105 102 27016 9001 59325
56 30 10 37 28 19715 7204 48976
57 44 26 96 65 33629 6572 43384
58 25 21 80 59 27084 7509 26692
59 42 21 83 80 32352 12920 53279
60 28 34 124 79 51845 5438 20652
61 33 29 116 107 26591 11489 38338
62 32 18 72 57 29677 6661 36735
63 28 16 55 44 54237 7941 42764
64 31 23 86 59 20284 6173 44331
65 13 22 85 80 22741 5562 41354
66 38 29 107 89 34178 9492 47879
67 39 31 124 115 69551 17456 103793
68 68 21 78 59 29653 9422 52235
69 32 21 83 66 38071 10913 49825
70 5 21 78 42 4157 1283 4105
71 53 15 59 35 28321 6198 58687
72 33 9 33 3 40195 4501 40745
73 48 21 84 68 48158 9560 33187
74 36 18 52 38 13310 3394 14063
75 52 31 121 107 78474 9871 37407
76 0 24 88 69 6386 2419 7190
77 52 24 99 80 31588 10630 49562
78 45 22 86 69 61254 8536 76324
79 16 21 75 46 21152 4911 21928
80 33 26 96 52 41272 9775 27860
81 48 22 81 58 34165 11227 28078
82 33 26 104 85 37054 6916 49577
83 24 20 76 13 12368 3424 28145
84 37 25 90 61 23168 8637 36241
85 16 19 75 49 16380 3189 10824
86 32 22 86 47 41242 8178 46892
87 55 25 100 93 48450 16739 61264
88 36 22 88 65 20790 6094 22933
89 29 21 80 64 34585 7237 20787
90 26 20 73 64 35672 7355 43978
91 37 23 88 57 52168 9734 51305
92 58 22 79 61 53933 11225 55593
93 35 21 81 71 34474 6213 51648
94 24 12 48 43 43753 4875 30552
95 18 9 33 18 36456 8159 23470
96 37 32 120 103 51183 11893 77530
97 86 24 90 76 52742 10754 57299
98 13 1 2 0 3895 786 9604
99 20 24 96 83 37076 9706 34684
100 32 22 79 70 24079 7796 41094
101 8 4 15 4 2325 593 3439
102 38 15 48 41 29354 5600 25171
103 45 21 81 57 30341 7245 23437
104 24 23 84 52 18992 7360 34086
105 23 12 46 24 15292 4574 24649
106 2 16 59 17 5842 522 2342
107 52 24 96 89 28918 10905 45571
108 5 9 29 20 3738 999 3255
109 0 0 0 0 0 0 0
110 43 22 79 45 95352 9016 30002
111 18 17 63 63 37478 5134 19360
112 41 18 68 48 26839 6608 43320
113 45 21 84 70 26783 8577 35513
114 29 17 54 32 33392 1543 23536
115 0 0 0 0 0 0 0
116 0 0 0 0 0 0 0
117 32 20 75 72 25446 9803 54438
118 58 26 87 56 59847 12140 56812
119 17 26 104 64 28162 6678 33838
120 24 20 80 77 33298 6420 32366
121 7 1 3 3 2781 4 13
122 62 24 93 73 37121 7979 55082
123 30 14 55 37 22698 5141 31334
124 49 26 96 54 27615 1311 16612
125 3 12 48 32 32689 443 5084
126 10 2 8 4 5752 2416 9927
127 42 16 60 55 23164 8396 47413
128 18 22 84 81 20304 5462 27389
129 40 28 112 90 34409 7271 30425
130 1 2 8 1 0 0 0
131 0 0 0 0 0 0 0
132 29 17 52 38 92538 4423 33510
133 0 1 4 0 0 0 0
134 46 17 57 36 46037 5331 40389
135 5 0 0 0 0 0 0
136 8 4 14 7 5444 775 6012
137 0 0 0 0 0 0 0
138 21 25 91 75 23924 6676 22205
139 21 26 89 52 52230 1489 17231
140 0 0 0 0 0 0 0
141 0 0 0 0 0 0 0
142 15 15 54 45 8019 3080 11017
143 40 18 69 60 34542 11409 46741
144 17 19 76 48 21157 6769 39869
CW:Hyperlinks CW:blogs
1 30 28
2 42 39
3 0 0
4 54 54
5 86 80
6 157 144
7 36 36
8 48 48
9 45 42
10 77 71
11 49 49
12 77 74
13 28 27
14 84 83
15 31 31
16 28 28
17 99 98
18 2 2
19 41 43
20 25 24
21 16 16
22 96 95
23 23 22
24 33 33
25 46 45
26 59 59
27 72 66
28 72 70
29 62 56
30 55 55
31 27 27
32 41 37
33 51 48
34 26 26
35 65 64
36 0 0
37 28 21
38 44 44
39 36 36
40 100 89
41 104 101
42 35 31
43 69 65
44 73 71
45 106 102
46 53 53
47 43 41
48 49 46
49 38 37
50 51 51
51 14 14
52 40 40
53 79 77
54 52 51
55 44 43
56 34 33
57 47 47
58 32 31
59 31 31
60 40 40
61 42 42
62 34 35
63 40 40
64 35 30
65 11 11
66 43 41
67 53 53
68 82 82
69 41 41
70 6 6
71 82 81
72 47 47
73 108 100
74 46 46
75 38 38
76 0 0
77 45 45
78 57 56
79 20 18
80 56 54
81 38 37
82 42 40
83 37 37
84 36 36
85 34 34
86 53 49
87 85 82
88 36 36
89 33 33
90 57 55
91 50 50
92 71 71
93 32 31
94 45 42
95 33 31
96 53 51
97 64 64
98 14 14
99 38 37
100 39 37
101 8 8
102 38 38
103 24 23
104 22 22
105 18 18
106 3 1
107 49 48
108 5 5
109 0 0
110 47 46
111 33 33
112 44 41
113 56 57
114 49 49
115 0 0
116 0 0
117 45 45
118 78 78
119 51 46
120 25 25
121 1 1
122 62 59
123 29 29
124 26 26
125 4 4
126 10 10
127 43 43
128 36 36
129 43 41
130 0 0
131 0 0
132 33 32
133 0 0
134 53 53
135 0 0
136 6 6
137 0 0
138 19 18
139 26 26
140 0 0
141 0 0
142 16 16
143 84 84
144 28 22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pageviews
2.847e+03 -8.213e+00
Logins CompendiumViews
2.710e+02 1.158e+02
`CompendiumViews(PRonly)` Shared
4.180e+01 -1.103e+03
Blogs Reviews
4.272e+02 -5.673e+02
Submits `Submits(+120)`
1.165e+02 3.947e+02
Characters `CW:Revisions`
3.603e-02 -2.763e+00
`CW:seconds` `CW:Hyperlinks`
1.145e+00 1.219e+03
`CW:blogs`
-1.474e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-73185 -10294 -1793 10306 60596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.847e+03 4.243e+03 0.671 0.503502
Pageviews -8.213e+00 1.214e+01 -0.677 0.499806
Logins 2.710e+02 1.011e+02 2.680 0.008320 **
CompendiumViews 1.158e+02 2.825e+01 4.098 7.31e-05 ***
`CompendiumViews(PRonly)` 4.180e+01 5.746e+01 0.727 0.468276
Shared -1.103e+03 8.763e+02 -1.259 0.210281
Blogs 4.272e+02 1.826e+02 2.339 0.020871 *
Reviews -5.673e+02 1.207e+03 -0.470 0.639170
Submits 1.165e+02 3.643e+02 0.320 0.749647
`Submits(+120)` 3.947e+02 1.710e+02 2.308 0.022603 *
Characters 3.603e-02 1.238e-01 0.291 0.771466
`CW:Revisions` -2.763e+00 7.963e-01 -3.470 0.000708 ***
`CW:seconds` 1.145e+00 1.426e-01 8.033 5.21e-13 ***
`CW:Hyperlinks` 1.219e+03 9.684e+02 1.259 0.210214
`CW:blogs` -1.474e+03 9.987e+02 -1.476 0.142317
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19260 on 129 degrees of freedom
Multiple R-squared: 0.9042, Adjusted R-squared: 0.8938
F-statistic: 86.98 on 14 and 129 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,] 0.8991493 2.017015e-01 1.008507e-01
[2,] 0.8355316 3.289368e-01 1.644684e-01
[3,] 0.7427600 5.144799e-01 2.572400e-01
[4,] 0.6428362 7.143276e-01 3.571638e-01
[5,] 0.7509044 4.981913e-01 2.490956e-01
[6,] 0.8275321 3.449358e-01 1.724679e-01
[7,] 0.7784209 4.431582e-01 2.215791e-01
[8,] 0.7130004 5.739993e-01 2.869996e-01
[9,] 0.6486944 7.026112e-01 3.513056e-01
[10,] 0.5753398 8.493204e-01 4.246602e-01
[11,] 0.4911972 9.823944e-01 5.088028e-01
[12,] 0.8011252 3.977495e-01 1.988748e-01
[13,] 0.7494451 5.011097e-01 2.505549e-01
[14,] 0.7147843 5.704314e-01 2.852157e-01
[15,] 0.6683515 6.632970e-01 3.316485e-01
[16,] 0.6293726 7.412549e-01 3.706274e-01
[17,] 0.5897597 8.204806e-01 4.102403e-01
[18,] 0.5253909 9.492182e-01 4.746091e-01
[19,] 0.4564067 9.128134e-01 5.435933e-01
[20,] 0.4875849 9.751698e-01 5.124151e-01
[21,] 0.4230577 8.461154e-01 5.769423e-01
[22,] 0.3628202 7.256403e-01 6.371798e-01
[23,] 0.7833318 4.333363e-01 2.166682e-01
[24,] 0.8336391 3.327218e-01 1.663609e-01
[25,] 0.8017378 3.965244e-01 1.982622e-01
[26,] 0.7603301 4.793397e-01 2.396699e-01
[27,] 0.7897671 4.204657e-01 2.102329e-01
[28,] 0.7774830 4.450340e-01 2.225170e-01
[29,] 0.8217887 3.564227e-01 1.782113e-01
[30,] 0.7981628 4.036743e-01 2.018372e-01
[31,] 0.7983449 4.033103e-01 2.016551e-01
[32,] 0.7632356 4.735288e-01 2.367644e-01
[33,] 0.7271987 5.456027e-01 2.728013e-01
[34,] 0.6910061 6.179878e-01 3.089939e-01
[35,] 0.6468533 7.062934e-01 3.531467e-01
[36,] 0.8397773 3.204455e-01 1.602227e-01
[37,] 0.9276140 1.447720e-01 7.238602e-02
[38,] 0.9123640 1.752720e-01 8.763599e-02
[39,] 0.8903442 2.193115e-01 1.096558e-01
[40,] 0.9533398 9.332033e-02 4.666017e-02
[41,] 0.9399191 1.201617e-01 6.008087e-02
[42,] 0.9296134 1.407731e-01 7.038657e-02
[43,] 0.9163840 1.672321e-01 8.361605e-02
[44,] 0.8949529 2.100941e-01 1.050471e-01
[45,] 0.9546435 9.071309e-02 4.535654e-02
[46,] 0.9462318 1.075365e-01 5.376824e-02
[47,] 0.9425369 1.149263e-01 5.746313e-02
[48,] 0.9256822 1.486356e-01 7.431782e-02
[49,] 0.9324988 1.350023e-01 6.750117e-02
[50,] 0.9648982 7.020356e-02 3.510178e-02
[51,] 0.9821701 3.565990e-02 1.782995e-02
[52,] 0.9758306 4.833889e-02 2.416945e-02
[53,] 0.9671895 6.562093e-02 3.281047e-02
[54,] 0.9616209 7.675830e-02 3.837915e-02
[55,] 0.9522259 9.554826e-02 4.777413e-02
[56,] 0.9490534 1.018932e-01 5.094661e-02
[57,] 0.9347431 1.305139e-01 6.525694e-02
[58,] 0.9481719 1.036562e-01 5.182809e-02
[59,] 0.9466019 1.067962e-01 5.339812e-02
[60,] 0.9481256 1.037488e-01 5.187438e-02
[61,] 0.9999860 2.792978e-05 1.396489e-05
[62,] 0.9999748 5.035689e-05 2.517844e-05
[63,] 0.9999623 7.535900e-05 3.767950e-05
[64,] 0.9999383 1.233550e-04 6.167752e-05
[65,] 0.9999995 1.006355e-06 5.031777e-07
[66,] 0.9999990 1.959656e-06 9.798279e-07
[67,] 0.9999981 3.826416e-06 1.913208e-06
[68,] 0.9999965 6.945411e-06 3.472705e-06
[69,] 0.9999944 1.117096e-05 5.585482e-06
[70,] 0.9999911 1.787653e-05 8.938263e-06
[71,] 0.9999911 1.784863e-05 8.924316e-06
[72,] 0.9999828 3.446920e-05 1.723460e-05
[73,] 0.9999672 6.565197e-05 3.282599e-05
[74,] 0.9999657 6.855260e-05 3.427630e-05
[75,] 0.9999815 3.703461e-05 1.851731e-05
[76,] 0.9999660 6.807087e-05 3.403543e-05
[77,] 0.9999523 9.539047e-05 4.769523e-05
[78,] 0.9999244 1.511414e-04 7.557068e-05
[79,] 0.9999679 6.420980e-05 3.210490e-05
[80,] 0.9999681 6.375269e-05 3.187635e-05
[81,] 0.9999362 1.276887e-04 6.384436e-05
[82,] 0.9998865 2.270394e-04 1.135197e-04
[83,] 0.9999506 9.889527e-05 4.944764e-05
[84,] 0.9999189 1.622355e-04 8.111777e-05
[85,] 0.9999198 1.604108e-04 8.020542e-05
[86,] 0.9999947 1.053091e-05 5.265456e-06
[87,] 0.9999881 2.377512e-05 1.188756e-05
[88,] 0.9999794 4.113788e-05 2.056894e-05
[89,] 0.9999557 8.859752e-05 4.429876e-05
[90,] 0.9999308 1.384748e-04 6.923739e-05
[91,] 0.9998850 2.300415e-04 1.150207e-04
[92,] 0.9997483 5.034668e-04 2.517334e-04
[93,] 0.9995424 9.151808e-04 4.575904e-04
[94,] 0.9996626 6.747329e-04 3.373665e-04
[95,] 0.9993275 1.345002e-03 6.725012e-04
[96,] 0.9990777 1.844576e-03 9.222880e-04
[97,] 0.9990506 1.898872e-03 9.494360e-04
[98,] 0.9979438 4.112317e-03 2.056158e-03
[99,] 0.9957347 8.530557e-03 4.265278e-03
[100,] 0.9999754 4.926906e-05 2.463453e-05
[101,] 0.9999883 2.333086e-05 1.166543e-05
[102,] 0.9999949 1.024615e-05 5.123077e-06
[103,] 0.9999773 4.548052e-05 2.274026e-05
[104,] 0.9999068 1.864251e-04 9.321255e-05
[105,] 0.9999601 7.978553e-05 3.989276e-05
[106,] 0.9998099 3.802863e-04 1.901432e-04
[107,] 0.9998609 2.782315e-04 1.391158e-04
[108,] 0.9997393 5.213268e-04 2.606634e-04
[109,] 0.9983056 3.388753e-03 1.694377e-03
> postscript(file="/var/www/rcomp/tmp/1nih51323874251.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2xw9d1323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3ceaz1323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4fgw21323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ur231323874251.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 = 144
Frequency = 1
1 2 3 4 5 6
18733.4268 -3714.2936 -7905.1782 -10489.9332 -13092.6886 -17048.3273
7 8 9 10 11 12
-7052.6622 -8905.7284 1114.3908 38663.7987 -11230.0682 15074.0514
13 14 15 16 17 18
-33082.0200 -18169.9666 20561.5384 -11003.5174 15755.2209 3789.1254
19 20 21 22 23 24
-28114.8228 -17321.0662 -23539.0647 4952.4005 23581.6699 -18837.0525
25 26 27 28 29 30
-17503.4066 16015.0277 -8901.4819 -6734.7633 54812.0897 8353.9266
31 32 33 34 35 36
17044.1274 15065.5826 -22295.4665 16664.4568 6220.6093 -3109.3298
37 38 39 40 41 42
9025.9188 -2764.1057 6934.4647 -35496.0355 20363.0540 -9417.3323
43 44 45 46 47 48
-4481.5797 -26587.7775 11238.4970 24623.8843 5541.1576 20064.2423
49 50 51 52 53 54
9013.1867 -11563.7755 -8109.0830 8105.9659 46419.3630 42591.4220
55 56 57 58 59 60
-6748.0257 2731.6290 43007.1963 -1421.8566 -11681.8178 13467.4030
61 62 63 64 65 66
-1313.3131 -40775.5231 12669.5136 -18236.9338 -612.1530 -23778.7486
67 68 69 70 71 72
-38196.0505 -35630.1854 -4541.7199 -1178.1442 14042.9252 5193.5351
73 74 75 76 77 78
-13814.6696 4414.6765 -20637.0908 -6626.3666 -14512.3698 -73185.2394
79 80 81 82 83 84
-6682.2247 -10441.4077 -2256.6385 60595.9054 -6105.7758 -5302.0494
85 86 87 88 89 90
-12890.3228 9994.5584 18155.5444 -18115.7341 3704.0511 -6028.3215
91 92 93 94 95 96
-10194.7921 -7776.0971 6912.5837 6118.2017 -10891.6181 -7562.4139
97 98 99 100 101 102
17458.3058 -3921.9037 17440.8465 -23354.9265 5920.5641 -16247.0889
103 104 105 106 107 108
18666.9788 12583.1373 4843.2887 -10159.3782 -2347.4365 12590.1066
109 110 111 112 113 114
-2846.5135 -1811.0851 -13098.3731 -6176.3228 -21750.3003 -12580.5713
115 116 117 118 119 120
-1774.8437 -2846.5135 38013.6336 -10245.0783 6211.8024 -4075.0655
121 122 123 124 125 126
1559.1517 -4433.8412 6262.8790 -264.6651 16449.6196 -369.0053
127 128 129 130 131 132
-3669.5138 13383.8550 1557.4284 -2072.0366 -180.4213 17249.7622
133 134 135 136 137 138
2131.9784 25765.9580 -1068.9521 6943.5385 -2846.5135 12748.4480
139 140 141 142 143 144
15106.6301 1843.0711 -2920.8472 8460.2557 21784.3450 -11636.5798
> postscript(file="/var/www/rcomp/tmp/6f03h1323874251.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 = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 18733.4268 NA
1 -3714.2936 18733.4268
2 -7905.1782 -3714.2936
3 -10489.9332 -7905.1782
4 -13092.6886 -10489.9332
5 -17048.3273 -13092.6886
6 -7052.6622 -17048.3273
7 -8905.7284 -7052.6622
8 1114.3908 -8905.7284
9 38663.7987 1114.3908
10 -11230.0682 38663.7987
11 15074.0514 -11230.0682
12 -33082.0200 15074.0514
13 -18169.9666 -33082.0200
14 20561.5384 -18169.9666
15 -11003.5174 20561.5384
16 15755.2209 -11003.5174
17 3789.1254 15755.2209
18 -28114.8228 3789.1254
19 -17321.0662 -28114.8228
20 -23539.0647 -17321.0662
21 4952.4005 -23539.0647
22 23581.6699 4952.4005
23 -18837.0525 23581.6699
24 -17503.4066 -18837.0525
25 16015.0277 -17503.4066
26 -8901.4819 16015.0277
27 -6734.7633 -8901.4819
28 54812.0897 -6734.7633
29 8353.9266 54812.0897
30 17044.1274 8353.9266
31 15065.5826 17044.1274
32 -22295.4665 15065.5826
33 16664.4568 -22295.4665
34 6220.6093 16664.4568
35 -3109.3298 6220.6093
36 9025.9188 -3109.3298
37 -2764.1057 9025.9188
38 6934.4647 -2764.1057
39 -35496.0355 6934.4647
40 20363.0540 -35496.0355
41 -9417.3323 20363.0540
42 -4481.5797 -9417.3323
43 -26587.7775 -4481.5797
44 11238.4970 -26587.7775
45 24623.8843 11238.4970
46 5541.1576 24623.8843
47 20064.2423 5541.1576
48 9013.1867 20064.2423
49 -11563.7755 9013.1867
50 -8109.0830 -11563.7755
51 8105.9659 -8109.0830
52 46419.3630 8105.9659
53 42591.4220 46419.3630
54 -6748.0257 42591.4220
55 2731.6290 -6748.0257
56 43007.1963 2731.6290
57 -1421.8566 43007.1963
58 -11681.8178 -1421.8566
59 13467.4030 -11681.8178
60 -1313.3131 13467.4030
61 -40775.5231 -1313.3131
62 12669.5136 -40775.5231
63 -18236.9338 12669.5136
64 -612.1530 -18236.9338
65 -23778.7486 -612.1530
66 -38196.0505 -23778.7486
67 -35630.1854 -38196.0505
68 -4541.7199 -35630.1854
69 -1178.1442 -4541.7199
70 14042.9252 -1178.1442
71 5193.5351 14042.9252
72 -13814.6696 5193.5351
73 4414.6765 -13814.6696
74 -20637.0908 4414.6765
75 -6626.3666 -20637.0908
76 -14512.3698 -6626.3666
77 -73185.2394 -14512.3698
78 -6682.2247 -73185.2394
79 -10441.4077 -6682.2247
80 -2256.6385 -10441.4077
81 60595.9054 -2256.6385
82 -6105.7758 60595.9054
83 -5302.0494 -6105.7758
84 -12890.3228 -5302.0494
85 9994.5584 -12890.3228
86 18155.5444 9994.5584
87 -18115.7341 18155.5444
88 3704.0511 -18115.7341
89 -6028.3215 3704.0511
90 -10194.7921 -6028.3215
91 -7776.0971 -10194.7921
92 6912.5837 -7776.0971
93 6118.2017 6912.5837
94 -10891.6181 6118.2017
95 -7562.4139 -10891.6181
96 17458.3058 -7562.4139
97 -3921.9037 17458.3058
98 17440.8465 -3921.9037
99 -23354.9265 17440.8465
100 5920.5641 -23354.9265
101 -16247.0889 5920.5641
102 18666.9788 -16247.0889
103 12583.1373 18666.9788
104 4843.2887 12583.1373
105 -10159.3782 4843.2887
106 -2347.4365 -10159.3782
107 12590.1066 -2347.4365
108 -2846.5135 12590.1066
109 -1811.0851 -2846.5135
110 -13098.3731 -1811.0851
111 -6176.3228 -13098.3731
112 -21750.3003 -6176.3228
113 -12580.5713 -21750.3003
114 -1774.8437 -12580.5713
115 -2846.5135 -1774.8437
116 38013.6336 -2846.5135
117 -10245.0783 38013.6336
118 6211.8024 -10245.0783
119 -4075.0655 6211.8024
120 1559.1517 -4075.0655
121 -4433.8412 1559.1517
122 6262.8790 -4433.8412
123 -264.6651 6262.8790
124 16449.6196 -264.6651
125 -369.0053 16449.6196
126 -3669.5138 -369.0053
127 13383.8550 -3669.5138
128 1557.4284 13383.8550
129 -2072.0366 1557.4284
130 -180.4213 -2072.0366
131 17249.7622 -180.4213
132 2131.9784 17249.7622
133 25765.9580 2131.9784
134 -1068.9521 25765.9580
135 6943.5385 -1068.9521
136 -2846.5135 6943.5385
137 12748.4480 -2846.5135
138 15106.6301 12748.4480
139 1843.0711 15106.6301
140 -2920.8472 1843.0711
141 8460.2557 -2920.8472
142 21784.3450 8460.2557
143 -11636.5798 21784.3450
144 NA -11636.5798
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3714.2936 18733.4268
[2,] -7905.1782 -3714.2936
[3,] -10489.9332 -7905.1782
[4,] -13092.6886 -10489.9332
[5,] -17048.3273 -13092.6886
[6,] -7052.6622 -17048.3273
[7,] -8905.7284 -7052.6622
[8,] 1114.3908 -8905.7284
[9,] 38663.7987 1114.3908
[10,] -11230.0682 38663.7987
[11,] 15074.0514 -11230.0682
[12,] -33082.0200 15074.0514
[13,] -18169.9666 -33082.0200
[14,] 20561.5384 -18169.9666
[15,] -11003.5174 20561.5384
[16,] 15755.2209 -11003.5174
[17,] 3789.1254 15755.2209
[18,] -28114.8228 3789.1254
[19,] -17321.0662 -28114.8228
[20,] -23539.0647 -17321.0662
[21,] 4952.4005 -23539.0647
[22,] 23581.6699 4952.4005
[23,] -18837.0525 23581.6699
[24,] -17503.4066 -18837.0525
[25,] 16015.0277 -17503.4066
[26,] -8901.4819 16015.0277
[27,] -6734.7633 -8901.4819
[28,] 54812.0897 -6734.7633
[29,] 8353.9266 54812.0897
[30,] 17044.1274 8353.9266
[31,] 15065.5826 17044.1274
[32,] -22295.4665 15065.5826
[33,] 16664.4568 -22295.4665
[34,] 6220.6093 16664.4568
[35,] -3109.3298 6220.6093
[36,] 9025.9188 -3109.3298
[37,] -2764.1057 9025.9188
[38,] 6934.4647 -2764.1057
[39,] -35496.0355 6934.4647
[40,] 20363.0540 -35496.0355
[41,] -9417.3323 20363.0540
[42,] -4481.5797 -9417.3323
[43,] -26587.7775 -4481.5797
[44,] 11238.4970 -26587.7775
[45,] 24623.8843 11238.4970
[46,] 5541.1576 24623.8843
[47,] 20064.2423 5541.1576
[48,] 9013.1867 20064.2423
[49,] -11563.7755 9013.1867
[50,] -8109.0830 -11563.7755
[51,] 8105.9659 -8109.0830
[52,] 46419.3630 8105.9659
[53,] 42591.4220 46419.3630
[54,] -6748.0257 42591.4220
[55,] 2731.6290 -6748.0257
[56,] 43007.1963 2731.6290
[57,] -1421.8566 43007.1963
[58,] -11681.8178 -1421.8566
[59,] 13467.4030 -11681.8178
[60,] -1313.3131 13467.4030
[61,] -40775.5231 -1313.3131
[62,] 12669.5136 -40775.5231
[63,] -18236.9338 12669.5136
[64,] -612.1530 -18236.9338
[65,] -23778.7486 -612.1530
[66,] -38196.0505 -23778.7486
[67,] -35630.1854 -38196.0505
[68,] -4541.7199 -35630.1854
[69,] -1178.1442 -4541.7199
[70,] 14042.9252 -1178.1442
[71,] 5193.5351 14042.9252
[72,] -13814.6696 5193.5351
[73,] 4414.6765 -13814.6696
[74,] -20637.0908 4414.6765
[75,] -6626.3666 -20637.0908
[76,] -14512.3698 -6626.3666
[77,] -73185.2394 -14512.3698
[78,] -6682.2247 -73185.2394
[79,] -10441.4077 -6682.2247
[80,] -2256.6385 -10441.4077
[81,] 60595.9054 -2256.6385
[82,] -6105.7758 60595.9054
[83,] -5302.0494 -6105.7758
[84,] -12890.3228 -5302.0494
[85,] 9994.5584 -12890.3228
[86,] 18155.5444 9994.5584
[87,] -18115.7341 18155.5444
[88,] 3704.0511 -18115.7341
[89,] -6028.3215 3704.0511
[90,] -10194.7921 -6028.3215
[91,] -7776.0971 -10194.7921
[92,] 6912.5837 -7776.0971
[93,] 6118.2017 6912.5837
[94,] -10891.6181 6118.2017
[95,] -7562.4139 -10891.6181
[96,] 17458.3058 -7562.4139
[97,] -3921.9037 17458.3058
[98,] 17440.8465 -3921.9037
[99,] -23354.9265 17440.8465
[100,] 5920.5641 -23354.9265
[101,] -16247.0889 5920.5641
[102,] 18666.9788 -16247.0889
[103,] 12583.1373 18666.9788
[104,] 4843.2887 12583.1373
[105,] -10159.3782 4843.2887
[106,] -2347.4365 -10159.3782
[107,] 12590.1066 -2347.4365
[108,] -2846.5135 12590.1066
[109,] -1811.0851 -2846.5135
[110,] -13098.3731 -1811.0851
[111,] -6176.3228 -13098.3731
[112,] -21750.3003 -6176.3228
[113,] -12580.5713 -21750.3003
[114,] -1774.8437 -12580.5713
[115,] -2846.5135 -1774.8437
[116,] 38013.6336 -2846.5135
[117,] -10245.0783 38013.6336
[118,] 6211.8024 -10245.0783
[119,] -4075.0655 6211.8024
[120,] 1559.1517 -4075.0655
[121,] -4433.8412 1559.1517
[122,] 6262.8790 -4433.8412
[123,] -264.6651 6262.8790
[124,] 16449.6196 -264.6651
[125,] -369.0053 16449.6196
[126,] -3669.5138 -369.0053
[127,] 13383.8550 -3669.5138
[128,] 1557.4284 13383.8550
[129,] -2072.0366 1557.4284
[130,] -180.4213 -2072.0366
[131,] 17249.7622 -180.4213
[132,] 2131.9784 17249.7622
[133,] 25765.9580 2131.9784
[134,] -1068.9521 25765.9580
[135,] 6943.5385 -1068.9521
[136,] -2846.5135 6943.5385
[137,] 12748.4480 -2846.5135
[138,] 15106.6301 12748.4480
[139,] 1843.0711 15106.6301
[140,] -2920.8472 1843.0711
[141,] 8460.2557 -2920.8472
[142,] 21784.3450 8460.2557
[143,] -11636.5798 21784.3450
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3714.2936 18733.4268
2 -7905.1782 -3714.2936
3 -10489.9332 -7905.1782
4 -13092.6886 -10489.9332
5 -17048.3273 -13092.6886
6 -7052.6622 -17048.3273
7 -8905.7284 -7052.6622
8 1114.3908 -8905.7284
9 38663.7987 1114.3908
10 -11230.0682 38663.7987
11 15074.0514 -11230.0682
12 -33082.0200 15074.0514
13 -18169.9666 -33082.0200
14 20561.5384 -18169.9666
15 -11003.5174 20561.5384
16 15755.2209 -11003.5174
17 3789.1254 15755.2209
18 -28114.8228 3789.1254
19 -17321.0662 -28114.8228
20 -23539.0647 -17321.0662
21 4952.4005 -23539.0647
22 23581.6699 4952.4005
23 -18837.0525 23581.6699
24 -17503.4066 -18837.0525
25 16015.0277 -17503.4066
26 -8901.4819 16015.0277
27 -6734.7633 -8901.4819
28 54812.0897 -6734.7633
29 8353.9266 54812.0897
30 17044.1274 8353.9266
31 15065.5826 17044.1274
32 -22295.4665 15065.5826
33 16664.4568 -22295.4665
34 6220.6093 16664.4568
35 -3109.3298 6220.6093
36 9025.9188 -3109.3298
37 -2764.1057 9025.9188
38 6934.4647 -2764.1057
39 -35496.0355 6934.4647
40 20363.0540 -35496.0355
41 -9417.3323 20363.0540
42 -4481.5797 -9417.3323
43 -26587.7775 -4481.5797
44 11238.4970 -26587.7775
45 24623.8843 11238.4970
46 5541.1576 24623.8843
47 20064.2423 5541.1576
48 9013.1867 20064.2423
49 -11563.7755 9013.1867
50 -8109.0830 -11563.7755
51 8105.9659 -8109.0830
52 46419.3630 8105.9659
53 42591.4220 46419.3630
54 -6748.0257 42591.4220
55 2731.6290 -6748.0257
56 43007.1963 2731.6290
57 -1421.8566 43007.1963
58 -11681.8178 -1421.8566
59 13467.4030 -11681.8178
60 -1313.3131 13467.4030
61 -40775.5231 -1313.3131
62 12669.5136 -40775.5231
63 -18236.9338 12669.5136
64 -612.1530 -18236.9338
65 -23778.7486 -612.1530
66 -38196.0505 -23778.7486
67 -35630.1854 -38196.0505
68 -4541.7199 -35630.1854
69 -1178.1442 -4541.7199
70 14042.9252 -1178.1442
71 5193.5351 14042.9252
72 -13814.6696 5193.5351
73 4414.6765 -13814.6696
74 -20637.0908 4414.6765
75 -6626.3666 -20637.0908
76 -14512.3698 -6626.3666
77 -73185.2394 -14512.3698
78 -6682.2247 -73185.2394
79 -10441.4077 -6682.2247
80 -2256.6385 -10441.4077
81 60595.9054 -2256.6385
82 -6105.7758 60595.9054
83 -5302.0494 -6105.7758
84 -12890.3228 -5302.0494
85 9994.5584 -12890.3228
86 18155.5444 9994.5584
87 -18115.7341 18155.5444
88 3704.0511 -18115.7341
89 -6028.3215 3704.0511
90 -10194.7921 -6028.3215
91 -7776.0971 -10194.7921
92 6912.5837 -7776.0971
93 6118.2017 6912.5837
94 -10891.6181 6118.2017
95 -7562.4139 -10891.6181
96 17458.3058 -7562.4139
97 -3921.9037 17458.3058
98 17440.8465 -3921.9037
99 -23354.9265 17440.8465
100 5920.5641 -23354.9265
101 -16247.0889 5920.5641
102 18666.9788 -16247.0889
103 12583.1373 18666.9788
104 4843.2887 12583.1373
105 -10159.3782 4843.2887
106 -2347.4365 -10159.3782
107 12590.1066 -2347.4365
108 -2846.5135 12590.1066
109 -1811.0851 -2846.5135
110 -13098.3731 -1811.0851
111 -6176.3228 -13098.3731
112 -21750.3003 -6176.3228
113 -12580.5713 -21750.3003
114 -1774.8437 -12580.5713
115 -2846.5135 -1774.8437
116 38013.6336 -2846.5135
117 -10245.0783 38013.6336
118 6211.8024 -10245.0783
119 -4075.0655 6211.8024
120 1559.1517 -4075.0655
121 -4433.8412 1559.1517
122 6262.8790 -4433.8412
123 -264.6651 6262.8790
124 16449.6196 -264.6651
125 -369.0053 16449.6196
126 -3669.5138 -369.0053
127 13383.8550 -3669.5138
128 1557.4284 13383.8550
129 -2072.0366 1557.4284
130 -180.4213 -2072.0366
131 17249.7622 -180.4213
132 2131.9784 17249.7622
133 25765.9580 2131.9784
134 -1068.9521 25765.9580
135 6943.5385 -1068.9521
136 -2846.5135 6943.5385
137 12748.4480 -2846.5135
138 15106.6301 12748.4480
139 1843.0711 15106.6301
140 -2920.8472 1843.0711
141 8460.2557 -2920.8472
142 21784.3450 8460.2557
143 -11636.5798 21784.3450
> 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/www/rcomp/tmp/7h1971323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8d4o01323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9m65j1323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/102fxf1323874251.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/www/rcomp/tmp/11t7e81323874251.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/www/rcomp/tmp/12obai1323874251.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/www/rcomp/tmp/132dmh1323874251.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/www/rcomp/tmp/14dpzv1323874251.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/www/rcomp/tmp/15r8341323874251.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/www/rcomp/tmp/16ds5z1323874251.tab")
+ }
>
> try(system("convert tmp/1nih51323874251.ps tmp/1nih51323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xw9d1323874251.ps tmp/2xw9d1323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ceaz1323874251.ps tmp/3ceaz1323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fgw21323874251.ps tmp/4fgw21323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ur231323874251.ps tmp/5ur231323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/6f03h1323874251.ps tmp/6f03h1323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/7h1971323874251.ps tmp/7h1971323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d4o01323874251.ps tmp/8d4o01323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m65j1323874251.ps tmp/9m65j1323874251.png",intern=TRUE))
character(0)
> try(system("convert tmp/102fxf1323874251.ps tmp/102fxf1323874251.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
7.036 0.712 8.525