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)
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> x <- array(list(30
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+ ,8914
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+ ,10
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+ ,7747
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+ ,43
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+ ,5432
+ ,27184
+ ,34
+ ,34
+ ,19
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+ ,4913
+ ,21610
+ ,32
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+ ,14
+ ,82206
+ ,2650
+ ,20484
+ ,20
+ ,19
+ ,11
+ ,32073
+ ,2370
+ ,20156
+ ,34
+ ,34
+ ,4
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+ ,775
+ ,6012
+ ,6
+ ,6
+ ,16
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+ ,12
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+ ,30884
+ ,10205
+ ,37623
+ ,72
+ ,72
+ ,16
+ ,19540
+ ,6095
+ ,35873
+ ,27
+ ,21)
+ ,dim=c(6
+ ,289)
+ ,dimnames=list(c('compendiums_reviewed'
+ ,'totsize'
+ ,'totrevisions'
+ ,'totseconds'
+ ,'tothyperlinks'
+ ,'totblogs')
+ ,1:289))
> y <- array(NA,dim=c(6,289),dimnames=list(c('compendiums_reviewed','totsize','totrevisions','totseconds','tothyperlinks','totblogs'),1:289))
> 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 = '1'
> #'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
compendiums_reviewed totsize totrevisions totseconds tothyperlinks totblogs
1 30 112285 24188 146283 144 145
2 28 84786 18273 98364 103 101
3 38 83123 14130 86146 98 98
4 30 101193 32287 96933 135 132
5 22 38361 8654 79234 61 60
6 26 68504 9245 42551 39 38
7 25 119182 33251 195663 150 144
8 18 22807 1271 6853 5 5
9 11 17140 5279 21529 28 28
10 26 116174 27101 95757 84 84
11 25 57635 16373 85584 80 79
12 38 66198 19716 143983 130 127
13 44 71701 17753 75851 82 78
14 30 57793 9028 59238 60 60
15 40 80444 18653 93163 131 131
16 34 53855 8828 96037 84 84
17 47 97668 29498 151511 140 133
18 30 133824 27563 136368 151 150
19 31 101481 18293 112642 91 91
20 23 99645 22530 94728 138 132
21 36 114789 15977 105499 150 136
22 36 99052 35082 121527 124 124
23 30 67654 16116 127766 119 118
24 25 65553 15849 98958 73 70
25 39 97500 16026 77900 110 107
26 34 69112 26569 85646 123 119
27 31 82753 24785 98579 90 89
28 31 85323 17569 130767 116 112
29 33 72654 23825 131741 113 108
30 25 30727 7869 53907 56 52
31 33 77873 14975 178812 115 112
32 35 117478 37791 146761 119 116
33 42 74007 9605 82036 129 123
34 43 90183 27295 163253 127 125
35 30 61542 2746 27032 27 27
36 33 101494 34461 171975 175 162
37 13 27570 8098 65990 35 32
38 32 55813 4787 86572 64 64
39 36 79215 24919 159676 96 92
40 0 1423 603 1929 0 0
41 28 55461 16329 85371 84 83
42 14 31081 12558 58391 41 41
43 17 22996 7784 31580 47 47
44 32 83122 28522 136815 126 120
45 30 70106 22265 120642 105 105
46 35 60578 14459 69107 80 79
47 20 39992 14526 50495 70 65
48 28 79892 22240 108016 73 70
49 28 49810 11802 46341 57 55
50 39 71570 7623 78348 40 39
51 34 100708 11912 79336 68 67
52 26 33032 7935 56968 21 21
53 39 82875 18220 93176 127 127
54 39 139077 19199 161632 154 152
55 33 71595 19918 87850 116 113
56 28 72260 21884 127969 102 99
57 4 5950 2694 15049 7 7
58 39 115762 15808 155135 148 141
59 18 32551 3597 25109 21 21
60 14 31701 5296 45824 35 35
61 29 80670 25239 102996 112 109
62 44 143558 29801 160604 137 133
63 21 117105 18450 158051 135 123
64 16 23789 7132 44547 26 26
65 28 120733 34861 162647 230 230
66 35 105195 35940 174141 181 166
67 28 73107 16688 60622 71 68
68 38 132068 24683 179566 147 147
69 23 149193 46230 184301 190 179
70 36 46821 10387 75661 64 61
71 32 87011 21436 96144 105 101
72 29 95260 30546 129847 107 108
73 25 55183 19746 117286 94 90
74 27 106671 15977 71180 116 114
75 36 73511 22583 109377 106 103
76 28 92945 17274 85298 143 142
77 23 78664 16469 73631 81 79
78 40 70054 14251 86767 89 88
79 23 22618 3007 23824 26 25
80 40 74011 16851 93487 84 83
81 28 83737 21113 82981 113 113
82 34 69094 17401 73815 120 118
83 33 93133 23958 94552 110 110
84 28 95536 23567 132190 134 129
85 34 225920 13065 128754 54 51
86 30 62133 15358 66363 96 93
87 33 61370 14587 67808 78 76
88 22 43836 12770 61724 51 49
89 38 106117 24021 131722 121 118
90 26 38692 9648 68580 38 38
91 35 84651 20537 106175 145 141
92 8 56622 7905 55792 59 58
93 24 15986 4527 25157 27 27
94 29 95364 30495 76669 91 91
95 20 26706 7117 57283 48 48
96 29 89691 17719 105805 68 63
97 45 67267 27056 129484 58 56
98 37 126846 33473 72413 150 144
99 33 41140 9758 87831 74 73
100 33 102860 21115 96971 181 168
101 25 51715 7236 71299 65 64
102 32 55801 13790 77494 97 97
103 29 111813 32902 120336 121 117
104 28 120293 25131 93913 99 100
105 28 138599 30910 136048 152 149
106 31 161647 35947 181248 188 187
107 52 115929 29848 146123 138 127
108 21 24266 6943 32036 40 37
109 24 162901 42705 186646 254 245
110 41 109825 31808 102255 87 87
111 33 129838 26675 168237 178 177
112 32 37510 8435 64219 51 49
113 19 43750 7409 19630 49 49
114 20 40652 14993 76825 73 73
115 31 87771 36867 115338 176 177
116 31 85872 33835 109427 94 94
117 32 89275 24164 118168 120 117
118 18 44418 12607 84845 66 60
119 23 192565 22609 153197 56 55
120 17 35232 5892 29877 39 39
121 20 40909 17014 63506 66 64
122 12 13294 5394 22445 27 26
123 17 32387 9178 47695 65 64
124 30 140867 6440 68370 58 58
125 31 120662 21916 146304 98 95
126 10 21233 4011 38233 25 25
127 13 44332 5818 42071 26 26
128 22 61056 18647 50517 77 76
129 42 101338 20556 103950 130 129
130 1 1168 238 5841 11 11
131 9 13497 70 2341 2 2
132 32 65567 22392 84396 101 101
133 11 25162 3913 24610 31 28
134 25 32334 12237 35753 36 36
135 36 40735 8388 55515 120 89
136 31 91413 22120 209056 195 193
137 0 855 338 6622 4 4
138 24 97068 11727 115814 89 84
139 13 44339 3704 11609 24 23
140 8 14116 3988 13155 39 39
141 13 10288 3030 18274 14 14
142 19 65622 13520 72875 78 78
143 18 16563 1421 10112 15 14
144 33 76643 20923 142775 106 101
145 40 110681 20237 68847 83 82
146 22 29011 3219 17659 24 24
147 38 92696 3769 20112 37 36
148 24 94785 12252 61023 77 75
149 8 8773 1888 13983 16 16
150 35 83209 14497 65176 56 55
151 43 93815 28864 132432 132 131
152 43 86687 21721 112494 144 131
153 14 34553 4821 45109 40 39
154 41 105547 33644 170875 153 144
155 38 103487 15923 180759 143 139
156 45 213688 42935 214921 220 211
157 31 71220 18864 100226 79 78
158 13 23517 4977 32043 50 50
159 28 56926 7785 54454 39 39
160 31 91721 17939 78876 95 90
161 40 115168 23436 170745 169 166
162 30 111194 325 6940 12 12
163 16 51009 13539 49025 63 57
164 37 135777 34538 122037 134 133
165 30 51513 12198 53782 69 69
166 35 74163 26924 127748 119 119
167 32 51633 12716 86839 119 119
168 27 75345 8172 44830 75 65
169 20 33416 10855 77395 63 61
170 18 83305 11932 89324 55 49
171 31 98952 14300 103300 103 101
172 31 102372 25515 112283 197 196
173 21 37238 2805 10901 16 15
174 39 103772 29402 120691 140 136
175 41 123969 16440 58106 89 89
176 13 27142 11221 57140 40 40
177 32 135400 28732 122422 125 123
178 18 21399 5250 25899 21 21
179 39 130115 28608 139296 167 163
180 14 24874 8092 52678 32 29
181 7 34988 4473 23853 36 35
182 17 45549 1572 17306 13 13
183 0 6023 2065 7953 5 5
184 30 64466 14817 89455 96 96
185 37 54990 16714 147866 151 151
186 0 1644 556 4245 6 6
187 5 6179 2089 21509 13 13
188 1 3926 2658 7670 3 3
189 16 32755 10695 66675 57 56
190 32 34777 1669 14336 23 23
191 24 73224 16267 53608 61 57
192 17 27114 7768 30059 21 14
193 11 20760 7252 29668 43 43
194 24 37636 6387 22097 20 20
195 22 65461 18715 96841 82 72
196 12 30080 7936 41907 90 87
197 19 24094 8643 27080 25 21
198 13 69008 7294 35885 60 56
199 17 54968 4570 41247 61 59
200 15 46090 7185 28313 85 82
201 16 27507 10058 36845 43 43
202 24 10672 2342 16548 25 25
203 15 34029 8509 36134 41 38
204 17 46300 13275 55764 26 25
205 18 24760 6816 28910 38 38
206 20 18779 1930 13339 12 12
207 16 21280 8086 25319 29 29
208 16 40662 10737 66956 49 47
209 18 28987 8033 47487 46 45
210 22 22827 7058 52785 41 40
211 8 18513 6782 44683 31 30
212 17 30594 5401 35619 41 41
213 18 24006 6521 21920 26 25
214 16 27913 10856 45608 23 23
215 23 42744 2154 7721 14 14
216 22 12934 6117 20634 16 16
217 13 22574 5238 29788 25 26
218 13 41385 4820 31931 21 21
219 16 18653 5615 37754 32 27
220 16 18472 4272 32505 9 9
221 20 30976 8702 40557 35 33
222 22 63339 15340 94238 42 42
223 17 25568 8030 44197 68 68
224 18 33747 9526 43228 32 32
225 17 4154 1278 4103 6 6
226 12 19474 4236 44144 68 67
227 7 35130 3023 32868 33 33
228 17 39067 7196 27640 84 77
229 14 13310 3394 14063 46 46
230 23 65892 6371 28990 30 30
231 17 4143 1574 4694 0 0
232 14 28579 9620 42648 36 36
233 15 51776 6978 64329 47 46
234 17 21152 4911 21928 20 18
235 21 38084 8645 25836 50 48
236 18 27717 8987 22779 30 29
237 18 32928 5544 40820 30 28
238 17 11342 3083 27530 34 34
239 17 19499 6909 32378 33 33
240 16 16380 3189 10824 34 34
241 15 36874 6745 39613 37 33
242 21 48259 16724 60865 83 80
243 16 16734 4850 19787 32 32
244 14 28207 7025 20107 30 30
245 15 30143 6047 36605 43 41
246 17 41369 7377 40961 41 41
247 15 45833 9078 48231 51 51
248 15 29156 4605 39725 19 18
249 10 35944 3238 21455 37 34
250 6 36278 8100 23430 33 31
251 22 45588 9653 62991 41 39
252 21 45097 8914 49363 54 54
253 1 3895 786 9604 14 14
254 18 28394 6700 24552 25 24
255 17 18632 5788 31493 25 24
256 4 2325 593 3439 8 8
257 10 25139 4506 19555 26 26
258 16 27975 6382 21228 20 19
259 16 14483 5621 23177 11 11
260 9 13127 3997 22094 14 14
261 16 5839 520 2342 3 1
262 17 24069 8891 38798 40 39
263 7 3738 999 3255 5 5
264 15 18625 7067 24261 38 37
265 14 36341 4639 18511 32 32
266 14 24548 5654 40798 41 38
267 18 21792 6928 28893 46 47
268 12 26263 1514 21425 47 47
269 16 23686 9238 50276 37 37
270 21 49303 8204 37643 51 51
271 19 25659 5926 30377 49 45
272 16 28904 5785 27126 21 21
273 1 2781 4 13 1 1
274 16 29236 5930 42097 44 42
275 10 19546 3710 24451 26 26
276 19 22818 705 14335 21 21
277 12 32689 443 5084 4 4
278 2 5752 2416 9927 10 10
279 14 22197 7747 43527 43 43
280 17 20055 5432 27184 34 34
281 19 25272 4913 21610 32 31
282 14 82206 2650 20484 20 19
283 11 32073 2370 20156 34 34
284 4 5444 775 6012 6 6
285 16 20154 5576 18475 12 11
286 20 36944 1352 12645 24 24
287 12 8019 3080 11017 16 16
288 15 30884 10205 37623 72 72
289 16 19540 6095 35873 27 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) totsize totrevisions totseconds tothyperlinks
1.115e+01 9.413e-05 2.585e-05 4.967e-05 7.822e-02
totblogs
-3.542e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-24.0469 -4.2228 -0.0887 4.0740 18.2241
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.115e+01 7.134e-01 15.628 < 2e-16 ***
totsize 9.413e-05 1.850e-05 5.087 6.62e-07 ***
totrevisions 2.585e-05 9.564e-05 0.270 0.7871
totseconds 4.967e-05 2.044e-05 2.431 0.0157 *
tothyperlinks 7.822e-02 1.346e-01 0.581 0.5617
totblogs -3.542e-02 1.388e-01 -0.255 0.7988
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 6.763 on 283 degrees of freedom
Multiple R-squared: 0.5996, Adjusted R-squared: 0.5925
F-statistic: 84.76 on 5 and 283 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,] 0.2294768 4.589536e-01 7.705232e-01
[2,] 0.1722682 3.445365e-01 8.277318e-01
[3,] 0.1629681 3.259362e-01 8.370319e-01
[4,] 0.3617171 7.234342e-01 6.382829e-01
[5,] 0.6673744 6.652511e-01 3.326256e-01
[6,] 0.5796478 8.407044e-01 4.203522e-01
[7,] 0.4867437 9.734874e-01 5.132563e-01
[8,] 0.4429280 8.858560e-01 5.570720e-01
[9,] 0.4625951 9.251902e-01 5.374049e-01
[10,] 0.6150881 7.698238e-01 3.849119e-01
[11,] 0.5707988 8.584025e-01 4.292012e-01
[12,] 0.9359014 1.281972e-01 6.409859e-02
[13,] 0.9182918 1.634164e-01 8.170821e-02
[14,] 0.9033692 1.932617e-01 9.663084e-02
[15,] 0.8766676 2.466648e-01 1.233324e-01
[16,] 0.8431110 3.137779e-01 1.568890e-01
[17,] 0.8455065 3.089871e-01 1.544935e-01
[18,] 0.8075214 3.849571e-01 1.924786e-01
[19,] 0.7667799 4.664402e-01 2.332201e-01
[20,] 0.7174298 5.651404e-01 2.825702e-01
[21,] 0.6662835 6.674330e-01 3.337165e-01
[22,] 0.6149928 7.700144e-01 3.850072e-01
[23,] 0.5584279 8.831441e-01 4.415721e-01
[24,] 0.5124462 9.751076e-01 4.875538e-01
[25,] 0.5316702 9.366596e-01 4.683298e-01
[26,] 0.5870144 8.259711e-01 4.129856e-01
[27,] 0.5976567 8.046866e-01 4.023433e-01
[28,] 0.5747770 8.504461e-01 4.252230e-01
[29,] 0.6230048 7.539903e-01 3.769952e-01
[30,] 0.5977618 8.044764e-01 4.022382e-01
[31,] 0.5969114 8.061773e-01 4.030886e-01
[32,] 0.7989505 4.020989e-01 2.010495e-01
[33,] 0.7632618 4.734765e-01 2.367382e-01
[34,] 0.7557297 4.885407e-01 2.442703e-01
[35,] 0.7256133 5.487734e-01 2.743867e-01
[36,] 0.6831407 6.337186e-01 3.168593e-01
[37,] 0.6388083 7.223835e-01 3.611917e-01
[38,] 0.6623964 6.752073e-01 3.376036e-01
[39,] 0.6239749 7.520502e-01 3.760251e-01
[40,] 0.5806107 8.387785e-01 4.193893e-01
[41,] 0.5607306 8.785388e-01 4.392694e-01
[42,] 0.6816941 6.366118e-01 3.183059e-01
[43,] 0.6489713 7.020574e-01 3.510287e-01
[44,] 0.6413998 7.172003e-01 3.586002e-01
[45,] 0.6335211 7.329578e-01 3.664789e-01
[46,] 0.6241129 7.517741e-01 3.758871e-01
[47,] 0.5902489 8.195023e-01 4.097511e-01
[48,] 0.5506133 8.987733e-01 4.493867e-01
[49,] 0.6332015 7.335970e-01 3.667985e-01
[50,] 0.6018541 7.962917e-01 3.981459e-01
[51,] 0.5637838 8.724323e-01 4.362162e-01
[52,] 0.5630227 8.739545e-01 4.369773e-01
[53,] 0.5220274 9.559451e-01 4.779726e-01
[54,] 0.4924198 9.848396e-01 5.075802e-01
[55,] 0.6721234 6.557532e-01 3.278766e-01
[56,] 0.6385146 7.229708e-01 3.614854e-01
[57,] 0.8038693 3.922614e-01 1.961307e-01
[58,] 0.7781209 4.437582e-01 2.218791e-01
[59,] 0.7493422 5.013155e-01 2.506578e-01
[60,] 0.7228968 5.542065e-01 2.771032e-01
[61,] 0.8849493 2.301015e-01 1.150507e-01
[62,] 0.9209273 1.581454e-01 7.907272e-02
[63,] 0.9069634 1.860731e-01 9.303656e-02
[64,] 0.8921802 2.156396e-01 1.078198e-01
[65,] 0.8742257 2.515487e-01 1.257743e-01
[66,] 0.8700865 2.598269e-01 1.299135e-01
[67,] 0.8720724 2.558552e-01 1.279276e-01
[68,] 0.8629525 2.740950e-01 1.370475e-01
[69,] 0.8510732 2.978536e-01 1.489268e-01
[70,] 0.8958816 2.082368e-01 1.041184e-01
[71,] 0.8882682 2.234636e-01 1.117318e-01
[72,] 0.9223510 1.552980e-01 7.764902e-02
[73,] 0.9089890 1.820220e-01 9.101098e-02
[74,] 0.9055255 1.889489e-01 9.447445e-02
[75,] 0.8910646 2.178708e-01 1.089354e-01
[76,] 0.8838065 2.323869e-01 1.161935e-01
[77,] 0.8858102 2.283796e-01 1.141898e-01
[78,] 0.8733019 2.533963e-01 1.266981e-01
[79,] 0.8783130 2.433740e-01 1.216870e-01
[80,] 0.8595908 2.808184e-01 1.404092e-01
[81,] 0.8485963 3.028075e-01 1.514037e-01
[82,] 0.8362174 3.275652e-01 1.637826e-01
[83,] 0.8183924 3.632151e-01 1.816076e-01
[84,] 0.9145554 1.708892e-01 8.544459e-02
[85,] 0.9146902 1.706196e-01 8.530979e-02
[86,] 0.9002662 1.994676e-01 9.973382e-02
[87,] 0.8880352 2.239295e-01 1.119648e-01
[88,] 0.8703012 2.593976e-01 1.296988e-01
[89,] 0.9538590 9.228197e-02 4.614099e-02
[90,] 0.9482631 1.034738e-01 5.173689e-02
[91,] 0.9564110 8.717795e-02 4.358897e-02
[92,] 0.9481973 1.036054e-01 5.180268e-02
[93,] 0.9407322 1.185356e-01 5.926779e-02
[94,] 0.9402716 1.194568e-01 5.972839e-02
[95,] 0.9346873 1.306254e-01 6.531272e-02
[96,] 0.9280287 1.439427e-01 7.197135e-02
[97,] 0.9408082 1.183836e-01 5.919182e-02
[98,] 0.9604770 7.904609e-02 3.952305e-02
[99,] 0.9867837 2.643265e-02 1.321633e-02
[100,] 0.9844217 3.115660e-02 1.557830e-02
[101,] 0.9992500 1.500100e-03 7.500500e-04
[102,] 0.9994513 1.097450e-03 5.487252e-04
[103,] 0.9994329 1.134108e-03 5.670542e-04
[104,] 0.9996945 6.110976e-04 3.055488e-04
[105,] 0.9996053 7.894567e-04 3.947283e-04
[106,] 0.9995327 9.346989e-04 4.673495e-04
[107,] 0.9994883 1.023317e-03 5.116583e-04
[108,] 0.9993137 1.372571e-03 6.862853e-04
[109,] 0.9990791 1.841795e-03 9.208975e-04
[110,] 0.9990647 1.870582e-03 9.352912e-04
[111,] 0.9997987 4.026054e-04 2.013027e-04
[112,] 0.9997542 4.915318e-04 2.457659e-04
[113,] 0.9996945 6.110747e-04 3.055374e-04
[114,] 0.9996806 6.387077e-04 3.193539e-04
[115,] 0.9996326 7.347789e-04 3.673895e-04
[116,] 0.9995092 9.816449e-04 4.908224e-04
[117,] 0.9994014 1.197186e-03 5.985928e-04
[118,] 0.9995049 9.901548e-04 4.950774e-04
[119,] 0.9995375 9.250111e-04 4.625056e-04
[120,] 0.9994141 1.171746e-03 5.858732e-04
[121,] 0.9995845 8.310456e-04 4.155228e-04
[122,] 0.9998309 3.382817e-04 1.691409e-04
[123,] 0.9998136 3.728217e-04 1.864108e-04
[124,] 0.9997819 4.361030e-04 2.180515e-04
[125,] 0.9997786 4.428262e-04 2.214131e-04
[126,] 0.9997798 4.403394e-04 2.201697e-04
[127,] 0.9999105 1.789651e-04 8.948253e-05
[128,] 0.9999141 1.717847e-04 8.589233e-05
[129,] 0.9999689 6.226369e-05 3.113184e-05
[130,] 0.9999690 6.201399e-05 3.100700e-05
[131,] 0.9999646 7.082563e-05 3.541281e-05
[132,] 0.9999690 6.199805e-05 3.099903e-05
[133,] 0.9999583 8.333084e-05 4.166542e-05
[134,] 0.9999585 8.300950e-05 4.150475e-05
[135,] 0.9999513 9.742703e-05 4.871351e-05
[136,] 0.9999347 1.306827e-04 6.534136e-05
[137,] 0.9999549 9.028292e-05 4.514146e-05
[138,] 0.9999534 9.322815e-05 4.661407e-05
[139,] 0.9999897 2.053108e-05 1.026554e-05
[140,] 0.9999867 2.665475e-05 1.332737e-05
[141,] 0.9999859 2.811420e-05 1.405710e-05
[142,] 0.9999906 1.881382e-05 9.406911e-06
[143,] 0.9999939 1.211576e-05 6.057882e-06
[144,] 0.9999984 3.247727e-06 1.623864e-06
[145,] 0.9999980 3.906901e-06 1.953451e-06
[146,] 0.9999978 4.404019e-06 2.202010e-06
[147,] 0.9999969 6.157824e-06 3.078912e-06
[148,] 0.9999981 3.808029e-06 1.904014e-06
[149,] 0.9999975 5.036917e-06 2.518459e-06
[150,] 0.9999969 6.238250e-06 3.119125e-06
[151,] 0.9999970 6.010836e-06 3.005418e-06
[152,] 0.9999957 8.506660e-06 4.253330e-06
[153,] 0.9999938 1.230009e-05 6.150043e-06
[154,] 0.9999936 1.272753e-05 6.363764e-06
[155,] 0.9999929 1.413737e-05 7.068684e-06
[156,] 0.9999909 1.812652e-05 9.063260e-06
[157,] 0.9999927 1.461472e-05 7.307359e-06
[158,] 0.9999906 1.881203e-05 9.406016e-06
[159,] 0.9999921 1.580906e-05 7.904529e-06
[160,] 0.9999914 1.711925e-05 8.559625e-06
[161,] 0.9999878 2.434825e-05 1.217413e-05
[162,] 0.9999897 2.062349e-05 1.031175e-05
[163,] 0.9999848 3.036472e-05 1.518236e-05
[164,] 0.9999824 3.511007e-05 1.755503e-05
[165,] 0.9999816 3.687750e-05 1.843875e-05
[166,] 0.9999771 4.582651e-05 2.291326e-05
[167,] 0.9999883 2.342395e-05 1.171198e-05
[168,] 0.9999879 2.427064e-05 1.213532e-05
[169,] 0.9999873 2.534279e-05 1.267140e-05
[170,] 0.9999833 3.331576e-05 1.665788e-05
[171,] 0.9999755 4.891372e-05 2.445686e-05
[172,] 0.9999680 6.400089e-05 3.200045e-05
[173,] 0.9999824 3.523165e-05 1.761583e-05
[174,] 0.9999748 5.034936e-05 2.517468e-05
[175,] 0.9999918 1.630277e-05 8.151385e-06
[176,] 0.9999898 2.044045e-05 1.022023e-05
[177,] 0.9999966 6.882472e-06 3.441236e-06
[178,] 0.9999990 1.967514e-06 9.837568e-07
[179,] 0.9999993 1.443775e-06 7.218875e-07
[180,] 0.9999998 3.147771e-07 1.573885e-07
[181,] 0.9999998 4.772425e-07 2.386213e-07
[182,] 1.0000000 5.912055e-09 2.956028e-09
[183,] 1.0000000 1.054614e-08 5.273071e-09
[184,] 1.0000000 1.850645e-08 9.253224e-09
[185,] 1.0000000 2.074192e-08 1.037096e-08
[186,] 1.0000000 1.441476e-08 7.207379e-09
[187,] 1.0000000 2.340782e-08 1.170391e-08
[188,] 1.0000000 2.489597e-08 1.244799e-08
[189,] 1.0000000 3.963913e-08 1.981956e-08
[190,] 1.0000000 3.011949e-08 1.505975e-08
[191,] 1.0000000 4.965303e-08 2.482652e-08
[192,] 1.0000000 6.759560e-08 3.379780e-08
[193,] 0.9999999 1.124374e-07 5.621869e-08
[194,] 1.0000000 1.687797e-08 8.438984e-09
[195,] 1.0000000 2.623745e-08 1.311873e-08
[196,] 1.0000000 3.537698e-08 1.768849e-08
[197,] 1.0000000 5.756933e-08 2.878466e-08
[198,] 1.0000000 3.516098e-08 1.758049e-08
[199,] 1.0000000 6.341385e-08 3.170693e-08
[200,] 1.0000000 9.151478e-08 4.575739e-08
[201,] 0.9999999 1.557882e-07 7.789411e-08
[202,] 0.9999999 1.099437e-07 5.497187e-08
[203,] 1.0000000 7.610855e-08 3.805427e-08
[204,] 0.9999999 1.254483e-07 6.272415e-08
[205,] 0.9999999 2.044821e-07 1.022410e-07
[206,] 0.9999998 3.313256e-07 1.656628e-07
[207,] 0.9999999 1.366961e-07 6.834806e-08
[208,] 1.0000000 6.788998e-08 3.394499e-08
[209,] 0.9999999 1.198840e-07 5.994202e-08
[210,] 0.9999999 1.863731e-07 9.318653e-08
[211,] 0.9999998 3.317121e-07 1.658561e-07
[212,] 0.9999997 5.086904e-07 2.543452e-07
[213,] 0.9999996 7.856403e-07 3.928201e-07
[214,] 0.9999993 1.347227e-06 6.736137e-07
[215,] 0.9999988 2.314620e-06 1.157310e-06
[216,] 0.9999980 4.054851e-06 2.027426e-06
[217,] 0.9999986 2.753462e-06 1.376731e-06
[218,] 0.9999979 4.279471e-06 2.139735e-06
[219,] 0.9999988 2.402261e-06 1.201131e-06
[220,] 0.9999980 4.040194e-06 2.020097e-06
[221,] 0.9999966 6.761906e-06 3.380953e-06
[222,] 0.9999961 7.862151e-06 3.931075e-06
[223,] 0.9999978 4.308383e-06 2.154192e-06
[224,] 0.9999968 6.388831e-06 3.194415e-06
[225,] 0.9999965 6.932020e-06 3.466010e-06
[226,] 0.9999952 9.510067e-06 4.755033e-06
[227,] 0.9999939 1.211754e-05 6.058769e-06
[228,] 0.9999913 1.742416e-05 8.712080e-06
[229,] 0.9999853 2.942628e-05 1.471314e-05
[230,] 0.9999819 3.629995e-05 1.814998e-05
[231,] 0.9999718 5.642415e-05 2.821207e-05
[232,] 0.9999689 6.228233e-05 3.114116e-05
[233,] 0.9999513 9.733177e-05 4.866588e-05
[234,] 0.9999292 1.415222e-04 7.076111e-05
[235,] 0.9999068 1.864672e-04 9.323361e-05
[236,] 0.9998417 3.165896e-04 1.582948e-04
[237,] 0.9997411 5.178874e-04 2.589437e-04
[238,] 0.9995724 8.551037e-04 4.275518e-04
[239,] 0.9995246 9.507448e-04 4.753724e-04
[240,] 0.9992240 1.551982e-03 7.759909e-04
[241,] 0.9991727 1.654517e-03 8.272584e-04
[242,] 0.9999114 1.771197e-04 8.855984e-05
[243,] 0.9998401 3.198206e-04 1.599103e-04
[244,] 0.9997222 5.556972e-04 2.778486e-04
[245,] 0.9998261 3.477180e-04 1.738590e-04
[246,] 0.9997098 5.804465e-04 2.902233e-04
[247,] 0.9995766 8.467955e-04 4.233978e-04
[248,] 0.9994727 1.054550e-03 5.272749e-04
[249,] 0.9992953 1.409385e-03 7.046925e-04
[250,] 0.9987485 2.502908e-03 1.251454e-03
[251,] 0.9984428 3.114352e-03 1.557176e-03
[252,] 0.9974815 5.037006e-03 2.518503e-03
[253,] 0.9984038 3.192324e-03 1.596162e-03
[254,] 0.9971382 5.723620e-03 2.861810e-03
[255,] 0.9950669 9.866266e-03 4.933133e-03
[256,] 0.9915297 1.694061e-02 8.470304e-03
[257,] 0.9862235 2.755300e-02 1.377650e-02
[258,] 0.9792048 4.159049e-02 2.079525e-02
[259,] 0.9712773 5.744541e-02 2.872271e-02
[260,] 0.9575714 8.485715e-02 4.242858e-02
[261,] 0.9342015 1.315971e-01 6.579853e-02
[262,] 0.9042772 1.914457e-01 9.572285e-02
[263,] 0.8616723 2.766554e-01 1.383277e-01
[264,] 0.8190552 3.618895e-01 1.809448e-01
[265,] 0.8371340 3.257320e-01 1.628660e-01
[266,] 0.7662062 4.675875e-01 2.337938e-01
[267,] 0.6955625 6.088750e-01 3.044375e-01
[268,] 0.7129876 5.740247e-01 2.870124e-01
[269,] 0.6049087 7.901826e-01 3.950913e-01
[270,] 0.6897441 6.205119e-01 3.102559e-01
[271,] 0.5484990 9.030020e-01 4.515010e-01
[272,] 0.5115469 9.769062e-01 4.884531e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1rlow1324312896.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29kce1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/34zey1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4hcdq1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5pguy1324312896.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 = 289
Frequency = 1
1 2 3 4 5 6
-5.73720530 -0.96720654 10.18816732 -2.20763116 0.43450528 4.34544883
7 8 9 10 11 12
-14.57834683 4.11676008 -4.16666230 -5.13662762 0.29216547 7.28830370
13 14 15 16 17 18
18.22405775 7.66716808 10.56269086 9.18805319 12.12953006 -7.72983445
19 20 21 22 23 24
0.33586575 -8.93478396 1.47717168 3.27695372 0.59144567 -0.87525540
25 26 27 28 29 30
9.57565219 5.99878694 2.63685686 -0.23628869 2.83910026 5.53918588
31 32 33 34 35 36
0.22368643 -0.67332361 13.82830330 9.04120604 10.48863484 -5.08573311
37 38 39 40 41 42
-5.83570096 8.43423348 4.56847952 -11.39450487 3.33735442 -5.05451711
43 44 45 46 47 48
-0.09499200 -0.11147265 1.19006346 10.88309577 -0.97007867 0.15982132
49 50 51 52 53 54
7.04508073 15.27780608 6.17684575 7.80789105 9.51557674 -0.42683536
55 56 57 58 59 60
5.16249677 -1.34469613 -8.82596710 2.25788157 1.54787880 -4.04412653
61 62 63 64 65 66
-0.41049955 4.58496484 -15.70249718 -0.89825611 -13.33681752 -3.90754551
67 68 69 70 71 72
3.38186870 -1.42930243 -21.06329926 13.57161124 2.69542344 -2.89921857
73 74 75 76 77 78
-1.84435403 -3.17388594 7.27184030 -2.73664578 -3.17427046 13.73410779
79 80 81 82 83 84
7.31258456 13.17456709 -0.53483345 7.02441617 3.06075728 -5.22909701
85 86 87 88 89 90
-7.56588394 5.09425567 8.91992367 1.07501943 4.41355889 5.92653115
91 92 93 94 95 96
3.73082729 -14.01502482 8.82398266 0.38312963 1.25346333 0.60725541
97 98 99 100 101 102
17.83463803 2.81679754 10.16102030 -1.40029942 2.43712445 7.24138863
103 104 105 106 107 108
-4.82204942 -3.98829461 -10.36358432 -13.37830071 15.61308454 3.97783323
109 110 111 112 113 114
-24.04689690 9.88816426 -7.07013013 11.65863114 0.46911584 -2.30345731
115 116 117 118 119 120
-2.58966440 1.43468123 0.71129685 -4.90764275 -16.90178807 -0.77093446
121 122 123 124 125 126
-1.48958742 -2.84573695 -2.62120626 -0.45383896 -3.64127479 -6.22053542
127 128 129 130 131 132
-5.67503564 -1.21835522 10.01824760 -11.02610938 -3.62331976 5.58569376
133 134 135 136 137 138
-5.27416130 7.17431620 11.80867830 -8.12590608 -11.73847769 -6.32807519
139 140 141 142 143 144
-4.05771255 -6.90338351 -0.70274345 -5.63359060 4.07540184 2.29008239
145 146 147 148 149 150
10.90198551 6.13256891 15.41000879 -2.78528279 -5.40304504 9.97411772
151 152 153 154 155 156
10.01121534 10.91866532 -4.51417884 3.69164590 1.45776008 -7.78307448
157 158 159 160 161 162
4.26445741 -4.22284258 6.91720256 2.59274533 1.58422333 7.51734780
163 164 165 166 167 168
-5.64446115 0.34535574 8.06224613 4.73564239 6.25585196 2.75668653
169 170 171 172 173 174
-1.18657469 -8.30245186 0.55685033 -4.48833469 5.01146779 5.19465077
175 176 177 178 179 180
11.06154357 -5.54423312 -4.13846716 2.51565572 0.65588603 -3.79213033
181 182 183 184 185 186
-10.31909000 0.10665735 -12.37850580 3.84783179 6.43567649 -11.78589398
187 188 189 190 191 192
-8.40953195 -11.09679971 -4.29553413 15.83773795 0.12265677 0.45799478
193 194 195 196 197 198
-5.60461286 7.18950113 -4.46862329 -8.22508680 2.80272606 -9.32534359
199 200 201 202 203 204
-4.17161324 -5.82348555 -1.66877071 9.89391393 -3.22801434 -2.76867883
205 206 207 208 209 210
1.28174961 5.85715056 0.14000748 -4.74795664 -0.44816355 4.10768429
211 212 213 214 215 216
-8.64871192 -0.69247187 2.18566202 -1.30705635 6.78898126 7.76556087
217 218 219 220 221 222
-2.92356858 -4.65417204 -0.47201290 1.00182976 2.12685085 -1.98638570
223 224 225 226 227 228
-1.86884089 -0.08874572 4.96622391 -6.22990767 -10.57897924 -3.22815194
229 230 231 232 233 234
-1.15680995 2.75980643 5.18700603 -3.74706453 -6.44545127 1.71687280
235 236 237 238 239 240
2.54859634 1.55874603 0.22564917 1.88103554 0.81626557 1.23391283
241 242 243 244 245 246
-3.48728102 -1.80566677 0.79800696 -2.26850368 -2.87212879 -2.02318177
247 248 249 250 251 252
-5.27641702 -1.83447540 -7.37167138 -11.42032052 1.35567515 0.61249458
253 254 255 256 257 258
-11.61227436 1.68003650 1.27773929 -7.89650238 -5.71599390 0.10677850
259 260 261 262 263 264
1.72021723 -5.18473674 3.97221752 -0.31903392 -4.90248823 -0.95173162
265 266 267 268 269 270
-2.97878995 -3.49342282 1.25216152 -4.73598335 -1.69830862 0.94548940
271 272 273 274 275 276
1.53485223 -0.26558215 -10.45446707 -2.09932851 -5.41214104 4.07401357
277 278 279 280 281 282
-2.66137506 -10.67409048 -3.44110746 1.01732669 2.86670557 -6.86462487
283 284 285 286 287 288
-5.68567558 -8.23702913 1.34291229 3.68315624 -1.21555381 -4.27008865
289
-0.29593930
> postscript(file="/var/wessaorg/rcomp/tmp/667jw1324312896.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.73720530 NA
1 -0.96720654 -5.73720530
2 10.18816732 -0.96720654
3 -2.20763116 10.18816732
4 0.43450528 -2.20763116
5 4.34544883 0.43450528
6 -14.57834683 4.34544883
7 4.11676008 -14.57834683
8 -4.16666230 4.11676008
9 -5.13662762 -4.16666230
10 0.29216547 -5.13662762
11 7.28830370 0.29216547
12 18.22405775 7.28830370
13 7.66716808 18.22405775
14 10.56269086 7.66716808
15 9.18805319 10.56269086
16 12.12953006 9.18805319
17 -7.72983445 12.12953006
18 0.33586575 -7.72983445
19 -8.93478396 0.33586575
20 1.47717168 -8.93478396
21 3.27695372 1.47717168
22 0.59144567 3.27695372
23 -0.87525540 0.59144567
24 9.57565219 -0.87525540
25 5.99878694 9.57565219
26 2.63685686 5.99878694
27 -0.23628869 2.63685686
28 2.83910026 -0.23628869
29 5.53918588 2.83910026
30 0.22368643 5.53918588
31 -0.67332361 0.22368643
32 13.82830330 -0.67332361
33 9.04120604 13.82830330
34 10.48863484 9.04120604
35 -5.08573311 10.48863484
36 -5.83570096 -5.08573311
37 8.43423348 -5.83570096
38 4.56847952 8.43423348
39 -11.39450487 4.56847952
40 3.33735442 -11.39450487
41 -5.05451711 3.33735442
42 -0.09499200 -5.05451711
43 -0.11147265 -0.09499200
44 1.19006346 -0.11147265
45 10.88309577 1.19006346
46 -0.97007867 10.88309577
47 0.15982132 -0.97007867
48 7.04508073 0.15982132
49 15.27780608 7.04508073
50 6.17684575 15.27780608
51 7.80789105 6.17684575
52 9.51557674 7.80789105
53 -0.42683536 9.51557674
54 5.16249677 -0.42683536
55 -1.34469613 5.16249677
56 -8.82596710 -1.34469613
57 2.25788157 -8.82596710
58 1.54787880 2.25788157
59 -4.04412653 1.54787880
60 -0.41049955 -4.04412653
61 4.58496484 -0.41049955
62 -15.70249718 4.58496484
63 -0.89825611 -15.70249718
64 -13.33681752 -0.89825611
65 -3.90754551 -13.33681752
66 3.38186870 -3.90754551
67 -1.42930243 3.38186870
68 -21.06329926 -1.42930243
69 13.57161124 -21.06329926
70 2.69542344 13.57161124
71 -2.89921857 2.69542344
72 -1.84435403 -2.89921857
73 -3.17388594 -1.84435403
74 7.27184030 -3.17388594
75 -2.73664578 7.27184030
76 -3.17427046 -2.73664578
77 13.73410779 -3.17427046
78 7.31258456 13.73410779
79 13.17456709 7.31258456
80 -0.53483345 13.17456709
81 7.02441617 -0.53483345
82 3.06075728 7.02441617
83 -5.22909701 3.06075728
84 -7.56588394 -5.22909701
85 5.09425567 -7.56588394
86 8.91992367 5.09425567
87 1.07501943 8.91992367
88 4.41355889 1.07501943
89 5.92653115 4.41355889
90 3.73082729 5.92653115
91 -14.01502482 3.73082729
92 8.82398266 -14.01502482
93 0.38312963 8.82398266
94 1.25346333 0.38312963
95 0.60725541 1.25346333
96 17.83463803 0.60725541
97 2.81679754 17.83463803
98 10.16102030 2.81679754
99 -1.40029942 10.16102030
100 2.43712445 -1.40029942
101 7.24138863 2.43712445
102 -4.82204942 7.24138863
103 -3.98829461 -4.82204942
104 -10.36358432 -3.98829461
105 -13.37830071 -10.36358432
106 15.61308454 -13.37830071
107 3.97783323 15.61308454
108 -24.04689690 3.97783323
109 9.88816426 -24.04689690
110 -7.07013013 9.88816426
111 11.65863114 -7.07013013
112 0.46911584 11.65863114
113 -2.30345731 0.46911584
114 -2.58966440 -2.30345731
115 1.43468123 -2.58966440
116 0.71129685 1.43468123
117 -4.90764275 0.71129685
118 -16.90178807 -4.90764275
119 -0.77093446 -16.90178807
120 -1.48958742 -0.77093446
121 -2.84573695 -1.48958742
122 -2.62120626 -2.84573695
123 -0.45383896 -2.62120626
124 -3.64127479 -0.45383896
125 -6.22053542 -3.64127479
126 -5.67503564 -6.22053542
127 -1.21835522 -5.67503564
128 10.01824760 -1.21835522
129 -11.02610938 10.01824760
130 -3.62331976 -11.02610938
131 5.58569376 -3.62331976
132 -5.27416130 5.58569376
133 7.17431620 -5.27416130
134 11.80867830 7.17431620
135 -8.12590608 11.80867830
136 -11.73847769 -8.12590608
137 -6.32807519 -11.73847769
138 -4.05771255 -6.32807519
139 -6.90338351 -4.05771255
140 -0.70274345 -6.90338351
141 -5.63359060 -0.70274345
142 4.07540184 -5.63359060
143 2.29008239 4.07540184
144 10.90198551 2.29008239
145 6.13256891 10.90198551
146 15.41000879 6.13256891
147 -2.78528279 15.41000879
148 -5.40304504 -2.78528279
149 9.97411772 -5.40304504
150 10.01121534 9.97411772
151 10.91866532 10.01121534
152 -4.51417884 10.91866532
153 3.69164590 -4.51417884
154 1.45776008 3.69164590
155 -7.78307448 1.45776008
156 4.26445741 -7.78307448
157 -4.22284258 4.26445741
158 6.91720256 -4.22284258
159 2.59274533 6.91720256
160 1.58422333 2.59274533
161 7.51734780 1.58422333
162 -5.64446115 7.51734780
163 0.34535574 -5.64446115
164 8.06224613 0.34535574
165 4.73564239 8.06224613
166 6.25585196 4.73564239
167 2.75668653 6.25585196
168 -1.18657469 2.75668653
169 -8.30245186 -1.18657469
170 0.55685033 -8.30245186
171 -4.48833469 0.55685033
172 5.01146779 -4.48833469
173 5.19465077 5.01146779
174 11.06154357 5.19465077
175 -5.54423312 11.06154357
176 -4.13846716 -5.54423312
177 2.51565572 -4.13846716
178 0.65588603 2.51565572
179 -3.79213033 0.65588603
180 -10.31909000 -3.79213033
181 0.10665735 -10.31909000
182 -12.37850580 0.10665735
183 3.84783179 -12.37850580
184 6.43567649 3.84783179
185 -11.78589398 6.43567649
186 -8.40953195 -11.78589398
187 -11.09679971 -8.40953195
188 -4.29553413 -11.09679971
189 15.83773795 -4.29553413
190 0.12265677 15.83773795
191 0.45799478 0.12265677
192 -5.60461286 0.45799478
193 7.18950113 -5.60461286
194 -4.46862329 7.18950113
195 -8.22508680 -4.46862329
196 2.80272606 -8.22508680
197 -9.32534359 2.80272606
198 -4.17161324 -9.32534359
199 -5.82348555 -4.17161324
200 -1.66877071 -5.82348555
201 9.89391393 -1.66877071
202 -3.22801434 9.89391393
203 -2.76867883 -3.22801434
204 1.28174961 -2.76867883
205 5.85715056 1.28174961
206 0.14000748 5.85715056
207 -4.74795664 0.14000748
208 -0.44816355 -4.74795664
209 4.10768429 -0.44816355
210 -8.64871192 4.10768429
211 -0.69247187 -8.64871192
212 2.18566202 -0.69247187
213 -1.30705635 2.18566202
214 6.78898126 -1.30705635
215 7.76556087 6.78898126
216 -2.92356858 7.76556087
217 -4.65417204 -2.92356858
218 -0.47201290 -4.65417204
219 1.00182976 -0.47201290
220 2.12685085 1.00182976
221 -1.98638570 2.12685085
222 -1.86884089 -1.98638570
223 -0.08874572 -1.86884089
224 4.96622391 -0.08874572
225 -6.22990767 4.96622391
226 -10.57897924 -6.22990767
227 -3.22815194 -10.57897924
228 -1.15680995 -3.22815194
229 2.75980643 -1.15680995
230 5.18700603 2.75980643
231 -3.74706453 5.18700603
232 -6.44545127 -3.74706453
233 1.71687280 -6.44545127
234 2.54859634 1.71687280
235 1.55874603 2.54859634
236 0.22564917 1.55874603
237 1.88103554 0.22564917
238 0.81626557 1.88103554
239 1.23391283 0.81626557
240 -3.48728102 1.23391283
241 -1.80566677 -3.48728102
242 0.79800696 -1.80566677
243 -2.26850368 0.79800696
244 -2.87212879 -2.26850368
245 -2.02318177 -2.87212879
246 -5.27641702 -2.02318177
247 -1.83447540 -5.27641702
248 -7.37167138 -1.83447540
249 -11.42032052 -7.37167138
250 1.35567515 -11.42032052
251 0.61249458 1.35567515
252 -11.61227436 0.61249458
253 1.68003650 -11.61227436
254 1.27773929 1.68003650
255 -7.89650238 1.27773929
256 -5.71599390 -7.89650238
257 0.10677850 -5.71599390
258 1.72021723 0.10677850
259 -5.18473674 1.72021723
260 3.97221752 -5.18473674
261 -0.31903392 3.97221752
262 -4.90248823 -0.31903392
263 -0.95173162 -4.90248823
264 -2.97878995 -0.95173162
265 -3.49342282 -2.97878995
266 1.25216152 -3.49342282
267 -4.73598335 1.25216152
268 -1.69830862 -4.73598335
269 0.94548940 -1.69830862
270 1.53485223 0.94548940
271 -0.26558215 1.53485223
272 -10.45446707 -0.26558215
273 -2.09932851 -10.45446707
274 -5.41214104 -2.09932851
275 4.07401357 -5.41214104
276 -2.66137506 4.07401357
277 -10.67409048 -2.66137506
278 -3.44110746 -10.67409048
279 1.01732669 -3.44110746
280 2.86670557 1.01732669
281 -6.86462487 2.86670557
282 -5.68567558 -6.86462487
283 -8.23702913 -5.68567558
284 1.34291229 -8.23702913
285 3.68315624 1.34291229
286 -1.21555381 3.68315624
287 -4.27008865 -1.21555381
288 -0.29593930 -4.27008865
289 NA -0.29593930
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.96720654 -5.73720530
[2,] 10.18816732 -0.96720654
[3,] -2.20763116 10.18816732
[4,] 0.43450528 -2.20763116
[5,] 4.34544883 0.43450528
[6,] -14.57834683 4.34544883
[7,] 4.11676008 -14.57834683
[8,] -4.16666230 4.11676008
[9,] -5.13662762 -4.16666230
[10,] 0.29216547 -5.13662762
[11,] 7.28830370 0.29216547
[12,] 18.22405775 7.28830370
[13,] 7.66716808 18.22405775
[14,] 10.56269086 7.66716808
[15,] 9.18805319 10.56269086
[16,] 12.12953006 9.18805319
[17,] -7.72983445 12.12953006
[18,] 0.33586575 -7.72983445
[19,] -8.93478396 0.33586575
[20,] 1.47717168 -8.93478396
[21,] 3.27695372 1.47717168
[22,] 0.59144567 3.27695372
[23,] -0.87525540 0.59144567
[24,] 9.57565219 -0.87525540
[25,] 5.99878694 9.57565219
[26,] 2.63685686 5.99878694
[27,] -0.23628869 2.63685686
[28,] 2.83910026 -0.23628869
[29,] 5.53918588 2.83910026
[30,] 0.22368643 5.53918588
[31,] -0.67332361 0.22368643
[32,] 13.82830330 -0.67332361
[33,] 9.04120604 13.82830330
[34,] 10.48863484 9.04120604
[35,] -5.08573311 10.48863484
[36,] -5.83570096 -5.08573311
[37,] 8.43423348 -5.83570096
[38,] 4.56847952 8.43423348
[39,] -11.39450487 4.56847952
[40,] 3.33735442 -11.39450487
[41,] -5.05451711 3.33735442
[42,] -0.09499200 -5.05451711
[43,] -0.11147265 -0.09499200
[44,] 1.19006346 -0.11147265
[45,] 10.88309577 1.19006346
[46,] -0.97007867 10.88309577
[47,] 0.15982132 -0.97007867
[48,] 7.04508073 0.15982132
[49,] 15.27780608 7.04508073
[50,] 6.17684575 15.27780608
[51,] 7.80789105 6.17684575
[52,] 9.51557674 7.80789105
[53,] -0.42683536 9.51557674
[54,] 5.16249677 -0.42683536
[55,] -1.34469613 5.16249677
[56,] -8.82596710 -1.34469613
[57,] 2.25788157 -8.82596710
[58,] 1.54787880 2.25788157
[59,] -4.04412653 1.54787880
[60,] -0.41049955 -4.04412653
[61,] 4.58496484 -0.41049955
[62,] -15.70249718 4.58496484
[63,] -0.89825611 -15.70249718
[64,] -13.33681752 -0.89825611
[65,] -3.90754551 -13.33681752
[66,] 3.38186870 -3.90754551
[67,] -1.42930243 3.38186870
[68,] -21.06329926 -1.42930243
[69,] 13.57161124 -21.06329926
[70,] 2.69542344 13.57161124
[71,] -2.89921857 2.69542344
[72,] -1.84435403 -2.89921857
[73,] -3.17388594 -1.84435403
[74,] 7.27184030 -3.17388594
[75,] -2.73664578 7.27184030
[76,] -3.17427046 -2.73664578
[77,] 13.73410779 -3.17427046
[78,] 7.31258456 13.73410779
[79,] 13.17456709 7.31258456
[80,] -0.53483345 13.17456709
[81,] 7.02441617 -0.53483345
[82,] 3.06075728 7.02441617
[83,] -5.22909701 3.06075728
[84,] -7.56588394 -5.22909701
[85,] 5.09425567 -7.56588394
[86,] 8.91992367 5.09425567
[87,] 1.07501943 8.91992367
[88,] 4.41355889 1.07501943
[89,] 5.92653115 4.41355889
[90,] 3.73082729 5.92653115
[91,] -14.01502482 3.73082729
[92,] 8.82398266 -14.01502482
[93,] 0.38312963 8.82398266
[94,] 1.25346333 0.38312963
[95,] 0.60725541 1.25346333
[96,] 17.83463803 0.60725541
[97,] 2.81679754 17.83463803
[98,] 10.16102030 2.81679754
[99,] -1.40029942 10.16102030
[100,] 2.43712445 -1.40029942
[101,] 7.24138863 2.43712445
[102,] -4.82204942 7.24138863
[103,] -3.98829461 -4.82204942
[104,] -10.36358432 -3.98829461
[105,] -13.37830071 -10.36358432
[106,] 15.61308454 -13.37830071
[107,] 3.97783323 15.61308454
[108,] -24.04689690 3.97783323
[109,] 9.88816426 -24.04689690
[110,] -7.07013013 9.88816426
[111,] 11.65863114 -7.07013013
[112,] 0.46911584 11.65863114
[113,] -2.30345731 0.46911584
[114,] -2.58966440 -2.30345731
[115,] 1.43468123 -2.58966440
[116,] 0.71129685 1.43468123
[117,] -4.90764275 0.71129685
[118,] -16.90178807 -4.90764275
[119,] -0.77093446 -16.90178807
[120,] -1.48958742 -0.77093446
[121,] -2.84573695 -1.48958742
[122,] -2.62120626 -2.84573695
[123,] -0.45383896 -2.62120626
[124,] -3.64127479 -0.45383896
[125,] -6.22053542 -3.64127479
[126,] -5.67503564 -6.22053542
[127,] -1.21835522 -5.67503564
[128,] 10.01824760 -1.21835522
[129,] -11.02610938 10.01824760
[130,] -3.62331976 -11.02610938
[131,] 5.58569376 -3.62331976
[132,] -5.27416130 5.58569376
[133,] 7.17431620 -5.27416130
[134,] 11.80867830 7.17431620
[135,] -8.12590608 11.80867830
[136,] -11.73847769 -8.12590608
[137,] -6.32807519 -11.73847769
[138,] -4.05771255 -6.32807519
[139,] -6.90338351 -4.05771255
[140,] -0.70274345 -6.90338351
[141,] -5.63359060 -0.70274345
[142,] 4.07540184 -5.63359060
[143,] 2.29008239 4.07540184
[144,] 10.90198551 2.29008239
[145,] 6.13256891 10.90198551
[146,] 15.41000879 6.13256891
[147,] -2.78528279 15.41000879
[148,] -5.40304504 -2.78528279
[149,] 9.97411772 -5.40304504
[150,] 10.01121534 9.97411772
[151,] 10.91866532 10.01121534
[152,] -4.51417884 10.91866532
[153,] 3.69164590 -4.51417884
[154,] 1.45776008 3.69164590
[155,] -7.78307448 1.45776008
[156,] 4.26445741 -7.78307448
[157,] -4.22284258 4.26445741
[158,] 6.91720256 -4.22284258
[159,] 2.59274533 6.91720256
[160,] 1.58422333 2.59274533
[161,] 7.51734780 1.58422333
[162,] -5.64446115 7.51734780
[163,] 0.34535574 -5.64446115
[164,] 8.06224613 0.34535574
[165,] 4.73564239 8.06224613
[166,] 6.25585196 4.73564239
[167,] 2.75668653 6.25585196
[168,] -1.18657469 2.75668653
[169,] -8.30245186 -1.18657469
[170,] 0.55685033 -8.30245186
[171,] -4.48833469 0.55685033
[172,] 5.01146779 -4.48833469
[173,] 5.19465077 5.01146779
[174,] 11.06154357 5.19465077
[175,] -5.54423312 11.06154357
[176,] -4.13846716 -5.54423312
[177,] 2.51565572 -4.13846716
[178,] 0.65588603 2.51565572
[179,] -3.79213033 0.65588603
[180,] -10.31909000 -3.79213033
[181,] 0.10665735 -10.31909000
[182,] -12.37850580 0.10665735
[183,] 3.84783179 -12.37850580
[184,] 6.43567649 3.84783179
[185,] -11.78589398 6.43567649
[186,] -8.40953195 -11.78589398
[187,] -11.09679971 -8.40953195
[188,] -4.29553413 -11.09679971
[189,] 15.83773795 -4.29553413
[190,] 0.12265677 15.83773795
[191,] 0.45799478 0.12265677
[192,] -5.60461286 0.45799478
[193,] 7.18950113 -5.60461286
[194,] -4.46862329 7.18950113
[195,] -8.22508680 -4.46862329
[196,] 2.80272606 -8.22508680
[197,] -9.32534359 2.80272606
[198,] -4.17161324 -9.32534359
[199,] -5.82348555 -4.17161324
[200,] -1.66877071 -5.82348555
[201,] 9.89391393 -1.66877071
[202,] -3.22801434 9.89391393
[203,] -2.76867883 -3.22801434
[204,] 1.28174961 -2.76867883
[205,] 5.85715056 1.28174961
[206,] 0.14000748 5.85715056
[207,] -4.74795664 0.14000748
[208,] -0.44816355 -4.74795664
[209,] 4.10768429 -0.44816355
[210,] -8.64871192 4.10768429
[211,] -0.69247187 -8.64871192
[212,] 2.18566202 -0.69247187
[213,] -1.30705635 2.18566202
[214,] 6.78898126 -1.30705635
[215,] 7.76556087 6.78898126
[216,] -2.92356858 7.76556087
[217,] -4.65417204 -2.92356858
[218,] -0.47201290 -4.65417204
[219,] 1.00182976 -0.47201290
[220,] 2.12685085 1.00182976
[221,] -1.98638570 2.12685085
[222,] -1.86884089 -1.98638570
[223,] -0.08874572 -1.86884089
[224,] 4.96622391 -0.08874572
[225,] -6.22990767 4.96622391
[226,] -10.57897924 -6.22990767
[227,] -3.22815194 -10.57897924
[228,] -1.15680995 -3.22815194
[229,] 2.75980643 -1.15680995
[230,] 5.18700603 2.75980643
[231,] -3.74706453 5.18700603
[232,] -6.44545127 -3.74706453
[233,] 1.71687280 -6.44545127
[234,] 2.54859634 1.71687280
[235,] 1.55874603 2.54859634
[236,] 0.22564917 1.55874603
[237,] 1.88103554 0.22564917
[238,] 0.81626557 1.88103554
[239,] 1.23391283 0.81626557
[240,] -3.48728102 1.23391283
[241,] -1.80566677 -3.48728102
[242,] 0.79800696 -1.80566677
[243,] -2.26850368 0.79800696
[244,] -2.87212879 -2.26850368
[245,] -2.02318177 -2.87212879
[246,] -5.27641702 -2.02318177
[247,] -1.83447540 -5.27641702
[248,] -7.37167138 -1.83447540
[249,] -11.42032052 -7.37167138
[250,] 1.35567515 -11.42032052
[251,] 0.61249458 1.35567515
[252,] -11.61227436 0.61249458
[253,] 1.68003650 -11.61227436
[254,] 1.27773929 1.68003650
[255,] -7.89650238 1.27773929
[256,] -5.71599390 -7.89650238
[257,] 0.10677850 -5.71599390
[258,] 1.72021723 0.10677850
[259,] -5.18473674 1.72021723
[260,] 3.97221752 -5.18473674
[261,] -0.31903392 3.97221752
[262,] -4.90248823 -0.31903392
[263,] -0.95173162 -4.90248823
[264,] -2.97878995 -0.95173162
[265,] -3.49342282 -2.97878995
[266,] 1.25216152 -3.49342282
[267,] -4.73598335 1.25216152
[268,] -1.69830862 -4.73598335
[269,] 0.94548940 -1.69830862
[270,] 1.53485223 0.94548940
[271,] -0.26558215 1.53485223
[272,] -10.45446707 -0.26558215
[273,] -2.09932851 -10.45446707
[274,] -5.41214104 -2.09932851
[275,] 4.07401357 -5.41214104
[276,] -2.66137506 4.07401357
[277,] -10.67409048 -2.66137506
[278,] -3.44110746 -10.67409048
[279,] 1.01732669 -3.44110746
[280,] 2.86670557 1.01732669
[281,] -6.86462487 2.86670557
[282,] -5.68567558 -6.86462487
[283,] -8.23702913 -5.68567558
[284,] 1.34291229 -8.23702913
[285,] 3.68315624 1.34291229
[286,] -1.21555381 3.68315624
[287,] -4.27008865 -1.21555381
[288,] -0.29593930 -4.27008865
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.96720654 -5.73720530
2 10.18816732 -0.96720654
3 -2.20763116 10.18816732
4 0.43450528 -2.20763116
5 4.34544883 0.43450528
6 -14.57834683 4.34544883
7 4.11676008 -14.57834683
8 -4.16666230 4.11676008
9 -5.13662762 -4.16666230
10 0.29216547 -5.13662762
11 7.28830370 0.29216547
12 18.22405775 7.28830370
13 7.66716808 18.22405775
14 10.56269086 7.66716808
15 9.18805319 10.56269086
16 12.12953006 9.18805319
17 -7.72983445 12.12953006
18 0.33586575 -7.72983445
19 -8.93478396 0.33586575
20 1.47717168 -8.93478396
21 3.27695372 1.47717168
22 0.59144567 3.27695372
23 -0.87525540 0.59144567
24 9.57565219 -0.87525540
25 5.99878694 9.57565219
26 2.63685686 5.99878694
27 -0.23628869 2.63685686
28 2.83910026 -0.23628869
29 5.53918588 2.83910026
30 0.22368643 5.53918588
31 -0.67332361 0.22368643
32 13.82830330 -0.67332361
33 9.04120604 13.82830330
34 10.48863484 9.04120604
35 -5.08573311 10.48863484
36 -5.83570096 -5.08573311
37 8.43423348 -5.83570096
38 4.56847952 8.43423348
39 -11.39450487 4.56847952
40 3.33735442 -11.39450487
41 -5.05451711 3.33735442
42 -0.09499200 -5.05451711
43 -0.11147265 -0.09499200
44 1.19006346 -0.11147265
45 10.88309577 1.19006346
46 -0.97007867 10.88309577
47 0.15982132 -0.97007867
48 7.04508073 0.15982132
49 15.27780608 7.04508073
50 6.17684575 15.27780608
51 7.80789105 6.17684575
52 9.51557674 7.80789105
53 -0.42683536 9.51557674
54 5.16249677 -0.42683536
55 -1.34469613 5.16249677
56 -8.82596710 -1.34469613
57 2.25788157 -8.82596710
58 1.54787880 2.25788157
59 -4.04412653 1.54787880
60 -0.41049955 -4.04412653
61 4.58496484 -0.41049955
62 -15.70249718 4.58496484
63 -0.89825611 -15.70249718
64 -13.33681752 -0.89825611
65 -3.90754551 -13.33681752
66 3.38186870 -3.90754551
67 -1.42930243 3.38186870
68 -21.06329926 -1.42930243
69 13.57161124 -21.06329926
70 2.69542344 13.57161124
71 -2.89921857 2.69542344
72 -1.84435403 -2.89921857
73 -3.17388594 -1.84435403
74 7.27184030 -3.17388594
75 -2.73664578 7.27184030
76 -3.17427046 -2.73664578
77 13.73410779 -3.17427046
78 7.31258456 13.73410779
79 13.17456709 7.31258456
80 -0.53483345 13.17456709
81 7.02441617 -0.53483345
82 3.06075728 7.02441617
83 -5.22909701 3.06075728
84 -7.56588394 -5.22909701
85 5.09425567 -7.56588394
86 8.91992367 5.09425567
87 1.07501943 8.91992367
88 4.41355889 1.07501943
89 5.92653115 4.41355889
90 3.73082729 5.92653115
91 -14.01502482 3.73082729
92 8.82398266 -14.01502482
93 0.38312963 8.82398266
94 1.25346333 0.38312963
95 0.60725541 1.25346333
96 17.83463803 0.60725541
97 2.81679754 17.83463803
98 10.16102030 2.81679754
99 -1.40029942 10.16102030
100 2.43712445 -1.40029942
101 7.24138863 2.43712445
102 -4.82204942 7.24138863
103 -3.98829461 -4.82204942
104 -10.36358432 -3.98829461
105 -13.37830071 -10.36358432
106 15.61308454 -13.37830071
107 3.97783323 15.61308454
108 -24.04689690 3.97783323
109 9.88816426 -24.04689690
110 -7.07013013 9.88816426
111 11.65863114 -7.07013013
112 0.46911584 11.65863114
113 -2.30345731 0.46911584
114 -2.58966440 -2.30345731
115 1.43468123 -2.58966440
116 0.71129685 1.43468123
117 -4.90764275 0.71129685
118 -16.90178807 -4.90764275
119 -0.77093446 -16.90178807
120 -1.48958742 -0.77093446
121 -2.84573695 -1.48958742
122 -2.62120626 -2.84573695
123 -0.45383896 -2.62120626
124 -3.64127479 -0.45383896
125 -6.22053542 -3.64127479
126 -5.67503564 -6.22053542
127 -1.21835522 -5.67503564
128 10.01824760 -1.21835522
129 -11.02610938 10.01824760
130 -3.62331976 -11.02610938
131 5.58569376 -3.62331976
132 -5.27416130 5.58569376
133 7.17431620 -5.27416130
134 11.80867830 7.17431620
135 -8.12590608 11.80867830
136 -11.73847769 -8.12590608
137 -6.32807519 -11.73847769
138 -4.05771255 -6.32807519
139 -6.90338351 -4.05771255
140 -0.70274345 -6.90338351
141 -5.63359060 -0.70274345
142 4.07540184 -5.63359060
143 2.29008239 4.07540184
144 10.90198551 2.29008239
145 6.13256891 10.90198551
146 15.41000879 6.13256891
147 -2.78528279 15.41000879
148 -5.40304504 -2.78528279
149 9.97411772 -5.40304504
150 10.01121534 9.97411772
151 10.91866532 10.01121534
152 -4.51417884 10.91866532
153 3.69164590 -4.51417884
154 1.45776008 3.69164590
155 -7.78307448 1.45776008
156 4.26445741 -7.78307448
157 -4.22284258 4.26445741
158 6.91720256 -4.22284258
159 2.59274533 6.91720256
160 1.58422333 2.59274533
161 7.51734780 1.58422333
162 -5.64446115 7.51734780
163 0.34535574 -5.64446115
164 8.06224613 0.34535574
165 4.73564239 8.06224613
166 6.25585196 4.73564239
167 2.75668653 6.25585196
168 -1.18657469 2.75668653
169 -8.30245186 -1.18657469
170 0.55685033 -8.30245186
171 -4.48833469 0.55685033
172 5.01146779 -4.48833469
173 5.19465077 5.01146779
174 11.06154357 5.19465077
175 -5.54423312 11.06154357
176 -4.13846716 -5.54423312
177 2.51565572 -4.13846716
178 0.65588603 2.51565572
179 -3.79213033 0.65588603
180 -10.31909000 -3.79213033
181 0.10665735 -10.31909000
182 -12.37850580 0.10665735
183 3.84783179 -12.37850580
184 6.43567649 3.84783179
185 -11.78589398 6.43567649
186 -8.40953195 -11.78589398
187 -11.09679971 -8.40953195
188 -4.29553413 -11.09679971
189 15.83773795 -4.29553413
190 0.12265677 15.83773795
191 0.45799478 0.12265677
192 -5.60461286 0.45799478
193 7.18950113 -5.60461286
194 -4.46862329 7.18950113
195 -8.22508680 -4.46862329
196 2.80272606 -8.22508680
197 -9.32534359 2.80272606
198 -4.17161324 -9.32534359
199 -5.82348555 -4.17161324
200 -1.66877071 -5.82348555
201 9.89391393 -1.66877071
202 -3.22801434 9.89391393
203 -2.76867883 -3.22801434
204 1.28174961 -2.76867883
205 5.85715056 1.28174961
206 0.14000748 5.85715056
207 -4.74795664 0.14000748
208 -0.44816355 -4.74795664
209 4.10768429 -0.44816355
210 -8.64871192 4.10768429
211 -0.69247187 -8.64871192
212 2.18566202 -0.69247187
213 -1.30705635 2.18566202
214 6.78898126 -1.30705635
215 7.76556087 6.78898126
216 -2.92356858 7.76556087
217 -4.65417204 -2.92356858
218 -0.47201290 -4.65417204
219 1.00182976 -0.47201290
220 2.12685085 1.00182976
221 -1.98638570 2.12685085
222 -1.86884089 -1.98638570
223 -0.08874572 -1.86884089
224 4.96622391 -0.08874572
225 -6.22990767 4.96622391
226 -10.57897924 -6.22990767
227 -3.22815194 -10.57897924
228 -1.15680995 -3.22815194
229 2.75980643 -1.15680995
230 5.18700603 2.75980643
231 -3.74706453 5.18700603
232 -6.44545127 -3.74706453
233 1.71687280 -6.44545127
234 2.54859634 1.71687280
235 1.55874603 2.54859634
236 0.22564917 1.55874603
237 1.88103554 0.22564917
238 0.81626557 1.88103554
239 1.23391283 0.81626557
240 -3.48728102 1.23391283
241 -1.80566677 -3.48728102
242 0.79800696 -1.80566677
243 -2.26850368 0.79800696
244 -2.87212879 -2.26850368
245 -2.02318177 -2.87212879
246 -5.27641702 -2.02318177
247 -1.83447540 -5.27641702
248 -7.37167138 -1.83447540
249 -11.42032052 -7.37167138
250 1.35567515 -11.42032052
251 0.61249458 1.35567515
252 -11.61227436 0.61249458
253 1.68003650 -11.61227436
254 1.27773929 1.68003650
255 -7.89650238 1.27773929
256 -5.71599390 -7.89650238
257 0.10677850 -5.71599390
258 1.72021723 0.10677850
259 -5.18473674 1.72021723
260 3.97221752 -5.18473674
261 -0.31903392 3.97221752
262 -4.90248823 -0.31903392
263 -0.95173162 -4.90248823
264 -2.97878995 -0.95173162
265 -3.49342282 -2.97878995
266 1.25216152 -3.49342282
267 -4.73598335 1.25216152
268 -1.69830862 -4.73598335
269 0.94548940 -1.69830862
270 1.53485223 0.94548940
271 -0.26558215 1.53485223
272 -10.45446707 -0.26558215
273 -2.09932851 -10.45446707
274 -5.41214104 -2.09932851
275 4.07401357 -5.41214104
276 -2.66137506 4.07401357
277 -10.67409048 -2.66137506
278 -3.44110746 -10.67409048
279 1.01732669 -3.44110746
280 2.86670557 1.01732669
281 -6.86462487 2.86670557
282 -5.68567558 -6.86462487
283 -8.23702913 -5.68567558
284 1.34291229 -8.23702913
285 3.68315624 1.34291229
286 -1.21555381 3.68315624
287 -4.27008865 -1.21555381
288 -0.29593930 -4.27008865
> 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/784eb1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8p81g1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9upd41324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/109pje1324312896.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pxc71324312896.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/12aqxz1324312896.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/136sxc1324312896.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/14zh981324312896.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/15mnjb1324312896.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/16wlsr1324312896.tab")
+ }
>
> try(system("convert tmp/1rlow1324312896.ps tmp/1rlow1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/29kce1324312896.ps tmp/29kce1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/34zey1324312896.ps tmp/34zey1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hcdq1324312896.ps tmp/4hcdq1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/5pguy1324312896.ps tmp/5pguy1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/667jw1324312896.ps tmp/667jw1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/784eb1324312896.ps tmp/784eb1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p81g1324312896.ps tmp/8p81g1324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/9upd41324312896.ps tmp/9upd41324312896.png",intern=TRUE))
character(0)
> try(system("convert tmp/109pje1324312896.ps tmp/109pje1324312896.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.135 0.692 8.851