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(94
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+ ,'totseconds')
+ ,1:289))
> y <- array(NA,dim=c(5,289),dimnames=list(c('Feedbackmessagesp120','compendiumsreveiwed','totsize','bloggedcomputations','totseconds'),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
Feedbackmessagesp120 compendiumsreveiwed totsize bloggedcomputations
1 94 30 112285 79
2 103 28 84786 58
3 93 38 83123 60
4 103 30 101193 108
5 51 22 38361 49
6 70 26 68504 0
7 91 25 119182 121
8 22 18 22807 1
9 38 11 17140 20
10 93 26 116174 43
11 60 25 57635 69
12 123 38 66198 78
13 148 44 71701 86
14 90 30 57793 44
15 124 40 80444 104
16 70 34 53855 63
17 168 47 97668 158
18 115 30 133824 102
19 71 31 101481 77
20 66 23 99645 82
21 134 36 114789 115
22 117 36 99052 101
23 108 30 67654 80
24 84 25 65553 50
25 156 39 97500 83
26 120 34 69112 123
27 114 31 82753 73
28 94 31 85323 81
29 120 33 72654 105
30 81 25 30727 47
31 110 33 77873 105
32 133 35 117478 94
33 122 42 74007 44
34 158 43 90183 114
35 109 30 61542 38
36 124 33 101494 107
37 39 13 27570 30
38 92 32 55813 71
39 126 36 79215 84
40 0 0 1423 0
41 70 28 55461 59
42 37 14 31081 33
43 38 17 22996 42
44 120 32 83122 96
45 93 30 70106 106
46 95 35 60578 56
47 77 20 39992 57
48 90 28 79892 59
49 80 28 49810 39
50 31 39 71570 34
51 110 34 100708 76
52 66 26 33032 20
53 138 39 82875 91
54 133 39 139077 115
55 113 33 71595 85
56 100 28 72260 76
57 7 4 5950 8
58 140 39 115762 79
59 61 18 32551 21
60 41 14 31701 30
61 96 29 80670 76
62 164 44 143558 101
63 78 21 117105 94
64 49 16 23789 27
65 102 28 120733 92
66 124 35 105195 123
67 99 28 73107 75
68 129 38 132068 128
69 62 23 149193 105
70 73 36 46821 55
71 114 32 87011 56
72 99 29 95260 41
73 70 25 55183 72
74 104 27 106671 67
75 116 36 73511 75
76 91 28 92945 114
77 74 23 78664 118
78 138 40 70054 77
79 67 23 22618 22
80 151 40 74011 66
81 72 28 83737 69
82 120 34 69094 105
83 115 33 93133 116
84 105 28 95536 88
85 104 34 225920 73
86 108 30 62133 99
87 98 33 61370 62
88 69 22 43836 53
89 111 38 106117 118
90 99 26 38692 30
91 71 35 84651 100
92 27 8 56622 49
93 69 24 15986 24
94 107 29 95364 67
95 73 20 26706 46
96 107 29 89691 57
97 93 45 67267 75
98 129 37 126846 135
99 69 33 41140 68
100 118 33 102860 124
101 73 25 51715 33
102 119 32 55801 98
103 104 29 111813 58
104 107 28 120293 68
105 99 28 138599 81
106 90 31 161647 131
107 197 52 115929 110
108 36 21 24266 37
109 85 24 162901 130
110 139 41 109825 93
111 106 33 129838 118
112 50 32 37510 39
113 64 19 43750 13
114 31 20 40652 74
115 63 31 87771 81
116 92 31 85872 109
117 106 32 89275 151
118 63 18 44418 51
119 69 23 192565 28
120 41 17 35232 40
121 56 20 40909 56
122 25 12 13294 27
123 65 17 32387 37
124 93 30 140867 83
125 114 31 120662 54
126 38 10 21233 27
127 44 13 44332 28
128 87 22 61056 59
129 110 42 101338 133
130 0 1 1168 12
131 27 9 13497 0
132 83 32 65567 106
133 30 11 25162 23
134 80 25 32334 44
135 98 36 40735 71
136 82 31 91413 116
137 0 0 855 4
138 60 24 97068 62
139 28 13 44339 12
140 9 8 14116 18
141 33 13 10288 14
142 59 19 65622 60
143 49 18 16563 7
144 115 33 76643 98
145 140 40 110681 64
146 49 22 29011 29
147 120 38 92696 32
148 66 24 94785 25
149 21 8 8773 16
150 124 35 83209 48
151 152 43 93815 100
152 139 43 86687 46
153 38 14 34553 45
154 144 41 105547 129
155 120 38 103487 130
156 160 45 213688 136
157 114 31 71220 59
158 39 13 23517 25
159 78 28 56926 32
160 119 31 91721 63
161 141 40 115168 95
162 101 30 111194 14
163 56 16 51009 36
164 133 37 135777 113
165 83 30 51513 47
166 116 35 74163 92
167 90 32 51633 70
168 36 27 75345 19
169 50 20 33416 50
170 61 18 83305 41
171 97 31 98952 91
172 98 31 102372 111
173 78 21 37238 41
174 117 39 103772 120
175 148 41 123969 135
176 41 13 27142 27
177 105 32 135400 87
178 55 18 21399 25
179 132 39 130115 131
180 44 14 24874 45
181 21 7 34988 29
182 50 17 45549 58
183 0 0 6023 4
184 73 30 64466 47
185 86 37 54990 109
186 0 0 1644 7
187 13 5 6179 12
188 4 1 3926 0
189 57 16 32755 37
190 48 32 34777 37
191 46 24 73224 46
192 48 17 27114 15
193 32 11 20760 42
194 68 24 37636 7
195 87 22 65461 54
196 43 12 30080 54
197 67 19 24094 14
198 46 13 69008 16
199 46 17 54968 33
200 56 15 46090 32
201 48 16 27507 21
202 44 24 10672 15
203 60 15 34029 38
204 65 17 46300 22
205 55 18 24760 28
206 38 20 18779 10
207 52 16 21280 31
208 60 16 40662 32
209 54 18 28987 32
210 86 22 22827 43
211 24 8 18513 27
212 52 17 30594 37
213 49 18 24006 20
214 61 16 27913 32
215 61 23 42744 0
216 81 22 12934 5
217 43 13 22574 26
218 40 13 41385 10
219 40 16 18653 27
220 56 16 18472 11
221 68 20 30976 29
222 79 22 63339 25
223 47 17 25568 55
224 57 18 33747 23
225 41 17 4154 5
226 29 12 19474 43
227 3 7 35130 23
228 60 17 39067 34
229 30 14 13310 36
230 79 23 65892 35
231 47 17 4143 0
232 40 14 28579 37
233 48 15 51776 28
234 36 17 21152 16
235 42 21 38084 26
236 49 18 27717 38
237 57 18 32928 23
238 12 17 11342 22
239 40 17 19499 30
240 43 16 16380 16
241 33 15 36874 18
242 77 21 48259 28
243 43 16 16734 32
244 45 14 28207 21
245 47 15 30143 23
246 43 17 41369 29
247 45 15 45833 50
248 50 15 29156 12
249 35 10 35944 21
250 7 6 36278 18
251 71 22 45588 27
252 67 21 45097 41
253 0 1 3895 13
254 62 18 28394 12
255 54 17 18632 21
256 4 4 2325 8
257 25 10 25139 26
258 40 16 27975 27
259 38 16 14483 13
260 19 9 13127 16
261 17 16 5839 2
262 67 17 24069 42
263 14 7 3738 5
264 30 15 18625 37
265 54 14 36341 17
266 35 14 24548 38
267 59 18 21792 37
268 24 12 26263 29
269 58 16 23686 32
270 42 21 49303 35
271 46 19 25659 17
272 61 16 28904 20
273 3 1 2781 7
274 52 16 29236 46
275 25 10 19546 24
276 40 19 22818 40
277 32 12 32689 3
278 4 2 5752 10
279 49 14 22197 37
280 63 17 20055 17
281 67 19 25272 28
282 32 14 82206 19
283 23 11 32073 29
284 7 4 5444 8
285 54 16 20154 10
286 37 20 36944 15
287 35 12 8019 15
288 51 15 30884 28
289 39 16 19540 17
totseconds
1 146283
2 98364
3 86146
4 96933
5 79234
6 42551
7 195663
8 6853
9 21529
10 95757
11 85584
12 143983
13 75851
14 59238
15 93163
16 96037
17 151511
18 136368
19 112642
20 94728
21 105499
22 121527
23 127766
24 98958
25 77900
26 85646
27 98579
28 130767
29 131741
30 53907
31 178812
32 146761
33 82036
34 163253
35 27032
36 171975
37 65990
38 86572
39 159676
40 1929
41 85371
42 58391
43 31580
44 136815
45 120642
46 69107
47 50495
48 108016
49 46341
50 78348
51 79336
52 56968
53 93176
54 161632
55 87850
56 127969
57 15049
58 155135
59 25109
60 45824
61 102996
62 160604
63 158051
64 44547
65 162647
66 174141
67 60622
68 179566
69 184301
70 75661
71 96144
72 129847
73 117286
74 71180
75 109377
76 85298
77 73631
78 86767
79 23824
80 93487
81 82981
82 73815
83 94552
84 132190
85 128754
86 66363
87 67808
88 61724
89 131722
90 68580
91 106175
92 55792
93 25157
94 76669
95 57283
96 105805
97 129484
98 72413
99 87831
100 96971
101 71299
102 77494
103 120336
104 93913
105 136048
106 181248
107 146123
108 32036
109 186646
110 102255
111 168237
112 64219
113 19630
114 76825
115 115338
116 109427
117 118168
118 84845
119 153197
120 29877
121 63506
122 22445
123 47695
124 68370
125 146304
126 38233
127 42071
128 50517
129 103950
130 5841
131 2341
132 84396
133 24610
134 35753
135 55515
136 209056
137 6622
138 115814
139 11609
140 13155
141 18274
142 72875
143 10112
144 142775
145 68847
146 17659
147 20112
148 61023
149 13983
150 65176
151 132432
152 112494
153 45109
154 170875
155 180759
156 214921
157 100226
158 32043
159 54454
160 78876
161 170745
162 6940
163 49025
164 122037
165 53782
166 127748
167 86839
168 44830
169 77395
170 89324
171 103300
172 112283
173 10901
174 120691
175 58106
176 57140
177 122422
178 25899
179 139296
180 52678
181 23853
182 17306
183 7953
184 89455
185 147866
186 4245
187 21509
188 7670
189 66675
190 14336
191 53608
192 30059
193 29668
194 22097
195 96841
196 41907
197 27080
198 35885
199 41247
200 28313
201 36845
202 16548
203 36134
204 55764
205 28910
206 13339
207 25319
208 66956
209 47487
210 52785
211 44683
212 35619
213 21920
214 45608
215 7721
216 20634
217 29788
218 31931
219 37754
220 32505
221 40557
222 94238
223 44197
224 43228
225 4103
226 44144
227 32868
228 27640
229 14063
230 28990
231 4694
232 42648
233 64329
234 21928
235 25836
236 22779
237 40820
238 27530
239 32378
240 10824
241 39613
242 60865
243 19787
244 20107
245 36605
246 40961
247 48231
248 39725
249 21455
250 23430
251 62991
252 49363
253 9604
254 24552
255 31493
256 3439
257 19555
258 21228
259 23177
260 22094
261 2342
262 38798
263 3255
264 24261
265 18511
266 40798
267 28893
268 21425
269 50276
270 37643
271 30377
272 27126
273 13
274 42097
275 24451
276 14335
277 5084
278 9927
279 43527
280 27184
281 21610
282 20484
283 20156
284 6012
285 18475
286 12645
287 11017
288 37623
289 35873
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) compendiumsreveiwed totsize
-4.756e+00 2.710e+00 1.170e-04
bloggedcomputations totseconds
1.191e-01 6.674e-06
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-82.872 -6.325 1.912 9.716 33.262
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.756e+00 2.108e+00 -2.257 0.02480 *
compendiumsreveiwed 2.710e+00 1.353e-01 20.023 < 2e-16 ***
totsize 1.170e-04 4.005e-05 2.922 0.00376 **
bloggedcomputations 1.191e-01 4.707e-02 2.530 0.01194 *
totseconds 6.674e-06 3.786e-05 0.176 0.86018
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 14.59 on 284 degrees of freedom
Multiple R-squared: 0.8618, Adjusted R-squared: 0.8598
F-statistic: 442.6 on 4 and 284 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,] 0.6074100 7.851800e-01 3.925900e-01
[2,] 0.5696087 8.607826e-01 4.303913e-01
[3,] 0.4329260 8.658520e-01 5.670740e-01
[4,] 0.3416695 6.833391e-01 6.583305e-01
[5,] 0.5991612 8.016776e-01 4.008388e-01
[6,] 0.6843416 6.313168e-01 3.156584e-01
[7,] 0.6005385 7.989230e-01 3.994615e-01
[8,] 0.5106935 9.786130e-01 4.893065e-01
[9,] 0.6691386 6.617229e-01 3.308614e-01
[10,] 0.6291459 7.417081e-01 3.708541e-01
[11,] 0.5507925 8.984149e-01 4.492075e-01
[12,] 0.7573139 4.853721e-01 2.426861e-01
[13,] 0.7436411 5.127178e-01 2.563589e-01
[14,] 0.7021045 5.957909e-01 2.978955e-01
[15,] 0.6373748 7.252504e-01 3.626252e-01
[16,] 0.6720780 6.558439e-01 3.279220e-01
[17,] 0.6557256 6.885488e-01 3.442744e-01
[18,] 0.8102338 3.795324e-01 1.897662e-01
[19,] 0.7684496 4.631007e-01 2.315504e-01
[20,] 0.7732419 4.535162e-01 2.267581e-01
[21,] 0.7277121 5.445759e-01 2.722879e-01
[22,] 0.7147099 5.705801e-01 2.852901e-01
[23,] 0.6999373 6.001254e-01 3.000627e-01
[24,] 0.6508766 6.982468e-01 3.491234e-01
[25,] 0.6457960 7.084080e-01 3.542040e-01
[26,] 0.5921826 8.156347e-01 4.078174e-01
[27,] 0.5982144 8.035713e-01 4.017856e-01
[28,] 0.6451673 7.096655e-01 3.548327e-01
[29,] 0.6228816 7.542368e-01 3.771184e-01
[30,] 0.5938080 8.123840e-01 4.061920e-01
[31,] 0.5537639 8.924723e-01 4.462361e-01
[32,] 0.5329511 9.340978e-01 4.670489e-01
[33,] 0.5276964 9.446073e-01 4.723036e-01
[34,] 0.5328866 9.342268e-01 4.671134e-01
[35,] 0.4829545 9.659089e-01 5.170455e-01
[36,] 0.4527691 9.055382e-01 5.472309e-01
[37,] 0.4427443 8.854885e-01 5.572557e-01
[38,] 0.4147666 8.295332e-01 5.852334e-01
[39,] 0.3896551 7.793102e-01 6.103449e-01
[40,] 0.4031006 8.062011e-01 5.968994e-01
[41,] 0.3582958 7.165916e-01 6.417042e-01
[42,] 0.3150248 6.300497e-01 6.849752e-01
[43,] 0.9914300 1.714001e-02 8.570005e-03
[44,] 0.9885425 2.291491e-02 1.145745e-02
[45,] 0.9855768 2.884631e-02 1.442315e-02
[46,] 0.9848982 3.020367e-02 1.510184e-02
[47,] 0.9808801 3.823983e-02 1.911992e-02
[48,] 0.9765692 4.686155e-02 2.343077e-02
[49,] 0.9732693 5.346150e-02 2.673075e-02
[50,] 0.9661399 6.772011e-02 3.386005e-02
[51,] 0.9687917 6.241653e-02 3.120826e-02
[52,] 0.9677061 6.458773e-02 3.229387e-02
[53,] 0.9595413 8.091745e-02 4.045872e-02
[54,] 0.9499473 1.001054e-01 5.005272e-02
[55,] 0.9542920 9.141606e-02 4.570803e-02
[56,] 0.9469232 1.061535e-01 5.307675e-02
[57,] 0.9377305 1.245390e-01 6.226949e-02
[58,] 0.9253095 1.493810e-01 7.469050e-02
[59,] 0.9128355 1.743290e-01 8.716452e-02
[60,] 0.8998531 2.002937e-01 1.001469e-01
[61,] 0.8891758 2.216485e-01 1.108242e-01
[62,] 0.9367007 1.265986e-01 6.329932e-02
[63,] 0.9669777 6.604462e-02 3.302231e-02
[64,] 0.9689517 6.209667e-02 3.104833e-02
[65,] 0.9683536 6.329277e-02 3.164639e-02
[66,] 0.9634257 7.314854e-02 3.657427e-02
[67,] 0.9608548 7.829036e-02 3.914518e-02
[68,] 0.9533218 9.335639e-02 4.667819e-02
[69,] 0.9526337 9.473263e-02 4.736631e-02
[70,] 0.9533155 9.336891e-02 4.668446e-02
[71,] 0.9545546 9.089079e-02 4.544539e-02
[72,] 0.9468356 1.063287e-01 5.316437e-02
[73,] 0.9714424 5.711518e-02 2.855759e-02
[74,] 0.9743865 5.122707e-02 2.561354e-02
[75,] 0.9710677 5.786470e-02 2.893235e-02
[76,] 0.9656627 6.867465e-02 3.433733e-02
[77,] 0.9623691 7.526188e-02 3.763094e-02
[78,] 0.9657730 6.845398e-02 3.422699e-02
[79,] 0.9624334 7.513320e-02 3.756660e-02
[80,] 0.9545502 9.089956e-02 4.544978e-02
[81,] 0.9454875 1.090250e-01 5.451252e-02
[82,] 0.9498824 1.002351e-01 5.011757e-02
[83,] 0.9668537 6.629259e-02 3.314630e-02
[84,] 0.9936927 1.261463e-02 6.307316e-03
[85,] 0.9919426 1.611478e-02 8.057389e-03
[86,] 0.9899660 2.006797e-02 1.003398e-02
[87,] 0.9896986 2.060284e-02 1.030142e-02
[88,] 0.9896491 2.070187e-02 1.035093e-02
[89,] 0.9900352 1.992952e-02 9.964759e-03
[90,] 0.9982704 3.459144e-03 1.729572e-03
[91,] 0.9978338 4.332305e-03 2.166152e-03
[92,] 0.9990393 1.921436e-03 9.607181e-04
[93,] 0.9988169 2.366216e-03 1.183108e-03
[94,] 0.9984323 3.135442e-03 1.567721e-03
[95,] 0.9986742 2.651649e-03 1.325825e-03
[96,] 0.9984535 3.093080e-03 1.546540e-03
[97,] 0.9983801 3.239776e-03 1.619888e-03
[98,] 0.9978716 4.256747e-03 2.128374e-03
[99,] 0.9987455 2.508987e-03 1.254494e-03
[100,] 0.9996532 6.935590e-04 3.467795e-04
[101,] 0.9997824 4.351422e-04 2.175711e-04
[102,] 0.9997462 5.075875e-04 2.537937e-04
[103,] 0.9996931 6.138840e-04 3.069420e-04
[104,] 0.9996230 7.540697e-04 3.770348e-04
[105,] 0.9999627 7.457661e-05 3.728831e-05
[106,] 0.9999569 8.626331e-05 4.313165e-05
[107,] 0.9999890 2.198876e-05 1.099438e-05
[108,] 0.9999987 2.591096e-06 1.295548e-06
[109,] 0.9999984 3.166672e-06 1.583336e-06
[110,] 0.9999978 4.379652e-06 2.189826e-06
[111,] 0.9999971 5.840006e-06 2.920003e-06
[112,] 0.9999980 4.033717e-06 2.016858e-06
[113,] 0.9999973 5.345148e-06 2.672574e-06
[114,] 0.9999961 7.724170e-06 3.862085e-06
[115,] 0.9999947 1.062438e-05 5.312189e-06
[116,] 0.9999952 9.629804e-06 4.814902e-06
[117,] 0.9999941 1.184794e-05 5.923970e-06
[118,] 0.9999932 1.358970e-05 6.794848e-06
[119,] 0.9999919 1.626377e-05 8.131886e-06
[120,] 0.9999887 2.268205e-05 1.134103e-05
[121,] 0.9999914 1.714898e-05 8.574491e-06
[122,] 0.9999962 7.588856e-06 3.794428e-06
[123,] 0.9999944 1.123751e-05 5.618757e-06
[124,] 0.9999922 1.551741e-05 7.758706e-06
[125,] 0.9999939 1.227762e-05 6.138811e-06
[126,] 0.9999910 1.805921e-05 9.029604e-06
[127,] 0.9999885 2.306551e-05 1.153275e-05
[128,] 0.9999847 3.057496e-05 1.528748e-05
[129,] 0.9999934 1.311031e-05 6.555155e-06
[130,] 0.9999907 1.865510e-05 9.327549e-06
[131,] 0.9999949 1.027962e-05 5.139809e-06
[132,] 0.9999936 1.289496e-05 6.447481e-06
[133,] 0.9999926 1.478342e-05 7.391710e-06
[134,] 0.9999891 2.170163e-05 1.085082e-05
[135,] 0.9999846 3.079347e-05 1.539673e-05
[136,] 0.9999780 4.407671e-05 2.203836e-05
[137,] 0.9999712 5.758994e-05 2.879497e-05
[138,] 0.9999737 5.261337e-05 2.630668e-05
[139,] 0.9999702 5.951071e-05 2.975535e-05
[140,] 0.9999635 7.308017e-05 3.654009e-05
[141,] 0.9999577 8.459792e-05 4.229896e-05
[142,] 0.9999400 1.199348e-04 5.996742e-05
[143,] 0.9999535 9.296437e-05 4.648219e-05
[144,] 0.9999602 7.965755e-05 3.982877e-05
[145,] 0.9999528 9.449627e-05 4.724813e-05
[146,] 0.9999358 1.283819e-04 6.419095e-05
[147,] 0.9999204 1.592422e-04 7.962111e-05
[148,] 0.9999005 1.990995e-04 9.954976e-05
[149,] 0.9998682 2.635778e-04 1.317889e-04
[150,] 0.9998948 2.103341e-04 1.051670e-04
[151,] 0.9998542 2.916033e-04 1.458017e-04
[152,] 0.9998003 3.993653e-04 1.996827e-04
[153,] 0.9998753 2.494505e-04 1.247253e-04
[154,] 0.9998539 2.922804e-04 1.461402e-04
[155,] 0.9998553 2.893917e-04 1.446959e-04
[156,] 0.9998118 3.764125e-04 1.882062e-04
[157,] 0.9997832 4.336216e-04 2.168108e-04
[158,] 0.9997058 5.884091e-04 2.942045e-04
[159,] 0.9996228 7.544567e-04 3.772284e-04
[160,] 0.9995034 9.932437e-04 4.966218e-04
[161,] 0.9999743 5.139243e-05 2.569622e-05
[162,] 0.9999701 5.988344e-05 2.994172e-05
[163,] 0.9999578 8.436321e-05 4.218160e-05
[164,] 0.9999413 1.173626e-04 5.868129e-05
[165,] 0.9999211 1.578790e-04 7.893951e-05
[166,] 0.9999510 9.797380e-05 4.898690e-05
[167,] 0.9999389 1.221115e-04 6.105574e-05
[168,] 0.9999856 2.875762e-05 1.437881e-05
[169,] 0.9999797 4.068128e-05 2.034064e-05
[170,] 0.9999706 5.882538e-05 2.941269e-05
[171,] 0.9999612 7.768975e-05 3.884488e-05
[172,] 0.9999556 8.871644e-05 4.435822e-05
[173,] 0.9999361 1.278845e-04 6.394224e-05
[174,] 0.9999080 1.839950e-04 9.199750e-05
[175,] 0.9998939 2.121269e-04 1.060634e-04
[176,] 0.9998560 2.880702e-04 1.440351e-04
[177,] 0.9999042 1.915481e-04 9.577406e-05
[178,] 0.9999932 1.364977e-05 6.824887e-06
[179,] 0.9999899 2.028987e-05 1.014494e-05
[180,] 0.9999852 2.954005e-05 1.477002e-05
[181,] 0.9999784 4.314045e-05 2.157023e-05
[182,] 0.9999700 5.995526e-05 2.997763e-05
[183,] 0.9999976 4.885839e-06 2.442919e-06
[184,] 0.9999997 6.602861e-07 3.301430e-07
[185,] 0.9999995 1.070089e-06 5.350444e-07
[186,] 0.9999991 1.735415e-06 8.677075e-07
[187,] 0.9999986 2.771347e-06 1.385673e-06
[188,] 0.9999981 3.711777e-06 1.855888e-06
[189,] 0.9999973 5.410271e-06 2.705136e-06
[190,] 0.9999975 4.965754e-06 2.482877e-06
[191,] 0.9999963 7.337322e-06 3.668661e-06
[192,] 0.9999949 1.024435e-05 5.122174e-06
[193,] 0.9999950 9.977163e-06 4.988581e-06
[194,] 0.9999922 1.558584e-05 7.792920e-06
[195,] 0.9999964 7.222570e-06 3.611285e-06
[196,] 0.9999973 5.370466e-06 2.685233e-06
[197,] 0.9999967 6.567370e-06 3.283685e-06
[198,] 0.9999950 1.005878e-05 5.029388e-06
[199,] 0.9999962 7.553607e-06 3.776804e-06
[200,] 0.9999950 1.009107e-05 5.045534e-06
[201,] 0.9999927 1.460200e-05 7.300998e-06
[202,] 0.9999887 2.268530e-05 1.134265e-05
[203,] 0.9999931 1.377984e-05 6.889918e-06
[204,] 0.9999891 2.175324e-05 1.087662e-05
[205,] 0.9999832 3.359760e-05 1.679880e-05
[206,] 0.9999736 5.285249e-05 2.642625e-05
[207,] 0.9999728 5.436338e-05 2.718169e-05
[208,] 0.9999579 8.427109e-05 4.213555e-05
[209,] 0.9999786 4.271035e-05 2.135517e-05
[210,] 0.9999703 5.945122e-05 2.972561e-05
[211,] 0.9999535 9.290245e-05 4.645123e-05
[212,] 0.9999360 1.279208e-04 6.396040e-05
[213,] 0.9999249 1.501810e-04 7.509048e-05
[214,] 0.9999087 1.826148e-04 9.130740e-05
[215,] 0.9998677 2.646973e-04 1.323486e-04
[216,] 0.9998020 3.959928e-04 1.979964e-04
[217,] 0.9997066 5.868502e-04 2.934251e-04
[218,] 0.9995592 8.816541e-04 4.408270e-04
[219,] 0.9994325 1.135079e-03 5.675395e-04
[220,] 0.9996632 6.736329e-04 3.368165e-04
[221,] 0.9996674 6.651263e-04 3.325631e-04
[222,] 0.9995217 9.565841e-04 4.782920e-04
[223,] 0.9996548 6.904378e-04 3.452189e-04
[224,] 0.9995438 9.123860e-04 4.561930e-04
[225,] 0.9993337 1.332556e-03 6.662780e-04
[226,] 0.9991274 1.745279e-03 8.726395e-04
[227,] 0.9989130 2.174083e-03 1.087041e-03
[228,] 0.9989997 2.000530e-03 1.000265e-03
[229,] 0.9985313 2.937303e-03 1.468652e-03
[230,] 0.9978674 4.265145e-03 2.132572e-03
[231,] 0.9999289 1.422374e-04 7.111869e-05
[232,] 0.9999175 1.649497e-04 8.247487e-05
[233,] 0.9998667 2.666417e-04 1.333209e-04
[234,] 0.9998979 2.042227e-04 1.021113e-04
[235,] 0.9998532 2.935051e-04 1.467526e-04
[236,] 0.9997565 4.869565e-04 2.434783e-04
[237,] 0.9996817 6.366592e-04 3.183296e-04
[238,] 0.9994815 1.036904e-03 5.184519e-04
[239,] 0.9993636 1.272881e-03 6.364406e-04
[240,] 0.9990054 1.989254e-03 9.946271e-04
[241,] 0.9984419 3.116189e-03 1.558095e-03
[242,] 0.9980573 3.885436e-03 1.942718e-03
[243,] 0.9977954 4.409239e-03 2.204619e-03
[244,] 0.9970267 5.946678e-03 2.973339e-03
[245,] 0.9953983 9.203354e-03 4.601677e-03
[246,] 0.9929994 1.400125e-02 7.000623e-03
[247,] 0.9927255 1.454891e-02 7.274455e-03
[248,] 0.9894266 2.114671e-02 1.057335e-02
[249,] 0.9845461 3.090789e-02 1.545394e-02
[250,] 0.9774930 4.501401e-02 2.250700e-02
[251,] 0.9677652 6.446962e-02 3.223481e-02
[252,] 0.9584163 8.316750e-02 4.158375e-02
[253,] 0.9501441 9.971178e-02 4.985589e-02
[254,] 0.9776860 4.462809e-02 2.231405e-02
[255,] 0.9849051 3.018978e-02 1.509489e-02
[256,] 0.9785816 4.283671e-02 2.141836e-02
[257,] 0.9747138 5.057246e-02 2.528623e-02
[258,] 0.9843266 3.134678e-02 1.567339e-02
[259,] 0.9805431 3.891385e-02 1.945692e-02
[260,] 0.9758214 4.835728e-02 2.417864e-02
[261,] 0.9673117 6.537667e-02 3.268833e-02
[262,] 0.9502055 9.958892e-02 4.979446e-02
[263,] 0.9665871 6.682577e-02 3.341288e-02
[264,] 0.9712173 5.756540e-02 2.878270e-02
[265,] 0.9740335 5.193292e-02 2.596646e-02
[266,] 0.9589186 8.216275e-02 4.108138e-02
[267,] 0.9324618 1.350764e-01 6.753820e-02
[268,] 0.8960040 2.079920e-01 1.039960e-01
[269,] 0.8720826 2.558347e-01 1.279174e-01
[270,] 0.8143928 3.712145e-01 1.856072e-01
[271,] 0.7182909 5.634182e-01 2.817091e-01
[272,] 0.5969111 8.061778e-01 4.030889e-01
[273,] 0.5584381 8.831238e-01 4.415619e-01
[274,] 0.5841877 8.316246e-01 4.158123e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1ou8l1324673265.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/20cfc1324673265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3foc81324673265.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4ngwi1324673266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5b6sx1324673266.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
-6.05986088 14.39794381 -22.66203225 1.11354533 -14.71199590 -3.99747391
7 8 9 10 11 12
-1.64940469 -24.85337821 8.41745706 7.94890123 -18.52072368 6.78850493
13 14 15 16 17 18
14.38786773 1.06503681 -2.05515736 -31.82176992 14.14055187 9.74704540
19 20 21 22 23 24
-30.04285515 -13.62562933 13.37321911 -0.22517892 13.16651965 6.72639530
25 26 27 28 29 30
33.26190866 9.31681906 15.71858361 -5.74969249 13.44824861 8.45904582
31 32 33 34 35 36
2.52346107 16.99399041 -1.50166629 21.01726071 20.55591202 13.56721232
37 38 39 40 41 42
1.28986843 -5.52086343 12.86577441 4.57676651 -15.20336724 -4.13728101
43 44 45 46 47 48
-11.21360695 15.97125439 -5.16927891 -9.30476551 15.75580688 1.78703573
49 50 51 52 53 54
-1.89981129 -82.87160312 1.25959803 -6.32531044 15.91834460 1.02747923
55 56 57 58 59 60
9.24697689 10.52221525 -0.83238129 15.08613171 10.50282701 0.23133522
61 62 63 64 65 66
2.99511541 19.62842773 -0.10065222 4.10327593 4.71384815 5.79464072
67 68 69 70 71 72
9.99169097 -1.11058957 -26.75980994 -32.32961501 14.55145932 8.27716616
73 74 75 76 77 78
-8.80270244 14.65673653 4.94070232 -5.13875980 -7.31742134 16.41872700
79 80 81 82 83 84
4.00553152 30.22094202 -17.68669353 11.54158780 4.99034032 11.34158450
85 86 87 88 89 90
-19.36292187 11.95949917 -1.68374712 2.28790693 -14.56402427 24.74401656
91 92 93 94 95 96
-41.60889548 -2.75503780 3.82460886 13.52345649 14.57502360 15.18369381
97 98 99 100 101 102
-41.85105138 2.09167181 -29.16501117 5.88336667 -0.44533297 18.32557622
103 104 105 106 107 108
9.37931229 13.08233558 1.11106511 -24.97135079 33.20753345 -23.60893145
109 110 111 112 113 114
-11.06646963 8.04679202 -9.03416787 -41.41917543 10.47206119 -32.52175021
115 116 117 118 119 120
-36.93313711 -11.00614720 -5.17440163 7.14285088 -15.45652630 -9.39568132
121 122 123 124 125 126
-5.31922578 -7.68212104 15.17555195 -10.36037356 13.22745049 9.70321472
127 128 129 130 131 132
4.72651769 17.63337210 -27.44510066 0.44156808 5.77322737 -19.81588053
133 134 135 136 137 138
-0.89897484 7.74946990 -8.38859996 -23.15304878 4.13552365 -19.79274711
139 140 141 142 143 144
-9.16548226 -11.80528118 -0.46413879 -3.03979930 2.14086065 8.74154940
145 146 147 148 149 150
15.33313898 -12.82518383 6.99327789 -8.75345449 1.05252029 18.02636735
151 152 153 154 155 156
16.46533758 10.86349607 -4.88399860 8.80194396 -7.01272314 0.18250164
157 158 159 160 161 162
18.72429199 2.58611385 -3.95287886 20.99176003 11.43614494 9.73892201
163 164 165 166 167 168
6.81675987 7.33561535 -5.52106385 5.42699097 -6.91448495 -43.78567724
169 170 171 172 173 174
-9.82066375 1.75406836 -5.35193223 -7.19392098 16.53800537 -11.16401331
175 176 177 178 179 180
10.68460954 3.75629448 -3.97743487 5.32597626 -0.68039333 2.19794182
181 182 183 184 185 186
-0.91898375 -3.66258605 3.52197705 -17.27466887 -29.90819825 3.70179034
187 188 189 190 191 192
1.91154209 5.53580541 9.71561126 -42.52829679 -28.68228312 1.53028446
193 194 195 196 197 198
-0.68048806 2.33655762 17.40428186 5.00844956 15.60302452 5.30981083
199 200 201 202 203 204
-5.94704444 10.71669261 3.43423575 -19.42433750 15.36108144 15.28027550
205 206 207 208 209 210
4.55535518 -14.91681635 7.04878426 12.38407817 2.46042712 22.99659489
211 212 213 214 215 216
1.39799151 2.46593208 -0.35701757 15.01820825 -1.62168654 23.89426097
217 218 219 220 221 222
6.59240180 3.28269768 -4.25046752 13.71125442 11.21166550 13.12367258
223 224 225 226 227 228
-4.14697060 6.00377604 -1.41918445 -6.45552365 -18.28119797 9.88511531
229 230 231 232 233 234
-9.11948225 9.35970475 5.17363075 -1.21585098 2.28742578 -9.83698037
235 236 237 238 239 240
-17.87423794 -2.94064422 6.11567147 -33.44115188 -7.38064472 0.50525023
241 242 243 244 245 246
-9.61311650 15.46329609 -1.50150061 5.88362697 4.59902322 -6.87765523
247 248 249 250 251 252
-2.52987428 9.00372008 5.80855214 -11.04726228 7.17092178 4.36179151
253 254 255 256 257 258
-0.02170400 13.06477006 7.79855276 -3.33076469 -3.51003926 -5.23085841
259 260 261 262 263 264
-3.99795982 -4.22082810 -22.53750472 17.61267678 -1.26694752 -12.63829239
265 266 267 268 269 270
14.41896735 -5.85096565 7.83087821 -10.43089394 12.48161851 -20.33752879
271 272 273 274 275 276
-5.95937081 16.45474497 3.88721409 4.21952493 -2.65013772 -14.25907755
277 278 279 280 281 282
0.02277402 1.40636170 8.52498556 17.13719655 13.83438100 -13.19865998
283 284 285 286 287 288
-9.39241229 -0.71286462 11.72719033 -18.63298795 4.44046742 7.91005834
289
-4.15074861
> postscript(file="/var/wessaorg/rcomp/tmp/62fnh1324673266.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 -6.05986088 NA
1 14.39794381 -6.05986088
2 -22.66203225 14.39794381
3 1.11354533 -22.66203225
4 -14.71199590 1.11354533
5 -3.99747391 -14.71199590
6 -1.64940469 -3.99747391
7 -24.85337821 -1.64940469
8 8.41745706 -24.85337821
9 7.94890123 8.41745706
10 -18.52072368 7.94890123
11 6.78850493 -18.52072368
12 14.38786773 6.78850493
13 1.06503681 14.38786773
14 -2.05515736 1.06503681
15 -31.82176992 -2.05515736
16 14.14055187 -31.82176992
17 9.74704540 14.14055187
18 -30.04285515 9.74704540
19 -13.62562933 -30.04285515
20 13.37321911 -13.62562933
21 -0.22517892 13.37321911
22 13.16651965 -0.22517892
23 6.72639530 13.16651965
24 33.26190866 6.72639530
25 9.31681906 33.26190866
26 15.71858361 9.31681906
27 -5.74969249 15.71858361
28 13.44824861 -5.74969249
29 8.45904582 13.44824861
30 2.52346107 8.45904582
31 16.99399041 2.52346107
32 -1.50166629 16.99399041
33 21.01726071 -1.50166629
34 20.55591202 21.01726071
35 13.56721232 20.55591202
36 1.28986843 13.56721232
37 -5.52086343 1.28986843
38 12.86577441 -5.52086343
39 4.57676651 12.86577441
40 -15.20336724 4.57676651
41 -4.13728101 -15.20336724
42 -11.21360695 -4.13728101
43 15.97125439 -11.21360695
44 -5.16927891 15.97125439
45 -9.30476551 -5.16927891
46 15.75580688 -9.30476551
47 1.78703573 15.75580688
48 -1.89981129 1.78703573
49 -82.87160312 -1.89981129
50 1.25959803 -82.87160312
51 -6.32531044 1.25959803
52 15.91834460 -6.32531044
53 1.02747923 15.91834460
54 9.24697689 1.02747923
55 10.52221525 9.24697689
56 -0.83238129 10.52221525
57 15.08613171 -0.83238129
58 10.50282701 15.08613171
59 0.23133522 10.50282701
60 2.99511541 0.23133522
61 19.62842773 2.99511541
62 -0.10065222 19.62842773
63 4.10327593 -0.10065222
64 4.71384815 4.10327593
65 5.79464072 4.71384815
66 9.99169097 5.79464072
67 -1.11058957 9.99169097
68 -26.75980994 -1.11058957
69 -32.32961501 -26.75980994
70 14.55145932 -32.32961501
71 8.27716616 14.55145932
72 -8.80270244 8.27716616
73 14.65673653 -8.80270244
74 4.94070232 14.65673653
75 -5.13875980 4.94070232
76 -7.31742134 -5.13875980
77 16.41872700 -7.31742134
78 4.00553152 16.41872700
79 30.22094202 4.00553152
80 -17.68669353 30.22094202
81 11.54158780 -17.68669353
82 4.99034032 11.54158780
83 11.34158450 4.99034032
84 -19.36292187 11.34158450
85 11.95949917 -19.36292187
86 -1.68374712 11.95949917
87 2.28790693 -1.68374712
88 -14.56402427 2.28790693
89 24.74401656 -14.56402427
90 -41.60889548 24.74401656
91 -2.75503780 -41.60889548
92 3.82460886 -2.75503780
93 13.52345649 3.82460886
94 14.57502360 13.52345649
95 15.18369381 14.57502360
96 -41.85105138 15.18369381
97 2.09167181 -41.85105138
98 -29.16501117 2.09167181
99 5.88336667 -29.16501117
100 -0.44533297 5.88336667
101 18.32557622 -0.44533297
102 9.37931229 18.32557622
103 13.08233558 9.37931229
104 1.11106511 13.08233558
105 -24.97135079 1.11106511
106 33.20753345 -24.97135079
107 -23.60893145 33.20753345
108 -11.06646963 -23.60893145
109 8.04679202 -11.06646963
110 -9.03416787 8.04679202
111 -41.41917543 -9.03416787
112 10.47206119 -41.41917543
113 -32.52175021 10.47206119
114 -36.93313711 -32.52175021
115 -11.00614720 -36.93313711
116 -5.17440163 -11.00614720
117 7.14285088 -5.17440163
118 -15.45652630 7.14285088
119 -9.39568132 -15.45652630
120 -5.31922578 -9.39568132
121 -7.68212104 -5.31922578
122 15.17555195 -7.68212104
123 -10.36037356 15.17555195
124 13.22745049 -10.36037356
125 9.70321472 13.22745049
126 4.72651769 9.70321472
127 17.63337210 4.72651769
128 -27.44510066 17.63337210
129 0.44156808 -27.44510066
130 5.77322737 0.44156808
131 -19.81588053 5.77322737
132 -0.89897484 -19.81588053
133 7.74946990 -0.89897484
134 -8.38859996 7.74946990
135 -23.15304878 -8.38859996
136 4.13552365 -23.15304878
137 -19.79274711 4.13552365
138 -9.16548226 -19.79274711
139 -11.80528118 -9.16548226
140 -0.46413879 -11.80528118
141 -3.03979930 -0.46413879
142 2.14086065 -3.03979930
143 8.74154940 2.14086065
144 15.33313898 8.74154940
145 -12.82518383 15.33313898
146 6.99327789 -12.82518383
147 -8.75345449 6.99327789
148 1.05252029 -8.75345449
149 18.02636735 1.05252029
150 16.46533758 18.02636735
151 10.86349607 16.46533758
152 -4.88399860 10.86349607
153 8.80194396 -4.88399860
154 -7.01272314 8.80194396
155 0.18250164 -7.01272314
156 18.72429199 0.18250164
157 2.58611385 18.72429199
158 -3.95287886 2.58611385
159 20.99176003 -3.95287886
160 11.43614494 20.99176003
161 9.73892201 11.43614494
162 6.81675987 9.73892201
163 7.33561535 6.81675987
164 -5.52106385 7.33561535
165 5.42699097 -5.52106385
166 -6.91448495 5.42699097
167 -43.78567724 -6.91448495
168 -9.82066375 -43.78567724
169 1.75406836 -9.82066375
170 -5.35193223 1.75406836
171 -7.19392098 -5.35193223
172 16.53800537 -7.19392098
173 -11.16401331 16.53800537
174 10.68460954 -11.16401331
175 3.75629448 10.68460954
176 -3.97743487 3.75629448
177 5.32597626 -3.97743487
178 -0.68039333 5.32597626
179 2.19794182 -0.68039333
180 -0.91898375 2.19794182
181 -3.66258605 -0.91898375
182 3.52197705 -3.66258605
183 -17.27466887 3.52197705
184 -29.90819825 -17.27466887
185 3.70179034 -29.90819825
186 1.91154209 3.70179034
187 5.53580541 1.91154209
188 9.71561126 5.53580541
189 -42.52829679 9.71561126
190 -28.68228312 -42.52829679
191 1.53028446 -28.68228312
192 -0.68048806 1.53028446
193 2.33655762 -0.68048806
194 17.40428186 2.33655762
195 5.00844956 17.40428186
196 15.60302452 5.00844956
197 5.30981083 15.60302452
198 -5.94704444 5.30981083
199 10.71669261 -5.94704444
200 3.43423575 10.71669261
201 -19.42433750 3.43423575
202 15.36108144 -19.42433750
203 15.28027550 15.36108144
204 4.55535518 15.28027550
205 -14.91681635 4.55535518
206 7.04878426 -14.91681635
207 12.38407817 7.04878426
208 2.46042712 12.38407817
209 22.99659489 2.46042712
210 1.39799151 22.99659489
211 2.46593208 1.39799151
212 -0.35701757 2.46593208
213 15.01820825 -0.35701757
214 -1.62168654 15.01820825
215 23.89426097 -1.62168654
216 6.59240180 23.89426097
217 3.28269768 6.59240180
218 -4.25046752 3.28269768
219 13.71125442 -4.25046752
220 11.21166550 13.71125442
221 13.12367258 11.21166550
222 -4.14697060 13.12367258
223 6.00377604 -4.14697060
224 -1.41918445 6.00377604
225 -6.45552365 -1.41918445
226 -18.28119797 -6.45552365
227 9.88511531 -18.28119797
228 -9.11948225 9.88511531
229 9.35970475 -9.11948225
230 5.17363075 9.35970475
231 -1.21585098 5.17363075
232 2.28742578 -1.21585098
233 -9.83698037 2.28742578
234 -17.87423794 -9.83698037
235 -2.94064422 -17.87423794
236 6.11567147 -2.94064422
237 -33.44115188 6.11567147
238 -7.38064472 -33.44115188
239 0.50525023 -7.38064472
240 -9.61311650 0.50525023
241 15.46329609 -9.61311650
242 -1.50150061 15.46329609
243 5.88362697 -1.50150061
244 4.59902322 5.88362697
245 -6.87765523 4.59902322
246 -2.52987428 -6.87765523
247 9.00372008 -2.52987428
248 5.80855214 9.00372008
249 -11.04726228 5.80855214
250 7.17092178 -11.04726228
251 4.36179151 7.17092178
252 -0.02170400 4.36179151
253 13.06477006 -0.02170400
254 7.79855276 13.06477006
255 -3.33076469 7.79855276
256 -3.51003926 -3.33076469
257 -5.23085841 -3.51003926
258 -3.99795982 -5.23085841
259 -4.22082810 -3.99795982
260 -22.53750472 -4.22082810
261 17.61267678 -22.53750472
262 -1.26694752 17.61267678
263 -12.63829239 -1.26694752
264 14.41896735 -12.63829239
265 -5.85096565 14.41896735
266 7.83087821 -5.85096565
267 -10.43089394 7.83087821
268 12.48161851 -10.43089394
269 -20.33752879 12.48161851
270 -5.95937081 -20.33752879
271 16.45474497 -5.95937081
272 3.88721409 16.45474497
273 4.21952493 3.88721409
274 -2.65013772 4.21952493
275 -14.25907755 -2.65013772
276 0.02277402 -14.25907755
277 1.40636170 0.02277402
278 8.52498556 1.40636170
279 17.13719655 8.52498556
280 13.83438100 17.13719655
281 -13.19865998 13.83438100
282 -9.39241229 -13.19865998
283 -0.71286462 -9.39241229
284 11.72719033 -0.71286462
285 -18.63298795 11.72719033
286 4.44046742 -18.63298795
287 7.91005834 4.44046742
288 -4.15074861 7.91005834
289 NA -4.15074861
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 14.39794381 -6.05986088
[2,] -22.66203225 14.39794381
[3,] 1.11354533 -22.66203225
[4,] -14.71199590 1.11354533
[5,] -3.99747391 -14.71199590
[6,] -1.64940469 -3.99747391
[7,] -24.85337821 -1.64940469
[8,] 8.41745706 -24.85337821
[9,] 7.94890123 8.41745706
[10,] -18.52072368 7.94890123
[11,] 6.78850493 -18.52072368
[12,] 14.38786773 6.78850493
[13,] 1.06503681 14.38786773
[14,] -2.05515736 1.06503681
[15,] -31.82176992 -2.05515736
[16,] 14.14055187 -31.82176992
[17,] 9.74704540 14.14055187
[18,] -30.04285515 9.74704540
[19,] -13.62562933 -30.04285515
[20,] 13.37321911 -13.62562933
[21,] -0.22517892 13.37321911
[22,] 13.16651965 -0.22517892
[23,] 6.72639530 13.16651965
[24,] 33.26190866 6.72639530
[25,] 9.31681906 33.26190866
[26,] 15.71858361 9.31681906
[27,] -5.74969249 15.71858361
[28,] 13.44824861 -5.74969249
[29,] 8.45904582 13.44824861
[30,] 2.52346107 8.45904582
[31,] 16.99399041 2.52346107
[32,] -1.50166629 16.99399041
[33,] 21.01726071 -1.50166629
[34,] 20.55591202 21.01726071
[35,] 13.56721232 20.55591202
[36,] 1.28986843 13.56721232
[37,] -5.52086343 1.28986843
[38,] 12.86577441 -5.52086343
[39,] 4.57676651 12.86577441
[40,] -15.20336724 4.57676651
[41,] -4.13728101 -15.20336724
[42,] -11.21360695 -4.13728101
[43,] 15.97125439 -11.21360695
[44,] -5.16927891 15.97125439
[45,] -9.30476551 -5.16927891
[46,] 15.75580688 -9.30476551
[47,] 1.78703573 15.75580688
[48,] -1.89981129 1.78703573
[49,] -82.87160312 -1.89981129
[50,] 1.25959803 -82.87160312
[51,] -6.32531044 1.25959803
[52,] 15.91834460 -6.32531044
[53,] 1.02747923 15.91834460
[54,] 9.24697689 1.02747923
[55,] 10.52221525 9.24697689
[56,] -0.83238129 10.52221525
[57,] 15.08613171 -0.83238129
[58,] 10.50282701 15.08613171
[59,] 0.23133522 10.50282701
[60,] 2.99511541 0.23133522
[61,] 19.62842773 2.99511541
[62,] -0.10065222 19.62842773
[63,] 4.10327593 -0.10065222
[64,] 4.71384815 4.10327593
[65,] 5.79464072 4.71384815
[66,] 9.99169097 5.79464072
[67,] -1.11058957 9.99169097
[68,] -26.75980994 -1.11058957
[69,] -32.32961501 -26.75980994
[70,] 14.55145932 -32.32961501
[71,] 8.27716616 14.55145932
[72,] -8.80270244 8.27716616
[73,] 14.65673653 -8.80270244
[74,] 4.94070232 14.65673653
[75,] -5.13875980 4.94070232
[76,] -7.31742134 -5.13875980
[77,] 16.41872700 -7.31742134
[78,] 4.00553152 16.41872700
[79,] 30.22094202 4.00553152
[80,] -17.68669353 30.22094202
[81,] 11.54158780 -17.68669353
[82,] 4.99034032 11.54158780
[83,] 11.34158450 4.99034032
[84,] -19.36292187 11.34158450
[85,] 11.95949917 -19.36292187
[86,] -1.68374712 11.95949917
[87,] 2.28790693 -1.68374712
[88,] -14.56402427 2.28790693
[89,] 24.74401656 -14.56402427
[90,] -41.60889548 24.74401656
[91,] -2.75503780 -41.60889548
[92,] 3.82460886 -2.75503780
[93,] 13.52345649 3.82460886
[94,] 14.57502360 13.52345649
[95,] 15.18369381 14.57502360
[96,] -41.85105138 15.18369381
[97,] 2.09167181 -41.85105138
[98,] -29.16501117 2.09167181
[99,] 5.88336667 -29.16501117
[100,] -0.44533297 5.88336667
[101,] 18.32557622 -0.44533297
[102,] 9.37931229 18.32557622
[103,] 13.08233558 9.37931229
[104,] 1.11106511 13.08233558
[105,] -24.97135079 1.11106511
[106,] 33.20753345 -24.97135079
[107,] -23.60893145 33.20753345
[108,] -11.06646963 -23.60893145
[109,] 8.04679202 -11.06646963
[110,] -9.03416787 8.04679202
[111,] -41.41917543 -9.03416787
[112,] 10.47206119 -41.41917543
[113,] -32.52175021 10.47206119
[114,] -36.93313711 -32.52175021
[115,] -11.00614720 -36.93313711
[116,] -5.17440163 -11.00614720
[117,] 7.14285088 -5.17440163
[118,] -15.45652630 7.14285088
[119,] -9.39568132 -15.45652630
[120,] -5.31922578 -9.39568132
[121,] -7.68212104 -5.31922578
[122,] 15.17555195 -7.68212104
[123,] -10.36037356 15.17555195
[124,] 13.22745049 -10.36037356
[125,] 9.70321472 13.22745049
[126,] 4.72651769 9.70321472
[127,] 17.63337210 4.72651769
[128,] -27.44510066 17.63337210
[129,] 0.44156808 -27.44510066
[130,] 5.77322737 0.44156808
[131,] -19.81588053 5.77322737
[132,] -0.89897484 -19.81588053
[133,] 7.74946990 -0.89897484
[134,] -8.38859996 7.74946990
[135,] -23.15304878 -8.38859996
[136,] 4.13552365 -23.15304878
[137,] -19.79274711 4.13552365
[138,] -9.16548226 -19.79274711
[139,] -11.80528118 -9.16548226
[140,] -0.46413879 -11.80528118
[141,] -3.03979930 -0.46413879
[142,] 2.14086065 -3.03979930
[143,] 8.74154940 2.14086065
[144,] 15.33313898 8.74154940
[145,] -12.82518383 15.33313898
[146,] 6.99327789 -12.82518383
[147,] -8.75345449 6.99327789
[148,] 1.05252029 -8.75345449
[149,] 18.02636735 1.05252029
[150,] 16.46533758 18.02636735
[151,] 10.86349607 16.46533758
[152,] -4.88399860 10.86349607
[153,] 8.80194396 -4.88399860
[154,] -7.01272314 8.80194396
[155,] 0.18250164 -7.01272314
[156,] 18.72429199 0.18250164
[157,] 2.58611385 18.72429199
[158,] -3.95287886 2.58611385
[159,] 20.99176003 -3.95287886
[160,] 11.43614494 20.99176003
[161,] 9.73892201 11.43614494
[162,] 6.81675987 9.73892201
[163,] 7.33561535 6.81675987
[164,] -5.52106385 7.33561535
[165,] 5.42699097 -5.52106385
[166,] -6.91448495 5.42699097
[167,] -43.78567724 -6.91448495
[168,] -9.82066375 -43.78567724
[169,] 1.75406836 -9.82066375
[170,] -5.35193223 1.75406836
[171,] -7.19392098 -5.35193223
[172,] 16.53800537 -7.19392098
[173,] -11.16401331 16.53800537
[174,] 10.68460954 -11.16401331
[175,] 3.75629448 10.68460954
[176,] -3.97743487 3.75629448
[177,] 5.32597626 -3.97743487
[178,] -0.68039333 5.32597626
[179,] 2.19794182 -0.68039333
[180,] -0.91898375 2.19794182
[181,] -3.66258605 -0.91898375
[182,] 3.52197705 -3.66258605
[183,] -17.27466887 3.52197705
[184,] -29.90819825 -17.27466887
[185,] 3.70179034 -29.90819825
[186,] 1.91154209 3.70179034
[187,] 5.53580541 1.91154209
[188,] 9.71561126 5.53580541
[189,] -42.52829679 9.71561126
[190,] -28.68228312 -42.52829679
[191,] 1.53028446 -28.68228312
[192,] -0.68048806 1.53028446
[193,] 2.33655762 -0.68048806
[194,] 17.40428186 2.33655762
[195,] 5.00844956 17.40428186
[196,] 15.60302452 5.00844956
[197,] 5.30981083 15.60302452
[198,] -5.94704444 5.30981083
[199,] 10.71669261 -5.94704444
[200,] 3.43423575 10.71669261
[201,] -19.42433750 3.43423575
[202,] 15.36108144 -19.42433750
[203,] 15.28027550 15.36108144
[204,] 4.55535518 15.28027550
[205,] -14.91681635 4.55535518
[206,] 7.04878426 -14.91681635
[207,] 12.38407817 7.04878426
[208,] 2.46042712 12.38407817
[209,] 22.99659489 2.46042712
[210,] 1.39799151 22.99659489
[211,] 2.46593208 1.39799151
[212,] -0.35701757 2.46593208
[213,] 15.01820825 -0.35701757
[214,] -1.62168654 15.01820825
[215,] 23.89426097 -1.62168654
[216,] 6.59240180 23.89426097
[217,] 3.28269768 6.59240180
[218,] -4.25046752 3.28269768
[219,] 13.71125442 -4.25046752
[220,] 11.21166550 13.71125442
[221,] 13.12367258 11.21166550
[222,] -4.14697060 13.12367258
[223,] 6.00377604 -4.14697060
[224,] -1.41918445 6.00377604
[225,] -6.45552365 -1.41918445
[226,] -18.28119797 -6.45552365
[227,] 9.88511531 -18.28119797
[228,] -9.11948225 9.88511531
[229,] 9.35970475 -9.11948225
[230,] 5.17363075 9.35970475
[231,] -1.21585098 5.17363075
[232,] 2.28742578 -1.21585098
[233,] -9.83698037 2.28742578
[234,] -17.87423794 -9.83698037
[235,] -2.94064422 -17.87423794
[236,] 6.11567147 -2.94064422
[237,] -33.44115188 6.11567147
[238,] -7.38064472 -33.44115188
[239,] 0.50525023 -7.38064472
[240,] -9.61311650 0.50525023
[241,] 15.46329609 -9.61311650
[242,] -1.50150061 15.46329609
[243,] 5.88362697 -1.50150061
[244,] 4.59902322 5.88362697
[245,] -6.87765523 4.59902322
[246,] -2.52987428 -6.87765523
[247,] 9.00372008 -2.52987428
[248,] 5.80855214 9.00372008
[249,] -11.04726228 5.80855214
[250,] 7.17092178 -11.04726228
[251,] 4.36179151 7.17092178
[252,] -0.02170400 4.36179151
[253,] 13.06477006 -0.02170400
[254,] 7.79855276 13.06477006
[255,] -3.33076469 7.79855276
[256,] -3.51003926 -3.33076469
[257,] -5.23085841 -3.51003926
[258,] -3.99795982 -5.23085841
[259,] -4.22082810 -3.99795982
[260,] -22.53750472 -4.22082810
[261,] 17.61267678 -22.53750472
[262,] -1.26694752 17.61267678
[263,] -12.63829239 -1.26694752
[264,] 14.41896735 -12.63829239
[265,] -5.85096565 14.41896735
[266,] 7.83087821 -5.85096565
[267,] -10.43089394 7.83087821
[268,] 12.48161851 -10.43089394
[269,] -20.33752879 12.48161851
[270,] -5.95937081 -20.33752879
[271,] 16.45474497 -5.95937081
[272,] 3.88721409 16.45474497
[273,] 4.21952493 3.88721409
[274,] -2.65013772 4.21952493
[275,] -14.25907755 -2.65013772
[276,] 0.02277402 -14.25907755
[277,] 1.40636170 0.02277402
[278,] 8.52498556 1.40636170
[279,] 17.13719655 8.52498556
[280,] 13.83438100 17.13719655
[281,] -13.19865998 13.83438100
[282,] -9.39241229 -13.19865998
[283,] -0.71286462 -9.39241229
[284,] 11.72719033 -0.71286462
[285,] -18.63298795 11.72719033
[286,] 4.44046742 -18.63298795
[287,] 7.91005834 4.44046742
[288,] -4.15074861 7.91005834
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 14.39794381 -6.05986088
2 -22.66203225 14.39794381
3 1.11354533 -22.66203225
4 -14.71199590 1.11354533
5 -3.99747391 -14.71199590
6 -1.64940469 -3.99747391
7 -24.85337821 -1.64940469
8 8.41745706 -24.85337821
9 7.94890123 8.41745706
10 -18.52072368 7.94890123
11 6.78850493 -18.52072368
12 14.38786773 6.78850493
13 1.06503681 14.38786773
14 -2.05515736 1.06503681
15 -31.82176992 -2.05515736
16 14.14055187 -31.82176992
17 9.74704540 14.14055187
18 -30.04285515 9.74704540
19 -13.62562933 -30.04285515
20 13.37321911 -13.62562933
21 -0.22517892 13.37321911
22 13.16651965 -0.22517892
23 6.72639530 13.16651965
24 33.26190866 6.72639530
25 9.31681906 33.26190866
26 15.71858361 9.31681906
27 -5.74969249 15.71858361
28 13.44824861 -5.74969249
29 8.45904582 13.44824861
30 2.52346107 8.45904582
31 16.99399041 2.52346107
32 -1.50166629 16.99399041
33 21.01726071 -1.50166629
34 20.55591202 21.01726071
35 13.56721232 20.55591202
36 1.28986843 13.56721232
37 -5.52086343 1.28986843
38 12.86577441 -5.52086343
39 4.57676651 12.86577441
40 -15.20336724 4.57676651
41 -4.13728101 -15.20336724
42 -11.21360695 -4.13728101
43 15.97125439 -11.21360695
44 -5.16927891 15.97125439
45 -9.30476551 -5.16927891
46 15.75580688 -9.30476551
47 1.78703573 15.75580688
48 -1.89981129 1.78703573
49 -82.87160312 -1.89981129
50 1.25959803 -82.87160312
51 -6.32531044 1.25959803
52 15.91834460 -6.32531044
53 1.02747923 15.91834460
54 9.24697689 1.02747923
55 10.52221525 9.24697689
56 -0.83238129 10.52221525
57 15.08613171 -0.83238129
58 10.50282701 15.08613171
59 0.23133522 10.50282701
60 2.99511541 0.23133522
61 19.62842773 2.99511541
62 -0.10065222 19.62842773
63 4.10327593 -0.10065222
64 4.71384815 4.10327593
65 5.79464072 4.71384815
66 9.99169097 5.79464072
67 -1.11058957 9.99169097
68 -26.75980994 -1.11058957
69 -32.32961501 -26.75980994
70 14.55145932 -32.32961501
71 8.27716616 14.55145932
72 -8.80270244 8.27716616
73 14.65673653 -8.80270244
74 4.94070232 14.65673653
75 -5.13875980 4.94070232
76 -7.31742134 -5.13875980
77 16.41872700 -7.31742134
78 4.00553152 16.41872700
79 30.22094202 4.00553152
80 -17.68669353 30.22094202
81 11.54158780 -17.68669353
82 4.99034032 11.54158780
83 11.34158450 4.99034032
84 -19.36292187 11.34158450
85 11.95949917 -19.36292187
86 -1.68374712 11.95949917
87 2.28790693 -1.68374712
88 -14.56402427 2.28790693
89 24.74401656 -14.56402427
90 -41.60889548 24.74401656
91 -2.75503780 -41.60889548
92 3.82460886 -2.75503780
93 13.52345649 3.82460886
94 14.57502360 13.52345649
95 15.18369381 14.57502360
96 -41.85105138 15.18369381
97 2.09167181 -41.85105138
98 -29.16501117 2.09167181
99 5.88336667 -29.16501117
100 -0.44533297 5.88336667
101 18.32557622 -0.44533297
102 9.37931229 18.32557622
103 13.08233558 9.37931229
104 1.11106511 13.08233558
105 -24.97135079 1.11106511
106 33.20753345 -24.97135079
107 -23.60893145 33.20753345
108 -11.06646963 -23.60893145
109 8.04679202 -11.06646963
110 -9.03416787 8.04679202
111 -41.41917543 -9.03416787
112 10.47206119 -41.41917543
113 -32.52175021 10.47206119
114 -36.93313711 -32.52175021
115 -11.00614720 -36.93313711
116 -5.17440163 -11.00614720
117 7.14285088 -5.17440163
118 -15.45652630 7.14285088
119 -9.39568132 -15.45652630
120 -5.31922578 -9.39568132
121 -7.68212104 -5.31922578
122 15.17555195 -7.68212104
123 -10.36037356 15.17555195
124 13.22745049 -10.36037356
125 9.70321472 13.22745049
126 4.72651769 9.70321472
127 17.63337210 4.72651769
128 -27.44510066 17.63337210
129 0.44156808 -27.44510066
130 5.77322737 0.44156808
131 -19.81588053 5.77322737
132 -0.89897484 -19.81588053
133 7.74946990 -0.89897484
134 -8.38859996 7.74946990
135 -23.15304878 -8.38859996
136 4.13552365 -23.15304878
137 -19.79274711 4.13552365
138 -9.16548226 -19.79274711
139 -11.80528118 -9.16548226
140 -0.46413879 -11.80528118
141 -3.03979930 -0.46413879
142 2.14086065 -3.03979930
143 8.74154940 2.14086065
144 15.33313898 8.74154940
145 -12.82518383 15.33313898
146 6.99327789 -12.82518383
147 -8.75345449 6.99327789
148 1.05252029 -8.75345449
149 18.02636735 1.05252029
150 16.46533758 18.02636735
151 10.86349607 16.46533758
152 -4.88399860 10.86349607
153 8.80194396 -4.88399860
154 -7.01272314 8.80194396
155 0.18250164 -7.01272314
156 18.72429199 0.18250164
157 2.58611385 18.72429199
158 -3.95287886 2.58611385
159 20.99176003 -3.95287886
160 11.43614494 20.99176003
161 9.73892201 11.43614494
162 6.81675987 9.73892201
163 7.33561535 6.81675987
164 -5.52106385 7.33561535
165 5.42699097 -5.52106385
166 -6.91448495 5.42699097
167 -43.78567724 -6.91448495
168 -9.82066375 -43.78567724
169 1.75406836 -9.82066375
170 -5.35193223 1.75406836
171 -7.19392098 -5.35193223
172 16.53800537 -7.19392098
173 -11.16401331 16.53800537
174 10.68460954 -11.16401331
175 3.75629448 10.68460954
176 -3.97743487 3.75629448
177 5.32597626 -3.97743487
178 -0.68039333 5.32597626
179 2.19794182 -0.68039333
180 -0.91898375 2.19794182
181 -3.66258605 -0.91898375
182 3.52197705 -3.66258605
183 -17.27466887 3.52197705
184 -29.90819825 -17.27466887
185 3.70179034 -29.90819825
186 1.91154209 3.70179034
187 5.53580541 1.91154209
188 9.71561126 5.53580541
189 -42.52829679 9.71561126
190 -28.68228312 -42.52829679
191 1.53028446 -28.68228312
192 -0.68048806 1.53028446
193 2.33655762 -0.68048806
194 17.40428186 2.33655762
195 5.00844956 17.40428186
196 15.60302452 5.00844956
197 5.30981083 15.60302452
198 -5.94704444 5.30981083
199 10.71669261 -5.94704444
200 3.43423575 10.71669261
201 -19.42433750 3.43423575
202 15.36108144 -19.42433750
203 15.28027550 15.36108144
204 4.55535518 15.28027550
205 -14.91681635 4.55535518
206 7.04878426 -14.91681635
207 12.38407817 7.04878426
208 2.46042712 12.38407817
209 22.99659489 2.46042712
210 1.39799151 22.99659489
211 2.46593208 1.39799151
212 -0.35701757 2.46593208
213 15.01820825 -0.35701757
214 -1.62168654 15.01820825
215 23.89426097 -1.62168654
216 6.59240180 23.89426097
217 3.28269768 6.59240180
218 -4.25046752 3.28269768
219 13.71125442 -4.25046752
220 11.21166550 13.71125442
221 13.12367258 11.21166550
222 -4.14697060 13.12367258
223 6.00377604 -4.14697060
224 -1.41918445 6.00377604
225 -6.45552365 -1.41918445
226 -18.28119797 -6.45552365
227 9.88511531 -18.28119797
228 -9.11948225 9.88511531
229 9.35970475 -9.11948225
230 5.17363075 9.35970475
231 -1.21585098 5.17363075
232 2.28742578 -1.21585098
233 -9.83698037 2.28742578
234 -17.87423794 -9.83698037
235 -2.94064422 -17.87423794
236 6.11567147 -2.94064422
237 -33.44115188 6.11567147
238 -7.38064472 -33.44115188
239 0.50525023 -7.38064472
240 -9.61311650 0.50525023
241 15.46329609 -9.61311650
242 -1.50150061 15.46329609
243 5.88362697 -1.50150061
244 4.59902322 5.88362697
245 -6.87765523 4.59902322
246 -2.52987428 -6.87765523
247 9.00372008 -2.52987428
248 5.80855214 9.00372008
249 -11.04726228 5.80855214
250 7.17092178 -11.04726228
251 4.36179151 7.17092178
252 -0.02170400 4.36179151
253 13.06477006 -0.02170400
254 7.79855276 13.06477006
255 -3.33076469 7.79855276
256 -3.51003926 -3.33076469
257 -5.23085841 -3.51003926
258 -3.99795982 -5.23085841
259 -4.22082810 -3.99795982
260 -22.53750472 -4.22082810
261 17.61267678 -22.53750472
262 -1.26694752 17.61267678
263 -12.63829239 -1.26694752
264 14.41896735 -12.63829239
265 -5.85096565 14.41896735
266 7.83087821 -5.85096565
267 -10.43089394 7.83087821
268 12.48161851 -10.43089394
269 -20.33752879 12.48161851
270 -5.95937081 -20.33752879
271 16.45474497 -5.95937081
272 3.88721409 16.45474497
273 4.21952493 3.88721409
274 -2.65013772 4.21952493
275 -14.25907755 -2.65013772
276 0.02277402 -14.25907755
277 1.40636170 0.02277402
278 8.52498556 1.40636170
279 17.13719655 8.52498556
280 13.83438100 17.13719655
281 -13.19865998 13.83438100
282 -9.39241229 -13.19865998
283 -0.71286462 -9.39241229
284 11.72719033 -0.71286462
285 -18.63298795 11.72719033
286 4.44046742 -18.63298795
287 7.91005834 4.44046742
288 -4.15074861 7.91005834
> 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/7rh731324673266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8bbq01324673266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/96zm61324673266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10c1jr1324673266.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11pv0d1324673266.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/12cwvn1324673266.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/133ok51324673266.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/1438nu1324673266.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/15pux01324673266.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/166o1c1324673266.tab")
+ }
>
> try(system("convert tmp/1ou8l1324673265.ps tmp/1ou8l1324673265.png",intern=TRUE))
character(0)
> try(system("convert tmp/20cfc1324673265.ps tmp/20cfc1324673265.png",intern=TRUE))
character(0)
> try(system("convert tmp/3foc81324673265.ps tmp/3foc81324673265.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ngwi1324673266.ps tmp/4ngwi1324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/5b6sx1324673266.ps tmp/5b6sx1324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/62fnh1324673266.ps tmp/62fnh1324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rh731324673266.ps tmp/7rh731324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bbq01324673266.ps tmp/8bbq01324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/96zm61324673266.ps tmp/96zm61324673266.png",intern=TRUE))
character(0)
> try(system("convert tmp/10c1jr1324673266.ps tmp/10c1jr1324673266.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.043 0.684 8.797