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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Type 'q()' to quit R.
> x <- array(list(279055
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+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('Y'
+ ,'X1'
+ ,'X2'
+ ,'X3'
+ ,'X4'
+ ,'X5'
+ ,'X6'
+ ,'X7')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('Y','X1','X2','X3','X4','X5','X6','X7'),1:164))
> 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'
> 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
Y X1 X2 X3 X4 X5 X6 X7
1 279055 96 42 130 140824 32033 165 165
2 209884 75 38 143 110459 20654 135 132
3 233432 70 46 118 105079 16346 121 121
4 222117 134 42 146 112098 35926 148 145
5 179751 72 30 73 43929 10621 73 71
6 70849 8 35 89 76173 10024 49 47
7 568125 169 40 146 187326 43068 185 177
8 33186 1 18 22 22807 1271 5 5
9 227332 88 38 132 144408 34416 125 124
10 258676 98 37 92 66485 20318 93 92
11 341549 101 46 147 79089 24409 154 149
12 260484 122 60 203 81625 20648 98 93
13 202918 57 37 113 68788 12347 70 70
14 367799 139 55 171 103297 21857 148 148
15 269455 87 44 87 69446 11034 100 100
16 394578 176 63 208 114948 33433 150 142
17 335567 114 40 153 167949 35902 197 194
18 423110 121 43 97 125081 22355 114 113
19 182016 103 32 95 125818 31219 169 162
20 267365 135 52 197 136588 21983 200 186
21 279428 123 49 160 112431 40085 148 147
22 506616 97 41 148 103037 18507 140 137
23 201993 74 25 84 82317 16278 74 71
24 200004 103 57 227 118906 24662 128 123
25 257139 158 45 154 83515 31452 140 134
26 256931 113 42 151 104581 32580 116 115
27 296850 102 45 142 103129 22883 147 138
28 307100 132 43 148 83243 27652 132 125
29 184160 62 36 110 37110 9845 70 66
30 393860 150 45 149 113344 20190 144 137
31 309877 138 50 179 139165 46201 155 152
32 252512 50 50 149 86652 10971 165 159
33 367819 141 51 187 112302 34811 161 159
34 115602 48 42 153 69652 3029 31 31
35 430118 141 44 163 119442 38941 199 185
36 273950 83 42 127 69867 4958 78 78
37 428028 112 44 151 101629 32344 121 117
38 251306 79 40 100 70168 19433 112 109
39 115658 33 17 46 31081 12558 41 41
40 388812 149 43 156 103925 36524 158 149
41 343783 126 41 128 92622 26041 123 123
42 198635 81 41 111 79011 16637 104 103
43 213258 84 40 119 93487 28395 94 87
44 182398 68 49 148 64520 16747 73 71
45 157164 50 52 65 93473 9105 52 51
46 459440 101 42 134 114360 11941 71 70
47 78800 20 26 66 33032 7935 21 21
48 217575 101 59 201 96125 19499 155 155
49 368086 150 50 177 151911 22938 174 172
50 206448 115 50 156 89256 25314 136 133
51 244640 99 47 158 95676 28527 128 125
52 24188 8 4 7 5950 2694 7 7
53 399093 88 51 175 149695 20867 165 158
54 65029 21 18 61 32551 3597 21 21
55 101097 30 14 41 31701 5296 35 35
56 297973 97 41 133 100087 32982 137 133
57 369627 163 61 228 169707 38975 174 169
58 367127 132 40 140 150491 42721 257 256
59 374143 161 44 155 120192 41455 207 190
60 270099 89 40 141 95893 23923 103 100
61 391871 160 51 181 151715 26719 171 171
62 315924 139 29 75 176225 53405 279 267
63 291391 104 43 97 59900 12526 83 80
64 286417 100 42 142 104767 26584 130 126
65 270324 66 41 136 114799 37062 131 132
66 267432 163 30 87 72128 25696 126 121
67 215924 93 39 140 143592 24634 158 156
68 249232 85 51 169 89626 27269 138 133
69 260919 150 40 129 131072 25270 200 199
70 182961 143 29 92 126817 24634 104 98
71 256967 107 47 160 81351 17828 111 109
72 73566 22 23 67 22618 3007 26 25
73 272362 85 48 179 88977 20065 115 113
74 216802 86 38 90 92059 24648 127 126
75 228835 131 42 144 81897 21588 140 137
76 371391 140 46 144 108146 25217 121 121
77 392330 156 40 144 126372 30927 183 178
78 220401 81 45 134 249771 18487 68 63
79 225825 137 42 146 71154 18050 112 109
80 217623 102 41 121 71571 17696 103 101
81 199011 72 37 112 55918 17326 63 61
82 483074 161 47 145 160141 39361 166 157
83 145943 30 26 99 38692 9648 38 38
84 295224 120 48 96 102812 26759 163 159
85 80953 49 8 27 56622 7905 59 58
86 171206 63 27 77 15986 4527 27 27
87 179344 76 38 137 123534 41517 108 108
88 415550 85 41 151 108535 21261 88 83
89 366035 146 61 126 93879 36099 92 88
90 180679 165 45 159 144551 39039 170 164
91 298696 89 41 101 56750 13841 98 96
92 292260 168 42 144 127654 23841 205 192
93 199481 48 35 102 65594 8589 96 94
94 282361 149 36 135 59938 15049 107 107
95 329281 75 40 147 146975 39038 150 144
96 230588 103 40 155 165904 36774 138 136
97 297995 116 38 138 169265 40076 177 171
98 305984 165 43 113 183500 43840 213 210
99 416463 155 65 248 165986 43146 208 193
100 412530 165 33 116 184923 50099 307 297
101 297080 121 51 176 140358 40312 125 125
102 318283 156 45 140 149959 32616 208 204
103 202726 81 36 59 57224 11338 73 70
104 43287 13 19 64 43750 7409 49 49
105 223456 113 25 40 48029 18213 82 82
106 258249 112 44 98 104978 45873 206 205
107 299566 133 45 139 100046 39844 112 111
108 321797 169 44 135 101047 28317 139 135
109 170299 28 35 97 197426 24797 60 59
110 169545 121 46 142 160902 7471 70 70
111 354041 82 44 155 147172 27259 112 108
112 303273 148 45 115 109432 23201 142 141
113 23623 12 1 0 1168 238 11 11
114 195880 146 40 103 83248 28830 130 130
115 61857 23 11 30 25162 3913 31 28
116 207339 84 51 130 45724 9935 132 101
117 431443 163 38 102 110529 27738 219 216
118 21054 4 0 0 855 338 4 4
119 252805 81 30 77 101382 13326 102 97
120 31961 18 8 9 14116 3988 39 39
121 354622 118 43 150 89506 24347 125 119
122 251240 76 48 163 135356 27111 121 118
123 187003 55 49 148 116066 3938 42 41
124 172481 57 32 94 144244 17416 111 107
125 38214 16 8 21 8773 1888 16 16
126 256082 93 43 151 102153 18700 70 69
127 358276 137 52 187 117440 36809 162 160
128 211775 50 53 171 104128 24959 173 158
129 445926 152 49 170 134238 37343 171 161
130 348017 163 48 145 134047 21849 172 165
131 441946 142 56 198 279488 49809 254 246
132 208962 77 45 152 79756 21654 90 89
133 105332 42 40 112 66089 8728 50 49
134 315219 94 48 173 102070 20920 113 107
135 460249 126 50 177 146760 27195 187 182
136 160740 63 43 153 154771 1037 16 16
137 412099 127 46 161 165933 42570 175 173
138 173747 59 40 115 64593 17672 90 90
139 284582 117 45 147 92280 34245 140 140
140 283913 110 46 124 67150 16786 145 142
141 234262 44 37 57 128692 20954 141 126
142 386740 95 45 144 124089 16378 125 123
143 246963 128 39 126 125386 31852 241 239
144 173260 41 21 78 37238 2805 16 15
145 346748 146 50 153 140015 38086 175 170
146 176654 147 55 196 150047 21166 132 123
147 264767 121 40 130 154451 34672 154 151
148 314070 185 48 159 156349 36171 198 194
149 1 0 0 0 0 0 0 0
150 14688 4 0 0 6023 2065 5 5
151 98 0 0 0 0 0 0 0
152 455 0 0 0 0 0 0 0
153 0 0 0 0 0 0 0 0
154 0 0 0 0 0 0 0 0
155 284420 85 46 94 84601 19354 125 122
156 410509 157 52 129 68946 22124 174 173
157 0 0 0 0 0 0 0 0
158 203 0 0 0 0 0 0 0
159 7199 7 0 0 1644 556 6 6
160 46660 12 5 13 6179 2089 13 13
161 17547 0 1 4 3926 2658 3 3
162 121550 37 48 89 52789 1813 35 35
163 969 0 0 0 0 0 0 0
164 242258 62 34 71 100350 17372 80 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X1 X2 X3 X4 X5
8398.8114 970.3663 1864.2919 62.0281 0.2172 0.3527
X6 X7
652.9319 -320.6603
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-185164 -32976 -7687 25793 242087
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8398.8114 13325.8229 0.630 0.5294
X1 970.3663 203.0562 4.779 4.04e-06 ***
X2 1864.2919 851.8164 2.189 0.0301 *
X3 62.0281 242.9566 0.255 0.7988
X4 0.2172 0.1729 1.257 0.2108
X5 0.3527 0.8139 0.433 0.6654
X6 652.9319 1328.8040 0.491 0.6239
X7 -320.6603 1381.7175 -0.232 0.8168
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 63790 on 156 degrees of freedom
Multiple R-squared: 0.748, Adjusted R-squared: 0.7367
F-statistic: 66.14 on 7 and 156 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,] 0.3832139 7.664278e-01 6.167861e-01
[2,] 0.3049206 6.098411e-01 6.950794e-01
[3,] 0.4884736 9.769472e-01 5.115264e-01
[4,] 0.3668778 7.337557e-01 6.331222e-01
[5,] 0.2828720 5.657440e-01 7.171280e-01
[6,] 0.2268317 4.536634e-01 7.731683e-01
[7,] 0.1781607 3.563213e-01 8.218393e-01
[8,] 0.1451352 2.902704e-01 8.548648e-01
[9,] 0.5071410 9.857180e-01 4.928590e-01
[10,] 0.5982992 8.034016e-01 4.017008e-01
[11,] 0.5175103 9.649794e-01 4.824897e-01
[12,] 0.9685019 6.299611e-02 3.149806e-02
[13,] 0.9597620 8.047606e-02 4.023803e-02
[14,] 0.9616698 7.666035e-02 3.833018e-02
[15,] 0.9646458 7.070840e-02 3.535420e-02
[16,] 0.9497474 1.005051e-01 5.025257e-02
[17,] 0.9476752 1.046496e-01 5.232481e-02
[18,] 0.9332989 1.334022e-01 6.670112e-02
[19,] 0.9187384 1.625231e-01 8.126156e-02
[20,] 0.9045379 1.909242e-01 9.546211e-02
[21,] 0.8825468 2.349064e-01 1.174532e-01
[22,] 0.8729149 2.541702e-01 1.270851e-01
[23,] 0.8456253 3.087494e-01 1.543747e-01
[24,] 0.8375862 3.248277e-01 1.624138e-01
[25,] 0.9002789 1.994422e-01 9.972110e-02
[26,] 0.8788576 2.422849e-01 1.211424e-01
[27,] 0.9705959 5.880827e-02 2.940413e-02
[28,] 0.9610620 7.787606e-02 3.893803e-02
[29,] 0.9486292 1.027415e-01 5.137076e-02
[30,] 0.9412045 1.175911e-01 5.879555e-02
[31,] 0.9294180 1.411641e-01 7.058203e-02
[32,] 0.9182951 1.634097e-01 8.170487e-02
[33,] 0.8971133 2.057734e-01 1.028867e-01
[34,] 0.8758414 2.483172e-01 1.241586e-01
[35,] 0.8564168 2.871664e-01 1.435832e-01
[36,] 0.9732648 5.347050e-02 2.673525e-02
[37,] 0.9648190 7.036204e-02 3.518102e-02
[38,] 0.9696128 6.077448e-02 3.038724e-02
[39,] 0.9709007 5.819863e-02 2.909931e-02
[40,] 0.9772012 4.559769e-02 2.279885e-02
[41,] 0.9710262 5.794752e-02 2.897376e-02
[42,] 0.9650095 6.998091e-02 3.499046e-02
[43,] 0.9757486 4.850287e-02 2.425144e-02
[44,] 0.9691778 6.164435e-02 3.082217e-02
[45,] 0.9602168 7.956646e-02 3.978323e-02
[46,] 0.9546352 9.072950e-02 4.536475e-02
[47,] 0.9488962 1.022077e-01 5.110385e-02
[48,] 0.9355775 1.288451e-01 6.442255e-02
[49,] 0.9201333 1.597335e-01 7.986673e-02
[50,] 0.9051028 1.897945e-01 9.489724e-02
[51,] 0.8966579 2.066842e-01 1.033421e-01
[52,] 0.8997094 2.005812e-01 1.002906e-01
[53,] 0.8866632 2.266735e-01 1.133368e-01
[54,] 0.8640282 2.719436e-01 1.359718e-01
[55,] 0.8662684 2.674633e-01 1.337316e-01
[56,] 0.8723937 2.552127e-01 1.276063e-01
[57,] 0.8828385 2.343230e-01 1.171615e-01
[58,] 0.8646823 2.706354e-01 1.353177e-01
[59,] 0.8979271 2.041459e-01 1.020729e-01
[60,] 0.9462520 1.074961e-01 5.374803e-02
[61,] 0.9329905 1.340191e-01 6.700953e-02
[62,] 0.9179620 1.640760e-01 8.203801e-02
[63,] 0.9005559 1.988882e-01 9.944412e-02
[64,] 0.8816339 2.367323e-01 1.183661e-01
[65,] 0.8831689 2.336622e-01 1.168311e-01
[66,] 0.8785797 2.428406e-01 1.214203e-01
[67,] 0.8686662 2.626676e-01 1.313338e-01
[68,] 0.8728838 2.542325e-01 1.271162e-01
[69,] 0.8740589 2.518822e-01 1.259411e-01
[70,] 0.8550699 2.898602e-01 1.449301e-01
[71,] 0.8271480 3.457041e-01 1.728520e-01
[72,] 0.8938386 2.123228e-01 1.061614e-01
[73,] 0.8755894 2.488213e-01 1.244106e-01
[74,] 0.8514153 2.971693e-01 1.485847e-01
[75,] 0.8291608 3.416784e-01 1.708392e-01
[76,] 0.8055862 3.888275e-01 1.944138e-01
[77,] 0.8036911 3.926177e-01 1.963089e-01
[78,] 0.9519848 9.603047e-02 4.801524e-02
[79,] 0.9454342 1.091315e-01 5.456575e-02
[80,] 0.9925091 1.498187e-02 7.490933e-03
[81,] 0.9933491 1.330176e-02 6.650881e-03
[82,] 0.9939646 1.207075e-02 6.035373e-03
[83,] 0.9918335 1.633302e-02 8.166512e-03
[84,] 0.9888364 2.232721e-02 1.116361e-02
[85,] 0.9888795 2.224109e-02 1.112054e-02
[86,] 0.9883927 2.321463e-02 1.160731e-02
[87,] 0.9844225 3.115492e-02 1.557746e-02
[88,] 0.9860138 2.797246e-02 1.398623e-02
[89,] 0.9811101 3.777984e-02 1.888992e-02
[90,] 0.9750310 4.993796e-02 2.496898e-02
[91,] 0.9680912 6.381765e-02 3.190882e-02
[92,] 0.9646962 7.060763e-02 3.530381e-02
[93,] 0.9551987 8.960267e-02 4.480134e-02
[94,] 0.9509318 9.813634e-02 4.906817e-02
[95,] 0.9401672 1.196655e-01 5.983276e-02
[96,] 0.9453237 1.093526e-01 5.467628e-02
[97,] 0.9300774 1.398452e-01 6.992258e-02
[98,] 0.9122825 1.754349e-01 8.771747e-02
[99,] 0.8910834 2.178333e-01 1.089166e-01
[100,] 0.9165011 1.669978e-01 8.349890e-02
[101,] 0.9368682 1.262637e-01 6.313184e-02
[102,] 0.9201803 1.596393e-01 7.981966e-02
[103,] 0.8988310 2.023380e-01 1.011690e-01
[104,] 0.9461731 1.076539e-01 5.382693e-02
[105,] 0.9301918 1.396165e-01 6.980825e-02
[106,] 0.9211757 1.576487e-01 7.882434e-02
[107,] 0.9270237 1.459525e-01 7.297625e-02
[108,] 0.9063797 1.872406e-01 9.362030e-02
[109,] 0.8962499 2.075001e-01 1.037501e-01
[110,] 0.8738213 2.523573e-01 1.261787e-01
[111,] 0.8793065 2.413869e-01 1.206935e-01
[112,] 0.8500577 2.998847e-01 1.499423e-01
[113,] 0.8149257 3.701485e-01 1.850743e-01
[114,] 0.7831264 4.337473e-01 2.168736e-01
[115,] 0.7385684 5.228632e-01 2.614316e-01
[116,] 0.6927370 6.145261e-01 3.072630e-01
[117,] 0.6397238 7.205524e-01 3.602762e-01
[118,] 0.6610726 6.778548e-01 3.389274e-01
[119,] 0.7076113 5.847773e-01 2.923887e-01
[120,] 0.6583465 6.833070e-01 3.416535e-01
[121,] 0.5999342 8.001315e-01 4.000658e-01
[122,] 0.5572685 8.854630e-01 4.427315e-01
[123,] 0.5976331 8.047338e-01 4.023669e-01
[124,] 0.5501435 8.997131e-01 4.498565e-01
[125,] 0.7690394 4.619211e-01 2.309606e-01
[126,] 0.7183626 5.632749e-01 2.816374e-01
[127,] 0.7724392 4.551217e-01 2.275608e-01
[128,] 0.7302793 5.394413e-01 2.697207e-01
[129,] 0.6654774 6.690452e-01 3.345226e-01
[130,] 0.5942354 8.115293e-01 4.057646e-01
[131,] 0.5443473 9.113053e-01 4.556527e-01
[132,] 0.9847926 3.041488e-02 1.520744e-02
[133,] 0.9828521 3.429582e-02 1.714791e-02
[134,] 0.9999994 1.188726e-06 5.943630e-07
[135,] 0.9999999 1.153694e-07 5.768472e-08
[136,] 0.9999997 5.606215e-07 2.803107e-07
[137,] 1.0000000 4.296554e-10 2.148277e-10
[138,] 1.0000000 6.228835e-12 3.114418e-12
[139,] 1.0000000 2.160929e-10 1.080464e-10
[140,] 1.0000000 2.351205e-13 1.175603e-13
[141,] 1.0000000 4.167348e-11 2.083674e-11
[142,] 1.0000000 6.708230e-09 3.354115e-09
[143,] 0.9999995 9.610508e-07 4.805254e-07
> postscript(file="/var/wessaorg/rcomp/tmp/1d3aa1324635175.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/2t2121324635175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3dynh1324635175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wd8d1324635175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5525q1324635175.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 164
Frequency = 1
1 2 3 4 5
-5.574855e+03 -2.810220e+04 -4.764257e+03 -9.082564e+04 2.843803e+03
6 7 8 9 10
-5.308769e+04 1.921909e+05 -1.816882e+04 -3.085055e+04 2.766642e+04
11 12 13 14 15
6.170629e+04 -4.992732e+04 2.066465e+04 3.205353e+04 3.700532e+04
16 17 18 19 20
-4.123605e+03 1.692119e+04 1.378617e+05 -8.861953e+04 -8.956219e+04
21 22 23 24 25
-3.765682e+04 2.420868e+05 2.079753e+04 -1.073482e+05 -7.569864e+04
26 27 28 29 30
-2.185718e+04 1.457080e+04 7.329389e+03 5.586077e+03 6.493798e+04
31 32 33 34 35
-3.573751e+04 1.369747e+04 2.511207e+04 -5.366352e+04 8.246804e+04
36 37 38 39 40
5.599061e+04 1.445827e+05 2.520188e+04 1.588739e+04 5.514707e+04
41 42 43 44 45
5.856963e+04 -2.959177e+04 -2.240427e+04 -3.733466e+04 -4.184234e+04
46 47 48 49 50
2.134575e+05 -1.852300e+04 -9.055095e+04 1.039417e+04 -9.090070e+04
51 52 53 54 55
-3.158355e+04 -4.433574e+03 1.024225e+05 -1.640551e+04 1.456056e+04
56 57 58 59 60
3.058622e+04 -3.483370e+04 1.391337e+04 2.912249e+03 2.756700e+04
61 62 63 64 65
2.271036e+04 -3.973833e+04 4.992315e+04 1.726228e+04 3.179475e+04
66 67 68 69 70
-2.866126e+04 -5.712943e+04 -2.375208e+04 -7.976672e+04 -9.668656e+04
71 72 73 74 75
-1.428932e+04 -1.814879e+04 1.563637e+04 -2.268295e+04 -6.679739e+04
76 77 78 79 80
5.986160e+04 4.828351e+04 -4.377562e+04 -6.286867e+04 -3.034749e+04
81 82 83 84 85
4.988446e+03 1.151198e+05 2.938719e+04 -1.227253e+04 -2.659493e+04
86 87 88 89 90
3.252168e+04 -5.950506e+04 1.769503e+05 2.945008e+04 -1.851635e+05
91 92 93 94 95
7.082119e+04 -7.481417e+04 2.311060e+04 9.059796e+00 6.695646e+04
96 97 98 99 100
-5.744646e+04 -1.400700e+04 -7.675611e+04 -4.099052e+03 1.225208e+04
101 102 103 104 105
-2.096861e+04 -4.854224e+04 3.306663e+03 -4.551562e+04 1.221495e+04
106 107 108 109 110
-5.468867e+04 -3.725667e+03 -2.040095e+04 -8.424343e+03 -1.116787e+05
111 112 113 114 115
9.434948e+04 -1.922295e+04 -2.277138e+03 -1.065990e+05 -9.336424e+03
116 117 118 119 120
-5.294948e+04 8.018336e+04 7.139709e+03 4.288453e+04 -2.680834e+04
121 122 123 124 125
7.076415e+04 -1.063361e+04 -1.617333e+04 -3.235650e+04 -9.815473e+03
126 127 128 129 130
1.554386e+04 1.543324e+04 -4.827048e+04 8.578216e+04 -1.325037e+04
131 132 133 134 135
1.383394e+04 -2.266301e+04 -5.970933e+04 4.636853e+04 1.201820e+05
136 137 138 139 140
-3.774838e+04 7.487339e+04 -2.377619e+04 -9.001423e+03 5.677238e+03
141 142 143 144 145
2.364790e+04 1.184251e+05 -8.535426e+04 6.637271e+04 -9.626162e+03
146 147 148 149 150
-1.758861e+05 -4.159120e+04 -8.698635e+04 -8.397811e+03 -1.290228e+03
151 152 153 154 155
-8.300811e+03 -7.943811e+03 -8.398811e+03 -8.398811e+03 3.425328e+04
156 157 158 159 160
6.390288e+04 -8.398811e+03 -8.195811e+03 -1.053920e+04 1.009049e+04
161 162 163 164
4.248755e+03 -4.149468e+04 -7.429811e+03 4.883475e+04
> postscript(file="/var/wessaorg/rcomp/tmp/6y0dd1324635175.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 = 164
Frequency = 1
lag(myerror, k = 1) myerror
0 -5.574855e+03 NA
1 -2.810220e+04 -5.574855e+03
2 -4.764257e+03 -2.810220e+04
3 -9.082564e+04 -4.764257e+03
4 2.843803e+03 -9.082564e+04
5 -5.308769e+04 2.843803e+03
6 1.921909e+05 -5.308769e+04
7 -1.816882e+04 1.921909e+05
8 -3.085055e+04 -1.816882e+04
9 2.766642e+04 -3.085055e+04
10 6.170629e+04 2.766642e+04
11 -4.992732e+04 6.170629e+04
12 2.066465e+04 -4.992732e+04
13 3.205353e+04 2.066465e+04
14 3.700532e+04 3.205353e+04
15 -4.123605e+03 3.700532e+04
16 1.692119e+04 -4.123605e+03
17 1.378617e+05 1.692119e+04
18 -8.861953e+04 1.378617e+05
19 -8.956219e+04 -8.861953e+04
20 -3.765682e+04 -8.956219e+04
21 2.420868e+05 -3.765682e+04
22 2.079753e+04 2.420868e+05
23 -1.073482e+05 2.079753e+04
24 -7.569864e+04 -1.073482e+05
25 -2.185718e+04 -7.569864e+04
26 1.457080e+04 -2.185718e+04
27 7.329389e+03 1.457080e+04
28 5.586077e+03 7.329389e+03
29 6.493798e+04 5.586077e+03
30 -3.573751e+04 6.493798e+04
31 1.369747e+04 -3.573751e+04
32 2.511207e+04 1.369747e+04
33 -5.366352e+04 2.511207e+04
34 8.246804e+04 -5.366352e+04
35 5.599061e+04 8.246804e+04
36 1.445827e+05 5.599061e+04
37 2.520188e+04 1.445827e+05
38 1.588739e+04 2.520188e+04
39 5.514707e+04 1.588739e+04
40 5.856963e+04 5.514707e+04
41 -2.959177e+04 5.856963e+04
42 -2.240427e+04 -2.959177e+04
43 -3.733466e+04 -2.240427e+04
44 -4.184234e+04 -3.733466e+04
45 2.134575e+05 -4.184234e+04
46 -1.852300e+04 2.134575e+05
47 -9.055095e+04 -1.852300e+04
48 1.039417e+04 -9.055095e+04
49 -9.090070e+04 1.039417e+04
50 -3.158355e+04 -9.090070e+04
51 -4.433574e+03 -3.158355e+04
52 1.024225e+05 -4.433574e+03
53 -1.640551e+04 1.024225e+05
54 1.456056e+04 -1.640551e+04
55 3.058622e+04 1.456056e+04
56 -3.483370e+04 3.058622e+04
57 1.391337e+04 -3.483370e+04
58 2.912249e+03 1.391337e+04
59 2.756700e+04 2.912249e+03
60 2.271036e+04 2.756700e+04
61 -3.973833e+04 2.271036e+04
62 4.992315e+04 -3.973833e+04
63 1.726228e+04 4.992315e+04
64 3.179475e+04 1.726228e+04
65 -2.866126e+04 3.179475e+04
66 -5.712943e+04 -2.866126e+04
67 -2.375208e+04 -5.712943e+04
68 -7.976672e+04 -2.375208e+04
69 -9.668656e+04 -7.976672e+04
70 -1.428932e+04 -9.668656e+04
71 -1.814879e+04 -1.428932e+04
72 1.563637e+04 -1.814879e+04
73 -2.268295e+04 1.563637e+04
74 -6.679739e+04 -2.268295e+04
75 5.986160e+04 -6.679739e+04
76 4.828351e+04 5.986160e+04
77 -4.377562e+04 4.828351e+04
78 -6.286867e+04 -4.377562e+04
79 -3.034749e+04 -6.286867e+04
80 4.988446e+03 -3.034749e+04
81 1.151198e+05 4.988446e+03
82 2.938719e+04 1.151198e+05
83 -1.227253e+04 2.938719e+04
84 -2.659493e+04 -1.227253e+04
85 3.252168e+04 -2.659493e+04
86 -5.950506e+04 3.252168e+04
87 1.769503e+05 -5.950506e+04
88 2.945008e+04 1.769503e+05
89 -1.851635e+05 2.945008e+04
90 7.082119e+04 -1.851635e+05
91 -7.481417e+04 7.082119e+04
92 2.311060e+04 -7.481417e+04
93 9.059796e+00 2.311060e+04
94 6.695646e+04 9.059796e+00
95 -5.744646e+04 6.695646e+04
96 -1.400700e+04 -5.744646e+04
97 -7.675611e+04 -1.400700e+04
98 -4.099052e+03 -7.675611e+04
99 1.225208e+04 -4.099052e+03
100 -2.096861e+04 1.225208e+04
101 -4.854224e+04 -2.096861e+04
102 3.306663e+03 -4.854224e+04
103 -4.551562e+04 3.306663e+03
104 1.221495e+04 -4.551562e+04
105 -5.468867e+04 1.221495e+04
106 -3.725667e+03 -5.468867e+04
107 -2.040095e+04 -3.725667e+03
108 -8.424343e+03 -2.040095e+04
109 -1.116787e+05 -8.424343e+03
110 9.434948e+04 -1.116787e+05
111 -1.922295e+04 9.434948e+04
112 -2.277138e+03 -1.922295e+04
113 -1.065990e+05 -2.277138e+03
114 -9.336424e+03 -1.065990e+05
115 -5.294948e+04 -9.336424e+03
116 8.018336e+04 -5.294948e+04
117 7.139709e+03 8.018336e+04
118 4.288453e+04 7.139709e+03
119 -2.680834e+04 4.288453e+04
120 7.076415e+04 -2.680834e+04
121 -1.063361e+04 7.076415e+04
122 -1.617333e+04 -1.063361e+04
123 -3.235650e+04 -1.617333e+04
124 -9.815473e+03 -3.235650e+04
125 1.554386e+04 -9.815473e+03
126 1.543324e+04 1.554386e+04
127 -4.827048e+04 1.543324e+04
128 8.578216e+04 -4.827048e+04
129 -1.325037e+04 8.578216e+04
130 1.383394e+04 -1.325037e+04
131 -2.266301e+04 1.383394e+04
132 -5.970933e+04 -2.266301e+04
133 4.636853e+04 -5.970933e+04
134 1.201820e+05 4.636853e+04
135 -3.774838e+04 1.201820e+05
136 7.487339e+04 -3.774838e+04
137 -2.377619e+04 7.487339e+04
138 -9.001423e+03 -2.377619e+04
139 5.677238e+03 -9.001423e+03
140 2.364790e+04 5.677238e+03
141 1.184251e+05 2.364790e+04
142 -8.535426e+04 1.184251e+05
143 6.637271e+04 -8.535426e+04
144 -9.626162e+03 6.637271e+04
145 -1.758861e+05 -9.626162e+03
146 -4.159120e+04 -1.758861e+05
147 -8.698635e+04 -4.159120e+04
148 -8.397811e+03 -8.698635e+04
149 -1.290228e+03 -8.397811e+03
150 -8.300811e+03 -1.290228e+03
151 -7.943811e+03 -8.300811e+03
152 -8.398811e+03 -7.943811e+03
153 -8.398811e+03 -8.398811e+03
154 3.425328e+04 -8.398811e+03
155 6.390288e+04 3.425328e+04
156 -8.398811e+03 6.390288e+04
157 -8.195811e+03 -8.398811e+03
158 -1.053920e+04 -8.195811e+03
159 1.009049e+04 -1.053920e+04
160 4.248755e+03 1.009049e+04
161 -4.149468e+04 4.248755e+03
162 -7.429811e+03 -4.149468e+04
163 4.883475e+04 -7.429811e+03
164 NA 4.883475e+04
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.810220e+04 -5.574855e+03
[2,] -4.764257e+03 -2.810220e+04
[3,] -9.082564e+04 -4.764257e+03
[4,] 2.843803e+03 -9.082564e+04
[5,] -5.308769e+04 2.843803e+03
[6,] 1.921909e+05 -5.308769e+04
[7,] -1.816882e+04 1.921909e+05
[8,] -3.085055e+04 -1.816882e+04
[9,] 2.766642e+04 -3.085055e+04
[10,] 6.170629e+04 2.766642e+04
[11,] -4.992732e+04 6.170629e+04
[12,] 2.066465e+04 -4.992732e+04
[13,] 3.205353e+04 2.066465e+04
[14,] 3.700532e+04 3.205353e+04
[15,] -4.123605e+03 3.700532e+04
[16,] 1.692119e+04 -4.123605e+03
[17,] 1.378617e+05 1.692119e+04
[18,] -8.861953e+04 1.378617e+05
[19,] -8.956219e+04 -8.861953e+04
[20,] -3.765682e+04 -8.956219e+04
[21,] 2.420868e+05 -3.765682e+04
[22,] 2.079753e+04 2.420868e+05
[23,] -1.073482e+05 2.079753e+04
[24,] -7.569864e+04 -1.073482e+05
[25,] -2.185718e+04 -7.569864e+04
[26,] 1.457080e+04 -2.185718e+04
[27,] 7.329389e+03 1.457080e+04
[28,] 5.586077e+03 7.329389e+03
[29,] 6.493798e+04 5.586077e+03
[30,] -3.573751e+04 6.493798e+04
[31,] 1.369747e+04 -3.573751e+04
[32,] 2.511207e+04 1.369747e+04
[33,] -5.366352e+04 2.511207e+04
[34,] 8.246804e+04 -5.366352e+04
[35,] 5.599061e+04 8.246804e+04
[36,] 1.445827e+05 5.599061e+04
[37,] 2.520188e+04 1.445827e+05
[38,] 1.588739e+04 2.520188e+04
[39,] 5.514707e+04 1.588739e+04
[40,] 5.856963e+04 5.514707e+04
[41,] -2.959177e+04 5.856963e+04
[42,] -2.240427e+04 -2.959177e+04
[43,] -3.733466e+04 -2.240427e+04
[44,] -4.184234e+04 -3.733466e+04
[45,] 2.134575e+05 -4.184234e+04
[46,] -1.852300e+04 2.134575e+05
[47,] -9.055095e+04 -1.852300e+04
[48,] 1.039417e+04 -9.055095e+04
[49,] -9.090070e+04 1.039417e+04
[50,] -3.158355e+04 -9.090070e+04
[51,] -4.433574e+03 -3.158355e+04
[52,] 1.024225e+05 -4.433574e+03
[53,] -1.640551e+04 1.024225e+05
[54,] 1.456056e+04 -1.640551e+04
[55,] 3.058622e+04 1.456056e+04
[56,] -3.483370e+04 3.058622e+04
[57,] 1.391337e+04 -3.483370e+04
[58,] 2.912249e+03 1.391337e+04
[59,] 2.756700e+04 2.912249e+03
[60,] 2.271036e+04 2.756700e+04
[61,] -3.973833e+04 2.271036e+04
[62,] 4.992315e+04 -3.973833e+04
[63,] 1.726228e+04 4.992315e+04
[64,] 3.179475e+04 1.726228e+04
[65,] -2.866126e+04 3.179475e+04
[66,] -5.712943e+04 -2.866126e+04
[67,] -2.375208e+04 -5.712943e+04
[68,] -7.976672e+04 -2.375208e+04
[69,] -9.668656e+04 -7.976672e+04
[70,] -1.428932e+04 -9.668656e+04
[71,] -1.814879e+04 -1.428932e+04
[72,] 1.563637e+04 -1.814879e+04
[73,] -2.268295e+04 1.563637e+04
[74,] -6.679739e+04 -2.268295e+04
[75,] 5.986160e+04 -6.679739e+04
[76,] 4.828351e+04 5.986160e+04
[77,] -4.377562e+04 4.828351e+04
[78,] -6.286867e+04 -4.377562e+04
[79,] -3.034749e+04 -6.286867e+04
[80,] 4.988446e+03 -3.034749e+04
[81,] 1.151198e+05 4.988446e+03
[82,] 2.938719e+04 1.151198e+05
[83,] -1.227253e+04 2.938719e+04
[84,] -2.659493e+04 -1.227253e+04
[85,] 3.252168e+04 -2.659493e+04
[86,] -5.950506e+04 3.252168e+04
[87,] 1.769503e+05 -5.950506e+04
[88,] 2.945008e+04 1.769503e+05
[89,] -1.851635e+05 2.945008e+04
[90,] 7.082119e+04 -1.851635e+05
[91,] -7.481417e+04 7.082119e+04
[92,] 2.311060e+04 -7.481417e+04
[93,] 9.059796e+00 2.311060e+04
[94,] 6.695646e+04 9.059796e+00
[95,] -5.744646e+04 6.695646e+04
[96,] -1.400700e+04 -5.744646e+04
[97,] -7.675611e+04 -1.400700e+04
[98,] -4.099052e+03 -7.675611e+04
[99,] 1.225208e+04 -4.099052e+03
[100,] -2.096861e+04 1.225208e+04
[101,] -4.854224e+04 -2.096861e+04
[102,] 3.306663e+03 -4.854224e+04
[103,] -4.551562e+04 3.306663e+03
[104,] 1.221495e+04 -4.551562e+04
[105,] -5.468867e+04 1.221495e+04
[106,] -3.725667e+03 -5.468867e+04
[107,] -2.040095e+04 -3.725667e+03
[108,] -8.424343e+03 -2.040095e+04
[109,] -1.116787e+05 -8.424343e+03
[110,] 9.434948e+04 -1.116787e+05
[111,] -1.922295e+04 9.434948e+04
[112,] -2.277138e+03 -1.922295e+04
[113,] -1.065990e+05 -2.277138e+03
[114,] -9.336424e+03 -1.065990e+05
[115,] -5.294948e+04 -9.336424e+03
[116,] 8.018336e+04 -5.294948e+04
[117,] 7.139709e+03 8.018336e+04
[118,] 4.288453e+04 7.139709e+03
[119,] -2.680834e+04 4.288453e+04
[120,] 7.076415e+04 -2.680834e+04
[121,] -1.063361e+04 7.076415e+04
[122,] -1.617333e+04 -1.063361e+04
[123,] -3.235650e+04 -1.617333e+04
[124,] -9.815473e+03 -3.235650e+04
[125,] 1.554386e+04 -9.815473e+03
[126,] 1.543324e+04 1.554386e+04
[127,] -4.827048e+04 1.543324e+04
[128,] 8.578216e+04 -4.827048e+04
[129,] -1.325037e+04 8.578216e+04
[130,] 1.383394e+04 -1.325037e+04
[131,] -2.266301e+04 1.383394e+04
[132,] -5.970933e+04 -2.266301e+04
[133,] 4.636853e+04 -5.970933e+04
[134,] 1.201820e+05 4.636853e+04
[135,] -3.774838e+04 1.201820e+05
[136,] 7.487339e+04 -3.774838e+04
[137,] -2.377619e+04 7.487339e+04
[138,] -9.001423e+03 -2.377619e+04
[139,] 5.677238e+03 -9.001423e+03
[140,] 2.364790e+04 5.677238e+03
[141,] 1.184251e+05 2.364790e+04
[142,] -8.535426e+04 1.184251e+05
[143,] 6.637271e+04 -8.535426e+04
[144,] -9.626162e+03 6.637271e+04
[145,] -1.758861e+05 -9.626162e+03
[146,] -4.159120e+04 -1.758861e+05
[147,] -8.698635e+04 -4.159120e+04
[148,] -8.397811e+03 -8.698635e+04
[149,] -1.290228e+03 -8.397811e+03
[150,] -8.300811e+03 -1.290228e+03
[151,] -7.943811e+03 -8.300811e+03
[152,] -8.398811e+03 -7.943811e+03
[153,] -8.398811e+03 -8.398811e+03
[154,] 3.425328e+04 -8.398811e+03
[155,] 6.390288e+04 3.425328e+04
[156,] -8.398811e+03 6.390288e+04
[157,] -8.195811e+03 -8.398811e+03
[158,] -1.053920e+04 -8.195811e+03
[159,] 1.009049e+04 -1.053920e+04
[160,] 4.248755e+03 1.009049e+04
[161,] -4.149468e+04 4.248755e+03
[162,] -7.429811e+03 -4.149468e+04
[163,] 4.883475e+04 -7.429811e+03
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.810220e+04 -5.574855e+03
2 -4.764257e+03 -2.810220e+04
3 -9.082564e+04 -4.764257e+03
4 2.843803e+03 -9.082564e+04
5 -5.308769e+04 2.843803e+03
6 1.921909e+05 -5.308769e+04
7 -1.816882e+04 1.921909e+05
8 -3.085055e+04 -1.816882e+04
9 2.766642e+04 -3.085055e+04
10 6.170629e+04 2.766642e+04
11 -4.992732e+04 6.170629e+04
12 2.066465e+04 -4.992732e+04
13 3.205353e+04 2.066465e+04
14 3.700532e+04 3.205353e+04
15 -4.123605e+03 3.700532e+04
16 1.692119e+04 -4.123605e+03
17 1.378617e+05 1.692119e+04
18 -8.861953e+04 1.378617e+05
19 -8.956219e+04 -8.861953e+04
20 -3.765682e+04 -8.956219e+04
21 2.420868e+05 -3.765682e+04
22 2.079753e+04 2.420868e+05
23 -1.073482e+05 2.079753e+04
24 -7.569864e+04 -1.073482e+05
25 -2.185718e+04 -7.569864e+04
26 1.457080e+04 -2.185718e+04
27 7.329389e+03 1.457080e+04
28 5.586077e+03 7.329389e+03
29 6.493798e+04 5.586077e+03
30 -3.573751e+04 6.493798e+04
31 1.369747e+04 -3.573751e+04
32 2.511207e+04 1.369747e+04
33 -5.366352e+04 2.511207e+04
34 8.246804e+04 -5.366352e+04
35 5.599061e+04 8.246804e+04
36 1.445827e+05 5.599061e+04
37 2.520188e+04 1.445827e+05
38 1.588739e+04 2.520188e+04
39 5.514707e+04 1.588739e+04
40 5.856963e+04 5.514707e+04
41 -2.959177e+04 5.856963e+04
42 -2.240427e+04 -2.959177e+04
43 -3.733466e+04 -2.240427e+04
44 -4.184234e+04 -3.733466e+04
45 2.134575e+05 -4.184234e+04
46 -1.852300e+04 2.134575e+05
47 -9.055095e+04 -1.852300e+04
48 1.039417e+04 -9.055095e+04
49 -9.090070e+04 1.039417e+04
50 -3.158355e+04 -9.090070e+04
51 -4.433574e+03 -3.158355e+04
52 1.024225e+05 -4.433574e+03
53 -1.640551e+04 1.024225e+05
54 1.456056e+04 -1.640551e+04
55 3.058622e+04 1.456056e+04
56 -3.483370e+04 3.058622e+04
57 1.391337e+04 -3.483370e+04
58 2.912249e+03 1.391337e+04
59 2.756700e+04 2.912249e+03
60 2.271036e+04 2.756700e+04
61 -3.973833e+04 2.271036e+04
62 4.992315e+04 -3.973833e+04
63 1.726228e+04 4.992315e+04
64 3.179475e+04 1.726228e+04
65 -2.866126e+04 3.179475e+04
66 -5.712943e+04 -2.866126e+04
67 -2.375208e+04 -5.712943e+04
68 -7.976672e+04 -2.375208e+04
69 -9.668656e+04 -7.976672e+04
70 -1.428932e+04 -9.668656e+04
71 -1.814879e+04 -1.428932e+04
72 1.563637e+04 -1.814879e+04
73 -2.268295e+04 1.563637e+04
74 -6.679739e+04 -2.268295e+04
75 5.986160e+04 -6.679739e+04
76 4.828351e+04 5.986160e+04
77 -4.377562e+04 4.828351e+04
78 -6.286867e+04 -4.377562e+04
79 -3.034749e+04 -6.286867e+04
80 4.988446e+03 -3.034749e+04
81 1.151198e+05 4.988446e+03
82 2.938719e+04 1.151198e+05
83 -1.227253e+04 2.938719e+04
84 -2.659493e+04 -1.227253e+04
85 3.252168e+04 -2.659493e+04
86 -5.950506e+04 3.252168e+04
87 1.769503e+05 -5.950506e+04
88 2.945008e+04 1.769503e+05
89 -1.851635e+05 2.945008e+04
90 7.082119e+04 -1.851635e+05
91 -7.481417e+04 7.082119e+04
92 2.311060e+04 -7.481417e+04
93 9.059796e+00 2.311060e+04
94 6.695646e+04 9.059796e+00
95 -5.744646e+04 6.695646e+04
96 -1.400700e+04 -5.744646e+04
97 -7.675611e+04 -1.400700e+04
98 -4.099052e+03 -7.675611e+04
99 1.225208e+04 -4.099052e+03
100 -2.096861e+04 1.225208e+04
101 -4.854224e+04 -2.096861e+04
102 3.306663e+03 -4.854224e+04
103 -4.551562e+04 3.306663e+03
104 1.221495e+04 -4.551562e+04
105 -5.468867e+04 1.221495e+04
106 -3.725667e+03 -5.468867e+04
107 -2.040095e+04 -3.725667e+03
108 -8.424343e+03 -2.040095e+04
109 -1.116787e+05 -8.424343e+03
110 9.434948e+04 -1.116787e+05
111 -1.922295e+04 9.434948e+04
112 -2.277138e+03 -1.922295e+04
113 -1.065990e+05 -2.277138e+03
114 -9.336424e+03 -1.065990e+05
115 -5.294948e+04 -9.336424e+03
116 8.018336e+04 -5.294948e+04
117 7.139709e+03 8.018336e+04
118 4.288453e+04 7.139709e+03
119 -2.680834e+04 4.288453e+04
120 7.076415e+04 -2.680834e+04
121 -1.063361e+04 7.076415e+04
122 -1.617333e+04 -1.063361e+04
123 -3.235650e+04 -1.617333e+04
124 -9.815473e+03 -3.235650e+04
125 1.554386e+04 -9.815473e+03
126 1.543324e+04 1.554386e+04
127 -4.827048e+04 1.543324e+04
128 8.578216e+04 -4.827048e+04
129 -1.325037e+04 8.578216e+04
130 1.383394e+04 -1.325037e+04
131 -2.266301e+04 1.383394e+04
132 -5.970933e+04 -2.266301e+04
133 4.636853e+04 -5.970933e+04
134 1.201820e+05 4.636853e+04
135 -3.774838e+04 1.201820e+05
136 7.487339e+04 -3.774838e+04
137 -2.377619e+04 7.487339e+04
138 -9.001423e+03 -2.377619e+04
139 5.677238e+03 -9.001423e+03
140 2.364790e+04 5.677238e+03
141 1.184251e+05 2.364790e+04
142 -8.535426e+04 1.184251e+05
143 6.637271e+04 -8.535426e+04
144 -9.626162e+03 6.637271e+04
145 -1.758861e+05 -9.626162e+03
146 -4.159120e+04 -1.758861e+05
147 -8.698635e+04 -4.159120e+04
148 -8.397811e+03 -8.698635e+04
149 -1.290228e+03 -8.397811e+03
150 -8.300811e+03 -1.290228e+03
151 -7.943811e+03 -8.300811e+03
152 -8.398811e+03 -7.943811e+03
153 -8.398811e+03 -8.398811e+03
154 3.425328e+04 -8.398811e+03
155 6.390288e+04 3.425328e+04
156 -8.398811e+03 6.390288e+04
157 -8.195811e+03 -8.398811e+03
158 -1.053920e+04 -8.195811e+03
159 1.009049e+04 -1.053920e+04
160 4.248755e+03 1.009049e+04
161 -4.149468e+04 4.248755e+03
162 -7.429811e+03 -4.149468e+04
163 4.883475e+04 -7.429811e+03
> 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/70grd1324635175.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/82c8f1324635175.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/9fjq81324635175.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/10lraz1324635175.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11pxhm1324635175.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12bqzg1324635175.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13t57r1324635175.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/146k461324635175.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/150vk21324635175.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/168sin1324635175.tab")
+ }
>
> try(system("convert tmp/1d3aa1324635175.ps tmp/1d3aa1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/2t2121324635175.ps tmp/2t2121324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/3dynh1324635175.ps tmp/3dynh1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wd8d1324635175.ps tmp/4wd8d1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/5525q1324635175.ps tmp/5525q1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/6y0dd1324635175.ps tmp/6y0dd1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/70grd1324635175.ps tmp/70grd1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/82c8f1324635175.ps tmp/82c8f1324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fjq81324635175.ps tmp/9fjq81324635175.png",intern=TRUE))
character(0)
> try(system("convert tmp/10lraz1324635175.ps tmp/10lraz1324635175.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.189 0.623 5.823