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(38
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+ ,2)
+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('PR'
+ ,'Pagevieuws'
+ ,'Time_RFC'
+ ,'Compendium_Vieuws'
+ ,'Course_compendium-vieuws'
+ ,'Compendium_writing_nbr'
+ ,'Inlcuded_hyperlinks'
+ ,'Shared')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('PR','Pagevieuws','Time_RFC','Compendium_Vieuws','Course_compendium-vieuws','Compendium_writing_nbr','Inlcuded_hyperlinks','Shared'),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'
> #'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
PR Pagevieuws Time_RFC Compendium_Vieuws Course_compendium-vieuws
1 38 1724 270018 90 476
2 34 1209 179444 63 429
3 42 1844 222373 59 673
4 38 2683 218443 135 1137
5 27 1149 162874 48 348
6 35 631 70849 46 179
7 33 4513 498732 109 2201
8 18 381 33186 46 111
9 34 1997 207822 75 735
10 33 1758 213274 72 595
11 44 2079 298841 80 780
12 55 2128 237633 61 660
13 37 1659 164107 60 633
14 52 2934 358752 114 1163
15 43 1944 222781 46 622
16 59 4764 369889 127 1650
17 36 2122 305704 58 746
18 39 2956 322896 90 1157
19 29 1438 176082 41 507
20 49 2320 263411 62 683
21 45 2471 271965 99 828
22 39 2769 425544 101 1203
23 25 1442 179306 62 461
24 52 1717 189897 65 601
25 41 3220 220665 150 1201
26 38 2733 214779 72 990
27 41 2824 267198 91 1061
28 43 1968 270750 73 617
29 32 1495 155915 53 559
30 41 2745 330118 140 1031
31 46 2290 281588 50 911
32 49 1830 204039 83 615
33 48 2090 318563 53 779
34 37 945 97717 40 310
35 39 3092 369331 72 1198
36 42 2764 273950 87 1186
37 43 3658 422946 74 1317
38 36 1842 215710 67 611
39 17 934 115469 36 276
40 39 3342 343095 45 1185
41 39 3246 324178 42 1490
42 41 1629 170369 75 646
43 36 1735 195153 82 635
44 42 1714 173510 85 470
45 45 2496 153778 82 1022
46 41 5501 455168 848 2068
47 26 918 78800 57 330
48 52 2228 208051 80 648
49 47 3942 334657 116 1342
50 45 2081 175523 68 868
51 40 1816 213060 48 559
52 4 496 24188 20 218
53 44 2533 372238 81 833
54 18 744 65029 21 255
55 14 1161 101097 70 454
56 37 3027 279012 125 1108
57 56 2433 302218 80 642
58 39 3576 323514 220 1079
59 42 2606 339837 63 1046
60 36 2175 252529 77 822
61 46 3937 370483 65 1298
62 28 3161 303406 146 1143
63 43 2790 250858 72 1124
64 42 2610 264889 59 931
65 37 1426 228595 58 557
66 30 1646 216027 58 436
67 35 1867 188780 54 566
68 44 2736 237856 89 832
69 36 2277 232765 78 834
70 28 1675 175699 62 621
71 45 2537 239314 64 865
72 23 893 73566 39 385
73 45 2190 242585 58 716
74 38 1694 187167 94 705
75 38 1948 191920 61 683
76 45 2314 359644 95 982
77 36 2645 341637 48 1056
78 41 1804 206059 50 522
79 38 2250 201783 58 690
80 37 1787 182231 67 644
81 28 1678 153613 41 622
82 45 4009 454794 114 1226
83 26 1369 145943 45 653
84 44 2306 280343 57 656
85 8 870 80953 31 437
86 27 1966 150216 175 822
87 36 1338 156923 68 390
88 37 3731 365448 278 1467
89 57 2617 318651 91 907
90 45 3085 179797 72 1044
91 37 2312 251466 58 786
92 38 2136 254506 71 655
93 31 1808 185890 86 590
94 36 2992 263577 89 1072
95 36 2474 314255 134 947
96 36 1624 189252 64 555
97 35 1606 222504 72 552
98 39 2091 285198 61 771
99 65 3930 376927 130 1291
100 30 3705 397681 73 1415
101 51 2676 287015 83 846
102 41 2296 285330 85 838
103 36 1997 186856 116 640
104 19 602 43287 43 214
105 23 2146 185468 85 716
106 40 2157 222268 72 755
107 40 2549 259692 110 1140
108 40 2649 301614 55 1030
109 30 1110 121726 44 356
110 41 3102 154165 79 906
111 40 1861 306952 58 606
112 45 2295 297982 70 684
113 1 398 23623 9 156
114 40 2205 195817 54 779
115 11 530 61857 25 192
116 45 1596 163766 107 457
117 38 2949 384053 63 1162
118 0 387 21054 2 146
119 30 2137 252805 67 866
120 8 492 31961 22 200
121 39 3397 311281 153 1211
122 48 2089 240153 79 696
123 48 1638 174892 112 485
124 29 1685 152043 47 670
125 8 568 38214 52 276
126 43 1917 199336 113 662
127 52 2759 353021 115 1010
128 53 1288 196269 64 445
129 48 3554 403932 134 1564
130 48 2387 316105 120 820
131 50 3328 396725 111 1151
132 40 1250 187992 49 473
133 36 1121 102424 55 401
134 40 2867 284271 149 949
135 46 4024 401260 155 1429
136 40 1721 137843 104 534
137 46 4061 383703 146 1698
138 39 1830 157429 76 689
139 41 1627 236370 83 528
140 46 2535 282399 192 897
141 32 1808 217478 69 610
142 39 3873 366774 117 1548
143 39 2181 236660 67 759
144 21 2035 173260 37 716
145 45 2960 323545 56 955
146 50 1915 168994 122 720
147 36 2604 246745 52 1011
148 44 2633 301703 64 818
149 0 2 1 0 0
150 0 207 14688 0 85
151 0 5 98 0 0
152 0 8 455 0 0
153 0 0 0 0 0
154 0 0 0 0 0
155 37 2030 233143 58 699
156 47 3179 372078 118 1052
157 0 0 0 0 0
158 0 4 203 0 0
159 0 151 7199 0 74
160 5 474 46660 7 259
161 1 141 17547 3 69
162 43 1047 116678 89 285
163 0 29 969 0 0
164 32 1767 201582 48 582
Compendium_writing_nbr Inlcuded_hyperlinks Shared
1 140824 165 3
2 110459 135 4
3 105079 121 16
4 112098 148 2
5 43929 73 1
6 76173 49 3
7 187326 185 0
8 22807 5 0
9 144408 125 7
10 66485 93 0
11 79089 154 0
12 81625 98 7
13 68788 70 8
14 103297 148 4
15 69446 100 10
16 114948 150 0
17 167949 197 6
18 125081 114 4
19 125818 169 3
20 136588 200 8
21 112431 148 0
22 103037 140 1
23 82317 74 5
24 118906 128 9
25 83515 140 1
26 104581 116 0
27 103129 147 5
28 83243 132 0
29 37110 70 0
30 113344 144 0
31 139165 155 3
32 86652 165 6
33 112302 161 1
34 69652 31 4
35 119442 199 4
36 69867 78 0
37 101629 121 0
38 70168 112 2
39 31081 41 1
40 103925 158 2
41 92622 123 10
42 79011 104 9
43 93487 94 5
44 64520 73 6
45 93473 52 1
46 114360 71 2
47 33032 21 2
48 96125 155 0
49 151911 174 10
50 89256 136 3
51 95671 128 0
52 5950 7 0
53 149695 165 8
54 32551 21 5
55 31701 35 3
56 100087 137 1
57 169707 174 5
58 150491 257 5
59 120192 207 0
60 95893 103 12
61 151715 171 10
62 176225 279 12
63 59900 83 11
64 104767 130 8
65 114799 131 2
66 72128 126 0
67 143592 158 6
68 89626 138 9
69 131072 200 2
70 126817 104 5
71 81351 111 13
72 22618 26 6
73 88977 115 7
74 92059 127 2
75 81897 140 1
76 108146 121 4
77 126372 183 3
78 249771 68 6
79 71154 112 2
80 71571 103 0
81 55918 63 1
82 160141 166 0
83 38692 38 5
84 102812 163 2
85 56622 59 0
86 15986 27 0
87 123534 108 6
88 108535 88 1
89 93879 92 0
90 144551 170 1
91 56750 98 1
92 127654 205 3
93 65594 96 9
94 59938 107 1
95 146975 150 4
96 143372 123 3
97 168553 176 5
98 183500 213 0
99 165986 208 12
100 184923 307 13
101 140358 125 8
102 149959 208 0
103 57224 73 0
104 43750 49 4
105 48029 82 4
106 104978 206 0
107 100046 112 0
108 101047 139 0
109 197426 60 0
110 160902 70 0
111 147172 112 4
112 109432 142 0
113 1168 11 0
114 83248 130 0
115 25162 31 4
116 45724 132 0
117 110529 219 1
118 855 4 0
119 101382 102 5
120 14116 39 0
121 89506 125 3
122 135356 121 7
123 116066 42 13
124 144244 111 3
125 8773 16 0
126 102153 70 2
127 117440 162 0
128 104128 173 0
129 134238 171 4
130 134047 172 0
131 279488 254 3
132 79756 90 0
133 66089 50 0
134 102070 113 4
135 146760 187 4
136 154771 16 15
137 165933 175 0
138 64593 90 4
139 92280 140 1
140 67150 145 1
141 128692 141 0
142 124089 125 9
143 125386 241 1
144 37238 16 3
145 140015 175 11
146 150047 132 5
147 154451 154 2
148 156349 198 1
149 0 0 9
150 6023 5 0
151 0 0 0
152 0 0 0
153 0 0 1
154 0 0 0
155 84601 125 2
156 68946 174 3
157 0 0 0
158 0 0 0
159 1644 6 0
160 6179 13 0
161 3926 3 0
162 52789 35 0
163 0 0 0
164 100350 80 2
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pagevieuws
1.086e+01 1.363e-02
Time_RFC Compendium_Vieuws
5.376e-05 -1.585e-03
`Course_compendium-vieuws` Compendium_writing_nbr
-2.992e-02 5.245e-05
Inlcuded_hyperlinks Shared
6.543e-03 2.263e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-24.9441 -6.3183 0.4893 5.5057 20.8524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.086e+01 1.555e+00 6.982 7.88e-11 ***
Pagevieuws 1.363e-02 3.033e-03 4.495 1.35e-05 ***
Time_RFC 5.376e-05 1.719e-05 3.127 0.0021 **
Compendium_Vieuws -1.585e-03 1.190e-02 -0.133 0.8942
`Course_compendium-vieuws` -2.992e-02 6.757e-03 -4.428 1.78e-05 ***
Compendium_writing_nbr 5.245e-05 2.127e-05 2.466 0.0148 *
Inlcuded_hyperlinks 6.543e-03 1.971e-02 0.332 0.7404
Shared 2.263e-01 1.877e-01 1.206 0.2298
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8.453 on 156 degrees of freedom
Multiple R-squared: 0.6657, Adjusted R-squared: 0.6507
F-statistic: 44.38 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,] 0.39877982 7.975596e-01 6.012202e-01
[2,] 0.50085494 9.982901e-01 4.991451e-01
[3,] 0.35868885 7.173777e-01 6.413111e-01
[4,] 0.37745770 7.549154e-01 6.225423e-01
[5,] 0.29602584 5.920517e-01 7.039742e-01
[6,] 0.21225901 4.245180e-01 7.877410e-01
[7,] 0.14799185 2.959837e-01 8.520081e-01
[8,] 0.10638833 2.127767e-01 8.936117e-01
[9,] 0.06675006 1.335001e-01 9.332499e-01
[10,] 0.04129765 8.259529e-02 9.587024e-01
[11,] 0.02534626 5.069252e-02 9.746537e-01
[12,] 0.01525210 3.050421e-02 9.847479e-01
[13,] 0.02871791 5.743582e-02 9.712821e-01
[14,] 0.08716005 1.743201e-01 9.128399e-01
[15,] 0.08820606 1.764121e-01 9.117939e-01
[16,] 0.06105735 1.221147e-01 9.389426e-01
[17,] 0.04521976 9.043952e-02 9.547802e-01
[18,] 0.03178995 6.357990e-02 9.682101e-01
[19,] 0.02150354 4.300707e-02 9.784965e-01
[20,] 0.01407761 2.815523e-02 9.859224e-01
[21,] 0.02156083 4.312166e-02 9.784392e-01
[22,] 0.02055863 4.111726e-02 9.794414e-01
[23,] 0.02237128 4.474255e-02 9.776287e-01
[24,] 0.03061014 6.122029e-02 9.693899e-01
[25,] 0.03546619 7.093238e-02 9.645338e-01
[26,] 0.03292988 6.585977e-02 9.670701e-01
[27,] 0.03005490 6.010980e-02 9.699451e-01
[28,] 0.02248694 4.497388e-02 9.775131e-01
[29,] 0.04143433 8.286867e-02 9.585657e-01
[30,] 0.03849952 7.699904e-02 9.615005e-01
[31,] 0.02785323 5.570647e-02 9.721468e-01
[32,] 0.02386748 4.773495e-02 9.761325e-01
[33,] 0.01731102 3.462204e-02 9.826890e-01
[34,] 0.01323335 2.646670e-02 9.867666e-01
[35,] 0.02722108 5.444217e-02 9.727789e-01
[36,] 0.05950758 1.190152e-01 9.404924e-01
[37,] 0.04815919 9.631838e-02 9.518408e-01
[38,] 0.06021794 1.204359e-01 9.397821e-01
[39,] 0.06614504 1.322901e-01 9.338550e-01
[40,] 0.08744315 1.748863e-01 9.125568e-01
[41,] 0.07085178 1.417036e-01 9.291482e-01
[42,] 0.19141290 3.828258e-01 8.085871e-01
[43,] 0.16933031 3.386606e-01 8.306697e-01
[44,] 0.17345512 3.469102e-01 8.265449e-01
[45,] 0.21715266 4.343053e-01 7.828473e-01
[46,] 0.19782221 3.956444e-01 8.021778e-01
[47,] 0.17714488 3.542898e-01 8.228551e-01
[48,] 0.38902894 7.780579e-01 6.109711e-01
[49,] 0.34592230 6.918446e-01 6.540777e-01
[50,] 0.31249642 6.249928e-01 6.875036e-01
[51,] 0.32784499 6.556900e-01 6.721550e-01
[52,] 0.65298481 6.940304e-01 3.470152e-01
[53,] 0.66220896 6.755821e-01 3.377910e-01
[54,] 0.62575237 7.484953e-01 3.742476e-01
[55,] 0.58874393 8.225121e-01 4.112561e-01
[56,] 0.58419910 8.316018e-01 4.158009e-01
[57,] 0.55350199 8.929960e-01 4.464980e-01
[58,] 0.50659535 9.868093e-01 4.934046e-01
[59,] 0.46305433 9.261087e-01 5.369457e-01
[60,] 0.44275562 8.855112e-01 5.572444e-01
[61,] 0.42681384 8.536277e-01 5.731862e-01
[62,] 0.41165772 8.233154e-01 5.883423e-01
[63,] 0.40442689 8.088538e-01 5.955731e-01
[64,] 0.39673336 7.934667e-01 6.032666e-01
[65,] 0.37005861 7.401172e-01 6.299414e-01
[66,] 0.35794011 7.158802e-01 6.420599e-01
[67,] 0.32849683 6.569937e-01 6.715032e-01
[68,] 0.29116407 5.823281e-01 7.088359e-01
[69,] 0.25912534 5.182507e-01 7.408747e-01
[70,] 0.24501867 4.900373e-01 7.549813e-01
[71,] 0.22659545 4.531909e-01 7.734045e-01
[72,] 0.34703886 6.940777e-01 6.529611e-01
[73,] 0.36932509 7.386502e-01 6.306749e-01
[74,] 0.32655555 6.531111e-01 6.734445e-01
[75,] 0.38951221 7.790244e-01 6.104878e-01
[76,] 0.34549427 6.909885e-01 6.545057e-01
[77,] 0.30421423 6.084285e-01 6.957858e-01
[78,] 0.52708593 9.458281e-01 4.729141e-01
[79,] 0.66769715 6.646057e-01 3.323029e-01
[80,] 0.66065269 6.786946e-01 3.393473e-01
[81,] 0.63669257 7.266149e-01 3.633074e-01
[82,] 0.60456205 7.908759e-01 3.954380e-01
[83,] 0.57218129 8.556374e-01 4.278187e-01
[84,] 0.53488988 9.302202e-01 4.651101e-01
[85,] 0.59892630 8.021474e-01 4.010737e-01
[86,] 0.55245860 8.950828e-01 4.475414e-01
[87,] 0.53478906 9.304219e-01 4.652109e-01
[88,] 0.50023462 9.995308e-01 4.997654e-01
[89,] 0.53107973 9.378405e-01 4.689203e-01
[90,] 0.76499717 4.700057e-01 2.350028e-01
[91,] 0.75193128 4.961374e-01 2.480687e-01
[92,] 0.73003101 5.399380e-01 2.699690e-01
[93,] 0.68808998 6.238200e-01 3.119100e-01
[94,] 0.65414202 6.917160e-01 3.458580e-01
[95,] 0.68658170 6.268366e-01 3.134183e-01
[96,] 0.64779543 7.044091e-01 3.522046e-01
[97,] 0.64496667 7.100667e-01 3.550333e-01
[98,] 0.64980792 7.003842e-01 3.501921e-01
[99,] 0.61665192 7.666962e-01 3.833481e-01
[100,] 0.58460532 8.307894e-01 4.153947e-01
[101,] 0.54356025 9.128795e-01 4.564398e-01
[102,] 0.49788111 9.957622e-01 5.021189e-01
[103,] 0.57026745 8.594651e-01 4.297325e-01
[104,] 0.65623137 6.875373e-01 3.437686e-01
[105,] 0.65201085 6.959783e-01 3.479891e-01
[106,] 0.69176909 6.164618e-01 3.082309e-01
[107,] 0.65288637 6.942273e-01 3.471136e-01
[108,] 0.69209950 6.158010e-01 3.079005e-01
[109,] 0.65680661 6.863868e-01 3.431934e-01
[110,] 0.63125444 7.374911e-01 3.687456e-01
[111,] 0.59813996 8.037201e-01 4.018600e-01
[112,] 0.58340486 8.331903e-01 4.165951e-01
[113,] 0.56429714 8.714057e-01 4.357029e-01
[114,] 0.50956095 9.808781e-01 4.904391e-01
[115,] 0.48336378 9.667276e-01 5.166362e-01
[116,] 0.45418060 9.083612e-01 5.458194e-01
[117,] 0.42246720 8.449344e-01 5.775328e-01
[118,] 0.67254810 6.549038e-01 3.274519e-01
[119,] 0.63167690 7.366462e-01 3.683231e-01
[120,] 0.57694260 8.461148e-01 4.230574e-01
[121,] 0.80340473 3.931905e-01 1.965953e-01
[122,] 0.86374772 2.725046e-01 1.362523e-01
[123,] 0.93676925 1.264615e-01 6.323075e-02
[124,] 0.95174053 9.651893e-02 4.825947e-02
[125,] 0.98932001 2.135997e-02 1.067999e-02
[126,] 0.99285525 1.428950e-02 7.144749e-03
[127,] 0.98835789 2.328422e-02 1.164211e-02
[128,] 0.99728723 5.425534e-03 2.712767e-03
[129,] 0.99706390 5.872208e-03 2.936104e-03
[130,] 0.99930308 1.393832e-03 6.969158e-04
[131,] 0.99972697 5.460645e-04 2.730323e-04
[132,] 0.99989507 2.098545e-04 1.049272e-04
[133,] 0.99994911 1.017752e-04 5.088759e-05
[134,] 0.99987830 2.434065e-04 1.217032e-04
[135,] 0.99983413 3.317367e-04 1.658684e-04
[136,] 0.99999636 7.279875e-06 3.639937e-06
[137,] 0.99999693 6.142592e-06 3.071296e-06
[138,] 1.00000000 4.388628e-09 2.194314e-09
[139,] 0.99999997 6.311858e-08 3.155929e-08
[140,] 0.99999954 9.206215e-07 4.603108e-07
[141,] 0.99999339 1.321007e-05 6.605033e-06
[142,] 0.99991361 1.727762e-04 8.638808e-05
[143,] 0.99901715 1.965692e-03 9.828459e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1kz771324128396.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2jkgy1324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/322c01324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4210k1324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ouho1324128396.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 6
-5.6385626 2.3648380 4.3525137 5.7540356 -0.7997135 12.1626912
7 8 9 10 11 12
-11.2026333 2.3267688 -3.1222951 0.5296238 7.0412053 15.6924715
13 14 15 16 17 18
7.8594877 9.5425368 5.7851406 5.8704123 -9.2659493 -2.9672755
19 20 21 22 23 24
-4.0788657 2.6026440 3.8992997 -2.8779843 -7.1993603 16.4983980
25 26 27 28 29 30
5.0307378 1.8275833 1.6649773 4.1025978 6.7824797 -0.8452485
31 32 33 34 35 36
7.1280347 13.7736840 7.7441324 12.5812463 -6.3798746 10.1811762
37 38 39 40 41 42
-7.0644284 1.9542319 -6.6102887 -7.2743205 3.1833534 11.3595647
43 44 45 46 47 48
3.4756393 7.4237574 17.0862051 -13.0164084 6.0304293 13.0417315
49 50 51 52 53 54
-6.6218714 16.1633721 3.8755838 -8.7258778 -7.0921298 -1.8121222
55 56 57 58 59 60
-6.9982864 -3.1469777 3.8897744 -16.0745085 1.0824334 -1.7893797
61 62 63 64 65 66
-10.8472643 -21.6166454 8.1897567 1.1122893 3.8372796 -6.3829750
67 68 69 70 71 72
-4.3629553 0.4489685 -1.9729443 -4.9246382 4.7363499 4.8778203
73 74 75 76 77 78
5.7550124 9.1153276 5.3610036 5.4229857 -6.1166768 -4.7359637
79 80 81 82 83 84
1.4389102 6.9292535 1.1105834 -17.5839917 4.8307272 -0.5610334
85 86 87 88 89 90
-9.3039528 5.1199202 1.6963997 -6.5294760 15.0894589 4.8496410
91 92 93 94 95 96
0.8683360 -4.6662958 -2.8170442 -1.6722288 -6.5294452 0.5304716
97 98 99 100 101 102
-4.2090776 -3.5504137 6.3520932 -24.9440757 3.6839157 -0.5174794
103 104 105 106 107 108
3.7246635 0.5564562 -9.4890492 3.6360159 8.7334508 1.5088535
109 110 111 112 113 114
-2.5619788 -2.0988968 -3.8654560 0.7418226 -12.0074320 6.7302430
115 116 117 118 119 120
-7.0545506 14.1599145 -6.2977920 -12.9674492 -4.6825689 -6.2622910
121 122 123 124 125 126
-4.6186914 7.2257449 10.7920793 -1.8539634 -4.8822965 9.0082636
127 128 129 130 131 132
7.7320195 20.8524400 4.9181796 4.1744697 -9.9417311 11.4507131
133 134 135 136 137 138
12.6433976 -3.5938593 -8.1124699 2.7933557 0.3395796 10.5828792
139 140 141 142 143 144
5.1997501 7.8460018 -4.5105977 -7.2367473 0.1211683 -8.1715694
145 146 147 148 149 150
-5.9211562 15.8198674 -2.8527466 -4.1194163 -12.9248349 -12.2773515
151 152 153 154 155 156
-10.9339189 -10.9940079 -11.0868273 -10.8604910 1.2308623 -0.9706344
157 158 159 160 161 162
-10.8604910 -10.9259322 -11.2173317 -7.4792970 -10.8822569 17.2646804
163 164
-11.3079105 -2.5352349
> postscript(file="/var/wessaorg/rcomp/tmp/6247j1324128396.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 -5.6385626 NA
1 2.3648380 -5.6385626
2 4.3525137 2.3648380
3 5.7540356 4.3525137
4 -0.7997135 5.7540356
5 12.1626912 -0.7997135
6 -11.2026333 12.1626912
7 2.3267688 -11.2026333
8 -3.1222951 2.3267688
9 0.5296238 -3.1222951
10 7.0412053 0.5296238
11 15.6924715 7.0412053
12 7.8594877 15.6924715
13 9.5425368 7.8594877
14 5.7851406 9.5425368
15 5.8704123 5.7851406
16 -9.2659493 5.8704123
17 -2.9672755 -9.2659493
18 -4.0788657 -2.9672755
19 2.6026440 -4.0788657
20 3.8992997 2.6026440
21 -2.8779843 3.8992997
22 -7.1993603 -2.8779843
23 16.4983980 -7.1993603
24 5.0307378 16.4983980
25 1.8275833 5.0307378
26 1.6649773 1.8275833
27 4.1025978 1.6649773
28 6.7824797 4.1025978
29 -0.8452485 6.7824797
30 7.1280347 -0.8452485
31 13.7736840 7.1280347
32 7.7441324 13.7736840
33 12.5812463 7.7441324
34 -6.3798746 12.5812463
35 10.1811762 -6.3798746
36 -7.0644284 10.1811762
37 1.9542319 -7.0644284
38 -6.6102887 1.9542319
39 -7.2743205 -6.6102887
40 3.1833534 -7.2743205
41 11.3595647 3.1833534
42 3.4756393 11.3595647
43 7.4237574 3.4756393
44 17.0862051 7.4237574
45 -13.0164084 17.0862051
46 6.0304293 -13.0164084
47 13.0417315 6.0304293
48 -6.6218714 13.0417315
49 16.1633721 -6.6218714
50 3.8755838 16.1633721
51 -8.7258778 3.8755838
52 -7.0921298 -8.7258778
53 -1.8121222 -7.0921298
54 -6.9982864 -1.8121222
55 -3.1469777 -6.9982864
56 3.8897744 -3.1469777
57 -16.0745085 3.8897744
58 1.0824334 -16.0745085
59 -1.7893797 1.0824334
60 -10.8472643 -1.7893797
61 -21.6166454 -10.8472643
62 8.1897567 -21.6166454
63 1.1122893 8.1897567
64 3.8372796 1.1122893
65 -6.3829750 3.8372796
66 -4.3629553 -6.3829750
67 0.4489685 -4.3629553
68 -1.9729443 0.4489685
69 -4.9246382 -1.9729443
70 4.7363499 -4.9246382
71 4.8778203 4.7363499
72 5.7550124 4.8778203
73 9.1153276 5.7550124
74 5.3610036 9.1153276
75 5.4229857 5.3610036
76 -6.1166768 5.4229857
77 -4.7359637 -6.1166768
78 1.4389102 -4.7359637
79 6.9292535 1.4389102
80 1.1105834 6.9292535
81 -17.5839917 1.1105834
82 4.8307272 -17.5839917
83 -0.5610334 4.8307272
84 -9.3039528 -0.5610334
85 5.1199202 -9.3039528
86 1.6963997 5.1199202
87 -6.5294760 1.6963997
88 15.0894589 -6.5294760
89 4.8496410 15.0894589
90 0.8683360 4.8496410
91 -4.6662958 0.8683360
92 -2.8170442 -4.6662958
93 -1.6722288 -2.8170442
94 -6.5294452 -1.6722288
95 0.5304716 -6.5294452
96 -4.2090776 0.5304716
97 -3.5504137 -4.2090776
98 6.3520932 -3.5504137
99 -24.9440757 6.3520932
100 3.6839157 -24.9440757
101 -0.5174794 3.6839157
102 3.7246635 -0.5174794
103 0.5564562 3.7246635
104 -9.4890492 0.5564562
105 3.6360159 -9.4890492
106 8.7334508 3.6360159
107 1.5088535 8.7334508
108 -2.5619788 1.5088535
109 -2.0988968 -2.5619788
110 -3.8654560 -2.0988968
111 0.7418226 -3.8654560
112 -12.0074320 0.7418226
113 6.7302430 -12.0074320
114 -7.0545506 6.7302430
115 14.1599145 -7.0545506
116 -6.2977920 14.1599145
117 -12.9674492 -6.2977920
118 -4.6825689 -12.9674492
119 -6.2622910 -4.6825689
120 -4.6186914 -6.2622910
121 7.2257449 -4.6186914
122 10.7920793 7.2257449
123 -1.8539634 10.7920793
124 -4.8822965 -1.8539634
125 9.0082636 -4.8822965
126 7.7320195 9.0082636
127 20.8524400 7.7320195
128 4.9181796 20.8524400
129 4.1744697 4.9181796
130 -9.9417311 4.1744697
131 11.4507131 -9.9417311
132 12.6433976 11.4507131
133 -3.5938593 12.6433976
134 -8.1124699 -3.5938593
135 2.7933557 -8.1124699
136 0.3395796 2.7933557
137 10.5828792 0.3395796
138 5.1997501 10.5828792
139 7.8460018 5.1997501
140 -4.5105977 7.8460018
141 -7.2367473 -4.5105977
142 0.1211683 -7.2367473
143 -8.1715694 0.1211683
144 -5.9211562 -8.1715694
145 15.8198674 -5.9211562
146 -2.8527466 15.8198674
147 -4.1194163 -2.8527466
148 -12.9248349 -4.1194163
149 -12.2773515 -12.9248349
150 -10.9339189 -12.2773515
151 -10.9940079 -10.9339189
152 -11.0868273 -10.9940079
153 -10.8604910 -11.0868273
154 1.2308623 -10.8604910
155 -0.9706344 1.2308623
156 -10.8604910 -0.9706344
157 -10.9259322 -10.8604910
158 -11.2173317 -10.9259322
159 -7.4792970 -11.2173317
160 -10.8822569 -7.4792970
161 17.2646804 -10.8822569
162 -11.3079105 17.2646804
163 -2.5352349 -11.3079105
164 NA -2.5352349
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.3648380 -5.6385626
[2,] 4.3525137 2.3648380
[3,] 5.7540356 4.3525137
[4,] -0.7997135 5.7540356
[5,] 12.1626912 -0.7997135
[6,] -11.2026333 12.1626912
[7,] 2.3267688 -11.2026333
[8,] -3.1222951 2.3267688
[9,] 0.5296238 -3.1222951
[10,] 7.0412053 0.5296238
[11,] 15.6924715 7.0412053
[12,] 7.8594877 15.6924715
[13,] 9.5425368 7.8594877
[14,] 5.7851406 9.5425368
[15,] 5.8704123 5.7851406
[16,] -9.2659493 5.8704123
[17,] -2.9672755 -9.2659493
[18,] -4.0788657 -2.9672755
[19,] 2.6026440 -4.0788657
[20,] 3.8992997 2.6026440
[21,] -2.8779843 3.8992997
[22,] -7.1993603 -2.8779843
[23,] 16.4983980 -7.1993603
[24,] 5.0307378 16.4983980
[25,] 1.8275833 5.0307378
[26,] 1.6649773 1.8275833
[27,] 4.1025978 1.6649773
[28,] 6.7824797 4.1025978
[29,] -0.8452485 6.7824797
[30,] 7.1280347 -0.8452485
[31,] 13.7736840 7.1280347
[32,] 7.7441324 13.7736840
[33,] 12.5812463 7.7441324
[34,] -6.3798746 12.5812463
[35,] 10.1811762 -6.3798746
[36,] -7.0644284 10.1811762
[37,] 1.9542319 -7.0644284
[38,] -6.6102887 1.9542319
[39,] -7.2743205 -6.6102887
[40,] 3.1833534 -7.2743205
[41,] 11.3595647 3.1833534
[42,] 3.4756393 11.3595647
[43,] 7.4237574 3.4756393
[44,] 17.0862051 7.4237574
[45,] -13.0164084 17.0862051
[46,] 6.0304293 -13.0164084
[47,] 13.0417315 6.0304293
[48,] -6.6218714 13.0417315
[49,] 16.1633721 -6.6218714
[50,] 3.8755838 16.1633721
[51,] -8.7258778 3.8755838
[52,] -7.0921298 -8.7258778
[53,] -1.8121222 -7.0921298
[54,] -6.9982864 -1.8121222
[55,] -3.1469777 -6.9982864
[56,] 3.8897744 -3.1469777
[57,] -16.0745085 3.8897744
[58,] 1.0824334 -16.0745085
[59,] -1.7893797 1.0824334
[60,] -10.8472643 -1.7893797
[61,] -21.6166454 -10.8472643
[62,] 8.1897567 -21.6166454
[63,] 1.1122893 8.1897567
[64,] 3.8372796 1.1122893
[65,] -6.3829750 3.8372796
[66,] -4.3629553 -6.3829750
[67,] 0.4489685 -4.3629553
[68,] -1.9729443 0.4489685
[69,] -4.9246382 -1.9729443
[70,] 4.7363499 -4.9246382
[71,] 4.8778203 4.7363499
[72,] 5.7550124 4.8778203
[73,] 9.1153276 5.7550124
[74,] 5.3610036 9.1153276
[75,] 5.4229857 5.3610036
[76,] -6.1166768 5.4229857
[77,] -4.7359637 -6.1166768
[78,] 1.4389102 -4.7359637
[79,] 6.9292535 1.4389102
[80,] 1.1105834 6.9292535
[81,] -17.5839917 1.1105834
[82,] 4.8307272 -17.5839917
[83,] -0.5610334 4.8307272
[84,] -9.3039528 -0.5610334
[85,] 5.1199202 -9.3039528
[86,] 1.6963997 5.1199202
[87,] -6.5294760 1.6963997
[88,] 15.0894589 -6.5294760
[89,] 4.8496410 15.0894589
[90,] 0.8683360 4.8496410
[91,] -4.6662958 0.8683360
[92,] -2.8170442 -4.6662958
[93,] -1.6722288 -2.8170442
[94,] -6.5294452 -1.6722288
[95,] 0.5304716 -6.5294452
[96,] -4.2090776 0.5304716
[97,] -3.5504137 -4.2090776
[98,] 6.3520932 -3.5504137
[99,] -24.9440757 6.3520932
[100,] 3.6839157 -24.9440757
[101,] -0.5174794 3.6839157
[102,] 3.7246635 -0.5174794
[103,] 0.5564562 3.7246635
[104,] -9.4890492 0.5564562
[105,] 3.6360159 -9.4890492
[106,] 8.7334508 3.6360159
[107,] 1.5088535 8.7334508
[108,] -2.5619788 1.5088535
[109,] -2.0988968 -2.5619788
[110,] -3.8654560 -2.0988968
[111,] 0.7418226 -3.8654560
[112,] -12.0074320 0.7418226
[113,] 6.7302430 -12.0074320
[114,] -7.0545506 6.7302430
[115,] 14.1599145 -7.0545506
[116,] -6.2977920 14.1599145
[117,] -12.9674492 -6.2977920
[118,] -4.6825689 -12.9674492
[119,] -6.2622910 -4.6825689
[120,] -4.6186914 -6.2622910
[121,] 7.2257449 -4.6186914
[122,] 10.7920793 7.2257449
[123,] -1.8539634 10.7920793
[124,] -4.8822965 -1.8539634
[125,] 9.0082636 -4.8822965
[126,] 7.7320195 9.0082636
[127,] 20.8524400 7.7320195
[128,] 4.9181796 20.8524400
[129,] 4.1744697 4.9181796
[130,] -9.9417311 4.1744697
[131,] 11.4507131 -9.9417311
[132,] 12.6433976 11.4507131
[133,] -3.5938593 12.6433976
[134,] -8.1124699 -3.5938593
[135,] 2.7933557 -8.1124699
[136,] 0.3395796 2.7933557
[137,] 10.5828792 0.3395796
[138,] 5.1997501 10.5828792
[139,] 7.8460018 5.1997501
[140,] -4.5105977 7.8460018
[141,] -7.2367473 -4.5105977
[142,] 0.1211683 -7.2367473
[143,] -8.1715694 0.1211683
[144,] -5.9211562 -8.1715694
[145,] 15.8198674 -5.9211562
[146,] -2.8527466 15.8198674
[147,] -4.1194163 -2.8527466
[148,] -12.9248349 -4.1194163
[149,] -12.2773515 -12.9248349
[150,] -10.9339189 -12.2773515
[151,] -10.9940079 -10.9339189
[152,] -11.0868273 -10.9940079
[153,] -10.8604910 -11.0868273
[154,] 1.2308623 -10.8604910
[155,] -0.9706344 1.2308623
[156,] -10.8604910 -0.9706344
[157,] -10.9259322 -10.8604910
[158,] -11.2173317 -10.9259322
[159,] -7.4792970 -11.2173317
[160,] -10.8822569 -7.4792970
[161,] 17.2646804 -10.8822569
[162,] -11.3079105 17.2646804
[163,] -2.5352349 -11.3079105
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.3648380 -5.6385626
2 4.3525137 2.3648380
3 5.7540356 4.3525137
4 -0.7997135 5.7540356
5 12.1626912 -0.7997135
6 -11.2026333 12.1626912
7 2.3267688 -11.2026333
8 -3.1222951 2.3267688
9 0.5296238 -3.1222951
10 7.0412053 0.5296238
11 15.6924715 7.0412053
12 7.8594877 15.6924715
13 9.5425368 7.8594877
14 5.7851406 9.5425368
15 5.8704123 5.7851406
16 -9.2659493 5.8704123
17 -2.9672755 -9.2659493
18 -4.0788657 -2.9672755
19 2.6026440 -4.0788657
20 3.8992997 2.6026440
21 -2.8779843 3.8992997
22 -7.1993603 -2.8779843
23 16.4983980 -7.1993603
24 5.0307378 16.4983980
25 1.8275833 5.0307378
26 1.6649773 1.8275833
27 4.1025978 1.6649773
28 6.7824797 4.1025978
29 -0.8452485 6.7824797
30 7.1280347 -0.8452485
31 13.7736840 7.1280347
32 7.7441324 13.7736840
33 12.5812463 7.7441324
34 -6.3798746 12.5812463
35 10.1811762 -6.3798746
36 -7.0644284 10.1811762
37 1.9542319 -7.0644284
38 -6.6102887 1.9542319
39 -7.2743205 -6.6102887
40 3.1833534 -7.2743205
41 11.3595647 3.1833534
42 3.4756393 11.3595647
43 7.4237574 3.4756393
44 17.0862051 7.4237574
45 -13.0164084 17.0862051
46 6.0304293 -13.0164084
47 13.0417315 6.0304293
48 -6.6218714 13.0417315
49 16.1633721 -6.6218714
50 3.8755838 16.1633721
51 -8.7258778 3.8755838
52 -7.0921298 -8.7258778
53 -1.8121222 -7.0921298
54 -6.9982864 -1.8121222
55 -3.1469777 -6.9982864
56 3.8897744 -3.1469777
57 -16.0745085 3.8897744
58 1.0824334 -16.0745085
59 -1.7893797 1.0824334
60 -10.8472643 -1.7893797
61 -21.6166454 -10.8472643
62 8.1897567 -21.6166454
63 1.1122893 8.1897567
64 3.8372796 1.1122893
65 -6.3829750 3.8372796
66 -4.3629553 -6.3829750
67 0.4489685 -4.3629553
68 -1.9729443 0.4489685
69 -4.9246382 -1.9729443
70 4.7363499 -4.9246382
71 4.8778203 4.7363499
72 5.7550124 4.8778203
73 9.1153276 5.7550124
74 5.3610036 9.1153276
75 5.4229857 5.3610036
76 -6.1166768 5.4229857
77 -4.7359637 -6.1166768
78 1.4389102 -4.7359637
79 6.9292535 1.4389102
80 1.1105834 6.9292535
81 -17.5839917 1.1105834
82 4.8307272 -17.5839917
83 -0.5610334 4.8307272
84 -9.3039528 -0.5610334
85 5.1199202 -9.3039528
86 1.6963997 5.1199202
87 -6.5294760 1.6963997
88 15.0894589 -6.5294760
89 4.8496410 15.0894589
90 0.8683360 4.8496410
91 -4.6662958 0.8683360
92 -2.8170442 -4.6662958
93 -1.6722288 -2.8170442
94 -6.5294452 -1.6722288
95 0.5304716 -6.5294452
96 -4.2090776 0.5304716
97 -3.5504137 -4.2090776
98 6.3520932 -3.5504137
99 -24.9440757 6.3520932
100 3.6839157 -24.9440757
101 -0.5174794 3.6839157
102 3.7246635 -0.5174794
103 0.5564562 3.7246635
104 -9.4890492 0.5564562
105 3.6360159 -9.4890492
106 8.7334508 3.6360159
107 1.5088535 8.7334508
108 -2.5619788 1.5088535
109 -2.0988968 -2.5619788
110 -3.8654560 -2.0988968
111 0.7418226 -3.8654560
112 -12.0074320 0.7418226
113 6.7302430 -12.0074320
114 -7.0545506 6.7302430
115 14.1599145 -7.0545506
116 -6.2977920 14.1599145
117 -12.9674492 -6.2977920
118 -4.6825689 -12.9674492
119 -6.2622910 -4.6825689
120 -4.6186914 -6.2622910
121 7.2257449 -4.6186914
122 10.7920793 7.2257449
123 -1.8539634 10.7920793
124 -4.8822965 -1.8539634
125 9.0082636 -4.8822965
126 7.7320195 9.0082636
127 20.8524400 7.7320195
128 4.9181796 20.8524400
129 4.1744697 4.9181796
130 -9.9417311 4.1744697
131 11.4507131 -9.9417311
132 12.6433976 11.4507131
133 -3.5938593 12.6433976
134 -8.1124699 -3.5938593
135 2.7933557 -8.1124699
136 0.3395796 2.7933557
137 10.5828792 0.3395796
138 5.1997501 10.5828792
139 7.8460018 5.1997501
140 -4.5105977 7.8460018
141 -7.2367473 -4.5105977
142 0.1211683 -7.2367473
143 -8.1715694 0.1211683
144 -5.9211562 -8.1715694
145 15.8198674 -5.9211562
146 -2.8527466 15.8198674
147 -4.1194163 -2.8527466
148 -12.9248349 -4.1194163
149 -12.2773515 -12.9248349
150 -10.9339189 -12.2773515
151 -10.9940079 -10.9339189
152 -11.0868273 -10.9940079
153 -10.8604910 -11.0868273
154 1.2308623 -10.8604910
155 -0.9706344 1.2308623
156 -10.8604910 -0.9706344
157 -10.9259322 -10.8604910
158 -11.2173317 -10.9259322
159 -7.4792970 -11.2173317
160 -10.8822569 -7.4792970
161 17.2646804 -10.8822569
162 -11.3079105 17.2646804
163 -2.5352349 -11.3079105
> 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/7ynp51324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8d5jy1324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/94r5n1324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10blme1324128396.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ozfq1324128396.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/120fao1324128397.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/13ardc1324128397.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/14wkwv1324128397.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/157l0l1324128397.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/16rkjv1324128397.tab")
+ }
>
> try(system("convert tmp/1kz771324128396.ps tmp/1kz771324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jkgy1324128396.ps tmp/2jkgy1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/322c01324128396.ps tmp/322c01324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/4210k1324128396.ps tmp/4210k1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ouho1324128396.ps tmp/5ouho1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/6247j1324128396.ps tmp/6247j1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ynp51324128396.ps tmp/7ynp51324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/8d5jy1324128396.ps tmp/8d5jy1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/94r5n1324128396.ps tmp/94r5n1324128396.png",intern=TRUE))
character(0)
> try(system("convert tmp/10blme1324128396.ps tmp/10blme1324128396.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.089 0.628 5.763