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(1565
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+ ,83484
+ ,16
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+ ,47
+ ,0
+ ,17
+ ,19
+ ,76
+ ,48
+ ,21157
+ ,6769
+ ,39869
+ ,28
+ ,22)
+ ,dim=c(15
+ ,144)
+ ,dimnames=list(c('1'
+ ,'2'
+ ,'3'
+ ,'4'
+ ,'5'
+ ,'6'
+ ,'7'
+ ,'8'
+ ,'9'
+ ,'10'
+ ,'11'
+ ,'12'
+ ,'13'
+ ,'14'
+ ,'15')
+ ,1:144))
> y <- array(NA,dim=c(15,144),dimnames=list(c('1','2','3','4','5','6','7','8','9','10','11','12','13','14','15'),1:144))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '2'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
2 1 3 4 5 6 7 8 9 10 11 12 13 14 15
1 129404 1565 80 500 109 0 20 18 70 63 18158 5636 22622 30 28
2 130358 1134 46 329 68 1 38 17 68 50 30461 9079 73570 42 39
3 7215 192 18 72 1 0 0 0 0 0 1423 603 1929 0 0
4 112861 2033 85 584 146 0 49 22 68 51 25629 8874 36294 54 54
5 219904 3283 126 1100 124 0 76 30 120 112 48758 17988 62378 86 80
6 402036 5877 218 1618 267 1 104 31 120 118 129230 21325 167760 157 144
7 117604 1322 50 442 83 1 37 19 72 59 27376 8325 52443 36 36
8 126737 1187 49 321 48 0 53 25 96 90 26706 7117 57283 48 48
9 99729 1463 38 406 87 0 42 30 109 50 26505 7996 36614 45 42
10 256310 2568 86 818 129 1 62 26 104 79 49801 14218 93268 77 71
11 113066 1810 69 568 146 2 50 20 54 49 46580 6321 35439 49 49
12 165392 1915 62 595 113 0 66 30 118 91 48352 19690 72405 77 74
13 77780 1443 89 529 60 0 38 15 49 32 13899 5659 24044 28 27
14 152673 2415 84 818 240 4 48 22 88 82 39342 11370 55909 84 83
15 134368 1254 47 359 50 4 42 17 60 58 27465 4778 44689 31 31
16 125769 1374 67 419 81 3 47 19 74 65 55211 5954 49319 28 28
17 123467 1504 50 364 85 0 71 28 112 111 74098 22924 62075 99 98
18 56232 999 47 284 62 5 0 12 45 36 13497 70 2341 2 2
19 108458 2222 79 683 127 0 50 28 110 89 38338 14369 40551 41 43
20 22762 634 21 188 44 0 12 13 39 28 52505 3706 11621 25 24
21 48633 849 50 291 37 0 16 14 55 35 10663 3147 18741 16 16
22 182081 2189 83 640 94 0 77 27 102 78 74484 16801 84202 96 95
23 140857 1469 59 520 127 0 29 25 96 67 28895 2162 15334 23 22
24 93773 1791 46 532 159 1 38 30 86 61 32827 4721 28024 33 33
25 133398 1743 78 547 41 1 50 21 78 58 36188 5290 53306 46 45
26 113933 1180 23 428 153 0 33 17 64 49 28173 6446 37918 59 59
27 153851 1749 139 561 86 0 49 22 82 77 54926 14711 54819 72 66
28 140711 1101 75 266 55 0 59 28 100 71 38900 13311 89058 72 70
29 303844 2391 105 783 78 0 55 26 99 85 88530 13577 103354 62 56
30 163810 1826 38 754 84 0 42 17 67 56 35482 14634 70239 55 55
31 123344 1301 40 394 71 0 40 23 87 71 26730 6931 33045 27 27
32 157640 1433 39 482 111 2 51 20 65 58 29806 9992 63852 41 37
33 103274 1893 90 593 82 4 45 16 63 34 41799 6185 30905 51 48
34 193500 2525 105 760 254 0 73 20 80 59 54289 3445 24242 26 26
35 178768 2033 43 668 66 1 51 21 84 77 36805 12327 78907 65 64
36 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0
37 181412 1817 55 855 58 0 46 27 105 75 33146 9898 36005 28 21
38 92342 1506 47 464 131 3 44 14 51 39 23333 8022 31972 44 44
39 100023 1820 41 418 258 9 31 29 98 83 47686 10765 35853 36 36
40 178277 1649 50 607 56 0 71 31 124 123 77783 22717 115301 100 89
41 145067 1672 58 540 90 2 61 19 75 67 36042 10090 47689 104 101
42 114146 1433 50 551 57 0 28 30 120 105 34541 12385 34223 35 31
43 86039 864 25 309 35 2 21 23 84 76 75620 8513 43431 69 65
44 125481 1683 66 647 53 1 42 21 82 57 60610 5508 52220 73 71
45 95535 1024 42 321 46 2 44 22 87 82 55041 9628 33863 106 102
46 129221 1029 78 262 38 2 40 21 78 64 32087 11872 46879 53 53
47 61554 629 26 180 45 1 15 32 97 57 16356 4186 23228 43 41
48 168048 1679 82 582 113 0 46 20 80 80 40161 10877 42827 49 46
49 159121 1715 75 544 104 1 43 26 104 94 55459 17066 65765 38 37
50 129362 2093 51 758 150 4 47 25 93 72 36679 9175 38167 51 51
51 48188 658 28 205 37 0 12 22 82 39 22346 2102 14812 14 14
52 95461 1234 56 317 49 0 46 19 73 60 27377 10807 32615 40 40
53 229864 2059 64 709 83 0 56 24 87 84 50273 13662 82188 79 77
54 191094 1725 68 590 67 0 47 26 95 69 32104 9224 51763 52 51
55 158572 1482 50 537 39 1 48 27 105 102 27016 9001 59325 44 43
56 111388 1454 47 443 69 6 35 10 37 28 19715 7204 48976 34 33
57 172614 1620 58 429 58 0 45 26 96 65 33629 6572 43384 47 47
58 63205 733 18 205 68 0 25 23 88 67 27084 7509 26692 32 31
59 109102 894 56 310 30 0 47 21 83 80 32352 12920 53279 31 31
60 137303 2343 74 785 54 10 28 34 124 79 51845 5438 20652 40 40
61 125304 1503 50 434 65 6 48 29 116 107 26591 11489 38338 42 42
62 88620 1627 65 602 81 0 32 19 76 60 29677 6661 36735 34 35
63 95808 1119 48 317 84 11 28 19 65 53 54237 7941 42764 40 40
64 83419 897 29 288 45 3 31 23 86 59 20284 6173 44331 35 30
65 101723 855 25 285 52 0 13 22 85 80 22741 5562 41354 11 11
66 94982 1229 37 391 36 0 38 29 107 89 34178 9492 47879 43 41
67 143566 1991 61 449 80 8 48 31 124 115 69551 17456 103793 53 53
68 113325 2393 63 715 144 2 68 21 78 59 29653 9422 52235 82 82
69 81518 820 32 208 45 0 32 21 83 66 38071 10913 49825 41 41
70 31970 340 15 101 40 0 5 21 78 42 4157 1283 4105 6 6
71 192268 2443 102 858 126 3 53 15 59 35 28321 6198 58687 82 81
72 91261 1030 55 306 75 1 33 9 33 3 40195 4501 40745 47 47
73 80820 1091 56 360 54 2 54 23 92 72 48158 9560 33187 108 100
74 85829 1414 59 424 84 1 37 18 52 38 13310 3394 14063 46 46
75 116322 2192 53 562 86 0 52 31 121 107 78474 9871 37407 38 38
76 56544 1082 32 292 62 2 0 25 92 73 6386 2419 7190 0 0
77 116173 1764 51 492 99 1 52 24 99 80 31588 10630 49562 45 45
78 118781 2072 80 690 63 0 51 22 86 69 61254 8536 76324 57 56
79 60138 816 23 253 76 0 16 21 75 46 21152 4911 21928 20 18
80 73422 1121 66 366 92 0 33 26 96 52 41272 9775 27860 56 54
81 67751 809 57 192 45 0 48 22 81 58 34165 11227 28078 38 37
82 214002 1699 53 620 57 0 33 26 104 85 37054 6916 49577 42 40
83 51185 751 24 221 44 0 24 20 76 13 12368 3424 28145 37 37
84 97181 1309 32 438 132 0 37 25 90 61 23168 8637 36241 36 36
85 45100 732 39 247 44 0 17 19 75 49 16380 3189 10824 34 34
86 115801 1327 43 388 67 0 32 22 86 47 41242 8178 46892 53 49
87 186310 2246 190 541 82 0 55 25 100 93 48450 16739 61264 85 82
88 71960 968 86 233 71 0 39 22 88 65 20790 6094 22933 36 36
89 80105 1015 48 333 44 5 31 21 80 64 34585 7237 20787 33 33
90 103613 1100 41 422 68 0 26 20 73 64 35672 7355 43978 57 55
91 98707 1300 33 452 54 3 37 23 88 57 52168 9734 51305 50 50
92 136234 1982 67 584 86 1 66 22 79 61 53933 11225 55593 71 71
93 136781 1091 52 366 59 0 35 21 81 71 34474 6213 51648 32 31
94 105863 1107 52 406 74 0 24 12 48 43 43753 4875 30552 45 42
95 42228 666 32 265 18 0 22 9 33 18 36456 8159 23470 33 31
96 179997 1903 91 606 156 0 37 32 120 103 51183 11893 77530 53 51
97 169406 1608 50 491 87 0 86 24 90 76 52742 10754 57299 64 64
98 19349 223 12 67 15 0 13 1 2 0 3895 786 9604 14 14
99 160819 1807 87 617 104 1 21 24 96 83 37076 9706 34684 38 37
100 109510 1466 53 597 54 0 32 25 86 73 24079 7796 41094 39 37
101 43803 552 24 240 11 0 8 4 15 4 2325 593 3439 8 8
102 47062 708 19 219 37 0 38 15 48 41 29354 5600 25171 38 38
103 110845 1079 44 349 80 0 45 21 81 57 30341 7245 23437 24 23
104 92517 957 52 241 66 1 24 23 84 52 18992 7360 34086 22 22
105 58660 585 36 136 27 0 23 12 46 24 15292 4574 24649 18 18
106 27676 596 22 194 59 0 2 16 59 17 5842 522 2342 3 1
107 98550 980 32 222 113 0 52 24 96 89 28918 10905 45571 49 48
108 43646 585 24 153 24 0 5 9 29 20 3738 999 3255 5 5
109 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
110 75566 975 28 281 58 0 43 25 91 51 95352 9016 30002 47 46
111 57359 750 48 240 43 0 18 17 63 63 37478 5134 19360 33 33
112 104330 1071 36 358 45 0 44 18 68 48 26839 6608 43320 44 41
113 70369 931 47 302 55 0 45 21 84 70 26783 8577 35513 56 57
114 65494 783 56 267 66 0 29 17 54 32 33392 1543 23536 49 49
115 3616 78 5 14 5 0 0 0 0 0 0 0 0 0 0
116 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
117 143931 874 37 287 67 0 32 20 75 72 25446 9803 54438 45 45
118 117946 1327 66 476 67 0 65 26 87 56 59847 12140 56812 78 78
119 137332 1831 85 519 118 1 26 27 108 66 28162 6678 33838 51 46
120 84336 750 33 243 51 0 24 20 80 77 33298 6420 32366 25 25
121 43410 778 19 292 63 0 7 1 3 3 2781 4 13 1 1
122 136250 1373 58 410 84 1 62 24 93 73 37121 7979 55082 62 59
123 79015 807 34 217 35 0 30 14 55 37 22698 5141 31334 29 29
124 100578 1544 45 448 58 8 49 27 99 57 27615 1311 16612 26 26
125 57586 685 38 160 29 3 3 12 48 32 32689 443 5084 4 4
126 19764 285 12 75 19 1 10 2 8 4 5752 2416 9927 10 10
127 105757 1336 42 412 51 2 42 16 60 55 23164 8396 47413 43 43
128 103651 954 25 332 64 0 23 23 88 84 20304 5462 27389 36 36
129 113402 1283 35 417 96 0 40 28 112 90 34409 7271 30425 43 41
130 11796 256 9 79 22 0 1 2 8 1 0 0 0 0 0
131 7627 81 9 25 7 0 0 0 0 0 0 0 0 0 0
132 121085 1214 49 431 34 0 29 17 52 38 92538 4423 33510 33 32
133 6836 41 3 11 5 0 0 1 4 0 0 0 0 0 0
134 139563 1634 46 564 43 5 46 17 57 36 46037 5331 40389 53 53
135 5118 42 3 6 1 0 5 0 0 0 0 0 0 0 0
136 40248 528 16 183 34 1 8 4 14 7 5444 775 6012 6 6
137 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
138 95079 890 42 295 49 0 21 25 91 75 23924 6676 22205 19 18
139 80763 1203 32 230 44 0 21 26 89 52 52230 1489 17231 26 26
140 7131 81 4 27 0 1 0 0 0 0 0 0 0 0 0
141 4194 61 11 14 4 0 0 0 0 0 0 0 0 0 0
142 60378 849 20 240 40 1 15 15 54 45 8019 3080 11017 16 16
143 109173 1035 44 251 52 0 47 20 77 66 34542 11409 46741 84 84
144 83484 964 16 347 47 0 17 19 76 48 21157 6769 39869 28 22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `1` `3` `4` `5` `6`
2.162e+03 -7.330e+00 3.053e+02 1.135e+02 2.958e+01 -8.505e+02
`7` `8` `9` `10` `11` `12`
3.981e+02 -6.877e+02 1.369e+02 4.322e+02 3.521e-02 -2.756e+00
`13` `14` `15`
1.167e+00 1.130e+03 -1.403e+03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-73115 -10406 -1784 9961 63857
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.162e+03 4.417e+03 0.489 0.62536
`1` -7.330e+00 1.235e+01 -0.593 0.55398
`3` 3.053e+02 1.030e+02 2.964 0.00361 **
`4` 1.135e+02 2.862e+01 3.966 0.00012 ***
`5` 2.958e+01 5.777e+01 0.512 0.60949
`6` -8.505e+02 8.882e+02 -0.958 0.34006
`7` 3.981e+02 1.927e+02 2.066 0.04082 *
`8` -6.877e+02 1.228e+03 -0.560 0.57657
`9` 1.369e+02 3.691e+02 0.371 0.71125
`10` 4.322e+02 1.752e+02 2.467 0.01493 *
`11` 3.521e-02 1.278e-01 0.276 0.78335
`12` -2.756e+00 8.366e-01 -3.295 0.00127 **
`13` 1.167e+00 1.483e-01 7.867 1.27e-12 ***
`14` 1.130e+03 1.008e+03 1.121 0.26432
`15` -1.403e+03 1.042e+03 -1.347 0.18023
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 19940 on 129 degrees of freedom
Multiple R-squared: 0.9033, Adjusted R-squared: 0.8928
F-statistic: 86.07 on 14 and 129 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.7943915 4.112171e-01 2.056085e-01
[2,] 0.6876821 6.246358e-01 3.123179e-01
[3,] 0.5697407 8.605187e-01 4.302593e-01
[4,] 0.4546379 9.092757e-01 5.453621e-01
[5,] 0.6423222 7.153555e-01 3.576778e-01
[6,] 0.7647026 4.705948e-01 2.352974e-01
[7,] 0.7222703 5.554595e-01 2.777297e-01
[8,] 0.6585382 6.829237e-01 3.414618e-01
[9,] 0.5991111 8.017779e-01 4.008889e-01
[10,] 0.5203022 9.593955e-01 4.796978e-01
[11,] 0.4362653 8.725305e-01 5.637347e-01
[12,] 0.7864334 4.271332e-01 2.135666e-01
[13,] 0.7351328 5.297344e-01 2.648672e-01
[14,] 0.6968122 6.063757e-01 3.031878e-01
[15,] 0.6429284 7.141432e-01 3.570716e-01
[16,] 0.5891715 8.216570e-01 4.108285e-01
[17,] 0.5848713 8.302575e-01 4.151287e-01
[18,] 0.5235703 9.528594e-01 4.764297e-01
[19,] 0.4549603 9.099206e-01 5.450397e-01
[20,] 0.5084420 9.831160e-01 4.915580e-01
[21,] 0.4501826 9.003652e-01 5.498174e-01
[22,] 0.3875577 7.751153e-01 6.124423e-01
[23,] 0.8315955 3.368091e-01 1.684045e-01
[24,] 0.8677387 2.645226e-01 1.322613e-01
[25,] 0.8464402 3.071197e-01 1.535598e-01
[26,] 0.8134678 3.730644e-01 1.865322e-01
[27,] 0.8368221 3.263558e-01 1.631779e-01
[28,] 0.8217750 3.564500e-01 1.782250e-01
[29,] 0.8958269 2.083461e-01 1.041731e-01
[30,] 0.8773157 2.453687e-01 1.226843e-01
[31,] 0.8769002 2.461997e-01 1.230998e-01
[32,] 0.8484083 3.031834e-01 1.515917e-01
[33,] 0.8203078 3.593844e-01 1.796922e-01
[34,] 0.7887970 4.224060e-01 2.112030e-01
[35,] 0.7523720 4.952561e-01 2.476280e-01
[36,] 0.8824053 2.351894e-01 1.175947e-01
[37,] 0.9528901 9.421990e-02 4.710995e-02
[38,] 0.9407837 1.184326e-01 5.921631e-02
[39,] 0.9255359 1.489283e-01 7.446413e-02
[40,] 0.9828959 3.420810e-02 1.710405e-02
[41,] 0.9771631 4.567383e-02 2.283692e-02
[42,] 0.9710654 5.786924e-02 2.893462e-02
[43,] 0.9623087 7.538263e-02 3.769132e-02
[44,] 0.9520964 9.580730e-02 4.790365e-02
[45,] 0.9831274 3.374517e-02 1.687259e-02
[46,] 0.9777160 4.456802e-02 2.228401e-02
[47,] 0.9758195 4.836103e-02 2.418051e-02
[48,] 0.9674255 6.514907e-02 3.257453e-02
[49,] 0.9707928 5.841439e-02 2.920720e-02
[50,] 0.9826037 3.479255e-02 1.739627e-02
[51,] 0.9921724 1.565511e-02 7.827555e-03
[52,] 0.9890671 2.186583e-02 1.093291e-02
[53,] 0.9845717 3.085652e-02 1.542826e-02
[54,] 0.9805659 3.886822e-02 1.943411e-02
[55,] 0.9745068 5.098637e-02 2.549318e-02
[56,] 0.9748717 5.025661e-02 2.512830e-02
[57,] 0.9664773 6.704546e-02 3.352273e-02
[58,] 0.9750521 4.989575e-02 2.494788e-02
[59,] 0.9741423 5.171535e-02 2.585768e-02
[60,] 0.9760830 4.783398e-02 2.391699e-02
[61,] 0.9999944 1.115286e-05 5.576431e-06
[62,] 0.9999896 2.088222e-05 1.044111e-05
[63,] 0.9999829 3.423701e-05 1.711850e-05
[64,] 0.9999711 5.772591e-05 2.886295e-05
[65,] 0.9999996 8.818383e-07 4.409192e-07
[66,] 0.9999991 1.777474e-06 8.887369e-07
[67,] 0.9999983 3.434952e-06 1.717476e-06
[68,] 0.9999970 5.929886e-06 2.964943e-06
[69,] 0.9999949 1.026526e-05 5.132629e-06
[70,] 0.9999904 1.913511e-05 9.567557e-06
[71,] 0.9999928 1.441080e-05 7.205399e-06
[72,] 0.9999860 2.808936e-05 1.404468e-05
[73,] 0.9999732 5.363505e-05 2.681753e-05
[74,] 0.9999730 5.397534e-05 2.698767e-05
[75,] 0.9999863 2.747851e-05 1.373926e-05
[76,] 0.9999746 5.082861e-05 2.541431e-05
[77,] 0.9999574 8.526972e-05 4.263486e-05
[78,] 0.9999407 1.186608e-04 5.933041e-05
[79,] 0.9999726 5.476345e-05 2.738173e-05
[80,] 0.9999747 5.062516e-05 2.531258e-05
[81,] 0.9999488 1.024645e-04 5.123224e-05
[82,] 0.9999184 1.631263e-04 8.156315e-05
[83,] 0.9999641 7.188375e-05 3.594187e-05
[84,] 0.9999402 1.196933e-04 5.984665e-05
[85,] 0.9999416 1.167334e-04 5.836668e-05
[86,] 0.9999933 1.344400e-05 6.722000e-06
[87,] 0.9999851 2.971052e-05 1.485526e-05
[88,] 0.9999715 5.708579e-05 2.854290e-05
[89,] 0.9999419 1.161981e-04 5.809907e-05
[90,] 0.9999273 1.453393e-04 7.266966e-05
[91,] 0.9998687 2.625585e-04 1.312793e-04
[92,] 0.9997135 5.729547e-04 2.864774e-04
[93,] 0.9994981 1.003742e-03 5.018711e-04
[94,] 0.9997536 4.927861e-04 2.463930e-04
[95,] 0.9996864 6.272111e-04 3.136056e-04
[96,] 0.9996404 7.191297e-04 3.595648e-04
[97,] 0.9998611 2.778929e-04 1.389464e-04
[98,] 0.9996520 6.960071e-04 3.480035e-04
[99,] 0.9991575 1.685090e-03 8.425452e-04
[100,] 0.9999838 3.240374e-05 1.620187e-05
[101,] 0.9999986 2.786796e-06 1.393398e-06
[102,] 0.9999988 2.428604e-06 1.214302e-06
[103,] 0.9999952 9.523140e-06 4.761570e-06
[104,] 0.9999738 5.237880e-05 2.618940e-05
[105,] 0.9999706 5.889303e-05 2.944652e-05
[106,] 0.9998886 2.227007e-04 1.113503e-04
[107,] 0.9997343 5.314520e-04 2.657260e-04
[108,] 0.9995611 8.777832e-04 4.388916e-04
[109,] 0.9982719 3.456115e-03 1.728057e-03
> postscript(file="/var/wessaorg/rcomp/tmp/1p1y01323945222.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2olmz1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3bxb11323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4thpi1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5iwb51323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 144
Frequency = 1
1 2 3 4 5 6
15789.33704 -5043.12429 -7877.01769 -10746.80933 -9530.06071 -18755.08954
7 8 9 10 11 12
-8320.13357 -10555.58110 1859.32288 36907.01524 -10908.30397 11213.05284
13 14 15 16 17 18
-34863.88043 -19214.68158 18423.92033 -13495.14796 14678.42595 1285.76059
19 20 21 22 23 24
-32066.15427 -16858.17138 -25301.70075 5664.18817 24132.19169 -18022.29491
25 26 27 28 29 30
-21211.91554 17535.47261 -7158.51300 -8464.37654 63856.93956 9085.95096
31 32 33 34 35 36
15947.86343 14821.92309 -19837.04619 18632.99656 7751.20142 -2459.62373
37 38 39 40 41 42
12385.63552 -2860.38834 5552.06657 -37514.60522 19824.40148 -12411.86129
43 44 45 46 47 48
-5378.56762 -27566.42560 10629.35080 33159.94743 6058.68728 21417.79240
49 50 51 52 53 54
5863.60302 -12098.36720 -8251.98343 8324.58411 45050.86046 44881.34810
55 56 57 58 59 60
-3016.40567 3973.83428 52068.25676 -3574.13282 -9126.82314 8614.25717
61 62 63 64 65 66
8974.55348 -43072.87876 9189.45251 -19185.04160 -2678.87717 -25197.19419
67 68 69 70 71 72
-34610.22862 -36705.06741 -5740.97360 -1071.19639 12893.81820 7265.46111
73 74 75 76 77 78
-16412.76296 5391.81128 -23322.17856 -9639.66925 -16247.55116 -73114.85569
79 80 81 82 83 84
-6254.38308 -10356.78616 -3198.01646 58375.88541 -4753.50110 -4477.09655
85 86 87 88 89 90
-13227.40895 9738.33221 12112.95084 -19318.55283 2846.55548 -6719.22994
91 92 93 94 95 96
-11179.11214 -4617.64506 5153.89158 5523.28582 -10283.12832 -10905.45213
97 98 99 100 101 102
20946.90430 -3008.29781 26146.30751 -25800.57888 6296.46292 -15366.47553
103 104 105 106 107 108
18676.58169 11169.80235 4202.87810 -9308.93364 -2710.93380 12139.07936
109 110 111 112 113 114
-2161.64349 1872.52470 -14556.28422 -3616.96735 -22505.73581 -12375.62076
115 116 117 118 119 120
-1237.66286 -2161.64349 36830.92828 -8412.75466 10823.76259 -5949.87285
121 122 123 124 125 126
2502.82540 -5067.63775 5606.31266 1450.98724 14258.99868 -24.53595
127 128 129 130 131 132
-4976.20010 11783.19680 1139.46721 -1406.31148 266.18193 16966.92910
133 134 135 136 137 138
2802.27762 24716.11342 -352.91717 7332.71878 -2161.64349 11069.61057
139 140 141 142 143 144
14354.13124 2127.27445 -2586.55527 7976.63777 25730.28421 -11518.18321
> postscript(file="/var/wessaorg/rcomp/tmp/6xcsb1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 144
Frequency = 1
lag(myerror, k = 1) myerror
0 15789.33704 NA
1 -5043.12429 15789.33704
2 -7877.01769 -5043.12429
3 -10746.80933 -7877.01769
4 -9530.06071 -10746.80933
5 -18755.08954 -9530.06071
6 -8320.13357 -18755.08954
7 -10555.58110 -8320.13357
8 1859.32288 -10555.58110
9 36907.01524 1859.32288
10 -10908.30397 36907.01524
11 11213.05284 -10908.30397
12 -34863.88043 11213.05284
13 -19214.68158 -34863.88043
14 18423.92033 -19214.68158
15 -13495.14796 18423.92033
16 14678.42595 -13495.14796
17 1285.76059 14678.42595
18 -32066.15427 1285.76059
19 -16858.17138 -32066.15427
20 -25301.70075 -16858.17138
21 5664.18817 -25301.70075
22 24132.19169 5664.18817
23 -18022.29491 24132.19169
24 -21211.91554 -18022.29491
25 17535.47261 -21211.91554
26 -7158.51300 17535.47261
27 -8464.37654 -7158.51300
28 63856.93956 -8464.37654
29 9085.95096 63856.93956
30 15947.86343 9085.95096
31 14821.92309 15947.86343
32 -19837.04619 14821.92309
33 18632.99656 -19837.04619
34 7751.20142 18632.99656
35 -2459.62373 7751.20142
36 12385.63552 -2459.62373
37 -2860.38834 12385.63552
38 5552.06657 -2860.38834
39 -37514.60522 5552.06657
40 19824.40148 -37514.60522
41 -12411.86129 19824.40148
42 -5378.56762 -12411.86129
43 -27566.42560 -5378.56762
44 10629.35080 -27566.42560
45 33159.94743 10629.35080
46 6058.68728 33159.94743
47 21417.79240 6058.68728
48 5863.60302 21417.79240
49 -12098.36720 5863.60302
50 -8251.98343 -12098.36720
51 8324.58411 -8251.98343
52 45050.86046 8324.58411
53 44881.34810 45050.86046
54 -3016.40567 44881.34810
55 3973.83428 -3016.40567
56 52068.25676 3973.83428
57 -3574.13282 52068.25676
58 -9126.82314 -3574.13282
59 8614.25717 -9126.82314
60 8974.55348 8614.25717
61 -43072.87876 8974.55348
62 9189.45251 -43072.87876
63 -19185.04160 9189.45251
64 -2678.87717 -19185.04160
65 -25197.19419 -2678.87717
66 -34610.22862 -25197.19419
67 -36705.06741 -34610.22862
68 -5740.97360 -36705.06741
69 -1071.19639 -5740.97360
70 12893.81820 -1071.19639
71 7265.46111 12893.81820
72 -16412.76296 7265.46111
73 5391.81128 -16412.76296
74 -23322.17856 5391.81128
75 -9639.66925 -23322.17856
76 -16247.55116 -9639.66925
77 -73114.85569 -16247.55116
78 -6254.38308 -73114.85569
79 -10356.78616 -6254.38308
80 -3198.01646 -10356.78616
81 58375.88541 -3198.01646
82 -4753.50110 58375.88541
83 -4477.09655 -4753.50110
84 -13227.40895 -4477.09655
85 9738.33221 -13227.40895
86 12112.95084 9738.33221
87 -19318.55283 12112.95084
88 2846.55548 -19318.55283
89 -6719.22994 2846.55548
90 -11179.11214 -6719.22994
91 -4617.64506 -11179.11214
92 5153.89158 -4617.64506
93 5523.28582 5153.89158
94 -10283.12832 5523.28582
95 -10905.45213 -10283.12832
96 20946.90430 -10905.45213
97 -3008.29781 20946.90430
98 26146.30751 -3008.29781
99 -25800.57888 26146.30751
100 6296.46292 -25800.57888
101 -15366.47553 6296.46292
102 18676.58169 -15366.47553
103 11169.80235 18676.58169
104 4202.87810 11169.80235
105 -9308.93364 4202.87810
106 -2710.93380 -9308.93364
107 12139.07936 -2710.93380
108 -2161.64349 12139.07936
109 1872.52470 -2161.64349
110 -14556.28422 1872.52470
111 -3616.96735 -14556.28422
112 -22505.73581 -3616.96735
113 -12375.62076 -22505.73581
114 -1237.66286 -12375.62076
115 -2161.64349 -1237.66286
116 36830.92828 -2161.64349
117 -8412.75466 36830.92828
118 10823.76259 -8412.75466
119 -5949.87285 10823.76259
120 2502.82540 -5949.87285
121 -5067.63775 2502.82540
122 5606.31266 -5067.63775
123 1450.98724 5606.31266
124 14258.99868 1450.98724
125 -24.53595 14258.99868
126 -4976.20010 -24.53595
127 11783.19680 -4976.20010
128 1139.46721 11783.19680
129 -1406.31148 1139.46721
130 266.18193 -1406.31148
131 16966.92910 266.18193
132 2802.27762 16966.92910
133 24716.11342 2802.27762
134 -352.91717 24716.11342
135 7332.71878 -352.91717
136 -2161.64349 7332.71878
137 11069.61057 -2161.64349
138 14354.13124 11069.61057
139 2127.27445 14354.13124
140 -2586.55527 2127.27445
141 7976.63777 -2586.55527
142 25730.28421 7976.63777
143 -11518.18321 25730.28421
144 NA -11518.18321
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -5043.12429 15789.33704
[2,] -7877.01769 -5043.12429
[3,] -10746.80933 -7877.01769
[4,] -9530.06071 -10746.80933
[5,] -18755.08954 -9530.06071
[6,] -8320.13357 -18755.08954
[7,] -10555.58110 -8320.13357
[8,] 1859.32288 -10555.58110
[9,] 36907.01524 1859.32288
[10,] -10908.30397 36907.01524
[11,] 11213.05284 -10908.30397
[12,] -34863.88043 11213.05284
[13,] -19214.68158 -34863.88043
[14,] 18423.92033 -19214.68158
[15,] -13495.14796 18423.92033
[16,] 14678.42595 -13495.14796
[17,] 1285.76059 14678.42595
[18,] -32066.15427 1285.76059
[19,] -16858.17138 -32066.15427
[20,] -25301.70075 -16858.17138
[21,] 5664.18817 -25301.70075
[22,] 24132.19169 5664.18817
[23,] -18022.29491 24132.19169
[24,] -21211.91554 -18022.29491
[25,] 17535.47261 -21211.91554
[26,] -7158.51300 17535.47261
[27,] -8464.37654 -7158.51300
[28,] 63856.93956 -8464.37654
[29,] 9085.95096 63856.93956
[30,] 15947.86343 9085.95096
[31,] 14821.92309 15947.86343
[32,] -19837.04619 14821.92309
[33,] 18632.99656 -19837.04619
[34,] 7751.20142 18632.99656
[35,] -2459.62373 7751.20142
[36,] 12385.63552 -2459.62373
[37,] -2860.38834 12385.63552
[38,] 5552.06657 -2860.38834
[39,] -37514.60522 5552.06657
[40,] 19824.40148 -37514.60522
[41,] -12411.86129 19824.40148
[42,] -5378.56762 -12411.86129
[43,] -27566.42560 -5378.56762
[44,] 10629.35080 -27566.42560
[45,] 33159.94743 10629.35080
[46,] 6058.68728 33159.94743
[47,] 21417.79240 6058.68728
[48,] 5863.60302 21417.79240
[49,] -12098.36720 5863.60302
[50,] -8251.98343 -12098.36720
[51,] 8324.58411 -8251.98343
[52,] 45050.86046 8324.58411
[53,] 44881.34810 45050.86046
[54,] -3016.40567 44881.34810
[55,] 3973.83428 -3016.40567
[56,] 52068.25676 3973.83428
[57,] -3574.13282 52068.25676
[58,] -9126.82314 -3574.13282
[59,] 8614.25717 -9126.82314
[60,] 8974.55348 8614.25717
[61,] -43072.87876 8974.55348
[62,] 9189.45251 -43072.87876
[63,] -19185.04160 9189.45251
[64,] -2678.87717 -19185.04160
[65,] -25197.19419 -2678.87717
[66,] -34610.22862 -25197.19419
[67,] -36705.06741 -34610.22862
[68,] -5740.97360 -36705.06741
[69,] -1071.19639 -5740.97360
[70,] 12893.81820 -1071.19639
[71,] 7265.46111 12893.81820
[72,] -16412.76296 7265.46111
[73,] 5391.81128 -16412.76296
[74,] -23322.17856 5391.81128
[75,] -9639.66925 -23322.17856
[76,] -16247.55116 -9639.66925
[77,] -73114.85569 -16247.55116
[78,] -6254.38308 -73114.85569
[79,] -10356.78616 -6254.38308
[80,] -3198.01646 -10356.78616
[81,] 58375.88541 -3198.01646
[82,] -4753.50110 58375.88541
[83,] -4477.09655 -4753.50110
[84,] -13227.40895 -4477.09655
[85,] 9738.33221 -13227.40895
[86,] 12112.95084 9738.33221
[87,] -19318.55283 12112.95084
[88,] 2846.55548 -19318.55283
[89,] -6719.22994 2846.55548
[90,] -11179.11214 -6719.22994
[91,] -4617.64506 -11179.11214
[92,] 5153.89158 -4617.64506
[93,] 5523.28582 5153.89158
[94,] -10283.12832 5523.28582
[95,] -10905.45213 -10283.12832
[96,] 20946.90430 -10905.45213
[97,] -3008.29781 20946.90430
[98,] 26146.30751 -3008.29781
[99,] -25800.57888 26146.30751
[100,] 6296.46292 -25800.57888
[101,] -15366.47553 6296.46292
[102,] 18676.58169 -15366.47553
[103,] 11169.80235 18676.58169
[104,] 4202.87810 11169.80235
[105,] -9308.93364 4202.87810
[106,] -2710.93380 -9308.93364
[107,] 12139.07936 -2710.93380
[108,] -2161.64349 12139.07936
[109,] 1872.52470 -2161.64349
[110,] -14556.28422 1872.52470
[111,] -3616.96735 -14556.28422
[112,] -22505.73581 -3616.96735
[113,] -12375.62076 -22505.73581
[114,] -1237.66286 -12375.62076
[115,] -2161.64349 -1237.66286
[116,] 36830.92828 -2161.64349
[117,] -8412.75466 36830.92828
[118,] 10823.76259 -8412.75466
[119,] -5949.87285 10823.76259
[120,] 2502.82540 -5949.87285
[121,] -5067.63775 2502.82540
[122,] 5606.31266 -5067.63775
[123,] 1450.98724 5606.31266
[124,] 14258.99868 1450.98724
[125,] -24.53595 14258.99868
[126,] -4976.20010 -24.53595
[127,] 11783.19680 -4976.20010
[128,] 1139.46721 11783.19680
[129,] -1406.31148 1139.46721
[130,] 266.18193 -1406.31148
[131,] 16966.92910 266.18193
[132,] 2802.27762 16966.92910
[133,] 24716.11342 2802.27762
[134,] -352.91717 24716.11342
[135,] 7332.71878 -352.91717
[136,] -2161.64349 7332.71878
[137,] 11069.61057 -2161.64349
[138,] 14354.13124 11069.61057
[139,] 2127.27445 14354.13124
[140,] -2586.55527 2127.27445
[141,] 7976.63777 -2586.55527
[142,] 25730.28421 7976.63777
[143,] -11518.18321 25730.28421
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -5043.12429 15789.33704
2 -7877.01769 -5043.12429
3 -10746.80933 -7877.01769
4 -9530.06071 -10746.80933
5 -18755.08954 -9530.06071
6 -8320.13357 -18755.08954
7 -10555.58110 -8320.13357
8 1859.32288 -10555.58110
9 36907.01524 1859.32288
10 -10908.30397 36907.01524
11 11213.05284 -10908.30397
12 -34863.88043 11213.05284
13 -19214.68158 -34863.88043
14 18423.92033 -19214.68158
15 -13495.14796 18423.92033
16 14678.42595 -13495.14796
17 1285.76059 14678.42595
18 -32066.15427 1285.76059
19 -16858.17138 -32066.15427
20 -25301.70075 -16858.17138
21 5664.18817 -25301.70075
22 24132.19169 5664.18817
23 -18022.29491 24132.19169
24 -21211.91554 -18022.29491
25 17535.47261 -21211.91554
26 -7158.51300 17535.47261
27 -8464.37654 -7158.51300
28 63856.93956 -8464.37654
29 9085.95096 63856.93956
30 15947.86343 9085.95096
31 14821.92309 15947.86343
32 -19837.04619 14821.92309
33 18632.99656 -19837.04619
34 7751.20142 18632.99656
35 -2459.62373 7751.20142
36 12385.63552 -2459.62373
37 -2860.38834 12385.63552
38 5552.06657 -2860.38834
39 -37514.60522 5552.06657
40 19824.40148 -37514.60522
41 -12411.86129 19824.40148
42 -5378.56762 -12411.86129
43 -27566.42560 -5378.56762
44 10629.35080 -27566.42560
45 33159.94743 10629.35080
46 6058.68728 33159.94743
47 21417.79240 6058.68728
48 5863.60302 21417.79240
49 -12098.36720 5863.60302
50 -8251.98343 -12098.36720
51 8324.58411 -8251.98343
52 45050.86046 8324.58411
53 44881.34810 45050.86046
54 -3016.40567 44881.34810
55 3973.83428 -3016.40567
56 52068.25676 3973.83428
57 -3574.13282 52068.25676
58 -9126.82314 -3574.13282
59 8614.25717 -9126.82314
60 8974.55348 8614.25717
61 -43072.87876 8974.55348
62 9189.45251 -43072.87876
63 -19185.04160 9189.45251
64 -2678.87717 -19185.04160
65 -25197.19419 -2678.87717
66 -34610.22862 -25197.19419
67 -36705.06741 -34610.22862
68 -5740.97360 -36705.06741
69 -1071.19639 -5740.97360
70 12893.81820 -1071.19639
71 7265.46111 12893.81820
72 -16412.76296 7265.46111
73 5391.81128 -16412.76296
74 -23322.17856 5391.81128
75 -9639.66925 -23322.17856
76 -16247.55116 -9639.66925
77 -73114.85569 -16247.55116
78 -6254.38308 -73114.85569
79 -10356.78616 -6254.38308
80 -3198.01646 -10356.78616
81 58375.88541 -3198.01646
82 -4753.50110 58375.88541
83 -4477.09655 -4753.50110
84 -13227.40895 -4477.09655
85 9738.33221 -13227.40895
86 12112.95084 9738.33221
87 -19318.55283 12112.95084
88 2846.55548 -19318.55283
89 -6719.22994 2846.55548
90 -11179.11214 -6719.22994
91 -4617.64506 -11179.11214
92 5153.89158 -4617.64506
93 5523.28582 5153.89158
94 -10283.12832 5523.28582
95 -10905.45213 -10283.12832
96 20946.90430 -10905.45213
97 -3008.29781 20946.90430
98 26146.30751 -3008.29781
99 -25800.57888 26146.30751
100 6296.46292 -25800.57888
101 -15366.47553 6296.46292
102 18676.58169 -15366.47553
103 11169.80235 18676.58169
104 4202.87810 11169.80235
105 -9308.93364 4202.87810
106 -2710.93380 -9308.93364
107 12139.07936 -2710.93380
108 -2161.64349 12139.07936
109 1872.52470 -2161.64349
110 -14556.28422 1872.52470
111 -3616.96735 -14556.28422
112 -22505.73581 -3616.96735
113 -12375.62076 -22505.73581
114 -1237.66286 -12375.62076
115 -2161.64349 -1237.66286
116 36830.92828 -2161.64349
117 -8412.75466 36830.92828
118 10823.76259 -8412.75466
119 -5949.87285 10823.76259
120 2502.82540 -5949.87285
121 -5067.63775 2502.82540
122 5606.31266 -5067.63775
123 1450.98724 5606.31266
124 14258.99868 1450.98724
125 -24.53595 14258.99868
126 -4976.20010 -24.53595
127 11783.19680 -4976.20010
128 1139.46721 11783.19680
129 -1406.31148 1139.46721
130 266.18193 -1406.31148
131 16966.92910 266.18193
132 2802.27762 16966.92910
133 24716.11342 2802.27762
134 -352.91717 24716.11342
135 7332.71878 -352.91717
136 -2161.64349 7332.71878
137 11069.61057 -2161.64349
138 14354.13124 11069.61057
139 2127.27445 14354.13124
140 -2586.55527 2127.27445
141 7976.63777 -2586.55527
142 25730.28421 7976.63777
143 -11518.18321 25730.28421
> 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/75alm1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/82xcr1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9zaro1323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10wen81323945222.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11cza61323945222.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/12g0oz1323945222.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/13wz5t1323945222.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/14bx4h1323945222.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/157p391323945222.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/16nf1n1323945222.tab")
+ }
>
> try(system("convert tmp/1p1y01323945222.ps tmp/1p1y01323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/2olmz1323945222.ps tmp/2olmz1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/3bxb11323945222.ps tmp/3bxb11323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/4thpi1323945222.ps tmp/4thpi1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/5iwb51323945222.ps tmp/5iwb51323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/6xcsb1323945222.ps tmp/6xcsb1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/75alm1323945222.ps tmp/75alm1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/82xcr1323945222.ps tmp/82xcr1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/9zaro1323945222.ps tmp/9zaro1323945222.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wen81323945222.ps tmp/10wen81323945222.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.580 0.666 6.353