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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+ ,16
+ ,347
+ ,47
+ ,17
+ ,19
+ ,76
+ ,21157
+ ,6769
+ ,39869
+ ,28
+ ,22)
+ ,dim=c(13
+ ,144)
+ ,dimnames=list(c('pageviews'
+ ,'timeRFC'
+ ,'logins'
+ ,'compviews'
+ ,'prviews'
+ ,'bloggedcomp'
+ ,'reviewedcomp'
+ ,'submittedfb'
+ ,'characters'
+ ,'revisions'
+ ,'seconds'
+ ,'inclhyperlinks'
+ ,'inclblogs')
+ ,1:144))
> y <- array(NA,dim=c(13,144),dimnames=list(c('pageviews','timeRFC','logins','compviews','prviews','bloggedcomp','reviewedcomp','submittedfb','characters','revisions','seconds','inclhyperlinks','inclblogs'),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 = '5'
> 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
prviews pageviews timeRFC logins compviews bloggedcomp reviewedcomp
1 109 1565 129404 80 500 20 18
2 68 1134 130358 46 329 38 17
3 1 192 7215 18 72 0 0
4 146 2032 112861 84 584 49 22
5 124 3283 219904 126 1100 76 30
6 267 5787 396382 216 1581 104 31
7 83 1322 117604 50 442 37 19
8 48 1187 126737 49 321 53 25
9 87 1463 99729 38 406 42 30
10 129 2568 256310 86 818 62 26
11 146 1810 113066 69 568 50 20
12 95 1802 157228 60 556 65 25
13 57 1335 69952 86 494 28 15
14 240 2415 152673 84 818 48 22
15 46 1193 130642 45 338 42 16
16 81 1374 125769 67 419 47 19
17 85 1504 123467 50 364 71 28
18 62 999 56232 47 284 0 12
19 126 2201 108330 78 674 50 28
20 44 633 22762 20 188 12 13
21 37 838 48554 49 286 16 14
22 94 2189 182081 83 640 77 27
23 127 1469 140857 59 520 29 25
24 159 1790 93773 45 532 38 30
25 41 1743 133398 78 547 50 21
26 153 1180 113933 23 428 33 17
27 86 1749 153851 139 561 49 22
28 55 1101 140711 75 266 59 28
29 78 2383 303804 104 783 55 26
30 84 1808 161651 37 746 40 17
31 71 1301 123344 40 394 40 23
32 111 1432 157640 38 482 51 20
33 71 1794 91279 89 568 41 11
34 243 2476 189374 103 746 73 20
35 66 2033 178768 43 668 51 21
36 0 1 0 1 0 0 0
37 58 1782 175403 54 835 46 27
38 131 1505 92342 46 464 44 14
39 258 1820 100023 41 418 31 29
40 56 1648 178277 49 607 71 31
41 90 1669 145062 57 539 61 19
42 57 1367 110980 48 519 28 30
43 35 864 86039 25 309 21 23
44 53 1683 125481 66 647 42 21
45 46 1024 95535 42 321 44 22
46 37 1020 126456 76 261 40 21
47 45 629 61554 26 180 15 32
48 111 1660 164752 81 576 46 19
49 104 1715 159121 75 544 43 26
50 150 2093 129362 51 758 47 25
51 37 658 48188 28 205 12 22
52 49 1234 95461 56 317 46 19
53 83 2059 229864 64 709 56 24
54 67 1725 191094 68 590 47 26
55 39 1447 150640 48 526 48 27
56 69 1454 111388 47 443 35 10
57 58 1557 165098 57 419 44 26
58 68 733 63205 18 205 25 23
59 30 894 109102 56 310 47 21
60 54 2343 137303 74 785 28 34
61 65 1503 125304 50 434 48 29
62 81 1580 85332 64 576 32 19
63 84 1119 95808 48 317 28 19
64 45 897 83419 29 288 31 23
65 52 855 101723 25 285 13 22
66 36 1229 94982 37 391 38 29
67 80 1939 129700 60 446 39 31
68 144 2393 113325 63 715 68 21
69 45 820 81518 32 208 32 21
70 40 340 31970 15 101 5 21
71 126 2443 192268 102 858 53 15
72 75 1020 91086 53 302 33 9
73 54 1091 80820 56 360 54 23
74 82 1380 83261 58 411 36 18
75 86 2187 116290 52 561 52 31
76 62 1082 56544 32 292 0 25
77 99 1764 116173 51 492 52 24
78 63 1996 111488 79 669 45 22
79 76 816 60138 23 253 16 21
80 92 1121 73422 66 366 33 26
81 45 809 67751 57 192 48 22
82 57 1691 213351 52 616 33 26
83 44 751 51185 24 221 24 20
84 132 1309 97181 32 438 37 25
85 44 732 45100 39 247 17 19
86 67 1327 115801 43 388 32 22
87 82 2246 186310 190 541 55 25
88 71 968 71960 86 233 39 22
89 44 1015 80105 48 333 31 21
90 68 1100 103613 41 422 26 20
91 54 1300 98707 33 452 37 23
92 86 1982 136234 67 584 66 22
93 59 1091 136781 52 366 35 21
94 74 1107 105863 52 406 24 12
95 18 633 38775 31 254 18 9
96 156 1903 179997 91 606 37 32
97 87 1608 169406 50 491 86 24
98 15 223 19349 12 67 13 1
99 104 1767 153069 86 607 21 24
100 54 1466 109510 53 597 32 25
101 11 552 43803 24 240 8 4
102 37 708 47062 19 219 38 15
103 80 1079 110845 44 349 45 21
104 66 957 92517 52 241 24 23
105 27 585 58660 36 136 23 12
106 59 596 27676 22 194 2 16
107 113 980 98550 32 222 52 24
108 24 585 43646 24 153 5 9
109 0 0 0 0 0 0 0
110 55 903 67312 27 251 43 23
111 43 750 57359 48 240 18 17
112 45 1071 104330 36 358 44 18
113 55 931 70369 47 302 45 21
114 66 782 65494 55 267 29 17
115 5 78 3616 5 14 0 0
116 0 0 0 0 0 0 0
117 67 874 143931 37 287 32 20
118 67 1327 117946 66 476 65 26
119 117 1796 131175 84 509 26 26
120 51 750 84336 33 243 24 20
121 63 778 43410 19 292 7 1
122 84 1373 136250 58 410 62 24
123 35 807 79015 34 217 30 14
124 57 1449 92937 43 422 49 26
125 29 685 57586 38 160 3 12
126 19 285 19764 12 75 10 2
127 51 1336 105757 42 412 42 16
128 63 898 97213 24 309 18 22
129 96 1283 113402 35 417 40 28
130 22 256 11796 9 79 1 2
131 7 81 7627 9 25 0 0
132 34 1214 121085 49 431 29 17
133 5 41 6836 3 11 0 1
134 43 1633 139563 45 564 46 17
135 1 42 5118 3 6 5 0
136 34 528 40248 16 183 8 4
137 0 0 0 0 0 0 0
138 49 890 95079 42 295 21 25
139 44 1203 80763 32 230 21 26
140 0 81 7131 4 27 0 0
141 4 61 4194 11 14 0 0
142 40 849 60378 20 240 15 15
143 52 1035 109173 44 251 47 20
144 47 964 83484 16 347 17 19
submittedfb characters revisions seconds inclhyperlinks inclblogs
1 70 18158 5636 22622 30 28
2 68 30461 9079 73570 42 39
3 0 1423 603 1929 0 0
4 68 25629 8874 36294 54 54
5 120 48758 17988 62378 86 80
6 120 129230 21325 167760 157 144
7 72 27376 8325 52443 36 36
8 96 26706 7117 57283 48 48
9 109 26505 7996 36614 45 42
10 104 49801 14218 93268 77 71
11 54 46580 6321 35439 49 49
12 98 48352 19690 72405 77 74
13 49 13899 5659 24044 28 27
14 88 39342 11370 55909 84 83
15 57 27465 4778 44689 31 31
16 74 55211 5954 49319 28 28
17 112 74098 22924 62075 99 98
18 45 13497 70 2341 2 2
19 110 38338 14369 40551 41 43
20 39 52505 3706 11621 25 24
21 55 10663 3147 18741 16 16
22 102 74484 16801 84202 96 95
23 96 28895 2162 15334 23 22
24 86 32827 4721 28024 33 33
25 78 36188 5290 53306 46 45
26 64 28173 6446 37918 59 59
27 82 54926 14711 54819 72 66
28 100 38900 13311 89058 72 70
29 99 88530 13577 103354 62 56
30 67 35482 14634 70239 55 55
31 87 26730 6931 33045 27 27
32 65 29806 9992 63852 41 37
33 43 41799 6185 30905 51 48
34 80 54289 3445 24242 26 26
35 84 36805 12327 78907 65 64
36 0 0 0 0 0 0
37 105 33146 9898 36005 28 21
38 51 23333 8022 31972 44 44
39 98 47686 10765 35853 36 36
40 124 77783 22717 115301 100 89
41 75 36042 10090 47689 104 101
42 120 34541 12385 34223 35 31
43 84 75620 8513 43431 69 65
44 82 60610 5508 52220 73 71
45 87 55041 9628 33863 106 102
46 78 32087 11872 46879 53 53
47 97 16356 4186 23228 43 41
48 76 40161 10877 42827 49 46
49 104 55459 17066 65765 38 37
50 93 36679 9175 38167 51 51
51 82 22346 2102 14812 14 14
52 73 27377 10807 32615 40 40
53 87 50273 13662 82188 79 77
54 95 32104 9224 51763 52 51
55 105 27016 9001 59325 44 43
56 37 19715 7204 48976 34 33
57 96 33629 6572 43384 47 47
58 88 27084 7509 26692 32 31
59 83 32352 12920 53279 31 31
60 124 51845 5438 20652 40 40
61 116 26591 11489 38338 42 42
62 76 29677 6661 36735 34 35
63 65 54237 7941 42764 40 40
64 86 20284 6173 44331 35 30
65 85 22741 5562 41354 11 11
66 107 34178 9492 47879 43 41
67 124 69551 17456 103793 53 53
68 78 29653 9422 52235 82 82
69 83 38071 10913 49825 41 41
70 78 4157 1283 4105 6 6
71 59 28321 6198 58687 82 81
72 33 40195 4501 40745 47 47
73 92 48158 9560 33187 108 100
74 52 13310 3394 14063 46 46
75 121 78474 9871 37407 38 38
76 92 6386 2419 7190 0 0
77 99 31588 10630 49562 45 45
78 86 61254 8536 76324 57 56
79 75 21152 4911 21928 20 18
80 96 41272 9775 27860 56 54
81 81 34165 11227 28078 38 37
82 104 37054 6916 49577 42 40
83 76 12368 3424 28145 37 37
84 90 23168 8637 36241 36 36
85 75 16380 3189 10824 34 34
86 86 41242 8178 46892 53 49
87 100 48450 16739 61264 85 82
88 88 20790 6094 22933 36 36
89 80 34585 7237 20787 33 33
90 73 35672 7355 43978 57 55
91 88 52168 9734 51305 50 50
92 79 53933 11225 55593 71 71
93 81 34474 6213 51648 32 31
94 48 43753 4875 30552 45 42
95 33 36456 8159 23470 33 31
96 120 51183 11893 77530 53 51
97 90 52742 10754 57299 64 64
98 2 3895 786 9604 14 14
99 96 37076 9706 34684 38 37
100 86 24079 7796 41094 39 37
101 15 2325 593 3439 8 8
102 48 29354 5600 25171 38 38
103 81 30341 7245 23437 24 23
104 84 18992 7360 34086 22 22
105 46 15292 4574 24649 18 18
106 59 5842 522 2342 3 1
107 96 28918 10905 45571 49 48
108 29 3738 999 3255 5 5
109 0 0 0 0 0 0
110 83 95352 9016 30002 47 46
111 63 37478 5134 19360 33 33
112 68 26839 6608 43320 44 41
113 84 26783 8577 35513 56 57
114 54 33392 1543 23536 49 49
115 0 0 0 0 0 0
116 0 0 0 0 0 0
117 75 25446 9803 54438 45 45
118 87 59847 12140 56812 78 78
119 104 28162 6678 33838 51 46
120 80 33298 6420 32366 25 25
121 3 2781 4 13 1 1
122 93 37121 7979 55082 62 59
123 55 22698 5141 31334 29 29
124 96 27615 1311 16612 26 26
125 48 32689 443 5084 4 4
126 8 5752 2416 9927 10 10
127 60 23164 8396 47413 43 43
128 84 20304 5462 27389 36 36
129 112 34409 7271 30425 43 41
130 8 0 0 0 0 0
131 0 0 0 0 0 0
132 52 92538 4423 33510 33 32
133 4 0 0 0 0 0
134 57 46037 5331 40389 53 53
135 0 0 0 0 0 0
136 14 5444 775 6012 6 6
137 0 0 0 0 0 0
138 91 23924 6676 22205 19 18
139 89 52230 1489 17231 26 26
140 0 0 0 0 0 0
141 0 0 0 0 0 0
142 54 8019 3080 11017 16 16
143 77 34542 11409 46741 84 84
144 76 21157 6769 39869 28 22
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) pageviews timeRFC logins compviews
5.8427073 0.0906212 0.0000754 -0.2334261 -0.0844621
bloggedcomp reviewedcomp submittedfb characters revisions
-0.0116968 3.5509104 -0.8937024 -0.0002793 0.0015854
seconds inclhyperlinks inclblogs
-0.0005771 -1.2299678 1.3289797
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-86.715 -14.666 -3.606 12.527 122.905
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.8427073 6.4764922 0.902 0.3686
pageviews 0.0906212 0.0163214 5.552 1.51e-07 ***
timeRFC 0.0000754 0.0001277 0.591 0.5558
logins -0.2334261 0.1552595 -1.503 0.1351
compviews -0.0844621 0.0447654 -1.887 0.0614 .
bloggedcomp -0.0116968 0.2849049 -0.041 0.9673
reviewedcomp 3.5509104 1.7803353 1.995 0.0482 *
submittedfb -0.8937024 0.4844765 -1.845 0.0673 .
characters -0.0002793 0.0001880 -1.486 0.1398
revisions 0.0015854 0.0011793 1.344 0.1811
seconds -0.0005771 0.0002604 -2.216 0.0284 *
inclhyperlinks -1.2299678 1.4690607 -0.837 0.4040
inclblogs 1.3289797 1.5210319 0.874 0.3839
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 29.7 on 131 degrees of freedom
Multiple R-squared: 0.642, Adjusted R-squared: 0.6092
F-statistic: 19.58 on 12 and 131 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,] 0.4558043 9.116085e-01 5.441957e-01
[2,] 0.4471541 8.943082e-01 5.528459e-01
[3,] 0.4247902 8.495803e-01 5.752098e-01
[4,] 0.3009918 6.019837e-01 6.990082e-01
[5,] 0.3723422 7.446844e-01 6.276578e-01
[6,] 0.2890397 5.780794e-01 7.109603e-01
[7,] 0.5075687 9.848626e-01 4.924313e-01
[8,] 0.4695912 9.391824e-01 5.304088e-01
[9,] 0.3856387 7.712775e-01 6.143613e-01
[10,] 0.4990388 9.980775e-01 5.009612e-01
[11,] 0.5080283 9.839434e-01 4.919717e-01
[12,] 0.4996779 9.993557e-01 5.003221e-01
[13,] 0.4268962 8.537924e-01 5.731038e-01
[14,] 0.5572387 8.855227e-01 4.427613e-01
[15,] 0.6454754 7.090493e-01 3.545246e-01
[16,] 0.5763228 8.473544e-01 4.236772e-01
[17,] 0.7356267 5.287466e-01 2.643733e-01
[18,] 0.6855817 6.288365e-01 3.144183e-01
[19,] 0.9905448 1.891046e-02 9.455230e-03
[20,] 0.9945741 1.085179e-02 5.425893e-03
[21,] 0.9917569 1.648621e-02 8.243104e-03
[22,] 0.9880385 2.392297e-02 1.196148e-02
[23,] 0.9877352 2.452961e-02 1.226480e-02
[24,] 0.9999983 3.327847e-06 1.663924e-06
[25,] 0.9999995 1.034651e-06 5.173254e-07
[26,] 0.9999993 1.457117e-06 7.285584e-07
[27,] 0.9999990 1.937761e-06 9.688805e-07
[28,] 0.9999985 2.964609e-06 1.482304e-06
[29,] 0.9999979 4.226613e-06 2.113306e-06
[30,] 0.9999969 6.183906e-06 3.091953e-06
[31,] 0.9999963 7.377018e-06 3.688509e-06
[32,] 0.9999949 1.021578e-05 5.107891e-06
[33,] 0.9999949 1.025128e-05 5.125642e-06
[34,] 0.9999928 1.442012e-05 7.210058e-06
[35,] 0.9999963 7.310360e-06 3.655180e-06
[36,] 0.9999936 1.271543e-05 6.357715e-06
[37,] 0.9999942 1.165824e-05 5.829121e-06
[38,] 0.9999934 1.317171e-05 6.585853e-06
[39,] 0.9999935 1.307097e-05 6.535487e-06
[40,] 0.9999958 8.435037e-06 4.217519e-06
[41,] 0.9999927 1.465655e-05 7.328276e-06
[42,] 0.9999958 8.412399e-06 4.206199e-06
[43,] 0.9999937 1.261442e-05 6.307209e-06
[44,] 0.9999919 1.610027e-05 8.050136e-06
[45,] 0.9999999 2.339009e-07 1.169505e-07
[46,] 0.9999999 2.318436e-07 1.159218e-07
[47,] 0.9999998 4.569231e-07 2.284615e-07
[48,] 0.9999998 4.272254e-07 2.136127e-07
[49,] 0.9999996 7.516851e-07 3.758425e-07
[50,] 0.9999993 1.395453e-06 6.977263e-07
[51,] 0.9999997 6.534657e-07 3.267328e-07
[52,] 0.9999995 1.058906e-06 5.294531e-07
[53,] 0.9999995 1.071871e-06 5.359355e-07
[54,] 0.9999990 1.908620e-06 9.543099e-07
[55,] 0.9999983 3.482473e-06 1.741236e-06
[56,] 0.9999981 3.733703e-06 1.866852e-06
[57,] 0.9999990 1.934607e-06 9.673034e-07
[58,] 0.9999985 2.917794e-06 1.458897e-06
[59,] 0.9999982 3.540311e-06 1.770155e-06
[60,] 0.9999987 2.595645e-06 1.297822e-06
[61,] 0.9999979 4.193794e-06 2.096897e-06
[62,] 0.9999961 7.844900e-06 3.922450e-06
[63,] 0.9999953 9.459917e-06 4.729959e-06
[64,] 0.9999934 1.323947e-05 6.619734e-06
[65,] 0.9999932 1.355317e-05 6.776584e-06
[66,] 0.9999894 2.124889e-05 1.062444e-05
[67,] 0.9999944 1.128228e-05 5.641138e-06
[68,] 0.9999905 1.909096e-05 9.545480e-06
[69,] 0.9999995 9.175071e-07 4.587536e-07
[70,] 0.9999991 1.706407e-06 8.532034e-07
[71,] 0.9999985 3.042718e-06 1.521359e-06
[72,] 0.9999997 5.740783e-07 2.870392e-07
[73,] 0.9999996 8.507964e-07 4.253982e-07
[74,] 0.9999995 1.075197e-06 5.375987e-07
[75,] 0.9999990 1.910320e-06 9.551599e-07
[76,] 0.9999982 3.686162e-06 1.843081e-06
[77,] 0.9999970 6.047188e-06 3.023594e-06
[78,] 0.9999956 8.785187e-06 4.392594e-06
[79,] 0.9999933 1.335697e-05 6.678486e-06
[80,] 0.9999914 1.726770e-05 8.633850e-06
[81,] 0.9999999 2.818431e-07 1.409215e-07
[82,] 0.9999997 6.656376e-07 3.328188e-07
[83,] 0.9999993 1.432466e-06 7.162328e-07
[84,] 0.9999984 3.171047e-06 1.585523e-06
[85,] 0.9999974 5.174244e-06 2.587122e-06
[86,] 0.9999970 5.940674e-06 2.970337e-06
[87,] 0.9999938 1.249184e-05 6.245922e-06
[88,] 0.9999864 2.721588e-05 1.360794e-05
[89,] 0.9999727 5.463177e-05 2.731588e-05
[90,] 0.9999502 9.964777e-05 4.982389e-05
[91,] 0.9999386 1.227521e-04 6.137604e-05
[92,] 0.9999991 1.832130e-06 9.160650e-07
[93,] 0.9999979 4.227326e-06 2.113663e-06
[94,] 0.9999947 1.060312e-05 5.301562e-06
[95,] 0.9999983 3.431242e-06 1.715621e-06
[96,] 0.9999958 8.394971e-06 4.197486e-06
[97,] 0.9999914 1.721098e-05 8.605490e-06
[98,] 0.9999806 3.878097e-05 1.939049e-05
[99,] 0.9999727 5.460799e-05 2.730400e-05
[100,] 0.9999274 1.451809e-04 7.259047e-05
[101,] 0.9998139 3.722220e-04 1.861110e-04
[102,] 0.9998441 3.117848e-04 1.558924e-04
[103,] 0.9997436 5.128978e-04 2.564489e-04
[104,] 0.9993794 1.241245e-03 6.206225e-04
[105,] 0.9986098 2.780391e-03 1.390196e-03
[106,] 0.9995624 8.751104e-04 4.375552e-04
[107,] 0.9999711 5.776862e-05 2.888431e-05
[108,] 0.9999092 1.816514e-04 9.082569e-05
[109,] 0.9996828 6.343382e-04 3.171691e-04
[110,] 0.9999740 5.194128e-05 2.597064e-05
[111,] 0.9998596 2.808469e-04 1.404234e-04
[112,] 0.9996707 6.585700e-04 3.292850e-04
[113,] 0.9969347 6.130565e-03 3.065283e-03
> postscript(file="/var/wessaorg/rcomp/tmp/15k051323895130.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/2musj1323895130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3nj1f1323895130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4zrsd1323895130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5zhzy1323895130.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
20.2375865 25.3363958 -11.9484952 8.3496269 -51.4537218 -9.9961863
7 8 9 10 11 12
15.9497350 -14.2948362 -9.0166378 7.1659365 28.1010892 -5.0284943
13 14 15 16 17 18
-15.1008691 111.8165678 -21.3198151 23.0734645 -10.8343140 -1.2380010
19 20 21 22 23 24
-8.2579965 2.8050344 -6.0030008 -19.7493010 45.0108173 24.6755275
25 26 27 28 29 30
-43.4662069 84.0575817 10.1217770 6.0640901 -14.8176427 -4.4533795
31 32 33 34 35 36
-10.0046376 31.3374121 -17.0626174 107.9983266 -38.8353127 -5.6999024
37 38 39 40 41 42
-20.0498857 36.0099426 122.9047709 8.6093651 -3.6449120 -13.3948266
43 44 45 46 47 48
1.1213710 -11.8021886 -8.3107017 -23.4750884 -18.2411760 28.6741717
49 50 51 52 53 54
19.2713101 28.2239034 -2.8809452 -32.3372342 -24.1114883 -30.3463364
55 56 57 58 59 60
-30.6912395 -10.3400511 -39.3527691 18.9723703 -9.4438816 -86.7146852
61 62 63 64 65 66
-29.7356543 4.1900926 21.7319861 2.8641711 9.2180644 -33.0963007
67 68 69 70 71 72
-12.0678958 -0.8452079 1.7447911 9.0674129 5.7946795 28.6031253
73 74 75 76 77 78
5.2371487 -22.0510213 -44.5209231 -18.4803930 -2.0213719 -14.6158788
79 80 81 82 83 84
22.5121195 28.4306071 -9.3994071 -26.5034154 -1.7026710 46.8898594
85 86 87 88 89 90
0.5452722 -0.2270265 -32.2233727 18.5603346 -16.3303142 15.0950419
91 92 93 94 95 96
-10.0131062 -29.1897094 13.0000793 28.5066655 -11.5157567 68.1959152
97 98 99 100 101 102
-3.5671309 -1.6639033 11.1982193 -24.8199232 -22.0980929 -13.4580714
103 104 105 106 107 108
15.5788008 3.7582010 -8.1596848 19.0349040 51.8816576 -24.7610030
109 110 111 112 113 114
-5.8427073 9.0855908 2.7215641 -8.1058420 4.4911243 23.5860001
115 116 117 118 119 120
-5.8342009 -5.8427073 18.8865034 -3.6637339 20.4350059 15.1412173
121 122 123 124 125 126
12.3698543 17.5979168 -10.4765060 -34.7895668 -9.5898459 -2.3450237
127 128 129 130 131 132
-25.7247724 6.5637611 23.9069121 0.9017053 -2.5456960 -20.4699005
133 134 135 136 137 138
-3.4203342 -49.6314002 -7.7691485 -1.9653053 -5.8427073 -8.7860220
139 140 141 142 143 144
-42.9976504 -10.5065051 -3.9366630 -25.0916815 -15.8358422 4.5852975
> postscript(file="/var/wessaorg/rcomp/tmp/62u2j1323895130.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 20.2375865 NA
1 25.3363958 20.2375865
2 -11.9484952 25.3363958
3 8.3496269 -11.9484952
4 -51.4537218 8.3496269
5 -9.9961863 -51.4537218
6 15.9497350 -9.9961863
7 -14.2948362 15.9497350
8 -9.0166378 -14.2948362
9 7.1659365 -9.0166378
10 28.1010892 7.1659365
11 -5.0284943 28.1010892
12 -15.1008691 -5.0284943
13 111.8165678 -15.1008691
14 -21.3198151 111.8165678
15 23.0734645 -21.3198151
16 -10.8343140 23.0734645
17 -1.2380010 -10.8343140
18 -8.2579965 -1.2380010
19 2.8050344 -8.2579965
20 -6.0030008 2.8050344
21 -19.7493010 -6.0030008
22 45.0108173 -19.7493010
23 24.6755275 45.0108173
24 -43.4662069 24.6755275
25 84.0575817 -43.4662069
26 10.1217770 84.0575817
27 6.0640901 10.1217770
28 -14.8176427 6.0640901
29 -4.4533795 -14.8176427
30 -10.0046376 -4.4533795
31 31.3374121 -10.0046376
32 -17.0626174 31.3374121
33 107.9983266 -17.0626174
34 -38.8353127 107.9983266
35 -5.6999024 -38.8353127
36 -20.0498857 -5.6999024
37 36.0099426 -20.0498857
38 122.9047709 36.0099426
39 8.6093651 122.9047709
40 -3.6449120 8.6093651
41 -13.3948266 -3.6449120
42 1.1213710 -13.3948266
43 -11.8021886 1.1213710
44 -8.3107017 -11.8021886
45 -23.4750884 -8.3107017
46 -18.2411760 -23.4750884
47 28.6741717 -18.2411760
48 19.2713101 28.6741717
49 28.2239034 19.2713101
50 -2.8809452 28.2239034
51 -32.3372342 -2.8809452
52 -24.1114883 -32.3372342
53 -30.3463364 -24.1114883
54 -30.6912395 -30.3463364
55 -10.3400511 -30.6912395
56 -39.3527691 -10.3400511
57 18.9723703 -39.3527691
58 -9.4438816 18.9723703
59 -86.7146852 -9.4438816
60 -29.7356543 -86.7146852
61 4.1900926 -29.7356543
62 21.7319861 4.1900926
63 2.8641711 21.7319861
64 9.2180644 2.8641711
65 -33.0963007 9.2180644
66 -12.0678958 -33.0963007
67 -0.8452079 -12.0678958
68 1.7447911 -0.8452079
69 9.0674129 1.7447911
70 5.7946795 9.0674129
71 28.6031253 5.7946795
72 5.2371487 28.6031253
73 -22.0510213 5.2371487
74 -44.5209231 -22.0510213
75 -18.4803930 -44.5209231
76 -2.0213719 -18.4803930
77 -14.6158788 -2.0213719
78 22.5121195 -14.6158788
79 28.4306071 22.5121195
80 -9.3994071 28.4306071
81 -26.5034154 -9.3994071
82 -1.7026710 -26.5034154
83 46.8898594 -1.7026710
84 0.5452722 46.8898594
85 -0.2270265 0.5452722
86 -32.2233727 -0.2270265
87 18.5603346 -32.2233727
88 -16.3303142 18.5603346
89 15.0950419 -16.3303142
90 -10.0131062 15.0950419
91 -29.1897094 -10.0131062
92 13.0000793 -29.1897094
93 28.5066655 13.0000793
94 -11.5157567 28.5066655
95 68.1959152 -11.5157567
96 -3.5671309 68.1959152
97 -1.6639033 -3.5671309
98 11.1982193 -1.6639033
99 -24.8199232 11.1982193
100 -22.0980929 -24.8199232
101 -13.4580714 -22.0980929
102 15.5788008 -13.4580714
103 3.7582010 15.5788008
104 -8.1596848 3.7582010
105 19.0349040 -8.1596848
106 51.8816576 19.0349040
107 -24.7610030 51.8816576
108 -5.8427073 -24.7610030
109 9.0855908 -5.8427073
110 2.7215641 9.0855908
111 -8.1058420 2.7215641
112 4.4911243 -8.1058420
113 23.5860001 4.4911243
114 -5.8342009 23.5860001
115 -5.8427073 -5.8342009
116 18.8865034 -5.8427073
117 -3.6637339 18.8865034
118 20.4350059 -3.6637339
119 15.1412173 20.4350059
120 12.3698543 15.1412173
121 17.5979168 12.3698543
122 -10.4765060 17.5979168
123 -34.7895668 -10.4765060
124 -9.5898459 -34.7895668
125 -2.3450237 -9.5898459
126 -25.7247724 -2.3450237
127 6.5637611 -25.7247724
128 23.9069121 6.5637611
129 0.9017053 23.9069121
130 -2.5456960 0.9017053
131 -20.4699005 -2.5456960
132 -3.4203342 -20.4699005
133 -49.6314002 -3.4203342
134 -7.7691485 -49.6314002
135 -1.9653053 -7.7691485
136 -5.8427073 -1.9653053
137 -8.7860220 -5.8427073
138 -42.9976504 -8.7860220
139 -10.5065051 -42.9976504
140 -3.9366630 -10.5065051
141 -25.0916815 -3.9366630
142 -15.8358422 -25.0916815
143 4.5852975 -15.8358422
144 NA 4.5852975
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 25.3363958 20.2375865
[2,] -11.9484952 25.3363958
[3,] 8.3496269 -11.9484952
[4,] -51.4537218 8.3496269
[5,] -9.9961863 -51.4537218
[6,] 15.9497350 -9.9961863
[7,] -14.2948362 15.9497350
[8,] -9.0166378 -14.2948362
[9,] 7.1659365 -9.0166378
[10,] 28.1010892 7.1659365
[11,] -5.0284943 28.1010892
[12,] -15.1008691 -5.0284943
[13,] 111.8165678 -15.1008691
[14,] -21.3198151 111.8165678
[15,] 23.0734645 -21.3198151
[16,] -10.8343140 23.0734645
[17,] -1.2380010 -10.8343140
[18,] -8.2579965 -1.2380010
[19,] 2.8050344 -8.2579965
[20,] -6.0030008 2.8050344
[21,] -19.7493010 -6.0030008
[22,] 45.0108173 -19.7493010
[23,] 24.6755275 45.0108173
[24,] -43.4662069 24.6755275
[25,] 84.0575817 -43.4662069
[26,] 10.1217770 84.0575817
[27,] 6.0640901 10.1217770
[28,] -14.8176427 6.0640901
[29,] -4.4533795 -14.8176427
[30,] -10.0046376 -4.4533795
[31,] 31.3374121 -10.0046376
[32,] -17.0626174 31.3374121
[33,] 107.9983266 -17.0626174
[34,] -38.8353127 107.9983266
[35,] -5.6999024 -38.8353127
[36,] -20.0498857 -5.6999024
[37,] 36.0099426 -20.0498857
[38,] 122.9047709 36.0099426
[39,] 8.6093651 122.9047709
[40,] -3.6449120 8.6093651
[41,] -13.3948266 -3.6449120
[42,] 1.1213710 -13.3948266
[43,] -11.8021886 1.1213710
[44,] -8.3107017 -11.8021886
[45,] -23.4750884 -8.3107017
[46,] -18.2411760 -23.4750884
[47,] 28.6741717 -18.2411760
[48,] 19.2713101 28.6741717
[49,] 28.2239034 19.2713101
[50,] -2.8809452 28.2239034
[51,] -32.3372342 -2.8809452
[52,] -24.1114883 -32.3372342
[53,] -30.3463364 -24.1114883
[54,] -30.6912395 -30.3463364
[55,] -10.3400511 -30.6912395
[56,] -39.3527691 -10.3400511
[57,] 18.9723703 -39.3527691
[58,] -9.4438816 18.9723703
[59,] -86.7146852 -9.4438816
[60,] -29.7356543 -86.7146852
[61,] 4.1900926 -29.7356543
[62,] 21.7319861 4.1900926
[63,] 2.8641711 21.7319861
[64,] 9.2180644 2.8641711
[65,] -33.0963007 9.2180644
[66,] -12.0678958 -33.0963007
[67,] -0.8452079 -12.0678958
[68,] 1.7447911 -0.8452079
[69,] 9.0674129 1.7447911
[70,] 5.7946795 9.0674129
[71,] 28.6031253 5.7946795
[72,] 5.2371487 28.6031253
[73,] -22.0510213 5.2371487
[74,] -44.5209231 -22.0510213
[75,] -18.4803930 -44.5209231
[76,] -2.0213719 -18.4803930
[77,] -14.6158788 -2.0213719
[78,] 22.5121195 -14.6158788
[79,] 28.4306071 22.5121195
[80,] -9.3994071 28.4306071
[81,] -26.5034154 -9.3994071
[82,] -1.7026710 -26.5034154
[83,] 46.8898594 -1.7026710
[84,] 0.5452722 46.8898594
[85,] -0.2270265 0.5452722
[86,] -32.2233727 -0.2270265
[87,] 18.5603346 -32.2233727
[88,] -16.3303142 18.5603346
[89,] 15.0950419 -16.3303142
[90,] -10.0131062 15.0950419
[91,] -29.1897094 -10.0131062
[92,] 13.0000793 -29.1897094
[93,] 28.5066655 13.0000793
[94,] -11.5157567 28.5066655
[95,] 68.1959152 -11.5157567
[96,] -3.5671309 68.1959152
[97,] -1.6639033 -3.5671309
[98,] 11.1982193 -1.6639033
[99,] -24.8199232 11.1982193
[100,] -22.0980929 -24.8199232
[101,] -13.4580714 -22.0980929
[102,] 15.5788008 -13.4580714
[103,] 3.7582010 15.5788008
[104,] -8.1596848 3.7582010
[105,] 19.0349040 -8.1596848
[106,] 51.8816576 19.0349040
[107,] -24.7610030 51.8816576
[108,] -5.8427073 -24.7610030
[109,] 9.0855908 -5.8427073
[110,] 2.7215641 9.0855908
[111,] -8.1058420 2.7215641
[112,] 4.4911243 -8.1058420
[113,] 23.5860001 4.4911243
[114,] -5.8342009 23.5860001
[115,] -5.8427073 -5.8342009
[116,] 18.8865034 -5.8427073
[117,] -3.6637339 18.8865034
[118,] 20.4350059 -3.6637339
[119,] 15.1412173 20.4350059
[120,] 12.3698543 15.1412173
[121,] 17.5979168 12.3698543
[122,] -10.4765060 17.5979168
[123,] -34.7895668 -10.4765060
[124,] -9.5898459 -34.7895668
[125,] -2.3450237 -9.5898459
[126,] -25.7247724 -2.3450237
[127,] 6.5637611 -25.7247724
[128,] 23.9069121 6.5637611
[129,] 0.9017053 23.9069121
[130,] -2.5456960 0.9017053
[131,] -20.4699005 -2.5456960
[132,] -3.4203342 -20.4699005
[133,] -49.6314002 -3.4203342
[134,] -7.7691485 -49.6314002
[135,] -1.9653053 -7.7691485
[136,] -5.8427073 -1.9653053
[137,] -8.7860220 -5.8427073
[138,] -42.9976504 -8.7860220
[139,] -10.5065051 -42.9976504
[140,] -3.9366630 -10.5065051
[141,] -25.0916815 -3.9366630
[142,] -15.8358422 -25.0916815
[143,] 4.5852975 -15.8358422
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 25.3363958 20.2375865
2 -11.9484952 25.3363958
3 8.3496269 -11.9484952
4 -51.4537218 8.3496269
5 -9.9961863 -51.4537218
6 15.9497350 -9.9961863
7 -14.2948362 15.9497350
8 -9.0166378 -14.2948362
9 7.1659365 -9.0166378
10 28.1010892 7.1659365
11 -5.0284943 28.1010892
12 -15.1008691 -5.0284943
13 111.8165678 -15.1008691
14 -21.3198151 111.8165678
15 23.0734645 -21.3198151
16 -10.8343140 23.0734645
17 -1.2380010 -10.8343140
18 -8.2579965 -1.2380010
19 2.8050344 -8.2579965
20 -6.0030008 2.8050344
21 -19.7493010 -6.0030008
22 45.0108173 -19.7493010
23 24.6755275 45.0108173
24 -43.4662069 24.6755275
25 84.0575817 -43.4662069
26 10.1217770 84.0575817
27 6.0640901 10.1217770
28 -14.8176427 6.0640901
29 -4.4533795 -14.8176427
30 -10.0046376 -4.4533795
31 31.3374121 -10.0046376
32 -17.0626174 31.3374121
33 107.9983266 -17.0626174
34 -38.8353127 107.9983266
35 -5.6999024 -38.8353127
36 -20.0498857 -5.6999024
37 36.0099426 -20.0498857
38 122.9047709 36.0099426
39 8.6093651 122.9047709
40 -3.6449120 8.6093651
41 -13.3948266 -3.6449120
42 1.1213710 -13.3948266
43 -11.8021886 1.1213710
44 -8.3107017 -11.8021886
45 -23.4750884 -8.3107017
46 -18.2411760 -23.4750884
47 28.6741717 -18.2411760
48 19.2713101 28.6741717
49 28.2239034 19.2713101
50 -2.8809452 28.2239034
51 -32.3372342 -2.8809452
52 -24.1114883 -32.3372342
53 -30.3463364 -24.1114883
54 -30.6912395 -30.3463364
55 -10.3400511 -30.6912395
56 -39.3527691 -10.3400511
57 18.9723703 -39.3527691
58 -9.4438816 18.9723703
59 -86.7146852 -9.4438816
60 -29.7356543 -86.7146852
61 4.1900926 -29.7356543
62 21.7319861 4.1900926
63 2.8641711 21.7319861
64 9.2180644 2.8641711
65 -33.0963007 9.2180644
66 -12.0678958 -33.0963007
67 -0.8452079 -12.0678958
68 1.7447911 -0.8452079
69 9.0674129 1.7447911
70 5.7946795 9.0674129
71 28.6031253 5.7946795
72 5.2371487 28.6031253
73 -22.0510213 5.2371487
74 -44.5209231 -22.0510213
75 -18.4803930 -44.5209231
76 -2.0213719 -18.4803930
77 -14.6158788 -2.0213719
78 22.5121195 -14.6158788
79 28.4306071 22.5121195
80 -9.3994071 28.4306071
81 -26.5034154 -9.3994071
82 -1.7026710 -26.5034154
83 46.8898594 -1.7026710
84 0.5452722 46.8898594
85 -0.2270265 0.5452722
86 -32.2233727 -0.2270265
87 18.5603346 -32.2233727
88 -16.3303142 18.5603346
89 15.0950419 -16.3303142
90 -10.0131062 15.0950419
91 -29.1897094 -10.0131062
92 13.0000793 -29.1897094
93 28.5066655 13.0000793
94 -11.5157567 28.5066655
95 68.1959152 -11.5157567
96 -3.5671309 68.1959152
97 -1.6639033 -3.5671309
98 11.1982193 -1.6639033
99 -24.8199232 11.1982193
100 -22.0980929 -24.8199232
101 -13.4580714 -22.0980929
102 15.5788008 -13.4580714
103 3.7582010 15.5788008
104 -8.1596848 3.7582010
105 19.0349040 -8.1596848
106 51.8816576 19.0349040
107 -24.7610030 51.8816576
108 -5.8427073 -24.7610030
109 9.0855908 -5.8427073
110 2.7215641 9.0855908
111 -8.1058420 2.7215641
112 4.4911243 -8.1058420
113 23.5860001 4.4911243
114 -5.8342009 23.5860001
115 -5.8427073 -5.8342009
116 18.8865034 -5.8427073
117 -3.6637339 18.8865034
118 20.4350059 -3.6637339
119 15.1412173 20.4350059
120 12.3698543 15.1412173
121 17.5979168 12.3698543
122 -10.4765060 17.5979168
123 -34.7895668 -10.4765060
124 -9.5898459 -34.7895668
125 -2.3450237 -9.5898459
126 -25.7247724 -2.3450237
127 6.5637611 -25.7247724
128 23.9069121 6.5637611
129 0.9017053 23.9069121
130 -2.5456960 0.9017053
131 -20.4699005 -2.5456960
132 -3.4203342 -20.4699005
133 -49.6314002 -3.4203342
134 -7.7691485 -49.6314002
135 -1.9653053 -7.7691485
136 -5.8427073 -1.9653053
137 -8.7860220 -5.8427073
138 -42.9976504 -8.7860220
139 -10.5065051 -42.9976504
140 -3.9366630 -10.5065051
141 -25.0916815 -3.9366630
142 -15.8358422 -25.0916815
143 4.5852975 -15.8358422
> 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/7jezd1323895130.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/8aipf1323895130.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/92qlp1323895130.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/10ke871323895130.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11xanw1323895130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/121ga11323895130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13y9z11323895130.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/1432kn1323895130.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/15gk0q1323895130.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/162q651323895131.tab")
+ }
>
> try(system("convert tmp/15k051323895130.ps tmp/15k051323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/2musj1323895130.ps tmp/2musj1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nj1f1323895130.ps tmp/3nj1f1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/4zrsd1323895130.ps tmp/4zrsd1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/5zhzy1323895130.ps tmp/5zhzy1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/62u2j1323895130.ps tmp/62u2j1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jezd1323895130.ps tmp/7jezd1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/8aipf1323895130.ps tmp/8aipf1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/92qlp1323895130.ps tmp/92qlp1323895130.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ke871323895130.ps tmp/10ke871323895130.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.316 0.571 6.230