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(252101
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+ ,dim=c(8
+ ,164)
+ ,dimnames=list(c('Time'
+ ,'Logins'
+ ,'CompendiumViews'
+ ,'BloggedComputations'
+ ,'ReviewedCompendiums'
+ ,'LongFeedbackmessages'
+ ,'Characters'
+ ,'WritingTime')
+ ,1:164))
> y <- array(NA,dim=c(8,164),dimnames=list(c('Time','Logins','CompendiumViews','BloggedComputations','ReviewedCompendiums','LongFeedbackmessages','Characters','WritingTime'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par20 = ''
> par19 = ''
> par18 = ''
> par17 = ''
> par16 = ''
> par15 = ''
> par14 = ''
> par13 = ''
> par12 = ''
> par11 = ''
> par10 = ''
> par9 = ''
> par8 = ''
> par7 = ''
> par6 = ''
> par5 = ''
> par4 = ''
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Time Logins CompendiumViews BloggedComputations ReviewedCompendiums
1 252101 62 438 92 34
2 134577 59 330 58 30
3 198520 62 609 62 38
4 189326 94 1015 108 34
5 137449 44 294 55 25
6 65295 27 164 8 31
7 439387 103 1912 134 29
8 33186 19 111 1 18
9 178368 51 698 64 30
10 186657 38 556 77 29
11 265539 97 717 86 39
12 191088 96 495 93 50
13 138866 57 544 44 33
14 296878 66 959 106 46
15 192648 72 540 63 38
16 333462 162 1486 160 52
17 243571 58 635 104 32
18 263451 130 940 86 35
19 155679 49 452 93 25
20 227053 71 617 119 42
21 240028 63 695 107 40
22 388549 90 1046 86 35
23 156540 34 405 50 25
24 148421 43 477 92 46
25 177732 97 1012 123 36
26 191441 106 842 81 35
27 249893 122 994 93 38
28 236812 76 530 113 35
29 142329 45 515 52 28
30 259667 53 766 113 37
31 231625 66 734 112 40
32 176062 67 551 44 42
33 286683 79 718 123 44
34 87485 33 280 38 33
35 322865 83 1055 111 35
36 247082 51 950 77 37
37 346011 106 1038 92 39
38 191653 74 552 74 32
39 114673 31 275 33 17
40 284224 162 986 105 34
41 284195 72 1336 108 33
42 155363 60 565 66 35
43 177306 67 571 69 32
44 144571 49 404 62 35
45 140319 73 985 50 45
46 405267 135 1851 91 38
47 78800 42 330 20 26
48 201970 69 611 101 45
49 302674 99 1249 129 44
50 164733 50 812 93 40
51 194221 68 501 89 33
52 24188 24 218 8 4
53 346142 282 787 80 41
54 65029 17 255 21 18
55 101097 64 454 30 14
56 246088 46 944 86 33
57 273108 75 600 116 49
58 282220 160 977 106 32
59 275505 120 872 127 37
60 214872 74 690 75 32
61 335121 124 1176 138 41
62 267171 107 1013 114 25
63 189637 89 894 55 42
64 229512 78 777 67 35
65 209798 61 521 45 33
66 201345 60 409 88 28
67 163833 114 493 67 31
68 204250 129 757 75 40
69 197813 67 736 114 32
70 132955 60 511 123 25
71 216092 59 789 86 42
72 73566 32 385 22 23
73 213198 67 644 67 42
74 181713 50 664 77 38
75 148698 49 505 105 34
76 300103 70 878 119 38
77 251437 78 769 88 32
78 197295 101 499 78 37
79 158163 55 546 112 34
80 155529 57 551 66 33
81 132672 41 565 58 25
82 377213 102 1087 132 40
83 145905 66 649 30 26
84 223701 87 540 100 40
85 80953 25 437 49 8
86 130805 47 732 26 27
87 135082 48 308 67 32
88 305270 160 1243 57 33
89 271806 95 783 95 50
90 150949 96 933 139 37
91 225805 79 710 73 33
92 197389 68 563 134 34
93 156583 56 508 37 28
94 232718 68 968 108 36
95 261601 70 838 58 32
96 178489 35 523 78 32
97 200657 44 500 88 31
98 259244 69 694 142 35
99 313075 130 1060 127 58
100 346933 100 1232 139 27
101 246440 104 735 108 45
102 252444 58 757 128 37
103 159965 159 574 62 32
104 43287 14 214 13 19
105 172239 68 661 89 22
106 185198 121 640 83 35
107 227681 43 1015 116 36
108 260464 81 893 157 36
109 106288 54 293 28 23
110 109632 77 446 83 36
111 268905 58 538 72 36
112 266805 78 627 134 42
113 23623 11 156 12 1
114 152474 66 577 106 32
115 61857 25 192 23 11
116 144889 43 437 83 40
117 346600 99 1054 126 34
118 21054 16 146 4 0
119 224051 45 751 71 27
120 31414 19 200 18 8
121 261043 105 1050 98 35
122 206108 58 601 66 44
123 154984 74 430 44 40
124 112933 45 467 29 28
125 38214 34 276 16 8
126 158671 33 528 56 35
127 302148 71 898 112 47
128 177918 55 411 46 46
129 350552 70 1362 129 42
130 275578 91 743 139 48
131 368746 106 1069 136 49
132 172464 31 431 66 35
133 94381 35 380 42 32
134 244295 281 790 70 36
135 382487 154 1367 97 42
136 114525 40 449 49 35
137 345884 120 1495 113 42
138 147989 72 651 55 34
139 216638 45 494 100 36
140 192862 72 667 80 36
141 184818 107 510 29 32
142 336707 105 1472 95 33
143 215836 76 675 114 35
144 173260 63 716 41 21
145 271773 89 814 128 40
146 130908 52 556 142 49
147 204009 75 887 88 33
148 245514 92 663 147 39
149 1 0 0 0 0
150 14688 10 85 4 0
151 98 1 0 0 0
152 455 2 0 0 0
153 0 0 0 0 0
154 0 0 0 0 0
155 195765 75 607 56 33
156 326038 121 934 121 42
157 0 0 0 0 0
158 203 4 0 0 0
159 7199 5 74 7 0
160 46660 20 259 12 5
161 17547 5 69 0 1
162 107465 38 267 37 38
163 969 2 0 0 0
164 173102 58 517 47 28
LongFeedbackmessages Characters WritingTime
1 104 124252 165119
2 111 98956 107269
3 93 98073 93497
4 119 106816 100269
5 57 41449 91627
6 80 76173 47552
7 107 177551 233933
8 22 22807 6853
9 103 126938 104380
10 72 61680 98431
11 127 72117 156949
12 168 79738 81817
13 100 57793 59238
14 143 91677 101138
15 79 64631 107158
16 183 106385 155499
17 123 161961 156274
18 81 112669 121777
19 74 114029 105037
20 158 124550 118661
21 133 105416 131187
22 128 72875 145026
23 84 81964 107016
24 184 104880 87242
25 127 76302 91699
26 128 96740 110087
27 118 93071 145447
28 125 78912 143307
29 89 35224 61678
30 122 90694 210080
31 151 125369 165005
32 122 80849 97806
33 162 104434 184471
34 121 65702 27786
35 132 108179 184458
36 110 63583 98765
37 135 95066 178441
38 80 62486 100619
39 46 31081 58391
40 127 94584 151672
41 103 87408 124437
42 95 68966 79929
43 100 88766 123064
44 102 57139 50466
45 45 90586 100991
46 122 109249 79367
47 66 33032 56968
48 159 96056 106257
49 153 146648 178412
50 131 80613 98520
51 113 87026 153670
52 7 5950 15049
53 147 131106 174478
54 61 32551 25109
55 41 31701 45824
56 108 91072 116772
57 184 159803 189150
58 115 143950 194404
59 132 112368 185881
60 113 82124 67508
61 141 144068 188597
62 65 162627 203618
63 94 55062 87232
64 121 95329 110875
65 112 105612 144756
66 81 62853 129825
67 116 125976 92189
68 132 79146 121158
69 104 108461 96219
70 80 99971 84128
71 145 77826 97960
72 67 22618 23824
73 159 84892 103515
74 90 92059 91313
75 120 77993 85407
76 126 104155 95871
77 118 109840 143846
78 112 238712 155387
79 123 67486 74429
80 98 68007 74004
81 78 48194 71987
82 119 134796 150629
83 99 38692 68580
84 81 93587 119855
85 27 56622 55792
86 77 15986 25157
87 118 113402 90895
88 122 97967 117510
89 103 74844 144774
90 129 136051 77529
91 69 50548 103123
92 121 112215 104669
93 81 59591 82414
94 135 59938 82390
95 116 137639 128446
96 123 143372 111542
97 111 138599 136048
98 100 174110 197257
99 221 135062 162079
100 95 175681 206286
101 153 130307 109858
102 118 139141 182125
103 50 44244 74168
104 64 43750 19630
105 34 48029 88634
106 76 95216 128321
107 112 92288 118936
108 115 94588 127044
109 69 197426 178377
110 108 151244 69581
111 130 139206 168019
112 110 106271 113598
113 0 1168 5841
114 83 71764 93116
115 30 25162 24610
116 106 45635 60611
117 91 101817 226620
118 0 855 6622
119 69 100174 121996
120 9 14116 13155
121 123 85008 154158
122 150 124254 78489
123 125 105793 22007
124 81 117129 72530
125 21 8773 13983
126 124 94747 73397
127 168 107549 143878
128 149 97392 119956
129 147 126893 181558
130 145 118850 208236
131 172 234853 237085
132 126 74783 110297
133 89 66089 61394
134 137 95684 81420
135 149 139537 191154
136 121 144253 11798
137 149 153824 135724
138 93 63995 68614
139 119 84891 139926
140 102 61263 105203
141 45 106221 80338
142 104 113587 121376
143 111 113864 124922
144 78 37238 10901
145 120 119906 135471
146 176 135096 66395
147 109 151611 134041
148 132 144645 153554
149 0 0 0
150 0 6023 7953
151 0 0 0
152 0 0 0
153 0 0 0
154 0 0 0
155 78 77457 98922
156 104 62464 165395
157 0 0 0
158 0 0 0
159 0 1644 4245
160 13 6179 21509
161 4 3926 7670
162 65 42087 15167
163 0 0 0
164 55 87656 63891
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Logins CompendiumViews
-5186.7698 191.1707 127.3480
BloggedComputations ReviewedCompendiums LongFeedbackmessages
40.5306 603.1585 190.1026
Characters WritingTime
-0.1496 0.7179
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-90563 -16569 1804 12996 101179
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.187e+03 5.950e+03 -0.872 0.38472
Logins 1.912e+02 7.182e+01 2.662 0.00859 **
CompendiumViews 1.273e+02 1.018e+01 12.513 < 2e-16 ***
BloggedComputations 4.053e+01 1.047e+02 0.387 0.69928
ReviewedCompendiums 6.032e+02 4.268e+02 1.413 0.15962
LongFeedbackmessages 1.901e+02 1.196e+02 1.589 0.11408
Characters -1.496e-01 7.897e-02 -1.894 0.06003 .
WritingTime 7.179e-01 7.411e-02 9.687 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 27800 on 156 degrees of freedom
Multiple R-squared: 0.9219, Adjusted R-squared: 0.9184
F-statistic: 263.2 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,] 0.11809367 2.361873e-01 8.819063e-01
[2,] 0.18527029 3.705406e-01 8.147297e-01
[3,] 0.10956082 2.191216e-01 8.904392e-01
[4,] 0.11236205 2.247241e-01 8.876380e-01
[5,] 0.09648752 1.929750e-01 9.035125e-01
[6,] 0.05876268 1.175254e-01 9.412373e-01
[7,] 0.03436978 6.873956e-02 9.656302e-01
[8,] 0.16421191 3.284238e-01 8.357881e-01
[9,] 0.11554418 2.310884e-01 8.844558e-01
[10,] 0.07892785 1.578557e-01 9.210722e-01
[11,] 0.05392037 1.078407e-01 9.460796e-01
[12,] 0.86239143 2.752171e-01 1.376086e-01
[13,] 0.82755240 3.448952e-01 1.724476e-01
[14,] 0.83684323 3.263135e-01 1.631568e-01
[15,] 0.88606050 2.278790e-01 1.139395e-01
[16,] 0.87533992 2.493202e-01 1.246601e-01
[17,] 0.88402506 2.319499e-01 1.159749e-01
[18,] 0.85332728 2.933454e-01 1.466727e-01
[19,] 0.81290163 3.741967e-01 1.870984e-01
[20,] 0.97145828 5.708344e-02 2.854172e-02
[21,] 0.97222559 5.554881e-02 2.777441e-02
[22,] 0.96650822 6.698355e-02 3.349178e-02
[23,] 0.95393714 9.212572e-02 4.606286e-02
[24,] 0.93973344 1.205331e-01 6.026656e-02
[25,] 0.92233588 1.553282e-01 7.766412e-02
[26,] 0.90099173 1.980165e-01 9.900827e-02
[27,] 0.90075868 1.984826e-01 9.924132e-02
[28,] 0.87718440 2.456312e-01 1.228156e-01
[29,] 0.86247452 2.750510e-01 1.375255e-01
[30,] 0.83731388 3.253722e-01 1.626861e-01
[31,] 0.80839932 3.832014e-01 1.916007e-01
[32,] 0.77715764 4.456847e-01 2.228424e-01
[33,] 0.76548249 4.690350e-01 2.345175e-01
[34,] 0.74815630 5.036874e-01 2.518437e-01
[35,] 0.95763790 8.472421e-02 4.236210e-02
[36,] 0.99119109 1.761782e-02 8.808909e-03
[37,] 0.99287854 1.424293e-02 7.121463e-03
[38,] 0.99001657 1.996686e-02 9.983429e-03
[39,] 0.99113036 1.773928e-02 8.869638e-03
[40,] 0.99615507 7.689853e-03 3.844926e-03
[41,] 0.99543128 9.137431e-03 4.568715e-03
[42,] 0.99516090 9.678192e-03 4.839096e-03
[43,] 0.99590105 8.197891e-03 4.098945e-03
[44,] 0.99421663 1.156673e-02 5.783366e-03
[45,] 0.99235897 1.528206e-02 7.641030e-03
[46,] 0.98974459 2.051082e-02 1.025541e-02
[47,] 0.98629534 2.740932e-02 1.370466e-02
[48,] 0.98737121 2.525758e-02 1.262879e-02
[49,] 0.98498099 3.003802e-02 1.501901e-02
[50,] 0.98870117 2.259765e-02 1.129883e-02
[51,] 0.98475933 3.048135e-02 1.524067e-02
[52,] 0.98453423 3.093153e-02 1.546577e-02
[53,] 0.98807047 2.385907e-02 1.192953e-02
[54,] 0.98405081 3.189838e-02 1.594919e-02
[55,] 0.97908013 4.183973e-02 2.091987e-02
[56,] 0.97898032 4.203935e-02 2.101968e-02
[57,] 0.97248433 5.503134e-02 2.751567e-02
[58,] 0.97880686 4.238628e-02 2.119314e-02
[59,] 0.97293950 5.412101e-02 2.706050e-02
[60,] 0.96731260 6.537481e-02 3.268740e-02
[61,] 0.95839321 8.321358e-02 4.160679e-02
[62,] 0.95350320 9.299359e-02 4.649680e-02
[63,] 0.94125385 1.174923e-01 5.874615e-02
[64,] 0.93060354 1.387929e-01 6.939646e-02
[65,] 0.91837727 1.632455e-01 8.162273e-02
[66,] 0.98516787 2.966425e-02 1.483213e-02
[67,] 0.98117974 3.764053e-02 1.882026e-02
[68,] 0.97521278 4.957444e-02 2.478722e-02
[69,] 0.96800570 6.398859e-02 3.199430e-02
[70,] 0.95927422 8.145157e-02 4.072578e-02
[71,] 0.95441445 9.117110e-02 4.558555e-02
[72,] 0.99628921 7.421588e-03 3.710794e-03
[73,] 0.99611890 7.762190e-03 3.881095e-03
[74,] 0.99611496 7.770080e-03 3.885040e-03
[75,] 0.99519874 9.602526e-03 4.801263e-03
[76,] 0.99462232 1.075536e-02 5.377680e-03
[77,] 0.99250495 1.499009e-02 7.495046e-03
[78,] 0.98979847 2.040306e-02 1.020153e-02
[79,] 0.98683582 2.632836e-02 1.316418e-02
[80,] 0.99815767 3.684667e-03 1.842333e-03
[81,] 0.99780120 4.397599e-03 2.198800e-03
[82,] 0.99702750 5.944996e-03 2.972498e-03
[83,] 0.99574382 8.512368e-03 4.256184e-03
[84,] 0.99416073 1.167854e-02 5.839271e-03
[85,] 0.99469295 1.061410e-02 5.307048e-03
[86,] 0.99269975 1.460050e-02 7.300252e-03
[87,] 0.99108864 1.782272e-02 8.911360e-03
[88,] 0.98790943 2.418115e-02 1.209057e-02
[89,] 0.98651888 2.696224e-02 1.348112e-02
[90,] 0.98418050 3.163901e-02 1.581950e-02
[91,] 0.98102170 3.795661e-02 1.897830e-02
[92,] 0.97498174 5.003653e-02 2.501826e-02
[93,] 0.97364965 5.270071e-02 2.635035e-02
[94,] 0.96838738 6.322524e-02 3.161262e-02
[95,] 0.95920883 8.158235e-02 4.079117e-02
[96,] 0.96802159 6.395682e-02 3.197841e-02
[97,] 0.97141323 5.717355e-02 2.858677e-02
[98,] 0.96275535 7.448930e-02 3.724465e-02
[99,] 0.98832608 2.334785e-02 1.167392e-02
[100,] 0.99257793 1.484414e-02 7.422070e-03
[101,] 0.99724947 5.501066e-03 2.750533e-03
[102,] 0.99948266 1.034678e-03 5.173391e-04
[103,] 0.99916969 1.660628e-03 8.303141e-04
[104,] 0.99928993 1.420131e-03 7.100653e-04
[105,] 0.99892131 2.157384e-03 1.078692e-03
[106,] 0.99832944 3.341114e-03 1.670557e-03
[107,] 0.99750398 4.992039e-03 2.496020e-03
[108,] 0.99616429 7.671416e-03 3.835708e-03
[109,] 0.99489557 1.020885e-02 5.104427e-03
[110,] 0.99335299 1.329402e-02 6.647009e-03
[111,] 0.99558839 8.823217e-03 4.411608e-03
[112,] 0.99576860 8.462796e-03 4.231398e-03
[113,] 0.99769715 4.605703e-03 2.302852e-03
[114,] 0.99770897 4.582063e-03 2.291031e-03
[115,] 0.99770717 4.585654e-03 2.292827e-03
[116,] 0.99643751 7.124990e-03 3.562495e-03
[117,] 0.99817904 3.641920e-03 1.820960e-03
[118,] 0.99706289 5.874224e-03 2.937112e-03
[119,] 0.99519038 9.619234e-03 4.809617e-03
[120,] 0.99504967 9.900654e-03 4.950327e-03
[121,] 0.99435380 1.129240e-02 5.646198e-03
[122,] 0.99440268 1.119464e-02 5.597322e-03
[123,] 0.99511641 9.767179e-03 4.883589e-03
[124,] 0.99404207 1.191587e-02 5.957934e-03
[125,] 0.99048364 1.903272e-02 9.516361e-03
[126,] 0.99634929 7.301419e-03 3.650710e-03
[127,] 0.99387569 1.224863e-02 6.124314e-03
[128,] 0.99718866 5.622686e-03 2.811343e-03
[129,] 0.99993404 1.319291e-04 6.596457e-05
[130,] 0.99985055 2.988922e-04 1.494461e-04
[131,] 0.99999897 2.061444e-06 1.030722e-06
[132,] 0.99999648 7.041966e-06 3.520983e-06
[133,] 0.99998977 2.045234e-05 1.022617e-05
[134,] 0.99999448 1.103195e-05 5.515977e-06
[135,] 0.99999957 8.579617e-07 4.289809e-07
[136,] 0.99999993 1.473384e-07 7.366922e-08
[137,] 0.99999988 2.428798e-07 1.214399e-07
[138,] 0.99999979 4.148129e-07 2.074064e-07
[139,] 0.99999815 3.694609e-06 1.847304e-06
[140,] 0.99999978 4.352294e-07 2.176147e-07
[141,] 0.99999650 7.007051e-06 3.503526e-06
[142,] 0.99994349 1.130288e-04 5.651441e-05
[143,] 0.99929716 1.405673e-03 7.028365e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1mvv41324649174.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/2jbic1324649174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3a5hz1324649174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4qnju1324649174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/51qf31324649174.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
45694.14685 -17294.81421 18734.29254 -56228.75265 9058.93000 -12538.72005
7 8 9 10 11 12
-3255.66073 4017.24542 -11300.60994 18034.72613 7832.13057 2210.60058
13 14 15 16 17 18
-10701.96590 49199.50811 7547.86512 -49920.08009 21940.70913 13511.97240
19 20 21 22 23 24
2670.82414 13343.41080 12504.59960 101178.66212 6008.76629 -28754.05651
25 26 27 28 29 30
-69761.59521 -44152.38941 -34445.76166 19442.85818 -1597.26327 -30171.15258
31 32 33 34 35 36
-26356.97367 -10159.74068 6196.61760 -3860.40579 10885.12942 13795.14048
37 38 39 40 41 42
31944.32377 11999.01498 21305.91750 -10770.23570 -14642.32692 -11783.47543
43 44 45 46 47 48
-19211.23061 18244.89484 -90563.48557 58487.59487 -31064.18991 -7220.72062
49 50 51 52 53 54
-37124.60208 -54515.17766 -19691.45316 -16956.79757 35629.25224 -1968.96658
55 56 57 58 59 60
-9377.42993 8133.76166 6423.12426 -31092.13785 -22493.24726 38039.37197
61 62 63 64 65 66
-4132.63627 -31011.53077 -35860.19269 8670.33761 5830.36751 23320.27672
67 68 69 70 71 72
-6360.73056 -39029.06332 -81.67056 -19117.99863 -5546.64769 -17615.15494
73 74 75 76 77 78
3673.06800 -2152.19302 -17017.19211 75153.19842 11642.72625 -2988.28692
79 80 81 82 83 84
-8464.92334 -4514.80892 -18660.37659 84398.98818 -23339.85327 27863.44549
85 86 87 88 89 90
-17818.66532 -13858.40670 -870.54229 6460.88111 12788.22330 -68813.15221
91 92 93 94 95 96
23019.03706 10580.77250 2332.40139 -306.05760 31360.12188 5905.95159
97 98 99 100 101 102
13453.83432 1414.56290 -19878.57628 14314.25674 18162.79564 -9733.90612
103 104 105 106 107 108
-16289.74261 -13156.47471 461.06607 -31053.13268 -23898.92476 9447.07766
109 110 111 112 113 114
-62813.11095 -29635.40041 45344.80387 59901.01661 1732.38099 -23926.96558
115 116 117 118 119 120
10639.65967 1875.66073 8256.97569 -199.11859 20118.20651 -7099.41699
121 122 123 124 125 126
-33981.17372 28180.26978 41619.30174 -17965.39505 -16439.36229 4836.74677
127 128 129 130 131 132
27373.07840 -9234.75300 -960.03424 -25120.48381 14692.83185 1101.02529
133 134 135 136 137 138
-27627.55779 423.54198 10198.96107 21897.08303 5079.01203 -23594.52908
139 140 141 142 143 144
14165.82545 -11366.69489 33784.65774 20692.52133 1050.34875 43810.47975
145 146 147 148 149 150
24837.05488 -40876.19497 -35842.73111 5504.58191 5187.76982 2167.70755
151 152 153 154 155 156
5093.59914 5259.42845 5186.76982 5186.76982 12880.31983 29745.08375
157 158 159 160 161 162
5186.76982 4625.08709 -1079.22063 -5450.85449 6708.14201 30016.47585
163 164
5773.42845 39357.09857
> postscript(file="/var/wessaorg/rcomp/tmp/6f8fe1324649174.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 45694.14685 NA
1 -17294.81421 45694.14685
2 18734.29254 -17294.81421
3 -56228.75265 18734.29254
4 9058.93000 -56228.75265
5 -12538.72005 9058.93000
6 -3255.66073 -12538.72005
7 4017.24542 -3255.66073
8 -11300.60994 4017.24542
9 18034.72613 -11300.60994
10 7832.13057 18034.72613
11 2210.60058 7832.13057
12 -10701.96590 2210.60058
13 49199.50811 -10701.96590
14 7547.86512 49199.50811
15 -49920.08009 7547.86512
16 21940.70913 -49920.08009
17 13511.97240 21940.70913
18 2670.82414 13511.97240
19 13343.41080 2670.82414
20 12504.59960 13343.41080
21 101178.66212 12504.59960
22 6008.76629 101178.66212
23 -28754.05651 6008.76629
24 -69761.59521 -28754.05651
25 -44152.38941 -69761.59521
26 -34445.76166 -44152.38941
27 19442.85818 -34445.76166
28 -1597.26327 19442.85818
29 -30171.15258 -1597.26327
30 -26356.97367 -30171.15258
31 -10159.74068 -26356.97367
32 6196.61760 -10159.74068
33 -3860.40579 6196.61760
34 10885.12942 -3860.40579
35 13795.14048 10885.12942
36 31944.32377 13795.14048
37 11999.01498 31944.32377
38 21305.91750 11999.01498
39 -10770.23570 21305.91750
40 -14642.32692 -10770.23570
41 -11783.47543 -14642.32692
42 -19211.23061 -11783.47543
43 18244.89484 -19211.23061
44 -90563.48557 18244.89484
45 58487.59487 -90563.48557
46 -31064.18991 58487.59487
47 -7220.72062 -31064.18991
48 -37124.60208 -7220.72062
49 -54515.17766 -37124.60208
50 -19691.45316 -54515.17766
51 -16956.79757 -19691.45316
52 35629.25224 -16956.79757
53 -1968.96658 35629.25224
54 -9377.42993 -1968.96658
55 8133.76166 -9377.42993
56 6423.12426 8133.76166
57 -31092.13785 6423.12426
58 -22493.24726 -31092.13785
59 38039.37197 -22493.24726
60 -4132.63627 38039.37197
61 -31011.53077 -4132.63627
62 -35860.19269 -31011.53077
63 8670.33761 -35860.19269
64 5830.36751 8670.33761
65 23320.27672 5830.36751
66 -6360.73056 23320.27672
67 -39029.06332 -6360.73056
68 -81.67056 -39029.06332
69 -19117.99863 -81.67056
70 -5546.64769 -19117.99863
71 -17615.15494 -5546.64769
72 3673.06800 -17615.15494
73 -2152.19302 3673.06800
74 -17017.19211 -2152.19302
75 75153.19842 -17017.19211
76 11642.72625 75153.19842
77 -2988.28692 11642.72625
78 -8464.92334 -2988.28692
79 -4514.80892 -8464.92334
80 -18660.37659 -4514.80892
81 84398.98818 -18660.37659
82 -23339.85327 84398.98818
83 27863.44549 -23339.85327
84 -17818.66532 27863.44549
85 -13858.40670 -17818.66532
86 -870.54229 -13858.40670
87 6460.88111 -870.54229
88 12788.22330 6460.88111
89 -68813.15221 12788.22330
90 23019.03706 -68813.15221
91 10580.77250 23019.03706
92 2332.40139 10580.77250
93 -306.05760 2332.40139
94 31360.12188 -306.05760
95 5905.95159 31360.12188
96 13453.83432 5905.95159
97 1414.56290 13453.83432
98 -19878.57628 1414.56290
99 14314.25674 -19878.57628
100 18162.79564 14314.25674
101 -9733.90612 18162.79564
102 -16289.74261 -9733.90612
103 -13156.47471 -16289.74261
104 461.06607 -13156.47471
105 -31053.13268 461.06607
106 -23898.92476 -31053.13268
107 9447.07766 -23898.92476
108 -62813.11095 9447.07766
109 -29635.40041 -62813.11095
110 45344.80387 -29635.40041
111 59901.01661 45344.80387
112 1732.38099 59901.01661
113 -23926.96558 1732.38099
114 10639.65967 -23926.96558
115 1875.66073 10639.65967
116 8256.97569 1875.66073
117 -199.11859 8256.97569
118 20118.20651 -199.11859
119 -7099.41699 20118.20651
120 -33981.17372 -7099.41699
121 28180.26978 -33981.17372
122 41619.30174 28180.26978
123 -17965.39505 41619.30174
124 -16439.36229 -17965.39505
125 4836.74677 -16439.36229
126 27373.07840 4836.74677
127 -9234.75300 27373.07840
128 -960.03424 -9234.75300
129 -25120.48381 -960.03424
130 14692.83185 -25120.48381
131 1101.02529 14692.83185
132 -27627.55779 1101.02529
133 423.54198 -27627.55779
134 10198.96107 423.54198
135 21897.08303 10198.96107
136 5079.01203 21897.08303
137 -23594.52908 5079.01203
138 14165.82545 -23594.52908
139 -11366.69489 14165.82545
140 33784.65774 -11366.69489
141 20692.52133 33784.65774
142 1050.34875 20692.52133
143 43810.47975 1050.34875
144 24837.05488 43810.47975
145 -40876.19497 24837.05488
146 -35842.73111 -40876.19497
147 5504.58191 -35842.73111
148 5187.76982 5504.58191
149 2167.70755 5187.76982
150 5093.59914 2167.70755
151 5259.42845 5093.59914
152 5186.76982 5259.42845
153 5186.76982 5186.76982
154 12880.31983 5186.76982
155 29745.08375 12880.31983
156 5186.76982 29745.08375
157 4625.08709 5186.76982
158 -1079.22063 4625.08709
159 -5450.85449 -1079.22063
160 6708.14201 -5450.85449
161 30016.47585 6708.14201
162 5773.42845 30016.47585
163 39357.09857 5773.42845
164 NA 39357.09857
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -17294.81421 45694.14685
[2,] 18734.29254 -17294.81421
[3,] -56228.75265 18734.29254
[4,] 9058.93000 -56228.75265
[5,] -12538.72005 9058.93000
[6,] -3255.66073 -12538.72005
[7,] 4017.24542 -3255.66073
[8,] -11300.60994 4017.24542
[9,] 18034.72613 -11300.60994
[10,] 7832.13057 18034.72613
[11,] 2210.60058 7832.13057
[12,] -10701.96590 2210.60058
[13,] 49199.50811 -10701.96590
[14,] 7547.86512 49199.50811
[15,] -49920.08009 7547.86512
[16,] 21940.70913 -49920.08009
[17,] 13511.97240 21940.70913
[18,] 2670.82414 13511.97240
[19,] 13343.41080 2670.82414
[20,] 12504.59960 13343.41080
[21,] 101178.66212 12504.59960
[22,] 6008.76629 101178.66212
[23,] -28754.05651 6008.76629
[24,] -69761.59521 -28754.05651
[25,] -44152.38941 -69761.59521
[26,] -34445.76166 -44152.38941
[27,] 19442.85818 -34445.76166
[28,] -1597.26327 19442.85818
[29,] -30171.15258 -1597.26327
[30,] -26356.97367 -30171.15258
[31,] -10159.74068 -26356.97367
[32,] 6196.61760 -10159.74068
[33,] -3860.40579 6196.61760
[34,] 10885.12942 -3860.40579
[35,] 13795.14048 10885.12942
[36,] 31944.32377 13795.14048
[37,] 11999.01498 31944.32377
[38,] 21305.91750 11999.01498
[39,] -10770.23570 21305.91750
[40,] -14642.32692 -10770.23570
[41,] -11783.47543 -14642.32692
[42,] -19211.23061 -11783.47543
[43,] 18244.89484 -19211.23061
[44,] -90563.48557 18244.89484
[45,] 58487.59487 -90563.48557
[46,] -31064.18991 58487.59487
[47,] -7220.72062 -31064.18991
[48,] -37124.60208 -7220.72062
[49,] -54515.17766 -37124.60208
[50,] -19691.45316 -54515.17766
[51,] -16956.79757 -19691.45316
[52,] 35629.25224 -16956.79757
[53,] -1968.96658 35629.25224
[54,] -9377.42993 -1968.96658
[55,] 8133.76166 -9377.42993
[56,] 6423.12426 8133.76166
[57,] -31092.13785 6423.12426
[58,] -22493.24726 -31092.13785
[59,] 38039.37197 -22493.24726
[60,] -4132.63627 38039.37197
[61,] -31011.53077 -4132.63627
[62,] -35860.19269 -31011.53077
[63,] 8670.33761 -35860.19269
[64,] 5830.36751 8670.33761
[65,] 23320.27672 5830.36751
[66,] -6360.73056 23320.27672
[67,] -39029.06332 -6360.73056
[68,] -81.67056 -39029.06332
[69,] -19117.99863 -81.67056
[70,] -5546.64769 -19117.99863
[71,] -17615.15494 -5546.64769
[72,] 3673.06800 -17615.15494
[73,] -2152.19302 3673.06800
[74,] -17017.19211 -2152.19302
[75,] 75153.19842 -17017.19211
[76,] 11642.72625 75153.19842
[77,] -2988.28692 11642.72625
[78,] -8464.92334 -2988.28692
[79,] -4514.80892 -8464.92334
[80,] -18660.37659 -4514.80892
[81,] 84398.98818 -18660.37659
[82,] -23339.85327 84398.98818
[83,] 27863.44549 -23339.85327
[84,] -17818.66532 27863.44549
[85,] -13858.40670 -17818.66532
[86,] -870.54229 -13858.40670
[87,] 6460.88111 -870.54229
[88,] 12788.22330 6460.88111
[89,] -68813.15221 12788.22330
[90,] 23019.03706 -68813.15221
[91,] 10580.77250 23019.03706
[92,] 2332.40139 10580.77250
[93,] -306.05760 2332.40139
[94,] 31360.12188 -306.05760
[95,] 5905.95159 31360.12188
[96,] 13453.83432 5905.95159
[97,] 1414.56290 13453.83432
[98,] -19878.57628 1414.56290
[99,] 14314.25674 -19878.57628
[100,] 18162.79564 14314.25674
[101,] -9733.90612 18162.79564
[102,] -16289.74261 -9733.90612
[103,] -13156.47471 -16289.74261
[104,] 461.06607 -13156.47471
[105,] -31053.13268 461.06607
[106,] -23898.92476 -31053.13268
[107,] 9447.07766 -23898.92476
[108,] -62813.11095 9447.07766
[109,] -29635.40041 -62813.11095
[110,] 45344.80387 -29635.40041
[111,] 59901.01661 45344.80387
[112,] 1732.38099 59901.01661
[113,] -23926.96558 1732.38099
[114,] 10639.65967 -23926.96558
[115,] 1875.66073 10639.65967
[116,] 8256.97569 1875.66073
[117,] -199.11859 8256.97569
[118,] 20118.20651 -199.11859
[119,] -7099.41699 20118.20651
[120,] -33981.17372 -7099.41699
[121,] 28180.26978 -33981.17372
[122,] 41619.30174 28180.26978
[123,] -17965.39505 41619.30174
[124,] -16439.36229 -17965.39505
[125,] 4836.74677 -16439.36229
[126,] 27373.07840 4836.74677
[127,] -9234.75300 27373.07840
[128,] -960.03424 -9234.75300
[129,] -25120.48381 -960.03424
[130,] 14692.83185 -25120.48381
[131,] 1101.02529 14692.83185
[132,] -27627.55779 1101.02529
[133,] 423.54198 -27627.55779
[134,] 10198.96107 423.54198
[135,] 21897.08303 10198.96107
[136,] 5079.01203 21897.08303
[137,] -23594.52908 5079.01203
[138,] 14165.82545 -23594.52908
[139,] -11366.69489 14165.82545
[140,] 33784.65774 -11366.69489
[141,] 20692.52133 33784.65774
[142,] 1050.34875 20692.52133
[143,] 43810.47975 1050.34875
[144,] 24837.05488 43810.47975
[145,] -40876.19497 24837.05488
[146,] -35842.73111 -40876.19497
[147,] 5504.58191 -35842.73111
[148,] 5187.76982 5504.58191
[149,] 2167.70755 5187.76982
[150,] 5093.59914 2167.70755
[151,] 5259.42845 5093.59914
[152,] 5186.76982 5259.42845
[153,] 5186.76982 5186.76982
[154,] 12880.31983 5186.76982
[155,] 29745.08375 12880.31983
[156,] 5186.76982 29745.08375
[157,] 4625.08709 5186.76982
[158,] -1079.22063 4625.08709
[159,] -5450.85449 -1079.22063
[160,] 6708.14201 -5450.85449
[161,] 30016.47585 6708.14201
[162,] 5773.42845 30016.47585
[163,] 39357.09857 5773.42845
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -17294.81421 45694.14685
2 18734.29254 -17294.81421
3 -56228.75265 18734.29254
4 9058.93000 -56228.75265
5 -12538.72005 9058.93000
6 -3255.66073 -12538.72005
7 4017.24542 -3255.66073
8 -11300.60994 4017.24542
9 18034.72613 -11300.60994
10 7832.13057 18034.72613
11 2210.60058 7832.13057
12 -10701.96590 2210.60058
13 49199.50811 -10701.96590
14 7547.86512 49199.50811
15 -49920.08009 7547.86512
16 21940.70913 -49920.08009
17 13511.97240 21940.70913
18 2670.82414 13511.97240
19 13343.41080 2670.82414
20 12504.59960 13343.41080
21 101178.66212 12504.59960
22 6008.76629 101178.66212
23 -28754.05651 6008.76629
24 -69761.59521 -28754.05651
25 -44152.38941 -69761.59521
26 -34445.76166 -44152.38941
27 19442.85818 -34445.76166
28 -1597.26327 19442.85818
29 -30171.15258 -1597.26327
30 -26356.97367 -30171.15258
31 -10159.74068 -26356.97367
32 6196.61760 -10159.74068
33 -3860.40579 6196.61760
34 10885.12942 -3860.40579
35 13795.14048 10885.12942
36 31944.32377 13795.14048
37 11999.01498 31944.32377
38 21305.91750 11999.01498
39 -10770.23570 21305.91750
40 -14642.32692 -10770.23570
41 -11783.47543 -14642.32692
42 -19211.23061 -11783.47543
43 18244.89484 -19211.23061
44 -90563.48557 18244.89484
45 58487.59487 -90563.48557
46 -31064.18991 58487.59487
47 -7220.72062 -31064.18991
48 -37124.60208 -7220.72062
49 -54515.17766 -37124.60208
50 -19691.45316 -54515.17766
51 -16956.79757 -19691.45316
52 35629.25224 -16956.79757
53 -1968.96658 35629.25224
54 -9377.42993 -1968.96658
55 8133.76166 -9377.42993
56 6423.12426 8133.76166
57 -31092.13785 6423.12426
58 -22493.24726 -31092.13785
59 38039.37197 -22493.24726
60 -4132.63627 38039.37197
61 -31011.53077 -4132.63627
62 -35860.19269 -31011.53077
63 8670.33761 -35860.19269
64 5830.36751 8670.33761
65 23320.27672 5830.36751
66 -6360.73056 23320.27672
67 -39029.06332 -6360.73056
68 -81.67056 -39029.06332
69 -19117.99863 -81.67056
70 -5546.64769 -19117.99863
71 -17615.15494 -5546.64769
72 3673.06800 -17615.15494
73 -2152.19302 3673.06800
74 -17017.19211 -2152.19302
75 75153.19842 -17017.19211
76 11642.72625 75153.19842
77 -2988.28692 11642.72625
78 -8464.92334 -2988.28692
79 -4514.80892 -8464.92334
80 -18660.37659 -4514.80892
81 84398.98818 -18660.37659
82 -23339.85327 84398.98818
83 27863.44549 -23339.85327
84 -17818.66532 27863.44549
85 -13858.40670 -17818.66532
86 -870.54229 -13858.40670
87 6460.88111 -870.54229
88 12788.22330 6460.88111
89 -68813.15221 12788.22330
90 23019.03706 -68813.15221
91 10580.77250 23019.03706
92 2332.40139 10580.77250
93 -306.05760 2332.40139
94 31360.12188 -306.05760
95 5905.95159 31360.12188
96 13453.83432 5905.95159
97 1414.56290 13453.83432
98 -19878.57628 1414.56290
99 14314.25674 -19878.57628
100 18162.79564 14314.25674
101 -9733.90612 18162.79564
102 -16289.74261 -9733.90612
103 -13156.47471 -16289.74261
104 461.06607 -13156.47471
105 -31053.13268 461.06607
106 -23898.92476 -31053.13268
107 9447.07766 -23898.92476
108 -62813.11095 9447.07766
109 -29635.40041 -62813.11095
110 45344.80387 -29635.40041
111 59901.01661 45344.80387
112 1732.38099 59901.01661
113 -23926.96558 1732.38099
114 10639.65967 -23926.96558
115 1875.66073 10639.65967
116 8256.97569 1875.66073
117 -199.11859 8256.97569
118 20118.20651 -199.11859
119 -7099.41699 20118.20651
120 -33981.17372 -7099.41699
121 28180.26978 -33981.17372
122 41619.30174 28180.26978
123 -17965.39505 41619.30174
124 -16439.36229 -17965.39505
125 4836.74677 -16439.36229
126 27373.07840 4836.74677
127 -9234.75300 27373.07840
128 -960.03424 -9234.75300
129 -25120.48381 -960.03424
130 14692.83185 -25120.48381
131 1101.02529 14692.83185
132 -27627.55779 1101.02529
133 423.54198 -27627.55779
134 10198.96107 423.54198
135 21897.08303 10198.96107
136 5079.01203 21897.08303
137 -23594.52908 5079.01203
138 14165.82545 -23594.52908
139 -11366.69489 14165.82545
140 33784.65774 -11366.69489
141 20692.52133 33784.65774
142 1050.34875 20692.52133
143 43810.47975 1050.34875
144 24837.05488 43810.47975
145 -40876.19497 24837.05488
146 -35842.73111 -40876.19497
147 5504.58191 -35842.73111
148 5187.76982 5504.58191
149 2167.70755 5187.76982
150 5093.59914 2167.70755
151 5259.42845 5093.59914
152 5186.76982 5259.42845
153 5186.76982 5186.76982
154 12880.31983 5186.76982
155 29745.08375 12880.31983
156 5186.76982 29745.08375
157 4625.08709 5186.76982
158 -1079.22063 4625.08709
159 -5450.85449 -1079.22063
160 6708.14201 -5450.85449
161 30016.47585 6708.14201
162 5773.42845 30016.47585
163 39357.09857 5773.42845
> 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/74tbu1324649174.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/8ucyg1324649174.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/98tls1324649174.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/10534f1324649174.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/119lfz1324649174.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/128zu91324649174.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13p9d41324649174.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14hsil1324649174.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/15mugg1324649174.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/166ewe1324649174.tab")
+ }
>
> try(system("convert tmp/1mvv41324649174.ps tmp/1mvv41324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jbic1324649174.ps tmp/2jbic1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/3a5hz1324649174.ps tmp/3a5hz1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/4qnju1324649174.ps tmp/4qnju1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/51qf31324649174.ps tmp/51qf31324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/6f8fe1324649174.ps tmp/6f8fe1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/74tbu1324649174.ps tmp/74tbu1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ucyg1324649174.ps tmp/8ucyg1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/98tls1324649174.ps tmp/98tls1324649174.png",intern=TRUE))
character(0)
> try(system("convert tmp/10534f1324649174.ps tmp/10534f1324649174.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.085 0.636 5.729