R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(170588
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+ ,48)
+ ,dim=c(7
+ ,164)
+ ,dimnames=list(c('TimeRFCSEC'
+ ,'#Logins'
+ ,'CW#characters'
+ ,'CW#revisions'
+ ,'CW#seconds'
+ ,'CWIncludedHyperlinks'
+ ,'CWIncludedBlogs')
+ ,1:164))
> y <- array(NA,dim=c(7,164),dimnames=list(c('TimeRFCSEC','#Logins','CW#characters','CW#revisions','CW#seconds','CWIncludedHyperlinks','CWIncludedBlogs'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '3'
> 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
CW#characters TimeRFCSEC #Logins CW#revisions CW#seconds
1 95556 170588 46 21387 114468
2 54565 86621 48 12341 88594
3 63016 113337 37 11397 74151
4 79774 152510 75 25533 77921
5 31258 86206 31 6630 53212
6 52491 37257 18 7745 34956
7 91256 306055 79 25304 149703
8 22807 32750 16 1271 6853
9 77411 116502 38 18035 58907
10 48821 130539 24 13284 67067
11 52295 161876 65 15628 110563
12 63262 128274 74 13990 58126
13 50466 102350 43 8532 57113
14 62932 193024 42 13953 77993
15 38439 141574 55 7210 68091
16 70817 253559 121 22436 124676
17 105965 181110 42 20238 109522
18 73795 198432 102 10244 75865
19 82043 113853 36 17390 79746
20 74349 159940 50 9917 77844
21 82204 166822 48 29625 98681
22 55709 286675 56 13193 105531
23 37137 91657 19 6815 51428
24 70780 108278 32 11807 65703
25 55027 146342 77 21472 72562
26 56699 145142 90 19589 81728
27 65911 161740 81 12266 95580
28 56316 160905 55 18391 98278
29 26982 106888 34 6711 46629
30 54628 188150 38 9004 115189
31 96750 189401 53 34301 124865
32 53009 129484 48 8061 59392
33 64664 204030 63 19463 127818
34 36990 62731 25 2053 17821
35 85224 243625 56 29618 154076
36 37048 167255 37 3963 64881
37 59635 264528 83 17609 136506
38 42051 122024 50 11738 66524
39 26998 80964 26 11082 45988
40 63717 209795 108 22648 107445
41 55071 224205 55 16538 102772
42 40001 115971 41 10149 46657
43 54506 138191 49 19787 97563
44 35838 81106 31 7740 36663
45 50838 93125 49 5873 55369
46 86997 305756 96 11694 77921
47 33032 78800 42 7935 56968
48 61704 158835 55 15093 77519
49 117986 221745 70 14533 129805
50 56733 131108 39 15834 72761
51 55064 128734 53 15699 81278
52 5950 24188 24 2694 15049
53 84607 257662 209 13834 113935
54 32551 65029 17 3597 25109
55 31701 98066 58 5296 45824
56 71170 173587 27 21637 89644
57 101773 180042 58 18081 109011
58 101653 197266 114 29016 134245
59 81493 212060 75 27279 136692
60 55901 141582 51 12889 50741
61 109104 245107 86 21550 149510
62 114425 206879 77 34042 147888
63 36311 145696 62 8190 54987
64 70027 170635 60 16163 74467
65 73713 142064 39 23471 100033
66 40671 114820 35 14220 85505
67 89041 113461 86 12759 62426
68 57231 145285 102 18142 82932
69 68608 150999 49 12416 72002
70 59155 91812 35 14069 65469
71 55827 118807 33 11131 63572
72 22618 69471 28 3007 23824
73 58425 126630 44 12530 73831
74 65724 145908 37 13205 63551
75 56979 98393 33 13025 56756
76 72369 190926 45 18778 81399
77 79194 198797 57 19793 117881
78 202316 106193 58 8238 70711
79 44970 89318 36 11285 50495
80 49319 120362 42 10490 53845
81 36252 98791 30 10457 51390
82 75741 274953 67 17313 104953
83 38417 132798 53 9592 65983
84 64102 135251 59 14282 76839
85 56622 80953 25 7905 55792
86 15430 109237 39 4525 25155
87 72571 96634 36 21179 55291
88 67271 226191 114 13724 84279
89 43460 171286 54 18446 99692
90 99501 117815 70 25961 59633
91 28340 133561 51 6602 63249
92 76013 152193 49 16795 82928
93 37361 112004 42 5463 50000
94 48204 169613 51 11299 69455
95 76168 187483 51 20390 84068
96 85168 130533 27 18558 76195
97 125410 142339 29 26262 114634
98 123328 189764 54 25267 139357
99 83038 201603 92 21091 110044
100 120087 246834 72 32425 155118
101 91939 155947 63 24380 83061
102 103646 182581 41 20460 127122
103 29467 106351 111 6515 45653
104 43750 43287 14 7409 19630
105 34497 127493 45 12300 67229
106 66477 127930 91 27127 86060
107 71181 149006 29 27687 88003
108 74482 187653 64 19255 95815
109 174949 74112 32 15070 85499
110 46765 94006 65 6291 27220
111 90257 176625 42 16577 109882
112 51370 141933 55 13027 72579
113 1168 22938 10 238 5841
114 51360 125927 53 17103 68369
115 25162 61857 25 3913 24610
116 21067 91290 33 5654 30995
117 58233 255100 66 14354 150662
118 855 21054 16 338 6622
119 85903 169093 35 8852 93694
120 14116 31414 19 3988 13155
121 57637 188701 76 15964 111908
122 94137 137544 35 14784 57550
123 62147 77166 46 2667 16356
124 62832 74567 29 7164 40174
125 8773 38214 34 1888 13983
126 63785 90961 25 12367 52316
127 65196 194224 48 20505 99585
128 73087 135261 38 18330 86271
129 72631 244272 50 24993 131012
130 86281 201748 65 11869 130274
131 162365 256402 72 31156 159051
132 56530 139144 23 15234 76506
133 35606 76470 29 6645 49145
134 70111 189502 194 15007 66398
135 92046 280334 114 16597 127546
136 63989 50999 15 317 6802
137 104911 253274 86 27627 99509
138 43448 103239 50 8658 43106
139 60029 168059 33 20493 108303
140 38650 128768 50 8877 64167
141 47261 75746 72 867 8579
142 73586 249232 81 13259 97811
143 83042 152366 54 20613 84365
144 37238 173260 63 2805 10901
145 63958 197197 69 20588 91346
146 78956 67507 39 9812 33660
147 99518 139409 49 20001 93634
148 111436 185366 67 23042 109348
149 0 0 0 0 0
150 6023 14688 10 2065 7953
151 0 98 1 0 0
152 0 455 2 0 0
153 0 0 0 0 0
154 0 0 0 0 0
155 42564 137885 57 10902 63538
156 38885 185288 72 11309 108281
157 0 0 0 0 0
158 0 203 4 0 0
159 1644 7199 5 556 4245
160 6179 46660 20 2089 21509
161 3926 17547 5 2658 7670
162 23238 73567 27 1419 10641
163 0 969 2 0 0
164 49288 105477 33 10699 41243
CWIncludedHyperlinks CWIncludedBlogs
1 127 128
2 90 89
3 68 68
4 111 108
5 51 51
6 33 33
7 123 119
8 5 5
9 63 63
10 66 66
11 99 98
12 72 71
13 55 55
14 116 116
15 71 71
16 125 120
17 123 122
18 74 74
19 116 111
20 117 103
21 98 98
22 101 100
23 43 42
24 103 100
25 107 105
26 77 77
27 87 83
28 99 98
29 46 46
30 96 95
31 92 91
32 96 91
33 96 94
34 15 15
35 147 137
36 56 56
37 81 78
38 69 68
39 34 34
40 98 94
41 82 82
42 64 63
43 61 58
44 45 43
45 37 36
46 64 64
47 21 21
48 104 104
49 126 124
50 104 101
51 87 85
52 7 7
53 130 124
54 21 21
55 35 35
56 97 95
57 103 102
58 210 212
59 151 141
60 57 54
61 117 117
62 152 145
63 52 50
64 83 80
65 87 87
66 80 78
67 88 86
68 83 82
69 120 119
70 76 75
71 70 70
72 26 25
73 66 66
74 89 89
75 100 99
76 98 98
77 109 104
78 51 48
79 82 81
80 65 64
81 46 44
82 104 104
83 36 36
84 123 120
85 59 58
86 27 27
87 84 84
88 61 56
89 46 46
90 125 119
91 58 57
92 152 139
93 52 51
94 85 85
95 95 91
96 78 79
97 144 142
98 149 149
99 101 96
100 205 198
101 61 61
102 145 145
103 28 26
104 49 49
105 68 68
106 142 145
107 82 82
108 105 102
109 52 52
110 56 56
111 81 80
112 100 99
113 11 11
114 87 87
115 31 28
116 67 67
117 150 150
118 4 4
119 75 71
120 39 39
121 88 87
122 67 66
123 24 23
124 58 56
125 16 16
126 49 49
127 109 108
128 124 112
129 115 110
130 128 126
131 159 155
132 75 75
133 30 30
134 83 78
135 135 135
136 8 8
137 115 114
138 60 60
139 99 99
140 98 98
141 36 33
142 93 93
143 158 157
144 16 15
145 100 98
146 49 49
147 89 88
148 153 151
149 0 0
150 5 5
151 0 0
152 0 0
153 0 0
154 0 0
155 80 80
156 122 122
157 0 0
158 0 0
159 6 6
160 13 13
161 3 3
162 18 18
163 0 0
164 49 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TimeRFCSEC `#Logins`
1.318e+04 -3.577e-02 6.771e+01
`CW#revisions` `CW#seconds` CWIncludedHyperlinks
1.344e+00 2.767e-01 4.955e+02
CWIncludedBlogs
-3.723e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-32732 -13185 -4121 7848 150971
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.318e+04 3.931e+03 3.352 0.00100 **
TimeRFCSEC -3.577e-02 6.146e-02 -0.582 0.56135
`#Logins` 6.771e+01 8.066e+01 0.839 0.40247
`CW#revisions` 1.344e+00 4.235e-01 3.174 0.00181 **
`CW#seconds` 2.767e-01 1.310e-01 2.111 0.03636 *
CWIncludedHyperlinks 4.955e+02 7.451e+02 0.665 0.50702
CWIncludedBlogs -3.723e+02 7.604e+02 -0.490 0.62510
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 22090 on 157 degrees of freedom
Multiple R-squared: 0.5812, Adjusted R-squared: 0.5652
F-statistic: 36.32 on 6 and 157 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,] 7.293457e-02 1.458691e-01 9.270654e-01
[2,] 8.233540e-02 1.646708e-01 9.176646e-01
[3,] 5.387120e-02 1.077424e-01 9.461288e-01
[4,] 2.438159e-02 4.876318e-02 9.756184e-01
[5,] 1.154563e-02 2.309125e-02 9.884544e-01
[6,] 4.686518e-03 9.373037e-03 9.953135e-01
[7,] 1.793916e-03 3.587832e-03 9.982061e-01
[8,] 7.871183e-03 1.574237e-02 9.921288e-01
[9,] 1.082271e-02 2.164543e-02 9.891773e-01
[10,] 1.882726e-02 3.765453e-02 9.811727e-01
[11,] 1.733659e-02 3.467319e-02 9.826634e-01
[12,] 1.194432e-02 2.388864e-02 9.880557e-01
[13,] 8.084313e-03 1.616863e-02 9.919157e-01
[14,] 4.534666e-03 9.069331e-03 9.954653e-01
[15,] 2.396103e-03 4.792206e-03 9.976039e-01
[16,] 4.420032e-03 8.840063e-03 9.955800e-01
[17,] 2.622776e-03 5.245552e-03 9.973772e-01
[18,] 1.548966e-03 3.097932e-03 9.984510e-01
[19,] 1.391630e-03 2.783260e-03 9.986084e-01
[20,] 1.157173e-03 2.314345e-03 9.988428e-01
[21,] 6.457953e-04 1.291591e-03 9.993542e-01
[22,] 3.476066e-04 6.952133e-04 9.996524e-01
[23,] 1.821644e-04 3.643289e-04 9.998178e-01
[24,] 1.094559e-04 2.189117e-04 9.998905e-01
[25,] 7.021327e-05 1.404265e-04 9.999298e-01
[26,] 6.903913e-05 1.380783e-04 9.999310e-01
[27,] 3.477842e-05 6.955684e-05 9.999652e-01
[28,] 1.938761e-05 3.877522e-05 9.999806e-01
[29,] 1.370895e-05 2.741789e-05 9.999863e-01
[30,] 1.491752e-05 2.983504e-05 9.999851e-01
[31,] 9.397259e-06 1.879452e-05 9.999906e-01
[32,] 5.290069e-06 1.058014e-05 9.999947e-01
[33,] 3.299902e-06 6.599804e-06 9.999967e-01
[34,] 1.915671e-06 3.831342e-06 9.999981e-01
[35,] 9.688615e-07 1.937723e-06 9.999990e-01
[36,] 9.089739e-07 1.817948e-06 9.999991e-01
[37,] 7.466552e-06 1.493310e-05 9.999925e-01
[38,] 3.989649e-06 7.979297e-06 9.999960e-01
[39,] 2.170814e-06 4.341628e-06 9.999978e-01
[40,] 5.931460e-05 1.186292e-04 9.999407e-01
[41,] 4.109179e-05 8.218358e-05 9.999589e-01
[42,] 2.582697e-05 5.165394e-05 9.999742e-01
[43,] 2.214379e-05 4.428758e-05 9.999779e-01
[44,] 1.284428e-05 2.568856e-05 9.999872e-01
[45,] 7.301025e-06 1.460205e-05 9.999927e-01
[46,] 4.064592e-06 8.129185e-06 9.999959e-01
[47,] 2.154531e-06 4.309063e-06 9.999978e-01
[48,] 5.478657e-06 1.095731e-05 9.999945e-01
[49,] 4.603539e-06 9.207079e-06 9.999954e-01
[50,] 4.322964e-06 8.645927e-06 9.999957e-01
[51,] 2.443413e-06 4.886825e-06 9.999976e-01
[52,] 3.365597e-06 6.731194e-06 9.999966e-01
[53,] 2.919093e-06 5.838185e-06 9.999971e-01
[54,] 1.835244e-06 3.670488e-06 9.999982e-01
[55,] 1.075276e-06 2.150551e-06 9.999989e-01
[56,] 6.215929e-07 1.243186e-06 9.999994e-01
[57,] 7.533263e-07 1.506653e-06 9.999992e-01
[58,] 1.999106e-06 3.998212e-06 9.999980e-01
[59,] 1.501377e-06 3.002755e-06 9.999985e-01
[60,] 8.620481e-07 1.724096e-06 9.999991e-01
[61,] 4.751845e-07 9.503691e-07 9.999995e-01
[62,] 2.545799e-07 5.091598e-07 9.999997e-01
[63,] 1.405744e-07 2.811489e-07 9.999999e-01
[64,] 7.498365e-08 1.499673e-07 9.999999e-01
[65,] 4.245438e-08 8.490876e-08 1.000000e+00
[66,] 2.212542e-08 4.425083e-08 1.000000e+00
[67,] 1.122535e-08 2.245070e-08 1.000000e+00
[68,] 6.371066e-09 1.274213e-08 1.000000e+00
[69,] 6.635322e-01 6.729355e-01 3.364678e-01
[70,] 6.252928e-01 7.494144e-01 3.747072e-01
[71,] 5.806769e-01 8.386462e-01 4.193231e-01
[72,] 5.477918e-01 9.044163e-01 4.522082e-01
[73,] 5.033988e-01 9.932024e-01 4.966012e-01
[74,] 4.686146e-01 9.372292e-01 5.313854e-01
[75,] 4.249462e-01 8.498924e-01 5.750538e-01
[76,] 3.890829e-01 7.781658e-01 6.109171e-01
[77,] 3.621904e-01 7.243809e-01 6.378096e-01
[78,] 3.273054e-01 6.546109e-01 6.726946e-01
[79,] 2.875260e-01 5.750519e-01 7.124740e-01
[80,] 3.283388e-01 6.566776e-01 6.716612e-01
[81,] 3.113091e-01 6.226183e-01 6.886909e-01
[82,] 2.990122e-01 5.980245e-01 7.009878e-01
[83,] 2.613971e-01 5.227941e-01 7.386029e-01
[84,] 2.261844e-01 4.523689e-01 7.738156e-01
[85,] 1.955962e-01 3.911925e-01 8.044038e-01
[86,] 1.672616e-01 3.345233e-01 8.327384e-01
[87,] 1.590426e-01 3.180852e-01 8.409574e-01
[88,] 1.813511e-01 3.627023e-01 8.186489e-01
[89,] 1.796225e-01 3.592451e-01 8.203775e-01
[90,] 1.580785e-01 3.161570e-01 8.419215e-01
[91,] 1.331057e-01 2.662114e-01 8.668943e-01
[92,] 1.207098e-01 2.414195e-01 8.792902e-01
[93,] 1.083759e-01 2.167518e-01 8.916241e-01
[94,] 1.053106e-01 2.106212e-01 8.946894e-01
[95,] 9.243353e-02 1.848671e-01 9.075665e-01
[96,] 9.187457e-02 1.837491e-01 9.081254e-01
[97,] 9.705571e-02 1.941114e-01 9.029443e-01
[98,] 1.019653e-01 2.039306e-01 8.980347e-01
[99,] 8.503917e-02 1.700783e-01 9.149608e-01
[100,] 9.341182e-01 1.317636e-01 6.588181e-02
[101,] 9.200965e-01 1.598070e-01 7.990351e-02
[102,] 9.162940e-01 1.674120e-01 8.370598e-02
[103,] 8.997906e-01 2.004188e-01 1.002094e-01
[104,] 8.852025e-01 2.295951e-01 1.147975e-01
[105,] 8.717187e-01 2.565627e-01 1.282813e-01
[106,] 8.441916e-01 3.116168e-01 1.558084e-01
[107,] 8.307330e-01 3.385340e-01 1.692670e-01
[108,] 8.376868e-01 3.246264e-01 1.623132e-01
[109,] 8.160883e-01 3.678234e-01 1.839117e-01
[110,] 8.538861e-01 2.922277e-01 1.461139e-01
[111,] 8.340594e-01 3.318813e-01 1.659406e-01
[112,] 8.114985e-01 3.770031e-01 1.885015e-01
[113,] 8.618373e-01 2.763254e-01 1.381627e-01
[114,] 9.145067e-01 1.709866e-01 8.549329e-02
[115,] 9.221382e-01 1.557236e-01 7.786181e-02
[116,] 9.051951e-01 1.896097e-01 9.480486e-02
[117,] 8.936058e-01 2.127885e-01 1.063942e-01
[118,] 8.849924e-01 2.300153e-01 1.150076e-01
[119,] 8.641590e-01 2.716820e-01 1.358410e-01
[120,] 9.564164e-01 8.716722e-02 4.358361e-02
[121,] 9.414601e-01 1.170798e-01 5.853988e-02
[122,] 9.657531e-01 6.849381e-02 3.424690e-02
[123,] 9.513423e-01 9.731545e-02 4.865773e-02
[124,] 9.379311e-01 1.241378e-01 6.206889e-02
[125,] 9.554433e-01 8.911345e-02 4.455673e-02
[126,] 9.390068e-01 1.219863e-01 6.099316e-02
[127,] 9.993305e-01 1.338996e-03 6.694981e-04
[128,] 9.996789e-01 6.422125e-04 3.211062e-04
[129,] 9.996079e-01 7.842123e-04 3.921062e-04
[130,] 9.992078e-01 1.584343e-03 7.921717e-04
[131,] 9.984270e-01 3.146060e-03 1.573030e-03
[132,] 9.977592e-01 4.481645e-03 2.240823e-03
[133,] 9.974969e-01 5.006140e-03 2.503070e-03
[134,] 9.979737e-01 4.052535e-03 2.026268e-03
[135,] 9.993706e-01 1.258812e-03 6.294059e-04
[136,] 9.998838e-01 2.323639e-04 1.161819e-04
[137,] 9.999833e-01 3.331250e-05 1.665625e-05
[138,] 1.000000e+00 6.912932e-11 3.456466e-11
[139,] 1.000000e+00 8.631617e-13 4.315809e-13
[140,] 1.000000e+00 2.526698e-11 1.263349e-11
[141,] 1.000000e+00 3.828531e-12 1.914266e-12
[142,] 1.000000e+00 2.206557e-10 1.103279e-10
[143,] 1.000000e+00 1.345042e-08 6.725208e-09
[144,] 9.999997e-01 5.410415e-07 2.705208e-07
[145,] 9.999913e-01 1.735985e-05 8.679923e-06
> postscript(file="/var/wessaorg/rcomp/tmp/1t9aq1321876963.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/287mi1321876963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3sh9g1321876963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4smry1321876963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5o7421321876963.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
9670.58386 -11326.70730 7172.50220 -3702.35589 -10854.06924 15277.46530
7 8 9 10 11 12
-8399.77867 5495.41379 17522.73922 -5857.38371 -23661.01635 5529.94528
13 14 15 16 17 18
3989.98775 -812.38019 -10677.57830 -23400.08955 23388.03313 16931.09527
19 20 21 22 23 24
8905.69622 9010.89320 -7457.77986 -10753.90056 -3109.30312 11450.78904
25 26 27 28 29 30
-20998.43064 -15813.11542 -2106.94414 -19313.34505 -12264.95644 -10565.26763
31 32 33 34 35 36
-5605.50204 254.70291 -19580.31802 14823.89457 -27307.86954 -2829.84564
37 38 39 40 41 42
-22234.56561 -13205.86557 -16854.85548 -23004.51761 -14579.19239 -6614.60153
43 44 45 46 47 48
-19271.12425 -3375.74510 9528.22661 33092.85530 -9186.95700 -4066.57620
49 50 51 52 53 54
36282.93301 -9742.85256 -12152.60857 -16636.39589 -1874.82724 6177.65891
55 56 57 58 59 60
-6006.37798 -4210.69328 23579.73658 -13463.68203 -25993.75782 4829.10426
61 62 63 64 65 66
14122.29943 -4577.87523 -9227.88212 5216.45188 -6970.85154 -24143.26348
67 68 69 70 71 72
28088.08417 -15587.94342 5744.72606 128.75790 3487.89350 -4180.95188
73 74 75 76 77 78
1394.91076 8960.26091 -819.50759 3135.59850 -5244.43434 150971.08338
79 80 81 82 83 84
-7067.26982 222.57940 -10111.33964 2737.90300 -9184.73711 -4962.64867
85 86 87 88 89 90
10942.51923 -12850.97846 6293.79908 3321.89437 -25292.92745 16764.19305
91 92 93 94 95 96
-17406.06441 -4127.34720 -2611.05932 -7237.88643 2380.17496 19564.81828
97 98 99 100 101 102
29854.11004 22402.43820 -2261.05035 -3504.53534 16803.56236 13683.53155
103 104 105 106 107 108
-13006.19110 9743.30939 -20680.70154 -24942.31148 -10300.88956 -2763.80705
109 110 111 112 113 114
111935.04137 9660.20483 17516.39213 -10740.66593 -15158.94928 -13528.56607
115 116 117 118 119 120
-4502.25799 -15511.56136 -29747.90933 -15433.63500 27852.59559 -13031.64768
121 122 123 124 125 126
-17572.65675 39085.29995 37174.06392 21721.01665 -13719.19152 15031.23163
127 128 129 130 131 132
-13202.98701 -6080.44874 -21069.06119 7406.20634 46518.20599 -4114.46736
133 134 135 136 137 138
-3026.55055 -56.07319 6945.63123 48324.93155 15758.99587 -380.56470
139 140 141 142 143 144
-19081.67669 -15068.23993 22824.96213 7495.83890 767.43567 16860.99029
145 146 147 148 149 150
-12852.20379 37011.17847 23878.42885 19528.57125 -13179.25197 -12900.18742
151 152 153 154 155 156
-13243.45667 -13298.39580 -13179.25197 -13179.25197 -11632.50619 -32731.54255
157 158 159 160 161 162
-13179.25197 -13442.83214 -14277.35564 -17045.74757 -15028.82452 3793.14953
163 164
-13280.00776 5445.23813
> postscript(file="/var/wessaorg/rcomp/tmp/64cqy1321876963.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 9670.58386 NA
1 -11326.70730 9670.58386
2 7172.50220 -11326.70730
3 -3702.35589 7172.50220
4 -10854.06924 -3702.35589
5 15277.46530 -10854.06924
6 -8399.77867 15277.46530
7 5495.41379 -8399.77867
8 17522.73922 5495.41379
9 -5857.38371 17522.73922
10 -23661.01635 -5857.38371
11 5529.94528 -23661.01635
12 3989.98775 5529.94528
13 -812.38019 3989.98775
14 -10677.57830 -812.38019
15 -23400.08955 -10677.57830
16 23388.03313 -23400.08955
17 16931.09527 23388.03313
18 8905.69622 16931.09527
19 9010.89320 8905.69622
20 -7457.77986 9010.89320
21 -10753.90056 -7457.77986
22 -3109.30312 -10753.90056
23 11450.78904 -3109.30312
24 -20998.43064 11450.78904
25 -15813.11542 -20998.43064
26 -2106.94414 -15813.11542
27 -19313.34505 -2106.94414
28 -12264.95644 -19313.34505
29 -10565.26763 -12264.95644
30 -5605.50204 -10565.26763
31 254.70291 -5605.50204
32 -19580.31802 254.70291
33 14823.89457 -19580.31802
34 -27307.86954 14823.89457
35 -2829.84564 -27307.86954
36 -22234.56561 -2829.84564
37 -13205.86557 -22234.56561
38 -16854.85548 -13205.86557
39 -23004.51761 -16854.85548
40 -14579.19239 -23004.51761
41 -6614.60153 -14579.19239
42 -19271.12425 -6614.60153
43 -3375.74510 -19271.12425
44 9528.22661 -3375.74510
45 33092.85530 9528.22661
46 -9186.95700 33092.85530
47 -4066.57620 -9186.95700
48 36282.93301 -4066.57620
49 -9742.85256 36282.93301
50 -12152.60857 -9742.85256
51 -16636.39589 -12152.60857
52 -1874.82724 -16636.39589
53 6177.65891 -1874.82724
54 -6006.37798 6177.65891
55 -4210.69328 -6006.37798
56 23579.73658 -4210.69328
57 -13463.68203 23579.73658
58 -25993.75782 -13463.68203
59 4829.10426 -25993.75782
60 14122.29943 4829.10426
61 -4577.87523 14122.29943
62 -9227.88212 -4577.87523
63 5216.45188 -9227.88212
64 -6970.85154 5216.45188
65 -24143.26348 -6970.85154
66 28088.08417 -24143.26348
67 -15587.94342 28088.08417
68 5744.72606 -15587.94342
69 128.75790 5744.72606
70 3487.89350 128.75790
71 -4180.95188 3487.89350
72 1394.91076 -4180.95188
73 8960.26091 1394.91076
74 -819.50759 8960.26091
75 3135.59850 -819.50759
76 -5244.43434 3135.59850
77 150971.08338 -5244.43434
78 -7067.26982 150971.08338
79 222.57940 -7067.26982
80 -10111.33964 222.57940
81 2737.90300 -10111.33964
82 -9184.73711 2737.90300
83 -4962.64867 -9184.73711
84 10942.51923 -4962.64867
85 -12850.97846 10942.51923
86 6293.79908 -12850.97846
87 3321.89437 6293.79908
88 -25292.92745 3321.89437
89 16764.19305 -25292.92745
90 -17406.06441 16764.19305
91 -4127.34720 -17406.06441
92 -2611.05932 -4127.34720
93 -7237.88643 -2611.05932
94 2380.17496 -7237.88643
95 19564.81828 2380.17496
96 29854.11004 19564.81828
97 22402.43820 29854.11004
98 -2261.05035 22402.43820
99 -3504.53534 -2261.05035
100 16803.56236 -3504.53534
101 13683.53155 16803.56236
102 -13006.19110 13683.53155
103 9743.30939 -13006.19110
104 -20680.70154 9743.30939
105 -24942.31148 -20680.70154
106 -10300.88956 -24942.31148
107 -2763.80705 -10300.88956
108 111935.04137 -2763.80705
109 9660.20483 111935.04137
110 17516.39213 9660.20483
111 -10740.66593 17516.39213
112 -15158.94928 -10740.66593
113 -13528.56607 -15158.94928
114 -4502.25799 -13528.56607
115 -15511.56136 -4502.25799
116 -29747.90933 -15511.56136
117 -15433.63500 -29747.90933
118 27852.59559 -15433.63500
119 -13031.64768 27852.59559
120 -17572.65675 -13031.64768
121 39085.29995 -17572.65675
122 37174.06392 39085.29995
123 21721.01665 37174.06392
124 -13719.19152 21721.01665
125 15031.23163 -13719.19152
126 -13202.98701 15031.23163
127 -6080.44874 -13202.98701
128 -21069.06119 -6080.44874
129 7406.20634 -21069.06119
130 46518.20599 7406.20634
131 -4114.46736 46518.20599
132 -3026.55055 -4114.46736
133 -56.07319 -3026.55055
134 6945.63123 -56.07319
135 48324.93155 6945.63123
136 15758.99587 48324.93155
137 -380.56470 15758.99587
138 -19081.67669 -380.56470
139 -15068.23993 -19081.67669
140 22824.96213 -15068.23993
141 7495.83890 22824.96213
142 767.43567 7495.83890
143 16860.99029 767.43567
144 -12852.20379 16860.99029
145 37011.17847 -12852.20379
146 23878.42885 37011.17847
147 19528.57125 23878.42885
148 -13179.25197 19528.57125
149 -12900.18742 -13179.25197
150 -13243.45667 -12900.18742
151 -13298.39580 -13243.45667
152 -13179.25197 -13298.39580
153 -13179.25197 -13179.25197
154 -11632.50619 -13179.25197
155 -32731.54255 -11632.50619
156 -13179.25197 -32731.54255
157 -13442.83214 -13179.25197
158 -14277.35564 -13442.83214
159 -17045.74757 -14277.35564
160 -15028.82452 -17045.74757
161 3793.14953 -15028.82452
162 -13280.00776 3793.14953
163 5445.23813 -13280.00776
164 NA 5445.23813
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -11326.70730 9670.58386
[2,] 7172.50220 -11326.70730
[3,] -3702.35589 7172.50220
[4,] -10854.06924 -3702.35589
[5,] 15277.46530 -10854.06924
[6,] -8399.77867 15277.46530
[7,] 5495.41379 -8399.77867
[8,] 17522.73922 5495.41379
[9,] -5857.38371 17522.73922
[10,] -23661.01635 -5857.38371
[11,] 5529.94528 -23661.01635
[12,] 3989.98775 5529.94528
[13,] -812.38019 3989.98775
[14,] -10677.57830 -812.38019
[15,] -23400.08955 -10677.57830
[16,] 23388.03313 -23400.08955
[17,] 16931.09527 23388.03313
[18,] 8905.69622 16931.09527
[19,] 9010.89320 8905.69622
[20,] -7457.77986 9010.89320
[21,] -10753.90056 -7457.77986
[22,] -3109.30312 -10753.90056
[23,] 11450.78904 -3109.30312
[24,] -20998.43064 11450.78904
[25,] -15813.11542 -20998.43064
[26,] -2106.94414 -15813.11542
[27,] -19313.34505 -2106.94414
[28,] -12264.95644 -19313.34505
[29,] -10565.26763 -12264.95644
[30,] -5605.50204 -10565.26763
[31,] 254.70291 -5605.50204
[32,] -19580.31802 254.70291
[33,] 14823.89457 -19580.31802
[34,] -27307.86954 14823.89457
[35,] -2829.84564 -27307.86954
[36,] -22234.56561 -2829.84564
[37,] -13205.86557 -22234.56561
[38,] -16854.85548 -13205.86557
[39,] -23004.51761 -16854.85548
[40,] -14579.19239 -23004.51761
[41,] -6614.60153 -14579.19239
[42,] -19271.12425 -6614.60153
[43,] -3375.74510 -19271.12425
[44,] 9528.22661 -3375.74510
[45,] 33092.85530 9528.22661
[46,] -9186.95700 33092.85530
[47,] -4066.57620 -9186.95700
[48,] 36282.93301 -4066.57620
[49,] -9742.85256 36282.93301
[50,] -12152.60857 -9742.85256
[51,] -16636.39589 -12152.60857
[52,] -1874.82724 -16636.39589
[53,] 6177.65891 -1874.82724
[54,] -6006.37798 6177.65891
[55,] -4210.69328 -6006.37798
[56,] 23579.73658 -4210.69328
[57,] -13463.68203 23579.73658
[58,] -25993.75782 -13463.68203
[59,] 4829.10426 -25993.75782
[60,] 14122.29943 4829.10426
[61,] -4577.87523 14122.29943
[62,] -9227.88212 -4577.87523
[63,] 5216.45188 -9227.88212
[64,] -6970.85154 5216.45188
[65,] -24143.26348 -6970.85154
[66,] 28088.08417 -24143.26348
[67,] -15587.94342 28088.08417
[68,] 5744.72606 -15587.94342
[69,] 128.75790 5744.72606
[70,] 3487.89350 128.75790
[71,] -4180.95188 3487.89350
[72,] 1394.91076 -4180.95188
[73,] 8960.26091 1394.91076
[74,] -819.50759 8960.26091
[75,] 3135.59850 -819.50759
[76,] -5244.43434 3135.59850
[77,] 150971.08338 -5244.43434
[78,] -7067.26982 150971.08338
[79,] 222.57940 -7067.26982
[80,] -10111.33964 222.57940
[81,] 2737.90300 -10111.33964
[82,] -9184.73711 2737.90300
[83,] -4962.64867 -9184.73711
[84,] 10942.51923 -4962.64867
[85,] -12850.97846 10942.51923
[86,] 6293.79908 -12850.97846
[87,] 3321.89437 6293.79908
[88,] -25292.92745 3321.89437
[89,] 16764.19305 -25292.92745
[90,] -17406.06441 16764.19305
[91,] -4127.34720 -17406.06441
[92,] -2611.05932 -4127.34720
[93,] -7237.88643 -2611.05932
[94,] 2380.17496 -7237.88643
[95,] 19564.81828 2380.17496
[96,] 29854.11004 19564.81828
[97,] 22402.43820 29854.11004
[98,] -2261.05035 22402.43820
[99,] -3504.53534 -2261.05035
[100,] 16803.56236 -3504.53534
[101,] 13683.53155 16803.56236
[102,] -13006.19110 13683.53155
[103,] 9743.30939 -13006.19110
[104,] -20680.70154 9743.30939
[105,] -24942.31148 -20680.70154
[106,] -10300.88956 -24942.31148
[107,] -2763.80705 -10300.88956
[108,] 111935.04137 -2763.80705
[109,] 9660.20483 111935.04137
[110,] 17516.39213 9660.20483
[111,] -10740.66593 17516.39213
[112,] -15158.94928 -10740.66593
[113,] -13528.56607 -15158.94928
[114,] -4502.25799 -13528.56607
[115,] -15511.56136 -4502.25799
[116,] -29747.90933 -15511.56136
[117,] -15433.63500 -29747.90933
[118,] 27852.59559 -15433.63500
[119,] -13031.64768 27852.59559
[120,] -17572.65675 -13031.64768
[121,] 39085.29995 -17572.65675
[122,] 37174.06392 39085.29995
[123,] 21721.01665 37174.06392
[124,] -13719.19152 21721.01665
[125,] 15031.23163 -13719.19152
[126,] -13202.98701 15031.23163
[127,] -6080.44874 -13202.98701
[128,] -21069.06119 -6080.44874
[129,] 7406.20634 -21069.06119
[130,] 46518.20599 7406.20634
[131,] -4114.46736 46518.20599
[132,] -3026.55055 -4114.46736
[133,] -56.07319 -3026.55055
[134,] 6945.63123 -56.07319
[135,] 48324.93155 6945.63123
[136,] 15758.99587 48324.93155
[137,] -380.56470 15758.99587
[138,] -19081.67669 -380.56470
[139,] -15068.23993 -19081.67669
[140,] 22824.96213 -15068.23993
[141,] 7495.83890 22824.96213
[142,] 767.43567 7495.83890
[143,] 16860.99029 767.43567
[144,] -12852.20379 16860.99029
[145,] 37011.17847 -12852.20379
[146,] 23878.42885 37011.17847
[147,] 19528.57125 23878.42885
[148,] -13179.25197 19528.57125
[149,] -12900.18742 -13179.25197
[150,] -13243.45667 -12900.18742
[151,] -13298.39580 -13243.45667
[152,] -13179.25197 -13298.39580
[153,] -13179.25197 -13179.25197
[154,] -11632.50619 -13179.25197
[155,] -32731.54255 -11632.50619
[156,] -13179.25197 -32731.54255
[157,] -13442.83214 -13179.25197
[158,] -14277.35564 -13442.83214
[159,] -17045.74757 -14277.35564
[160,] -15028.82452 -17045.74757
[161,] 3793.14953 -15028.82452
[162,] -13280.00776 3793.14953
[163,] 5445.23813 -13280.00776
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -11326.70730 9670.58386
2 7172.50220 -11326.70730
3 -3702.35589 7172.50220
4 -10854.06924 -3702.35589
5 15277.46530 -10854.06924
6 -8399.77867 15277.46530
7 5495.41379 -8399.77867
8 17522.73922 5495.41379
9 -5857.38371 17522.73922
10 -23661.01635 -5857.38371
11 5529.94528 -23661.01635
12 3989.98775 5529.94528
13 -812.38019 3989.98775
14 -10677.57830 -812.38019
15 -23400.08955 -10677.57830
16 23388.03313 -23400.08955
17 16931.09527 23388.03313
18 8905.69622 16931.09527
19 9010.89320 8905.69622
20 -7457.77986 9010.89320
21 -10753.90056 -7457.77986
22 -3109.30312 -10753.90056
23 11450.78904 -3109.30312
24 -20998.43064 11450.78904
25 -15813.11542 -20998.43064
26 -2106.94414 -15813.11542
27 -19313.34505 -2106.94414
28 -12264.95644 -19313.34505
29 -10565.26763 -12264.95644
30 -5605.50204 -10565.26763
31 254.70291 -5605.50204
32 -19580.31802 254.70291
33 14823.89457 -19580.31802
34 -27307.86954 14823.89457
35 -2829.84564 -27307.86954
36 -22234.56561 -2829.84564
37 -13205.86557 -22234.56561
38 -16854.85548 -13205.86557
39 -23004.51761 -16854.85548
40 -14579.19239 -23004.51761
41 -6614.60153 -14579.19239
42 -19271.12425 -6614.60153
43 -3375.74510 -19271.12425
44 9528.22661 -3375.74510
45 33092.85530 9528.22661
46 -9186.95700 33092.85530
47 -4066.57620 -9186.95700
48 36282.93301 -4066.57620
49 -9742.85256 36282.93301
50 -12152.60857 -9742.85256
51 -16636.39589 -12152.60857
52 -1874.82724 -16636.39589
53 6177.65891 -1874.82724
54 -6006.37798 6177.65891
55 -4210.69328 -6006.37798
56 23579.73658 -4210.69328
57 -13463.68203 23579.73658
58 -25993.75782 -13463.68203
59 4829.10426 -25993.75782
60 14122.29943 4829.10426
61 -4577.87523 14122.29943
62 -9227.88212 -4577.87523
63 5216.45188 -9227.88212
64 -6970.85154 5216.45188
65 -24143.26348 -6970.85154
66 28088.08417 -24143.26348
67 -15587.94342 28088.08417
68 5744.72606 -15587.94342
69 128.75790 5744.72606
70 3487.89350 128.75790
71 -4180.95188 3487.89350
72 1394.91076 -4180.95188
73 8960.26091 1394.91076
74 -819.50759 8960.26091
75 3135.59850 -819.50759
76 -5244.43434 3135.59850
77 150971.08338 -5244.43434
78 -7067.26982 150971.08338
79 222.57940 -7067.26982
80 -10111.33964 222.57940
81 2737.90300 -10111.33964
82 -9184.73711 2737.90300
83 -4962.64867 -9184.73711
84 10942.51923 -4962.64867
85 -12850.97846 10942.51923
86 6293.79908 -12850.97846
87 3321.89437 6293.79908
88 -25292.92745 3321.89437
89 16764.19305 -25292.92745
90 -17406.06441 16764.19305
91 -4127.34720 -17406.06441
92 -2611.05932 -4127.34720
93 -7237.88643 -2611.05932
94 2380.17496 -7237.88643
95 19564.81828 2380.17496
96 29854.11004 19564.81828
97 22402.43820 29854.11004
98 -2261.05035 22402.43820
99 -3504.53534 -2261.05035
100 16803.56236 -3504.53534
101 13683.53155 16803.56236
102 -13006.19110 13683.53155
103 9743.30939 -13006.19110
104 -20680.70154 9743.30939
105 -24942.31148 -20680.70154
106 -10300.88956 -24942.31148
107 -2763.80705 -10300.88956
108 111935.04137 -2763.80705
109 9660.20483 111935.04137
110 17516.39213 9660.20483
111 -10740.66593 17516.39213
112 -15158.94928 -10740.66593
113 -13528.56607 -15158.94928
114 -4502.25799 -13528.56607
115 -15511.56136 -4502.25799
116 -29747.90933 -15511.56136
117 -15433.63500 -29747.90933
118 27852.59559 -15433.63500
119 -13031.64768 27852.59559
120 -17572.65675 -13031.64768
121 39085.29995 -17572.65675
122 37174.06392 39085.29995
123 21721.01665 37174.06392
124 -13719.19152 21721.01665
125 15031.23163 -13719.19152
126 -13202.98701 15031.23163
127 -6080.44874 -13202.98701
128 -21069.06119 -6080.44874
129 7406.20634 -21069.06119
130 46518.20599 7406.20634
131 -4114.46736 46518.20599
132 -3026.55055 -4114.46736
133 -56.07319 -3026.55055
134 6945.63123 -56.07319
135 48324.93155 6945.63123
136 15758.99587 48324.93155
137 -380.56470 15758.99587
138 -19081.67669 -380.56470
139 -15068.23993 -19081.67669
140 22824.96213 -15068.23993
141 7495.83890 22824.96213
142 767.43567 7495.83890
143 16860.99029 767.43567
144 -12852.20379 16860.99029
145 37011.17847 -12852.20379
146 23878.42885 37011.17847
147 19528.57125 23878.42885
148 -13179.25197 19528.57125
149 -12900.18742 -13179.25197
150 -13243.45667 -12900.18742
151 -13298.39580 -13243.45667
152 -13179.25197 -13298.39580
153 -13179.25197 -13179.25197
154 -11632.50619 -13179.25197
155 -32731.54255 -11632.50619
156 -13179.25197 -32731.54255
157 -13442.83214 -13179.25197
158 -14277.35564 -13442.83214
159 -17045.74757 -14277.35564
160 -15028.82452 -17045.74757
161 3793.14953 -15028.82452
162 -13280.00776 3793.14953
163 5445.23813 -13280.00776
> 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/7vcbj1321876963.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/86ig01321876963.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/9rqq01321876963.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/10xdjq1321876963.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11vs6n1321876963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12fym41321876963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/135r6a1321876963.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/144x901321876963.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/15akjh1321876963.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/1630hv1321876963.tab")
+ }
>
> try(system("convert tmp/1t9aq1321876963.ps tmp/1t9aq1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/287mi1321876963.ps tmp/287mi1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sh9g1321876963.ps tmp/3sh9g1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/4smry1321876963.ps tmp/4smry1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o7421321876963.ps tmp/5o7421321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/64cqy1321876963.ps tmp/64cqy1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/7vcbj1321876963.ps tmp/7vcbj1321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/86ig01321876963.ps tmp/86ig01321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rqq01321876963.ps tmp/9rqq01321876963.png",intern=TRUE))
character(0)
> try(system("convert tmp/10xdjq1321876963.ps tmp/10xdjq1321876963.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.621 0.594 6.404