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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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(84
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+ ,1020)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('Feedback_messages'
+ ,'Blogged_Computations'
+ ,'Time_Rfc'
+ ,'Aantal_karakters'
+ ,'Logins'
+ ,'Pageviews
')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('Feedback_messages','Blogged_Computations','Time_Rfc','Aantal_karakters','Logins','Pageviews
'),1:164))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Feedback_messages Blogged_Computations Time_Rfc Aantal_karakters Logins
1 84 65 170588 95556 47
2 72 54 86621 54565 48
3 41 58 118522 63016 40
4 85 99 152510 79774 75
5 30 41 86206 31258 32
6 53 0 37257 52491 18
7 74 111 306055 91256 80
8 22 1 32750 22807 16
9 68 37 116502 77411 38
10 47 60 130539 48821 25
11 102 64 161876 52295 65
12 123 71 128274 63262 74
13 69 38 104367 50466 45
14 108 76 193024 62932 42
15 59 62 141574 38439 56
16 122 126 254150 70817 124
17 91 85 181110 105965 42
18 45 74 198432 73795 102
19 53 78 113853 82043 36
20 112 100 159940 74349 51
21 82 79 166822 82204 49
22 92 76 286675 55709 57
23 51 42 95297 37137 21
24 120 81 108278 70780 32
25 99 103 146342 55027 77
26 86 70 145142 56699 90
27 59 75 161740 65911 82
28 98 93 162716 56316 56
29 71 42 106888 26982 34
30 100 95 188150 54628 39
31 113 87 189401 96750 53
32 92 44 129484 53009 48
33 107 88 204030 64664 64
34 75 29 68538 36990 27
35 100 89 243625 85224 56
36 69 71 167255 37048 37
37 106 70 264528 59635 83
38 51 50 122024 42051 50
39 18 30 80964 26998 26
40 91 87 209795 63717 109
41 75 78 224205 55071 56
42 63 48 115971 40001 42
43 72 57 138191 54506 49
44 59 31 81106 35838 31
45 29 30 93125 50838 49
46 85 70 307743 86997 97
47 66 20 78800 33032 42
48 106 84 158835 61704 55
49 113 81 223590 117986 71
50 101 79 131108 56733 39
51 65 72 128734 55064 54
52 7 8 24188 5950 24
53 111 67 257677 84607 213
54 61 21 65029 32551 17
55 41 30 98066 31701 58
56 70 70 173587 71170 27
57 136 87 180042 101773 59
58 87 87 197266 101653 114
59 90 116 212060 81493 76
60 76 54 141582 55901 51
61 101 96 245107 109104 87
62 57 94 206879 114425 78
63 61 51 145696 36311 62
64 92 51 173535 70027 61
65 80 38 142064 73713 39
66 35 65 117926 40671 37
67 72 64 113461 89041 87
68 88 66 145285 57231 102
69 80 98 150999 68608 50
70 62 100 91838 59155 37
71 81 56 118807 55827 33
72 63 22 69471 22618 28
73 91 51 126630 58425 44
74 65 61 145908 65724 38
75 79 94 98393 56979 34
76 85 98 190926 72369 45
77 75 76 198797 79194 58
78 70 57 106193 202316 59
79 78 75 89318 44970 36
80 75 48 120362 49319 43
81 55 48 98791 36252 30
82 80 109 283982 75741 68
83 83 27 132798 38417 53
84 38 85 135251 64102 59
85 27 49 80953 56622 25
86 62 24 109237 15430 39
87 82 46 96634 72571 36
88 88 44 226191 67271 115
89 59 49 172071 43460 55
90 92 108 117815 99501 71
91 40 42 133561 28340 52
92 91 110 152193 76013 49
93 63 28 112004 37361 43
94 88 79 169613 48204 52
95 85 49 187483 76168 51
96 76 64 130533 85168 27
97 67 75 142339 125410 29
98 69 122 199232 123328 56
99 150 95 201744 83038 94
100 77 106 247024 120087 74
101 103 73 158054 91939 66
102 81 108 182581 103646 42
103 37 30 106351 29467 112
104 64 13 43287 43750 14
105 22 69 127493 34497 45
106 35 75 127930 66477 92
107 61 82 149006 71181 29
108 80 108 187714 74482 66
109 54 28 74112 174949 32
110 76 83 94006 46765 66
111 87 51 176625 90257 43
112 75 90 141933 51370 56
113 0 12 22938 1168 10
114 61 87 125927 51360 53
115 30 23 61857 25162 25
116 66 57 91290 21067 34
117 56 93 255100 58233 66
118 0 4 21054 855 16
119 40 56 174150 85903 38
120 9 18 31414 14116 19
121 82 86 189461 57637 77
122 110 40 137544 94137 35
123 71 16 77166 62147 46
124 50 18 74567 62832 30
125 21 16 38214 8773 34
126 78 42 90961 63785 25
127 118 78 194652 65196 50
128 102 31 135261 73087 38
129 109 104 244272 72631 51
130 104 121 201748 86281 66
131 124 111 256402 162365 73
132 76 57 139144 56530 23
133 57 28 76470 35606 29
134 91 56 193518 70111 196
135 101 82 280334 92046 115
136 66 2 50999 63989 16
137 98 91 254825 104911 88
138 63 41 103239 43448 51
139 85 84 168059 60029 33
140 74 55 129762 38650 53
141 19 3 78256 47261 74
142 57 68 249232 73586 82
143 74 93 152366 83042 54
144 78 41 173260 37238 63
145 91 94 197197 63958 70
146 112 105 68388 78956 41
147 79 70 139409 99518 49
148 100 114 185366 111436 68
149 0 0 0 0 0
150 0 4 14688 6023 10
151 0 0 98 0 1
152 0 0 455 0 2
153 0 0 0 0 0
154 0 0 0 0 0
155 48 42 137885 42564 58
156 55 97 185288 38885 72
157 0 0 0 0 0
158 0 0 203 0 4
159 0 7 7199 1644 5
160 13 12 46660 6179 20
161 4 0 17547 3926 5
162 31 37 73567 23238 27
163 0 0 969 0 2
164 29 39 105477 49288 33
Pageviews\r
1 1168
2 669
3 1098
4 1939
5 679
6 321
7 2667
8 345
9 1367
10 1159
11 1385
12 1155
13 1154
14 1703
15 1190
16 3103
17 1357
18 1892
19 883
20 1627
21 1412
22 1901
23 825
24 904
25 2115
26 1858
27 1781
28 1304
29 1035
30 1557
31 1527
32 1220
33 1368
34 564
35 1990
36 1557
37 2057
38 1111
39 686
40 2012
41 2232
42 1033
43 1166
44 1020
45 1735
46 3644
47 918
48 1579
49 2805
50 1496
51 1108
52 496
53 1753
54 744
55 1101
56 1612
57 1806
58 2460
59 1653
60 1234
61 2368
62 2204
63 1633
64 1664
65 958
66 1118
67 1258
68 1964
69 1483
70 1034
71 1348
72 837
73 1310
74 1144
75 987
76 1334
77 1452
78 957
79 911
80 1114
81 1209
82 2541
83 1176
84 1253
85 870
86 1473
87 811
88 2435
89 1410
90 1982
91 1214
92 1356
93 1197
94 1971
95 1432
96 1030
97 1145
98 1509
99 2230
100 2236
101 1324
102 1599
103 999
104 602
105 1379
106 1172
107 1337
108 1709
109 668
110 1128
111 1209
112 1324
113 391
114 1264
115 530
116 983
117 1926
118 387
119 1481
120 449
121 2135
122 1128
123 800
124 964
125 568
126 901
127 1568
128 859
129 2229
130 1566
131 2153
132 828
133 809
134 1848
135 2914
136 589
137 2613
138 1298
139 1109
140 1437
141 682
142 2799
143 1281
144 2035
145 1752
146 1133
147 1667
148 1558
149 0
150 207
151 5
152 8
153 0
154 0
155 1300
156 1718
157 0
158 4
159 151
160 474
161 141
162 705
163 29
164 1020
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Blogged_Computations Time_Rfc
1.483e+01 2.819e-01 1.121e-04
Aantal_karakters Logins `Pageviews\r`
2.449e-04 6.802e-02 2.331e-03
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-45.978 -14.926 -0.421 14.314 53.853
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.483e+01 3.908e+00 3.795 0.000210 ***
Blogged_Computations 2.819e-01 8.382e-02 3.363 0.000968 ***
Time_Rfc 1.121e-04 6.408e-05 1.749 0.082192 .
Aantal_karakters 2.449e-04 6.660e-05 3.677 0.000323 ***
Logins 6.802e-02 7.847e-02 0.867 0.387366
`Pageviews\r` 2.331e-03 6.367e-03 0.366 0.714808
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 21.08 on 158 degrees of freedom
Multiple R-squared: 0.5952, Adjusted R-squared: 0.5824
F-statistic: 46.46 on 5 and 158 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,] 0.01718346 3.436693e-02 9.828165e-01
[2,] 0.11828139 2.365628e-01 8.817186e-01
[3,] 0.37081752 7.416350e-01 6.291825e-01
[4,] 0.35699603 7.139921e-01 6.430040e-01
[5,] 0.25514335 5.102867e-01 7.448566e-01
[6,] 0.82416231 3.516754e-01 1.758377e-01
[7,] 0.77903907 4.419219e-01 2.209609e-01
[8,] 0.70528923 5.894215e-01 2.947108e-01
[9,] 0.62707381 7.458524e-01 3.729262e-01
[10,] 0.90903989 1.819202e-01 9.096011e-02
[11,] 0.92086446 1.582711e-01 7.913554e-02
[12,] 0.92145355 1.570929e-01 7.854645e-02
[13,] 0.89027665 2.194467e-01 1.097234e-01
[14,] 0.87661226 2.467755e-01 1.233877e-01
[15,] 0.83699371 3.260126e-01 1.630063e-01
[16,] 0.90685999 1.862800e-01 9.314001e-02
[17,] 0.87921367 2.415727e-01 1.207863e-01
[18,] 0.84783168 3.043366e-01 1.521683e-01
[19,] 0.84693309 3.061338e-01 1.530669e-01
[20,] 0.81337112 3.732578e-01 1.866289e-01
[21,] 0.79009533 4.198093e-01 2.099047e-01
[22,] 0.75218262 4.956348e-01 2.478174e-01
[23,] 0.75076174 4.984765e-01 2.492383e-01
[24,] 0.79504342 4.099132e-01 2.049566e-01
[25,] 0.77440138 4.511972e-01 2.255986e-01
[26,] 0.78141863 4.371627e-01 2.185814e-01
[27,] 0.74175842 5.164832e-01 2.582416e-01
[28,] 0.69952604 6.009479e-01 3.004740e-01
[29,] 0.70173694 5.965261e-01 2.982631e-01
[30,] 0.67896675 6.420665e-01 3.210332e-01
[31,] 0.73616790 5.276642e-01 2.638321e-01
[32,] 0.69047699 6.190460e-01 3.095230e-01
[33,] 0.64447483 7.110503e-01 3.555252e-01
[34,] 0.59497981 8.100404e-01 4.050202e-01
[35,] 0.54415677 9.116865e-01 4.558432e-01
[36,] 0.50557046 9.888591e-01 4.944295e-01
[37,] 0.49229252 9.845850e-01 5.077075e-01
[38,] 0.48234605 9.646921e-01 5.176540e-01
[39,] 0.48689422 9.737884e-01 5.131058e-01
[40,] 0.49585908 9.917182e-01 5.041409e-01
[41,] 0.48740576 9.748115e-01 5.125942e-01
[42,] 0.49960726 9.992145e-01 5.003927e-01
[43,] 0.47150358 9.430072e-01 5.284964e-01
[44,] 0.47256541 9.451308e-01 5.274346e-01
[45,] 0.43931179 8.786236e-01 5.606882e-01
[46,] 0.43520019 8.704004e-01 5.647998e-01
[47,] 0.39572307 7.914461e-01 6.042769e-01
[48,] 0.35863171 7.172634e-01 6.413683e-01
[49,] 0.47645581 9.529116e-01 5.235442e-01
[50,] 0.45425839 9.085168e-01 5.457416e-01
[51,] 0.45248397 9.049679e-01 5.475160e-01
[52,] 0.41341126 8.268225e-01 5.865887e-01
[53,] 0.37342136 7.468427e-01 6.265786e-01
[54,] 0.57700740 8.459852e-01 4.229926e-01
[55,] 0.53042726 9.391455e-01 4.695727e-01
[56,] 0.52207801 9.558440e-01 4.779220e-01
[57,] 0.50026345 9.994731e-01 4.997366e-01
[58,] 0.55624168 8.875166e-01 4.437583e-01
[59,] 0.51753900 9.649220e-01 4.824610e-01
[60,] 0.48690387 9.738077e-01 5.130961e-01
[61,] 0.44929585 8.985917e-01 5.507041e-01
[62,] 0.43618194 8.723639e-01 5.638181e-01
[63,] 0.41697167 8.339433e-01 5.830283e-01
[64,] 0.42367051 8.473410e-01 5.763295e-01
[65,] 0.44694736 8.938947e-01 5.530526e-01
[66,] 0.40956253 8.191251e-01 5.904375e-01
[67,] 0.37548683 7.509737e-01 6.245132e-01
[68,] 0.33961982 6.792396e-01 6.603802e-01
[69,] 0.31036618 6.207324e-01 6.896338e-01
[70,] 0.34181459 6.836292e-01 6.581854e-01
[71,] 0.32755304 6.551061e-01 6.724470e-01
[72,] 0.30837110 6.167422e-01 6.916289e-01
[73,] 0.27124334 5.424867e-01 7.287567e-01
[74,] 0.28563738 5.712748e-01 7.143626e-01
[75,] 0.32394562 6.478912e-01 6.760544e-01
[76,] 0.43581311 8.716262e-01 5.641869e-01
[77,] 0.48581394 9.716279e-01 5.141861e-01
[78,] 0.46671798 9.334360e-01 5.332820e-01
[79,] 0.46886498 9.377300e-01 5.311350e-01
[80,] 0.42607265 8.521453e-01 5.739274e-01
[81,] 0.38930996 7.786199e-01 6.106900e-01
[82,] 0.34832892 6.966578e-01 6.516711e-01
[83,] 0.33380851 6.676170e-01 6.661915e-01
[84,] 0.29644312 5.928862e-01 7.035569e-01
[85,] 0.27131552 5.426310e-01 7.286845e-01
[86,] 0.24668673 4.933735e-01 7.533133e-01
[87,] 0.22332304 4.466461e-01 7.766770e-01
[88,] 0.19230134 3.846027e-01 8.076987e-01
[89,] 0.18733833 3.746767e-01 8.126617e-01
[90,] 0.26724144 5.344829e-01 7.327586e-01
[91,] 0.54816993 9.036601e-01 4.518301e-01
[92,] 0.62381312 7.523738e-01 3.761869e-01
[93,] 0.62303327 7.539335e-01 3.769667e-01
[94,] 0.60892845 7.821431e-01 3.910716e-01
[95,] 0.59544671 8.091066e-01 4.045533e-01
[96,] 0.63478445 7.304311e-01 3.652155e-01
[97,] 0.75212875 4.957425e-01 2.478712e-01
[98,] 0.84954729 3.009054e-01 1.504527e-01
[99,] 0.83950272 3.209946e-01 1.604973e-01
[100,] 0.82313531 3.537294e-01 1.768647e-01
[101,] 0.92265680 1.546864e-01 7.734320e-02
[102,] 0.90633288 1.873342e-01 9.366712e-02
[103,] 0.88610681 2.277864e-01 1.138932e-01
[104,] 0.86025232 2.794954e-01 1.397477e-01
[105,] 0.86188364 2.762327e-01 1.381164e-01
[106,] 0.84102732 3.179454e-01 1.589727e-01
[107,] 0.81272102 3.745580e-01 1.872790e-01
[108,] 0.82256231 3.548754e-01 1.774377e-01
[109,] 0.86027571 2.794486e-01 1.397243e-01
[110,] 0.85081654 2.983669e-01 1.491835e-01
[111,] 0.94266264 1.146747e-01 5.733736e-02
[112,] 0.93736974 1.252605e-01 6.263026e-02
[113,] 0.91983553 1.603289e-01 8.016447e-02
[114,] 0.94383957 1.123209e-01 5.616043e-02
[115,] 0.94534002 1.093200e-01 5.465998e-02
[116,] 0.92753353 1.449329e-01 7.246647e-02
[117,] 0.90855671 1.828866e-01 9.144329e-02
[118,] 0.90821380 1.835724e-01 9.178620e-02
[119,] 0.95908059 8.183882e-02 4.091941e-02
[120,] 0.99316773 1.366453e-02 6.832266e-03
[121,] 0.99304567 1.390867e-02 6.954335e-03
[122,] 0.98938433 2.123135e-02 1.061567e-02
[123,] 0.98615183 2.769634e-02 1.384817e-02
[124,] 0.99050258 1.899484e-02 9.497419e-03
[125,] 0.99187808 1.624384e-02 8.121918e-03
[126,] 0.99047460 1.905079e-02 9.525396e-03
[127,] 0.98591110 2.817779e-02 1.408890e-02
[128,] 0.99738506 5.229880e-03 2.614940e-03
[129,] 0.99540441 9.191172e-03 4.595586e-03
[130,] 0.99453389 1.093223e-02 5.466115e-03
[131,] 0.99848989 3.020224e-03 1.510112e-03
[132,] 0.99887581 2.248387e-03 1.124193e-03
[133,] 0.99900931 1.981373e-03 9.906865e-04
[134,] 0.99986119 2.776135e-04 1.388068e-04
[135,] 0.99966278 6.744489e-04 3.372244e-04
[136,] 0.99994744 1.051157e-04 5.255786e-05
[137,] 0.99999971 5.860842e-07 2.930421e-07
[138,] 0.99999976 4.847101e-07 2.423550e-07
[139,] 0.99999993 1.301396e-07 6.506979e-08
[140,] 0.99999974 5.293935e-07 2.646968e-07
[141,] 0.99999836 3.282164e-06 1.641082e-06
[142,] 0.99999550 9.007359e-06 4.503680e-06
[143,] 0.99997167 5.665753e-05 2.832877e-05
[144,] 0.99983702 3.259635e-04 1.629818e-04
[145,] 0.99908196 1.836075e-03 9.180375e-04
[146,] 0.99521434 9.571314e-03 4.785657e-03
[147,] 0.98078649 3.842703e-02 1.921351e-02
> postscript(file="/var/wessaorg/rcomp/tmp/19f6d1321960619.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/29jun1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3e5sb1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42ojd1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/55z601321960619.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
2.40801117 14.05435950 -24.17447742 -3.98531089 -17.46237114 19.16776677
7 8 9 10 11 12
-40.42853006 -4.25945565 4.95476629 -15.73097464 30.53052036 50.56249711
13 14 15 16 17 18
13.65213017 27.87492205 -5.17006817 10.15835636 -0.05809638 -42.34946190
19 20 21 22 23 24
-21.17450384 25.58813765 -0.55079954 1.66383740 1.20487227 48.58619816
25 26 27 28 29 30
15.09271043 10.83379377 -20.96839611 18.07881684 21.01867497 17.64396643
31 32 33 34 35 36
21.56099565 31.16477314 21.11919349 32.10488156 3.45820789 0.19199118
37 38 39 40 41 42
16.74408817 -7.88857273 -24.33931871 0.42481162 -9.44433854 6.58164788
43 44 45 46 47 48
6.21586131 13.07948060 -24.55022297 -20.45226905 23.61509694 27.15813160
49 50 51 52 53 54
10.01593207 29.17466462 -4.29340148 -17.04065457 9.10846778 22.10076918
55 56 57 58 59 60
-7.55203826 -7.04010719 43.32162227 -12.84516660 -10.27467268 10.04514424
61 62 63 64 65 66
-6.51851137 -45.97813079 -1.45128903 18.16705661 15.59835853 -26.45115351
67 68 69 70 71 72
-4.24133152 12.75161262 -3.03630311 -10.72241263 18.01120819 24.78849151
73 74 75 76 77 78
27.24768674 -4.72431845 8.08039905 -2.74570060 -10.25786424 -28.58740434
79 80 81 82 83 84
16.43493491 15.55072136 1.83157802 -26.48109522 29.92071779 -38.57944752
85 86 87 88 89 90
-28.30881362 18.29670595 21.26250855 5.44221610 -6.59898275 -0.29184622
91 92 93 94 95 96
-14.94544279 2.99821739 12.85967671 11.95557660 9.88429260 3.40591241
97 98 99 100 101 102
-20.27687315 -40.07676035 53.85310225 -35.04967026 19.78801633 -16.70183572
103 104 105 106 107 108
-15.36892601 27.58537451 -41.29157412 -40.57763569 -16.16461811 -13.02410384
109 110 111 112 113 114
-23.60535574 8.66865224 10.15142478 -0.58126617 -22.65989307 -11.59509572
115 116 117 118 119 120
-7.34321381 15.10908684 -36.87672935 -20.51599254 -37.20806289 -20.21935956
121 122 123 124 125 126
-2.63510554 40.41552881 22.79868979 2.06460276 -8.40711943 21.71588871
127 128 129 130 131 132
36.34480612 40.78602704 11.02476168 3.18244624 -0.60217779 12.16968206
133 134 135 136 137 138
13.12939948 3.88580469 -5.52126630 26.75921876 -8.81086655 7.90789365
139 140 141 142 143 144
8.12623477 12.70368472 -23.64341679 -35.05548691 -11.11624225 14.04533973
145 146 147 148 149 150
3.06376003 35.14512903 -2.77553921 -3.28576162 -14.82873043 -20.24029813
151 152 153 154 155 156
-14.91938866 -15.03441866 -14.82873043 -14.82873043 -11.52203360 -26.36348914
157 158 159 160 161 162
-14.82873043 -15.13288280 -18.70336588 -14.41988136 -14.42590422 -11.67463200
163 164
-15.14098555 -25.33711911
> postscript(file="/var/wessaorg/rcomp/tmp/6hk0d1321960619.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 2.40801117 NA
1 14.05435950 2.40801117
2 -24.17447742 14.05435950
3 -3.98531089 -24.17447742
4 -17.46237114 -3.98531089
5 19.16776677 -17.46237114
6 -40.42853006 19.16776677
7 -4.25945565 -40.42853006
8 4.95476629 -4.25945565
9 -15.73097464 4.95476629
10 30.53052036 -15.73097464
11 50.56249711 30.53052036
12 13.65213017 50.56249711
13 27.87492205 13.65213017
14 -5.17006817 27.87492205
15 10.15835636 -5.17006817
16 -0.05809638 10.15835636
17 -42.34946190 -0.05809638
18 -21.17450384 -42.34946190
19 25.58813765 -21.17450384
20 -0.55079954 25.58813765
21 1.66383740 -0.55079954
22 1.20487227 1.66383740
23 48.58619816 1.20487227
24 15.09271043 48.58619816
25 10.83379377 15.09271043
26 -20.96839611 10.83379377
27 18.07881684 -20.96839611
28 21.01867497 18.07881684
29 17.64396643 21.01867497
30 21.56099565 17.64396643
31 31.16477314 21.56099565
32 21.11919349 31.16477314
33 32.10488156 21.11919349
34 3.45820789 32.10488156
35 0.19199118 3.45820789
36 16.74408817 0.19199118
37 -7.88857273 16.74408817
38 -24.33931871 -7.88857273
39 0.42481162 -24.33931871
40 -9.44433854 0.42481162
41 6.58164788 -9.44433854
42 6.21586131 6.58164788
43 13.07948060 6.21586131
44 -24.55022297 13.07948060
45 -20.45226905 -24.55022297
46 23.61509694 -20.45226905
47 27.15813160 23.61509694
48 10.01593207 27.15813160
49 29.17466462 10.01593207
50 -4.29340148 29.17466462
51 -17.04065457 -4.29340148
52 9.10846778 -17.04065457
53 22.10076918 9.10846778
54 -7.55203826 22.10076918
55 -7.04010719 -7.55203826
56 43.32162227 -7.04010719
57 -12.84516660 43.32162227
58 -10.27467268 -12.84516660
59 10.04514424 -10.27467268
60 -6.51851137 10.04514424
61 -45.97813079 -6.51851137
62 -1.45128903 -45.97813079
63 18.16705661 -1.45128903
64 15.59835853 18.16705661
65 -26.45115351 15.59835853
66 -4.24133152 -26.45115351
67 12.75161262 -4.24133152
68 -3.03630311 12.75161262
69 -10.72241263 -3.03630311
70 18.01120819 -10.72241263
71 24.78849151 18.01120819
72 27.24768674 24.78849151
73 -4.72431845 27.24768674
74 8.08039905 -4.72431845
75 -2.74570060 8.08039905
76 -10.25786424 -2.74570060
77 -28.58740434 -10.25786424
78 16.43493491 -28.58740434
79 15.55072136 16.43493491
80 1.83157802 15.55072136
81 -26.48109522 1.83157802
82 29.92071779 -26.48109522
83 -38.57944752 29.92071779
84 -28.30881362 -38.57944752
85 18.29670595 -28.30881362
86 21.26250855 18.29670595
87 5.44221610 21.26250855
88 -6.59898275 5.44221610
89 -0.29184622 -6.59898275
90 -14.94544279 -0.29184622
91 2.99821739 -14.94544279
92 12.85967671 2.99821739
93 11.95557660 12.85967671
94 9.88429260 11.95557660
95 3.40591241 9.88429260
96 -20.27687315 3.40591241
97 -40.07676035 -20.27687315
98 53.85310225 -40.07676035
99 -35.04967026 53.85310225
100 19.78801633 -35.04967026
101 -16.70183572 19.78801633
102 -15.36892601 -16.70183572
103 27.58537451 -15.36892601
104 -41.29157412 27.58537451
105 -40.57763569 -41.29157412
106 -16.16461811 -40.57763569
107 -13.02410384 -16.16461811
108 -23.60535574 -13.02410384
109 8.66865224 -23.60535574
110 10.15142478 8.66865224
111 -0.58126617 10.15142478
112 -22.65989307 -0.58126617
113 -11.59509572 -22.65989307
114 -7.34321381 -11.59509572
115 15.10908684 -7.34321381
116 -36.87672935 15.10908684
117 -20.51599254 -36.87672935
118 -37.20806289 -20.51599254
119 -20.21935956 -37.20806289
120 -2.63510554 -20.21935956
121 40.41552881 -2.63510554
122 22.79868979 40.41552881
123 2.06460276 22.79868979
124 -8.40711943 2.06460276
125 21.71588871 -8.40711943
126 36.34480612 21.71588871
127 40.78602704 36.34480612
128 11.02476168 40.78602704
129 3.18244624 11.02476168
130 -0.60217779 3.18244624
131 12.16968206 -0.60217779
132 13.12939948 12.16968206
133 3.88580469 13.12939948
134 -5.52126630 3.88580469
135 26.75921876 -5.52126630
136 -8.81086655 26.75921876
137 7.90789365 -8.81086655
138 8.12623477 7.90789365
139 12.70368472 8.12623477
140 -23.64341679 12.70368472
141 -35.05548691 -23.64341679
142 -11.11624225 -35.05548691
143 14.04533973 -11.11624225
144 3.06376003 14.04533973
145 35.14512903 3.06376003
146 -2.77553921 35.14512903
147 -3.28576162 -2.77553921
148 -14.82873043 -3.28576162
149 -20.24029813 -14.82873043
150 -14.91938866 -20.24029813
151 -15.03441866 -14.91938866
152 -14.82873043 -15.03441866
153 -14.82873043 -14.82873043
154 -11.52203360 -14.82873043
155 -26.36348914 -11.52203360
156 -14.82873043 -26.36348914
157 -15.13288280 -14.82873043
158 -18.70336588 -15.13288280
159 -14.41988136 -18.70336588
160 -14.42590422 -14.41988136
161 -11.67463200 -14.42590422
162 -15.14098555 -11.67463200
163 -25.33711911 -15.14098555
164 NA -25.33711911
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 14.05435950 2.40801117
[2,] -24.17447742 14.05435950
[3,] -3.98531089 -24.17447742
[4,] -17.46237114 -3.98531089
[5,] 19.16776677 -17.46237114
[6,] -40.42853006 19.16776677
[7,] -4.25945565 -40.42853006
[8,] 4.95476629 -4.25945565
[9,] -15.73097464 4.95476629
[10,] 30.53052036 -15.73097464
[11,] 50.56249711 30.53052036
[12,] 13.65213017 50.56249711
[13,] 27.87492205 13.65213017
[14,] -5.17006817 27.87492205
[15,] 10.15835636 -5.17006817
[16,] -0.05809638 10.15835636
[17,] -42.34946190 -0.05809638
[18,] -21.17450384 -42.34946190
[19,] 25.58813765 -21.17450384
[20,] -0.55079954 25.58813765
[21,] 1.66383740 -0.55079954
[22,] 1.20487227 1.66383740
[23,] 48.58619816 1.20487227
[24,] 15.09271043 48.58619816
[25,] 10.83379377 15.09271043
[26,] -20.96839611 10.83379377
[27,] 18.07881684 -20.96839611
[28,] 21.01867497 18.07881684
[29,] 17.64396643 21.01867497
[30,] 21.56099565 17.64396643
[31,] 31.16477314 21.56099565
[32,] 21.11919349 31.16477314
[33,] 32.10488156 21.11919349
[34,] 3.45820789 32.10488156
[35,] 0.19199118 3.45820789
[36,] 16.74408817 0.19199118
[37,] -7.88857273 16.74408817
[38,] -24.33931871 -7.88857273
[39,] 0.42481162 -24.33931871
[40,] -9.44433854 0.42481162
[41,] 6.58164788 -9.44433854
[42,] 6.21586131 6.58164788
[43,] 13.07948060 6.21586131
[44,] -24.55022297 13.07948060
[45,] -20.45226905 -24.55022297
[46,] 23.61509694 -20.45226905
[47,] 27.15813160 23.61509694
[48,] 10.01593207 27.15813160
[49,] 29.17466462 10.01593207
[50,] -4.29340148 29.17466462
[51,] -17.04065457 -4.29340148
[52,] 9.10846778 -17.04065457
[53,] 22.10076918 9.10846778
[54,] -7.55203826 22.10076918
[55,] -7.04010719 -7.55203826
[56,] 43.32162227 -7.04010719
[57,] -12.84516660 43.32162227
[58,] -10.27467268 -12.84516660
[59,] 10.04514424 -10.27467268
[60,] -6.51851137 10.04514424
[61,] -45.97813079 -6.51851137
[62,] -1.45128903 -45.97813079
[63,] 18.16705661 -1.45128903
[64,] 15.59835853 18.16705661
[65,] -26.45115351 15.59835853
[66,] -4.24133152 -26.45115351
[67,] 12.75161262 -4.24133152
[68,] -3.03630311 12.75161262
[69,] -10.72241263 -3.03630311
[70,] 18.01120819 -10.72241263
[71,] 24.78849151 18.01120819
[72,] 27.24768674 24.78849151
[73,] -4.72431845 27.24768674
[74,] 8.08039905 -4.72431845
[75,] -2.74570060 8.08039905
[76,] -10.25786424 -2.74570060
[77,] -28.58740434 -10.25786424
[78,] 16.43493491 -28.58740434
[79,] 15.55072136 16.43493491
[80,] 1.83157802 15.55072136
[81,] -26.48109522 1.83157802
[82,] 29.92071779 -26.48109522
[83,] -38.57944752 29.92071779
[84,] -28.30881362 -38.57944752
[85,] 18.29670595 -28.30881362
[86,] 21.26250855 18.29670595
[87,] 5.44221610 21.26250855
[88,] -6.59898275 5.44221610
[89,] -0.29184622 -6.59898275
[90,] -14.94544279 -0.29184622
[91,] 2.99821739 -14.94544279
[92,] 12.85967671 2.99821739
[93,] 11.95557660 12.85967671
[94,] 9.88429260 11.95557660
[95,] 3.40591241 9.88429260
[96,] -20.27687315 3.40591241
[97,] -40.07676035 -20.27687315
[98,] 53.85310225 -40.07676035
[99,] -35.04967026 53.85310225
[100,] 19.78801633 -35.04967026
[101,] -16.70183572 19.78801633
[102,] -15.36892601 -16.70183572
[103,] 27.58537451 -15.36892601
[104,] -41.29157412 27.58537451
[105,] -40.57763569 -41.29157412
[106,] -16.16461811 -40.57763569
[107,] -13.02410384 -16.16461811
[108,] -23.60535574 -13.02410384
[109,] 8.66865224 -23.60535574
[110,] 10.15142478 8.66865224
[111,] -0.58126617 10.15142478
[112,] -22.65989307 -0.58126617
[113,] -11.59509572 -22.65989307
[114,] -7.34321381 -11.59509572
[115,] 15.10908684 -7.34321381
[116,] -36.87672935 15.10908684
[117,] -20.51599254 -36.87672935
[118,] -37.20806289 -20.51599254
[119,] -20.21935956 -37.20806289
[120,] -2.63510554 -20.21935956
[121,] 40.41552881 -2.63510554
[122,] 22.79868979 40.41552881
[123,] 2.06460276 22.79868979
[124,] -8.40711943 2.06460276
[125,] 21.71588871 -8.40711943
[126,] 36.34480612 21.71588871
[127,] 40.78602704 36.34480612
[128,] 11.02476168 40.78602704
[129,] 3.18244624 11.02476168
[130,] -0.60217779 3.18244624
[131,] 12.16968206 -0.60217779
[132,] 13.12939948 12.16968206
[133,] 3.88580469 13.12939948
[134,] -5.52126630 3.88580469
[135,] 26.75921876 -5.52126630
[136,] -8.81086655 26.75921876
[137,] 7.90789365 -8.81086655
[138,] 8.12623477 7.90789365
[139,] 12.70368472 8.12623477
[140,] -23.64341679 12.70368472
[141,] -35.05548691 -23.64341679
[142,] -11.11624225 -35.05548691
[143,] 14.04533973 -11.11624225
[144,] 3.06376003 14.04533973
[145,] 35.14512903 3.06376003
[146,] -2.77553921 35.14512903
[147,] -3.28576162 -2.77553921
[148,] -14.82873043 -3.28576162
[149,] -20.24029813 -14.82873043
[150,] -14.91938866 -20.24029813
[151,] -15.03441866 -14.91938866
[152,] -14.82873043 -15.03441866
[153,] -14.82873043 -14.82873043
[154,] -11.52203360 -14.82873043
[155,] -26.36348914 -11.52203360
[156,] -14.82873043 -26.36348914
[157,] -15.13288280 -14.82873043
[158,] -18.70336588 -15.13288280
[159,] -14.41988136 -18.70336588
[160,] -14.42590422 -14.41988136
[161,] -11.67463200 -14.42590422
[162,] -15.14098555 -11.67463200
[163,] -25.33711911 -15.14098555
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 14.05435950 2.40801117
2 -24.17447742 14.05435950
3 -3.98531089 -24.17447742
4 -17.46237114 -3.98531089
5 19.16776677 -17.46237114
6 -40.42853006 19.16776677
7 -4.25945565 -40.42853006
8 4.95476629 -4.25945565
9 -15.73097464 4.95476629
10 30.53052036 -15.73097464
11 50.56249711 30.53052036
12 13.65213017 50.56249711
13 27.87492205 13.65213017
14 -5.17006817 27.87492205
15 10.15835636 -5.17006817
16 -0.05809638 10.15835636
17 -42.34946190 -0.05809638
18 -21.17450384 -42.34946190
19 25.58813765 -21.17450384
20 -0.55079954 25.58813765
21 1.66383740 -0.55079954
22 1.20487227 1.66383740
23 48.58619816 1.20487227
24 15.09271043 48.58619816
25 10.83379377 15.09271043
26 -20.96839611 10.83379377
27 18.07881684 -20.96839611
28 21.01867497 18.07881684
29 17.64396643 21.01867497
30 21.56099565 17.64396643
31 31.16477314 21.56099565
32 21.11919349 31.16477314
33 32.10488156 21.11919349
34 3.45820789 32.10488156
35 0.19199118 3.45820789
36 16.74408817 0.19199118
37 -7.88857273 16.74408817
38 -24.33931871 -7.88857273
39 0.42481162 -24.33931871
40 -9.44433854 0.42481162
41 6.58164788 -9.44433854
42 6.21586131 6.58164788
43 13.07948060 6.21586131
44 -24.55022297 13.07948060
45 -20.45226905 -24.55022297
46 23.61509694 -20.45226905
47 27.15813160 23.61509694
48 10.01593207 27.15813160
49 29.17466462 10.01593207
50 -4.29340148 29.17466462
51 -17.04065457 -4.29340148
52 9.10846778 -17.04065457
53 22.10076918 9.10846778
54 -7.55203826 22.10076918
55 -7.04010719 -7.55203826
56 43.32162227 -7.04010719
57 -12.84516660 43.32162227
58 -10.27467268 -12.84516660
59 10.04514424 -10.27467268
60 -6.51851137 10.04514424
61 -45.97813079 -6.51851137
62 -1.45128903 -45.97813079
63 18.16705661 -1.45128903
64 15.59835853 18.16705661
65 -26.45115351 15.59835853
66 -4.24133152 -26.45115351
67 12.75161262 -4.24133152
68 -3.03630311 12.75161262
69 -10.72241263 -3.03630311
70 18.01120819 -10.72241263
71 24.78849151 18.01120819
72 27.24768674 24.78849151
73 -4.72431845 27.24768674
74 8.08039905 -4.72431845
75 -2.74570060 8.08039905
76 -10.25786424 -2.74570060
77 -28.58740434 -10.25786424
78 16.43493491 -28.58740434
79 15.55072136 16.43493491
80 1.83157802 15.55072136
81 -26.48109522 1.83157802
82 29.92071779 -26.48109522
83 -38.57944752 29.92071779
84 -28.30881362 -38.57944752
85 18.29670595 -28.30881362
86 21.26250855 18.29670595
87 5.44221610 21.26250855
88 -6.59898275 5.44221610
89 -0.29184622 -6.59898275
90 -14.94544279 -0.29184622
91 2.99821739 -14.94544279
92 12.85967671 2.99821739
93 11.95557660 12.85967671
94 9.88429260 11.95557660
95 3.40591241 9.88429260
96 -20.27687315 3.40591241
97 -40.07676035 -20.27687315
98 53.85310225 -40.07676035
99 -35.04967026 53.85310225
100 19.78801633 -35.04967026
101 -16.70183572 19.78801633
102 -15.36892601 -16.70183572
103 27.58537451 -15.36892601
104 -41.29157412 27.58537451
105 -40.57763569 -41.29157412
106 -16.16461811 -40.57763569
107 -13.02410384 -16.16461811
108 -23.60535574 -13.02410384
109 8.66865224 -23.60535574
110 10.15142478 8.66865224
111 -0.58126617 10.15142478
112 -22.65989307 -0.58126617
113 -11.59509572 -22.65989307
114 -7.34321381 -11.59509572
115 15.10908684 -7.34321381
116 -36.87672935 15.10908684
117 -20.51599254 -36.87672935
118 -37.20806289 -20.51599254
119 -20.21935956 -37.20806289
120 -2.63510554 -20.21935956
121 40.41552881 -2.63510554
122 22.79868979 40.41552881
123 2.06460276 22.79868979
124 -8.40711943 2.06460276
125 21.71588871 -8.40711943
126 36.34480612 21.71588871
127 40.78602704 36.34480612
128 11.02476168 40.78602704
129 3.18244624 11.02476168
130 -0.60217779 3.18244624
131 12.16968206 -0.60217779
132 13.12939948 12.16968206
133 3.88580469 13.12939948
134 -5.52126630 3.88580469
135 26.75921876 -5.52126630
136 -8.81086655 26.75921876
137 7.90789365 -8.81086655
138 8.12623477 7.90789365
139 12.70368472 8.12623477
140 -23.64341679 12.70368472
141 -35.05548691 -23.64341679
142 -11.11624225 -35.05548691
143 14.04533973 -11.11624225
144 3.06376003 14.04533973
145 35.14512903 3.06376003
146 -2.77553921 35.14512903
147 -3.28576162 -2.77553921
148 -14.82873043 -3.28576162
149 -20.24029813 -14.82873043
150 -14.91938866 -20.24029813
151 -15.03441866 -14.91938866
152 -14.82873043 -15.03441866
153 -14.82873043 -14.82873043
154 -11.52203360 -14.82873043
155 -26.36348914 -11.52203360
156 -14.82873043 -26.36348914
157 -15.13288280 -14.82873043
158 -18.70336588 -15.13288280
159 -14.41988136 -18.70336588
160 -14.42590422 -14.41988136
161 -11.67463200 -14.42590422
162 -15.14098555 -11.67463200
163 -25.33711911 -15.14098555
> 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/7slgy1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8enlo1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/93s2c1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10bw0p1321960619.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/112s141321960619.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/12gkhm1321960619.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/13jon01321960619.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/149ne21321960619.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/159u0o1321960619.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/16ildl1321960619.tab")
+ }
>
> try(system("convert tmp/19f6d1321960619.ps tmp/19f6d1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/29jun1321960619.ps tmp/29jun1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/3e5sb1321960619.ps tmp/3e5sb1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/42ojd1321960619.ps tmp/42ojd1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/55z601321960619.ps tmp/55z601321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hk0d1321960619.ps tmp/6hk0d1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/7slgy1321960619.ps tmp/7slgy1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/8enlo1321960619.ps tmp/8enlo1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/93s2c1321960619.ps tmp/93s2c1321960619.png",intern=TRUE))
character(0)
> try(system("convert tmp/10bw0p1321960619.ps tmp/10bw0p1321960619.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.428 0.669 6.247