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(65
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+ ,dim=c(7
+ ,164)
+ ,dimnames=list(c('BlogdComputations'
+ ,'TotalTime'
+ ,'Shared'
+ ,'Caracters'
+ ,'Writing'
+ ,'Hyperlink'
+ ,'Blogs')
+ ,1:164))
> y <- array(NA,dim=c(7,164),dimnames=list(c('BlogdComputations','TotalTime','Shared','Caracters','Writing','Hyperlink','Blogs'),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
BlogdComputations TotalTime Shared Caracters Writing Hyperlink Blogs
1 65 146455 1 95556 114468 127 128
2 54 84944 4 54565 88594 90 89
3 58 113337 9 63016 74151 68 68
4 75 128655 2 79774 77921 111 108
5 41 74398 1 31258 53212 51 51
6 0 35523 2 52491 34956 33 33
7 111 293403 0 91256 149703 123 119
8 1 32750 0 22807 6853 5 5
9 36 106539 5 77411 58907 63 63
10 60 130539 0 48821 67067 66 66
11 63 154991 0 52295 110563 99 98
12 71 126683 7 63262 58126 72 71
13 38 100672 6 50466 57113 55 55
14 76 179562 3 62932 77993 116 116
15 61 125971 4 38439 68091 71 71
16 125 234509 0 70817 124676 125 120
17 84 158980 4 105965 109522 123 122
18 69 184217 3 73795 75865 74 74
19 77 107342 0 82043 79746 116 111
20 95 141371 5 74349 77844 117 103
21 78 154730 0 82204 98681 98 98
22 76 264020 1 55709 105531 101 100
23 40 90938 3 37137 51428 43 42
24 81 101324 5 70780 65703 103 100
25 102 130232 0 55027 72562 107 105
26 70 137793 0 56699 81728 77 77
27 75 161678 4 65911 95580 87 83
28 93 151503 0 56316 98278 99 98
29 42 105324 0 26982 46629 46 46
30 95 175914 0 54628 115189 96 95
31 87 181853 3 96750 124865 92 91
32 44 114928 4 53009 59392 96 91
33 84 190410 1 64664 127818 96 94
34 28 61499 4 36990 17821 15 15
35 87 223004 1 85224 154076 147 137
36 71 167131 0 37048 64881 56 56
37 68 233482 0 59635 136506 81 78
38 50 121185 2 42051 66524 69 68
39 30 78776 1 26998 45988 34 34
40 86 188967 2 63717 107445 98 94
41 75 199512 8 55071 102772 82 82
42 46 102531 5 40001 46657 64 63
43 52 118958 3 54506 97563 61 58
44 31 68948 4 35838 36663 45 43
45 30 93125 1 50838 55369 37 36
46 70 277108 2 86997 77921 64 64
47 20 78800 2 33032 56968 21 21
48 84 157250 0 61704 77519 104 104
49 81 210554 6 117986 129805 126 124
50 79 127324 3 56733 72761 104 101
51 70 114397 0 55064 81278 87 85
52 8 24188 0 5950 15049 7 7
53 67 246209 6 84607 113935 130 124
54 21 65029 5 32551 25109 21 21
55 30 98030 3 31701 45824 35 35
56 70 173587 1 71170 89644 97 95
57 87 172684 5 101773 109011 103 102
58 87 191381 5 101653 134245 210 212
59 112 191276 0 81493 136692 151 141
60 54 134043 9 55901 50741 57 54
61 96 233406 6 109104 149510 117 117
62 93 195304 6 114425 147888 152 145
63 49 127619 5 36311 54987 52 50
64 49 162810 6 70027 74467 83 80
65 38 129100 2 73713 100033 87 87
66 64 108715 0 40671 85505 80 78
67 62 106469 3 89041 62426 88 86
68 66 142069 8 57231 82932 83 82
69 98 143937 2 78792 79169 140 139
70 97 84256 5 59155 65469 76 75
71 56 118807 11 55827 63572 70 70
72 22 69471 6 22618 23824 26 25
73 51 122433 5 58425 73831 66 66
74 56 131122 1 65724 63551 89 89
75 94 94763 0 56979 56756 100 99
76 98 188780 3 72369 81399 98 98
77 76 191467 3 79194 117881 109 104
78 57 105615 6 202316 70711 51 48
79 75 89318 1 44970 50495 82 81
80 48 107335 0 49319 53845 65 64
81 48 98599 1 36252 51390 46 44
82 109 260646 0 75741 104953 104 104
83 27 131876 5 38417 65983 36 36
84 83 119291 2 64102 76839 123 120
85 49 80953 0 56622 55792 59 58
86 24 99768 0 15430 25155 27 27
87 43 84572 5 72571 55291 84 84
88 44 202373 1 67271 84279 61 56
89 49 166790 0 43460 99692 46 46
90 106 99946 1 99501 59633 125 119
91 42 116900 1 28340 63249 58 57
92 108 142146 2 76013 82928 152 139
93 27 99246 4 37361 50000 52 51
94 79 156833 1 48204 69455 85 85
95 49 175078 4 76168 84068 95 91
96 64 130533 0 85168 76195 78 79
97 75 142339 2 125410 114634 144 142
98 115 176789 0 123328 139357 149 149
99 92 181379 7 83038 110044 101 96
100 106 228548 7 120087 155118 205 198
101 73 142141 6 91939 83061 61 61
102 105 167845 0 103646 127122 145 145
103 30 103012 0 29467 45653 28 26
104 13 43287 4 43750 19630 49 49
105 69 125366 4 34497 67229 68 68
106 72 118372 0 66477 86060 142 145
107 80 135171 0 71181 88003 82 82
108 106 175568 0 74482 95815 105 102
109 28 74112 0 174949 85499 52 52
110 70 88817 0 46765 27220 56 56
111 51 164767 4 90257 109882 81 80
112 90 141933 0 51370 72579 100 99
113 12 22938 0 1168 5841 11 11
114 84 115199 0 51360 68369 87 87
115 23 61857 4 25162 24610 31 28
116 57 91185 0 21067 30995 67 67
117 84 213765 1 58233 150662 150 150
118 4 21054 0 855 6622 4 4
119 56 167105 5 85903 93694 75 71
120 18 31414 0 14116 13155 39 39
121 86 178863 1 57637 111908 88 87
122 39 126681 7 94137 57550 67 66
123 16 64320 5 62147 16356 24 23
124 18 67746 2 62832 40174 58 56
125 16 38214 0 8773 13983 16 16
126 42 90961 1 63785 52316 49 49
127 75 181510 0 65196 99585 109 108
128 30 116775 0 73087 86271 124 112
129 104 223914 2 72631 131012 115 110
130 121 185139 0 86281 130274 128 126
131 106 242879 2 162365 159051 159 155
132 57 139144 0 56530 76506 75 75
133 28 75812 0 35606 49145 30 30
134 56 178218 4 70111 66398 83 78
135 81 246834 4 92046 127546 135 135
136 2 50999 8 63989 6802 8 8
137 88 223842 0 104911 99509 115 114
138 41 93577 4 43448 43106 60 60
139 83 155383 0 60029 108303 99 99
140 55 111664 1 38650 64167 98 98
141 3 75426 0 47261 8579 36 33
142 54 243551 9 73586 97811 93 93
143 89 136548 0 83042 84365 158 157
144 41 173260 3 37238 10901 16 15
145 94 185039 7 63958 91346 100 98
146 101 67507 5 78956 33660 49 49
147 70 139350 2 99518 93634 89 88
148 111 172964 1 111436 109348 153 151
149 0 0 9 0 0 0 0
150 4 14688 0 6023 7953 5 5
151 0 98 0 0 0 0 0
152 0 455 0 0 0 0 0
153 0 0 1 0 0 0 0
154 0 0 0 0 0 0 0
155 42 128066 2 42564 63538 80 80
156 97 176460 1 38885 108281 122 122
157 0 0 0 0 0 0 0
158 0 203 0 0 0 0 0
159 7 7199 0 1644 4245 6 6
160 12 46660 0 6179 21509 13 13
161 0 17547 0 3926 7670 3 3
162 37 73567 0 23238 10641 18 18
163 0 969 0 0 0 0 0
164 39 101060 2 49288 41243 49 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) TotalTime Shared Caracters Writing Hyperlink
4.619e+00 1.573e-04 -8.440e-01 3.196e-05 -3.734e-06 1.969e-01
Blogs
2.538e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-47.846 -8.842 -0.905 8.634 65.498
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.619e+00 2.918e+00 1.583 0.115389
TotalTime 1.573e-04 3.977e-05 3.956 0.000115 ***
Shared -8.440e-01 4.909e-01 -1.719 0.087570 .
Caracters 3.196e-05 5.614e-05 0.569 0.569973
Writing -3.734e-06 8.566e-05 -0.044 0.965280
Hyperlink 1.969e-01 5.182e-01 0.380 0.704459
Blogs 2.538e-01 5.290e-01 0.480 0.632097
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 15.46 on 157 degrees of freedom
Multiple R-squared: 0.7787, Adjusted R-squared: 0.7702
F-statistic: 92.07 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,] 0.003825078 7.650156e-03 9.961749e-01
[2,] 0.027485424 5.497085e-02 9.725146e-01
[3,] 0.008962119 1.792424e-02 9.910379e-01
[4,] 0.014907944 2.981589e-02 9.850921e-01
[5,] 0.083005569 1.660111e-01 9.169944e-01
[6,] 0.044568185 8.913637e-02 9.554318e-01
[7,] 0.039832434 7.966487e-02 9.601676e-01
[8,] 0.033398630 6.679726e-02 9.666014e-01
[9,] 0.021094564 4.218913e-02 9.789054e-01
[10,] 0.010999655 2.199931e-02 9.890003e-01
[11,] 0.026222542 5.244508e-02 9.737775e-01
[12,] 0.027376337 5.475267e-02 9.726237e-01
[13,] 0.082102439 1.642049e-01 9.178976e-01
[14,] 0.056434395 1.128688e-01 9.435656e-01
[15,] 0.054594881 1.091898e-01 9.454051e-01
[16,] 0.134991731 2.699835e-01 8.650083e-01
[17,] 0.125479612 2.509592e-01 8.745204e-01
[18,] 0.092994102 1.859882e-01 9.070059e-01
[19,] 0.107687538 2.153751e-01 8.923125e-01
[20,] 0.078442751 1.568855e-01 9.215572e-01
[21,] 0.080861135 1.617223e-01 9.191389e-01
[22,] 0.085573521 1.711470e-01 9.144265e-01
[23,] 0.178198735 3.563975e-01 8.218013e-01
[24,] 0.143079632 2.861593e-01 8.569204e-01
[25,] 0.136523003 2.730460e-01 8.634770e-01
[26,] 0.270967221 5.419344e-01 7.290328e-01
[27,] 0.249246269 4.984925e-01 7.507537e-01
[28,] 0.238112771 4.762255e-01 7.618872e-01
[29,] 0.202143646 4.042873e-01 7.978564e-01
[30,] 0.163824677 3.276494e-01 8.361753e-01
[31,] 0.135084983 2.701700e-01 8.649150e-01
[32,] 0.107941470 2.158829e-01 8.920585e-01
[33,] 0.086148074 1.722961e-01 9.138519e-01
[34,] 0.066810223 1.336204e-01 9.331898e-01
[35,] 0.051487052 1.029741e-01 9.485129e-01
[36,] 0.039199524 7.839905e-02 9.608005e-01
[37,] 0.031401979 6.280396e-02 9.685980e-01
[38,] 0.023275919 4.655184e-02 9.767241e-01
[39,] 0.017274622 3.454924e-02 9.827254e-01
[40,] 0.014796344 2.959269e-02 9.852037e-01
[41,] 0.010964710 2.192942e-02 9.890353e-01
[42,] 0.008225090 1.645018e-02 9.917749e-01
[43,] 0.005791559 1.158312e-02 9.942084e-01
[44,] 0.029488501 5.897700e-02 9.705115e-01
[45,] 0.021761762 4.352352e-02 9.782382e-01
[46,] 0.016356173 3.271235e-02 9.836438e-01
[47,] 0.012520972 2.504194e-02 9.874790e-01
[48,] 0.011579179 2.315836e-02 9.884208e-01
[49,] 0.063705496 1.274110e-01 9.362945e-01
[50,] 0.054282724 1.085654e-01 9.457173e-01
[51,] 0.045165333 9.033067e-02 9.548347e-01
[52,] 0.036505501 7.301100e-02 9.634945e-01
[53,] 0.029587727 5.917545e-02 9.704123e-01
[54,] 0.022576254 4.515251e-02 9.774237e-01
[55,] 0.022733204 4.546641e-02 9.772668e-01
[56,] 0.037428402 7.485680e-02 9.625716e-01
[57,] 0.029511414 5.902283e-02 9.704886e-01
[58,] 0.022774050 4.554810e-02 9.772260e-01
[59,] 0.018245780 3.649156e-02 9.817542e-01
[60,] 0.015934506 3.186901e-02 9.840655e-01
[61,] 0.139247020 2.784940e-01 8.607530e-01
[62,] 0.121256229 2.425125e-01 8.787438e-01
[63,] 0.101568140 2.031363e-01 8.984319e-01
[64,] 0.082229325 1.644587e-01 9.177707e-01
[65,] 0.072332322 1.446646e-01 9.276677e-01
[66,] 0.117358006 2.347160e-01 8.826420e-01
[67,] 0.135851265 2.717025e-01 8.641487e-01
[68,] 0.115718880 2.314378e-01 8.842811e-01
[69,] 0.109625939 2.192519e-01 8.903741e-01
[70,] 0.117806569 2.356131e-01 8.821934e-01
[71,] 0.099014711 1.980294e-01 9.009853e-01
[72,] 0.083893871 1.677877e-01 9.161061e-01
[73,] 0.082030237 1.640605e-01 9.179698e-01
[74,] 0.074174160 1.483483e-01 9.258258e-01
[75,] 0.060514341 1.210287e-01 9.394857e-01
[76,] 0.048387009 9.677402e-02 9.516130e-01
[77,] 0.042890032 8.578006e-02 9.571100e-01
[78,] 0.037959967 7.591993e-02 9.620400e-01
[79,] 0.045551232 9.110246e-02 9.544488e-01
[80,] 0.035873032 7.174606e-02 9.641270e-01
[81,] 0.065473053 1.309461e-01 9.345269e-01
[82,] 0.054853364 1.097067e-01 9.451466e-01
[83,] 0.090924322 1.818486e-01 9.090757e-01
[84,] 0.088896756 1.777935e-01 9.111032e-01
[85,] 0.078596481 1.571930e-01 9.214035e-01
[86,] 0.099543668 1.990873e-01 9.004563e-01
[87,] 0.080739822 1.614796e-01 9.192602e-01
[88,] 0.082925436 1.658509e-01 9.170746e-01
[89,] 0.076310483 1.526210e-01 9.236895e-01
[90,] 0.094389089 1.887782e-01 9.056109e-01
[91,] 0.096535712 1.930714e-01 9.034643e-01
[92,] 0.110346824 2.206936e-01 8.896532e-01
[93,] 0.092735968 1.854719e-01 9.072640e-01
[94,] 0.076291194 1.525824e-01 9.237088e-01
[95,] 0.081818644 1.636373e-01 9.181814e-01
[96,] 0.083368034 1.667361e-01 9.166320e-01
[97,] 0.099928252 1.998565e-01 9.000717e-01
[98,] 0.095869126 1.917383e-01 9.041309e-01
[99,] 0.151593056 3.031861e-01 8.484069e-01
[100,] 0.176574060 3.531481e-01 8.234259e-01
[101,] 0.223330273 4.466605e-01 7.766697e-01
[102,] 0.231900928 4.638019e-01 7.680991e-01
[103,] 0.257820060 5.156401e-01 7.421799e-01
[104,] 0.221632737 4.432655e-01 7.783673e-01
[105,] 0.255732207 5.114644e-01 7.442678e-01
[106,] 0.226855060 4.537101e-01 7.731449e-01
[107,] 0.221438114 4.428762e-01 7.785619e-01
[108,] 0.280215078 5.604302e-01 7.197849e-01
[109,] 0.244475866 4.889517e-01 7.555241e-01
[110,] 0.208459815 4.169196e-01 7.915402e-01
[111,] 0.181673554 3.633471e-01 8.183264e-01
[112,] 0.163935019 3.278700e-01 8.360650e-01
[113,] 0.158353768 3.167075e-01 8.416462e-01
[114,] 0.134583295 2.691666e-01 8.654167e-01
[115,] 0.163824512 3.276490e-01 8.361755e-01
[116,] 0.133162014 2.663240e-01 8.668380e-01
[117,] 0.107898771 2.157975e-01 8.921012e-01
[118,] 0.088085180 1.761704e-01 9.119148e-01
[119,] 0.190889034 3.817781e-01 8.091110e-01
[120,] 0.196332795 3.926656e-01 8.036672e-01
[121,] 0.323274257 6.465485e-01 6.767257e-01
[122,] 0.305495718 6.109914e-01 6.945043e-01
[123,] 0.256447504 5.128950e-01 7.435525e-01
[124,] 0.211807256 4.236145e-01 7.881927e-01
[125,] 0.175257142 3.505143e-01 8.247429e-01
[126,] 0.220801004 4.416020e-01 7.791990e-01
[127,] 0.343405600 6.868112e-01 6.565944e-01
[128,] 0.309390978 6.187820e-01 6.906090e-01
[129,] 0.270568713 5.411374e-01 7.294313e-01
[130,] 0.228155875 4.563117e-01 7.718441e-01
[131,] 0.189623065 3.792461e-01 8.103769e-01
[132,] 0.277929165 5.558583e-01 7.220708e-01
[133,] 0.703963803 5.920724e-01 2.960362e-01
[134,] 0.720685755 5.586285e-01 2.793142e-01
[135,] 0.671457990 6.570840e-01 3.285420e-01
[136,] 0.890585390 2.188292e-01 1.094146e-01
[137,] 0.994339390 1.132122e-02 5.660610e-03
[138,] 0.998627075 2.745851e-03 1.372925e-03
[139,] 0.999910212 1.795762e-04 8.978808e-05
[140,] 0.999886937 2.261258e-04 1.130629e-04
[141,] 0.999998939 2.121043e-06 1.060522e-06
[142,] 0.999989722 2.055520e-05 1.027760e-05
[143,] 0.999913880 1.722398e-04 8.611991e-05
[144,] 0.999999541 9.185387e-07 4.592693e-07
[145,] 0.999978260 4.348031e-05 2.174015e-05
> postscript(file="/var/wessaorg/rcomp/tmp/1i1ph1321542431.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2ovja1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3gd3y1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wo561321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/546xq1321542431.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
-21.934823483 -2.329126551 10.761664826 0.303669151 1.734581785
6 7 8 9 10
-24.939949476 3.444650293 -11.728073999 -11.808230869 3.788562952
11 12 13 14 15
-11.626213583 18.357457576 -3.581137918 -8.337122467 6.965143313
16 17 18 19 20
26.621797658 -0.414138824 2.505015743 2.156945752 21.095486051
21 22 23 24 25
2.611443748 -15.963584489 3.485330010 16.982622600 27.687673290
26 27 28 29 30
7.492592674 8.376262806 18.748106083 -0.608919478 18.377085289
31 32 33 34 35
12.467292367 -18.794549007 5.920750463 9.205666305 -17.721654028
36 37 38 39 40
13.907231693 -10.491594629 -3.935973952 -2.182985877 8.552035626
41 42 43 44 45
7.411960143 -0.224677082 3.089331804 -1.872591576 -6.265603838
46 47 48 49 50
-7.859423880 -5.635499496 6.086977943 -11.246455445 9.229120788
51 52 53 54 55
7.224233647 -3.713146981 -30.635682248 -0.040832996 -4.125670619
56 57 58 59 60
-6.233933854 10.420131508 -41.409856420 9.677223906 9.363336673
61 62 63 64 65
4.064833947 -7.115498272 4.639857112 -14.775261759 -26.434440251
66 67 68 69 70
5.748855671 1.396214304 7.108600691 7.357631945 47.699862853
71 72 73 74 75
8.877920706 -0.582741946 0.001844499 -10.378793571 28.047257737
76 77 78 79 80
20.036514529 -6.156984975 12.402686613 19.220939189 -3.921893716
81 82 83 84 85
7.522021441 14.474928336 -11.352354974 4.865192636 3.706495611
86 87 88 89 90
-8.882447445 -10.676075300 -19.671159739 -3.607018040 28.728731916
91 92 93 94 95
-6.722074726 15.379058920 -14.046529362 10.961301797 -23.708238335
96 97 98 99 100
0.999631261 -18.297600273 11.992754114 18.259353718 -22.542675115
101 102 103 104 105
20.962224661 5.785998645 -3.708046359 -18.462564536 16.535210033
106 107 108 109 110
-17.805646830 15.212107026 25.175787230 -16.987211755 24.776055480
111 112 113 114 115
-14.891519849 16.865028965 -1.200864011 20.660701443 -1.897207101
116 117 118 119 120
7.281244958 -22.308104251 -5.736626817 -5.871111907 -9.540540808
121 122 123 124 125
13.254327469 -12.377692264 -7.006282672 -23.080252141 -2.070214351
126 127 128 129 130
-0.012618675 -8.758467057 -47.845624643 13.449203564 27.801697592
131 132 133 134 135
-10.382471293 -4.832631623 -3.021181226 -11.412373927 -22.385014529
136 137 138 139 140
-9.515856409 -6.391864411 -3.234415261 7.802709508 -11.506432514
141 142 143 144 145
-30.427381909 -25.240183849 -10.396994191 3.549213588 19.912978560
146 147 148 149 150
65.498327445 2.456748810 8.411198890 2.976532971 -5.346068566
151 152 153 154 155
-4.634495600 -4.690657415 -3.775121787 -4.619078632 -18.257537527
156 157 158 159 160
9.640437802 -4.619078632 -4.651013781 -1.492518083 -5.935775260
161 162 163 164
-8.828457452 11.991967645 -4.771517843 -3.081617679
> postscript(file="/var/wessaorg/rcomp/tmp/6hf8w1321542431.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 -21.934823483 NA
1 -2.329126551 -21.934823483
2 10.761664826 -2.329126551
3 0.303669151 10.761664826
4 1.734581785 0.303669151
5 -24.939949476 1.734581785
6 3.444650293 -24.939949476
7 -11.728073999 3.444650293
8 -11.808230869 -11.728073999
9 3.788562952 -11.808230869
10 -11.626213583 3.788562952
11 18.357457576 -11.626213583
12 -3.581137918 18.357457576
13 -8.337122467 -3.581137918
14 6.965143313 -8.337122467
15 26.621797658 6.965143313
16 -0.414138824 26.621797658
17 2.505015743 -0.414138824
18 2.156945752 2.505015743
19 21.095486051 2.156945752
20 2.611443748 21.095486051
21 -15.963584489 2.611443748
22 3.485330010 -15.963584489
23 16.982622600 3.485330010
24 27.687673290 16.982622600
25 7.492592674 27.687673290
26 8.376262806 7.492592674
27 18.748106083 8.376262806
28 -0.608919478 18.748106083
29 18.377085289 -0.608919478
30 12.467292367 18.377085289
31 -18.794549007 12.467292367
32 5.920750463 -18.794549007
33 9.205666305 5.920750463
34 -17.721654028 9.205666305
35 13.907231693 -17.721654028
36 -10.491594629 13.907231693
37 -3.935973952 -10.491594629
38 -2.182985877 -3.935973952
39 8.552035626 -2.182985877
40 7.411960143 8.552035626
41 -0.224677082 7.411960143
42 3.089331804 -0.224677082
43 -1.872591576 3.089331804
44 -6.265603838 -1.872591576
45 -7.859423880 -6.265603838
46 -5.635499496 -7.859423880
47 6.086977943 -5.635499496
48 -11.246455445 6.086977943
49 9.229120788 -11.246455445
50 7.224233647 9.229120788
51 -3.713146981 7.224233647
52 -30.635682248 -3.713146981
53 -0.040832996 -30.635682248
54 -4.125670619 -0.040832996
55 -6.233933854 -4.125670619
56 10.420131508 -6.233933854
57 -41.409856420 10.420131508
58 9.677223906 -41.409856420
59 9.363336673 9.677223906
60 4.064833947 9.363336673
61 -7.115498272 4.064833947
62 4.639857112 -7.115498272
63 -14.775261759 4.639857112
64 -26.434440251 -14.775261759
65 5.748855671 -26.434440251
66 1.396214304 5.748855671
67 7.108600691 1.396214304
68 7.357631945 7.108600691
69 47.699862853 7.357631945
70 8.877920706 47.699862853
71 -0.582741946 8.877920706
72 0.001844499 -0.582741946
73 -10.378793571 0.001844499
74 28.047257737 -10.378793571
75 20.036514529 28.047257737
76 -6.156984975 20.036514529
77 12.402686613 -6.156984975
78 19.220939189 12.402686613
79 -3.921893716 19.220939189
80 7.522021441 -3.921893716
81 14.474928336 7.522021441
82 -11.352354974 14.474928336
83 4.865192636 -11.352354974
84 3.706495611 4.865192636
85 -8.882447445 3.706495611
86 -10.676075300 -8.882447445
87 -19.671159739 -10.676075300
88 -3.607018040 -19.671159739
89 28.728731916 -3.607018040
90 -6.722074726 28.728731916
91 15.379058920 -6.722074726
92 -14.046529362 15.379058920
93 10.961301797 -14.046529362
94 -23.708238335 10.961301797
95 0.999631261 -23.708238335
96 -18.297600273 0.999631261
97 11.992754114 -18.297600273
98 18.259353718 11.992754114
99 -22.542675115 18.259353718
100 20.962224661 -22.542675115
101 5.785998645 20.962224661
102 -3.708046359 5.785998645
103 -18.462564536 -3.708046359
104 16.535210033 -18.462564536
105 -17.805646830 16.535210033
106 15.212107026 -17.805646830
107 25.175787230 15.212107026
108 -16.987211755 25.175787230
109 24.776055480 -16.987211755
110 -14.891519849 24.776055480
111 16.865028965 -14.891519849
112 -1.200864011 16.865028965
113 20.660701443 -1.200864011
114 -1.897207101 20.660701443
115 7.281244958 -1.897207101
116 -22.308104251 7.281244958
117 -5.736626817 -22.308104251
118 -5.871111907 -5.736626817
119 -9.540540808 -5.871111907
120 13.254327469 -9.540540808
121 -12.377692264 13.254327469
122 -7.006282672 -12.377692264
123 -23.080252141 -7.006282672
124 -2.070214351 -23.080252141
125 -0.012618675 -2.070214351
126 -8.758467057 -0.012618675
127 -47.845624643 -8.758467057
128 13.449203564 -47.845624643
129 27.801697592 13.449203564
130 -10.382471293 27.801697592
131 -4.832631623 -10.382471293
132 -3.021181226 -4.832631623
133 -11.412373927 -3.021181226
134 -22.385014529 -11.412373927
135 -9.515856409 -22.385014529
136 -6.391864411 -9.515856409
137 -3.234415261 -6.391864411
138 7.802709508 -3.234415261
139 -11.506432514 7.802709508
140 -30.427381909 -11.506432514
141 -25.240183849 -30.427381909
142 -10.396994191 -25.240183849
143 3.549213588 -10.396994191
144 19.912978560 3.549213588
145 65.498327445 19.912978560
146 2.456748810 65.498327445
147 8.411198890 2.456748810
148 2.976532971 8.411198890
149 -5.346068566 2.976532971
150 -4.634495600 -5.346068566
151 -4.690657415 -4.634495600
152 -3.775121787 -4.690657415
153 -4.619078632 -3.775121787
154 -18.257537527 -4.619078632
155 9.640437802 -18.257537527
156 -4.619078632 9.640437802
157 -4.651013781 -4.619078632
158 -1.492518083 -4.651013781
159 -5.935775260 -1.492518083
160 -8.828457452 -5.935775260
161 11.991967645 -8.828457452
162 -4.771517843 11.991967645
163 -3.081617679 -4.771517843
164 NA -3.081617679
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -2.329126551 -21.934823483
[2,] 10.761664826 -2.329126551
[3,] 0.303669151 10.761664826
[4,] 1.734581785 0.303669151
[5,] -24.939949476 1.734581785
[6,] 3.444650293 -24.939949476
[7,] -11.728073999 3.444650293
[8,] -11.808230869 -11.728073999
[9,] 3.788562952 -11.808230869
[10,] -11.626213583 3.788562952
[11,] 18.357457576 -11.626213583
[12,] -3.581137918 18.357457576
[13,] -8.337122467 -3.581137918
[14,] 6.965143313 -8.337122467
[15,] 26.621797658 6.965143313
[16,] -0.414138824 26.621797658
[17,] 2.505015743 -0.414138824
[18,] 2.156945752 2.505015743
[19,] 21.095486051 2.156945752
[20,] 2.611443748 21.095486051
[21,] -15.963584489 2.611443748
[22,] 3.485330010 -15.963584489
[23,] 16.982622600 3.485330010
[24,] 27.687673290 16.982622600
[25,] 7.492592674 27.687673290
[26,] 8.376262806 7.492592674
[27,] 18.748106083 8.376262806
[28,] -0.608919478 18.748106083
[29,] 18.377085289 -0.608919478
[30,] 12.467292367 18.377085289
[31,] -18.794549007 12.467292367
[32,] 5.920750463 -18.794549007
[33,] 9.205666305 5.920750463
[34,] -17.721654028 9.205666305
[35,] 13.907231693 -17.721654028
[36,] -10.491594629 13.907231693
[37,] -3.935973952 -10.491594629
[38,] -2.182985877 -3.935973952
[39,] 8.552035626 -2.182985877
[40,] 7.411960143 8.552035626
[41,] -0.224677082 7.411960143
[42,] 3.089331804 -0.224677082
[43,] -1.872591576 3.089331804
[44,] -6.265603838 -1.872591576
[45,] -7.859423880 -6.265603838
[46,] -5.635499496 -7.859423880
[47,] 6.086977943 -5.635499496
[48,] -11.246455445 6.086977943
[49,] 9.229120788 -11.246455445
[50,] 7.224233647 9.229120788
[51,] -3.713146981 7.224233647
[52,] -30.635682248 -3.713146981
[53,] -0.040832996 -30.635682248
[54,] -4.125670619 -0.040832996
[55,] -6.233933854 -4.125670619
[56,] 10.420131508 -6.233933854
[57,] -41.409856420 10.420131508
[58,] 9.677223906 -41.409856420
[59,] 9.363336673 9.677223906
[60,] 4.064833947 9.363336673
[61,] -7.115498272 4.064833947
[62,] 4.639857112 -7.115498272
[63,] -14.775261759 4.639857112
[64,] -26.434440251 -14.775261759
[65,] 5.748855671 -26.434440251
[66,] 1.396214304 5.748855671
[67,] 7.108600691 1.396214304
[68,] 7.357631945 7.108600691
[69,] 47.699862853 7.357631945
[70,] 8.877920706 47.699862853
[71,] -0.582741946 8.877920706
[72,] 0.001844499 -0.582741946
[73,] -10.378793571 0.001844499
[74,] 28.047257737 -10.378793571
[75,] 20.036514529 28.047257737
[76,] -6.156984975 20.036514529
[77,] 12.402686613 -6.156984975
[78,] 19.220939189 12.402686613
[79,] -3.921893716 19.220939189
[80,] 7.522021441 -3.921893716
[81,] 14.474928336 7.522021441
[82,] -11.352354974 14.474928336
[83,] 4.865192636 -11.352354974
[84,] 3.706495611 4.865192636
[85,] -8.882447445 3.706495611
[86,] -10.676075300 -8.882447445
[87,] -19.671159739 -10.676075300
[88,] -3.607018040 -19.671159739
[89,] 28.728731916 -3.607018040
[90,] -6.722074726 28.728731916
[91,] 15.379058920 -6.722074726
[92,] -14.046529362 15.379058920
[93,] 10.961301797 -14.046529362
[94,] -23.708238335 10.961301797
[95,] 0.999631261 -23.708238335
[96,] -18.297600273 0.999631261
[97,] 11.992754114 -18.297600273
[98,] 18.259353718 11.992754114
[99,] -22.542675115 18.259353718
[100,] 20.962224661 -22.542675115
[101,] 5.785998645 20.962224661
[102,] -3.708046359 5.785998645
[103,] -18.462564536 -3.708046359
[104,] 16.535210033 -18.462564536
[105,] -17.805646830 16.535210033
[106,] 15.212107026 -17.805646830
[107,] 25.175787230 15.212107026
[108,] -16.987211755 25.175787230
[109,] 24.776055480 -16.987211755
[110,] -14.891519849 24.776055480
[111,] 16.865028965 -14.891519849
[112,] -1.200864011 16.865028965
[113,] 20.660701443 -1.200864011
[114,] -1.897207101 20.660701443
[115,] 7.281244958 -1.897207101
[116,] -22.308104251 7.281244958
[117,] -5.736626817 -22.308104251
[118,] -5.871111907 -5.736626817
[119,] -9.540540808 -5.871111907
[120,] 13.254327469 -9.540540808
[121,] -12.377692264 13.254327469
[122,] -7.006282672 -12.377692264
[123,] -23.080252141 -7.006282672
[124,] -2.070214351 -23.080252141
[125,] -0.012618675 -2.070214351
[126,] -8.758467057 -0.012618675
[127,] -47.845624643 -8.758467057
[128,] 13.449203564 -47.845624643
[129,] 27.801697592 13.449203564
[130,] -10.382471293 27.801697592
[131,] -4.832631623 -10.382471293
[132,] -3.021181226 -4.832631623
[133,] -11.412373927 -3.021181226
[134,] -22.385014529 -11.412373927
[135,] -9.515856409 -22.385014529
[136,] -6.391864411 -9.515856409
[137,] -3.234415261 -6.391864411
[138,] 7.802709508 -3.234415261
[139,] -11.506432514 7.802709508
[140,] -30.427381909 -11.506432514
[141,] -25.240183849 -30.427381909
[142,] -10.396994191 -25.240183849
[143,] 3.549213588 -10.396994191
[144,] 19.912978560 3.549213588
[145,] 65.498327445 19.912978560
[146,] 2.456748810 65.498327445
[147,] 8.411198890 2.456748810
[148,] 2.976532971 8.411198890
[149,] -5.346068566 2.976532971
[150,] -4.634495600 -5.346068566
[151,] -4.690657415 -4.634495600
[152,] -3.775121787 -4.690657415
[153,] -4.619078632 -3.775121787
[154,] -18.257537527 -4.619078632
[155,] 9.640437802 -18.257537527
[156,] -4.619078632 9.640437802
[157,] -4.651013781 -4.619078632
[158,] -1.492518083 -4.651013781
[159,] -5.935775260 -1.492518083
[160,] -8.828457452 -5.935775260
[161,] 11.991967645 -8.828457452
[162,] -4.771517843 11.991967645
[163,] -3.081617679 -4.771517843
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -2.329126551 -21.934823483
2 10.761664826 -2.329126551
3 0.303669151 10.761664826
4 1.734581785 0.303669151
5 -24.939949476 1.734581785
6 3.444650293 -24.939949476
7 -11.728073999 3.444650293
8 -11.808230869 -11.728073999
9 3.788562952 -11.808230869
10 -11.626213583 3.788562952
11 18.357457576 -11.626213583
12 -3.581137918 18.357457576
13 -8.337122467 -3.581137918
14 6.965143313 -8.337122467
15 26.621797658 6.965143313
16 -0.414138824 26.621797658
17 2.505015743 -0.414138824
18 2.156945752 2.505015743
19 21.095486051 2.156945752
20 2.611443748 21.095486051
21 -15.963584489 2.611443748
22 3.485330010 -15.963584489
23 16.982622600 3.485330010
24 27.687673290 16.982622600
25 7.492592674 27.687673290
26 8.376262806 7.492592674
27 18.748106083 8.376262806
28 -0.608919478 18.748106083
29 18.377085289 -0.608919478
30 12.467292367 18.377085289
31 -18.794549007 12.467292367
32 5.920750463 -18.794549007
33 9.205666305 5.920750463
34 -17.721654028 9.205666305
35 13.907231693 -17.721654028
36 -10.491594629 13.907231693
37 -3.935973952 -10.491594629
38 -2.182985877 -3.935973952
39 8.552035626 -2.182985877
40 7.411960143 8.552035626
41 -0.224677082 7.411960143
42 3.089331804 -0.224677082
43 -1.872591576 3.089331804
44 -6.265603838 -1.872591576
45 -7.859423880 -6.265603838
46 -5.635499496 -7.859423880
47 6.086977943 -5.635499496
48 -11.246455445 6.086977943
49 9.229120788 -11.246455445
50 7.224233647 9.229120788
51 -3.713146981 7.224233647
52 -30.635682248 -3.713146981
53 -0.040832996 -30.635682248
54 -4.125670619 -0.040832996
55 -6.233933854 -4.125670619
56 10.420131508 -6.233933854
57 -41.409856420 10.420131508
58 9.677223906 -41.409856420
59 9.363336673 9.677223906
60 4.064833947 9.363336673
61 -7.115498272 4.064833947
62 4.639857112 -7.115498272
63 -14.775261759 4.639857112
64 -26.434440251 -14.775261759
65 5.748855671 -26.434440251
66 1.396214304 5.748855671
67 7.108600691 1.396214304
68 7.357631945 7.108600691
69 47.699862853 7.357631945
70 8.877920706 47.699862853
71 -0.582741946 8.877920706
72 0.001844499 -0.582741946
73 -10.378793571 0.001844499
74 28.047257737 -10.378793571
75 20.036514529 28.047257737
76 -6.156984975 20.036514529
77 12.402686613 -6.156984975
78 19.220939189 12.402686613
79 -3.921893716 19.220939189
80 7.522021441 -3.921893716
81 14.474928336 7.522021441
82 -11.352354974 14.474928336
83 4.865192636 -11.352354974
84 3.706495611 4.865192636
85 -8.882447445 3.706495611
86 -10.676075300 -8.882447445
87 -19.671159739 -10.676075300
88 -3.607018040 -19.671159739
89 28.728731916 -3.607018040
90 -6.722074726 28.728731916
91 15.379058920 -6.722074726
92 -14.046529362 15.379058920
93 10.961301797 -14.046529362
94 -23.708238335 10.961301797
95 0.999631261 -23.708238335
96 -18.297600273 0.999631261
97 11.992754114 -18.297600273
98 18.259353718 11.992754114
99 -22.542675115 18.259353718
100 20.962224661 -22.542675115
101 5.785998645 20.962224661
102 -3.708046359 5.785998645
103 -18.462564536 -3.708046359
104 16.535210033 -18.462564536
105 -17.805646830 16.535210033
106 15.212107026 -17.805646830
107 25.175787230 15.212107026
108 -16.987211755 25.175787230
109 24.776055480 -16.987211755
110 -14.891519849 24.776055480
111 16.865028965 -14.891519849
112 -1.200864011 16.865028965
113 20.660701443 -1.200864011
114 -1.897207101 20.660701443
115 7.281244958 -1.897207101
116 -22.308104251 7.281244958
117 -5.736626817 -22.308104251
118 -5.871111907 -5.736626817
119 -9.540540808 -5.871111907
120 13.254327469 -9.540540808
121 -12.377692264 13.254327469
122 -7.006282672 -12.377692264
123 -23.080252141 -7.006282672
124 -2.070214351 -23.080252141
125 -0.012618675 -2.070214351
126 -8.758467057 -0.012618675
127 -47.845624643 -8.758467057
128 13.449203564 -47.845624643
129 27.801697592 13.449203564
130 -10.382471293 27.801697592
131 -4.832631623 -10.382471293
132 -3.021181226 -4.832631623
133 -11.412373927 -3.021181226
134 -22.385014529 -11.412373927
135 -9.515856409 -22.385014529
136 -6.391864411 -9.515856409
137 -3.234415261 -6.391864411
138 7.802709508 -3.234415261
139 -11.506432514 7.802709508
140 -30.427381909 -11.506432514
141 -25.240183849 -30.427381909
142 -10.396994191 -25.240183849
143 3.549213588 -10.396994191
144 19.912978560 3.549213588
145 65.498327445 19.912978560
146 2.456748810 65.498327445
147 8.411198890 2.456748810
148 2.976532971 8.411198890
149 -5.346068566 2.976532971
150 -4.634495600 -5.346068566
151 -4.690657415 -4.634495600
152 -3.775121787 -4.690657415
153 -4.619078632 -3.775121787
154 -18.257537527 -4.619078632
155 9.640437802 -18.257537527
156 -4.619078632 9.640437802
157 -4.651013781 -4.619078632
158 -1.492518083 -4.651013781
159 -5.935775260 -1.492518083
160 -8.828457452 -5.935775260
161 11.991967645 -8.828457452
162 -4.771517843 11.991967645
163 -3.081617679 -4.771517843
> 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/7a1p11321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/89cfs1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9d4a21321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/100tdg1321542431.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/117he31321542431.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/125j991321542431.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/13q6z71321542431.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/14m74w1321542431.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/159uxj1321542431.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/166dck1321542432.tab")
+ }
>
> try(system("convert tmp/1i1ph1321542431.ps tmp/1i1ph1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ovja1321542431.ps tmp/2ovja1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/3gd3y1321542431.ps tmp/3gd3y1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wo561321542431.ps tmp/4wo561321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/546xq1321542431.ps tmp/546xq1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hf8w1321542431.ps tmp/6hf8w1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/7a1p11321542431.ps tmp/7a1p11321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/89cfs1321542431.ps tmp/89cfs1321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d4a21321542431.ps tmp/9d4a21321542431.png",intern=TRUE))
character(0)
> try(system("convert tmp/100tdg1321542431.ps tmp/100tdg1321542431.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.268 0.547 5.845