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(146455
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+ ,49
+ ,48)
+ ,dim=c(6
+ ,164)
+ ,dimnames=list(c('TotalTime'
+ ,'Shared'
+ ,'Caracters'
+ ,'Writing'
+ ,'Hyperlink'
+ ,'Blogs')
+ ,1:164))
> y <- array(NA,dim=c(6,164),dimnames=list(c('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
TotalTime Shared Caracters Writing Hyperlink Blogs
1 146455 1 95556 114468 127 128
2 84944 4 54565 88594 90 89
3 113337 9 63016 74151 68 68
4 128655 2 79774 77921 111 108
5 74398 1 31258 53212 51 51
6 35523 2 52491 34956 33 33
7 293403 0 91256 149703 123 119
8 32750 0 22807 6853 5 5
9 106539 5 77411 58907 63 63
10 130539 0 48821 67067 66 66
11 154991 0 52295 110563 99 98
12 126683 7 63262 58126 72 71
13 100672 6 50466 57113 55 55
14 179562 3 62932 77993 116 116
15 125971 4 38439 68091 71 71
16 234509 0 70817 124676 125 120
17 158980 4 105965 109522 123 122
18 184217 3 73795 75865 74 74
19 107342 0 82043 79746 116 111
20 141371 5 74349 77844 117 103
21 154730 0 82204 98681 98 98
22 264020 1 55709 105531 101 100
23 90938 3 37137 51428 43 42
24 101324 5 70780 65703 103 100
25 130232 0 55027 72562 107 105
26 137793 0 56699 81728 77 77
27 161678 4 65911 95580 87 83
28 151503 0 56316 98278 99 98
29 105324 0 26982 46629 46 46
30 175914 0 54628 115189 96 95
31 181853 3 96750 124865 92 91
32 114928 4 53009 59392 96 91
33 190410 1 64664 127818 96 94
34 61499 4 36990 17821 15 15
35 223004 1 85224 154076 147 137
36 167131 0 37048 64881 56 56
37 233482 0 59635 136506 81 78
38 121185 2 42051 66524 69 68
39 78776 1 26998 45988 34 34
40 188967 2 63717 107445 98 94
41 199512 8 55071 102772 82 82
42 102531 5 40001 46657 64 63
43 118958 3 54506 97563 61 58
44 68948 4 35838 36663 45 43
45 93125 1 50838 55369 37 36
46 277108 2 86997 77921 64 64
47 78800 2 33032 56968 21 21
48 157250 0 61704 77519 104 104
49 210554 6 117986 129805 126 124
50 127324 3 56733 72761 104 101
51 114397 0 55064 81278 87 85
52 24188 0 5950 15049 7 7
53 246209 6 84607 113935 130 124
54 65029 5 32551 25109 21 21
55 98030 3 31701 45824 35 35
56 173587 1 71170 89644 97 95
57 172684 5 101773 109011 103 102
58 191381 5 101653 134245 210 212
59 191276 0 81493 136692 151 141
60 134043 9 55901 50741 57 54
61 233406 6 109104 149510 117 117
62 195304 6 114425 147888 152 145
63 127619 5 36311 54987 52 50
64 162810 6 70027 74467 83 80
65 129100 2 73713 100033 87 87
66 108715 0 40671 85505 80 78
67 106469 3 89041 62426 88 86
68 142069 8 57231 82932 83 82
69 143937 2 78792 79169 140 139
70 84256 5 59155 65469 76 75
71 118807 11 55827 63572 70 70
72 69471 6 22618 23824 26 25
73 122433 5 58425 73831 66 66
74 131122 1 65724 63551 89 89
75 94763 0 56979 56756 100 99
76 188780 3 72369 81399 98 98
77 191467 3 79194 117881 109 104
78 105615 6 202316 70711 51 48
79 89318 1 44970 50495 82 81
80 107335 0 49319 53845 65 64
81 98599 1 36252 51390 46 44
82 260646 0 75741 104953 104 104
83 131876 5 38417 65983 36 36
84 119291 2 64102 76839 123 120
85 80953 0 56622 55792 59 58
86 99768 0 15430 25155 27 27
87 84572 5 72571 55291 84 84
88 202373 1 67271 84279 61 56
89 166790 0 43460 99692 46 46
90 99946 1 99501 59633 125 119
91 116900 1 28340 63249 58 57
92 142146 2 76013 82928 152 139
93 99246 4 37361 50000 52 51
94 156833 1 48204 69455 85 85
95 175078 4 76168 84068 95 91
96 130533 0 85168 76195 78 79
97 142339 2 125410 114634 144 142
98 176789 0 123328 139357 149 149
99 181379 7 83038 110044 101 96
100 228548 7 120087 155118 205 198
101 142141 6 91939 83061 61 61
102 167845 0 103646 127122 145 145
103 103012 0 29467 45653 28 26
104 43287 4 43750 19630 49 49
105 125366 4 34497 67229 68 68
106 118372 0 66477 86060 142 145
107 135171 0 71181 88003 82 82
108 175568 0 74482 95815 105 102
109 74112 0 174949 85499 52 52
110 88817 0 46765 27220 56 56
111 164767 4 90257 109882 81 80
112 141933 0 51370 72579 100 99
113 22938 0 1168 5841 11 11
114 115199 0 51360 68369 87 87
115 61857 4 25162 24610 31 28
116 91185 0 21067 30995 67 67
117 213765 1 58233 150662 150 150
118 21054 0 855 6622 4 4
119 167105 5 85903 93694 75 71
120 31414 0 14116 13155 39 39
121 178863 1 57637 111908 88 87
122 126681 7 94137 57550 67 66
123 64320 5 62147 16356 24 23
124 67746 2 62832 40174 58 56
125 38214 0 8773 13983 16 16
126 90961 1 63785 52316 49 49
127 181510 0 65196 99585 109 108
128 116775 0 73087 86271 124 112
129 223914 2 72631 131012 115 110
130 185139 0 86281 130274 128 126
131 242879 2 162365 159051 159 155
132 139144 0 56530 76506 75 75
133 75812 0 35606 49145 30 30
134 178218 4 70111 66398 83 78
135 246834 4 92046 127546 135 135
136 50999 8 63989 6802 8 8
137 223842 0 104911 99509 115 114
138 93577 4 43448 43106 60 60
139 155383 0 60029 108303 99 99
140 111664 1 38650 64167 98 98
141 75426 0 47261 8579 36 33
142 243551 9 73586 97811 93 93
143 136548 0 83042 84365 158 157
144 173260 3 37238 10901 16 15
145 185039 7 63958 91346 100 98
146 67507 5 78956 33660 49 49
147 139350 2 99518 93634 89 88
148 172964 1 111436 109348 153 151
149 0 9 0 0 0 0
150 14688 0 6023 7953 5 5
151 98 0 0 0 0 0
152 455 0 0 0 0 0
153 0 1 0 0 0 0
154 0 0 0 0 0 0
155 128066 2 42564 63538 80 80
156 176460 1 38885 108281 122 122
157 0 0 0 0 0 0
158 203 0 0 0 0 0
159 7199 0 1644 4245 6 6
160 46660 0 6179 21509 13 13
161 17547 0 3926 7670 3 3
162 73567 0 23238 10641 18 18
163 969 0 0 0 0 0
164 101060 2 49288 41243 49 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Shared Caracters Writing Hyperlink Blogs
2.454e+04 1.893e+03 -8.151e-02 1.442e+00 1.979e+02 -1.989e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-70627 -20432 -4341 11637 143536
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.454e+04 5.501e+03 4.462 1.54e-05 ***
Shared 1.893e+03 9.705e+02 1.951 0.0528 .
Caracters -8.151e-02 1.121e-01 -0.727 0.4683
Writing 1.442e+00 1.273e-01 11.335 < 2e-16 ***
Hyperlink 1.979e+02 1.037e+03 0.191 0.8488
Blogs -1.989e+02 1.058e+03 -0.188 0.8511
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30920 on 158 degrees of freedom
Multiple R-squared: 0.7734, Adjusted R-squared: 0.7662
F-statistic: 107.9 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,] 0.58760707 8.247859e-01 4.123929e-01
[2,] 0.61210237 7.757953e-01 3.878976e-01
[3,] 0.48227045 9.645409e-01 5.177296e-01
[4,] 0.57374991 8.525002e-01 4.262501e-01
[5,] 0.47857713 9.571543e-01 5.214229e-01
[6,] 0.84247297 3.150541e-01 1.575270e-01
[7,] 0.79796662 4.040668e-01 2.020334e-01
[8,] 0.73426017 5.314797e-01 2.657398e-01
[9,] 0.68111194 6.377761e-01 3.188881e-01
[10,] 0.84578691 3.084262e-01 1.542131e-01
[11,] 0.88165091 2.366982e-01 1.183491e-01
[12,] 0.84484179 3.103164e-01 1.551582e-01
[13,] 0.79791833 4.041633e-01 2.020817e-01
[14,] 0.95828163 8.343674e-02 4.171837e-02
[15,] 0.94129173 1.174165e-01 5.870827e-02
[16,] 0.92150403 1.569919e-01 7.849597e-02
[17,] 0.89442241 2.111552e-01 1.055776e-01
[18,] 0.86189273 2.762145e-01 1.381073e-01
[19,] 0.82545343 3.490931e-01 1.745466e-01
[20,] 0.79859863 4.028027e-01 2.014014e-01
[21,] 0.76304964 4.739007e-01 2.369504e-01
[22,] 0.74426551 5.114690e-01 2.557345e-01
[23,] 0.71456739 5.708652e-01 2.854326e-01
[24,] 0.66124195 6.775161e-01 3.387581e-01
[25,] 0.63537042 7.292592e-01 3.646296e-01
[26,] 0.60068390 7.986322e-01 3.993161e-01
[27,] 0.58823027 8.235395e-01 4.117697e-01
[28,] 0.65788611 6.842278e-01 3.421139e-01
[29,] 0.61177463 7.764507e-01 3.882254e-01
[30,] 0.55671361 8.865728e-01 4.432864e-01
[31,] 0.51430879 9.713824e-01 4.856912e-01
[32,] 0.46343736 9.268747e-01 5.365626e-01
[33,] 0.43423321 8.684664e-01 5.657668e-01
[34,] 0.38344158 7.668832e-01 6.165584e-01
[35,] 0.44623280 8.924656e-01 5.537672e-01
[36,] 0.39913960 7.982792e-01 6.008604e-01
[37,] 0.34955082 6.991016e-01 6.504492e-01
[38,] 0.98155544 3.688912e-02 1.844456e-02
[39,] 0.98022023 3.955954e-02 1.977977e-02
[40,] 0.97676555 4.646889e-02 2.323445e-02
[41,] 0.96941075 6.117849e-02 3.058925e-02
[42,] 0.95987662 8.024676e-02 4.012338e-02
[43,] 0.95604043 8.791914e-02 4.395957e-02
[44,] 0.94799876 1.040025e-01 5.200124e-02
[45,] 0.96760154 6.479692e-02 3.239846e-02
[46,] 0.95818821 8.362358e-02 4.181179e-02
[47,] 0.94707035 1.058593e-01 5.292965e-02
[48,] 0.93901558 1.219688e-01 6.098442e-02
[49,] 0.92716957 1.456609e-01 7.283043e-02
[50,] 0.92391499 1.521700e-01 7.608501e-02
[51,] 0.91843929 1.631214e-01 8.156071e-02
[52,] 0.91374406 1.725119e-01 8.625594e-02
[53,] 0.89690799 2.061840e-01 1.030920e-01
[54,] 0.91835577 1.632885e-01 8.164423e-02
[55,] 0.90675989 1.864802e-01 9.324011e-02
[56,] 0.89911708 2.017658e-01 1.008829e-01
[57,] 0.90882028 1.823594e-01 9.117972e-02
[58,] 0.91338229 1.732354e-01 8.661771e-02
[59,] 0.89594326 2.081135e-01 1.040567e-01
[60,] 0.87695935 2.460813e-01 1.230407e-01
[61,] 0.85323779 2.935244e-01 1.467622e-01
[62,] 0.86786548 2.642690e-01 1.321345e-01
[63,] 0.84938596 3.012281e-01 1.506140e-01
[64,] 0.82205028 3.558994e-01 1.779497e-01
[65,] 0.79778534 4.044293e-01 2.022147e-01
[66,] 0.77403933 4.519213e-01 2.259607e-01
[67,] 0.74013880 5.197224e-01 2.598612e-01
[68,] 0.78052298 4.389540e-01 2.194770e-01
[69,] 0.74626818 5.074636e-01 2.537318e-01
[70,] 0.72986764 5.402647e-01 2.701324e-01
[71,] 0.69252301 6.149540e-01 3.074770e-01
[72,] 0.65417949 6.916410e-01 3.458205e-01
[73,] 0.61075505 7.784899e-01 3.892449e-01
[74,] 0.87192782 2.561444e-01 1.280722e-01
[75,] 0.84720514 3.055897e-01 1.527949e-01
[76,] 0.82633925 3.473215e-01 1.736608e-01
[77,] 0.80841855 3.831629e-01 1.915815e-01
[78,] 0.82962654 3.407469e-01 1.703735e-01
[79,] 0.82000822 3.599836e-01 1.799918e-01
[80,] 0.89265986 2.146803e-01 1.073401e-01
[81,] 0.87393635 2.521273e-01 1.260636e-01
[82,] 0.85051037 2.989793e-01 1.494896e-01
[83,] 0.82252512 3.549498e-01 1.774749e-01
[84,] 0.79556544 4.088691e-01 2.044346e-01
[85,] 0.76158989 4.768202e-01 2.384101e-01
[86,] 0.77060853 4.587829e-01 2.293915e-01
[87,] 0.75887666 4.822467e-01 2.411233e-01
[88,] 0.72517775 5.496445e-01 2.748223e-01
[89,] 0.76182085 4.763583e-01 2.381792e-01
[90,] 0.77533098 4.493380e-01 2.246690e-01
[91,] 0.74642452 5.071510e-01 2.535755e-01
[92,] 0.78787455 4.242509e-01 2.121255e-01
[93,] 0.75446552 4.910690e-01 2.455345e-01
[94,] 0.75591905 4.881619e-01 2.440810e-01
[95,] 0.73850500 5.229900e-01 2.614950e-01
[96,] 0.72286947 5.542611e-01 2.771305e-01
[97,] 0.68175874 6.364825e-01 3.182413e-01
[98,] 0.69788622 6.042276e-01 3.021138e-01
[99,] 0.65694576 6.861085e-01 3.430542e-01
[100,] 0.62903224 7.419355e-01 3.709678e-01
[101,] 0.75163503 4.967299e-01 2.483650e-01
[102,] 0.73422261 5.315548e-01 2.657774e-01
[103,] 0.71622027 5.675595e-01 2.837797e-01
[104,] 0.68366362 6.326728e-01 3.163364e-01
[105,] 0.64406243 7.118751e-01 3.559376e-01
[106,] 0.59686925 8.062615e-01 4.031307e-01
[107,] 0.54821886 9.035623e-01 4.517811e-01
[108,] 0.53290859 9.341828e-01 4.670914e-01
[109,] 0.50557494 9.888501e-01 4.944251e-01
[110,] 0.46232250 9.246450e-01 5.376775e-01
[111,] 0.41494971 8.298994e-01 5.850503e-01
[112,] 0.36924189 7.384838e-01 6.307581e-01
[113,] 0.32084457 6.416891e-01 6.791554e-01
[114,] 0.29123856 5.824771e-01 7.087614e-01
[115,] 0.25131268 5.026254e-01 7.486873e-01
[116,] 0.23126347 4.625269e-01 7.687365e-01
[117,] 0.19439036 3.887807e-01 8.056096e-01
[118,] 0.16418087 3.283617e-01 8.358191e-01
[119,] 0.14352878 2.870576e-01 8.564712e-01
[120,] 0.17263725 3.452745e-01 8.273628e-01
[121,] 0.14039874 2.807975e-01 8.596013e-01
[122,] 0.12972019 2.594404e-01 8.702798e-01
[123,] 0.21609895 4.321979e-01 7.839011e-01
[124,] 0.17703743 3.540749e-01 8.229626e-01
[125,] 0.15196863 3.039373e-01 8.480314e-01
[126,] 0.14416095 2.883219e-01 8.558390e-01
[127,] 0.13844886 2.768977e-01 8.615511e-01
[128,] 0.11727889 2.345578e-01 8.827211e-01
[129,] 0.15948218 3.189644e-01 8.405178e-01
[130,] 0.12305519 2.461104e-01 8.769448e-01
[131,] 0.09732996 1.946599e-01 9.026700e-01
[132,] 0.07437717 1.487543e-01 9.256228e-01
[133,] 0.05857503 1.171501e-01 9.414250e-01
[134,] 0.13578921 2.715784e-01 8.642108e-01
[135,] 0.10276765 2.055353e-01 8.972324e-01
[136,] 0.98367073 3.265853e-02 1.632927e-02
[137,] 0.99090199 1.819602e-02 9.098010e-03
[138,] 0.99147321 1.705358e-02 8.526788e-03
[139,] 0.99997028 5.943623e-05 2.971811e-05
[140,] 0.99998876 2.248771e-05 1.124386e-05
[141,] 0.99999252 1.495965e-05 7.479827e-06
[142,] 0.99999471 1.058476e-05 5.292380e-06
[143,] 0.99996850 6.299007e-05 3.149504e-05
[144,] 0.99981959 3.608251e-04 1.804125e-04
[145,] 0.99999068 1.864032e-05 9.320161e-06
[146,] 0.99987729 2.454272e-04 1.227136e-04
[147,] 0.99937018 1.259645e-03 6.298226e-04
> postscript(file="/var/wessaorg/rcomp/tmp/1p0si1321541513.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2p42e1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wj5j1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40iwh1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5yvkz1321541513.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
-36981.82969 -70627.21007 -30002.35686 -6055.78394 -26195.64210 -38917.77386
7 8 9 10 11 12
59685.56871 185.94015 -6069.20370 13300.19294 -24871.75090 10071.16315
13 14 15 16 17 18
-13446.31477 42082.71315 -1160.06435 35029.03561 -22556.15737 50650.75553
19 20 21 22 23 24
-26422.16536 -1533.23439 -5357.52711 89802.52379 -10596.91669 -22184.37431
25 26 27 28 29 30
5215.96320 59.46288 -3645.06573 -10311.36258 15765.98766 -10434.39672
31 32 33 34 35 36
-20703.50375 563.07058 -15429.51351 6706.26807 -20575.86543 52075.73825
37 38 39 40 41 42
16380.03305 194.74951 -11761.56643 10148.25482 16147.84411 4345.57126
43 44 45 46 47 48
-48088.76737 -13485.25747 -9196.87306 143535.68589 -28990.39118 26022.75923
49 50 51 52 53 54
-3242.52836 -3722.34253 -23208.50231 -21570.55058 51791.84111 -2525.69495
55 56 57 58 59 60
4326.41244 23343.27970 -10370.24527 -27378.35029 -25634.80124 23283.95406
61 62 63 64 65 66
-9149.41449 -45835.29120 16906.19295 24685.05187 -37427.82139 -36168.01931
67 68 69 70 71 72
-6853.05224 -12698.58085 7772.77894 -39491.65126 -13643.53023 872.81027
73 74 75 76 77 78
-13247.10815 18462.25048 -7102.50350 47138.94595 -3225.02589 -16340.59556
79 80 81 82 83 84
-6406.64664 9009.09180 637.80582 90990.53733 5855.37005 -15124.53707
85 86 87 88 89 90
-19592.07734 40224.71090 -23193.79729 58917.55205 2033.94115 -5466.26227
91 92 93 94 95 96
1398.63200 -2041.73624 -2095.29767 34224.83278 27204.62671 3301.12882
97 98 99 100 101 102
-41376.91018 -38568.81157 -9277.02263 -24399.20816 -6019.60787 -31472.68582
103 104 105 106 107 108
14648.59324 -13530.09975 -845.99466 -24151.52145 -10428.47811 18395.41275
109 110 111 112 113 114
-59446.96118 28878.29268 -18610.87777 16786.24040 -9924.18306 -3689.92204
115 116 117 118 119 120
-4273.43895 23717.24754 -25098.48202 -12967.25895 4226.57706 -10914.88061
121 122 123 124 125 126
-4408.88356 13411.78089 11607.76170 -13751.38132 -5767.77195 -5690.77489
127 128 129 130 131 132
18544.19268 -28515.54784 11645.52926 -20555.61772 -2277.07150 8927.21166
133 134 135 136 137 138
-16688.26144 55128.25767 38375.24645 6720.64763 64229.17024 2883.09832
139 140 141 142 143 144
-20390.45991 -4082.22846 41799.91914 66969.65619 -2959.85581 130164.88076
145 146 147 148 149 150
20394.48804 -8571.48326 -16040.93679 -2363.67727 -41584.24952 -20830.90566
151 152 153 154 155 156
-24444.98567 -24087.98567 -26436.45943 -24542.98567 11634.65098 -2875.74055
157 158 159 160 161 162
-24542.98567 -24339.98567 -23327.21687 -8392.17326 -17736.62574 35587.03667
163 164
-23573.98567 17106.49372
> postscript(file="/var/wessaorg/rcomp/tmp/6h7d91321541513.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 -36981.82969 NA
1 -70627.21007 -36981.82969
2 -30002.35686 -70627.21007
3 -6055.78394 -30002.35686
4 -26195.64210 -6055.78394
5 -38917.77386 -26195.64210
6 59685.56871 -38917.77386
7 185.94015 59685.56871
8 -6069.20370 185.94015
9 13300.19294 -6069.20370
10 -24871.75090 13300.19294
11 10071.16315 -24871.75090
12 -13446.31477 10071.16315
13 42082.71315 -13446.31477
14 -1160.06435 42082.71315
15 35029.03561 -1160.06435
16 -22556.15737 35029.03561
17 50650.75553 -22556.15737
18 -26422.16536 50650.75553
19 -1533.23439 -26422.16536
20 -5357.52711 -1533.23439
21 89802.52379 -5357.52711
22 -10596.91669 89802.52379
23 -22184.37431 -10596.91669
24 5215.96320 -22184.37431
25 59.46288 5215.96320
26 -3645.06573 59.46288
27 -10311.36258 -3645.06573
28 15765.98766 -10311.36258
29 -10434.39672 15765.98766
30 -20703.50375 -10434.39672
31 563.07058 -20703.50375
32 -15429.51351 563.07058
33 6706.26807 -15429.51351
34 -20575.86543 6706.26807
35 52075.73825 -20575.86543
36 16380.03305 52075.73825
37 194.74951 16380.03305
38 -11761.56643 194.74951
39 10148.25482 -11761.56643
40 16147.84411 10148.25482
41 4345.57126 16147.84411
42 -48088.76737 4345.57126
43 -13485.25747 -48088.76737
44 -9196.87306 -13485.25747
45 143535.68589 -9196.87306
46 -28990.39118 143535.68589
47 26022.75923 -28990.39118
48 -3242.52836 26022.75923
49 -3722.34253 -3242.52836
50 -23208.50231 -3722.34253
51 -21570.55058 -23208.50231
52 51791.84111 -21570.55058
53 -2525.69495 51791.84111
54 4326.41244 -2525.69495
55 23343.27970 4326.41244
56 -10370.24527 23343.27970
57 -27378.35029 -10370.24527
58 -25634.80124 -27378.35029
59 23283.95406 -25634.80124
60 -9149.41449 23283.95406
61 -45835.29120 -9149.41449
62 16906.19295 -45835.29120
63 24685.05187 16906.19295
64 -37427.82139 24685.05187
65 -36168.01931 -37427.82139
66 -6853.05224 -36168.01931
67 -12698.58085 -6853.05224
68 7772.77894 -12698.58085
69 -39491.65126 7772.77894
70 -13643.53023 -39491.65126
71 872.81027 -13643.53023
72 -13247.10815 872.81027
73 18462.25048 -13247.10815
74 -7102.50350 18462.25048
75 47138.94595 -7102.50350
76 -3225.02589 47138.94595
77 -16340.59556 -3225.02589
78 -6406.64664 -16340.59556
79 9009.09180 -6406.64664
80 637.80582 9009.09180
81 90990.53733 637.80582
82 5855.37005 90990.53733
83 -15124.53707 5855.37005
84 -19592.07734 -15124.53707
85 40224.71090 -19592.07734
86 -23193.79729 40224.71090
87 58917.55205 -23193.79729
88 2033.94115 58917.55205
89 -5466.26227 2033.94115
90 1398.63200 -5466.26227
91 -2041.73624 1398.63200
92 -2095.29767 -2041.73624
93 34224.83278 -2095.29767
94 27204.62671 34224.83278
95 3301.12882 27204.62671
96 -41376.91018 3301.12882
97 -38568.81157 -41376.91018
98 -9277.02263 -38568.81157
99 -24399.20816 -9277.02263
100 -6019.60787 -24399.20816
101 -31472.68582 -6019.60787
102 14648.59324 -31472.68582
103 -13530.09975 14648.59324
104 -845.99466 -13530.09975
105 -24151.52145 -845.99466
106 -10428.47811 -24151.52145
107 18395.41275 -10428.47811
108 -59446.96118 18395.41275
109 28878.29268 -59446.96118
110 -18610.87777 28878.29268
111 16786.24040 -18610.87777
112 -9924.18306 16786.24040
113 -3689.92204 -9924.18306
114 -4273.43895 -3689.92204
115 23717.24754 -4273.43895
116 -25098.48202 23717.24754
117 -12967.25895 -25098.48202
118 4226.57706 -12967.25895
119 -10914.88061 4226.57706
120 -4408.88356 -10914.88061
121 13411.78089 -4408.88356
122 11607.76170 13411.78089
123 -13751.38132 11607.76170
124 -5767.77195 -13751.38132
125 -5690.77489 -5767.77195
126 18544.19268 -5690.77489
127 -28515.54784 18544.19268
128 11645.52926 -28515.54784
129 -20555.61772 11645.52926
130 -2277.07150 -20555.61772
131 8927.21166 -2277.07150
132 -16688.26144 8927.21166
133 55128.25767 -16688.26144
134 38375.24645 55128.25767
135 6720.64763 38375.24645
136 64229.17024 6720.64763
137 2883.09832 64229.17024
138 -20390.45991 2883.09832
139 -4082.22846 -20390.45991
140 41799.91914 -4082.22846
141 66969.65619 41799.91914
142 -2959.85581 66969.65619
143 130164.88076 -2959.85581
144 20394.48804 130164.88076
145 -8571.48326 20394.48804
146 -16040.93679 -8571.48326
147 -2363.67727 -16040.93679
148 -41584.24952 -2363.67727
149 -20830.90566 -41584.24952
150 -24444.98567 -20830.90566
151 -24087.98567 -24444.98567
152 -26436.45943 -24087.98567
153 -24542.98567 -26436.45943
154 11634.65098 -24542.98567
155 -2875.74055 11634.65098
156 -24542.98567 -2875.74055
157 -24339.98567 -24542.98567
158 -23327.21687 -24339.98567
159 -8392.17326 -23327.21687
160 -17736.62574 -8392.17326
161 35587.03667 -17736.62574
162 -23573.98567 35587.03667
163 17106.49372 -23573.98567
164 NA 17106.49372
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -70627.21007 -36981.82969
[2,] -30002.35686 -70627.21007
[3,] -6055.78394 -30002.35686
[4,] -26195.64210 -6055.78394
[5,] -38917.77386 -26195.64210
[6,] 59685.56871 -38917.77386
[7,] 185.94015 59685.56871
[8,] -6069.20370 185.94015
[9,] 13300.19294 -6069.20370
[10,] -24871.75090 13300.19294
[11,] 10071.16315 -24871.75090
[12,] -13446.31477 10071.16315
[13,] 42082.71315 -13446.31477
[14,] -1160.06435 42082.71315
[15,] 35029.03561 -1160.06435
[16,] -22556.15737 35029.03561
[17,] 50650.75553 -22556.15737
[18,] -26422.16536 50650.75553
[19,] -1533.23439 -26422.16536
[20,] -5357.52711 -1533.23439
[21,] 89802.52379 -5357.52711
[22,] -10596.91669 89802.52379
[23,] -22184.37431 -10596.91669
[24,] 5215.96320 -22184.37431
[25,] 59.46288 5215.96320
[26,] -3645.06573 59.46288
[27,] -10311.36258 -3645.06573
[28,] 15765.98766 -10311.36258
[29,] -10434.39672 15765.98766
[30,] -20703.50375 -10434.39672
[31,] 563.07058 -20703.50375
[32,] -15429.51351 563.07058
[33,] 6706.26807 -15429.51351
[34,] -20575.86543 6706.26807
[35,] 52075.73825 -20575.86543
[36,] 16380.03305 52075.73825
[37,] 194.74951 16380.03305
[38,] -11761.56643 194.74951
[39,] 10148.25482 -11761.56643
[40,] 16147.84411 10148.25482
[41,] 4345.57126 16147.84411
[42,] -48088.76737 4345.57126
[43,] -13485.25747 -48088.76737
[44,] -9196.87306 -13485.25747
[45,] 143535.68589 -9196.87306
[46,] -28990.39118 143535.68589
[47,] 26022.75923 -28990.39118
[48,] -3242.52836 26022.75923
[49,] -3722.34253 -3242.52836
[50,] -23208.50231 -3722.34253
[51,] -21570.55058 -23208.50231
[52,] 51791.84111 -21570.55058
[53,] -2525.69495 51791.84111
[54,] 4326.41244 -2525.69495
[55,] 23343.27970 4326.41244
[56,] -10370.24527 23343.27970
[57,] -27378.35029 -10370.24527
[58,] -25634.80124 -27378.35029
[59,] 23283.95406 -25634.80124
[60,] -9149.41449 23283.95406
[61,] -45835.29120 -9149.41449
[62,] 16906.19295 -45835.29120
[63,] 24685.05187 16906.19295
[64,] -37427.82139 24685.05187
[65,] -36168.01931 -37427.82139
[66,] -6853.05224 -36168.01931
[67,] -12698.58085 -6853.05224
[68,] 7772.77894 -12698.58085
[69,] -39491.65126 7772.77894
[70,] -13643.53023 -39491.65126
[71,] 872.81027 -13643.53023
[72,] -13247.10815 872.81027
[73,] 18462.25048 -13247.10815
[74,] -7102.50350 18462.25048
[75,] 47138.94595 -7102.50350
[76,] -3225.02589 47138.94595
[77,] -16340.59556 -3225.02589
[78,] -6406.64664 -16340.59556
[79,] 9009.09180 -6406.64664
[80,] 637.80582 9009.09180
[81,] 90990.53733 637.80582
[82,] 5855.37005 90990.53733
[83,] -15124.53707 5855.37005
[84,] -19592.07734 -15124.53707
[85,] 40224.71090 -19592.07734
[86,] -23193.79729 40224.71090
[87,] 58917.55205 -23193.79729
[88,] 2033.94115 58917.55205
[89,] -5466.26227 2033.94115
[90,] 1398.63200 -5466.26227
[91,] -2041.73624 1398.63200
[92,] -2095.29767 -2041.73624
[93,] 34224.83278 -2095.29767
[94,] 27204.62671 34224.83278
[95,] 3301.12882 27204.62671
[96,] -41376.91018 3301.12882
[97,] -38568.81157 -41376.91018
[98,] -9277.02263 -38568.81157
[99,] -24399.20816 -9277.02263
[100,] -6019.60787 -24399.20816
[101,] -31472.68582 -6019.60787
[102,] 14648.59324 -31472.68582
[103,] -13530.09975 14648.59324
[104,] -845.99466 -13530.09975
[105,] -24151.52145 -845.99466
[106,] -10428.47811 -24151.52145
[107,] 18395.41275 -10428.47811
[108,] -59446.96118 18395.41275
[109,] 28878.29268 -59446.96118
[110,] -18610.87777 28878.29268
[111,] 16786.24040 -18610.87777
[112,] -9924.18306 16786.24040
[113,] -3689.92204 -9924.18306
[114,] -4273.43895 -3689.92204
[115,] 23717.24754 -4273.43895
[116,] -25098.48202 23717.24754
[117,] -12967.25895 -25098.48202
[118,] 4226.57706 -12967.25895
[119,] -10914.88061 4226.57706
[120,] -4408.88356 -10914.88061
[121,] 13411.78089 -4408.88356
[122,] 11607.76170 13411.78089
[123,] -13751.38132 11607.76170
[124,] -5767.77195 -13751.38132
[125,] -5690.77489 -5767.77195
[126,] 18544.19268 -5690.77489
[127,] -28515.54784 18544.19268
[128,] 11645.52926 -28515.54784
[129,] -20555.61772 11645.52926
[130,] -2277.07150 -20555.61772
[131,] 8927.21166 -2277.07150
[132,] -16688.26144 8927.21166
[133,] 55128.25767 -16688.26144
[134,] 38375.24645 55128.25767
[135,] 6720.64763 38375.24645
[136,] 64229.17024 6720.64763
[137,] 2883.09832 64229.17024
[138,] -20390.45991 2883.09832
[139,] -4082.22846 -20390.45991
[140,] 41799.91914 -4082.22846
[141,] 66969.65619 41799.91914
[142,] -2959.85581 66969.65619
[143,] 130164.88076 -2959.85581
[144,] 20394.48804 130164.88076
[145,] -8571.48326 20394.48804
[146,] -16040.93679 -8571.48326
[147,] -2363.67727 -16040.93679
[148,] -41584.24952 -2363.67727
[149,] -20830.90566 -41584.24952
[150,] -24444.98567 -20830.90566
[151,] -24087.98567 -24444.98567
[152,] -26436.45943 -24087.98567
[153,] -24542.98567 -26436.45943
[154,] 11634.65098 -24542.98567
[155,] -2875.74055 11634.65098
[156,] -24542.98567 -2875.74055
[157,] -24339.98567 -24542.98567
[158,] -23327.21687 -24339.98567
[159,] -8392.17326 -23327.21687
[160,] -17736.62574 -8392.17326
[161,] 35587.03667 -17736.62574
[162,] -23573.98567 35587.03667
[163,] 17106.49372 -23573.98567
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -70627.21007 -36981.82969
2 -30002.35686 -70627.21007
3 -6055.78394 -30002.35686
4 -26195.64210 -6055.78394
5 -38917.77386 -26195.64210
6 59685.56871 -38917.77386
7 185.94015 59685.56871
8 -6069.20370 185.94015
9 13300.19294 -6069.20370
10 -24871.75090 13300.19294
11 10071.16315 -24871.75090
12 -13446.31477 10071.16315
13 42082.71315 -13446.31477
14 -1160.06435 42082.71315
15 35029.03561 -1160.06435
16 -22556.15737 35029.03561
17 50650.75553 -22556.15737
18 -26422.16536 50650.75553
19 -1533.23439 -26422.16536
20 -5357.52711 -1533.23439
21 89802.52379 -5357.52711
22 -10596.91669 89802.52379
23 -22184.37431 -10596.91669
24 5215.96320 -22184.37431
25 59.46288 5215.96320
26 -3645.06573 59.46288
27 -10311.36258 -3645.06573
28 15765.98766 -10311.36258
29 -10434.39672 15765.98766
30 -20703.50375 -10434.39672
31 563.07058 -20703.50375
32 -15429.51351 563.07058
33 6706.26807 -15429.51351
34 -20575.86543 6706.26807
35 52075.73825 -20575.86543
36 16380.03305 52075.73825
37 194.74951 16380.03305
38 -11761.56643 194.74951
39 10148.25482 -11761.56643
40 16147.84411 10148.25482
41 4345.57126 16147.84411
42 -48088.76737 4345.57126
43 -13485.25747 -48088.76737
44 -9196.87306 -13485.25747
45 143535.68589 -9196.87306
46 -28990.39118 143535.68589
47 26022.75923 -28990.39118
48 -3242.52836 26022.75923
49 -3722.34253 -3242.52836
50 -23208.50231 -3722.34253
51 -21570.55058 -23208.50231
52 51791.84111 -21570.55058
53 -2525.69495 51791.84111
54 4326.41244 -2525.69495
55 23343.27970 4326.41244
56 -10370.24527 23343.27970
57 -27378.35029 -10370.24527
58 -25634.80124 -27378.35029
59 23283.95406 -25634.80124
60 -9149.41449 23283.95406
61 -45835.29120 -9149.41449
62 16906.19295 -45835.29120
63 24685.05187 16906.19295
64 -37427.82139 24685.05187
65 -36168.01931 -37427.82139
66 -6853.05224 -36168.01931
67 -12698.58085 -6853.05224
68 7772.77894 -12698.58085
69 -39491.65126 7772.77894
70 -13643.53023 -39491.65126
71 872.81027 -13643.53023
72 -13247.10815 872.81027
73 18462.25048 -13247.10815
74 -7102.50350 18462.25048
75 47138.94595 -7102.50350
76 -3225.02589 47138.94595
77 -16340.59556 -3225.02589
78 -6406.64664 -16340.59556
79 9009.09180 -6406.64664
80 637.80582 9009.09180
81 90990.53733 637.80582
82 5855.37005 90990.53733
83 -15124.53707 5855.37005
84 -19592.07734 -15124.53707
85 40224.71090 -19592.07734
86 -23193.79729 40224.71090
87 58917.55205 -23193.79729
88 2033.94115 58917.55205
89 -5466.26227 2033.94115
90 1398.63200 -5466.26227
91 -2041.73624 1398.63200
92 -2095.29767 -2041.73624
93 34224.83278 -2095.29767
94 27204.62671 34224.83278
95 3301.12882 27204.62671
96 -41376.91018 3301.12882
97 -38568.81157 -41376.91018
98 -9277.02263 -38568.81157
99 -24399.20816 -9277.02263
100 -6019.60787 -24399.20816
101 -31472.68582 -6019.60787
102 14648.59324 -31472.68582
103 -13530.09975 14648.59324
104 -845.99466 -13530.09975
105 -24151.52145 -845.99466
106 -10428.47811 -24151.52145
107 18395.41275 -10428.47811
108 -59446.96118 18395.41275
109 28878.29268 -59446.96118
110 -18610.87777 28878.29268
111 16786.24040 -18610.87777
112 -9924.18306 16786.24040
113 -3689.92204 -9924.18306
114 -4273.43895 -3689.92204
115 23717.24754 -4273.43895
116 -25098.48202 23717.24754
117 -12967.25895 -25098.48202
118 4226.57706 -12967.25895
119 -10914.88061 4226.57706
120 -4408.88356 -10914.88061
121 13411.78089 -4408.88356
122 11607.76170 13411.78089
123 -13751.38132 11607.76170
124 -5767.77195 -13751.38132
125 -5690.77489 -5767.77195
126 18544.19268 -5690.77489
127 -28515.54784 18544.19268
128 11645.52926 -28515.54784
129 -20555.61772 11645.52926
130 -2277.07150 -20555.61772
131 8927.21166 -2277.07150
132 -16688.26144 8927.21166
133 55128.25767 -16688.26144
134 38375.24645 55128.25767
135 6720.64763 38375.24645
136 64229.17024 6720.64763
137 2883.09832 64229.17024
138 -20390.45991 2883.09832
139 -4082.22846 -20390.45991
140 41799.91914 -4082.22846
141 66969.65619 41799.91914
142 -2959.85581 66969.65619
143 130164.88076 -2959.85581
144 20394.48804 130164.88076
145 -8571.48326 20394.48804
146 -16040.93679 -8571.48326
147 -2363.67727 -16040.93679
148 -41584.24952 -2363.67727
149 -20830.90566 -41584.24952
150 -24444.98567 -20830.90566
151 -24087.98567 -24444.98567
152 -26436.45943 -24087.98567
153 -24542.98567 -26436.45943
154 11634.65098 -24542.98567
155 -2875.74055 11634.65098
156 -24542.98567 -2875.74055
157 -24339.98567 -24542.98567
158 -23327.21687 -24339.98567
159 -8392.17326 -23327.21687
160 -17736.62574 -8392.17326
161 35587.03667 -17736.62574
162 -23573.98567 35587.03667
163 17106.49372 -23573.98567
> 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/7opyy1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8box01321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9ppje1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10umsr1321541513.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/115ya61321541513.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/12g7nf1321541513.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/1328fk1321541513.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/14fsu41321541513.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/15it3t1321541513.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/16h0u61321541513.tab")
+ }
>
> try(system("convert tmp/1p0si1321541513.ps tmp/1p0si1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/2p42e1321541513.ps tmp/2p42e1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wj5j1321541513.ps tmp/3wj5j1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/40iwh1321541513.ps tmp/40iwh1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/5yvkz1321541513.ps tmp/5yvkz1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h7d91321541513.ps tmp/6h7d91321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/7opyy1321541513.ps tmp/7opyy1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/8box01321541513.ps tmp/8box01321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ppje1321541513.ps tmp/9ppje1321541513.png",intern=TRUE))
character(0)
> try(system("convert tmp/10umsr1321541513.ps tmp/10umsr1321541513.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.092 0.568 5.730