R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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(170650
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+ ,48)
+ ,dim=c(3
+ ,164)
+ ,dimnames=list(c('TijdRFC'
+ ,'Karakters'
+ ,'Blogs')
+ ,1:164))
> y <- array(NA,dim=c(3,164),dimnames=list(c('TijdRFC','Karakters','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'
> 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
TijdRFC Karakters Blogs
1 170650 95556 128
2 86621 54565 89
3 127843 63016 68
4 152526 79774 108
5 92389 31258 51
6 38138 52491 33
7 316392 91256 119
8 32750 22807 5
9 1323444 77411 63
10 137034 48821 66
11 176816 52295 98
12 140146 63262 71
13 113286 50466 55
14 195452 62932 116
15 144513 38439 71
16 263581 70817 120
17 183271 105965 122
18 210763 73795 74
19 113853 82043 111
20 159968 74349 103
21 174585 82204 98
22 294675 55709 100
23 96213 37137 42
24 116390 70780 100
25 146342 55027 105
26 152647 56699 77
27 166661 65911 83
28 175505 56316 98
29 112485 26982 46
30 197053 54628 95
31 191822 96750 91
32 139127 53009 91
33 221991 64664 94
34 75339 36990 15
35 247985 85224 137
36 167351 37048 56
37 266609 59635 78
38 122024 42051 68
39 80964 26998 34
40 215183 63717 94
41 225469 55071 82
42 125382 40001 63
43 141437 54506 58
44 81106 35838 43
45 93125 50838 36
46 318668 86997 64
47 78800 33032 21
48 161048 61704 104
49 236367 117986 124
50 131108 56733 101
51 131096 55064 85
52 24188 84607 7
53 267003 84607 124
54 65029 32551 21
55 100147 31701 35
56 178549 71170 95
57 186965 101773 102
58 197266 101653 212
59 217300 81493 141
60 149594 55901 54
61 263413 109104 117
62 209228 114425 145
63 145699 36311 50
64 187197 70027 80
65 150752 73713 87
66 125555 40671 78
67 118697 89041 86
68 147913 57231 82
69 155015 68608 119
70 96487 59155 75
71 128780 55827 70
72 71972 22618 25
73 140266 58425 66
74 148454 65724 89
75 110655 56979 99
76 203795 72369 98
77 211093 79194 104
78 113421 202316 48
79 103660 44970 81
80 128390 49319 64
81 105502 36252 44
82 299359 75741 104
83 141493 38417 36
84 146390 64102 120
85 80953 56622 58
86 109237 15430 27
87 102104 72571 84
88 233139 67271 56
89 176507 43460 46
90 118217 99501 119
91 142694 28340 57
92 152193 76013 139
93 126500 37361 51
94 147410 48204 85
95 187772 76168 91
96 140903 85168 79
97 150587 125410 142
98 202077 123328 149
99 213875 83038 96
100 252952 120087 198
101 166981 91939 61
102 190562 103646 145
103 106351 29467 26
104 43287 43750 49
105 127493 34497 68
106 132143 66477 145
107 157469 71181 82
108 197727 74482 102
109 88077 174949 52
110 94968 46765 56
111 191351 90257 80
112 153332 51370 99
113 22938 1168 11
114 125927 51360 87
115 61857 25162 28
116 103749 21067 67
117 269909 58233 150
118 21054 855 4
119 174409 85903 71
120 31414 14116 39
121 200405 57637 87
122 139456 94137 66
123 78001 62147 23
124 82724 62832 56
125 38214 8773 16
126 91390 63785 49
127 197612 65196 108
128 137161 73087 112
129 251103 72631 110
130 209835 86281 126
131 269470 162365 155
132 139215 56530 75
133 76470 35606 30
134 197114 70111 78
135 291962 92046 135
136 56727 63989 8
137 254843 104911 114
138 105908 43448 60
139 170155 60029 99
140 136745 38650 98
141 86706 47261 33
142 251448 73586 93
143 152366 83042 157
144 173260 37238 15
145 212582 63958 98
146 87850 78956 49
147 148363 99518 88
148 185455 111436 151
149 0 0 0
150 14688 6023 5
151 98 0 0
152 455 0 0
153 0 0 0
154 0 0 0
155 137891 42564 80
156 200096 38885 122
157 0 0 0
158 203 0 0
159 7199 1644 6
160 46660 6179 13
161 17547 3926 3
162 73567 23238 18
163 969 0 0
164 106662 49288 48
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Karakters Blogs
4.279e+04 6.785e-01 8.679e+02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-118553 -37908 -11355 14708 1173451
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.279e+04 1.768e+04 2.421 0.0166 *
Karakters 6.785e-01 3.358e-01 2.021 0.0450 *
Blogs 8.679e+02 2.611e+02 3.324 0.0011 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 103900 on 161 degrees of freedom
Multiple R-squared: 0.2221, Adjusted R-squared: 0.2125
F-statistic: 22.99 on 2 and 161 DF, p-value: 1.652e-09
> 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,] 0.03063666 6.127332e-02 9.693633e-01
[2,] 0.21334470 4.266894e-01 7.866553e-01
[3,] 0.12345810 2.469162e-01 8.765419e-01
[4,] 1.00000000 9.779955e-39 4.889977e-39
[5,] 1.00000000 5.177864e-38 2.588932e-38
[6,] 1.00000000 1.511294e-39 7.556472e-40
[7,] 1.00000000 2.336786e-39 1.168393e-39
[8,] 1.00000000 9.922993e-39 4.961497e-39
[9,] 1.00000000 7.719170e-39 3.859585e-39
[10,] 1.00000000 9.512138e-39 4.756069e-39
[11,] 1.00000000 1.285570e-38 6.427852e-39
[12,] 1.00000000 3.649144e-40 1.824572e-40
[13,] 1.00000000 4.640189e-40 2.320094e-40
[14,] 1.00000000 3.810875e-40 1.905438e-40
[15,] 1.00000000 1.840740e-39 9.203701e-40
[16,] 1.00000000 5.412876e-39 2.706438e-39
[17,] 1.00000000 1.937227e-40 9.686135e-41
[18,] 1.00000000 9.791752e-40 4.895876e-40
[19,] 1.00000000 2.215548e-39 1.107774e-39
[20,] 1.00000000 8.731858e-39 4.365929e-39
[21,] 1.00000000 4.410813e-38 2.205407e-38
[22,] 1.00000000 1.941174e-37 9.705872e-38
[23,] 1.00000000 7.994769e-37 3.997384e-37
[24,] 1.00000000 3.476933e-36 1.738466e-36
[25,] 1.00000000 9.912117e-36 4.956059e-36
[26,] 1.00000000 1.194852e-35 5.974262e-36
[27,] 1.00000000 5.326575e-35 2.663287e-35
[28,] 1.00000000 1.192290e-34 5.961452e-35
[29,] 1.00000000 3.090743e-34 1.545371e-34
[30,] 1.00000000 1.064211e-33 5.321053e-34
[31,] 1.00000000 2.335253e-33 1.167627e-33
[32,] 1.00000000 4.441749e-34 2.220875e-34
[33,] 1.00000000 1.935651e-33 9.678257e-34
[34,] 1.00000000 8.101902e-33 4.050951e-33
[35,] 1.00000000 1.811961e-32 9.059806e-33
[36,] 1.00000000 1.751252e-32 8.756258e-33
[37,] 1.00000000 7.144328e-32 3.572164e-32
[38,] 1.00000000 2.328215e-31 1.164107e-31
[39,] 1.00000000 8.303941e-31 4.151970e-31
[40,] 1.00000000 2.127570e-30 1.063785e-30
[41,] 1.00000000 9.621386e-33 4.810693e-33
[42,] 1.00000000 3.494321e-32 1.747161e-32
[43,] 1.00000000 1.414893e-31 7.074464e-32
[44,] 1.00000000 2.439621e-31 1.219811e-31
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[52,] 1.00000000 1.142284e-28 5.711422e-29
[53,] 1.00000000 4.763521e-29 2.381761e-29
[54,] 1.00000000 1.801400e-28 9.006998e-29
[55,] 1.00000000 4.878812e-28 2.439406e-28
[56,] 1.00000000 6.890596e-28 3.445298e-28
[57,] 1.00000000 1.896157e-27 9.480783e-28
[58,] 1.00000000 4.298275e-27 2.149138e-27
[59,] 1.00000000 1.023889e-26 5.119446e-27
[60,] 1.00000000 3.426915e-26 1.713458e-26
[61,] 1.00000000 1.207630e-25 6.038152e-26
[62,] 1.00000000 2.212486e-25 1.106243e-25
[63,] 1.00000000 7.600762e-25 3.800381e-25
[64,] 1.00000000 2.077305e-24 1.038652e-24
[65,] 1.00000000 4.570975e-24 2.285488e-24
[66,] 1.00000000 1.511288e-23 7.556441e-24
[67,] 1.00000000 4.992772e-23 2.496386e-23
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[70,] 1.00000000 8.984446e-22 4.492223e-22
[71,] 1.00000000 2.151300e-21 1.075650e-21
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[73,] 1.00000000 3.130244e-21 1.565122e-21
[74,] 1.00000000 7.713779e-21 3.856890e-21
[75,] 1.00000000 2.387395e-20 1.193698e-20
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[77,] 1.00000000 6.636760e-21 3.318380e-21
[78,] 1.00000000 1.054112e-20 5.270562e-21
[79,] 1.00000000 2.438112e-20 1.219056e-20
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[83,] 1.00000000 2.642879e-20 1.321439e-20
[84,] 1.00000000 1.620812e-20 8.104059e-21
[85,] 1.00000000 9.101647e-21 4.550823e-21
[86,] 1.00000000 1.892643e-20 9.463216e-21
[87,] 1.00000000 2.378043e-20 1.189021e-20
[88,] 1.00000000 6.352920e-20 3.176460e-20
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[90,] 1.00000000 5.418551e-19 2.709276e-19
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[92,] 1.00000000 6.345604e-19 3.172802e-19
[93,] 1.00000000 1.115539e-18 5.577695e-19
[94,] 1.00000000 2.157235e-18 1.078617e-18
[95,] 1.00000000 3.121395e-18 1.560698e-18
[96,] 1.00000000 7.431273e-18 3.715636e-18
[97,] 1.00000000 1.177621e-17 5.888104e-18
[98,] 1.00000000 2.207504e-17 1.103752e-17
[99,] 1.00000000 2.814744e-17 1.407372e-17
[100,] 1.00000000 8.966836e-17 4.483418e-17
[101,] 1.00000000 1.426192e-17 7.130959e-18
[102,] 1.00000000 4.820106e-17 2.410053e-17
[103,] 1.00000000 1.476336e-16 7.381678e-17
[104,] 1.00000000 5.336854e-17 2.668427e-17
[105,] 1.00000000 1.660507e-16 8.302533e-17
[106,] 1.00000000 4.597362e-16 2.298681e-16
[107,] 1.00000000 1.498022e-15 7.490109e-16
[108,] 1.00000000 4.684028e-15 2.342014e-15
[109,] 1.00000000 1.274760e-14 6.373801e-15
[110,] 1.00000000 4.030018e-14 2.015009e-14
[111,] 1.00000000 1.274358e-13 6.371789e-14
[112,] 1.00000000 2.180284e-13 1.090142e-13
[113,] 1.00000000 6.648197e-13 3.324099e-13
[114,] 1.00000000 1.816079e-12 9.080394e-13
[115,] 1.00000000 3.559430e-12 1.779715e-12
[116,] 1.00000000 5.485475e-12 2.742738e-12
[117,] 1.00000000 1.603263e-11 8.016317e-12
[118,] 1.00000000 4.728728e-11 2.364364e-11
[119,] 1.00000000 9.171645e-11 4.585822e-11
[120,] 1.00000000 2.670084e-10 1.335042e-10
[121,] 1.00000000 6.541652e-10 3.270826e-10
[122,] 1.00000000 1.718961e-09 8.594807e-10
[123,] 1.00000000 2.400835e-09 1.200418e-09
[124,] 1.00000000 2.304935e-09 1.152467e-09
[125,] 1.00000000 6.714269e-09 3.357134e-09
[126,] 0.99999999 1.313223e-08 6.566115e-09
[127,] 0.99999998 3.734845e-08 1.867422e-08
[128,] 0.99999995 1.035904e-07 5.179522e-08
[129,] 0.99999991 1.814704e-07 9.073522e-08
[130,] 0.99999992 1.513588e-07 7.567938e-08
[131,] 0.99999982 3.546889e-07 1.773444e-07
[132,] 0.99999970 5.940813e-07 2.970406e-07
[133,] 0.99999918 1.643252e-06 8.216258e-07
[134,] 0.99999791 4.181163e-06 2.090582e-06
[135,] 0.99999448 1.103817e-05 5.519087e-06
[136,] 0.99998576 2.847421e-05 1.423711e-05
[137,] 0.99999599 8.011007e-06 4.005504e-06
[138,] 0.99999714 5.712693e-06 2.856347e-06
[139,] 1.00000000 3.981293e-09 1.990646e-09
[140,] 1.00000000 5.816835e-10 2.908418e-10
[141,] 1.00000000 3.294852e-09 1.647426e-09
[142,] 0.99999999 1.389382e-08 6.946912e-09
[143,] 1.00000000 8.529110e-12 4.264555e-12
[144,] 1.00000000 8.701486e-11 4.350743e-11
[145,] 1.00000000 9.089790e-10 4.544895e-10
[146,] 1.00000000 8.574771e-09 4.287386e-09
[147,] 0.99999996 7.795041e-08 3.897520e-08
[148,] 0.99999967 6.587049e-07 3.293524e-07
[149,] 0.99999740 5.209096e-06 2.604548e-06
[150,] 0.99998401 3.197481e-05 1.598741e-05
[151,] 0.99986558 2.688356e-04 1.344178e-04
[152,] 0.99907895 1.842091e-03 9.210456e-04
[153,] 0.99432409 1.135182e-02 5.675910e-03
> postscript(file="/var/www/rcomp/tmp/16ikp1321989110.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/www/rcomp/tmp/2r44c1321989110.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3ycid1321989110.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4lkgp1321989110.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/546ci1321989110.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-48070.15807 -70436.28016 -16722.31892 -38127.05755 -15872.62176
6 7 8 9 10
-68908.51554 108400.83610 -29852.99949 1173450.69438 3836.39388
11 12 13 14 15
13487.73310 -7189.99722 -11480.69361 9283.55891 14020.39978
16 17 18 19 20
68590.59483 -37304.56539 53676.18409 -80943.42215 -22664.38258
21 22 23 24 25
-9037.71805 127294.35942 -8226.48567 -61216.91606 -24915.47212
26 27 28 29 30
4556.74548 7112.52059 9448.32399 11464.41082 34745.45688
31 32 33 34 35
4404.67404 -18610.31032 53741.54917 -5566.92307 28464.24421
36 37 38 39 40
50821.03619 115658.63305 -8315.72902 -9652.41594 47576.12650
41 42 43 44 45
74143.81273 772.87364 11325.24756 -23319.98144 -15403.64649
46 47 48 49 50
161302.29134 -4627.77691 -13872.16416 5898.86765 -37835.38464
51 52 53 54 55
-22828.19496 -82084.60627 59183.85239 -18072.39920 5471.49156
56 57 58 59 60
5017.04915 -13407.74281 -98496.42526 -3160.79989 22007.36096
61 62 63 64 65
45047.09351 -37050.15391 34876.63507 27459.40766 -17562.12467
66 67 68 69 70
-12527.53547 -59149.86576 -4877.83355 -37608.59128 -51533.91030
71 72 73 74 75
-12643.13359 -7861.13858 551.69627 -16175.10738 -56719.46715
76 77 78 79 80
26845.72229 24305.17224 -108306.54981 -39943.33616 -3409.68070
81 82 83 84 85
-72.81613 114914.17066 41392.49814 -44044.00945 -50594.54296
86 87 88 89 90
32545.36822 -62831.47459 96101.52350 64305.42964 -95368.72573
91 92 93 94 95
31204.84285 -62813.56269 14097.24897 -1859.41096 14320.38314
96 97 98 99 100
-28240.44646 -100541.15771 -53713.87165 31422.21795 -43167.76237
101 102 103 104 105
8865.70441 -48402.17187 21002.62220 -71715.10441 2278.96170
106 107 108 109 110
-81600.52074 -4787.46575 15872.29076 -118552.62383 -28155.33643
111 112 113 114 115
17886.54461 -10236.53661 -30189.58861 -27219.72133 -22306.10166
116 117 118 119 120
-11484.32771 57419.76677 -25785.77151 11710.17994 -54801.06018
121 122 123 124 125
42999.08345 -24490.32217 -26918.30566 -51301.43769 -24413.48066
126 127 128 129 130
-37206.65216 16850.69773 -52426.33758 63560.91494 -855.86178
131 132 133 134 135
-18016.55033 -7024.74295 -16515.61114 39055.24861 69548.08304
136 137 138 139 140
-36423.38888 41925.96896 -18436.29607 710.98603 -17324.58938
141 142 143 144 145
-16791.75164 78012.53496 -83032.56477 92185.79903 41339.92175
146 147 148 149 150
-51040.78163 -38328.76705 -64002.51296 -42787.94323 -36526.38509
151 152 153 154 155
-42689.94323 -42332.94323 -42787.94323 -42787.94323 -3211.84990
156 157 158 159 160
25036.89397 -42787.94323 -42584.94323 -41911.97784 -11603.59058
161 162 163 164
-30508.64852 -611.39924 -41818.94323 -11229.93946
> postscript(file="/var/www/rcomp/tmp/6vmpu1321989110.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 -48070.15807 NA
1 -70436.28016 -48070.15807
2 -16722.31892 -70436.28016
3 -38127.05755 -16722.31892
4 -15872.62176 -38127.05755
5 -68908.51554 -15872.62176
6 108400.83610 -68908.51554
7 -29852.99949 108400.83610
8 1173450.69438 -29852.99949
9 3836.39388 1173450.69438
10 13487.73310 3836.39388
11 -7189.99722 13487.73310
12 -11480.69361 -7189.99722
13 9283.55891 -11480.69361
14 14020.39978 9283.55891
15 68590.59483 14020.39978
16 -37304.56539 68590.59483
17 53676.18409 -37304.56539
18 -80943.42215 53676.18409
19 -22664.38258 -80943.42215
20 -9037.71805 -22664.38258
21 127294.35942 -9037.71805
22 -8226.48567 127294.35942
23 -61216.91606 -8226.48567
24 -24915.47212 -61216.91606
25 4556.74548 -24915.47212
26 7112.52059 4556.74548
27 9448.32399 7112.52059
28 11464.41082 9448.32399
29 34745.45688 11464.41082
30 4404.67404 34745.45688
31 -18610.31032 4404.67404
32 53741.54917 -18610.31032
33 -5566.92307 53741.54917
34 28464.24421 -5566.92307
35 50821.03619 28464.24421
36 115658.63305 50821.03619
37 -8315.72902 115658.63305
38 -9652.41594 -8315.72902
39 47576.12650 -9652.41594
40 74143.81273 47576.12650
41 772.87364 74143.81273
42 11325.24756 772.87364
43 -23319.98144 11325.24756
44 -15403.64649 -23319.98144
45 161302.29134 -15403.64649
46 -4627.77691 161302.29134
47 -13872.16416 -4627.77691
48 5898.86765 -13872.16416
49 -37835.38464 5898.86765
50 -22828.19496 -37835.38464
51 -82084.60627 -22828.19496
52 59183.85239 -82084.60627
53 -18072.39920 59183.85239
54 5471.49156 -18072.39920
55 5017.04915 5471.49156
56 -13407.74281 5017.04915
57 -98496.42526 -13407.74281
58 -3160.79989 -98496.42526
59 22007.36096 -3160.79989
60 45047.09351 22007.36096
61 -37050.15391 45047.09351
62 34876.63507 -37050.15391
63 27459.40766 34876.63507
64 -17562.12467 27459.40766
65 -12527.53547 -17562.12467
66 -59149.86576 -12527.53547
67 -4877.83355 -59149.86576
68 -37608.59128 -4877.83355
69 -51533.91030 -37608.59128
70 -12643.13359 -51533.91030
71 -7861.13858 -12643.13359
72 551.69627 -7861.13858
73 -16175.10738 551.69627
74 -56719.46715 -16175.10738
75 26845.72229 -56719.46715
76 24305.17224 26845.72229
77 -108306.54981 24305.17224
78 -39943.33616 -108306.54981
79 -3409.68070 -39943.33616
80 -72.81613 -3409.68070
81 114914.17066 -72.81613
82 41392.49814 114914.17066
83 -44044.00945 41392.49814
84 -50594.54296 -44044.00945
85 32545.36822 -50594.54296
86 -62831.47459 32545.36822
87 96101.52350 -62831.47459
88 64305.42964 96101.52350
89 -95368.72573 64305.42964
90 31204.84285 -95368.72573
91 -62813.56269 31204.84285
92 14097.24897 -62813.56269
93 -1859.41096 14097.24897
94 14320.38314 -1859.41096
95 -28240.44646 14320.38314
96 -100541.15771 -28240.44646
97 -53713.87165 -100541.15771
98 31422.21795 -53713.87165
99 -43167.76237 31422.21795
100 8865.70441 -43167.76237
101 -48402.17187 8865.70441
102 21002.62220 -48402.17187
103 -71715.10441 21002.62220
104 2278.96170 -71715.10441
105 -81600.52074 2278.96170
106 -4787.46575 -81600.52074
107 15872.29076 -4787.46575
108 -118552.62383 15872.29076
109 -28155.33643 -118552.62383
110 17886.54461 -28155.33643
111 -10236.53661 17886.54461
112 -30189.58861 -10236.53661
113 -27219.72133 -30189.58861
114 -22306.10166 -27219.72133
115 -11484.32771 -22306.10166
116 57419.76677 -11484.32771
117 -25785.77151 57419.76677
118 11710.17994 -25785.77151
119 -54801.06018 11710.17994
120 42999.08345 -54801.06018
121 -24490.32217 42999.08345
122 -26918.30566 -24490.32217
123 -51301.43769 -26918.30566
124 -24413.48066 -51301.43769
125 -37206.65216 -24413.48066
126 16850.69773 -37206.65216
127 -52426.33758 16850.69773
128 63560.91494 -52426.33758
129 -855.86178 63560.91494
130 -18016.55033 -855.86178
131 -7024.74295 -18016.55033
132 -16515.61114 -7024.74295
133 39055.24861 -16515.61114
134 69548.08304 39055.24861
135 -36423.38888 69548.08304
136 41925.96896 -36423.38888
137 -18436.29607 41925.96896
138 710.98603 -18436.29607
139 -17324.58938 710.98603
140 -16791.75164 -17324.58938
141 78012.53496 -16791.75164
142 -83032.56477 78012.53496
143 92185.79903 -83032.56477
144 41339.92175 92185.79903
145 -51040.78163 41339.92175
146 -38328.76705 -51040.78163
147 -64002.51296 -38328.76705
148 -42787.94323 -64002.51296
149 -36526.38509 -42787.94323
150 -42689.94323 -36526.38509
151 -42332.94323 -42689.94323
152 -42787.94323 -42332.94323
153 -42787.94323 -42787.94323
154 -3211.84990 -42787.94323
155 25036.89397 -3211.84990
156 -42787.94323 25036.89397
157 -42584.94323 -42787.94323
158 -41911.97784 -42584.94323
159 -11603.59058 -41911.97784
160 -30508.64852 -11603.59058
161 -611.39924 -30508.64852
162 -41818.94323 -611.39924
163 -11229.93946 -41818.94323
164 NA -11229.93946
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -70436.28016 -48070.15807
[2,] -16722.31892 -70436.28016
[3,] -38127.05755 -16722.31892
[4,] -15872.62176 -38127.05755
[5,] -68908.51554 -15872.62176
[6,] 108400.83610 -68908.51554
[7,] -29852.99949 108400.83610
[8,] 1173450.69438 -29852.99949
[9,] 3836.39388 1173450.69438
[10,] 13487.73310 3836.39388
[11,] -7189.99722 13487.73310
[12,] -11480.69361 -7189.99722
[13,] 9283.55891 -11480.69361
[14,] 14020.39978 9283.55891
[15,] 68590.59483 14020.39978
[16,] -37304.56539 68590.59483
[17,] 53676.18409 -37304.56539
[18,] -80943.42215 53676.18409
[19,] -22664.38258 -80943.42215
[20,] -9037.71805 -22664.38258
[21,] 127294.35942 -9037.71805
[22,] -8226.48567 127294.35942
[23,] -61216.91606 -8226.48567
[24,] -24915.47212 -61216.91606
[25,] 4556.74548 -24915.47212
[26,] 7112.52059 4556.74548
[27,] 9448.32399 7112.52059
[28,] 11464.41082 9448.32399
[29,] 34745.45688 11464.41082
[30,] 4404.67404 34745.45688
[31,] -18610.31032 4404.67404
[32,] 53741.54917 -18610.31032
[33,] -5566.92307 53741.54917
[34,] 28464.24421 -5566.92307
[35,] 50821.03619 28464.24421
[36,] 115658.63305 50821.03619
[37,] -8315.72902 115658.63305
[38,] -9652.41594 -8315.72902
[39,] 47576.12650 -9652.41594
[40,] 74143.81273 47576.12650
[41,] 772.87364 74143.81273
[42,] 11325.24756 772.87364
[43,] -23319.98144 11325.24756
[44,] -15403.64649 -23319.98144
[45,] 161302.29134 -15403.64649
[46,] -4627.77691 161302.29134
[47,] -13872.16416 -4627.77691
[48,] 5898.86765 -13872.16416
[49,] -37835.38464 5898.86765
[50,] -22828.19496 -37835.38464
[51,] -82084.60627 -22828.19496
[52,] 59183.85239 -82084.60627
[53,] -18072.39920 59183.85239
[54,] 5471.49156 -18072.39920
[55,] 5017.04915 5471.49156
[56,] -13407.74281 5017.04915
[57,] -98496.42526 -13407.74281
[58,] -3160.79989 -98496.42526
[59,] 22007.36096 -3160.79989
[60,] 45047.09351 22007.36096
[61,] -37050.15391 45047.09351
[62,] 34876.63507 -37050.15391
[63,] 27459.40766 34876.63507
[64,] -17562.12467 27459.40766
[65,] -12527.53547 -17562.12467
[66,] -59149.86576 -12527.53547
[67,] -4877.83355 -59149.86576
[68,] -37608.59128 -4877.83355
[69,] -51533.91030 -37608.59128
[70,] -12643.13359 -51533.91030
[71,] -7861.13858 -12643.13359
[72,] 551.69627 -7861.13858
[73,] -16175.10738 551.69627
[74,] -56719.46715 -16175.10738
[75,] 26845.72229 -56719.46715
[76,] 24305.17224 26845.72229
[77,] -108306.54981 24305.17224
[78,] -39943.33616 -108306.54981
[79,] -3409.68070 -39943.33616
[80,] -72.81613 -3409.68070
[81,] 114914.17066 -72.81613
[82,] 41392.49814 114914.17066
[83,] -44044.00945 41392.49814
[84,] -50594.54296 -44044.00945
[85,] 32545.36822 -50594.54296
[86,] -62831.47459 32545.36822
[87,] 96101.52350 -62831.47459
[88,] 64305.42964 96101.52350
[89,] -95368.72573 64305.42964
[90,] 31204.84285 -95368.72573
[91,] -62813.56269 31204.84285
[92,] 14097.24897 -62813.56269
[93,] -1859.41096 14097.24897
[94,] 14320.38314 -1859.41096
[95,] -28240.44646 14320.38314
[96,] -100541.15771 -28240.44646
[97,] -53713.87165 -100541.15771
[98,] 31422.21795 -53713.87165
[99,] -43167.76237 31422.21795
[100,] 8865.70441 -43167.76237
[101,] -48402.17187 8865.70441
[102,] 21002.62220 -48402.17187
[103,] -71715.10441 21002.62220
[104,] 2278.96170 -71715.10441
[105,] -81600.52074 2278.96170
[106,] -4787.46575 -81600.52074
[107,] 15872.29076 -4787.46575
[108,] -118552.62383 15872.29076
[109,] -28155.33643 -118552.62383
[110,] 17886.54461 -28155.33643
[111,] -10236.53661 17886.54461
[112,] -30189.58861 -10236.53661
[113,] -27219.72133 -30189.58861
[114,] -22306.10166 -27219.72133
[115,] -11484.32771 -22306.10166
[116,] 57419.76677 -11484.32771
[117,] -25785.77151 57419.76677
[118,] 11710.17994 -25785.77151
[119,] -54801.06018 11710.17994
[120,] 42999.08345 -54801.06018
[121,] -24490.32217 42999.08345
[122,] -26918.30566 -24490.32217
[123,] -51301.43769 -26918.30566
[124,] -24413.48066 -51301.43769
[125,] -37206.65216 -24413.48066
[126,] 16850.69773 -37206.65216
[127,] -52426.33758 16850.69773
[128,] 63560.91494 -52426.33758
[129,] -855.86178 63560.91494
[130,] -18016.55033 -855.86178
[131,] -7024.74295 -18016.55033
[132,] -16515.61114 -7024.74295
[133,] 39055.24861 -16515.61114
[134,] 69548.08304 39055.24861
[135,] -36423.38888 69548.08304
[136,] 41925.96896 -36423.38888
[137,] -18436.29607 41925.96896
[138,] 710.98603 -18436.29607
[139,] -17324.58938 710.98603
[140,] -16791.75164 -17324.58938
[141,] 78012.53496 -16791.75164
[142,] -83032.56477 78012.53496
[143,] 92185.79903 -83032.56477
[144,] 41339.92175 92185.79903
[145,] -51040.78163 41339.92175
[146,] -38328.76705 -51040.78163
[147,] -64002.51296 -38328.76705
[148,] -42787.94323 -64002.51296
[149,] -36526.38509 -42787.94323
[150,] -42689.94323 -36526.38509
[151,] -42332.94323 -42689.94323
[152,] -42787.94323 -42332.94323
[153,] -42787.94323 -42787.94323
[154,] -3211.84990 -42787.94323
[155,] 25036.89397 -3211.84990
[156,] -42787.94323 25036.89397
[157,] -42584.94323 -42787.94323
[158,] -41911.97784 -42584.94323
[159,] -11603.59058 -41911.97784
[160,] -30508.64852 -11603.59058
[161,] -611.39924 -30508.64852
[162,] -41818.94323 -611.39924
[163,] -11229.93946 -41818.94323
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -70436.28016 -48070.15807
2 -16722.31892 -70436.28016
3 -38127.05755 -16722.31892
4 -15872.62176 -38127.05755
5 -68908.51554 -15872.62176
6 108400.83610 -68908.51554
7 -29852.99949 108400.83610
8 1173450.69438 -29852.99949
9 3836.39388 1173450.69438
10 13487.73310 3836.39388
11 -7189.99722 13487.73310
12 -11480.69361 -7189.99722
13 9283.55891 -11480.69361
14 14020.39978 9283.55891
15 68590.59483 14020.39978
16 -37304.56539 68590.59483
17 53676.18409 -37304.56539
18 -80943.42215 53676.18409
19 -22664.38258 -80943.42215
20 -9037.71805 -22664.38258
21 127294.35942 -9037.71805
22 -8226.48567 127294.35942
23 -61216.91606 -8226.48567
24 -24915.47212 -61216.91606
25 4556.74548 -24915.47212
26 7112.52059 4556.74548
27 9448.32399 7112.52059
28 11464.41082 9448.32399
29 34745.45688 11464.41082
30 4404.67404 34745.45688
31 -18610.31032 4404.67404
32 53741.54917 -18610.31032
33 -5566.92307 53741.54917
34 28464.24421 -5566.92307
35 50821.03619 28464.24421
36 115658.63305 50821.03619
37 -8315.72902 115658.63305
38 -9652.41594 -8315.72902
39 47576.12650 -9652.41594
40 74143.81273 47576.12650
41 772.87364 74143.81273
42 11325.24756 772.87364
43 -23319.98144 11325.24756
44 -15403.64649 -23319.98144
45 161302.29134 -15403.64649
46 -4627.77691 161302.29134
47 -13872.16416 -4627.77691
48 5898.86765 -13872.16416
49 -37835.38464 5898.86765
50 -22828.19496 -37835.38464
51 -82084.60627 -22828.19496
52 59183.85239 -82084.60627
53 -18072.39920 59183.85239
54 5471.49156 -18072.39920
55 5017.04915 5471.49156
56 -13407.74281 5017.04915
57 -98496.42526 -13407.74281
58 -3160.79989 -98496.42526
59 22007.36096 -3160.79989
60 45047.09351 22007.36096
61 -37050.15391 45047.09351
62 34876.63507 -37050.15391
63 27459.40766 34876.63507
64 -17562.12467 27459.40766
65 -12527.53547 -17562.12467
66 -59149.86576 -12527.53547
67 -4877.83355 -59149.86576
68 -37608.59128 -4877.83355
69 -51533.91030 -37608.59128
70 -12643.13359 -51533.91030
71 -7861.13858 -12643.13359
72 551.69627 -7861.13858
73 -16175.10738 551.69627
74 -56719.46715 -16175.10738
75 26845.72229 -56719.46715
76 24305.17224 26845.72229
77 -108306.54981 24305.17224
78 -39943.33616 -108306.54981
79 -3409.68070 -39943.33616
80 -72.81613 -3409.68070
81 114914.17066 -72.81613
82 41392.49814 114914.17066
83 -44044.00945 41392.49814
84 -50594.54296 -44044.00945
85 32545.36822 -50594.54296
86 -62831.47459 32545.36822
87 96101.52350 -62831.47459
88 64305.42964 96101.52350
89 -95368.72573 64305.42964
90 31204.84285 -95368.72573
91 -62813.56269 31204.84285
92 14097.24897 -62813.56269
93 -1859.41096 14097.24897
94 14320.38314 -1859.41096
95 -28240.44646 14320.38314
96 -100541.15771 -28240.44646
97 -53713.87165 -100541.15771
98 31422.21795 -53713.87165
99 -43167.76237 31422.21795
100 8865.70441 -43167.76237
101 -48402.17187 8865.70441
102 21002.62220 -48402.17187
103 -71715.10441 21002.62220
104 2278.96170 -71715.10441
105 -81600.52074 2278.96170
106 -4787.46575 -81600.52074
107 15872.29076 -4787.46575
108 -118552.62383 15872.29076
109 -28155.33643 -118552.62383
110 17886.54461 -28155.33643
111 -10236.53661 17886.54461
112 -30189.58861 -10236.53661
113 -27219.72133 -30189.58861
114 -22306.10166 -27219.72133
115 -11484.32771 -22306.10166
116 57419.76677 -11484.32771
117 -25785.77151 57419.76677
118 11710.17994 -25785.77151
119 -54801.06018 11710.17994
120 42999.08345 -54801.06018
121 -24490.32217 42999.08345
122 -26918.30566 -24490.32217
123 -51301.43769 -26918.30566
124 -24413.48066 -51301.43769
125 -37206.65216 -24413.48066
126 16850.69773 -37206.65216
127 -52426.33758 16850.69773
128 63560.91494 -52426.33758
129 -855.86178 63560.91494
130 -18016.55033 -855.86178
131 -7024.74295 -18016.55033
132 -16515.61114 -7024.74295
133 39055.24861 -16515.61114
134 69548.08304 39055.24861
135 -36423.38888 69548.08304
136 41925.96896 -36423.38888
137 -18436.29607 41925.96896
138 710.98603 -18436.29607
139 -17324.58938 710.98603
140 -16791.75164 -17324.58938
141 78012.53496 -16791.75164
142 -83032.56477 78012.53496
143 92185.79903 -83032.56477
144 41339.92175 92185.79903
145 -51040.78163 41339.92175
146 -38328.76705 -51040.78163
147 -64002.51296 -38328.76705
148 -42787.94323 -64002.51296
149 -36526.38509 -42787.94323
150 -42689.94323 -36526.38509
151 -42332.94323 -42689.94323
152 -42787.94323 -42332.94323
153 -42787.94323 -42787.94323
154 -3211.84990 -42787.94323
155 25036.89397 -3211.84990
156 -42787.94323 25036.89397
157 -42584.94323 -42787.94323
158 -41911.97784 -42584.94323
159 -11603.59058 -41911.97784
160 -30508.64852 -11603.59058
161 -611.39924 -30508.64852
162 -41818.94323 -611.39924
163 -11229.93946 -41818.94323
> 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/www/rcomp/tmp/72b1c1321989110.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/www/rcomp/tmp/8spby1321989110.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/www/rcomp/tmp/98voz1321989110.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/www/rcomp/tmp/100scy1321989110.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/114vbq1321989110.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12fsgj1321989110.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13ys1u1321989110.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14erog1321989110.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/www/rcomp/tmp/153ptv1321989110.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/www/rcomp/tmp/16f5uo1321989110.tab")
+ }
>
> try(system("convert tmp/16ikp1321989110.ps tmp/16ikp1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r44c1321989110.ps tmp/2r44c1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ycid1321989110.ps tmp/3ycid1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/4lkgp1321989110.ps tmp/4lkgp1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/546ci1321989110.ps tmp/546ci1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vmpu1321989110.ps tmp/6vmpu1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/72b1c1321989110.ps tmp/72b1c1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/8spby1321989110.ps tmp/8spby1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/98voz1321989110.ps tmp/98voz1321989110.png",intern=TRUE))
character(0)
> try(system("convert tmp/100scy1321989110.ps tmp/100scy1321989110.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.090 0.280 6.395