R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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Type '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(140824
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+ ,dim=c(5
+ ,164)
+ ,dimnames=list(c('#Karakters'
+ ,'Totale_tijd_RFC'
+ ,'#Feedback_Messages(+120karakters)'
+ ,'#Blogs'
+ ,'#Compendium_views(PR)')
+ ,1:164))
> y <- array(NA,dim=c(5,164),dimnames=list(c('#Karakters','Totale_tijd_RFC','#Feedback_Messages(+120karakters)','#Blogs','#Compendium_views(PR)'),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
#Karakters Totale_tijd_RFC #Feedback_Messages(+120karakters) #Blogs
1 140824 269998 116 165
2 110459 176565 127 132
3 105079 222373 106 121
4 112098 218443 133 145
5 43929 157206 64 71
6 76173 70849 89 47
7 187326 482608 122 177
8 22807 33186 22 5
9 144408 207822 117 124
10 66485 211698 82 92
11 79089 292874 136 149
12 81625 235891 184 93
13 68788 156623 106 70
14 103297 344166 162 148
15 69446 211787 86 100
16 114948 369753 199 142
17 167949 292100 139 194
18 125081 315018 92 113
19 125818 168686 85 162
20 136588 256016 174 186
21 112431 269240 148 147
22 103037 425544 144 137
23 82317 161962 84 71
24 118906 189897 208 123
25 83515 200545 144 134
26 104581 203723 139 115
27 103129 267198 127 138
28 83243 263212 136 125
29 37110 155915 99 66
30 113344 326805 135 137
31 139165 271661 165 152
32 86652 197192 139 159
33 112302 318563 178 159
34 69652 97717 137 31
35 119442 346931 148 185
36 69867 273950 127 78
37 101629 411809 141 117
38 70168 208192 89 109
39 31081 115469 46 41
40 103925 328339 143 149
41 92622 324178 122 123
42 79011 157897 103 103
43 93487 192883 108 87
44 64520 173450 126 71
45 93473 153778 45 51
46 114360 445562 122 70
47 33032 78800 66 21
48 96125 208051 180 155
49 151911 323152 165 172
50 89256 175523 146 133
51 95671 213050 137 125
52 5950 24188 7 7
53 149695 372225 157 158
54 32551 65029 61 21
55 31701 101097 41 35
56 100087 269593 120 133
57 169707 302218 208 169
58 150491 315889 127 256
59 120192 322546 147 190
60 95893 246873 127 100
61 151715 360665 161 171
62 176225 296186 73 267
63 59900 232336 94 80
64 104767 254550 142 126
65 114799 228595 125 132
66 72128 216027 87 121
67 143592 187959 128 156
68 89626 227699 148 133
69 131072 229698 116 199
70 126817 166791 89 98
71 81351 239277 154 109
72 22618 73566 67 25
73 88977 242498 171 113
74 92059 187167 90 126
75 81897 178281 133 137
76 108146 349060 137 121
77 126372 323126 133 178
78 249771 206059 125 63
79 71154 184970 134 109
80 71571 168990 110 101
81 55918 153613 89 61
82 160141 429481 138 157
83 38692 145919 99 38
84 102812 280343 92 159
85 56622 80953 27 58
86 15986 148106 77 27
87 123534 146777 127 108
88 108535 336054 137 83
89 93879 307486 122 88
90 144551 178495 143 164
91 56750 251466 85 96
92 127654 230961 131 192
93 65594 175244 90 94
94 59938 261494 135 107
95 146975 301883 132 144
96 143372 189252 139 123
97 168553 222504 127 170
98 183500 278170 104 210
99 165986 367723 221 193
100 184923 392346 106 297
101 140358 281033 161 125
102 149959 273642 130 204
103 57224 186856 59 70
104 43750 43287 64 49
105 48029 185302 36 82
106 104978 203088 88 205
107 100046 259692 125 111
108 101047 301456 124 135
109 197426 119969 83 59
110 160902 153028 127 70
111 147172 306952 143 108
112 109432 297807 115 141
113 1168 23623 0 11
114 83248 175532 94 130
115 25162 61857 30 28
116 45724 163766 119 101
117 110529 384053 102 216
118 855 21054 0 4
119 101382 252805 77 97
120 14116 31961 9 39
121 89506 294609 137 119
122 135356 235069 157 118
123 116066 174862 146 41
124 144244 152043 84 107
125 8773 38214 21 16
126 102153 189451 139 69
127 117440 344802 168 160
128 104128 190943 163 158
129 134238 396160 167 161
130 134047 314212 145 165
131 279488 396712 175 246
132 79756 187992 137 89
133 66089 102424 100 49
134 102070 283392 150 107
135 146760 401260 163 182
136 154771 135936 137 16
137 165933 373146 149 173
138 64593 157429 112 90
139 92280 236370 135 140
140 67150 258959 114 142
141 128692 214338 45 126
142 124089 363154 120 123
143 125386 232339 115 239
144 37238 173260 78 15
145 140015 317676 136 170
146 150047 168994 179 123
147 154451 233293 118 151
148 156349 301585 147 194
149 0 1 0 0
150 6023 14688 0 5
151 0 98 0 0
152 0 455 0 0
153 0 0 0 0
154 0 0 0 0
155 84601 216803 88 122
156 68946 365230 115 173
157 0 0 0 0
158 0 203 0 0
159 1644 7199 0 6
160 6179 46660 13 13
161 3926 17547 4 3
162 52789 116678 76 35
163 0 969 0 0
164 100350 195592 63 72
#Compendium_views(PR)
1 90
2 63
3 59
4 135
5 48
6 46
7 109
8 46
9 75
10 72
11 78
12 61
13 58
14 114
15 45
16 127
17 58
18 90
19 41
20 59
21 99
22 101
23 62
24 65
25 150
26 72
27 91
28 60
29 53
30 140
31 49
32 81
33 53
34 40
35 72
36 87
37 72
38 67
39 36
40 45
41 42
42 70
43 82
44 85
45 82
46 792
47 57
48 80
49 116
50 68
51 48
52 20
53 81
54 21
55 70
56 124
57 80
58 206
59 62
60 77
61 65
62 146
63 71
64 59
65 58
66 58
67 54
68 89
69 78
70 62
71 63
72 39
73 58
74 94
75 61
76 92
77 48
78 50
79 58
80 67
81 41
82 114
83 45
84 57
85 31
86 175
87 63
88 278
89 91
90 68
91 58
92 71
93 86
94 89
95 134
96 64
97 72
98 61
99 123
100 73
101 80
102 85
103 116
104 43
105 85
106 72
107 110
108 55
109 44
110 79
111 58
112 70
113 9
114 49
115 25
116 107
117 63
118 2
119 67
120 22
121 152
122 78
123 112
124 47
125 52
126 108
127 110
128 61
129 134
130 120
131 111
132 49
133 55
134 149
135 155
136 103
137 142
138 76
139 83
140 185
141 69
142 117
143 63
144 37
145 56
146 122
147 52
148 64
149 0
150 0
151 0
152 0
153 0
154 0
155 58
156 109
157 0
158 0
159 0
160 7
161 3
162 89
163 0
164 46
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Totale_tijd_RFC
6.104e+03 5.263e-02
`#Feedback_Messages(+120karakters)` `#Blogs`
3.084e+02 3.888e+02
`#Compendium_views(PR)`
1.641e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-60896 -17959 -6285 10102 168960
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.104e+03 5.892e+03 1.036 0.302
Totale_tijd_RFC 5.263e-02 4.498e-02 1.170 0.244
`#Feedback_Messages(+120karakters)` 3.084e+02 7.386e+01 4.175 4.89e-05 ***
`#Blogs` 3.888e+02 6.582e+01 5.907 2.05e-08 ***
`#Compendium_views(PR)` 1.641e+01 4.146e+01 0.396 0.693
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 30880 on 159 degrees of freedom
Multiple R-squared: 0.6561, Adjusted R-squared: 0.6474
F-statistic: 75.82 on 4 and 159 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,] 2.647971e-01 5.295942e-01 7.352029e-01
[2,] 2.874201e-01 5.748403e-01 7.125799e-01
[3,] 2.698695e-01 5.397391e-01 7.301305e-01
[4,] 6.355893e-01 7.288214e-01 3.644107e-01
[5,] 5.389255e-01 9.221490e-01 4.610745e-01
[6,] 4.281721e-01 8.563442e-01 5.718279e-01
[7,] 3.890286e-01 7.780572e-01 6.109714e-01
[8,] 3.374436e-01 6.748873e-01 6.625564e-01
[9,] 2.594444e-01 5.188888e-01 7.405556e-01
[10,] 2.042691e-01 4.085382e-01 7.957309e-01
[11,] 1.771245e-01 3.542491e-01 8.228755e-01
[12,] 1.295287e-01 2.590573e-01 8.704713e-01
[13,] 9.105392e-02 1.821078e-01 9.089461e-01
[14,] 6.334746e-02 1.266949e-01 9.366525e-01
[15,] 5.238907e-02 1.047781e-01 9.476109e-01
[16,] 3.841768e-02 7.683535e-02 9.615823e-01
[17,] 3.462648e-02 6.925297e-02 9.653735e-01
[18,] 2.932669e-02 5.865338e-02 9.706733e-01
[19,] 1.953560e-02 3.907120e-02 9.804644e-01
[20,] 1.330785e-02 2.661571e-02 9.866921e-01
[21,] 1.296343e-02 2.592685e-02 9.870366e-01
[22,] 1.357801e-02 2.715602e-02 9.864220e-01
[23,] 8.683663e-03 1.736733e-02 9.913163e-01
[24,] 6.136728e-03 1.227346e-02 9.938633e-01
[25,] 9.162434e-03 1.832487e-02 9.908376e-01
[26,] 7.583879e-03 1.516776e-02 9.924161e-01
[27,] 8.021781e-03 1.604356e-02 9.919782e-01
[28,] 7.479353e-03 1.495871e-02 9.925206e-01
[29,] 5.182731e-03 1.036546e-02 9.948173e-01
[30,] 3.447146e-03 6.894292e-03 9.965529e-01
[31,] 2.915988e-03 5.831977e-03 9.970840e-01
[32,] 2.197643e-03 4.395286e-03 9.978024e-01
[33,] 1.727729e-03 3.455458e-03 9.982723e-01
[34,] 1.192785e-03 2.385570e-03 9.988072e-01
[35,] 7.435756e-04 1.487151e-03 9.992564e-01
[36,] 5.441169e-04 1.088234e-03 9.994559e-01
[37,] 3.436360e-04 6.872719e-04 9.996564e-01
[38,] 6.545990e-04 1.309198e-03 9.993454e-01
[39,] 4.295768e-04 8.591537e-04 9.995704e-01
[40,] 2.632265e-04 5.264529e-04 9.997368e-01
[41,] 2.534032e-04 5.068064e-04 9.997466e-01
[42,] 2.020464e-04 4.040928e-04 9.997980e-01
[43,] 1.490190e-04 2.980379e-04 9.998510e-01
[44,] 9.329169e-05 1.865834e-04 9.999067e-01
[45,] 7.049383e-05 1.409877e-04 9.999295e-01
[46,] 6.004708e-05 1.200942e-04 9.999400e-01
[47,] 3.492376e-05 6.984752e-05 9.999651e-01
[48,] 2.213806e-05 4.427613e-05 9.999779e-01
[49,] 1.345642e-05 2.691285e-05 9.999865e-01
[50,] 2.342921e-05 4.685841e-05 9.999766e-01
[51,] 2.114047e-05 4.228094e-05 9.999789e-01
[52,] 1.718690e-05 3.437380e-05 9.999828e-01
[53,] 1.012803e-05 2.025606e-05 9.999899e-01
[54,] 7.197493e-06 1.439499e-05 9.999928e-01
[55,] 5.544705e-06 1.108941e-05 9.999945e-01
[56,] 4.113404e-06 8.226807e-06 9.999959e-01
[57,] 2.439842e-06 4.879684e-06 9.999976e-01
[58,] 1.488651e-06 2.977303e-06 9.999985e-01
[59,] 1.238702e-06 2.477403e-06 9.999988e-01
[60,] 1.522393e-06 3.044785e-06 9.999985e-01
[61,] 1.290095e-06 2.580190e-06 9.999987e-01
[62,] 7.103963e-07 1.420793e-06 9.999993e-01
[63,] 2.538818e-06 5.077637e-06 9.999975e-01
[64,] 2.330625e-06 4.661250e-06 9.999977e-01
[65,] 1.652758e-06 3.305517e-06 9.999983e-01
[66,] 1.638164e-06 3.276328e-06 9.999984e-01
[67,] 9.199310e-07 1.839862e-06 9.999991e-01
[68,] 9.613387e-07 1.922677e-06 9.999990e-01
[69,] 5.697382e-07 1.139476e-06 9.999994e-01
[70,] 3.497559e-07 6.995118e-07 9.999997e-01
[71,] 1.910986e-01 3.821971e-01 8.089014e-01
[72,] 1.982938e-01 3.965876e-01 8.017062e-01
[73,] 1.815748e-01 3.631496e-01 8.184252e-01
[74,] 1.595630e-01 3.191260e-01 8.404370e-01
[75,] 1.512967e-01 3.025935e-01 8.487033e-01
[76,] 1.456682e-01 2.913364e-01 8.543318e-01
[77,] 1.247704e-01 2.495408e-01 8.752296e-01
[78,] 1.071873e-01 2.143746e-01 8.928127e-01
[79,] 1.065361e-01 2.130721e-01 8.934639e-01
[80,] 1.043714e-01 2.087428e-01 8.956286e-01
[81,] 9.292142e-02 1.858428e-01 9.070786e-01
[82,] 7.578006e-02 1.515601e-01 9.242199e-01
[83,] 6.794752e-02 1.358950e-01 9.320525e-01
[84,] 6.758549e-02 1.351710e-01 9.324145e-01
[85,] 5.575130e-02 1.115026e-01 9.442487e-01
[86,] 4.673969e-02 9.347939e-02 9.532603e-01
[87,] 6.790077e-02 1.358015e-01 9.320992e-01
[88,] 6.622658e-02 1.324532e-01 9.337734e-01
[89,] 6.878773e-02 1.375755e-01 9.312123e-01
[90,] 8.221313e-02 1.644263e-01 9.177869e-01
[91,] 1.073400e-01 2.146799e-01 8.926600e-01
[92,] 9.855469e-02 1.971094e-01 9.014453e-01
[93,] 8.980683e-02 1.796137e-01 9.101932e-01
[94,] 7.938233e-02 1.587647e-01 9.206177e-01
[95,] 6.495265e-02 1.299053e-01 9.350474e-01
[96,] 5.264520e-02 1.052904e-01 9.473548e-01
[97,] 4.178904e-02 8.357809e-02 9.582110e-01
[98,] 3.428193e-02 6.856387e-02 9.657181e-01
[99,] 2.897718e-02 5.795437e-02 9.710228e-01
[100,] 2.224149e-02 4.448297e-02 9.777585e-01
[101,] 1.958761e-02 3.917523e-02 9.804124e-01
[102,] 4.841137e-01 9.682274e-01 5.158863e-01
[103,] 6.936797e-01 6.126405e-01 3.063203e-01
[104,] 6.805405e-01 6.389190e-01 3.194595e-01
[105,] 6.382737e-01 7.234525e-01 3.617263e-01
[106,] 5.962740e-01 8.074519e-01 4.037260e-01
[107,] 5.564948e-01 8.870103e-01 4.435052e-01
[108,] 5.076991e-01 9.846018e-01 4.923009e-01
[109,] 5.752503e-01 8.494995e-01 4.247497e-01
[110,] 5.774022e-01 8.451956e-01 4.225978e-01
[111,] 5.294498e-01 9.411005e-01 4.705502e-01
[112,] 4.953845e-01 9.907690e-01 5.046155e-01
[113,] 4.475750e-01 8.951500e-01 5.524250e-01
[114,] 4.236985e-01 8.473969e-01 5.763015e-01
[115,] 3.814689e-01 7.629378e-01 6.185311e-01
[116,] 3.744736e-01 7.489471e-01 6.255264e-01
[117,] 5.171243e-01 9.657514e-01 4.828757e-01
[118,] 4.666028e-01 9.332055e-01 5.333972e-01
[119,] 4.165756e-01 8.331512e-01 5.834244e-01
[120,] 4.213554e-01 8.427107e-01 5.786446e-01
[121,] 4.584229e-01 9.168457e-01 5.415771e-01
[122,] 4.262350e-01 8.524699e-01 5.737650e-01
[123,] 3.729230e-01 7.458460e-01 6.270770e-01
[124,] 8.462924e-01 3.074152e-01 1.537076e-01
[125,] 8.645129e-01 2.709742e-01 1.354871e-01
[126,] 8.364027e-01 3.271946e-01 1.635973e-01
[127,] 8.141106e-01 3.717787e-01 1.858894e-01
[128,] 7.696163e-01 4.607674e-01 2.303837e-01
[129,] 9.433982e-01 1.132036e-01 5.660181e-02
[130,] 9.464661e-01 1.070678e-01 5.353389e-02
[131,] 9.384607e-01 1.230787e-01 6.153935e-02
[132,] 9.414445e-01 1.171111e-01 5.855554e-02
[133,] 9.472930e-01 1.054140e-01 5.270699e-02
[134,] 9.975846e-01 4.830809e-03 2.415405e-03
[135,] 9.987618e-01 2.476498e-03 1.238249e-03
[136,] 9.983708e-01 3.258464e-03 1.629232e-03
[137,] 9.997660e-01 4.680964e-04 2.340482e-04
[138,] 9.999927e-01 1.450437e-05 7.252186e-06
[139,] 9.999856e-01 2.870663e-05 1.435331e-05
[140,] 9.999958e-01 8.313709e-06 4.156855e-06
[141,] 9.999922e-01 1.552412e-05 7.762058e-06
[142,] 9.999681e-01 6.385216e-05 3.192608e-05
[143,] 9.999043e-01 1.913215e-04 9.566077e-05
[144,] 9.996355e-01 7.289084e-04 3.644542e-04
[145,] 9.986832e-01 2.633626e-03 1.316813e-03
[146,] 9.955515e-01 8.896991e-03 4.448496e-03
[147,] 9.859979e-01 2.800426e-02 1.400213e-02
[148,] 9.848596e-01 3.028071e-02 1.514036e-02
[149,] 9.893568e-01 2.128637e-02 1.064319e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1osox1324314933.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/2118s1324314933.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3drog1324314933.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/418pj1324314933.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5i08n1324314933.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
19107.7854 3541.8473 6569.4603 -5109.2772 -18578.0379 19865.9032
7 8 9 10 11 12
47592.7620 5473.1841 41843.2050 -12999.4474 -43580.6034 -30794.8555
13 14 15 16 17 18
-6415.1932 -30291.4952 -13944.1506 -29277.2125 27226.4211 28614.8295
19 20 21 22 23 24
20963.5506 -9934.4060 -12262.1625 -24793.3113 13162.5412 -10224.3061
25 26 27 28 29 30
-32111.5479 -1003.6636 -11350.2986 -28238.6215 -34260.0333 -7154.4102
31 32 33 34 35 36
7978.3999 -35844.5409 -28149.1308 3448.5920 -23671.9228 -21573.0245
37 38 39 40 41 42
-16301.2102 -17818.3742 -11817.4388 -22228.2156 -16677.9887 -8361.9603
43 44 45 46 47 48
8755.1145 -18567.9991 44223.9022 6973.6138 -6672.2614 -38014.5664
49 50 51 52 53 54
9138.9083 -23936.2158 -13282.1742 -6635.7472 12824.9430 -4296.0027
55 56 57 58 59 60
-7124.1614 -10957.1039 16533.7845 -14317.9978 -23110.2251 -2511.9035
61 62 63 64 65 66
9427.7421 25812.4221 -19688.8328 -8481.3920 5842.3854 -20172.1059
67 68 69 70 71 72
26582.9013 -27273.4346 -1546.1756 45368.4998 -28249.8034 -18379.1118
73 74 75 76 77 78
-27508.8547 -2182.1164 -28871.9564 -7131.7770 -7748.0558 168960.0089
79 80 81 82 83 84
-29339.2729 -17717.5059 -10106.2037 25964.0931 -21133.8426 -9173.4095
85 86 87 88 89 90
14871.2774 -35026.7439 27516.1292 5664.9764 -1737.8998 20074.1750
91 92 93 94 95 96
-27078.0389 -6819.6431 -15446.2453 -44621.9497 26090.0286 35569.6440
97 98 99 100 101 102
44295.4724 48033.2291 -4680.5512 8806.6980 19901.0729 8652.0078
103 104 105 106 107 108
-6028.4850 -4125.6976 -12206.3406 -19839.8096 -3235.2230 -12553.0075
109 110 111 112 113 114
135751.0533 79067.6613 37872.5234 -3779.6370 -10604.2013 -12431.2106
115 116 117 118 119 120
-4745.9106 -46721.2068 -32259.1640 -7945.4333 19413.9779 -11970.5099
121 122 123 124 125 126
-23112.9281 21306.0850 37957.7687 61860.2747 -12892.4842 14614.3414
127 128 129 130 131 132
-22632.3614 -24723.6092 -9011.4559 568.9095 101070.4908 -13897.4325
133 134 135 136 137 138
3802.7915 -9252.4016 -4034.0747 91354.7871 24648.3487 -20574.4984
139 140 141 142 143 144
-23690.2220 -45984.4235 47307.8015 12123.9741 -22369.3461 -8476.9193
145 146 147 148 149 150
8235.9282 30024.3225 40116.6657 12561.8320 -6104.3732 -2798.4047
151 152 153 154 155 156
-6109.4782 -6128.2664 -6104.3206 -6104.3206 -8437.1328 -60895.9936
157 158 159 160 161 162
-6104.3206 -6115.0041 -7172.0893 -11559.2361 -5550.9412 2039.1174
163 164
-6155.3172 35775.1487
> postscript(file="/var/wessaorg/rcomp/tmp/6oksf1324314933.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 19107.7854 NA
1 3541.8473 19107.7854
2 6569.4603 3541.8473
3 -5109.2772 6569.4603
4 -18578.0379 -5109.2772
5 19865.9032 -18578.0379
6 47592.7620 19865.9032
7 5473.1841 47592.7620
8 41843.2050 5473.1841
9 -12999.4474 41843.2050
10 -43580.6034 -12999.4474
11 -30794.8555 -43580.6034
12 -6415.1932 -30794.8555
13 -30291.4952 -6415.1932
14 -13944.1506 -30291.4952
15 -29277.2125 -13944.1506
16 27226.4211 -29277.2125
17 28614.8295 27226.4211
18 20963.5506 28614.8295
19 -9934.4060 20963.5506
20 -12262.1625 -9934.4060
21 -24793.3113 -12262.1625
22 13162.5412 -24793.3113
23 -10224.3061 13162.5412
24 -32111.5479 -10224.3061
25 -1003.6636 -32111.5479
26 -11350.2986 -1003.6636
27 -28238.6215 -11350.2986
28 -34260.0333 -28238.6215
29 -7154.4102 -34260.0333
30 7978.3999 -7154.4102
31 -35844.5409 7978.3999
32 -28149.1308 -35844.5409
33 3448.5920 -28149.1308
34 -23671.9228 3448.5920
35 -21573.0245 -23671.9228
36 -16301.2102 -21573.0245
37 -17818.3742 -16301.2102
38 -11817.4388 -17818.3742
39 -22228.2156 -11817.4388
40 -16677.9887 -22228.2156
41 -8361.9603 -16677.9887
42 8755.1145 -8361.9603
43 -18567.9991 8755.1145
44 44223.9022 -18567.9991
45 6973.6138 44223.9022
46 -6672.2614 6973.6138
47 -38014.5664 -6672.2614
48 9138.9083 -38014.5664
49 -23936.2158 9138.9083
50 -13282.1742 -23936.2158
51 -6635.7472 -13282.1742
52 12824.9430 -6635.7472
53 -4296.0027 12824.9430
54 -7124.1614 -4296.0027
55 -10957.1039 -7124.1614
56 16533.7845 -10957.1039
57 -14317.9978 16533.7845
58 -23110.2251 -14317.9978
59 -2511.9035 -23110.2251
60 9427.7421 -2511.9035
61 25812.4221 9427.7421
62 -19688.8328 25812.4221
63 -8481.3920 -19688.8328
64 5842.3854 -8481.3920
65 -20172.1059 5842.3854
66 26582.9013 -20172.1059
67 -27273.4346 26582.9013
68 -1546.1756 -27273.4346
69 45368.4998 -1546.1756
70 -28249.8034 45368.4998
71 -18379.1118 -28249.8034
72 -27508.8547 -18379.1118
73 -2182.1164 -27508.8547
74 -28871.9564 -2182.1164
75 -7131.7770 -28871.9564
76 -7748.0558 -7131.7770
77 168960.0089 -7748.0558
78 -29339.2729 168960.0089
79 -17717.5059 -29339.2729
80 -10106.2037 -17717.5059
81 25964.0931 -10106.2037
82 -21133.8426 25964.0931
83 -9173.4095 -21133.8426
84 14871.2774 -9173.4095
85 -35026.7439 14871.2774
86 27516.1292 -35026.7439
87 5664.9764 27516.1292
88 -1737.8998 5664.9764
89 20074.1750 -1737.8998
90 -27078.0389 20074.1750
91 -6819.6431 -27078.0389
92 -15446.2453 -6819.6431
93 -44621.9497 -15446.2453
94 26090.0286 -44621.9497
95 35569.6440 26090.0286
96 44295.4724 35569.6440
97 48033.2291 44295.4724
98 -4680.5512 48033.2291
99 8806.6980 -4680.5512
100 19901.0729 8806.6980
101 8652.0078 19901.0729
102 -6028.4850 8652.0078
103 -4125.6976 -6028.4850
104 -12206.3406 -4125.6976
105 -19839.8096 -12206.3406
106 -3235.2230 -19839.8096
107 -12553.0075 -3235.2230
108 135751.0533 -12553.0075
109 79067.6613 135751.0533
110 37872.5234 79067.6613
111 -3779.6370 37872.5234
112 -10604.2013 -3779.6370
113 -12431.2106 -10604.2013
114 -4745.9106 -12431.2106
115 -46721.2068 -4745.9106
116 -32259.1640 -46721.2068
117 -7945.4333 -32259.1640
118 19413.9779 -7945.4333
119 -11970.5099 19413.9779
120 -23112.9281 -11970.5099
121 21306.0850 -23112.9281
122 37957.7687 21306.0850
123 61860.2747 37957.7687
124 -12892.4842 61860.2747
125 14614.3414 -12892.4842
126 -22632.3614 14614.3414
127 -24723.6092 -22632.3614
128 -9011.4559 -24723.6092
129 568.9095 -9011.4559
130 101070.4908 568.9095
131 -13897.4325 101070.4908
132 3802.7915 -13897.4325
133 -9252.4016 3802.7915
134 -4034.0747 -9252.4016
135 91354.7871 -4034.0747
136 24648.3487 91354.7871
137 -20574.4984 24648.3487
138 -23690.2220 -20574.4984
139 -45984.4235 -23690.2220
140 47307.8015 -45984.4235
141 12123.9741 47307.8015
142 -22369.3461 12123.9741
143 -8476.9193 -22369.3461
144 8235.9282 -8476.9193
145 30024.3225 8235.9282
146 40116.6657 30024.3225
147 12561.8320 40116.6657
148 -6104.3732 12561.8320
149 -2798.4047 -6104.3732
150 -6109.4782 -2798.4047
151 -6128.2664 -6109.4782
152 -6104.3206 -6128.2664
153 -6104.3206 -6104.3206
154 -8437.1328 -6104.3206
155 -60895.9936 -8437.1328
156 -6104.3206 -60895.9936
157 -6115.0041 -6104.3206
158 -7172.0893 -6115.0041
159 -11559.2361 -7172.0893
160 -5550.9412 -11559.2361
161 2039.1174 -5550.9412
162 -6155.3172 2039.1174
163 35775.1487 -6155.3172
164 NA 35775.1487
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 3541.8473 19107.7854
[2,] 6569.4603 3541.8473
[3,] -5109.2772 6569.4603
[4,] -18578.0379 -5109.2772
[5,] 19865.9032 -18578.0379
[6,] 47592.7620 19865.9032
[7,] 5473.1841 47592.7620
[8,] 41843.2050 5473.1841
[9,] -12999.4474 41843.2050
[10,] -43580.6034 -12999.4474
[11,] -30794.8555 -43580.6034
[12,] -6415.1932 -30794.8555
[13,] -30291.4952 -6415.1932
[14,] -13944.1506 -30291.4952
[15,] -29277.2125 -13944.1506
[16,] 27226.4211 -29277.2125
[17,] 28614.8295 27226.4211
[18,] 20963.5506 28614.8295
[19,] -9934.4060 20963.5506
[20,] -12262.1625 -9934.4060
[21,] -24793.3113 -12262.1625
[22,] 13162.5412 -24793.3113
[23,] -10224.3061 13162.5412
[24,] -32111.5479 -10224.3061
[25,] -1003.6636 -32111.5479
[26,] -11350.2986 -1003.6636
[27,] -28238.6215 -11350.2986
[28,] -34260.0333 -28238.6215
[29,] -7154.4102 -34260.0333
[30,] 7978.3999 -7154.4102
[31,] -35844.5409 7978.3999
[32,] -28149.1308 -35844.5409
[33,] 3448.5920 -28149.1308
[34,] -23671.9228 3448.5920
[35,] -21573.0245 -23671.9228
[36,] -16301.2102 -21573.0245
[37,] -17818.3742 -16301.2102
[38,] -11817.4388 -17818.3742
[39,] -22228.2156 -11817.4388
[40,] -16677.9887 -22228.2156
[41,] -8361.9603 -16677.9887
[42,] 8755.1145 -8361.9603
[43,] -18567.9991 8755.1145
[44,] 44223.9022 -18567.9991
[45,] 6973.6138 44223.9022
[46,] -6672.2614 6973.6138
[47,] -38014.5664 -6672.2614
[48,] 9138.9083 -38014.5664
[49,] -23936.2158 9138.9083
[50,] -13282.1742 -23936.2158
[51,] -6635.7472 -13282.1742
[52,] 12824.9430 -6635.7472
[53,] -4296.0027 12824.9430
[54,] -7124.1614 -4296.0027
[55,] -10957.1039 -7124.1614
[56,] 16533.7845 -10957.1039
[57,] -14317.9978 16533.7845
[58,] -23110.2251 -14317.9978
[59,] -2511.9035 -23110.2251
[60,] 9427.7421 -2511.9035
[61,] 25812.4221 9427.7421
[62,] -19688.8328 25812.4221
[63,] -8481.3920 -19688.8328
[64,] 5842.3854 -8481.3920
[65,] -20172.1059 5842.3854
[66,] 26582.9013 -20172.1059
[67,] -27273.4346 26582.9013
[68,] -1546.1756 -27273.4346
[69,] 45368.4998 -1546.1756
[70,] -28249.8034 45368.4998
[71,] -18379.1118 -28249.8034
[72,] -27508.8547 -18379.1118
[73,] -2182.1164 -27508.8547
[74,] -28871.9564 -2182.1164
[75,] -7131.7770 -28871.9564
[76,] -7748.0558 -7131.7770
[77,] 168960.0089 -7748.0558
[78,] -29339.2729 168960.0089
[79,] -17717.5059 -29339.2729
[80,] -10106.2037 -17717.5059
[81,] 25964.0931 -10106.2037
[82,] -21133.8426 25964.0931
[83,] -9173.4095 -21133.8426
[84,] 14871.2774 -9173.4095
[85,] -35026.7439 14871.2774
[86,] 27516.1292 -35026.7439
[87,] 5664.9764 27516.1292
[88,] -1737.8998 5664.9764
[89,] 20074.1750 -1737.8998
[90,] -27078.0389 20074.1750
[91,] -6819.6431 -27078.0389
[92,] -15446.2453 -6819.6431
[93,] -44621.9497 -15446.2453
[94,] 26090.0286 -44621.9497
[95,] 35569.6440 26090.0286
[96,] 44295.4724 35569.6440
[97,] 48033.2291 44295.4724
[98,] -4680.5512 48033.2291
[99,] 8806.6980 -4680.5512
[100,] 19901.0729 8806.6980
[101,] 8652.0078 19901.0729
[102,] -6028.4850 8652.0078
[103,] -4125.6976 -6028.4850
[104,] -12206.3406 -4125.6976
[105,] -19839.8096 -12206.3406
[106,] -3235.2230 -19839.8096
[107,] -12553.0075 -3235.2230
[108,] 135751.0533 -12553.0075
[109,] 79067.6613 135751.0533
[110,] 37872.5234 79067.6613
[111,] -3779.6370 37872.5234
[112,] -10604.2013 -3779.6370
[113,] -12431.2106 -10604.2013
[114,] -4745.9106 -12431.2106
[115,] -46721.2068 -4745.9106
[116,] -32259.1640 -46721.2068
[117,] -7945.4333 -32259.1640
[118,] 19413.9779 -7945.4333
[119,] -11970.5099 19413.9779
[120,] -23112.9281 -11970.5099
[121,] 21306.0850 -23112.9281
[122,] 37957.7687 21306.0850
[123,] 61860.2747 37957.7687
[124,] -12892.4842 61860.2747
[125,] 14614.3414 -12892.4842
[126,] -22632.3614 14614.3414
[127,] -24723.6092 -22632.3614
[128,] -9011.4559 -24723.6092
[129,] 568.9095 -9011.4559
[130,] 101070.4908 568.9095
[131,] -13897.4325 101070.4908
[132,] 3802.7915 -13897.4325
[133,] -9252.4016 3802.7915
[134,] -4034.0747 -9252.4016
[135,] 91354.7871 -4034.0747
[136,] 24648.3487 91354.7871
[137,] -20574.4984 24648.3487
[138,] -23690.2220 -20574.4984
[139,] -45984.4235 -23690.2220
[140,] 47307.8015 -45984.4235
[141,] 12123.9741 47307.8015
[142,] -22369.3461 12123.9741
[143,] -8476.9193 -22369.3461
[144,] 8235.9282 -8476.9193
[145,] 30024.3225 8235.9282
[146,] 40116.6657 30024.3225
[147,] 12561.8320 40116.6657
[148,] -6104.3732 12561.8320
[149,] -2798.4047 -6104.3732
[150,] -6109.4782 -2798.4047
[151,] -6128.2664 -6109.4782
[152,] -6104.3206 -6128.2664
[153,] -6104.3206 -6104.3206
[154,] -8437.1328 -6104.3206
[155,] -60895.9936 -8437.1328
[156,] -6104.3206 -60895.9936
[157,] -6115.0041 -6104.3206
[158,] -7172.0893 -6115.0041
[159,] -11559.2361 -7172.0893
[160,] -5550.9412 -11559.2361
[161,] 2039.1174 -5550.9412
[162,] -6155.3172 2039.1174
[163,] 35775.1487 -6155.3172
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 3541.8473 19107.7854
2 6569.4603 3541.8473
3 -5109.2772 6569.4603
4 -18578.0379 -5109.2772
5 19865.9032 -18578.0379
6 47592.7620 19865.9032
7 5473.1841 47592.7620
8 41843.2050 5473.1841
9 -12999.4474 41843.2050
10 -43580.6034 -12999.4474
11 -30794.8555 -43580.6034
12 -6415.1932 -30794.8555
13 -30291.4952 -6415.1932
14 -13944.1506 -30291.4952
15 -29277.2125 -13944.1506
16 27226.4211 -29277.2125
17 28614.8295 27226.4211
18 20963.5506 28614.8295
19 -9934.4060 20963.5506
20 -12262.1625 -9934.4060
21 -24793.3113 -12262.1625
22 13162.5412 -24793.3113
23 -10224.3061 13162.5412
24 -32111.5479 -10224.3061
25 -1003.6636 -32111.5479
26 -11350.2986 -1003.6636
27 -28238.6215 -11350.2986
28 -34260.0333 -28238.6215
29 -7154.4102 -34260.0333
30 7978.3999 -7154.4102
31 -35844.5409 7978.3999
32 -28149.1308 -35844.5409
33 3448.5920 -28149.1308
34 -23671.9228 3448.5920
35 -21573.0245 -23671.9228
36 -16301.2102 -21573.0245
37 -17818.3742 -16301.2102
38 -11817.4388 -17818.3742
39 -22228.2156 -11817.4388
40 -16677.9887 -22228.2156
41 -8361.9603 -16677.9887
42 8755.1145 -8361.9603
43 -18567.9991 8755.1145
44 44223.9022 -18567.9991
45 6973.6138 44223.9022
46 -6672.2614 6973.6138
47 -38014.5664 -6672.2614
48 9138.9083 -38014.5664
49 -23936.2158 9138.9083
50 -13282.1742 -23936.2158
51 -6635.7472 -13282.1742
52 12824.9430 -6635.7472
53 -4296.0027 12824.9430
54 -7124.1614 -4296.0027
55 -10957.1039 -7124.1614
56 16533.7845 -10957.1039
57 -14317.9978 16533.7845
58 -23110.2251 -14317.9978
59 -2511.9035 -23110.2251
60 9427.7421 -2511.9035
61 25812.4221 9427.7421
62 -19688.8328 25812.4221
63 -8481.3920 -19688.8328
64 5842.3854 -8481.3920
65 -20172.1059 5842.3854
66 26582.9013 -20172.1059
67 -27273.4346 26582.9013
68 -1546.1756 -27273.4346
69 45368.4998 -1546.1756
70 -28249.8034 45368.4998
71 -18379.1118 -28249.8034
72 -27508.8547 -18379.1118
73 -2182.1164 -27508.8547
74 -28871.9564 -2182.1164
75 -7131.7770 -28871.9564
76 -7748.0558 -7131.7770
77 168960.0089 -7748.0558
78 -29339.2729 168960.0089
79 -17717.5059 -29339.2729
80 -10106.2037 -17717.5059
81 25964.0931 -10106.2037
82 -21133.8426 25964.0931
83 -9173.4095 -21133.8426
84 14871.2774 -9173.4095
85 -35026.7439 14871.2774
86 27516.1292 -35026.7439
87 5664.9764 27516.1292
88 -1737.8998 5664.9764
89 20074.1750 -1737.8998
90 -27078.0389 20074.1750
91 -6819.6431 -27078.0389
92 -15446.2453 -6819.6431
93 -44621.9497 -15446.2453
94 26090.0286 -44621.9497
95 35569.6440 26090.0286
96 44295.4724 35569.6440
97 48033.2291 44295.4724
98 -4680.5512 48033.2291
99 8806.6980 -4680.5512
100 19901.0729 8806.6980
101 8652.0078 19901.0729
102 -6028.4850 8652.0078
103 -4125.6976 -6028.4850
104 -12206.3406 -4125.6976
105 -19839.8096 -12206.3406
106 -3235.2230 -19839.8096
107 -12553.0075 -3235.2230
108 135751.0533 -12553.0075
109 79067.6613 135751.0533
110 37872.5234 79067.6613
111 -3779.6370 37872.5234
112 -10604.2013 -3779.6370
113 -12431.2106 -10604.2013
114 -4745.9106 -12431.2106
115 -46721.2068 -4745.9106
116 -32259.1640 -46721.2068
117 -7945.4333 -32259.1640
118 19413.9779 -7945.4333
119 -11970.5099 19413.9779
120 -23112.9281 -11970.5099
121 21306.0850 -23112.9281
122 37957.7687 21306.0850
123 61860.2747 37957.7687
124 -12892.4842 61860.2747
125 14614.3414 -12892.4842
126 -22632.3614 14614.3414
127 -24723.6092 -22632.3614
128 -9011.4559 -24723.6092
129 568.9095 -9011.4559
130 101070.4908 568.9095
131 -13897.4325 101070.4908
132 3802.7915 -13897.4325
133 -9252.4016 3802.7915
134 -4034.0747 -9252.4016
135 91354.7871 -4034.0747
136 24648.3487 91354.7871
137 -20574.4984 24648.3487
138 -23690.2220 -20574.4984
139 -45984.4235 -23690.2220
140 47307.8015 -45984.4235
141 12123.9741 47307.8015
142 -22369.3461 12123.9741
143 -8476.9193 -22369.3461
144 8235.9282 -8476.9193
145 30024.3225 8235.9282
146 40116.6657 30024.3225
147 12561.8320 40116.6657
148 -6104.3732 12561.8320
149 -2798.4047 -6104.3732
150 -6109.4782 -2798.4047
151 -6128.2664 -6109.4782
152 -6104.3206 -6128.2664
153 -6104.3206 -6104.3206
154 -8437.1328 -6104.3206
155 -60895.9936 -8437.1328
156 -6104.3206 -60895.9936
157 -6115.0041 -6104.3206
158 -7172.0893 -6115.0041
159 -11559.2361 -7172.0893
160 -5550.9412 -11559.2361
161 2039.1174 -5550.9412
162 -6155.3172 2039.1174
163 35775.1487 -6155.3172
> 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/7v4ho1324314933.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/8hjl61324314933.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/9b93c1324314933.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/10ac5o1324314933.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/113t451324314933.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/122tok1324314933.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13ux7j1324314933.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14h0yc1324314933.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/154f1m1324314933.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/16yns91324314933.tab")
+ }
>
> try(system("convert tmp/1osox1324314933.ps tmp/1osox1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/2118s1324314933.ps tmp/2118s1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/3drog1324314933.ps tmp/3drog1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/418pj1324314933.ps tmp/418pj1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/5i08n1324314933.ps tmp/5i08n1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/6oksf1324314933.ps tmp/6oksf1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v4ho1324314933.ps tmp/7v4ho1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hjl61324314933.ps tmp/8hjl61324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/9b93c1324314933.ps tmp/9b93c1324314933.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ac5o1324314933.ps tmp/10ac5o1324314933.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
5.088 0.748 5.843