R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'contributors()' for more information and
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Type 'q()' to quit R.
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+ ,17.28194444
+ ,24
+ ,10
+ ,26
+ ,26
+ ,19.25111111
+ ,40
+ ,19
+ ,21
+ ,21
+ ,14.75472222
+ ,3
+ ,12
+ ,4
+ ,4
+ ,5.49
+ ,10
+ ,2
+ ,10
+ ,10
+ ,24.07777778
+ ,37
+ ,14
+ ,43
+ ,43
+ ,23.3625
+ ,17
+ ,17
+ ,34
+ ,34
+ ,21.65138889
+ ,28
+ ,19
+ ,32
+ ,31
+ ,24.75361111
+ ,19
+ ,14
+ ,20
+ ,19
+ ,25.27916667
+ ,29
+ ,11
+ ,34
+ ,34
+ ,11.18
+ ,8
+ ,4
+ ,6
+ ,6
+ ,17.82972222
+ ,10
+ ,16
+ ,12
+ ,11
+ ,14.12694444
+ ,15
+ ,20
+ ,24
+ ,24
+ ,15.72583333
+ ,15
+ ,12
+ ,16
+ ,16
+ ,17.44222222
+ ,28
+ ,15
+ ,72
+ ,72
+ ,20.14861111
+ ,17
+ ,16
+ ,27
+ ,21)
+ ,dim=c(5
+ ,289)
+ ,dimnames=list(c('Time_in_rfc'
+ ,'blogged_computations'
+ ,'compendiums_reviewed'
+ ,'tothyperlinks'
+ ,'totblogs
')
+ ,1:289))
> y <- array(NA,dim=c(5,289),dimnames=list(c('Time_in_rfc','blogged_computations','compendiums_reviewed','tothyperlinks','totblogs
'),1:289))
> 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'
> 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
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> 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
Time_in_rfc blogged_computations compendiums_reviewed tothyperlinks
1 58.585278 79 30 144
2 33.606111 58 28 103
3 49.030000 60 38 98
4 49.811389 108 30 135
5 34.218056 49 22 61
6 14.651667 0 26 39
7 107.092778 121 25 150
8 9.213889 1 18 5
9 28.234722 20 11 28
10 41.405833 43 26 84
11 45.957222 69 25 80
12 65.892500 78 38 130
13 48.146111 86 44 82
14 36.980833 44 30 60
15 71.909167 104 40 131
16 50.023056 63 34 84
17 90.221944 158 47 140
18 64.156667 102 30 151
19 65.773611 77 31 91
20 37.631389 82 23 138
21 56.368056 115 36 150
22 59.763056 101 36 124
23 95.638056 80 30 119
24 42.759722 50 25 73
25 36.928611 83 39 110
26 48.534444 123 34 123
27 48.448611 73 31 90
28 62.652222 81 31 116
29 62.120000 105 33 113
30 34.671389 47 25 56
31 61.582778 105 33 115
32 58.546389 94 35 119
33 47.296111 44 42 129
34 72.378056 114 43 127
35 23.570278 38 30 27
36 81.784444 107 33 175
37 28.058611 30 13 35
38 59.900278 71 32 64
39 90.307500 84 36 96
40 1.993333 0 0 0
41 46.539444 59 28 84
42 29.557778 33 14 41
43 26.822222 42 17 47
44 73.824722 96 32 126
45 74.903056 106 30 105
46 41.420000 56 35 80
47 48.840000 57 20 70
48 42.464167 59 28 73
49 31.018056 39 28 57
50 32.335556 34 39 40
51 100.639167 76 34 68
52 21.888889 20 26 21
53 50.879722 91 39 127
54 77.212500 115 39 154
55 41.841389 85 33 116
56 46.891389 76 28 102
57 6.718889 8 4 7
58 91.463056 79 39 148
59 18.063611 21 18 21
60 28.082500 30 14 35
61 60.818333 76 29 112
62 67.792222 101 44 137
63 94.880556 94 21 135
64 28.776944 27 16 26
65 64.813333 92 28 230
66 71.239444 123 35 181
67 57.266944 75 28 71
68 86.520278 128 38 147
69 65.500000 105 23 190
70 49.427500 55 36 64
71 57.548889 56 32 105
72 54.598056 41 29 107
73 48.384444 72 25 94
74 39.790556 67 27 116
75 52.099722 75 36 106
76 52.133611 114 28 143
77 33.060000 118 23 81
78 50.608889 77 40 89
79 20.435000 22 23 26
80 54.160833 66 40 84
81 46.524444 69 28 113
82 39.932222 105 34 120
83 76.539167 116 33 110
84 67.555278 88 28 134
85 50.833056 73 34 54
86 37.680278 99 30 96
87 42.305278 62 33 78
88 33.394722 53 22 51
89 96.245833 118 38 121
90 40.497222 30 26 38
91 53.705278 100 35 145
92 22.486944 49 8 59
93 34.103889 24 24 27
94 36.273611 67 29 91
95 31.280833 46 20 48
96 79.574444 57 29 68
97 66.962778 75 45 58
98 41.235000 135 37 150
99 56.864722 68 33 74
100 50.577500 124 33 181
101 38.984444 33 25 65
102 61.254444 98 32 97
103 67.516667 58 29 121
104 45.212500 68 28 99
105 50.725833 81 28 152
106 64.482778 131 31 188
107 73.699444 110 52 138
108 23.770556 37 21 40
109 86.344167 130 24 254
110 62.516667 93 41 87
111 64.532500 118 33 178
112 40.268333 39 32 51
113 12.024167 13 19 49
114 43.265000 74 20 73
115 45.752500 81 31 176
116 56.094444 109 31 94
117 65.403889 151 32 120
118 61.333611 51 18 66
119 27.629444 28 23 56
120 25.739167 40 17 39
121 37.035556 56 20 66
122 17.044722 27 12 27
123 34.980556 37 17 65
124 27.986111 83 30 58
125 62.374722 54 31 98
126 22.865556 27 10 25
127 28.336111 28 13 26
128 28.200833 59 22 77
129 67.641944 133 42 130
130 6.371667 12 1 11
131 11.546111 0 9 2
132 42.353889 106 32 101
133 17.182500 23 11 31
134 27.756389 44 25 36
135 36.801944 71 36 120
136 88.165000 116 31 195
137 5.848333 4 0 4
138 58.233611 62 24 89
139 6.291111 12 13 24
140 8.726111 18 8 39
141 12.971667 14 13 14
142 36.582778 60 19 78
143 25.481944 7 18 15
144 67.985833 98 33 106
145 51.252778 64 40 83
146 22.184167 29 22 24
147 35.673056 32 38 37
148 27.177500 25 24 77
149 10.615000 16 8 16
150 41.972500 48 35 56
151 75.682778 100 43 132
152 47.915000 46 43 144
153 30.011944 45 14 40
154 91.140833 129 41 153
155 69.605278 130 38 143
156 97.518611 136 45 220
157 43.893056 59 31 79
158 27.462778 25 13 50
159 23.733056 32 28 39
160 63.678333 63 31 95
161 97.671944 95 40 169
162 23.390833 14 30 12
163 33.456944 36 16 63
164 90.166111 113 37 134
165 36.408056 47 30 69
166 56.741944 92 35 119
167 45.984167 70 32 119
168 39.367222 19 27 75
169 32.235556 50 20 63
170 69.457500 41 18 55
171 83.270833 91 31 103
172 54.399444 111 31 197
173 48.127778 41 21 16
174 70.691111 120 39 140
175 28.996944 135 41 89
176 37.801111 27 13 40
177 55.410000 87 32 125
178 25.694167 25 18 21
179 62.313889 131 39 167
180 37.716944 45 14 32
181 20.668889 29 7 36
182 22.566667 58 17 13
183 4.080000 4 0 5
184 50.453611 47 30 96
185 75.515556 109 37 151
186 1.999722 7 0 6
187 12.961111 12 5 13
188 4.874167 0 1 3
189 37.046667 37 16 57
190 26.451944 37 32 23
191 42.389167 46 24 61
192 27.262778 15 17 21
193 22.116389 42 11 43
194 16.442778 7 24 20
195 38.872778 54 22 82
196 32.947778 54 12 90
197 20.244444 14 19 25
198 18.187500 16 13 60
199 27.678611 33 17 61
200 19.990278 32 15 85
201 21.464444 21 16 43
202 13.691389 15 24 25
203 37.536389 38 15 41
204 30.123889 22 17 26
205 24.929444 28 18 38
206 12.304444 10 20 12
207 21.568889 31 16 29
208 50.424444 32 16 49
209 37.227500 32 18 46
210 34.462222 43 22 41
211 25.730556 27 8 31
212 33.846667 37 17 41
213 14.698611 20 18 26
214 22.742222 32 16 23
215 16.383611 0 23 14
216 14.865278 5 22 16
217 16.892222 26 13 25
218 15.659722 10 13 21
219 18.191667 27 16 32
220 22.485833 11 16 9
221 21.195000 29 20 35
222 28.891944 25 22 42
223 27.251111 55 17 68
224 18.885833 23 18 32
225 8.608056 5 17 6
226 37.627222 43 12 68
227 20.417778 23 7 33
228 17.534167 34 17 84
229 17.015000 36 14 46
230 20.809444 35 23 30
231 8.826111 0 17 0
232 22.621389 37 14 36
233 24.218333 28 15 47
234 13.913889 16 17 20
235 18.262500 26 21 50
236 15.736944 38 18 30
237 43.999722 23 18 30
238 12.904167 22 17 34
239 20.451111 30 17 33
240 10.665278 16 16 34
241 25.527500 18 15 37
242 38.757222 28 21 83
243 14.490000 32 16 32
244 14.324167 21 14 30
245 19.597500 23 15 43
246 23.571111 29 17 41
247 28.482778 50 15 51
248 24.077222 12 15 19
249 23.808056 21 10 37
250 9.628333 18 6 33
251 41.827778 27 22 41
252 27.669722 41 21 54
253 5.374722 13 1 14
254 27.603611 12 18 25
255 23.952778 21 17 25
256 8.565833 8 4 8
257 8.807222 26 10 26
258 24.946111 27 16 20
259 17.246667 13 16 11
260 11.153056 16 9 14
261 7.676111 2 16 3
262 21.386111 42 17 40
263 10.405556 5 7 5
264 15.043611 37 15 38
265 13.850556 17 14 32
266 23.426944 38 14 41
267 17.826389 37 18 46
268 16.495000 29 12 47
269 33.141111 32 16 37
270 21.306111 35 21 51
271 28.729167 17 19 49
272 19.540000 20 16 21
273 12.058333 7 1 1
274 29.121667 46 16 44
275 17.281944 24 10 26
276 19.251111 40 19 21
277 14.754722 3 12 4
278 5.490000 10 2 10
279 24.077778 37 14 43
280 23.362500 17 17 34
281 21.651389 28 19 32
282 24.753611 19 14 20
283 25.279167 29 11 34
284 11.180000 8 4 6
285 17.829722 10 16 12
286 14.126944 15 20 24
287 15.725833 15 12 16
288 17.442222 28 15 72
289 20.148611 17 16 27
totblogs\r
1 145
2 101
3 98
4 132
5 60
6 38
7 144
8 5
9 28
10 84
11 79
12 127
13 78
14 60
15 131
16 84
17 133
18 150
19 91
20 132
21 136
22 124
23 118
24 70
25 107
26 119
27 89
28 112
29 108
30 52
31 112
32 116
33 123
34 125
35 27
36 162
37 32
38 64
39 92
40 0
41 83
42 41
43 47
44 120
45 105
46 79
47 65
48 70
49 55
50 39
51 67
52 21
53 127
54 152
55 113
56 99
57 7
58 141
59 21
60 35
61 109
62 133
63 123
64 26
65 230
66 166
67 68
68 147
69 179
70 61
71 101
72 108
73 90
74 114
75 103
76 142
77 79
78 88
79 25
80 83
81 113
82 118
83 110
84 129
85 51
86 93
87 76
88 49
89 118
90 38
91 141
92 58
93 27
94 91
95 48
96 63
97 56
98 144
99 73
100 168
101 64
102 97
103 117
104 100
105 149
106 187
107 127
108 37
109 245
110 87
111 177
112 49
113 49
114 73
115 177
116 94
117 117
118 60
119 55
120 39
121 64
122 26
123 64
124 58
125 95
126 25
127 26
128 76
129 129
130 11
131 2
132 101
133 28
134 36
135 89
136 193
137 4
138 84
139 23
140 39
141 14
142 78
143 14
144 101
145 82
146 24
147 36
148 75
149 16
150 55
151 131
152 131
153 39
154 144
155 139
156 211
157 78
158 50
159 39
160 90
161 166
162 12
163 57
164 133
165 69
166 119
167 119
168 65
169 61
170 49
171 101
172 196
173 15
174 136
175 89
176 40
177 123
178 21
179 163
180 29
181 35
182 13
183 5
184 96
185 151
186 6
187 13
188 3
189 56
190 23
191 57
192 14
193 43
194 20
195 72
196 87
197 21
198 56
199 59
200 82
201 43
202 25
203 38
204 25
205 38
206 12
207 29
208 47
209 45
210 40
211 30
212 41
213 25
214 23
215 14
216 16
217 26
218 21
219 27
220 9
221 33
222 42
223 68
224 32
225 6
226 67
227 33
228 77
229 46
230 30
231 0
232 36
233 46
234 18
235 48
236 29
237 28
238 34
239 33
240 34
241 33
242 80
243 32
244 30
245 41
246 41
247 51
248 18
249 34
250 31
251 39
252 54
253 14
254 24
255 24
256 8
257 26
258 19
259 11
260 14
261 1
262 39
263 5
264 37
265 32
266 38
267 47
268 47
269 37
270 51
271 45
272 21
273 1
274 42
275 26
276 21
277 4
278 10
279 43
280 34
281 31
282 19
283 34
284 6
285 11
286 24
287 16
288 72
289 21
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) blogged_computations compendiums_reviewed
3.8289 0.2216 0.5617
tothyperlinks `totblogs\\r`
0.3915 -0.2567
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-39.779 -6.136 -1.728 4.791 51.446
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.8289 1.6610 2.305 0.0219 *
blogged_computations 0.2216 0.0429 5.167 4.49e-07 ***
compendiums_reviewed 0.5617 0.1002 5.607 4.87e-08 ***
tothyperlinks 0.3915 0.2288 1.711 0.0881 .
`totblogs\\r` -0.2567 0.2366 -1.085 0.2788
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 11.51 on 284 degrees of freedom
Multiple R-squared: 0.7502, Adjusted R-squared: 0.7467
F-statistic: 213.2 on 4 and 284 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,] 0.9675373 6.492533e-02 3.246267e-02
[2,] 0.9403818 1.192363e-01 5.961816e-02
[3,] 0.9016625 1.966750e-01 9.833748e-02
[4,] 0.8418442 3.163116e-01 1.581558e-01
[5,] 0.7881176 4.237648e-01 2.118824e-01
[6,] 0.7080062 5.839876e-01 2.919938e-01
[7,] 0.6606809 6.786382e-01 3.393191e-01
[8,] 0.6412276 7.175448e-01 3.587724e-01
[9,] 0.5887498 8.225004e-01 4.112502e-01
[10,] 0.5065478 9.869044e-01 4.934522e-01
[11,] 0.4615102 9.230204e-01 5.384898e-01
[12,] 0.5175623 9.648755e-01 4.824377e-01
[13,] 0.6880284 6.239432e-01 3.119716e-01
[14,] 0.6315355 7.369290e-01 3.684645e-01
[15,] 0.6081989 7.836023e-01 3.918011e-01
[16,] 0.9576068 8.478647e-02 4.239323e-02
[17,] 0.9437781 1.124438e-01 5.622192e-02
[18,] 0.9602412 7.951763e-02 3.975882e-02
[19,] 0.9858590 2.828207e-02 1.414104e-02
[20,] 0.9798500 4.030002e-02 2.015001e-02
[21,] 0.9757281 4.854388e-02 2.427194e-02
[22,] 0.9663109 6.737814e-02 3.368907e-02
[23,] 0.9547571 9.048584e-02 4.524292e-02
[24,] 0.9408094 1.183813e-01 5.919063e-02
[25,] 0.9231170 1.537660e-01 7.688301e-02
[26,] 0.9141310 1.717379e-01 8.586895e-02
[27,] 0.8921315 2.157371e-01 1.078685e-01
[28,] 0.8708781 2.582438e-01 1.291219e-01
[29,] 0.8770404 2.459192e-01 1.229596e-01
[30,] 0.8503743 2.992513e-01 1.496257e-01
[31,] 0.8642251 2.715498e-01 1.357749e-01
[32,] 0.9800018 3.999640e-02 1.999820e-02
[33,] 0.9761095 4.778103e-02 2.389051e-02
[34,] 0.9685430 6.291393e-02 3.145696e-02
[35,] 0.9594514 8.109714e-02 4.054857e-02
[36,] 0.9504226 9.915488e-02 4.957744e-02
[37,] 0.9486484 1.027031e-01 5.135155e-02
[38,] 0.9461990 1.076020e-01 5.380099e-02
[39,] 0.9335690 1.328619e-01 6.643096e-02
[40,] 0.9274608 1.450784e-01 7.253922e-02
[41,] 0.9103217 1.793567e-01 8.967835e-02
[42,] 0.8920286 2.159427e-01 1.079714e-01
[43,] 0.8728930 2.542140e-01 1.271070e-01
[44,] 0.9980199 3.960286e-03 1.980143e-03
[45,] 0.9972822 5.435512e-03 2.717756e-03
[46,] 0.9975210 4.957961e-03 2.478980e-03
[47,] 0.9966478 6.704470e-03 3.352235e-03
[48,] 0.9974678 5.064309e-03 2.532155e-03
[49,] 0.9967530 6.494078e-03 3.247039e-03
[50,] 0.9959258 8.148303e-03 4.074151e-03
[51,] 0.9991765 1.646906e-03 8.234532e-04
[52,] 0.9988728 2.254386e-03 1.127193e-03
[53,] 0.9984700 3.060049e-03 1.530025e-03
[54,] 0.9980802 3.839624e-03 1.919812e-03
[55,] 0.9974100 5.180038e-03 2.590019e-03
[56,] 0.9997183 5.634048e-04 2.817024e-04
[57,] 0.9996212 7.575371e-04 3.787685e-04
[58,] 0.9995321 9.358118e-04 4.679059e-04
[59,] 0.9995251 9.498519e-04 4.749260e-04
[60,] 0.9994427 1.114686e-03 5.573432e-04
[61,] 0.9993987 1.202500e-03 6.012501e-04
[62,] 0.9992439 1.512251e-03 7.561255e-04
[63,] 0.9990049 1.990185e-03 9.950926e-04
[64,] 0.9988825 2.234941e-03 1.117471e-03
[65,] 0.9989220 2.155905e-03 1.077953e-03
[66,] 0.9985564 2.887244e-03 1.443622e-03
[67,] 0.9984956 3.008791e-03 1.504395e-03
[68,] 0.9980179 3.964129e-03 1.982064e-03
[69,] 0.9984857 3.028549e-03 1.514275e-03
[70,] 0.9995291 9.417158e-04 4.708579e-04
[71,] 0.9993812 1.237690e-03 6.188450e-04
[72,] 0.9991983 1.603479e-03 8.017396e-04
[73,] 0.9989140 2.171918e-03 1.085959e-03
[74,] 0.9985809 2.838219e-03 1.419109e-03
[75,] 0.9994245 1.151031e-03 5.755157e-04
[76,] 0.9994553 1.089380e-03 5.446902e-04
[77,] 0.9993666 1.266812e-03 6.334062e-04
[78,] 0.9991573 1.685425e-03 8.427125e-04
[79,] 0.9995042 9.916423e-04 4.958211e-04
[80,] 0.9993529 1.294139e-03 6.470693e-04
[81,] 0.9991334 1.733280e-03 8.666402e-04
[82,] 0.9997900 4.199629e-04 2.099814e-04
[83,] 0.9997707 4.585552e-04 2.292776e-04
[84,] 0.9997817 4.366505e-04 2.183253e-04
[85,] 0.9997254 5.492653e-04 2.746326e-04
[86,] 0.9996667 6.665551e-04 3.332775e-04
[87,] 0.9996537 6.926514e-04 3.463257e-04
[88,] 0.9995259 9.482025e-04 4.741012e-04
[89,] 0.9999732 5.359756e-05 2.679878e-05
[90,] 0.9999759 4.816152e-05 2.408076e-05
[91,] 0.9999989 2.117988e-06 1.058994e-06
[92,] 0.9999988 2.459625e-06 1.229813e-06
[93,] 0.9999998 3.844804e-07 1.922402e-07
[94,] 0.9999997 5.642113e-07 2.821057e-07
[95,] 0.9999996 8.049846e-07 4.024923e-07
[96,] 0.9999998 4.689300e-07 2.344650e-07
[97,] 0.9999996 7.173054e-07 3.586527e-07
[98,] 0.9999995 9.121518e-07 4.560759e-07
[99,] 0.9999995 9.686912e-07 4.843456e-07
[100,] 0.9999993 1.403640e-06 7.018198e-07
[101,] 0.9999991 1.839705e-06 9.198526e-07
[102,] 0.9999987 2.631868e-06 1.315934e-06
[103,] 0.9999981 3.728916e-06 1.864458e-06
[104,] 0.9999977 4.611151e-06 2.305575e-06
[105,] 0.9999967 6.686990e-06 3.343495e-06
[106,] 0.9999969 6.169649e-06 3.084825e-06
[107,] 0.9999955 9.096335e-06 4.548168e-06
[108,] 0.9999975 5.056432e-06 2.528216e-06
[109,] 0.9999963 7.447593e-06 3.723796e-06
[110,] 0.9999951 9.899468e-06 4.949734e-06
[111,] 0.9999990 2.053102e-06 1.026551e-06
[112,] 0.9999985 2.967763e-06 1.483882e-06
[113,] 0.9999978 4.377298e-06 2.188649e-06
[114,] 0.9999967 6.531891e-06 3.265946e-06
[115,] 0.9999954 9.154605e-06 4.577303e-06
[116,] 0.9999935 1.307911e-05 6.539553e-06
[117,] 0.9999964 7.182781e-06 3.591391e-06
[118,] 0.9999973 5.373499e-06 2.686750e-06
[119,] 0.9999961 7.702772e-06 3.851386e-06
[120,] 0.9999951 9.763671e-06 4.881835e-06
[121,] 0.9999954 9.241944e-06 4.620972e-06
[122,] 0.9999939 1.228985e-05 6.144927e-06
[123,] 0.9999913 1.743774e-05 8.718872e-06
[124,] 0.9999875 2.501831e-05 1.250915e-05
[125,] 0.9999918 1.645733e-05 8.228667e-06
[126,] 0.9999885 2.300842e-05 1.150421e-05
[127,] 0.9999843 3.134826e-05 1.567413e-05
[128,] 0.9999988 2.332787e-06 1.166394e-06
[129,] 0.9999991 1.759454e-06 8.797269e-07
[130,] 0.9999987 2.665336e-06 1.332668e-06
[131,] 0.9999988 2.390035e-06 1.195018e-06
[132,] 0.9999988 2.366183e-06 1.183091e-06
[133,] 0.9999986 2.762767e-06 1.381383e-06
[134,] 0.9999980 4.048982e-06 2.024491e-06
[135,] 0.9999970 6.042494e-06 3.021247e-06
[136,] 0.9999964 7.287968e-06 3.643984e-06
[137,] 0.9999954 9.229086e-06 4.614543e-06
[138,] 0.9999932 1.359342e-05 6.796709e-06
[139,] 0.9999903 1.936061e-05 9.680307e-06
[140,] 0.9999860 2.808964e-05 1.404482e-05
[141,] 0.9999818 3.637820e-05 1.818910e-05
[142,] 0.9999746 5.081418e-05 2.540709e-05
[143,] 0.9999637 7.252618e-05 3.626309e-05
[144,] 0.9999591 8.179328e-05 4.089664e-05
[145,] 0.9999785 4.300324e-05 2.150162e-05
[146,] 0.9999698 6.049980e-05 3.024990e-05
[147,] 0.9999680 6.401949e-05 3.200974e-05
[148,] 0.9999570 8.603006e-05 4.301503e-05
[149,] 0.9999420 1.159962e-04 5.799811e-05
[150,] 0.9999179 1.642273e-04 8.211365e-05
[151,] 0.9998909 2.181712e-04 1.090856e-04
[152,] 0.9998671 2.658402e-04 1.329201e-04
[153,] 0.9998837 2.326260e-04 1.163130e-04
[154,] 0.9999849 3.018829e-05 1.509415e-05
[155,] 0.9999781 4.387971e-05 2.193986e-05
[156,] 0.9999684 6.321331e-05 3.160666e-05
[157,] 0.9999955 9.084891e-06 4.542445e-06
[158,] 0.9999933 1.331949e-05 6.659743e-06
[159,] 0.9999907 1.864584e-05 9.322918e-06
[160,] 0.9999871 2.588272e-05 1.294136e-05
[161,] 0.9999818 3.649920e-05 1.824960e-05
[162,] 0.9999737 5.264727e-05 2.632364e-05
[163,] 0.9999997 6.375884e-07 3.187942e-07
[164,] 1.0000000 6.905002e-09 3.452501e-09
[165,] 1.0000000 5.273034e-09 2.636517e-09
[166,] 1.0000000 3.047600e-10 1.523800e-10
[167,] 1.0000000 3.897700e-10 1.948850e-10
[168,] 1.0000000 9.741249e-13 4.870625e-13
[169,] 1.0000000 2.437670e-13 1.218835e-13
[170,] 1.0000000 4.830813e-13 2.415406e-13
[171,] 1.0000000 8.205608e-13 4.102804e-13
[172,] 1.0000000 3.153840e-13 1.576920e-13
[173,] 1.0000000 2.640192e-13 1.320096e-13
[174,] 1.0000000 5.204650e-13 2.602325e-13
[175,] 1.0000000 8.114146e-13 4.057073e-13
[176,] 1.0000000 1.602792e-12 8.013959e-13
[177,] 1.0000000 1.532234e-12 7.661171e-13
[178,] 1.0000000 1.097347e-12 5.486736e-13
[179,] 1.0000000 1.733820e-12 8.669099e-13
[180,] 1.0000000 3.419855e-12 1.709928e-12
[181,] 1.0000000 6.761524e-12 3.380762e-12
[182,] 1.0000000 5.638483e-12 2.819242e-12
[183,] 1.0000000 9.520842e-12 4.760421e-12
[184,] 1.0000000 1.250181e-11 6.250907e-12
[185,] 1.0000000 2.197673e-11 1.098836e-11
[186,] 1.0000000 4.157094e-11 2.078547e-11
[187,] 1.0000000 7.565438e-11 3.782719e-11
[188,] 1.0000000 1.340926e-10 6.704632e-11
[189,] 1.0000000 2.553488e-10 1.276744e-10
[190,] 1.0000000 4.729847e-10 2.364924e-10
[191,] 1.0000000 7.709536e-10 3.854768e-10
[192,] 1.0000000 1.438453e-09 7.192264e-10
[193,] 1.0000000 1.409075e-09 7.045375e-10
[194,] 1.0000000 2.617667e-09 1.308833e-09
[195,] 1.0000000 3.103459e-09 1.551729e-09
[196,] 1.0000000 2.468800e-09 1.234400e-09
[197,] 1.0000000 2.459236e-09 1.229618e-09
[198,] 1.0000000 4.493844e-09 2.246922e-09
[199,] 1.0000000 6.831353e-09 3.415677e-09
[200,] 1.0000000 1.262751e-08 6.313757e-09
[201,] 1.0000000 1.479263e-10 7.396316e-11
[202,] 1.0000000 7.314109e-11 3.657055e-11
[203,] 1.0000000 1.012688e-10 5.063440e-11
[204,] 1.0000000 1.101645e-10 5.508227e-11
[205,] 1.0000000 8.281493e-11 4.140747e-11
[206,] 1.0000000 1.232943e-10 6.164714e-11
[207,] 1.0000000 2.402676e-10 1.201338e-10
[208,] 1.0000000 4.800969e-10 2.400484e-10
[209,] 1.0000000 8.423336e-10 4.211668e-10
[210,] 1.0000000 1.677604e-09 8.388018e-10
[211,] 1.0000000 3.359478e-09 1.679739e-09
[212,] 1.0000000 5.047265e-09 2.523632e-09
[213,] 1.0000000 6.739968e-09 3.369984e-09
[214,] 1.0000000 1.192620e-08 5.963101e-09
[215,] 1.0000000 1.885842e-08 9.429208e-09
[216,] 1.0000000 3.481118e-08 1.740559e-08
[217,] 1.0000000 6.499367e-08 3.249684e-08
[218,] 1.0000000 8.851929e-08 4.425965e-08
[219,] 1.0000000 3.556970e-08 1.778485e-08
[220,] 1.0000000 5.010749e-08 2.505374e-08
[221,] 1.0000000 7.518962e-09 3.759481e-09
[222,] 1.0000000 1.254075e-08 6.270375e-09
[223,] 1.0000000 2.185835e-08 1.092918e-08
[224,] 1.0000000 3.424387e-08 1.712194e-08
[225,] 1.0000000 6.596431e-08 3.298216e-08
[226,] 0.9999999 1.281357e-07 6.406786e-08
[227,] 0.9999999 1.486345e-07 7.431724e-08
[228,] 0.9999999 1.174258e-07 5.871291e-08
[229,] 0.9999999 1.071582e-07 5.357911e-08
[230,] 1.0000000 2.135569e-09 1.067784e-09
[231,] 1.0000000 2.230616e-09 1.115308e-09
[232,] 1.0000000 5.072750e-09 2.536375e-09
[233,] 1.0000000 3.893766e-09 1.946883e-09
[234,] 1.0000000 8.960035e-09 4.480017e-09
[235,] 1.0000000 1.028315e-08 5.141576e-09
[236,] 1.0000000 1.175137e-08 5.875683e-09
[237,] 1.0000000 2.131239e-08 1.065620e-08
[238,] 1.0000000 4.476666e-08 2.238333e-08
[239,] 0.9999999 1.010478e-07 5.052388e-08
[240,] 0.9999999 1.780804e-07 8.904021e-08
[241,] 0.9999999 2.682566e-07 1.341283e-07
[242,] 0.9999998 4.400200e-07 2.200100e-07
[243,] 0.9999996 7.245579e-07 3.622789e-07
[244,] 1.0000000 5.043502e-08 2.521751e-08
[245,] 0.9999999 1.193720e-07 5.968601e-08
[246,] 0.9999999 2.332751e-07 1.166375e-07
[247,] 0.9999999 1.600334e-07 8.001669e-08
[248,] 0.9999999 2.917519e-07 1.458760e-07
[249,] 0.9999996 7.009418e-07 3.504709e-07
[250,] 0.9999997 5.110989e-07 2.555494e-07
[251,] 0.9999996 8.517926e-07 4.258963e-07
[252,] 0.9999990 2.085274e-06 1.042637e-06
[253,] 0.9999978 4.353256e-06 2.176628e-06
[254,] 0.9999984 3.267049e-06 1.633524e-06
[255,] 0.9999964 7.281391e-06 3.640696e-06
[256,] 0.9999912 1.760599e-05 8.802993e-06
[257,] 0.9999930 1.391023e-05 6.955113e-06
[258,] 0.9999862 2.761166e-05 1.380583e-05
[259,] 0.9999676 6.470642e-05 3.235321e-05
[260,] 0.9999500 1.000650e-04 5.003248e-05
[261,] 0.9999082 1.836459e-04 9.182295e-05
[262,] 0.9999813 3.734879e-05 1.867439e-05
[263,] 0.9999586 8.283220e-05 4.141610e-05
[264,] 0.9999415 1.170926e-04 5.854630e-05
[265,] 0.9998280 3.440701e-04 1.720351e-04
[266,] 0.9995437 9.125687e-04 4.562843e-04
[267,] 0.9989181 2.163747e-03 1.081873e-03
[268,] 0.9970861 5.827769e-03 2.913885e-03
[269,] 0.9977246 4.550861e-03 2.275431e-03
[270,] 0.9943870 1.122596e-02 5.612982e-03
[271,] 0.9931068 1.378639e-02 6.893195e-03
[272,] 0.9820040 3.599209e-02 1.799604e-02
[273,] 0.9837490 3.250205e-02 1.625102e-02
[274,] 0.9692939 6.141225e-02 3.070612e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1d4h51354984890.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2vh211354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3zftz1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4gzxp1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5okp11354984890.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 = 289
Frequency = 1
1 2 3 4 5 6
1.24287432 -13.20219374 -2.65107162 -13.77220553 -1.30732595 -9.29486759
7 8 9 10 11 12
40.64429815 -5.62104622 10.02029273 2.11997342 1.75297377 5.13841772
13 14 15 16 17 18
-11.53783572 -1.53838067 4.90455049 1.81095133 4.30694870 0.25963741
19 20 21 22 23 24
15.20012432 -17.43239042 -16.98300358 -3.38633373 40.93040810 3.19666057
25 26 27 28 29 30
-22.79946935 -19.25965190 -1.36026299 6.79545669 -0.03174946 -2.19199842
31 32 33 34 35 36
-0.32511089 -2.58606289 -8.80449780 1.49773473 -9.17093315 8.77842906
37 38 39 40 41 42
4.79071462 13.73434140 33.67324337 -1.83555800 2.32730600 5.02469328
43 44 45 46 47 48
-2.19941909 12.22034517 16.57671230 -5.51989457 10.42487633 -0.77869026
49 50 51 52 53 54
-5.37866455 -6.58337296 51.44577091 -3.80735219 -12.14276347 4.71788095
55 56 57 58 59 60
-15.76857796 -4.02829021 -2.07340819 26.47246586 -3.36073683 5.02308434
61 62 63 64 65 66
7.98902722 -2.62990857 37.14456611 6.47219617 -6.13592556 -7.75833499
67 68 69 70 71 72
10.74750237 13.16308792 -2.95428857 3.79079955 8.15405598 11.22686528
73 74 75 76 77 78
0.85800649 -10.20309676 -3.63102438 -12.22131645 -21.27253653 -5.00695473
79 80 81 82 83 84
-4.95013120 1.65695113 -3.55634181 -22.95460153 13.63742442 9.14912662
85 86 87 88 89 90
3.67822936 -18.65164473 -4.82818599 -1.92599046 27.83946254 10.29313242
91 92 93 94 95 96
-12.51834592 -4.90517429 7.83572542 -10.96013575 -0.44721800 36.37368792
97 98 99 100 101 102
12.90362741 -35.05667380 9.19734217 -27.00508501 4.78098756 4.65615383
103 104 105 106 107 108
17.20693814 -2.50281625 -8.04170408 -11.39074273 -5.14279865 -6.21628818
109 110 111 112 113 114
3.67395099 3.31927077 -8.23520183 2.43361030 -11.96300671 1.96130738
115 116 117 118 119 120
-16.90826097 -1.97576776 -6.81152204 25.65413467 -3.12930906 -1.76085439
121 122 123 124 125 126
0.15141396 -3.40477917 4.38408451 -18.90731075 15.18513108 4.06574756
127 128 129 130 131 132
7.49480041 -11.69760330 -7.03542169 -2.16127451 2.39241257 -16.55666224
133 134 135 136 137 138
-2.87138951 -4.71932340 -27.11764083 14.41573694 0.59372456 13.90246594
139 140 141 142 143 144
-10.99116407 -8.84270298 -3.14922018 -1.73029089 7.71253484 8.32908939
145 146 147 148 149 150
-0.67303929 -3.66441265 -1.83652954 -6.56542666 -3.41029000 0.04073773
151 152 153 154 155 156
7.48811377 -13.00954327 2.69730030 12.75765698 -4.68290519 6.30604944
157 158 159 160 161 162
-1.33018828 4.05132825 -8.17248159 14.38495306 26.76972186 -2.00921528
163 164 165 166 167 168
2.62976754 22.19074159 -3.98920805 -3.17706386 -7.37378134 3.48483216
169 170 171 172 173 174
-2.91439065 37.47711074 27.46347472 -18.25450297 21.00288506 -1.53802451
175 176 177 178 179 180
-39.77874098 15.29432593 -3.03796560 3.38327565 -15.99266711 10.96720105
181 182 183 184 185 186
1.37142923 -5.41816494 -1.30940236 6.41692059 6.39196209 -4.18938104
187 188 189 190 191 192
1.91181830 0.07920345 8.09023545 -6.65183859 5.63513830 5.93286771
193 194 195 196 197 198
-2.99593138 -5.11402247 -2.90196379 -2.49133348 -1.75621450 -5.60405942
199 200 201 202 203 204
-1.74886648 -11.58395194 -1.80198023 -10.31246547 10.56326247 8.10890437
205 206 207 208 209 210
-0.33784490 -6.59214995 -2.02678323 23.39781680 9.73859488 2.96252712
211 212 213 214 215 216
6.98864427 6.74196574 -7.43479298 -0.26632414 -2.25128610 -4.58569434
217 218 219 220 221 222
-3.11429937 -0.51817667 -6.20546305 6.01874839 -5.52637454 1.50362360
223 224 225 226 227 228
-7.48246059 -4.46451563 -6.68652510 8.10501135 3.11123749 -16.49881915
229 230 231 232 233 234
-8.85695976 -7.73940215 -4.55152916 -2.12427075 -0.83374895 -6.21924346
235 236 237 238 239 240
-10.37756218 -10.92507787 20.40551248 -9.93244258 -4.02379071 -10.27982573
241 242 243 244 245 246
3.26953936 4.96897608 -9.73168866 -6.06655911 -4.06395306 -1.76050366
247 248 249 250 251 252
-1.72774182 6.34553275 3.95036716 -6.52178496 13.61753102 -4.32060290
253 254 255 256 257 258
-3.78423549 7.37808677 2.29422262 -0.36125733 -9.90574360 3.19340039
259 260 261 262 263 264
0.06672302 -3.16433837 -6.50093832 -6.94869906 0.86267963 -10.79005080
265 266 267 268 269 270
-5.92321432 -2.98449086 -10.25724712 -6.83692073 8.24545450 -8.95001862
271 272 273 274 275 276
2.82855428 -0.53932994 5.98150690 -0.33389381 -0.98774984 -6.94600757
277 278 279 280 281 282
2.98145585 -3.02656716 -1.61143698 1.63406960 -3.62555401 5.89736878
283 284 285 286 287 288
4.26125371 2.52249651 0.92316831 -7.49535187 -0.32458535 -10.72297581
289
-1.61491694
> postscript(file="/var/wessaorg/rcomp/tmp/6ghqq1354984890.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 = 289
Frequency = 1
lag(myerror, k = 1) myerror
0 1.24287432 NA
1 -13.20219374 1.24287432
2 -2.65107162 -13.20219374
3 -13.77220553 -2.65107162
4 -1.30732595 -13.77220553
5 -9.29486759 -1.30732595
6 40.64429815 -9.29486759
7 -5.62104622 40.64429815
8 10.02029273 -5.62104622
9 2.11997342 10.02029273
10 1.75297377 2.11997342
11 5.13841772 1.75297377
12 -11.53783572 5.13841772
13 -1.53838067 -11.53783572
14 4.90455049 -1.53838067
15 1.81095133 4.90455049
16 4.30694870 1.81095133
17 0.25963741 4.30694870
18 15.20012432 0.25963741
19 -17.43239042 15.20012432
20 -16.98300358 -17.43239042
21 -3.38633373 -16.98300358
22 40.93040810 -3.38633373
23 3.19666057 40.93040810
24 -22.79946935 3.19666057
25 -19.25965190 -22.79946935
26 -1.36026299 -19.25965190
27 6.79545669 -1.36026299
28 -0.03174946 6.79545669
29 -2.19199842 -0.03174946
30 -0.32511089 -2.19199842
31 -2.58606289 -0.32511089
32 -8.80449780 -2.58606289
33 1.49773473 -8.80449780
34 -9.17093315 1.49773473
35 8.77842906 -9.17093315
36 4.79071462 8.77842906
37 13.73434140 4.79071462
38 33.67324337 13.73434140
39 -1.83555800 33.67324337
40 2.32730600 -1.83555800
41 5.02469328 2.32730600
42 -2.19941909 5.02469328
43 12.22034517 -2.19941909
44 16.57671230 12.22034517
45 -5.51989457 16.57671230
46 10.42487633 -5.51989457
47 -0.77869026 10.42487633
48 -5.37866455 -0.77869026
49 -6.58337296 -5.37866455
50 51.44577091 -6.58337296
51 -3.80735219 51.44577091
52 -12.14276347 -3.80735219
53 4.71788095 -12.14276347
54 -15.76857796 4.71788095
55 -4.02829021 -15.76857796
56 -2.07340819 -4.02829021
57 26.47246586 -2.07340819
58 -3.36073683 26.47246586
59 5.02308434 -3.36073683
60 7.98902722 5.02308434
61 -2.62990857 7.98902722
62 37.14456611 -2.62990857
63 6.47219617 37.14456611
64 -6.13592556 6.47219617
65 -7.75833499 -6.13592556
66 10.74750237 -7.75833499
67 13.16308792 10.74750237
68 -2.95428857 13.16308792
69 3.79079955 -2.95428857
70 8.15405598 3.79079955
71 11.22686528 8.15405598
72 0.85800649 11.22686528
73 -10.20309676 0.85800649
74 -3.63102438 -10.20309676
75 -12.22131645 -3.63102438
76 -21.27253653 -12.22131645
77 -5.00695473 -21.27253653
78 -4.95013120 -5.00695473
79 1.65695113 -4.95013120
80 -3.55634181 1.65695113
81 -22.95460153 -3.55634181
82 13.63742442 -22.95460153
83 9.14912662 13.63742442
84 3.67822936 9.14912662
85 -18.65164473 3.67822936
86 -4.82818599 -18.65164473
87 -1.92599046 -4.82818599
88 27.83946254 -1.92599046
89 10.29313242 27.83946254
90 -12.51834592 10.29313242
91 -4.90517429 -12.51834592
92 7.83572542 -4.90517429
93 -10.96013575 7.83572542
94 -0.44721800 -10.96013575
95 36.37368792 -0.44721800
96 12.90362741 36.37368792
97 -35.05667380 12.90362741
98 9.19734217 -35.05667380
99 -27.00508501 9.19734217
100 4.78098756 -27.00508501
101 4.65615383 4.78098756
102 17.20693814 4.65615383
103 -2.50281625 17.20693814
104 -8.04170408 -2.50281625
105 -11.39074273 -8.04170408
106 -5.14279865 -11.39074273
107 -6.21628818 -5.14279865
108 3.67395099 -6.21628818
109 3.31927077 3.67395099
110 -8.23520183 3.31927077
111 2.43361030 -8.23520183
112 -11.96300671 2.43361030
113 1.96130738 -11.96300671
114 -16.90826097 1.96130738
115 -1.97576776 -16.90826097
116 -6.81152204 -1.97576776
117 25.65413467 -6.81152204
118 -3.12930906 25.65413467
119 -1.76085439 -3.12930906
120 0.15141396 -1.76085439
121 -3.40477917 0.15141396
122 4.38408451 -3.40477917
123 -18.90731075 4.38408451
124 15.18513108 -18.90731075
125 4.06574756 15.18513108
126 7.49480041 4.06574756
127 -11.69760330 7.49480041
128 -7.03542169 -11.69760330
129 -2.16127451 -7.03542169
130 2.39241257 -2.16127451
131 -16.55666224 2.39241257
132 -2.87138951 -16.55666224
133 -4.71932340 -2.87138951
134 -27.11764083 -4.71932340
135 14.41573694 -27.11764083
136 0.59372456 14.41573694
137 13.90246594 0.59372456
138 -10.99116407 13.90246594
139 -8.84270298 -10.99116407
140 -3.14922018 -8.84270298
141 -1.73029089 -3.14922018
142 7.71253484 -1.73029089
143 8.32908939 7.71253484
144 -0.67303929 8.32908939
145 -3.66441265 -0.67303929
146 -1.83652954 -3.66441265
147 -6.56542666 -1.83652954
148 -3.41029000 -6.56542666
149 0.04073773 -3.41029000
150 7.48811377 0.04073773
151 -13.00954327 7.48811377
152 2.69730030 -13.00954327
153 12.75765698 2.69730030
154 -4.68290519 12.75765698
155 6.30604944 -4.68290519
156 -1.33018828 6.30604944
157 4.05132825 -1.33018828
158 -8.17248159 4.05132825
159 14.38495306 -8.17248159
160 26.76972186 14.38495306
161 -2.00921528 26.76972186
162 2.62976754 -2.00921528
163 22.19074159 2.62976754
164 -3.98920805 22.19074159
165 -3.17706386 -3.98920805
166 -7.37378134 -3.17706386
167 3.48483216 -7.37378134
168 -2.91439065 3.48483216
169 37.47711074 -2.91439065
170 27.46347472 37.47711074
171 -18.25450297 27.46347472
172 21.00288506 -18.25450297
173 -1.53802451 21.00288506
174 -39.77874098 -1.53802451
175 15.29432593 -39.77874098
176 -3.03796560 15.29432593
177 3.38327565 -3.03796560
178 -15.99266711 3.38327565
179 10.96720105 -15.99266711
180 1.37142923 10.96720105
181 -5.41816494 1.37142923
182 -1.30940236 -5.41816494
183 6.41692059 -1.30940236
184 6.39196209 6.41692059
185 -4.18938104 6.39196209
186 1.91181830 -4.18938104
187 0.07920345 1.91181830
188 8.09023545 0.07920345
189 -6.65183859 8.09023545
190 5.63513830 -6.65183859
191 5.93286771 5.63513830
192 -2.99593138 5.93286771
193 -5.11402247 -2.99593138
194 -2.90196379 -5.11402247
195 -2.49133348 -2.90196379
196 -1.75621450 -2.49133348
197 -5.60405942 -1.75621450
198 -1.74886648 -5.60405942
199 -11.58395194 -1.74886648
200 -1.80198023 -11.58395194
201 -10.31246547 -1.80198023
202 10.56326247 -10.31246547
203 8.10890437 10.56326247
204 -0.33784490 8.10890437
205 -6.59214995 -0.33784490
206 -2.02678323 -6.59214995
207 23.39781680 -2.02678323
208 9.73859488 23.39781680
209 2.96252712 9.73859488
210 6.98864427 2.96252712
211 6.74196574 6.98864427
212 -7.43479298 6.74196574
213 -0.26632414 -7.43479298
214 -2.25128610 -0.26632414
215 -4.58569434 -2.25128610
216 -3.11429937 -4.58569434
217 -0.51817667 -3.11429937
218 -6.20546305 -0.51817667
219 6.01874839 -6.20546305
220 -5.52637454 6.01874839
221 1.50362360 -5.52637454
222 -7.48246059 1.50362360
223 -4.46451563 -7.48246059
224 -6.68652510 -4.46451563
225 8.10501135 -6.68652510
226 3.11123749 8.10501135
227 -16.49881915 3.11123749
228 -8.85695976 -16.49881915
229 -7.73940215 -8.85695976
230 -4.55152916 -7.73940215
231 -2.12427075 -4.55152916
232 -0.83374895 -2.12427075
233 -6.21924346 -0.83374895
234 -10.37756218 -6.21924346
235 -10.92507787 -10.37756218
236 20.40551248 -10.92507787
237 -9.93244258 20.40551248
238 -4.02379071 -9.93244258
239 -10.27982573 -4.02379071
240 3.26953936 -10.27982573
241 4.96897608 3.26953936
242 -9.73168866 4.96897608
243 -6.06655911 -9.73168866
244 -4.06395306 -6.06655911
245 -1.76050366 -4.06395306
246 -1.72774182 -1.76050366
247 6.34553275 -1.72774182
248 3.95036716 6.34553275
249 -6.52178496 3.95036716
250 13.61753102 -6.52178496
251 -4.32060290 13.61753102
252 -3.78423549 -4.32060290
253 7.37808677 -3.78423549
254 2.29422262 7.37808677
255 -0.36125733 2.29422262
256 -9.90574360 -0.36125733
257 3.19340039 -9.90574360
258 0.06672302 3.19340039
259 -3.16433837 0.06672302
260 -6.50093832 -3.16433837
261 -6.94869906 -6.50093832
262 0.86267963 -6.94869906
263 -10.79005080 0.86267963
264 -5.92321432 -10.79005080
265 -2.98449086 -5.92321432
266 -10.25724712 -2.98449086
267 -6.83692073 -10.25724712
268 8.24545450 -6.83692073
269 -8.95001862 8.24545450
270 2.82855428 -8.95001862
271 -0.53932994 2.82855428
272 5.98150690 -0.53932994
273 -0.33389381 5.98150690
274 -0.98774984 -0.33389381
275 -6.94600757 -0.98774984
276 2.98145585 -6.94600757
277 -3.02656716 2.98145585
278 -1.61143698 -3.02656716
279 1.63406960 -1.61143698
280 -3.62555401 1.63406960
281 5.89736878 -3.62555401
282 4.26125371 5.89736878
283 2.52249651 4.26125371
284 0.92316831 2.52249651
285 -7.49535187 0.92316831
286 -0.32458535 -7.49535187
287 -10.72297581 -0.32458535
288 -1.61491694 -10.72297581
289 NA -1.61491694
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -13.20219374 1.24287432
[2,] -2.65107162 -13.20219374
[3,] -13.77220553 -2.65107162
[4,] -1.30732595 -13.77220553
[5,] -9.29486759 -1.30732595
[6,] 40.64429815 -9.29486759
[7,] -5.62104622 40.64429815
[8,] 10.02029273 -5.62104622
[9,] 2.11997342 10.02029273
[10,] 1.75297377 2.11997342
[11,] 5.13841772 1.75297377
[12,] -11.53783572 5.13841772
[13,] -1.53838067 -11.53783572
[14,] 4.90455049 -1.53838067
[15,] 1.81095133 4.90455049
[16,] 4.30694870 1.81095133
[17,] 0.25963741 4.30694870
[18,] 15.20012432 0.25963741
[19,] -17.43239042 15.20012432
[20,] -16.98300358 -17.43239042
[21,] -3.38633373 -16.98300358
[22,] 40.93040810 -3.38633373
[23,] 3.19666057 40.93040810
[24,] -22.79946935 3.19666057
[25,] -19.25965190 -22.79946935
[26,] -1.36026299 -19.25965190
[27,] 6.79545669 -1.36026299
[28,] -0.03174946 6.79545669
[29,] -2.19199842 -0.03174946
[30,] -0.32511089 -2.19199842
[31,] -2.58606289 -0.32511089
[32,] -8.80449780 -2.58606289
[33,] 1.49773473 -8.80449780
[34,] -9.17093315 1.49773473
[35,] 8.77842906 -9.17093315
[36,] 4.79071462 8.77842906
[37,] 13.73434140 4.79071462
[38,] 33.67324337 13.73434140
[39,] -1.83555800 33.67324337
[40,] 2.32730600 -1.83555800
[41,] 5.02469328 2.32730600
[42,] -2.19941909 5.02469328
[43,] 12.22034517 -2.19941909
[44,] 16.57671230 12.22034517
[45,] -5.51989457 16.57671230
[46,] 10.42487633 -5.51989457
[47,] -0.77869026 10.42487633
[48,] -5.37866455 -0.77869026
[49,] -6.58337296 -5.37866455
[50,] 51.44577091 -6.58337296
[51,] -3.80735219 51.44577091
[52,] -12.14276347 -3.80735219
[53,] 4.71788095 -12.14276347
[54,] -15.76857796 4.71788095
[55,] -4.02829021 -15.76857796
[56,] -2.07340819 -4.02829021
[57,] 26.47246586 -2.07340819
[58,] -3.36073683 26.47246586
[59,] 5.02308434 -3.36073683
[60,] 7.98902722 5.02308434
[61,] -2.62990857 7.98902722
[62,] 37.14456611 -2.62990857
[63,] 6.47219617 37.14456611
[64,] -6.13592556 6.47219617
[65,] -7.75833499 -6.13592556
[66,] 10.74750237 -7.75833499
[67,] 13.16308792 10.74750237
[68,] -2.95428857 13.16308792
[69,] 3.79079955 -2.95428857
[70,] 8.15405598 3.79079955
[71,] 11.22686528 8.15405598
[72,] 0.85800649 11.22686528
[73,] -10.20309676 0.85800649
[74,] -3.63102438 -10.20309676
[75,] -12.22131645 -3.63102438
[76,] -21.27253653 -12.22131645
[77,] -5.00695473 -21.27253653
[78,] -4.95013120 -5.00695473
[79,] 1.65695113 -4.95013120
[80,] -3.55634181 1.65695113
[81,] -22.95460153 -3.55634181
[82,] 13.63742442 -22.95460153
[83,] 9.14912662 13.63742442
[84,] 3.67822936 9.14912662
[85,] -18.65164473 3.67822936
[86,] -4.82818599 -18.65164473
[87,] -1.92599046 -4.82818599
[88,] 27.83946254 -1.92599046
[89,] 10.29313242 27.83946254
[90,] -12.51834592 10.29313242
[91,] -4.90517429 -12.51834592
[92,] 7.83572542 -4.90517429
[93,] -10.96013575 7.83572542
[94,] -0.44721800 -10.96013575
[95,] 36.37368792 -0.44721800
[96,] 12.90362741 36.37368792
[97,] -35.05667380 12.90362741
[98,] 9.19734217 -35.05667380
[99,] -27.00508501 9.19734217
[100,] 4.78098756 -27.00508501
[101,] 4.65615383 4.78098756
[102,] 17.20693814 4.65615383
[103,] -2.50281625 17.20693814
[104,] -8.04170408 -2.50281625
[105,] -11.39074273 -8.04170408
[106,] -5.14279865 -11.39074273
[107,] -6.21628818 -5.14279865
[108,] 3.67395099 -6.21628818
[109,] 3.31927077 3.67395099
[110,] -8.23520183 3.31927077
[111,] 2.43361030 -8.23520183
[112,] -11.96300671 2.43361030
[113,] 1.96130738 -11.96300671
[114,] -16.90826097 1.96130738
[115,] -1.97576776 -16.90826097
[116,] -6.81152204 -1.97576776
[117,] 25.65413467 -6.81152204
[118,] -3.12930906 25.65413467
[119,] -1.76085439 -3.12930906
[120,] 0.15141396 -1.76085439
[121,] -3.40477917 0.15141396
[122,] 4.38408451 -3.40477917
[123,] -18.90731075 4.38408451
[124,] 15.18513108 -18.90731075
[125,] 4.06574756 15.18513108
[126,] 7.49480041 4.06574756
[127,] -11.69760330 7.49480041
[128,] -7.03542169 -11.69760330
[129,] -2.16127451 -7.03542169
[130,] 2.39241257 -2.16127451
[131,] -16.55666224 2.39241257
[132,] -2.87138951 -16.55666224
[133,] -4.71932340 -2.87138951
[134,] -27.11764083 -4.71932340
[135,] 14.41573694 -27.11764083
[136,] 0.59372456 14.41573694
[137,] 13.90246594 0.59372456
[138,] -10.99116407 13.90246594
[139,] -8.84270298 -10.99116407
[140,] -3.14922018 -8.84270298
[141,] -1.73029089 -3.14922018
[142,] 7.71253484 -1.73029089
[143,] 8.32908939 7.71253484
[144,] -0.67303929 8.32908939
[145,] -3.66441265 -0.67303929
[146,] -1.83652954 -3.66441265
[147,] -6.56542666 -1.83652954
[148,] -3.41029000 -6.56542666
[149,] 0.04073773 -3.41029000
[150,] 7.48811377 0.04073773
[151,] -13.00954327 7.48811377
[152,] 2.69730030 -13.00954327
[153,] 12.75765698 2.69730030
[154,] -4.68290519 12.75765698
[155,] 6.30604944 -4.68290519
[156,] -1.33018828 6.30604944
[157,] 4.05132825 -1.33018828
[158,] -8.17248159 4.05132825
[159,] 14.38495306 -8.17248159
[160,] 26.76972186 14.38495306
[161,] -2.00921528 26.76972186
[162,] 2.62976754 -2.00921528
[163,] 22.19074159 2.62976754
[164,] -3.98920805 22.19074159
[165,] -3.17706386 -3.98920805
[166,] -7.37378134 -3.17706386
[167,] 3.48483216 -7.37378134
[168,] -2.91439065 3.48483216
[169,] 37.47711074 -2.91439065
[170,] 27.46347472 37.47711074
[171,] -18.25450297 27.46347472
[172,] 21.00288506 -18.25450297
[173,] -1.53802451 21.00288506
[174,] -39.77874098 -1.53802451
[175,] 15.29432593 -39.77874098
[176,] -3.03796560 15.29432593
[177,] 3.38327565 -3.03796560
[178,] -15.99266711 3.38327565
[179,] 10.96720105 -15.99266711
[180,] 1.37142923 10.96720105
[181,] -5.41816494 1.37142923
[182,] -1.30940236 -5.41816494
[183,] 6.41692059 -1.30940236
[184,] 6.39196209 6.41692059
[185,] -4.18938104 6.39196209
[186,] 1.91181830 -4.18938104
[187,] 0.07920345 1.91181830
[188,] 8.09023545 0.07920345
[189,] -6.65183859 8.09023545
[190,] 5.63513830 -6.65183859
[191,] 5.93286771 5.63513830
[192,] -2.99593138 5.93286771
[193,] -5.11402247 -2.99593138
[194,] -2.90196379 -5.11402247
[195,] -2.49133348 -2.90196379
[196,] -1.75621450 -2.49133348
[197,] -5.60405942 -1.75621450
[198,] -1.74886648 -5.60405942
[199,] -11.58395194 -1.74886648
[200,] -1.80198023 -11.58395194
[201,] -10.31246547 -1.80198023
[202,] 10.56326247 -10.31246547
[203,] 8.10890437 10.56326247
[204,] -0.33784490 8.10890437
[205,] -6.59214995 -0.33784490
[206,] -2.02678323 -6.59214995
[207,] 23.39781680 -2.02678323
[208,] 9.73859488 23.39781680
[209,] 2.96252712 9.73859488
[210,] 6.98864427 2.96252712
[211,] 6.74196574 6.98864427
[212,] -7.43479298 6.74196574
[213,] -0.26632414 -7.43479298
[214,] -2.25128610 -0.26632414
[215,] -4.58569434 -2.25128610
[216,] -3.11429937 -4.58569434
[217,] -0.51817667 -3.11429937
[218,] -6.20546305 -0.51817667
[219,] 6.01874839 -6.20546305
[220,] -5.52637454 6.01874839
[221,] 1.50362360 -5.52637454
[222,] -7.48246059 1.50362360
[223,] -4.46451563 -7.48246059
[224,] -6.68652510 -4.46451563
[225,] 8.10501135 -6.68652510
[226,] 3.11123749 8.10501135
[227,] -16.49881915 3.11123749
[228,] -8.85695976 -16.49881915
[229,] -7.73940215 -8.85695976
[230,] -4.55152916 -7.73940215
[231,] -2.12427075 -4.55152916
[232,] -0.83374895 -2.12427075
[233,] -6.21924346 -0.83374895
[234,] -10.37756218 -6.21924346
[235,] -10.92507787 -10.37756218
[236,] 20.40551248 -10.92507787
[237,] -9.93244258 20.40551248
[238,] -4.02379071 -9.93244258
[239,] -10.27982573 -4.02379071
[240,] 3.26953936 -10.27982573
[241,] 4.96897608 3.26953936
[242,] -9.73168866 4.96897608
[243,] -6.06655911 -9.73168866
[244,] -4.06395306 -6.06655911
[245,] -1.76050366 -4.06395306
[246,] -1.72774182 -1.76050366
[247,] 6.34553275 -1.72774182
[248,] 3.95036716 6.34553275
[249,] -6.52178496 3.95036716
[250,] 13.61753102 -6.52178496
[251,] -4.32060290 13.61753102
[252,] -3.78423549 -4.32060290
[253,] 7.37808677 -3.78423549
[254,] 2.29422262 7.37808677
[255,] -0.36125733 2.29422262
[256,] -9.90574360 -0.36125733
[257,] 3.19340039 -9.90574360
[258,] 0.06672302 3.19340039
[259,] -3.16433837 0.06672302
[260,] -6.50093832 -3.16433837
[261,] -6.94869906 -6.50093832
[262,] 0.86267963 -6.94869906
[263,] -10.79005080 0.86267963
[264,] -5.92321432 -10.79005080
[265,] -2.98449086 -5.92321432
[266,] -10.25724712 -2.98449086
[267,] -6.83692073 -10.25724712
[268,] 8.24545450 -6.83692073
[269,] -8.95001862 8.24545450
[270,] 2.82855428 -8.95001862
[271,] -0.53932994 2.82855428
[272,] 5.98150690 -0.53932994
[273,] -0.33389381 5.98150690
[274,] -0.98774984 -0.33389381
[275,] -6.94600757 -0.98774984
[276,] 2.98145585 -6.94600757
[277,] -3.02656716 2.98145585
[278,] -1.61143698 -3.02656716
[279,] 1.63406960 -1.61143698
[280,] -3.62555401 1.63406960
[281,] 5.89736878 -3.62555401
[282,] 4.26125371 5.89736878
[283,] 2.52249651 4.26125371
[284,] 0.92316831 2.52249651
[285,] -7.49535187 0.92316831
[286,] -0.32458535 -7.49535187
[287,] -10.72297581 -0.32458535
[288,] -1.61491694 -10.72297581
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -13.20219374 1.24287432
2 -2.65107162 -13.20219374
3 -13.77220553 -2.65107162
4 -1.30732595 -13.77220553
5 -9.29486759 -1.30732595
6 40.64429815 -9.29486759
7 -5.62104622 40.64429815
8 10.02029273 -5.62104622
9 2.11997342 10.02029273
10 1.75297377 2.11997342
11 5.13841772 1.75297377
12 -11.53783572 5.13841772
13 -1.53838067 -11.53783572
14 4.90455049 -1.53838067
15 1.81095133 4.90455049
16 4.30694870 1.81095133
17 0.25963741 4.30694870
18 15.20012432 0.25963741
19 -17.43239042 15.20012432
20 -16.98300358 -17.43239042
21 -3.38633373 -16.98300358
22 40.93040810 -3.38633373
23 3.19666057 40.93040810
24 -22.79946935 3.19666057
25 -19.25965190 -22.79946935
26 -1.36026299 -19.25965190
27 6.79545669 -1.36026299
28 -0.03174946 6.79545669
29 -2.19199842 -0.03174946
30 -0.32511089 -2.19199842
31 -2.58606289 -0.32511089
32 -8.80449780 -2.58606289
33 1.49773473 -8.80449780
34 -9.17093315 1.49773473
35 8.77842906 -9.17093315
36 4.79071462 8.77842906
37 13.73434140 4.79071462
38 33.67324337 13.73434140
39 -1.83555800 33.67324337
40 2.32730600 -1.83555800
41 5.02469328 2.32730600
42 -2.19941909 5.02469328
43 12.22034517 -2.19941909
44 16.57671230 12.22034517
45 -5.51989457 16.57671230
46 10.42487633 -5.51989457
47 -0.77869026 10.42487633
48 -5.37866455 -0.77869026
49 -6.58337296 -5.37866455
50 51.44577091 -6.58337296
51 -3.80735219 51.44577091
52 -12.14276347 -3.80735219
53 4.71788095 -12.14276347
54 -15.76857796 4.71788095
55 -4.02829021 -15.76857796
56 -2.07340819 -4.02829021
57 26.47246586 -2.07340819
58 -3.36073683 26.47246586
59 5.02308434 -3.36073683
60 7.98902722 5.02308434
61 -2.62990857 7.98902722
62 37.14456611 -2.62990857
63 6.47219617 37.14456611
64 -6.13592556 6.47219617
65 -7.75833499 -6.13592556
66 10.74750237 -7.75833499
67 13.16308792 10.74750237
68 -2.95428857 13.16308792
69 3.79079955 -2.95428857
70 8.15405598 3.79079955
71 11.22686528 8.15405598
72 0.85800649 11.22686528
73 -10.20309676 0.85800649
74 -3.63102438 -10.20309676
75 -12.22131645 -3.63102438
76 -21.27253653 -12.22131645
77 -5.00695473 -21.27253653
78 -4.95013120 -5.00695473
79 1.65695113 -4.95013120
80 -3.55634181 1.65695113
81 -22.95460153 -3.55634181
82 13.63742442 -22.95460153
83 9.14912662 13.63742442
84 3.67822936 9.14912662
85 -18.65164473 3.67822936
86 -4.82818599 -18.65164473
87 -1.92599046 -4.82818599
88 27.83946254 -1.92599046
89 10.29313242 27.83946254
90 -12.51834592 10.29313242
91 -4.90517429 -12.51834592
92 7.83572542 -4.90517429
93 -10.96013575 7.83572542
94 -0.44721800 -10.96013575
95 36.37368792 -0.44721800
96 12.90362741 36.37368792
97 -35.05667380 12.90362741
98 9.19734217 -35.05667380
99 -27.00508501 9.19734217
100 4.78098756 -27.00508501
101 4.65615383 4.78098756
102 17.20693814 4.65615383
103 -2.50281625 17.20693814
104 -8.04170408 -2.50281625
105 -11.39074273 -8.04170408
106 -5.14279865 -11.39074273
107 -6.21628818 -5.14279865
108 3.67395099 -6.21628818
109 3.31927077 3.67395099
110 -8.23520183 3.31927077
111 2.43361030 -8.23520183
112 -11.96300671 2.43361030
113 1.96130738 -11.96300671
114 -16.90826097 1.96130738
115 -1.97576776 -16.90826097
116 -6.81152204 -1.97576776
117 25.65413467 -6.81152204
118 -3.12930906 25.65413467
119 -1.76085439 -3.12930906
120 0.15141396 -1.76085439
121 -3.40477917 0.15141396
122 4.38408451 -3.40477917
123 -18.90731075 4.38408451
124 15.18513108 -18.90731075
125 4.06574756 15.18513108
126 7.49480041 4.06574756
127 -11.69760330 7.49480041
128 -7.03542169 -11.69760330
129 -2.16127451 -7.03542169
130 2.39241257 -2.16127451
131 -16.55666224 2.39241257
132 -2.87138951 -16.55666224
133 -4.71932340 -2.87138951
134 -27.11764083 -4.71932340
135 14.41573694 -27.11764083
136 0.59372456 14.41573694
137 13.90246594 0.59372456
138 -10.99116407 13.90246594
139 -8.84270298 -10.99116407
140 -3.14922018 -8.84270298
141 -1.73029089 -3.14922018
142 7.71253484 -1.73029089
143 8.32908939 7.71253484
144 -0.67303929 8.32908939
145 -3.66441265 -0.67303929
146 -1.83652954 -3.66441265
147 -6.56542666 -1.83652954
148 -3.41029000 -6.56542666
149 0.04073773 -3.41029000
150 7.48811377 0.04073773
151 -13.00954327 7.48811377
152 2.69730030 -13.00954327
153 12.75765698 2.69730030
154 -4.68290519 12.75765698
155 6.30604944 -4.68290519
156 -1.33018828 6.30604944
157 4.05132825 -1.33018828
158 -8.17248159 4.05132825
159 14.38495306 -8.17248159
160 26.76972186 14.38495306
161 -2.00921528 26.76972186
162 2.62976754 -2.00921528
163 22.19074159 2.62976754
164 -3.98920805 22.19074159
165 -3.17706386 -3.98920805
166 -7.37378134 -3.17706386
167 3.48483216 -7.37378134
168 -2.91439065 3.48483216
169 37.47711074 -2.91439065
170 27.46347472 37.47711074
171 -18.25450297 27.46347472
172 21.00288506 -18.25450297
173 -1.53802451 21.00288506
174 -39.77874098 -1.53802451
175 15.29432593 -39.77874098
176 -3.03796560 15.29432593
177 3.38327565 -3.03796560
178 -15.99266711 3.38327565
179 10.96720105 -15.99266711
180 1.37142923 10.96720105
181 -5.41816494 1.37142923
182 -1.30940236 -5.41816494
183 6.41692059 -1.30940236
184 6.39196209 6.41692059
185 -4.18938104 6.39196209
186 1.91181830 -4.18938104
187 0.07920345 1.91181830
188 8.09023545 0.07920345
189 -6.65183859 8.09023545
190 5.63513830 -6.65183859
191 5.93286771 5.63513830
192 -2.99593138 5.93286771
193 -5.11402247 -2.99593138
194 -2.90196379 -5.11402247
195 -2.49133348 -2.90196379
196 -1.75621450 -2.49133348
197 -5.60405942 -1.75621450
198 -1.74886648 -5.60405942
199 -11.58395194 -1.74886648
200 -1.80198023 -11.58395194
201 -10.31246547 -1.80198023
202 10.56326247 -10.31246547
203 8.10890437 10.56326247
204 -0.33784490 8.10890437
205 -6.59214995 -0.33784490
206 -2.02678323 -6.59214995
207 23.39781680 -2.02678323
208 9.73859488 23.39781680
209 2.96252712 9.73859488
210 6.98864427 2.96252712
211 6.74196574 6.98864427
212 -7.43479298 6.74196574
213 -0.26632414 -7.43479298
214 -2.25128610 -0.26632414
215 -4.58569434 -2.25128610
216 -3.11429937 -4.58569434
217 -0.51817667 -3.11429937
218 -6.20546305 -0.51817667
219 6.01874839 -6.20546305
220 -5.52637454 6.01874839
221 1.50362360 -5.52637454
222 -7.48246059 1.50362360
223 -4.46451563 -7.48246059
224 -6.68652510 -4.46451563
225 8.10501135 -6.68652510
226 3.11123749 8.10501135
227 -16.49881915 3.11123749
228 -8.85695976 -16.49881915
229 -7.73940215 -8.85695976
230 -4.55152916 -7.73940215
231 -2.12427075 -4.55152916
232 -0.83374895 -2.12427075
233 -6.21924346 -0.83374895
234 -10.37756218 -6.21924346
235 -10.92507787 -10.37756218
236 20.40551248 -10.92507787
237 -9.93244258 20.40551248
238 -4.02379071 -9.93244258
239 -10.27982573 -4.02379071
240 3.26953936 -10.27982573
241 4.96897608 3.26953936
242 -9.73168866 4.96897608
243 -6.06655911 -9.73168866
244 -4.06395306 -6.06655911
245 -1.76050366 -4.06395306
246 -1.72774182 -1.76050366
247 6.34553275 -1.72774182
248 3.95036716 6.34553275
249 -6.52178496 3.95036716
250 13.61753102 -6.52178496
251 -4.32060290 13.61753102
252 -3.78423549 -4.32060290
253 7.37808677 -3.78423549
254 2.29422262 7.37808677
255 -0.36125733 2.29422262
256 -9.90574360 -0.36125733
257 3.19340039 -9.90574360
258 0.06672302 3.19340039
259 -3.16433837 0.06672302
260 -6.50093832 -3.16433837
261 -6.94869906 -6.50093832
262 0.86267963 -6.94869906
263 -10.79005080 0.86267963
264 -5.92321432 -10.79005080
265 -2.98449086 -5.92321432
266 -10.25724712 -2.98449086
267 -6.83692073 -10.25724712
268 8.24545450 -6.83692073
269 -8.95001862 8.24545450
270 2.82855428 -8.95001862
271 -0.53932994 2.82855428
272 5.98150690 -0.53932994
273 -0.33389381 5.98150690
274 -0.98774984 -0.33389381
275 -6.94600757 -0.98774984
276 2.98145585 -6.94600757
277 -3.02656716 2.98145585
278 -1.61143698 -3.02656716
279 1.63406960 -1.61143698
280 -3.62555401 1.63406960
281 5.89736878 -3.62555401
282 4.26125371 5.89736878
283 2.52249651 4.26125371
284 0.92316831 2.52249651
285 -7.49535187 0.92316831
286 -0.32458535 -7.49535187
287 -10.72297581 -0.32458535
288 -1.61491694 -10.72297581
> 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/7utnt1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8uofj1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9eeez1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/102pef1354984890.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11nhik1354984890.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/12kdqv1354984890.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/1394r61354984890.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/1457f61354984890.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/15g0mt1354984890.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/16k9mr1354984890.tab")
+ }
>
> try(system("convert tmp/1d4h51354984890.ps tmp/1d4h51354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vh211354984890.ps tmp/2vh211354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zftz1354984890.ps tmp/3zftz1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/4gzxp1354984890.ps tmp/4gzxp1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/5okp11354984890.ps tmp/5okp11354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ghqq1354984890.ps tmp/6ghqq1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/7utnt1354984890.ps tmp/7utnt1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/8uofj1354984890.ps tmp/8uofj1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/9eeez1354984890.ps tmp/9eeez1354984890.png",intern=TRUE))
character(0)
> try(system("convert tmp/102pef1354984890.ps tmp/102pef1354984890.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.718 1.099 11.959