R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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+ ,12
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+ ,43
+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '6'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects 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
Depression Connected Separate Learning Software Happiness Sport1 Sport2
1 12.0 41 38 13 12 14 53 32
2 11.0 39 32 16 11 18 83 51
3 14.0 30 35 19 15 11 66 42
4 12.0 31 33 15 6 12 67 41
5 21.0 34 37 14 13 16 76 46
6 12.0 35 29 13 10 18 78 47
7 22.0 39 31 19 12 14 53 37
8 11.0 34 36 15 14 14 80 49
9 10.0 36 35 14 12 15 74 45
10 13.0 37 38 15 9 15 76 47
11 10.0 38 31 16 10 17 79 49
12 8.0 36 34 16 12 19 54 33
13 15.0 38 35 16 12 10 67 42
14 14.0 39 38 16 11 16 54 33
15 10.0 33 37 17 15 18 87 53
16 14.0 32 33 15 12 14 58 36
17 14.0 36 32 15 10 14 75 45
18 11.0 38 38 20 12 17 88 54
19 10.0 39 38 18 11 14 64 41
20 13.0 32 32 16 12 16 57 36
21 9.5 32 33 16 11 18 66 41
22 14.0 31 31 16 12 11 68 44
23 12.0 39 38 19 13 14 54 33
24 14.0 37 39 16 11 12 56 37
25 11.0 39 32 17 12 17 86 52
26 9.0 41 32 17 13 9 80 47
27 11.0 36 35 16 10 16 76 43
28 15.0 33 37 15 14 14 69 44
29 14.0 33 33 16 12 15 78 45
30 13.0 34 33 14 10 11 67 44
31 9.0 31 31 15 12 16 80 49
32 15.0 27 32 12 8 13 54 33
33 10.0 37 31 14 10 17 71 43
34 11.0 34 37 16 12 15 84 54
35 13.0 34 30 14 12 14 74 42
36 8.0 32 33 10 7 16 71 44
37 20.0 29 31 10 9 9 63 37
38 12.0 36 33 14 12 15 71 43
39 10.0 29 31 16 10 17 76 46
40 10.0 35 33 16 10 13 69 42
41 9.0 37 32 16 10 15 74 45
42 14.0 34 33 14 12 16 75 44
43 8.0 38 32 20 15 16 54 33
44 14.0 35 33 14 10 12 52 31
45 11.0 38 28 14 10 15 69 42
46 13.0 37 35 11 12 11 68 40
47 9.0 38 39 14 13 15 65 43
48 11.0 33 34 15 11 15 75 46
49 15.0 36 38 16 11 17 74 42
50 11.0 38 32 14 12 13 75 45
51 10.0 32 38 16 14 16 72 44
52 14.0 32 30 14 10 14 67 40
53 18.0 32 33 12 12 11 63 37
54 14.0 34 38 16 13 12 62 46
55 11.0 32 32 9 5 12 63 36
56 14.5 37 35 14 6 15 76 47
57 13.0 39 34 16 12 16 74 45
58 9.0 29 34 16 12 15 67 42
59 10.0 37 36 15 11 12 73 43
60 15.0 35 34 16 10 12 70 43
61 20.0 30 28 12 7 8 53 32
62 12.0 38 34 16 12 13 77 45
63 12.0 34 35 16 14 11 80 48
64 14.0 31 35 14 11 14 52 31
65 13.0 34 31 16 12 15 54 33
66 11.0 35 37 17 13 10 80 49
67 17.0 36 35 18 14 11 66 42
68 12.0 30 27 18 11 12 73 41
69 13.0 39 40 12 12 15 63 38
70 14.0 35 37 16 12 15 69 42
71 13.0 38 36 10 8 14 67 44
72 15.0 31 38 14 11 16 54 33
73 13.0 34 39 18 14 15 81 48
74 10.0 38 41 18 14 15 69 40
75 11.0 34 27 16 12 13 84 50
76 19.0 39 30 17 9 12 80 49
77 13.0 37 37 16 13 17 70 43
78 17.0 34 31 16 11 13 69 44
79 13.0 28 31 13 12 15 77 47
80 9.0 37 27 16 12 13 54 33
81 11.0 33 36 16 12 15 79 46
82 9.0 35 37 16 12 15 71 45
83 12.0 37 33 15 12 16 73 43
84 12.0 32 34 15 11 15 72 44
85 13.0 33 31 16 10 14 77 47
86 13.0 38 39 14 9 15 75 45
87 12.0 33 34 16 12 14 69 42
88 15.0 29 32 16 12 13 54 33
89 22.0 33 33 15 12 7 70 43
90 13.0 31 36 12 9 17 73 46
91 15.0 36 32 17 15 13 54 33
92 13.0 35 41 16 12 15 77 46
93 15.0 32 28 15 12 14 82 48
94 12.5 29 30 13 12 13 80 47
95 11.0 39 36 16 10 16 80 47
96 16.0 37 35 16 13 12 69 43
97 11.0 35 31 16 9 14 78 46
98 11.0 37 34 16 12 17 81 48
99 10.0 32 36 14 10 15 76 46
100 10.0 38 36 16 14 17 76 45
101 16.0 37 35 16 11 12 73 45
102 12.0 36 37 20 15 16 85 52
103 11.0 32 28 15 11 11 66 42
104 16.0 33 39 16 11 15 79 47
105 19.0 40 32 13 12 9 68 41
106 11.0 38 35 17 12 16 76 47
107 16.0 41 39 16 12 15 71 43
108 15.0 36 35 16 11 10 54 33
109 24.0 43 42 12 7 10 46 30
110 14.0 30 34 16 12 15 85 52
111 15.0 31 33 16 14 11 74 44
112 11.0 32 41 17 11 13 88 55
113 15.0 32 33 13 11 14 38 11
114 12.0 37 34 12 10 18 76 47
115 10.0 37 32 18 13 16 86 53
116 14.0 33 40 14 13 14 54 33
117 13.0 34 40 14 8 14 67 44
118 9.0 33 35 13 11 14 69 42
119 15.0 38 36 16 12 14 90 55
120 15.0 33 37 13 11 12 54 33
121 14.0 31 27 16 13 14 76 46
122 11.0 38 39 13 12 15 89 54
123 8.0 37 38 16 14 15 76 47
124 11.0 36 31 15 13 15 73 45
125 11.0 31 33 16 15 13 79 47
126 8.0 39 32 15 10 17 90 55
127 10.0 44 39 17 11 17 74 44
128 11.0 33 36 15 9 19 81 53
129 13.0 35 33 12 11 15 72 44
130 11.0 32 33 16 10 13 71 42
131 20.0 28 32 10 11 9 66 40
132 10.0 40 37 16 8 15 77 46
133 15.0 27 30 12 11 15 65 40
134 12.0 37 38 14 12 15 74 46
135 14.0 32 29 15 12 16 85 53
136 23.0 28 22 13 9 11 54 33
137 14.0 34 35 15 11 14 63 42
138 16.0 30 35 11 10 11 54 35
139 11.0 35 34 12 8 15 64 40
140 12.0 31 35 11 9 13 69 41
141 10.0 32 34 16 8 15 54 33
142 14.0 30 37 15 9 16 84 51
143 12.0 30 35 17 15 14 86 53
144 12.0 31 23 16 11 15 77 46
145 11.0 40 31 10 8 16 89 55
146 12.0 32 27 18 13 16 76 47
147 13.0 36 36 13 12 11 60 38
148 11.0 32 31 16 12 12 75 46
149 19.0 35 32 13 9 9 73 46
150 12.0 38 39 10 7 16 85 53
151 17.0 42 37 15 13 13 79 47
152 9.0 34 38 16 9 16 71 41
153 12.0 35 39 16 6 12 72 44
154 19.0 38 34 14 8 9 69 43
155 18.0 33 31 10 8 13 78 51
156 15.0 36 32 17 15 13 54 33
157 14.0 32 37 13 6 14 69 43
158 11.0 33 36 15 9 19 81 53
159 9.0 34 32 16 11 13 84 51
160 18.0 32 38 12 8 12 84 50
161 16.0 34 36 13 8 13 69 46
162 24.0 27 26 13 10 10 66 43
163 14.0 31 26 12 8 14 81 47
164 20.0 38 33 17 14 16 82 50
165 18.0 34 39 15 10 10 72 43
166 23.0 24 30 10 8 11 54 33
167 12.0 30 33 14 11 14 78 48
168 14.0 26 25 11 12 12 74 44
169 16.0 34 38 13 12 9 82 50
170 18.0 27 37 16 12 9 73 41
171 20.0 37 31 12 5 11 55 34
172 12.0 36 37 16 12 16 72 44
173 12.0 41 35 12 10 9 78 47
174 17.0 29 25 9 7 13 59 35
175 13.0 36 28 12 12 16 72 44
176 9.0 32 35 15 11 13 78 44
177 16.0 37 33 12 8 9 68 43
178 18.0 30 30 12 9 12 69 41
179 10.0 31 31 14 10 16 67 41
180 14.0 38 37 12 9 11 74 42
181 11.0 36 36 16 12 14 54 33
182 9.0 35 30 11 6 13 67 41
183 11.0 31 36 19 15 15 70 44
184 10.0 38 32 15 12 14 80 48
185 11.0 22 28 8 12 16 89 55
186 19.0 32 36 16 12 13 76 44
187 14.0 36 34 17 11 14 74 43
188 12.0 39 31 12 7 15 87 52
189 14.0 28 28 11 7 13 54 30
190 21.0 32 36 11 5 11 61 39
191 13.0 32 36 14 12 11 38 11
192 10.0 38 40 16 12 14 75 44
193 15.0 32 33 12 3 15 69 42
194 16.0 35 37 16 11 11 62 41
195 14.0 32 32 13 10 15 72 44
196 12.0 37 38 15 12 12 70 44
197 19.0 34 31 16 9 14 79 48
198 15.0 33 37 16 12 14 87 53
199 19.0 33 33 14 9 8 62 37
200 13.0 26 32 16 12 13 77 44
201 17.0 30 30 16 12 9 69 44
202 12.0 24 30 14 10 15 69 40
203 11.0 34 31 11 9 17 75 42
204 14.0 34 32 12 12 13 54 35
205 11.0 33 34 15 8 15 72 43
206 13.0 34 36 15 11 15 74 45
207 12.0 35 37 16 11 14 85 55
208 15.0 35 36 16 12 16 52 31
209 14.0 36 33 11 10 13 70 44
210 12.0 34 33 15 10 16 84 50
211 17.0 34 33 12 12 9 64 40
212 11.0 41 44 12 12 16 84 53
213 18.0 32 39 15 11 11 87 54
214 13.0 30 32 15 8 10 79 49
215 17.0 35 35 16 12 11 67 40
216 13.0 28 25 14 10 15 65 41
217 11.0 33 35 17 11 17 85 52
218 12.0 39 34 14 10 14 83 52
219 22.0 36 35 13 8 8 61 36
220 14.0 36 39 15 12 15 82 52
221 12.0 35 33 13 12 11 76 46
222 12.0 38 36 14 10 16 58 31
223 17.0 33 32 15 12 10 72 44
224 9.0 31 32 12 9 15 72 44
225 21.0 34 36 13 9 9 38 11
226 10.0 32 36 8 6 16 78 46
227 11.0 31 32 14 10 19 54 33
228 12.0 33 34 14 9 12 63 34
229 23.0 34 33 11 9 8 66 42
230 13.0 34 35 12 9 11 70 43
231 12.0 34 30 13 6 14 71 43
232 16.0 33 38 10 10 9 67 44
233 9.0 32 34 16 6 15 58 36
234 17.0 41 33 18 14 13 72 46
235 9.0 34 32 13 10 16 72 44
236 14.0 36 31 11 10 11 70 43
237 17.0 37 30 4 6 12 76 50
238 13.0 36 27 13 12 13 50 33
239 11.0 29 31 16 12 10 72 43
240 12.0 37 30 10 7 11 72 44
241 10.0 27 32 12 8 12 88 53
242 19.0 35 35 12 11 8 53 34
243 16.0 28 28 10 3 12 58 35
244 16.0 35 33 13 6 12 66 40
245 14.0 37 31 15 10 15 82 53
246 20.0 29 35 12 8 11 69 42
247 15.0 32 35 14 9 13 68 43
248 23.0 36 32 10 9 14 44 29
249 20.0 19 21 12 8 10 56 36
250 16.0 21 20 12 9 12 53 30
251 14.0 31 34 11 7 15 70 42
252 17.0 33 32 10 7 13 78 47
253 11.0 36 34 12 6 13 71 44
254 13.0 33 32 16 9 13 72 45
255 17.0 37 33 12 10 12 68 44
256 15.0 34 33 14 11 12 67 43
257 21.0 35 37 16 12 9 75 43
258 18.0 31 32 14 8 9 62 40
259 15.0 37 34 13 11 15 67 41
260 8.0 35 30 4 3 10 83 52
261 12.0 27 30 15 11 14 64 38
262 12.0 34 38 11 12 15 68 41
263 22.0 40 36 11 7 7 62 39
264 12.0 29 32 14 9 14 72 43
Month t
1 9 1
2 9 2
3 9 3
4 9 4
5 9 5
6 9 6
7 9 7
8 9 8
9 9 9
10 9 10
11 9 11
12 9 12
13 9 13
14 9 14
15 9 15
16 9 16
17 9 17
18 9 18
19 9 19
20 9 20
21 9 21
22 9 22
23 9 23
24 9 24
25 9 25
26 9 26
27 9 27
28 9 28
29 9 29
30 9 30
31 9 31
32 9 32
33 9 33
34 9 34
35 9 35
36 9 36
37 9 37
38 9 38
39 9 39
40 9 40
41 9 41
42 9 42
43 9 43
44 9 44
45 9 45
46 9 46
47 9 47
48 9 48
49 9 49
50 9 50
51 9 51
52 9 52
53 9 53
54 9 54
55 9 55
56 9 56
57 9 57
58 9 58
59 9 59
60 9 60
61 9 61
62 9 62
63 9 63
64 9 64
65 9 65
66 10 66
67 10 67
68 10 68
69 10 69
70 10 70
71 10 71
72 10 72
73 10 73
74 10 74
75 10 75
76 10 76
77 10 77
78 10 78
79 10 79
80 10 80
81 10 81
82 10 82
83 10 83
84 10 84
85 10 85
86 10 86
87 10 87
88 10 88
89 10 89
90 10 90
91 10 91
92 10 92
93 10 93
94 10 94
95 10 95
96 10 96
97 10 97
98 10 98
99 10 99
100 10 100
101 10 101
102 10 102
103 10 103
104 10 104
105 10 105
106 10 106
107 10 107
108 10 108
109 10 109
110 10 110
111 10 111
112 10 112
113 10 113
114 10 114
115 10 115
116 10 116
117 10 117
118 10 118
119 10 119
120 10 120
121 10 121
122 10 122
123 10 123
124 10 124
125 10 125
126 10 126
127 10 127
128 10 128
129 10 129
130 10 130
131 10 131
132 10 132
133 10 133
134 10 134
135 10 135
136 10 136
137 10 137
138 10 138
139 10 139
140 10 140
141 10 141
142 10 142
143 10 143
144 10 144
145 10 145
146 10 146
147 10 147
148 10 148
149 10 149
150 10 150
151 10 151
152 10 152
153 10 153
154 9 154
155 10 155
156 10 156
157 10 157
158 10 158
159 10 159
160 10 160
161 10 161
162 11 162
163 11 163
164 11 164
165 11 165
166 11 166
167 11 167
168 11 168
169 11 169
170 11 170
171 11 171
172 11 172
173 11 173
174 11 174
175 11 175
176 11 176
177 11 177
178 11 178
179 11 179
180 11 180
181 11 181
182 11 182
183 11 183
184 11 184
185 11 185
186 11 186
187 11 187
188 11 188
189 11 189
190 11 190
191 11 191
192 11 192
193 11 193
194 11 194
195 11 195
196 11 196
197 11 197
198 11 198
199 11 199
200 11 200
201 11 201
202 11 202
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204 11 204
205 11 205
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209 11 209
210 11 210
211 11 211
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223 11 223
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225 11 225
226 11 226
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228 11 228
229 11 229
230 11 230
231 11 231
232 11 232
233 11 233
234 11 234
235 11 235
236 11 236
237 11 237
238 11 238
239 11 239
240 11 240
241 11 241
242 11 242
243 11 243
244 11 244
245 11 245
246 11 246
247 11 247
248 11 248
249 11 249
250 11 250
251 11 251
252 11 252
253 11 253
254 11 254
255 11 255
256 11 256
257 11 257
258 11 258
259 11 259
260 11 260
261 11 261
262 11 262
263 11 263
264 11 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Software Happiness
18.939369 -0.028251 0.002961 -0.067509 -0.026789 -0.687128
Sport1 Sport2 Month t
-0.165979 0.168248 1.145774 -0.008206
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.2605 -1.7810 -0.1709 1.5673 9.8031
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.939369 5.636628 3.360 0.000899 ***
Connected -0.028251 0.051258 -0.551 0.582011
Separate 0.002961 0.052112 0.057 0.954727
Learning -0.067509 0.092538 -0.730 0.466353
Software -0.026789 0.095616 -0.280 0.779573
Happiness -0.687128 0.074436 -9.231 < 2e-16 ***
Sport1 -0.165979 0.055129 -3.011 0.002869 **
Sport2 0.168248 0.082648 2.036 0.042817 *
Month 1.145774 0.599336 1.912 0.057035 .
t -0.008206 0.006404 -1.281 0.201246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.746 on 254 degrees of freedom
Multiple R-squared: 0.3949, Adjusted R-squared: 0.3735
F-statistic: 18.42 on 9 and 254 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,] 0.98227830 0.03544340 0.01772170
[2,] 0.98946281 0.02107438 0.01053719
[3,] 0.97870498 0.04259003 0.02129502
[4,] 0.96016006 0.07967989 0.03983994
[5,] 0.98307581 0.03384838 0.01692419
[6,] 0.97420788 0.05158424 0.02579212
[7,] 0.98370902 0.03258195 0.01629098
[8,] 0.97310315 0.05379369 0.02689685
[9,] 0.96318754 0.07362492 0.03681246
[10,] 0.94962270 0.10075461 0.05037730
[11,] 0.93245192 0.13509615 0.06754808
[12,] 0.90712183 0.18575633 0.09287817
[13,] 0.88061951 0.23876098 0.11938049
[14,] 0.86532167 0.26935665 0.13467833
[15,] 0.89167876 0.21664248 0.10832124
[16,] 0.86729771 0.26540458 0.13270229
[17,] 0.90251203 0.19497594 0.09748797
[18,] 0.88278920 0.23442160 0.11721080
[19,] 0.86943246 0.26113508 0.13056754
[20,] 0.85159504 0.29680992 0.14840496
[21,] 0.81456028 0.37087945 0.18543972
[22,] 0.77535526 0.44928947 0.22464474
[23,] 0.75982000 0.48036001 0.24018000
[24,] 0.76104800 0.47790401 0.23895200
[25,] 0.85884208 0.28231584 0.14115792
[26,] 0.82608145 0.34783710 0.17391855
[27,] 0.78878441 0.42243118 0.21121559
[28,] 0.76626005 0.46747991 0.23373995
[29,] 0.73294946 0.53410108 0.26705054
[30,] 0.75030593 0.49938814 0.24969407
[31,] 0.75605910 0.48788180 0.24394090
[32,] 0.72146171 0.55707658 0.27853829
[33,] 0.67841206 0.64317587 0.32158794
[34,] 0.63555560 0.72888880 0.36444440
[35,] 0.62496890 0.75006220 0.37503110
[36,] 0.58028058 0.83943885 0.41971942
[37,] 0.72870510 0.54258981 0.27129490
[38,] 0.69093107 0.61813785 0.30906893
[39,] 0.65263782 0.69472436 0.34736218
[40,] 0.63368432 0.73263137 0.36631568
[41,] 0.66276360 0.67447280 0.33723640
[42,] 0.63901651 0.72196697 0.36098349
[43,] 0.63068940 0.73862120 0.36931060
[44,] 0.69356867 0.61286266 0.30643133
[45,] 0.69329471 0.61341058 0.30670529
[46,] 0.70071127 0.59857746 0.29928873
[47,] 0.69302152 0.61395697 0.30697848
[48,] 0.68858338 0.62283323 0.31141662
[49,] 0.71515387 0.56969227 0.28484613
[50,] 0.67841498 0.64317005 0.32158502
[51,] 0.64605965 0.70788070 0.35394035
[52,] 0.60770318 0.78459364 0.39229682
[53,] 0.56843234 0.86313533 0.43156766
[54,] 0.55912924 0.88174152 0.44087076
[55,] 0.58609018 0.82781964 0.41390982
[56,] 0.56044228 0.87911545 0.43955772
[57,] 0.51986564 0.96026871 0.48013436
[58,] 0.49259339 0.98518678 0.50740661
[59,] 0.45663257 0.91326514 0.54336743
[60,] 0.42588533 0.85177065 0.57411467
[61,] 0.39437732 0.78875463 0.60562268
[62,] 0.37772475 0.75544951 0.62227525
[63,] 0.35183408 0.70366816 0.64816592
[64,] 0.49068410 0.98136821 0.50931590
[65,] 0.46043200 0.92086401 0.53956800
[66,] 0.45327260 0.90654520 0.54672740
[67,] 0.41456706 0.82913412 0.58543294
[68,] 0.55015185 0.89969630 0.44984815
[69,] 0.51613832 0.96772335 0.48386168
[70,] 0.54999460 0.90001080 0.45000540
[71,] 0.51188567 0.97622865 0.48811433
[72,] 0.47516422 0.95032844 0.52483578
[73,] 0.43678923 0.87357846 0.56321077
[74,] 0.40129873 0.80259746 0.59870127
[75,] 0.37183183 0.74366366 0.62816817
[76,] 0.33849440 0.67698880 0.66150560
[77,] 0.41450008 0.82900016 0.58549992
[78,] 0.38127413 0.76254826 0.61872587
[79,] 0.35133151 0.70266302 0.64866849
[80,] 0.32005241 0.64010481 0.67994759
[81,] 0.31310163 0.62620327 0.68689837
[82,] 0.28557651 0.57115303 0.71442349
[83,] 0.25383322 0.50766644 0.74616678
[84,] 0.23590211 0.47180423 0.76409789
[85,] 0.21746963 0.43493926 0.78253037
[86,] 0.19168038 0.38336076 0.80831962
[87,] 0.18765092 0.37530184 0.81234908
[88,] 0.16393182 0.32786365 0.83606818
[89,] 0.15292143 0.30584286 0.84707857
[90,] 0.13662130 0.27324260 0.86337870
[91,] 0.18311087 0.36622174 0.81688913
[92,] 0.21138773 0.42277545 0.78861227
[93,] 0.21517292 0.43034584 0.78482708
[94,] 0.18969701 0.37939402 0.81030299
[95,] 0.20968257 0.41936514 0.79031743
[96,] 0.19757366 0.39514732 0.80242634
[97,] 0.29891324 0.59782648 0.70108676
[98,] 0.28709845 0.57419690 0.71290155
[99,] 0.25753728 0.51507457 0.74246272
[100,] 0.24876574 0.49753148 0.75123426
[101,] 0.22601313 0.45202626 0.77398687
[102,] 0.20593056 0.41186112 0.79406944
[103,] 0.18232206 0.36464412 0.81767794
[104,] 0.15980935 0.31961871 0.84019065
[105,] 0.14343520 0.28687040 0.85656480
[106,] 0.18276719 0.36553437 0.81723281
[107,] 0.18822169 0.37644338 0.81177831
[108,] 0.16723665 0.33447330 0.83276335
[109,] 0.15224756 0.30449512 0.84775244
[110,] 0.13364308 0.26728616 0.86635692
[111,] 0.15985380 0.31970759 0.84014620
[112,] 0.14326512 0.28653024 0.85673488
[113,] 0.13397048 0.26794096 0.86602952
[114,] 0.12381776 0.24763552 0.87618224
[115,] 0.10705138 0.21410277 0.89294862
[116,] 0.09462368 0.18924736 0.90537632
[117,] 0.08069517 0.16139033 0.91930483
[118,] 0.07973960 0.15947920 0.92026040
[119,] 0.08118296 0.16236593 0.91881704
[120,] 0.07402150 0.14804301 0.92597850
[121,] 0.06721223 0.13442447 0.93278777
[122,] 0.05645164 0.11290329 0.94354836
[123,] 0.05845026 0.11690053 0.94154974
[124,] 0.11740406 0.23480811 0.88259594
[125,] 0.10113805 0.20227610 0.89886195
[126,] 0.08919954 0.17839908 0.91080046
[127,] 0.08664628 0.17329255 0.91335372
[128,] 0.08268639 0.16537277 0.91731361
[129,] 0.09617491 0.19234981 0.90382509
[130,] 0.09768729 0.19537458 0.90231271
[131,] 0.08333769 0.16667538 0.91666231
[132,] 0.07026089 0.14052179 0.92973911
[133,] 0.05905611 0.11811223 0.94094389
[134,] 0.04974759 0.09949519 0.95025241
[135,] 0.05654720 0.11309440 0.94345280
[136,] 0.06344911 0.12689821 0.93655089
[137,] 0.05948227 0.11896454 0.94051773
[138,] 0.04963707 0.09927413 0.95036293
[139,] 0.06182789 0.12365577 0.93817211
[140,] 0.05963320 0.11926640 0.94036680
[141,] 0.06119247 0.12238495 0.93880753
[142,] 0.06315941 0.12631881 0.93684059
[143,] 0.06722614 0.13445228 0.93277386
[144,] 0.05730271 0.11460542 0.94269729
[145,] 0.04799511 0.09599023 0.95200489
[146,] 0.04038548 0.08077097 0.95961452
[147,] 0.05571964 0.11143929 0.94428036
[148,] 0.06135768 0.12271536 0.93864232
[149,] 0.05191238 0.10382475 0.94808762
[150,] 0.09075412 0.18150824 0.90924588
[151,] 0.08056482 0.16112965 0.91943518
[152,] 0.27626600 0.55253200 0.72373400
[153,] 0.25113542 0.50227084 0.74886458
[154,] 0.31617719 0.63235439 0.68382281
[155,] 0.30009410 0.60018821 0.69990590
[156,] 0.28305504 0.56611008 0.71694496
[157,] 0.25569332 0.51138664 0.74430668
[158,] 0.23324390 0.46648781 0.76675610
[159,] 0.23128711 0.46257422 0.76871289
[160,] 0.20514679 0.41029359 0.79485321
[161,] 0.25745899 0.51491798 0.74254101
[162,] 0.24814949 0.49629897 0.75185051
[163,] 0.23258588 0.46517176 0.76741412
[164,] 0.27432716 0.54865431 0.72567284
[165,] 0.25594774 0.51189548 0.74405226
[166,] 0.26540612 0.53081223 0.73459388
[167,] 0.26064030 0.52128061 0.73935970
[168,] 0.23439469 0.46878937 0.76560531
[169,] 0.26488420 0.52976840 0.73511580
[170,] 0.38217495 0.76434991 0.61782505
[171,] 0.37372346 0.74744692 0.62627654
[172,] 0.37213882 0.74427765 0.62786118
[173,] 0.35336239 0.70672477 0.64663761
[174,] 0.46254522 0.92509043 0.53745478
[175,] 0.42704787 0.85409573 0.57295213
[176,] 0.39402251 0.78804502 0.60597749
[177,] 0.36192518 0.72385037 0.63807482
[178,] 0.39178946 0.78357892 0.60821054
[179,] 0.42035577 0.84071153 0.57964423
[180,] 0.44129537 0.88259074 0.55870463
[181,] 0.42899901 0.85799803 0.57100099
[182,] 0.39857155 0.79714310 0.60142845
[183,] 0.37055775 0.74111551 0.62944225
[184,] 0.40842318 0.81684636 0.59157682
[185,] 0.61286376 0.77427247 0.38713624
[186,] 0.62218101 0.75563797 0.37781899
[187,] 0.58158422 0.83683156 0.41841578
[188,] 0.54018127 0.91963747 0.45981873
[189,] 0.49697267 0.99394535 0.50302733
[190,] 0.45726953 0.91453907 0.54273047
[191,] 0.43795459 0.87590918 0.56204541
[192,] 0.42636529 0.85273058 0.57363471
[193,] 0.39000440 0.78000880 0.60999560
[194,] 0.34936825 0.69873650 0.65063175
[195,] 0.31105249 0.62210499 0.68894751
[196,] 0.27787576 0.55575151 0.72212424
[197,] 0.24145246 0.48290492 0.75854754
[198,] 0.23791191 0.47582383 0.76208809
[199,] 0.21219776 0.42439551 0.78780224
[200,] 0.18253287 0.36506574 0.81746713
[201,] 0.20975892 0.41951785 0.79024108
[202,] 0.19192341 0.38384682 0.80807659
[203,] 0.16458481 0.32916963 0.83541519
[204,] 0.14035685 0.28071369 0.85964315
[205,] 0.13710300 0.27420600 0.86289700
[206,] 0.11548856 0.23097713 0.88451144
[207,] 0.12819942 0.25639885 0.87180058
[208,] 0.13460223 0.26920446 0.86539777
[209,] 0.12266682 0.24533363 0.87733318
[210,] 0.09847097 0.19694195 0.90152903
[211,] 0.08483527 0.16967054 0.91516473
[212,] 0.07822410 0.15644821 0.92177590
[213,] 0.07234866 0.14469732 0.92765134
[214,] 0.06031428 0.12062856 0.93968572
[215,] 0.04583346 0.09166691 0.95416654
[216,] 0.03782687 0.07565374 0.96217313
[217,] 0.06740715 0.13481429 0.93259285
[218,] 0.05550374 0.11100748 0.94449626
[219,] 0.04269888 0.08539777 0.95730112
[220,] 0.03612996 0.07225993 0.96387004
[221,] 0.06892598 0.13785196 0.93107402
[222,] 0.07476554 0.14953107 0.92523446
[223,] 0.06785872 0.13571744 0.93214128
[224,] 0.05018722 0.10037443 0.94981278
[225,] 0.07007413 0.14014826 0.92992587
[226,] 0.09432158 0.18864316 0.90567842
[227,] 0.18392844 0.36785688 0.81607156
[228,] 0.22589250 0.45178501 0.77410750
[229,] 0.20918514 0.41837028 0.79081486
[230,] 0.63628756 0.72742488 0.36371244
[231,] 0.55193271 0.89613458 0.44806729
[232,] 0.54309221 0.91381559 0.45690779
[233,] 0.47558787 0.95117574 0.52441213
[234,] 0.38920292 0.77840584 0.61079708
[235,] 0.37507133 0.75014266 0.62492867
[236,] 0.32330262 0.64660524 0.67669738
[237,] 0.32787021 0.65574042 0.67212979
[238,] 0.27918104 0.55836209 0.72081896
[239,] 0.17285122 0.34570244 0.82714878
> postscript(file="/var/wessaorg/rcomp/tmp/1h4fk1384952525.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/2mzrv1384952525.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3k1x81384952525.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4fkif1384952525.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5khnb1384952525.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 264
Frequency = 1
1 2 3 4 5 6
-1.965539258 1.710833260 -1.351729121 -2.799131568 9.803078475 2.253316746
7 8 9 10 11 12
7.611735685 -1.290131307 -1.979300593 1.030875586 -0.281942683 -2.368788864
13 14 15 16 17 18
-0.847706575 1.632356430 1.135274867 0.210009241 1.588009632 1.730949316
19 20 21 22 23 24
-3.252051272 0.521580162 -0.973139403 -1.443158893 -1.411944941 -1.434600071
25 26 27 28 29 30
1.636419513 -5.923742857 0.605421950 1.858241817 2.904912938 -2.653257891
31 32 33 34 35 36
-1.850671015 0.047020896 -0.583030942 -0.556013214 1.009964577 -3.911373320
37 38 39 40 41 42
3.111586491 0.103144034 -0.299640350 -3.365222271 -2.598146239 3.262259012
43 44 45 46 47 48
-3.762972270 -1.125456566 -0.985394674 -1.753115745 -3.753354752 -0.702624593
49 50 51 52 53 54
5.327268630 -1.785762885 -1.044540381 1.214023843 2.911349021 -1.735006907
55 56 57 58 59 60
-3.603952208 2.769340606 2.324429411 -3.294113682 -3.393879305 1.106530317
61 62 63 64 65 66
1.921420612 -0.226243087 -1.661490915 0.320772884 0.269974999 -4.574976040
67 68 69 70 71 72
1.102864169 -2.097886558 -0.345840484 1.151160510 -1.620710836 1.601477591
73 74 75 76 77 78
1.312455845 -2.218016588 -2.037009073 4.907915708 1.663878808 2.468780366
79 80 81 82 83 84
0.329082372 -6.030372538 -0.825324161 -3.923159075 0.441467578 -0.742689025
85 86 87 88 89 90
-0.018606727 0.637024187 -1.444091427 -0.205545446 3.695603838 1.220004691
91 92 93 94 95 96
0.164706670 0.974675499 2.675385354 -0.892941807 0.090343098 1.224086893
97 98 99 100 101 102
-1.556199324 0.802817132 -2.392407260 -0.430014713 1.538955660 1.452701632
103 104 105 106 107 108
-4.976904507 4.159483083 2.271391176 -0.387513205 3.782061742 -1.940732924
109 110 111 112 113 114
6.044224161 2.320190852 0.184891683 -1.967969769 1.584995792 1.635975053
115 116 117 118 119 120
-0.588432889 -0.307575640 -1.098068557 -4.421995887 3.252150067 -0.761213027
121 122 123 124 125 126
1.314773580 -0.245246846 -3.986213250 -1.241267416 -1.974034722 -1.710015739
127 128 129 130 131 132
-0.224407533 1.315197266 0.511750480 -2.525290189 2.752694852 -1.651154907
133 134 135 136 137 138
1.838587420 -0.248257382 3.048011453 6.532534224 -0.098692645 -0.877775267
139 140 141 142 143 144
-2.144359789 -2.005452213 -3.424717439 3.115407016 0.046492127 0.314876752
145 146 147 148 149 150
0.232872623 0.889191485 -2.957654025 -3.014294454 2.399466797 0.839498864
151 152 153 154 155 156
4.417142408 -2.100227380 -2.234377891 3.546649162 3.903010062 0.698069695
157 158 159 160 161 162
0.561654765 1.561364816 -3.557583938 4.507066936 1.515646696 6.208933568
163 164 165 166 167 168
0.774259817 8.493265775 1.523701902 5.266915064 -1.692697980 -1.314724374
169 170 171 172 173 174
-0.727031799 1.309326749 2.724630644 -0.280823976 -4.767823902 1.261979701
175 176 177 178 179 180
0.500409091 -4.514895149 -1.882457231 2.527522285 -2.860618828 -1.276268547
181 182 183 184 185 186
-3.715157962 -6.091103850 -2.065049425 -2.897975520 -1.112179790 5.326539932
187 188 189 190 191 192
1.017812666 0.005574726 -1.505707437 3.811596330 -2.896713994 -2.945414492
193 194 195 196 197 198
1.430627733 -0.746026704 0.866472535 -3.206580196 5.919816506 2.448959092
199 200 201 202 203 204
0.673354819 -0.550265524 -0.499475295 -1.053611217 0.038463165 -1.864747282
205 206 207 208 209 210
-1.779454949 0.326907596 -1.115930472 1.856936938 -0.749837781 0.847500674
211 212 213 214 215 216
-0.740236133 -0.624597471 3.213937400 -3.067715016 1.457144003 -0.643083642
217 218 219 220 221 222
0.549183894 -0.892800073 3.824268927 1.666286911 -3.205906492 -0.136154708
223 224 225 226 227 228
0.877434279 -4.018114896 3.916645626 -1.989196546 -0.220066131 -2.672408161
229 230 231 232 233 234
4.567921306 -2.805234707 -1.567716108 -1.974629197 -4.709984827 3.171739357
235 236 237 238 239 240
-3.061672993 -1.728373018 1.236590452 -2.774357545 -4.865563186 -3.648504610
241 242 243 244 245 246
-3.938372124 0.006270271 -0.101724548 0.858917233 1.701543008 4.160278314
247 248 249 250 251 252
0.455075114 7.374245268 2.108477665 0.088745077 1.080964620 3.196419435
253 254 255 256 257 258
-3.265421674 -0.987913293 1.704293656 -0.208178306 5.244684661 0.259538447
259 260 261 262 263 264
2.228603258 -8.260452585 -1.570955649 -0.785629954 3.107657692 -1.130256422
> postscript(file="/var/wessaorg/rcomp/tmp/603mp1384952525.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 = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.965539258 NA
1 1.710833260 -1.965539258
2 -1.351729121 1.710833260
3 -2.799131568 -1.351729121
4 9.803078475 -2.799131568
5 2.253316746 9.803078475
6 7.611735685 2.253316746
7 -1.290131307 7.611735685
8 -1.979300593 -1.290131307
9 1.030875586 -1.979300593
10 -0.281942683 1.030875586
11 -2.368788864 -0.281942683
12 -0.847706575 -2.368788864
13 1.632356430 -0.847706575
14 1.135274867 1.632356430
15 0.210009241 1.135274867
16 1.588009632 0.210009241
17 1.730949316 1.588009632
18 -3.252051272 1.730949316
19 0.521580162 -3.252051272
20 -0.973139403 0.521580162
21 -1.443158893 -0.973139403
22 -1.411944941 -1.443158893
23 -1.434600071 -1.411944941
24 1.636419513 -1.434600071
25 -5.923742857 1.636419513
26 0.605421950 -5.923742857
27 1.858241817 0.605421950
28 2.904912938 1.858241817
29 -2.653257891 2.904912938
30 -1.850671015 -2.653257891
31 0.047020896 -1.850671015
32 -0.583030942 0.047020896
33 -0.556013214 -0.583030942
34 1.009964577 -0.556013214
35 -3.911373320 1.009964577
36 3.111586491 -3.911373320
37 0.103144034 3.111586491
38 -0.299640350 0.103144034
39 -3.365222271 -0.299640350
40 -2.598146239 -3.365222271
41 3.262259012 -2.598146239
42 -3.762972270 3.262259012
43 -1.125456566 -3.762972270
44 -0.985394674 -1.125456566
45 -1.753115745 -0.985394674
46 -3.753354752 -1.753115745
47 -0.702624593 -3.753354752
48 5.327268630 -0.702624593
49 -1.785762885 5.327268630
50 -1.044540381 -1.785762885
51 1.214023843 -1.044540381
52 2.911349021 1.214023843
53 -1.735006907 2.911349021
54 -3.603952208 -1.735006907
55 2.769340606 -3.603952208
56 2.324429411 2.769340606
57 -3.294113682 2.324429411
58 -3.393879305 -3.294113682
59 1.106530317 -3.393879305
60 1.921420612 1.106530317
61 -0.226243087 1.921420612
62 -1.661490915 -0.226243087
63 0.320772884 -1.661490915
64 0.269974999 0.320772884
65 -4.574976040 0.269974999
66 1.102864169 -4.574976040
67 -2.097886558 1.102864169
68 -0.345840484 -2.097886558
69 1.151160510 -0.345840484
70 -1.620710836 1.151160510
71 1.601477591 -1.620710836
72 1.312455845 1.601477591
73 -2.218016588 1.312455845
74 -2.037009073 -2.218016588
75 4.907915708 -2.037009073
76 1.663878808 4.907915708
77 2.468780366 1.663878808
78 0.329082372 2.468780366
79 -6.030372538 0.329082372
80 -0.825324161 -6.030372538
81 -3.923159075 -0.825324161
82 0.441467578 -3.923159075
83 -0.742689025 0.441467578
84 -0.018606727 -0.742689025
85 0.637024187 -0.018606727
86 -1.444091427 0.637024187
87 -0.205545446 -1.444091427
88 3.695603838 -0.205545446
89 1.220004691 3.695603838
90 0.164706670 1.220004691
91 0.974675499 0.164706670
92 2.675385354 0.974675499
93 -0.892941807 2.675385354
94 0.090343098 -0.892941807
95 1.224086893 0.090343098
96 -1.556199324 1.224086893
97 0.802817132 -1.556199324
98 -2.392407260 0.802817132
99 -0.430014713 -2.392407260
100 1.538955660 -0.430014713
101 1.452701632 1.538955660
102 -4.976904507 1.452701632
103 4.159483083 -4.976904507
104 2.271391176 4.159483083
105 -0.387513205 2.271391176
106 3.782061742 -0.387513205
107 -1.940732924 3.782061742
108 6.044224161 -1.940732924
109 2.320190852 6.044224161
110 0.184891683 2.320190852
111 -1.967969769 0.184891683
112 1.584995792 -1.967969769
113 1.635975053 1.584995792
114 -0.588432889 1.635975053
115 -0.307575640 -0.588432889
116 -1.098068557 -0.307575640
117 -4.421995887 -1.098068557
118 3.252150067 -4.421995887
119 -0.761213027 3.252150067
120 1.314773580 -0.761213027
121 -0.245246846 1.314773580
122 -3.986213250 -0.245246846
123 -1.241267416 -3.986213250
124 -1.974034722 -1.241267416
125 -1.710015739 -1.974034722
126 -0.224407533 -1.710015739
127 1.315197266 -0.224407533
128 0.511750480 1.315197266
129 -2.525290189 0.511750480
130 2.752694852 -2.525290189
131 -1.651154907 2.752694852
132 1.838587420 -1.651154907
133 -0.248257382 1.838587420
134 3.048011453 -0.248257382
135 6.532534224 3.048011453
136 -0.098692645 6.532534224
137 -0.877775267 -0.098692645
138 -2.144359789 -0.877775267
139 -2.005452213 -2.144359789
140 -3.424717439 -2.005452213
141 3.115407016 -3.424717439
142 0.046492127 3.115407016
143 0.314876752 0.046492127
144 0.232872623 0.314876752
145 0.889191485 0.232872623
146 -2.957654025 0.889191485
147 -3.014294454 -2.957654025
148 2.399466797 -3.014294454
149 0.839498864 2.399466797
150 4.417142408 0.839498864
151 -2.100227380 4.417142408
152 -2.234377891 -2.100227380
153 3.546649162 -2.234377891
154 3.903010062 3.546649162
155 0.698069695 3.903010062
156 0.561654765 0.698069695
157 1.561364816 0.561654765
158 -3.557583938 1.561364816
159 4.507066936 -3.557583938
160 1.515646696 4.507066936
161 6.208933568 1.515646696
162 0.774259817 6.208933568
163 8.493265775 0.774259817
164 1.523701902 8.493265775
165 5.266915064 1.523701902
166 -1.692697980 5.266915064
167 -1.314724374 -1.692697980
168 -0.727031799 -1.314724374
169 1.309326749 -0.727031799
170 2.724630644 1.309326749
171 -0.280823976 2.724630644
172 -4.767823902 -0.280823976
173 1.261979701 -4.767823902
174 0.500409091 1.261979701
175 -4.514895149 0.500409091
176 -1.882457231 -4.514895149
177 2.527522285 -1.882457231
178 -2.860618828 2.527522285
179 -1.276268547 -2.860618828
180 -3.715157962 -1.276268547
181 -6.091103850 -3.715157962
182 -2.065049425 -6.091103850
183 -2.897975520 -2.065049425
184 -1.112179790 -2.897975520
185 5.326539932 -1.112179790
186 1.017812666 5.326539932
187 0.005574726 1.017812666
188 -1.505707437 0.005574726
189 3.811596330 -1.505707437
190 -2.896713994 3.811596330
191 -2.945414492 -2.896713994
192 1.430627733 -2.945414492
193 -0.746026704 1.430627733
194 0.866472535 -0.746026704
195 -3.206580196 0.866472535
196 5.919816506 -3.206580196
197 2.448959092 5.919816506
198 0.673354819 2.448959092
199 -0.550265524 0.673354819
200 -0.499475295 -0.550265524
201 -1.053611217 -0.499475295
202 0.038463165 -1.053611217
203 -1.864747282 0.038463165
204 -1.779454949 -1.864747282
205 0.326907596 -1.779454949
206 -1.115930472 0.326907596
207 1.856936938 -1.115930472
208 -0.749837781 1.856936938
209 0.847500674 -0.749837781
210 -0.740236133 0.847500674
211 -0.624597471 -0.740236133
212 3.213937400 -0.624597471
213 -3.067715016 3.213937400
214 1.457144003 -3.067715016
215 -0.643083642 1.457144003
216 0.549183894 -0.643083642
217 -0.892800073 0.549183894
218 3.824268927 -0.892800073
219 1.666286911 3.824268927
220 -3.205906492 1.666286911
221 -0.136154708 -3.205906492
222 0.877434279 -0.136154708
223 -4.018114896 0.877434279
224 3.916645626 -4.018114896
225 -1.989196546 3.916645626
226 -0.220066131 -1.989196546
227 -2.672408161 -0.220066131
228 4.567921306 -2.672408161
229 -2.805234707 4.567921306
230 -1.567716108 -2.805234707
231 -1.974629197 -1.567716108
232 -4.709984827 -1.974629197
233 3.171739357 -4.709984827
234 -3.061672993 3.171739357
235 -1.728373018 -3.061672993
236 1.236590452 -1.728373018
237 -2.774357545 1.236590452
238 -4.865563186 -2.774357545
239 -3.648504610 -4.865563186
240 -3.938372124 -3.648504610
241 0.006270271 -3.938372124
242 -0.101724548 0.006270271
243 0.858917233 -0.101724548
244 1.701543008 0.858917233
245 4.160278314 1.701543008
246 0.455075114 4.160278314
247 7.374245268 0.455075114
248 2.108477665 7.374245268
249 0.088745077 2.108477665
250 1.080964620 0.088745077
251 3.196419435 1.080964620
252 -3.265421674 3.196419435
253 -0.987913293 -3.265421674
254 1.704293656 -0.987913293
255 -0.208178306 1.704293656
256 5.244684661 -0.208178306
257 0.259538447 5.244684661
258 2.228603258 0.259538447
259 -8.260452585 2.228603258
260 -1.570955649 -8.260452585
261 -0.785629954 -1.570955649
262 3.107657692 -0.785629954
263 -1.130256422 3.107657692
264 NA -1.130256422
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.710833260 -1.965539258
[2,] -1.351729121 1.710833260
[3,] -2.799131568 -1.351729121
[4,] 9.803078475 -2.799131568
[5,] 2.253316746 9.803078475
[6,] 7.611735685 2.253316746
[7,] -1.290131307 7.611735685
[8,] -1.979300593 -1.290131307
[9,] 1.030875586 -1.979300593
[10,] -0.281942683 1.030875586
[11,] -2.368788864 -0.281942683
[12,] -0.847706575 -2.368788864
[13,] 1.632356430 -0.847706575
[14,] 1.135274867 1.632356430
[15,] 0.210009241 1.135274867
[16,] 1.588009632 0.210009241
[17,] 1.730949316 1.588009632
[18,] -3.252051272 1.730949316
[19,] 0.521580162 -3.252051272
[20,] -0.973139403 0.521580162
[21,] -1.443158893 -0.973139403
[22,] -1.411944941 -1.443158893
[23,] -1.434600071 -1.411944941
[24,] 1.636419513 -1.434600071
[25,] -5.923742857 1.636419513
[26,] 0.605421950 -5.923742857
[27,] 1.858241817 0.605421950
[28,] 2.904912938 1.858241817
[29,] -2.653257891 2.904912938
[30,] -1.850671015 -2.653257891
[31,] 0.047020896 -1.850671015
[32,] -0.583030942 0.047020896
[33,] -0.556013214 -0.583030942
[34,] 1.009964577 -0.556013214
[35,] -3.911373320 1.009964577
[36,] 3.111586491 -3.911373320
[37,] 0.103144034 3.111586491
[38,] -0.299640350 0.103144034
[39,] -3.365222271 -0.299640350
[40,] -2.598146239 -3.365222271
[41,] 3.262259012 -2.598146239
[42,] -3.762972270 3.262259012
[43,] -1.125456566 -3.762972270
[44,] -0.985394674 -1.125456566
[45,] -1.753115745 -0.985394674
[46,] -3.753354752 -1.753115745
[47,] -0.702624593 -3.753354752
[48,] 5.327268630 -0.702624593
[49,] -1.785762885 5.327268630
[50,] -1.044540381 -1.785762885
[51,] 1.214023843 -1.044540381
[52,] 2.911349021 1.214023843
[53,] -1.735006907 2.911349021
[54,] -3.603952208 -1.735006907
[55,] 2.769340606 -3.603952208
[56,] 2.324429411 2.769340606
[57,] -3.294113682 2.324429411
[58,] -3.393879305 -3.294113682
[59,] 1.106530317 -3.393879305
[60,] 1.921420612 1.106530317
[61,] -0.226243087 1.921420612
[62,] -1.661490915 -0.226243087
[63,] 0.320772884 -1.661490915
[64,] 0.269974999 0.320772884
[65,] -4.574976040 0.269974999
[66,] 1.102864169 -4.574976040
[67,] -2.097886558 1.102864169
[68,] -0.345840484 -2.097886558
[69,] 1.151160510 -0.345840484
[70,] -1.620710836 1.151160510
[71,] 1.601477591 -1.620710836
[72,] 1.312455845 1.601477591
[73,] -2.218016588 1.312455845
[74,] -2.037009073 -2.218016588
[75,] 4.907915708 -2.037009073
[76,] 1.663878808 4.907915708
[77,] 2.468780366 1.663878808
[78,] 0.329082372 2.468780366
[79,] -6.030372538 0.329082372
[80,] -0.825324161 -6.030372538
[81,] -3.923159075 -0.825324161
[82,] 0.441467578 -3.923159075
[83,] -0.742689025 0.441467578
[84,] -0.018606727 -0.742689025
[85,] 0.637024187 -0.018606727
[86,] -1.444091427 0.637024187
[87,] -0.205545446 -1.444091427
[88,] 3.695603838 -0.205545446
[89,] 1.220004691 3.695603838
[90,] 0.164706670 1.220004691
[91,] 0.974675499 0.164706670
[92,] 2.675385354 0.974675499
[93,] -0.892941807 2.675385354
[94,] 0.090343098 -0.892941807
[95,] 1.224086893 0.090343098
[96,] -1.556199324 1.224086893
[97,] 0.802817132 -1.556199324
[98,] -2.392407260 0.802817132
[99,] -0.430014713 -2.392407260
[100,] 1.538955660 -0.430014713
[101,] 1.452701632 1.538955660
[102,] -4.976904507 1.452701632
[103,] 4.159483083 -4.976904507
[104,] 2.271391176 4.159483083
[105,] -0.387513205 2.271391176
[106,] 3.782061742 -0.387513205
[107,] -1.940732924 3.782061742
[108,] 6.044224161 -1.940732924
[109,] 2.320190852 6.044224161
[110,] 0.184891683 2.320190852
[111,] -1.967969769 0.184891683
[112,] 1.584995792 -1.967969769
[113,] 1.635975053 1.584995792
[114,] -0.588432889 1.635975053
[115,] -0.307575640 -0.588432889
[116,] -1.098068557 -0.307575640
[117,] -4.421995887 -1.098068557
[118,] 3.252150067 -4.421995887
[119,] -0.761213027 3.252150067
[120,] 1.314773580 -0.761213027
[121,] -0.245246846 1.314773580
[122,] -3.986213250 -0.245246846
[123,] -1.241267416 -3.986213250
[124,] -1.974034722 -1.241267416
[125,] -1.710015739 -1.974034722
[126,] -0.224407533 -1.710015739
[127,] 1.315197266 -0.224407533
[128,] 0.511750480 1.315197266
[129,] -2.525290189 0.511750480
[130,] 2.752694852 -2.525290189
[131,] -1.651154907 2.752694852
[132,] 1.838587420 -1.651154907
[133,] -0.248257382 1.838587420
[134,] 3.048011453 -0.248257382
[135,] 6.532534224 3.048011453
[136,] -0.098692645 6.532534224
[137,] -0.877775267 -0.098692645
[138,] -2.144359789 -0.877775267
[139,] -2.005452213 -2.144359789
[140,] -3.424717439 -2.005452213
[141,] 3.115407016 -3.424717439
[142,] 0.046492127 3.115407016
[143,] 0.314876752 0.046492127
[144,] 0.232872623 0.314876752
[145,] 0.889191485 0.232872623
[146,] -2.957654025 0.889191485
[147,] -3.014294454 -2.957654025
[148,] 2.399466797 -3.014294454
[149,] 0.839498864 2.399466797
[150,] 4.417142408 0.839498864
[151,] -2.100227380 4.417142408
[152,] -2.234377891 -2.100227380
[153,] 3.546649162 -2.234377891
[154,] 3.903010062 3.546649162
[155,] 0.698069695 3.903010062
[156,] 0.561654765 0.698069695
[157,] 1.561364816 0.561654765
[158,] -3.557583938 1.561364816
[159,] 4.507066936 -3.557583938
[160,] 1.515646696 4.507066936
[161,] 6.208933568 1.515646696
[162,] 0.774259817 6.208933568
[163,] 8.493265775 0.774259817
[164,] 1.523701902 8.493265775
[165,] 5.266915064 1.523701902
[166,] -1.692697980 5.266915064
[167,] -1.314724374 -1.692697980
[168,] -0.727031799 -1.314724374
[169,] 1.309326749 -0.727031799
[170,] 2.724630644 1.309326749
[171,] -0.280823976 2.724630644
[172,] -4.767823902 -0.280823976
[173,] 1.261979701 -4.767823902
[174,] 0.500409091 1.261979701
[175,] -4.514895149 0.500409091
[176,] -1.882457231 -4.514895149
[177,] 2.527522285 -1.882457231
[178,] -2.860618828 2.527522285
[179,] -1.276268547 -2.860618828
[180,] -3.715157962 -1.276268547
[181,] -6.091103850 -3.715157962
[182,] -2.065049425 -6.091103850
[183,] -2.897975520 -2.065049425
[184,] -1.112179790 -2.897975520
[185,] 5.326539932 -1.112179790
[186,] 1.017812666 5.326539932
[187,] 0.005574726 1.017812666
[188,] -1.505707437 0.005574726
[189,] 3.811596330 -1.505707437
[190,] -2.896713994 3.811596330
[191,] -2.945414492 -2.896713994
[192,] 1.430627733 -2.945414492
[193,] -0.746026704 1.430627733
[194,] 0.866472535 -0.746026704
[195,] -3.206580196 0.866472535
[196,] 5.919816506 -3.206580196
[197,] 2.448959092 5.919816506
[198,] 0.673354819 2.448959092
[199,] -0.550265524 0.673354819
[200,] -0.499475295 -0.550265524
[201,] -1.053611217 -0.499475295
[202,] 0.038463165 -1.053611217
[203,] -1.864747282 0.038463165
[204,] -1.779454949 -1.864747282
[205,] 0.326907596 -1.779454949
[206,] -1.115930472 0.326907596
[207,] 1.856936938 -1.115930472
[208,] -0.749837781 1.856936938
[209,] 0.847500674 -0.749837781
[210,] -0.740236133 0.847500674
[211,] -0.624597471 -0.740236133
[212,] 3.213937400 -0.624597471
[213,] -3.067715016 3.213937400
[214,] 1.457144003 -3.067715016
[215,] -0.643083642 1.457144003
[216,] 0.549183894 -0.643083642
[217,] -0.892800073 0.549183894
[218,] 3.824268927 -0.892800073
[219,] 1.666286911 3.824268927
[220,] -3.205906492 1.666286911
[221,] -0.136154708 -3.205906492
[222,] 0.877434279 -0.136154708
[223,] -4.018114896 0.877434279
[224,] 3.916645626 -4.018114896
[225,] -1.989196546 3.916645626
[226,] -0.220066131 -1.989196546
[227,] -2.672408161 -0.220066131
[228,] 4.567921306 -2.672408161
[229,] -2.805234707 4.567921306
[230,] -1.567716108 -2.805234707
[231,] -1.974629197 -1.567716108
[232,] -4.709984827 -1.974629197
[233,] 3.171739357 -4.709984827
[234,] -3.061672993 3.171739357
[235,] -1.728373018 -3.061672993
[236,] 1.236590452 -1.728373018
[237,] -2.774357545 1.236590452
[238,] -4.865563186 -2.774357545
[239,] -3.648504610 -4.865563186
[240,] -3.938372124 -3.648504610
[241,] 0.006270271 -3.938372124
[242,] -0.101724548 0.006270271
[243,] 0.858917233 -0.101724548
[244,] 1.701543008 0.858917233
[245,] 4.160278314 1.701543008
[246,] 0.455075114 4.160278314
[247,] 7.374245268 0.455075114
[248,] 2.108477665 7.374245268
[249,] 0.088745077 2.108477665
[250,] 1.080964620 0.088745077
[251,] 3.196419435 1.080964620
[252,] -3.265421674 3.196419435
[253,] -0.987913293 -3.265421674
[254,] 1.704293656 -0.987913293
[255,] -0.208178306 1.704293656
[256,] 5.244684661 -0.208178306
[257,] 0.259538447 5.244684661
[258,] 2.228603258 0.259538447
[259,] -8.260452585 2.228603258
[260,] -1.570955649 -8.260452585
[261,] -0.785629954 -1.570955649
[262,] 3.107657692 -0.785629954
[263,] -1.130256422 3.107657692
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.710833260 -1.965539258
2 -1.351729121 1.710833260
3 -2.799131568 -1.351729121
4 9.803078475 -2.799131568
5 2.253316746 9.803078475
6 7.611735685 2.253316746
7 -1.290131307 7.611735685
8 -1.979300593 -1.290131307
9 1.030875586 -1.979300593
10 -0.281942683 1.030875586
11 -2.368788864 -0.281942683
12 -0.847706575 -2.368788864
13 1.632356430 -0.847706575
14 1.135274867 1.632356430
15 0.210009241 1.135274867
16 1.588009632 0.210009241
17 1.730949316 1.588009632
18 -3.252051272 1.730949316
19 0.521580162 -3.252051272
20 -0.973139403 0.521580162
21 -1.443158893 -0.973139403
22 -1.411944941 -1.443158893
23 -1.434600071 -1.411944941
24 1.636419513 -1.434600071
25 -5.923742857 1.636419513
26 0.605421950 -5.923742857
27 1.858241817 0.605421950
28 2.904912938 1.858241817
29 -2.653257891 2.904912938
30 -1.850671015 -2.653257891
31 0.047020896 -1.850671015
32 -0.583030942 0.047020896
33 -0.556013214 -0.583030942
34 1.009964577 -0.556013214
35 -3.911373320 1.009964577
36 3.111586491 -3.911373320
37 0.103144034 3.111586491
38 -0.299640350 0.103144034
39 -3.365222271 -0.299640350
40 -2.598146239 -3.365222271
41 3.262259012 -2.598146239
42 -3.762972270 3.262259012
43 -1.125456566 -3.762972270
44 -0.985394674 -1.125456566
45 -1.753115745 -0.985394674
46 -3.753354752 -1.753115745
47 -0.702624593 -3.753354752
48 5.327268630 -0.702624593
49 -1.785762885 5.327268630
50 -1.044540381 -1.785762885
51 1.214023843 -1.044540381
52 2.911349021 1.214023843
53 -1.735006907 2.911349021
54 -3.603952208 -1.735006907
55 2.769340606 -3.603952208
56 2.324429411 2.769340606
57 -3.294113682 2.324429411
58 -3.393879305 -3.294113682
59 1.106530317 -3.393879305
60 1.921420612 1.106530317
61 -0.226243087 1.921420612
62 -1.661490915 -0.226243087
63 0.320772884 -1.661490915
64 0.269974999 0.320772884
65 -4.574976040 0.269974999
66 1.102864169 -4.574976040
67 -2.097886558 1.102864169
68 -0.345840484 -2.097886558
69 1.151160510 -0.345840484
70 -1.620710836 1.151160510
71 1.601477591 -1.620710836
72 1.312455845 1.601477591
73 -2.218016588 1.312455845
74 -2.037009073 -2.218016588
75 4.907915708 -2.037009073
76 1.663878808 4.907915708
77 2.468780366 1.663878808
78 0.329082372 2.468780366
79 -6.030372538 0.329082372
80 -0.825324161 -6.030372538
81 -3.923159075 -0.825324161
82 0.441467578 -3.923159075
83 -0.742689025 0.441467578
84 -0.018606727 -0.742689025
85 0.637024187 -0.018606727
86 -1.444091427 0.637024187
87 -0.205545446 -1.444091427
88 3.695603838 -0.205545446
89 1.220004691 3.695603838
90 0.164706670 1.220004691
91 0.974675499 0.164706670
92 2.675385354 0.974675499
93 -0.892941807 2.675385354
94 0.090343098 -0.892941807
95 1.224086893 0.090343098
96 -1.556199324 1.224086893
97 0.802817132 -1.556199324
98 -2.392407260 0.802817132
99 -0.430014713 -2.392407260
100 1.538955660 -0.430014713
101 1.452701632 1.538955660
102 -4.976904507 1.452701632
103 4.159483083 -4.976904507
104 2.271391176 4.159483083
105 -0.387513205 2.271391176
106 3.782061742 -0.387513205
107 -1.940732924 3.782061742
108 6.044224161 -1.940732924
109 2.320190852 6.044224161
110 0.184891683 2.320190852
111 -1.967969769 0.184891683
112 1.584995792 -1.967969769
113 1.635975053 1.584995792
114 -0.588432889 1.635975053
115 -0.307575640 -0.588432889
116 -1.098068557 -0.307575640
117 -4.421995887 -1.098068557
118 3.252150067 -4.421995887
119 -0.761213027 3.252150067
120 1.314773580 -0.761213027
121 -0.245246846 1.314773580
122 -3.986213250 -0.245246846
123 -1.241267416 -3.986213250
124 -1.974034722 -1.241267416
125 -1.710015739 -1.974034722
126 -0.224407533 -1.710015739
127 1.315197266 -0.224407533
128 0.511750480 1.315197266
129 -2.525290189 0.511750480
130 2.752694852 -2.525290189
131 -1.651154907 2.752694852
132 1.838587420 -1.651154907
133 -0.248257382 1.838587420
134 3.048011453 -0.248257382
135 6.532534224 3.048011453
136 -0.098692645 6.532534224
137 -0.877775267 -0.098692645
138 -2.144359789 -0.877775267
139 -2.005452213 -2.144359789
140 -3.424717439 -2.005452213
141 3.115407016 -3.424717439
142 0.046492127 3.115407016
143 0.314876752 0.046492127
144 0.232872623 0.314876752
145 0.889191485 0.232872623
146 -2.957654025 0.889191485
147 -3.014294454 -2.957654025
148 2.399466797 -3.014294454
149 0.839498864 2.399466797
150 4.417142408 0.839498864
151 -2.100227380 4.417142408
152 -2.234377891 -2.100227380
153 3.546649162 -2.234377891
154 3.903010062 3.546649162
155 0.698069695 3.903010062
156 0.561654765 0.698069695
157 1.561364816 0.561654765
158 -3.557583938 1.561364816
159 4.507066936 -3.557583938
160 1.515646696 4.507066936
161 6.208933568 1.515646696
162 0.774259817 6.208933568
163 8.493265775 0.774259817
164 1.523701902 8.493265775
165 5.266915064 1.523701902
166 -1.692697980 5.266915064
167 -1.314724374 -1.692697980
168 -0.727031799 -1.314724374
169 1.309326749 -0.727031799
170 2.724630644 1.309326749
171 -0.280823976 2.724630644
172 -4.767823902 -0.280823976
173 1.261979701 -4.767823902
174 0.500409091 1.261979701
175 -4.514895149 0.500409091
176 -1.882457231 -4.514895149
177 2.527522285 -1.882457231
178 -2.860618828 2.527522285
179 -1.276268547 -2.860618828
180 -3.715157962 -1.276268547
181 -6.091103850 -3.715157962
182 -2.065049425 -6.091103850
183 -2.897975520 -2.065049425
184 -1.112179790 -2.897975520
185 5.326539932 -1.112179790
186 1.017812666 5.326539932
187 0.005574726 1.017812666
188 -1.505707437 0.005574726
189 3.811596330 -1.505707437
190 -2.896713994 3.811596330
191 -2.945414492 -2.896713994
192 1.430627733 -2.945414492
193 -0.746026704 1.430627733
194 0.866472535 -0.746026704
195 -3.206580196 0.866472535
196 5.919816506 -3.206580196
197 2.448959092 5.919816506
198 0.673354819 2.448959092
199 -0.550265524 0.673354819
200 -0.499475295 -0.550265524
201 -1.053611217 -0.499475295
202 0.038463165 -1.053611217
203 -1.864747282 0.038463165
204 -1.779454949 -1.864747282
205 0.326907596 -1.779454949
206 -1.115930472 0.326907596
207 1.856936938 -1.115930472
208 -0.749837781 1.856936938
209 0.847500674 -0.749837781
210 -0.740236133 0.847500674
211 -0.624597471 -0.740236133
212 3.213937400 -0.624597471
213 -3.067715016 3.213937400
214 1.457144003 -3.067715016
215 -0.643083642 1.457144003
216 0.549183894 -0.643083642
217 -0.892800073 0.549183894
218 3.824268927 -0.892800073
219 1.666286911 3.824268927
220 -3.205906492 1.666286911
221 -0.136154708 -3.205906492
222 0.877434279 -0.136154708
223 -4.018114896 0.877434279
224 3.916645626 -4.018114896
225 -1.989196546 3.916645626
226 -0.220066131 -1.989196546
227 -2.672408161 -0.220066131
228 4.567921306 -2.672408161
229 -2.805234707 4.567921306
230 -1.567716108 -2.805234707
231 -1.974629197 -1.567716108
232 -4.709984827 -1.974629197
233 3.171739357 -4.709984827
234 -3.061672993 3.171739357
235 -1.728373018 -3.061672993
236 1.236590452 -1.728373018
237 -2.774357545 1.236590452
238 -4.865563186 -2.774357545
239 -3.648504610 -4.865563186
240 -3.938372124 -3.648504610
241 0.006270271 -3.938372124
242 -0.101724548 0.006270271
243 0.858917233 -0.101724548
244 1.701543008 0.858917233
245 4.160278314 1.701543008
246 0.455075114 4.160278314
247 7.374245268 0.455075114
248 2.108477665 7.374245268
249 0.088745077 2.108477665
250 1.080964620 0.088745077
251 3.196419435 1.080964620
252 -3.265421674 3.196419435
253 -0.987913293 -3.265421674
254 1.704293656 -0.987913293
255 -0.208178306 1.704293656
256 5.244684661 -0.208178306
257 0.259538447 5.244684661
258 2.228603258 0.259538447
259 -8.260452585 2.228603258
260 -1.570955649 -8.260452585
261 -0.785629954 -1.570955649
262 3.107657692 -0.785629954
263 -1.130256422 3.107657692
> 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/73lq51384952525.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/8g37r1384952525.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/9ztsx1384952525.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/10m3su1384952525.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11tgfc1384952525.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12xuu31384952526.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> 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, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1357il1384952526.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14ziy41384952526.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15lsn41384952526.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16whon1384952526.tab")
+ }
>
> try(system("convert tmp/1h4fk1384952525.ps tmp/1h4fk1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mzrv1384952525.ps tmp/2mzrv1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/3k1x81384952525.ps tmp/3k1x81384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fkif1384952525.ps tmp/4fkif1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/5khnb1384952525.ps tmp/5khnb1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/603mp1384952525.ps tmp/603mp1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/73lq51384952525.ps tmp/73lq51384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/8g37r1384952525.ps tmp/8g37r1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/9ztsx1384952525.ps tmp/9ztsx1384952525.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m3su1384952525.ps tmp/10m3su1384952525.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.052 3.121 20.149