R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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Type 'q()' to quit R.
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+ ,4
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+ ,0
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+ ,14
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+ ,0
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+ ,12
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+ ,72)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Pop'
+ ,'month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Pop','month','Connected','Separate','Learning','Software','Happiness','Depression','Belonging'),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 = '5'
> #'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.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
Learning Pop month Connected Separate Software Happiness Depression
1 13 1 9 41 38 12 14 12.0
2 16 1 9 39 32 11 18 11.0
3 19 1 9 30 35 15 11 14.0
4 15 1 9 31 33 6 12 12.0
5 14 1 9 34 37 13 16 21.0
6 13 1 9 35 29 10 18 12.0
7 19 1 9 39 31 12 14 22.0
8 15 1 9 34 36 14 14 11.0
9 14 1 9 36 35 12 15 10.0
10 15 1 9 37 38 9 15 13.0
11 16 1 9 38 31 10 17 10.0
12 16 1 9 36 34 12 19 8.0
13 16 1 9 38 35 12 10 15.0
14 16 1 9 39 38 11 16 14.0
15 17 1 9 33 37 15 18 10.0
16 15 1 9 32 33 12 14 14.0
17 15 1 9 36 32 10 14 14.0
18 20 1 9 38 38 12 17 11.0
19 18 1 9 39 38 11 14 10.0
20 16 1 9 32 32 12 16 13.0
21 16 1 9 32 33 11 18 9.5
22 16 1 9 31 31 12 11 14.0
23 19 1 9 39 38 13 14 12.0
24 16 1 9 37 39 11 12 14.0
25 17 1 9 39 32 12 17 11.0
26 17 1 9 41 32 13 9 9.0
27 16 1 9 36 35 10 16 11.0
28 15 1 9 33 37 14 14 15.0
29 16 1 9 33 33 12 15 14.0
30 14 1 9 34 33 10 11 13.0
31 15 1 9 31 31 12 16 9.0
32 12 1 9 27 32 8 13 15.0
33 14 1 9 37 31 10 17 10.0
34 16 1 9 34 37 12 15 11.0
35 14 1 9 34 30 12 14 13.0
36 10 1 9 32 33 7 16 8.0
37 10 1 9 29 31 9 9 20.0
38 14 1 9 36 33 12 15 12.0
39 16 1 9 29 31 10 17 10.0
40 16 1 9 35 33 10 13 10.0
41 16 1 9 37 32 10 15 9.0
42 14 1 9 34 33 12 16 14.0
43 20 1 9 38 32 15 16 8.0
44 14 1 9 35 33 10 12 14.0
45 14 1 9 38 28 10 15 11.0
46 11 1 9 37 35 12 11 13.0
47 14 1 9 38 39 13 15 9.0
48 15 1 9 33 34 11 15 11.0
49 16 1 9 36 38 11 17 15.0
50 14 1 9 38 32 12 13 11.0
51 16 1 9 32 38 14 16 10.0
52 14 1 9 32 30 10 14 14.0
53 12 1 9 32 33 12 11 18.0
54 16 1 9 34 38 13 12 14.0
55 9 1 9 32 32 5 12 11.0
56 14 1 9 37 35 6 15 14.5
57 16 1 9 39 34 12 16 13.0
58 16 1 9 29 34 12 15 9.0
59 15 1 9 37 36 11 12 10.0
60 16 1 9 35 34 10 12 15.0
61 12 1 9 30 28 7 8 20.0
62 16 1 9 38 34 12 13 12.0
63 16 1 9 34 35 14 11 12.0
64 14 1 9 31 35 11 14 14.0
65 16 1 9 34 31 12 15 13.0
66 17 1 10 35 37 13 10 11.0
67 18 1 10 36 35 14 11 17.0
68 18 1 10 30 27 11 12 12.0
69 12 1 10 39 40 12 15 13.0
70 16 1 10 35 37 12 15 14.0
71 10 1 10 38 36 8 14 13.0
72 14 1 10 31 38 11 16 15.0
73 18 1 10 34 39 14 15 13.0
74 18 1 10 38 41 14 15 10.0
75 16 1 10 34 27 12 13 11.0
76 17 1 10 39 30 9 12 19.0
77 16 1 10 37 37 13 17 13.0
78 16 1 10 34 31 11 13 17.0
79 13 1 10 28 31 12 15 13.0
80 16 1 10 37 27 12 13 9.0
81 16 1 10 33 36 12 15 11.0
82 16 1 10 35 37 12 15 9.0
83 15 1 10 37 33 12 16 12.0
84 15 1 10 32 34 11 15 12.0
85 16 1 10 33 31 10 14 13.0
86 14 1 10 38 39 9 15 13.0
87 16 1 10 33 34 12 14 12.0
88 16 1 10 29 32 12 13 15.0
89 15 1 10 33 33 12 7 22.0
90 12 1 10 31 36 9 17 13.0
91 17 1 10 36 32 15 13 15.0
92 16 1 10 35 41 12 15 13.0
93 15 1 10 32 28 12 14 15.0
94 13 1 10 29 30 12 13 12.5
95 16 1 10 39 36 10 16 11.0
96 16 1 10 37 35 13 12 16.0
97 16 1 10 35 31 9 14 11.0
98 16 1 10 37 34 12 17 11.0
99 14 1 10 32 36 10 15 10.0
100 16 1 10 38 36 14 17 10.0
101 16 1 10 37 35 11 12 16.0
102 20 1 10 36 37 15 16 12.0
103 15 1 10 32 28 11 11 11.0
104 16 1 10 33 39 11 15 16.0
105 13 1 10 40 32 12 9 19.0
106 17 1 10 38 35 12 16 11.0
107 16 1 10 41 39 12 15 16.0
108 16 1 10 36 35 11 10 15.0
109 12 1 10 43 42 7 10 24.0
110 16 1 10 30 34 12 15 14.0
111 16 1 10 31 33 14 11 15.0
112 17 1 10 32 41 11 13 11.0
113 13 1 10 32 33 11 14 15.0
114 12 1 10 37 34 10 18 12.0
115 18 1 10 37 32 13 16 10.0
116 14 1 10 33 40 13 14 14.0
117 14 1 10 34 40 8 14 13.0
118 13 1 10 33 35 11 14 9.0
119 16 1 10 38 36 12 14 15.0
120 13 1 10 33 37 11 12 15.0
121 16 1 10 31 27 13 14 14.0
122 13 1 10 38 39 12 15 11.0
123 16 1 10 37 38 14 15 8.0
124 15 1 10 36 31 13 15 11.0
125 16 1 10 31 33 15 13 11.0
126 15 1 10 39 32 10 17 8.0
127 17 1 10 44 39 11 17 10.0
128 15 1 10 33 36 9 19 11.0
129 12 1 10 35 33 11 15 13.0
130 16 1 10 32 33 10 13 11.0
131 10 1 10 28 32 11 9 20.0
132 16 1 10 40 37 8 15 10.0
133 12 1 10 27 30 11 15 15.0
134 14 1 10 37 38 12 15 12.0
135 15 1 10 32 29 12 16 14.0
136 13 1 10 28 22 9 11 23.0
137 15 1 10 34 35 11 14 14.0
138 11 1 10 30 35 10 11 16.0
139 12 1 10 35 34 8 15 11.0
140 11 1 10 31 35 9 13 12.0
141 16 1 10 32 34 8 15 10.0
142 15 1 10 30 37 9 16 14.0
143 17 1 10 30 35 15 14 12.0
144 16 1 10 31 23 11 15 12.0
145 10 1 10 40 31 8 16 11.0
146 18 1 10 32 27 13 16 12.0
147 13 1 10 36 36 12 11 13.0
148 16 1 10 32 31 12 12 11.0
149 13 1 10 35 32 9 9 19.0
150 10 1 10 38 39 7 16 12.0
151 15 1 10 42 37 13 13 17.0
152 16 1 10 34 38 9 16 9.0
153 16 1 10 35 39 6 12 12.0
154 14 1 9 38 34 8 9 19.0
155 10 1 10 33 31 8 13 18.0
156 17 1 10 36 32 15 13 15.0
157 13 1 10 32 37 6 14 14.0
158 15 1 10 33 36 9 19 11.0
159 16 1 10 34 32 11 13 9.0
160 12 1 10 32 38 8 12 18.0
161 13 1 10 34 36 8 13 16.0
162 13 0 11 27 26 10 10 24.0
163 12 0 11 31 26 8 14 14.0
164 17 0 11 38 33 14 16 20.0
165 15 0 11 34 39 10 10 18.0
166 10 0 11 24 30 8 11 23.0
167 14 0 11 30 33 11 14 12.0
168 11 0 11 26 25 12 12 14.0
169 13 0 11 34 38 12 9 16.0
170 16 0 11 27 37 12 9 18.0
171 12 0 11 37 31 5 11 20.0
172 16 0 11 36 37 12 16 12.0
173 12 0 11 41 35 10 9 12.0
174 9 0 11 29 25 7 13 17.0
175 12 0 11 36 28 12 16 13.0
176 15 0 11 32 35 11 13 9.0
177 12 0 11 37 33 8 9 16.0
178 12 0 11 30 30 9 12 18.0
179 14 0 11 31 31 10 16 10.0
180 12 0 11 38 37 9 11 14.0
181 16 0 11 36 36 12 14 11.0
182 11 0 11 35 30 6 13 9.0
183 19 0 11 31 36 15 15 11.0
184 15 0 11 38 32 12 14 10.0
185 8 0 11 22 28 12 16 11.0
186 16 0 11 32 36 12 13 19.0
187 17 0 11 36 34 11 14 14.0
188 12 0 11 39 31 7 15 12.0
189 11 0 11 28 28 7 13 14.0
190 11 0 11 32 36 5 11 21.0
191 14 0 11 32 36 12 11 13.0
192 16 0 11 38 40 12 14 10.0
193 12 0 11 32 33 3 15 15.0
194 16 0 11 35 37 11 11 16.0
195 13 0 11 32 32 10 15 14.0
196 15 0 11 37 38 12 12 12.0
197 16 0 11 34 31 9 14 19.0
198 16 0 11 33 37 12 14 15.0
199 14 0 11 33 33 9 8 19.0
200 16 0 11 26 32 12 13 13.0
201 16 0 11 30 30 12 9 17.0
202 14 0 11 24 30 10 15 12.0
203 11 0 11 34 31 9 17 11.0
204 12 0 11 34 32 12 13 14.0
205 15 0 11 33 34 8 15 11.0
206 15 0 11 34 36 11 15 13.0
207 16 0 11 35 37 11 14 12.0
208 16 0 11 35 36 12 16 15.0
209 11 0 11 36 33 10 13 14.0
210 15 0 11 34 33 10 16 12.0
211 12 0 11 34 33 12 9 17.0
212 12 0 11 41 44 12 16 11.0
213 15 0 11 32 39 11 11 18.0
214 15 0 11 30 32 8 10 13.0
215 16 0 11 35 35 12 11 17.0
216 14 0 11 28 25 10 15 13.0
217 17 0 11 33 35 11 17 11.0
218 14 0 11 39 34 10 14 12.0
219 13 0 11 36 35 8 8 22.0
220 15 0 11 36 39 12 15 14.0
221 13 0 11 35 33 12 11 12.0
222 14 0 11 38 36 10 16 12.0
223 15 0 11 33 32 12 10 17.0
224 12 0 11 31 32 9 15 9.0
225 13 0 11 34 36 9 9 21.0
226 8 0 11 32 36 6 16 10.0
227 14 0 11 31 32 10 19 11.0
228 14 0 11 33 34 9 12 12.0
229 11 0 11 34 33 9 8 23.0
230 12 0 11 34 35 9 11 13.0
231 13 0 11 34 30 6 14 12.0
232 10 0 11 33 38 10 9 16.0
233 16 0 11 32 34 6 15 9.0
234 18 0 11 41 33 14 13 17.0
235 13 0 11 34 32 10 16 9.0
236 11 0 11 36 31 10 11 14.0
237 4 0 11 37 30 6 12 17.0
238 13 0 11 36 27 12 13 13.0
239 16 0 11 29 31 12 10 11.0
240 10 0 11 37 30 7 11 12.0
241 12 0 11 27 32 8 12 10.0
242 12 0 11 35 35 11 8 19.0
243 10 0 11 28 28 3 12 16.0
244 13 0 11 35 33 6 12 16.0
245 15 0 11 37 31 10 15 14.0
246 12 0 11 29 35 8 11 20.0
247 14 0 11 32 35 9 13 15.0
248 10 0 11 36 32 9 14 23.0
249 12 0 11 19 21 8 10 20.0
250 12 0 11 21 20 9 12 16.0
251 11 0 11 31 34 7 15 14.0
252 10 0 11 33 32 7 13 17.0
253 12 0 11 36 34 6 13 11.0
254 16 0 11 33 32 9 13 13.0
255 12 0 11 37 33 10 12 17.0
256 14 0 11 34 33 11 12 15.0
257 16 0 11 35 37 12 9 21.0
258 14 0 11 31 32 8 9 18.0
259 13 0 11 37 34 11 15 15.0
260 4 0 11 35 30 3 10 8.0
261 15 0 11 27 30 11 14 12.0
262 11 0 11 34 38 12 15 12.0
263 11 0 11 40 36 7 7 22.0
264 14 0 11 29 32 9 14 12.0
Belonging t
1 53 1
2 83 2
3 66 3
4 67 4
5 76 5
6 78 6
7 53 7
8 80 8
9 74 9
10 76 10
11 79 11
12 54 12
13 67 13
14 54 14
15 87 15
16 58 16
17 75 17
18 88 18
19 64 19
20 57 20
21 66 21
22 68 22
23 54 23
24 56 24
25 86 25
26 80 26
27 76 27
28 69 28
29 78 29
30 67 30
31 80 31
32 54 32
33 71 33
34 84 34
35 74 35
36 71 36
37 63 37
38 71 38
39 76 39
40 69 40
41 74 41
42 75 42
43 54 43
44 52 44
45 69 45
46 68 46
47 65 47
48 75 48
49 74 49
50 75 50
51 72 51
52 67 52
53 63 53
54 62 54
55 63 55
56 76 56
57 74 57
58 67 58
59 73 59
60 70 60
61 53 61
62 77 62
63 80 63
64 52 64
65 54 65
66 80 66
67 66 67
68 73 68
69 63 69
70 69 70
71 67 71
72 54 72
73 81 73
74 69 74
75 84 75
76 80 76
77 70 77
78 69 78
79 77 79
80 54 80
81 79 81
82 71 82
83 73 83
84 72 84
85 77 85
86 75 86
87 69 87
88 54 88
89 70 89
90 73 90
91 54 91
92 77 92
93 82 93
94 80 94
95 80 95
96 69 96
97 78 97
98 81 98
99 76 99
100 76 100
101 73 101
102 85 102
103 66 103
104 79 104
105 68 105
106 76 106
107 71 107
108 54 108
109 46 109
110 85 110
111 74 111
112 88 112
113 38 113
114 76 114
115 86 115
116 54 116
117 67 117
118 69 118
119 90 119
120 54 120
121 76 121
122 89 122
123 76 123
124 73 124
125 79 125
126 90 126
127 74 127
128 81 128
129 72 129
130 71 130
131 66 131
132 77 132
133 65 133
134 74 134
135 85 135
136 54 136
137 63 137
138 54 138
139 64 139
140 69 140
141 54 141
142 84 142
143 86 143
144 77 144
145 89 145
146 76 146
147 60 147
148 75 148
149 73 149
150 85 150
151 79 151
152 71 152
153 72 153
154 69 154
155 78 155
156 54 156
157 69 157
158 81 158
159 84 159
160 84 160
161 69 161
162 66 162
163 81 163
164 82 164
165 72 165
166 54 166
167 78 167
168 74 168
169 82 169
170 73 170
171 55 171
172 72 172
173 78 173
174 59 174
175 72 175
176 78 176
177 68 177
178 69 178
179 67 179
180 74 180
181 54 181
182 67 182
183 70 183
184 80 184
185 89 185
186 76 186
187 74 187
188 87 188
189 54 189
190 61 190
191 38 191
192 75 192
193 69 193
194 62 194
195 72 195
196 70 196
197 79 197
198 87 198
199 62 199
200 77 200
201 69 201
202 69 202
203 75 203
204 54 204
205 72 205
206 74 206
207 85 207
208 52 208
209 70 209
210 84 210
211 64 211
212 84 212
213 87 213
214 79 214
215 67 215
216 65 216
217 85 217
218 83 218
219 61 219
220 82 220
221 76 221
222 58 222
223 72 223
224 72 224
225 38 225
226 78 226
227 54 227
228 63 228
229 66 229
230 70 230
231 71 231
232 67 232
233 58 233
234 72 234
235 72 235
236 70 236
237 76 237
238 50 238
239 72 239
240 72 240
241 88 241
242 53 242
243 58 243
244 66 244
245 82 245
246 69 246
247 68 247
248 44 248
249 56 249
250 53 250
251 70 251
252 78 252
253 71 253
254 72 254
255 68 255
256 67 256
257 75 257
258 62 258
259 67 259
260 83 260
261 64 261
262 68 262
263 62 263
264 72 264
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop month Connected Separate Software
2.710369 0.267704 0.298791 0.033134 0.040902 0.552244
Happiness Depression Belonging t
0.066924 -0.034004 0.006428 -0.006408
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9584 -1.0770 0.2715 1.2098 4.6609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.710369 4.475042 0.606 0.545
Pop 0.267704 0.498621 0.537 0.592
month 0.298791 0.455163 0.656 0.512
Connected 0.033134 0.034572 0.958 0.339
Separate 0.040902 0.035311 1.158 0.248
Software 0.552244 0.054773 10.082 <2e-16 ***
Happiness 0.066924 0.058168 1.151 0.251
Depression -0.034004 0.042131 -0.807 0.420
Belonging 0.006428 0.011998 0.536 0.593
t -0.006408 0.004338 -1.477 0.141
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.86 on 254 degrees of freedom
Multiple R-squared: 0.4463, Adjusted R-squared: 0.4266
F-statistic: 22.74 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,] 0.917403150 0.165193700 0.08259685
[2,] 0.856823094 0.286353811 0.14317691
[3,] 0.806455661 0.387088678 0.19354434
[4,] 0.836164817 0.327670366 0.16383518
[5,] 0.774902414 0.450195172 0.22509759
[6,] 0.932137792 0.135724415 0.06786221
[7,] 0.913328021 0.173343958 0.08667198
[8,] 0.877506157 0.244987686 0.12249384
[9,] 0.828716671 0.342566658 0.17128333
[10,] 0.795546058 0.408907884 0.20445394
[11,] 0.762164165 0.475671671 0.23783584
[12,] 0.731894807 0.536210386 0.26810519
[13,] 0.673513862 0.652972275 0.32648614
[14,] 0.624787689 0.750424622 0.37521231
[15,] 0.564973144 0.870053712 0.43502686
[16,] 0.611400340 0.777199320 0.38859966
[17,] 0.550499936 0.899000129 0.44950006
[18,] 0.556628870 0.886742261 0.44337113
[19,] 0.504560110 0.990879781 0.49543989
[20,] 0.496822025 0.993644051 0.50317797
[21,] 0.474006984 0.948013967 0.52599302
[22,] 0.412571359 0.825142717 0.58742864
[23,] 0.396862823 0.793725647 0.60313718
[24,] 0.496170487 0.992340974 0.50382951
[25,] 0.572158070 0.855683861 0.42784193
[26,] 0.545605511 0.908788978 0.45439449
[27,] 0.590779185 0.818441630 0.40922082
[28,] 0.571323792 0.857352416 0.42867621
[29,] 0.535885603 0.928228795 0.46411440
[30,] 0.502633773 0.994732453 0.49736623
[31,] 0.520200882 0.959598235 0.47979912
[32,] 0.471050166 0.942100333 0.52894983
[33,] 0.429396411 0.858792823 0.57060359
[34,] 0.645357706 0.709284587 0.35464229
[35,] 0.658790113 0.682419774 0.34120989
[36,] 0.621062936 0.757874128 0.37893706
[37,] 0.608035135 0.783929731 0.39196487
[38,] 0.579420730 0.841158540 0.42057927
[39,] 0.534229518 0.931540964 0.46577048
[40,] 0.491438972 0.982877944 0.50856103
[41,] 0.497723623 0.995447246 0.50227638
[42,] 0.466751375 0.933502749 0.53324863
[43,] 0.466011255 0.932022511 0.53398874
[44,] 0.479803897 0.959607793 0.52019610
[45,] 0.437413942 0.874827885 0.56258606
[46,] 0.421304302 0.842608604 0.57869570
[47,] 0.380217388 0.760434776 0.61978261
[48,] 0.405372307 0.810744613 0.59462769
[49,] 0.377129995 0.754259990 0.62287000
[50,] 0.340275237 0.680550474 0.65972476
[51,] 0.304335903 0.608671806 0.69566410
[52,] 0.272223504 0.544447008 0.72777650
[53,] 0.245832773 0.491665545 0.75416723
[54,] 0.213374540 0.426749081 0.78662546
[55,] 0.187961703 0.375923406 0.81203830
[56,] 0.205203238 0.410406477 0.79479676
[57,] 0.434631299 0.869262597 0.56536870
[58,] 0.393373492 0.786746984 0.60662651
[59,] 0.501301217 0.997397566 0.49869878
[60,] 0.465185202 0.930370404 0.53481480
[61,] 0.448463787 0.896927574 0.55153621
[62,] 0.419194900 0.838389799 0.58080510
[63,] 0.381714616 0.763429233 0.61828538
[64,] 0.442938195 0.885876391 0.55706180
[65,] 0.406560645 0.813121290 0.59343936
[66,] 0.379393562 0.758787125 0.62060644
[67,] 0.402363301 0.804726603 0.59763670
[68,] 0.368263037 0.736526074 0.63173696
[69,] 0.333157446 0.666314892 0.66684255
[70,] 0.298449480 0.596898961 0.70155052
[71,] 0.273337000 0.546674000 0.72666300
[72,] 0.241558177 0.483116354 0.75844182
[73,] 0.234207814 0.468415628 0.76579219
[74,] 0.205001382 0.410002765 0.79499862
[75,] 0.180407099 0.360814198 0.81959290
[76,] 0.163874819 0.327749638 0.83612518
[77,] 0.141648302 0.283296604 0.85835170
[78,] 0.138202988 0.276405976 0.86179701
[79,] 0.118527462 0.237054924 0.88147254
[80,] 0.102804298 0.205608596 0.89719570
[81,] 0.087984501 0.175969001 0.91201550
[82,] 0.092907809 0.185815619 0.90709219
[83,] 0.083744946 0.167489893 0.91625505
[84,] 0.069993747 0.139987495 0.93000625
[85,] 0.074559624 0.149119247 0.92544038
[86,] 0.062059767 0.124119535 0.93794023
[87,] 0.051537343 0.103074686 0.94846266
[88,] 0.045040025 0.090080051 0.95495997
[89,] 0.040193335 0.080386670 0.95980667
[90,] 0.049543025 0.099086050 0.95045697
[91,] 0.041751054 0.083502108 0.95824895
[92,] 0.037677941 0.075355883 0.96232206
[93,] 0.043753188 0.087506375 0.95624681
[94,] 0.039027677 0.078055355 0.96097232
[95,] 0.031891538 0.063783076 0.96810846
[96,] 0.030636182 0.061272365 0.96936382
[97,] 0.024835614 0.049671228 0.97516439
[98,] 0.020757042 0.041514084 0.97924296
[99,] 0.016540053 0.033080105 0.98345995
[100,] 0.017853009 0.035706017 0.98214699
[101,] 0.015407131 0.030814262 0.98459287
[102,] 0.019734499 0.039468997 0.98026550
[103,] 0.019380945 0.038761890 0.98061906
[104,] 0.018654959 0.037309918 0.98134504
[105,] 0.015885058 0.031770116 0.98411494
[106,] 0.015580981 0.031161962 0.98441902
[107,] 0.012622084 0.025244167 0.98737792
[108,] 0.011214369 0.022428737 0.98878563
[109,] 0.009116782 0.018233564 0.99088322
[110,] 0.012695495 0.025390991 0.98730450
[111,] 0.010385021 0.020770041 0.98961498
[112,] 0.008511951 0.017023903 0.99148805
[113,] 0.006809267 0.013618534 0.99319073
[114,] 0.005375740 0.010751480 0.99462426
[115,] 0.005069355 0.010138710 0.99493065
[116,] 0.004382794 0.008765588 0.99561721
[117,] 0.005649477 0.011298955 0.99435052
[118,] 0.006370793 0.012741586 0.99362921
[119,] 0.012202200 0.024404399 0.98779780
[120,] 0.015208149 0.030416298 0.98479185
[121,] 0.015878984 0.031757968 0.98412102
[122,] 0.014540660 0.029081321 0.98545934
[123,] 0.011524687 0.023049373 0.98847531
[124,] 0.010013664 0.020027328 0.98998634
[125,] 0.008227319 0.016454638 0.99177268
[126,] 0.010349349 0.020698698 0.98965065
[127,] 0.008801965 0.017603930 0.99119803
[128,] 0.010654189 0.021308378 0.98934581
[129,] 0.017171396 0.034342792 0.98282860
[130,] 0.016346821 0.032693641 0.98365318
[131,] 0.013561945 0.027123890 0.98643805
[132,] 0.014080024 0.028160047 0.98591998
[133,] 0.022916051 0.045832101 0.97708395
[134,] 0.028638574 0.057277149 0.97136143
[135,] 0.029976156 0.059952313 0.97002384
[136,] 0.026586614 0.053173228 0.97341339
[137,] 0.021592136 0.043184272 0.97840786
[138,] 0.029618184 0.059236369 0.97038182
[139,] 0.024921851 0.049843702 0.97507815
[140,] 0.026801618 0.053603237 0.97319838
[141,] 0.054628126 0.109256252 0.94537187
[142,] 0.051676367 0.103352734 0.94832363
[143,] 0.059435748 0.118871495 0.94056425
[144,] 0.050405020 0.100810040 0.94959498
[145,] 0.044617037 0.089234074 0.95538296
[146,] 0.038731176 0.077462353 0.96126882
[147,] 0.037886525 0.075773050 0.96211347
[148,] 0.031653984 0.063307968 0.96834602
[149,] 0.025809077 0.051618153 0.97419092
[150,] 0.020780599 0.041561198 0.97921940
[151,] 0.017282240 0.034564479 0.98271776
[152,] 0.014736020 0.029472040 0.98526398
[153,] 0.012440611 0.024881223 0.98755939
[154,] 0.013492007 0.026984015 0.98650799
[155,] 0.010747907 0.021495814 0.98925209
[156,] 0.015663949 0.031327897 0.98433605
[157,] 0.015357170 0.030714340 0.98464283
[158,] 0.014671010 0.029342021 0.98532899
[159,] 0.013425810 0.026851619 0.98657419
[160,] 0.010788604 0.021577208 0.98921140
[161,] 0.010335700 0.020671401 0.98966430
[162,] 0.011576511 0.023153023 0.98842349
[163,] 0.014028703 0.028057406 0.98597130
[164,] 0.011342052 0.022684104 0.98865795
[165,] 0.008814624 0.017629247 0.99118538
[166,] 0.007311733 0.014623466 0.99268827
[167,] 0.005692231 0.011384462 0.99430777
[168,] 0.004828665 0.009657329 0.99517134
[169,] 0.004003999 0.008007998 0.99599600
[170,] 0.003022346 0.006044692 0.99697765
[171,] 0.003405048 0.006810095 0.99659495
[172,] 0.002556437 0.005112874 0.99744356
[173,] 0.072457472 0.144914944 0.92754253
[174,] 0.065408020 0.130816041 0.93459198
[175,] 0.075627019 0.151254038 0.92437298
[176,] 0.062836742 0.125673485 0.93716326
[177,] 0.055541308 0.111082616 0.94445869
[178,] 0.046751818 0.093503637 0.95324818
[179,] 0.040058920 0.080117840 0.95994108
[180,] 0.033148623 0.066297247 0.96685138
[181,] 0.033667268 0.067334536 0.96633273
[182,] 0.032163753 0.064327506 0.96783625
[183,] 0.027619225 0.055238451 0.97238077
[184,] 0.021712019 0.043424037 0.97828798
[185,] 0.029166797 0.058333594 0.97083320
[186,] 0.023728366 0.047456732 0.97627163
[187,] 0.021216190 0.042432379 0.97878381
[188,] 0.017959665 0.035919331 0.98204033
[189,] 0.016389197 0.032778394 0.98361080
[190,] 0.013575346 0.027150692 0.98642465
[191,] 0.015000268 0.030000535 0.98499973
[192,] 0.019846743 0.039693487 0.98015326
[193,] 0.020968591 0.041937182 0.97903141
[194,] 0.016173882 0.032347764 0.98382612
[195,] 0.014312305 0.028624611 0.98568769
[196,] 0.011304534 0.022609068 0.98869547
[197,] 0.013091882 0.026183763 0.98690812
[198,] 0.010712932 0.021425864 0.98928707
[199,] 0.013091017 0.026182034 0.98690898
[200,] 0.023087900 0.046175800 0.97691210
[201,] 0.017784611 0.035569222 0.98221539
[202,] 0.022264574 0.044529147 0.97773543
[203,] 0.018731492 0.037462984 0.98126851
[204,] 0.014263796 0.028527592 0.98573620
[205,] 0.015960692 0.031921383 0.98403931
[206,] 0.013296668 0.026593337 0.98670333
[207,] 0.012269659 0.024539318 0.98773034
[208,] 0.009004209 0.018008418 0.99099579
[209,] 0.007304319 0.014608638 0.99269568
[210,] 0.005193192 0.010386384 0.99480681
[211,] 0.003925994 0.007851988 0.99607401
[212,] 0.002931170 0.005862340 0.99706883
[213,] 0.001996579 0.003993159 0.99800342
[214,] 0.004162431 0.008324862 0.99583757
[215,] 0.003236732 0.006473464 0.99676327
[216,] 0.002297809 0.004595618 0.99770219
[217,] 0.001576222 0.003152444 0.99842378
[218,] 0.001077294 0.002154588 0.99892271
[219,] 0.001164090 0.002328180 0.99883591
[220,] 0.004142608 0.008285217 0.99585739
[221,] 0.016806438 0.033612876 0.98319356
[222,] 0.026260013 0.052520026 0.97373999
[223,] 0.018497415 0.036994830 0.98150258
[224,] 0.014556095 0.029112190 0.98544390
[225,] 0.157747989 0.315495978 0.84225201
[226,] 0.121209604 0.242419208 0.87879040
[227,] 0.098626713 0.197253426 0.90137329
[228,] 0.074645367 0.149290734 0.92535463
[229,] 0.064255554 0.128511108 0.93574445
[230,] 0.101261068 0.202522136 0.89873893
[231,] 0.081063808 0.162127616 0.91893619
[232,] 0.079191795 0.158383590 0.92080821
[233,] 0.065521865 0.131043730 0.93447813
[234,] 0.053667116 0.107334232 0.94633288
[235,] 0.032448679 0.064897359 0.96755132
[236,] 0.026231087 0.052462175 0.97376891
[237,] 0.017404806 0.034809611 0.98259519
[238,] 0.044724686 0.089449372 0.95527531
[239,] 0.029966433 0.059932865 0.97003357
> postscript(file="/var/www/html/freestat/rcomp/tmp/1pa8p1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/2zk7s1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/3zk7s1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/4sbpd1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/5sbpd1289986122.ps",horizontal=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
-3.070065590 0.305732424 1.958423048 2.842334252 -2.299489473 -1.795006041
7 8 9 10 11 12
3.661000818 -2.023506594 -2.000339503 0.596114350 1.048315105 -0.147362586
13 14 15 16 17 18
0.508657122 0.559480169 -0.885358037 -0.435359193 0.474627775 3.678522423
19 20 21 22 23 24
2.525075597 0.469749825 0.676788002 0.854521377 2.578504823 0.903767276
25 26 27 28 29 30
0.948510469 0.842359383 1.173713167 -1.696396655 0.419328514 -0.198511580
31 32 33 34 35 36
-0.669580919 -0.790645268 -0.726155176 0.114047769 -1.394021043 -2.967415164
37 38 39 40 41 42
-2.956351361 -1.645416905 1.545228794 1.583717511 1.364767287 -1.578144671
43 44 45 46 47 48
2.610857930 -0.078439814 -0.378981577 -4.388108329 -2.515114881 -0.030307975
49 50 51 52 53 54
0.721682340 -1.519755748 -0.879933484 -0.035332935 -2.893619851 0.093254409
55 56 57 58 59 60
-2.279146097 1.721318459 0.283823078 0.597479837 0.005461434 1.901487455
61 62 63 64 65 66
0.522698330 0.496482687 -0.395395746 -0.585640930 0.818944285 0.795262750
67 68 69 70 71 72
1.525183016 3.432407625 -4.045855529 0.211233072 -3.586109520 -1.068575461
73 74 75 76 77 78
0.966162690 0.733351238 0.620847518 3.360272711 -0.536704506 1.329145509
79 80 81 82 83 84
-2.339168936 0.678309761 0.222601671 0.105253630 -0.768768435 -0.011994467
85 86 87 88 89 90
1.705017552 -0.283287816 0.508058603 0.994160315 0.363854889 -2.024002129
91 92 93 94 95 96
0.124711566 0.103172148 -0.156498164 -2.137720119 1.144640239 0.109908939
97 98 99 100 101 102
2.193451574 0.134097285 -0.539155258 -1.074377509 1.220724473 2.488642952
103 104 105 106 107 108
0.627427009 0.969538426 -1.947662519 1.210386941 0.222864884 1.520686847
109 110 111 112 113 114
-0.424730635 0.653081226 -0.064824352 1.878112083 -1.397784527 -2.659671257
115 116 117 118 119 120
1.773372914 -1.939344916 0.677582041 -1.883966910 0.432661257 -1.518667941
121 122 123 124 125 126
0.549277806 -2.867332169 -0.909824919 -0.910430725 -0.829360038 0.273678809
127 128 129 130 131 132
1.446708633 0.899949535 -2.748138474 1.982185540 -3.784341609 2.464386039
133 134 135 136 137 138
-2.221723543 -1.585980928 -0.115405526 0.806500733 0.413232922 -2.568947454
139 140 141 142 143 144
-1.084815764 -2.403302443 3.057677592 1.331661413 0.159396819 1.823394586
145 146 147 148 149 150
-3.316953939 2.474484036 -1.996050511 1.116056771 0.124547851 -2.933903096
151 152 153 154 155 156
-0.882333493 2.135843204 4.088225929 1.852125602 -2.511430068 0.541222566
157 158 159 160 161 162
1.248506832 1.092185381 1.438834369 -0.704212630 0.279215515 0.283095536
163 164 165 166 167 168
-0.442697433 0.795738640 1.296063480 -1.674784652 -0.375700396 -3.234215486
169 170 171 172 173 174
-1.807251341 1.597857143 1.433896950 0.646398648 -1.896671252 -2.402449826
175 176 177 178 179 180
-2.932256265 0.498810810 -0.351918154 -0.682300099 0.170956952 -1.322103228
181 182 183 184 185 186
0.960516475 -0.525711401 2.312504595 -0.124047048 -6.581574775 1.322635739
187 188 189 190 191 192
2.606466478 -0.373341154 -0.465775377 0.512245649 -0.471242324 0.632138904
193 194 195 196 197 198
2.235521301 1.907663590 -0.629753808 0.006703564 3.101886627 1.051849121
199 200 201 202 203 204
1.576850907 1.564310908 1.975118850 0.713257009 -2.306757022 -2.493290634
205 206 207 208 209 210
2.321862823 0.611752892 1.506339279 1.181684784 -2.566779467 1.147128186
211 212 213 214 215 216
-2.183907421 -3.660410208 0.954324991 2.918375114 1.573653668 0.934653915
217 218 219 220 221 222
2.483714320 0.132093104 1.184483165 -0.057174280 -1.533963485 0.135901172
223 224 225 226 227 228
0.848676791 -1.028565875 0.742964784 -3.627248856 0.354424359 1.209626515
229 230 231 232 233 234
-1.153744299 -0.795660805 1.830783777 -3.169517413 4.660886528 2.307925333
235 236 237 238 239 240
-0.676650766 -2.178114678 -6.958444628 -1.145474846 1.920620582 -1.568847509
241 242 243 244 245 246
-0.102918790 -1.342321241 1.198444309 2.060247615 1.501591044 0.269234279
247 248 249 250 251 252
1.326556074 -2.317496234 1.342915834 0.521133297 -0.649996427 -1.443616347
253 254 255 256 257 258
0.774800495 3.367264160 -1.123361527 0.368626554 1.979420708 2.513401424
259 260 261 262 263 264
-0.953228086 -5.305242539 1.538736966 -3.658891436 -0.094270601 1.462952724
> postscript(file="/var/www/html/freestat/rcomp/tmp/6sbpd1289986122.ps",horizontal=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 -3.070065590 NA
1 0.305732424 -3.070065590
2 1.958423048 0.305732424
3 2.842334252 1.958423048
4 -2.299489473 2.842334252
5 -1.795006041 -2.299489473
6 3.661000818 -1.795006041
7 -2.023506594 3.661000818
8 -2.000339503 -2.023506594
9 0.596114350 -2.000339503
10 1.048315105 0.596114350
11 -0.147362586 1.048315105
12 0.508657122 -0.147362586
13 0.559480169 0.508657122
14 -0.885358037 0.559480169
15 -0.435359193 -0.885358037
16 0.474627775 -0.435359193
17 3.678522423 0.474627775
18 2.525075597 3.678522423
19 0.469749825 2.525075597
20 0.676788002 0.469749825
21 0.854521377 0.676788002
22 2.578504823 0.854521377
23 0.903767276 2.578504823
24 0.948510469 0.903767276
25 0.842359383 0.948510469
26 1.173713167 0.842359383
27 -1.696396655 1.173713167
28 0.419328514 -1.696396655
29 -0.198511580 0.419328514
30 -0.669580919 -0.198511580
31 -0.790645268 -0.669580919
32 -0.726155176 -0.790645268
33 0.114047769 -0.726155176
34 -1.394021043 0.114047769
35 -2.967415164 -1.394021043
36 -2.956351361 -2.967415164
37 -1.645416905 -2.956351361
38 1.545228794 -1.645416905
39 1.583717511 1.545228794
40 1.364767287 1.583717511
41 -1.578144671 1.364767287
42 2.610857930 -1.578144671
43 -0.078439814 2.610857930
44 -0.378981577 -0.078439814
45 -4.388108329 -0.378981577
46 -2.515114881 -4.388108329
47 -0.030307975 -2.515114881
48 0.721682340 -0.030307975
49 -1.519755748 0.721682340
50 -0.879933484 -1.519755748
51 -0.035332935 -0.879933484
52 -2.893619851 -0.035332935
53 0.093254409 -2.893619851
54 -2.279146097 0.093254409
55 1.721318459 -2.279146097
56 0.283823078 1.721318459
57 0.597479837 0.283823078
58 0.005461434 0.597479837
59 1.901487455 0.005461434
60 0.522698330 1.901487455
61 0.496482687 0.522698330
62 -0.395395746 0.496482687
63 -0.585640930 -0.395395746
64 0.818944285 -0.585640930
65 0.795262750 0.818944285
66 1.525183016 0.795262750
67 3.432407625 1.525183016
68 -4.045855529 3.432407625
69 0.211233072 -4.045855529
70 -3.586109520 0.211233072
71 -1.068575461 -3.586109520
72 0.966162690 -1.068575461
73 0.733351238 0.966162690
74 0.620847518 0.733351238
75 3.360272711 0.620847518
76 -0.536704506 3.360272711
77 1.329145509 -0.536704506
78 -2.339168936 1.329145509
79 0.678309761 -2.339168936
80 0.222601671 0.678309761
81 0.105253630 0.222601671
82 -0.768768435 0.105253630
83 -0.011994467 -0.768768435
84 1.705017552 -0.011994467
85 -0.283287816 1.705017552
86 0.508058603 -0.283287816
87 0.994160315 0.508058603
88 0.363854889 0.994160315
89 -2.024002129 0.363854889
90 0.124711566 -2.024002129
91 0.103172148 0.124711566
92 -0.156498164 0.103172148
93 -2.137720119 -0.156498164
94 1.144640239 -2.137720119
95 0.109908939 1.144640239
96 2.193451574 0.109908939
97 0.134097285 2.193451574
98 -0.539155258 0.134097285
99 -1.074377509 -0.539155258
100 1.220724473 -1.074377509
101 2.488642952 1.220724473
102 0.627427009 2.488642952
103 0.969538426 0.627427009
104 -1.947662519 0.969538426
105 1.210386941 -1.947662519
106 0.222864884 1.210386941
107 1.520686847 0.222864884
108 -0.424730635 1.520686847
109 0.653081226 -0.424730635
110 -0.064824352 0.653081226
111 1.878112083 -0.064824352
112 -1.397784527 1.878112083
113 -2.659671257 -1.397784527
114 1.773372914 -2.659671257
115 -1.939344916 1.773372914
116 0.677582041 -1.939344916
117 -1.883966910 0.677582041
118 0.432661257 -1.883966910
119 -1.518667941 0.432661257
120 0.549277806 -1.518667941
121 -2.867332169 0.549277806
122 -0.909824919 -2.867332169
123 -0.910430725 -0.909824919
124 -0.829360038 -0.910430725
125 0.273678809 -0.829360038
126 1.446708633 0.273678809
127 0.899949535 1.446708633
128 -2.748138474 0.899949535
129 1.982185540 -2.748138474
130 -3.784341609 1.982185540
131 2.464386039 -3.784341609
132 -2.221723543 2.464386039
133 -1.585980928 -2.221723543
134 -0.115405526 -1.585980928
135 0.806500733 -0.115405526
136 0.413232922 0.806500733
137 -2.568947454 0.413232922
138 -1.084815764 -2.568947454
139 -2.403302443 -1.084815764
140 3.057677592 -2.403302443
141 1.331661413 3.057677592
142 0.159396819 1.331661413
143 1.823394586 0.159396819
144 -3.316953939 1.823394586
145 2.474484036 -3.316953939
146 -1.996050511 2.474484036
147 1.116056771 -1.996050511
148 0.124547851 1.116056771
149 -2.933903096 0.124547851
150 -0.882333493 -2.933903096
151 2.135843204 -0.882333493
152 4.088225929 2.135843204
153 1.852125602 4.088225929
154 -2.511430068 1.852125602
155 0.541222566 -2.511430068
156 1.248506832 0.541222566
157 1.092185381 1.248506832
158 1.438834369 1.092185381
159 -0.704212630 1.438834369
160 0.279215515 -0.704212630
161 0.283095536 0.279215515
162 -0.442697433 0.283095536
163 0.795738640 -0.442697433
164 1.296063480 0.795738640
165 -1.674784652 1.296063480
166 -0.375700396 -1.674784652
167 -3.234215486 -0.375700396
168 -1.807251341 -3.234215486
169 1.597857143 -1.807251341
170 1.433896950 1.597857143
171 0.646398648 1.433896950
172 -1.896671252 0.646398648
173 -2.402449826 -1.896671252
174 -2.932256265 -2.402449826
175 0.498810810 -2.932256265
176 -0.351918154 0.498810810
177 -0.682300099 -0.351918154
178 0.170956952 -0.682300099
179 -1.322103228 0.170956952
180 0.960516475 -1.322103228
181 -0.525711401 0.960516475
182 2.312504595 -0.525711401
183 -0.124047048 2.312504595
184 -6.581574775 -0.124047048
185 1.322635739 -6.581574775
186 2.606466478 1.322635739
187 -0.373341154 2.606466478
188 -0.465775377 -0.373341154
189 0.512245649 -0.465775377
190 -0.471242324 0.512245649
191 0.632138904 -0.471242324
192 2.235521301 0.632138904
193 1.907663590 2.235521301
194 -0.629753808 1.907663590
195 0.006703564 -0.629753808
196 3.101886627 0.006703564
197 1.051849121 3.101886627
198 1.576850907 1.051849121
199 1.564310908 1.576850907
200 1.975118850 1.564310908
201 0.713257009 1.975118850
202 -2.306757022 0.713257009
203 -2.493290634 -2.306757022
204 2.321862823 -2.493290634
205 0.611752892 2.321862823
206 1.506339279 0.611752892
207 1.181684784 1.506339279
208 -2.566779467 1.181684784
209 1.147128186 -2.566779467
210 -2.183907421 1.147128186
211 -3.660410208 -2.183907421
212 0.954324991 -3.660410208
213 2.918375114 0.954324991
214 1.573653668 2.918375114
215 0.934653915 1.573653668
216 2.483714320 0.934653915
217 0.132093104 2.483714320
218 1.184483165 0.132093104
219 -0.057174280 1.184483165
220 -1.533963485 -0.057174280
221 0.135901172 -1.533963485
222 0.848676791 0.135901172
223 -1.028565875 0.848676791
224 0.742964784 -1.028565875
225 -3.627248856 0.742964784
226 0.354424359 -3.627248856
227 1.209626515 0.354424359
228 -1.153744299 1.209626515
229 -0.795660805 -1.153744299
230 1.830783777 -0.795660805
231 -3.169517413 1.830783777
232 4.660886528 -3.169517413
233 2.307925333 4.660886528
234 -0.676650766 2.307925333
235 -2.178114678 -0.676650766
236 -6.958444628 -2.178114678
237 -1.145474846 -6.958444628
238 1.920620582 -1.145474846
239 -1.568847509 1.920620582
240 -0.102918790 -1.568847509
241 -1.342321241 -0.102918790
242 1.198444309 -1.342321241
243 2.060247615 1.198444309
244 1.501591044 2.060247615
245 0.269234279 1.501591044
246 1.326556074 0.269234279
247 -2.317496234 1.326556074
248 1.342915834 -2.317496234
249 0.521133297 1.342915834
250 -0.649996427 0.521133297
251 -1.443616347 -0.649996427
252 0.774800495 -1.443616347
253 3.367264160 0.774800495
254 -1.123361527 3.367264160
255 0.368626554 -1.123361527
256 1.979420708 0.368626554
257 2.513401424 1.979420708
258 -0.953228086 2.513401424
259 -5.305242539 -0.953228086
260 1.538736966 -5.305242539
261 -3.658891436 1.538736966
262 -0.094270601 -3.658891436
263 1.462952724 -0.094270601
264 NA 1.462952724
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.305732424 -3.070065590
[2,] 1.958423048 0.305732424
[3,] 2.842334252 1.958423048
[4,] -2.299489473 2.842334252
[5,] -1.795006041 -2.299489473
[6,] 3.661000818 -1.795006041
[7,] -2.023506594 3.661000818
[8,] -2.000339503 -2.023506594
[9,] 0.596114350 -2.000339503
[10,] 1.048315105 0.596114350
[11,] -0.147362586 1.048315105
[12,] 0.508657122 -0.147362586
[13,] 0.559480169 0.508657122
[14,] -0.885358037 0.559480169
[15,] -0.435359193 -0.885358037
[16,] 0.474627775 -0.435359193
[17,] 3.678522423 0.474627775
[18,] 2.525075597 3.678522423
[19,] 0.469749825 2.525075597
[20,] 0.676788002 0.469749825
[21,] 0.854521377 0.676788002
[22,] 2.578504823 0.854521377
[23,] 0.903767276 2.578504823
[24,] 0.948510469 0.903767276
[25,] 0.842359383 0.948510469
[26,] 1.173713167 0.842359383
[27,] -1.696396655 1.173713167
[28,] 0.419328514 -1.696396655
[29,] -0.198511580 0.419328514
[30,] -0.669580919 -0.198511580
[31,] -0.790645268 -0.669580919
[32,] -0.726155176 -0.790645268
[33,] 0.114047769 -0.726155176
[34,] -1.394021043 0.114047769
[35,] -2.967415164 -1.394021043
[36,] -2.956351361 -2.967415164
[37,] -1.645416905 -2.956351361
[38,] 1.545228794 -1.645416905
[39,] 1.583717511 1.545228794
[40,] 1.364767287 1.583717511
[41,] -1.578144671 1.364767287
[42,] 2.610857930 -1.578144671
[43,] -0.078439814 2.610857930
[44,] -0.378981577 -0.078439814
[45,] -4.388108329 -0.378981577
[46,] -2.515114881 -4.388108329
[47,] -0.030307975 -2.515114881
[48,] 0.721682340 -0.030307975
[49,] -1.519755748 0.721682340
[50,] -0.879933484 -1.519755748
[51,] -0.035332935 -0.879933484
[52,] -2.893619851 -0.035332935
[53,] 0.093254409 -2.893619851
[54,] -2.279146097 0.093254409
[55,] 1.721318459 -2.279146097
[56,] 0.283823078 1.721318459
[57,] 0.597479837 0.283823078
[58,] 0.005461434 0.597479837
[59,] 1.901487455 0.005461434
[60,] 0.522698330 1.901487455
[61,] 0.496482687 0.522698330
[62,] -0.395395746 0.496482687
[63,] -0.585640930 -0.395395746
[64,] 0.818944285 -0.585640930
[65,] 0.795262750 0.818944285
[66,] 1.525183016 0.795262750
[67,] 3.432407625 1.525183016
[68,] -4.045855529 3.432407625
[69,] 0.211233072 -4.045855529
[70,] -3.586109520 0.211233072
[71,] -1.068575461 -3.586109520
[72,] 0.966162690 -1.068575461
[73,] 0.733351238 0.966162690
[74,] 0.620847518 0.733351238
[75,] 3.360272711 0.620847518
[76,] -0.536704506 3.360272711
[77,] 1.329145509 -0.536704506
[78,] -2.339168936 1.329145509
[79,] 0.678309761 -2.339168936
[80,] 0.222601671 0.678309761
[81,] 0.105253630 0.222601671
[82,] -0.768768435 0.105253630
[83,] -0.011994467 -0.768768435
[84,] 1.705017552 -0.011994467
[85,] -0.283287816 1.705017552
[86,] 0.508058603 -0.283287816
[87,] 0.994160315 0.508058603
[88,] 0.363854889 0.994160315
[89,] -2.024002129 0.363854889
[90,] 0.124711566 -2.024002129
[91,] 0.103172148 0.124711566
[92,] -0.156498164 0.103172148
[93,] -2.137720119 -0.156498164
[94,] 1.144640239 -2.137720119
[95,] 0.109908939 1.144640239
[96,] 2.193451574 0.109908939
[97,] 0.134097285 2.193451574
[98,] -0.539155258 0.134097285
[99,] -1.074377509 -0.539155258
[100,] 1.220724473 -1.074377509
[101,] 2.488642952 1.220724473
[102,] 0.627427009 2.488642952
[103,] 0.969538426 0.627427009
[104,] -1.947662519 0.969538426
[105,] 1.210386941 -1.947662519
[106,] 0.222864884 1.210386941
[107,] 1.520686847 0.222864884
[108,] -0.424730635 1.520686847
[109,] 0.653081226 -0.424730635
[110,] -0.064824352 0.653081226
[111,] 1.878112083 -0.064824352
[112,] -1.397784527 1.878112083
[113,] -2.659671257 -1.397784527
[114,] 1.773372914 -2.659671257
[115,] -1.939344916 1.773372914
[116,] 0.677582041 -1.939344916
[117,] -1.883966910 0.677582041
[118,] 0.432661257 -1.883966910
[119,] -1.518667941 0.432661257
[120,] 0.549277806 -1.518667941
[121,] -2.867332169 0.549277806
[122,] -0.909824919 -2.867332169
[123,] -0.910430725 -0.909824919
[124,] -0.829360038 -0.910430725
[125,] 0.273678809 -0.829360038
[126,] 1.446708633 0.273678809
[127,] 0.899949535 1.446708633
[128,] -2.748138474 0.899949535
[129,] 1.982185540 -2.748138474
[130,] -3.784341609 1.982185540
[131,] 2.464386039 -3.784341609
[132,] -2.221723543 2.464386039
[133,] -1.585980928 -2.221723543
[134,] -0.115405526 -1.585980928
[135,] 0.806500733 -0.115405526
[136,] 0.413232922 0.806500733
[137,] -2.568947454 0.413232922
[138,] -1.084815764 -2.568947454
[139,] -2.403302443 -1.084815764
[140,] 3.057677592 -2.403302443
[141,] 1.331661413 3.057677592
[142,] 0.159396819 1.331661413
[143,] 1.823394586 0.159396819
[144,] -3.316953939 1.823394586
[145,] 2.474484036 -3.316953939
[146,] -1.996050511 2.474484036
[147,] 1.116056771 -1.996050511
[148,] 0.124547851 1.116056771
[149,] -2.933903096 0.124547851
[150,] -0.882333493 -2.933903096
[151,] 2.135843204 -0.882333493
[152,] 4.088225929 2.135843204
[153,] 1.852125602 4.088225929
[154,] -2.511430068 1.852125602
[155,] 0.541222566 -2.511430068
[156,] 1.248506832 0.541222566
[157,] 1.092185381 1.248506832
[158,] 1.438834369 1.092185381
[159,] -0.704212630 1.438834369
[160,] 0.279215515 -0.704212630
[161,] 0.283095536 0.279215515
[162,] -0.442697433 0.283095536
[163,] 0.795738640 -0.442697433
[164,] 1.296063480 0.795738640
[165,] -1.674784652 1.296063480
[166,] -0.375700396 -1.674784652
[167,] -3.234215486 -0.375700396
[168,] -1.807251341 -3.234215486
[169,] 1.597857143 -1.807251341
[170,] 1.433896950 1.597857143
[171,] 0.646398648 1.433896950
[172,] -1.896671252 0.646398648
[173,] -2.402449826 -1.896671252
[174,] -2.932256265 -2.402449826
[175,] 0.498810810 -2.932256265
[176,] -0.351918154 0.498810810
[177,] -0.682300099 -0.351918154
[178,] 0.170956952 -0.682300099
[179,] -1.322103228 0.170956952
[180,] 0.960516475 -1.322103228
[181,] -0.525711401 0.960516475
[182,] 2.312504595 -0.525711401
[183,] -0.124047048 2.312504595
[184,] -6.581574775 -0.124047048
[185,] 1.322635739 -6.581574775
[186,] 2.606466478 1.322635739
[187,] -0.373341154 2.606466478
[188,] -0.465775377 -0.373341154
[189,] 0.512245649 -0.465775377
[190,] -0.471242324 0.512245649
[191,] 0.632138904 -0.471242324
[192,] 2.235521301 0.632138904
[193,] 1.907663590 2.235521301
[194,] -0.629753808 1.907663590
[195,] 0.006703564 -0.629753808
[196,] 3.101886627 0.006703564
[197,] 1.051849121 3.101886627
[198,] 1.576850907 1.051849121
[199,] 1.564310908 1.576850907
[200,] 1.975118850 1.564310908
[201,] 0.713257009 1.975118850
[202,] -2.306757022 0.713257009
[203,] -2.493290634 -2.306757022
[204,] 2.321862823 -2.493290634
[205,] 0.611752892 2.321862823
[206,] 1.506339279 0.611752892
[207,] 1.181684784 1.506339279
[208,] -2.566779467 1.181684784
[209,] 1.147128186 -2.566779467
[210,] -2.183907421 1.147128186
[211,] -3.660410208 -2.183907421
[212,] 0.954324991 -3.660410208
[213,] 2.918375114 0.954324991
[214,] 1.573653668 2.918375114
[215,] 0.934653915 1.573653668
[216,] 2.483714320 0.934653915
[217,] 0.132093104 2.483714320
[218,] 1.184483165 0.132093104
[219,] -0.057174280 1.184483165
[220,] -1.533963485 -0.057174280
[221,] 0.135901172 -1.533963485
[222,] 0.848676791 0.135901172
[223,] -1.028565875 0.848676791
[224,] 0.742964784 -1.028565875
[225,] -3.627248856 0.742964784
[226,] 0.354424359 -3.627248856
[227,] 1.209626515 0.354424359
[228,] -1.153744299 1.209626515
[229,] -0.795660805 -1.153744299
[230,] 1.830783777 -0.795660805
[231,] -3.169517413 1.830783777
[232,] 4.660886528 -3.169517413
[233,] 2.307925333 4.660886528
[234,] -0.676650766 2.307925333
[235,] -2.178114678 -0.676650766
[236,] -6.958444628 -2.178114678
[237,] -1.145474846 -6.958444628
[238,] 1.920620582 -1.145474846
[239,] -1.568847509 1.920620582
[240,] -0.102918790 -1.568847509
[241,] -1.342321241 -0.102918790
[242,] 1.198444309 -1.342321241
[243,] 2.060247615 1.198444309
[244,] 1.501591044 2.060247615
[245,] 0.269234279 1.501591044
[246,] 1.326556074 0.269234279
[247,] -2.317496234 1.326556074
[248,] 1.342915834 -2.317496234
[249,] 0.521133297 1.342915834
[250,] -0.649996427 0.521133297
[251,] -1.443616347 -0.649996427
[252,] 0.774800495 -1.443616347
[253,] 3.367264160 0.774800495
[254,] -1.123361527 3.367264160
[255,] 0.368626554 -1.123361527
[256,] 1.979420708 0.368626554
[257,] 2.513401424 1.979420708
[258,] -0.953228086 2.513401424
[259,] -5.305242539 -0.953228086
[260,] 1.538736966 -5.305242539
[261,] -3.658891436 1.538736966
[262,] -0.094270601 -3.658891436
[263,] 1.462952724 -0.094270601
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.305732424 -3.070065590
2 1.958423048 0.305732424
3 2.842334252 1.958423048
4 -2.299489473 2.842334252
5 -1.795006041 -2.299489473
6 3.661000818 -1.795006041
7 -2.023506594 3.661000818
8 -2.000339503 -2.023506594
9 0.596114350 -2.000339503
10 1.048315105 0.596114350
11 -0.147362586 1.048315105
12 0.508657122 -0.147362586
13 0.559480169 0.508657122
14 -0.885358037 0.559480169
15 -0.435359193 -0.885358037
16 0.474627775 -0.435359193
17 3.678522423 0.474627775
18 2.525075597 3.678522423
19 0.469749825 2.525075597
20 0.676788002 0.469749825
21 0.854521377 0.676788002
22 2.578504823 0.854521377
23 0.903767276 2.578504823
24 0.948510469 0.903767276
25 0.842359383 0.948510469
26 1.173713167 0.842359383
27 -1.696396655 1.173713167
28 0.419328514 -1.696396655
29 -0.198511580 0.419328514
30 -0.669580919 -0.198511580
31 -0.790645268 -0.669580919
32 -0.726155176 -0.790645268
33 0.114047769 -0.726155176
34 -1.394021043 0.114047769
35 -2.967415164 -1.394021043
36 -2.956351361 -2.967415164
37 -1.645416905 -2.956351361
38 1.545228794 -1.645416905
39 1.583717511 1.545228794
40 1.364767287 1.583717511
41 -1.578144671 1.364767287
42 2.610857930 -1.578144671
43 -0.078439814 2.610857930
44 -0.378981577 -0.078439814
45 -4.388108329 -0.378981577
46 -2.515114881 -4.388108329
47 -0.030307975 -2.515114881
48 0.721682340 -0.030307975
49 -1.519755748 0.721682340
50 -0.879933484 -1.519755748
51 -0.035332935 -0.879933484
52 -2.893619851 -0.035332935
53 0.093254409 -2.893619851
54 -2.279146097 0.093254409
55 1.721318459 -2.279146097
56 0.283823078 1.721318459
57 0.597479837 0.283823078
58 0.005461434 0.597479837
59 1.901487455 0.005461434
60 0.522698330 1.901487455
61 0.496482687 0.522698330
62 -0.395395746 0.496482687
63 -0.585640930 -0.395395746
64 0.818944285 -0.585640930
65 0.795262750 0.818944285
66 1.525183016 0.795262750
67 3.432407625 1.525183016
68 -4.045855529 3.432407625
69 0.211233072 -4.045855529
70 -3.586109520 0.211233072
71 -1.068575461 -3.586109520
72 0.966162690 -1.068575461
73 0.733351238 0.966162690
74 0.620847518 0.733351238
75 3.360272711 0.620847518
76 -0.536704506 3.360272711
77 1.329145509 -0.536704506
78 -2.339168936 1.329145509
79 0.678309761 -2.339168936
80 0.222601671 0.678309761
81 0.105253630 0.222601671
82 -0.768768435 0.105253630
83 -0.011994467 -0.768768435
84 1.705017552 -0.011994467
85 -0.283287816 1.705017552
86 0.508058603 -0.283287816
87 0.994160315 0.508058603
88 0.363854889 0.994160315
89 -2.024002129 0.363854889
90 0.124711566 -2.024002129
91 0.103172148 0.124711566
92 -0.156498164 0.103172148
93 -2.137720119 -0.156498164
94 1.144640239 -2.137720119
95 0.109908939 1.144640239
96 2.193451574 0.109908939
97 0.134097285 2.193451574
98 -0.539155258 0.134097285
99 -1.074377509 -0.539155258
100 1.220724473 -1.074377509
101 2.488642952 1.220724473
102 0.627427009 2.488642952
103 0.969538426 0.627427009
104 -1.947662519 0.969538426
105 1.210386941 -1.947662519
106 0.222864884 1.210386941
107 1.520686847 0.222864884
108 -0.424730635 1.520686847
109 0.653081226 -0.424730635
110 -0.064824352 0.653081226
111 1.878112083 -0.064824352
112 -1.397784527 1.878112083
113 -2.659671257 -1.397784527
114 1.773372914 -2.659671257
115 -1.939344916 1.773372914
116 0.677582041 -1.939344916
117 -1.883966910 0.677582041
118 0.432661257 -1.883966910
119 -1.518667941 0.432661257
120 0.549277806 -1.518667941
121 -2.867332169 0.549277806
122 -0.909824919 -2.867332169
123 -0.910430725 -0.909824919
124 -0.829360038 -0.910430725
125 0.273678809 -0.829360038
126 1.446708633 0.273678809
127 0.899949535 1.446708633
128 -2.748138474 0.899949535
129 1.982185540 -2.748138474
130 -3.784341609 1.982185540
131 2.464386039 -3.784341609
132 -2.221723543 2.464386039
133 -1.585980928 -2.221723543
134 -0.115405526 -1.585980928
135 0.806500733 -0.115405526
136 0.413232922 0.806500733
137 -2.568947454 0.413232922
138 -1.084815764 -2.568947454
139 -2.403302443 -1.084815764
140 3.057677592 -2.403302443
141 1.331661413 3.057677592
142 0.159396819 1.331661413
143 1.823394586 0.159396819
144 -3.316953939 1.823394586
145 2.474484036 -3.316953939
146 -1.996050511 2.474484036
147 1.116056771 -1.996050511
148 0.124547851 1.116056771
149 -2.933903096 0.124547851
150 -0.882333493 -2.933903096
151 2.135843204 -0.882333493
152 4.088225929 2.135843204
153 1.852125602 4.088225929
154 -2.511430068 1.852125602
155 0.541222566 -2.511430068
156 1.248506832 0.541222566
157 1.092185381 1.248506832
158 1.438834369 1.092185381
159 -0.704212630 1.438834369
160 0.279215515 -0.704212630
161 0.283095536 0.279215515
162 -0.442697433 0.283095536
163 0.795738640 -0.442697433
164 1.296063480 0.795738640
165 -1.674784652 1.296063480
166 -0.375700396 -1.674784652
167 -3.234215486 -0.375700396
168 -1.807251341 -3.234215486
169 1.597857143 -1.807251341
170 1.433896950 1.597857143
171 0.646398648 1.433896950
172 -1.896671252 0.646398648
173 -2.402449826 -1.896671252
174 -2.932256265 -2.402449826
175 0.498810810 -2.932256265
176 -0.351918154 0.498810810
177 -0.682300099 -0.351918154
178 0.170956952 -0.682300099
179 -1.322103228 0.170956952
180 0.960516475 -1.322103228
181 -0.525711401 0.960516475
182 2.312504595 -0.525711401
183 -0.124047048 2.312504595
184 -6.581574775 -0.124047048
185 1.322635739 -6.581574775
186 2.606466478 1.322635739
187 -0.373341154 2.606466478
188 -0.465775377 -0.373341154
189 0.512245649 -0.465775377
190 -0.471242324 0.512245649
191 0.632138904 -0.471242324
192 2.235521301 0.632138904
193 1.907663590 2.235521301
194 -0.629753808 1.907663590
195 0.006703564 -0.629753808
196 3.101886627 0.006703564
197 1.051849121 3.101886627
198 1.576850907 1.051849121
199 1.564310908 1.576850907
200 1.975118850 1.564310908
201 0.713257009 1.975118850
202 -2.306757022 0.713257009
203 -2.493290634 -2.306757022
204 2.321862823 -2.493290634
205 0.611752892 2.321862823
206 1.506339279 0.611752892
207 1.181684784 1.506339279
208 -2.566779467 1.181684784
209 1.147128186 -2.566779467
210 -2.183907421 1.147128186
211 -3.660410208 -2.183907421
212 0.954324991 -3.660410208
213 2.918375114 0.954324991
214 1.573653668 2.918375114
215 0.934653915 1.573653668
216 2.483714320 0.934653915
217 0.132093104 2.483714320
218 1.184483165 0.132093104
219 -0.057174280 1.184483165
220 -1.533963485 -0.057174280
221 0.135901172 -1.533963485
222 0.848676791 0.135901172
223 -1.028565875 0.848676791
224 0.742964784 -1.028565875
225 -3.627248856 0.742964784
226 0.354424359 -3.627248856
227 1.209626515 0.354424359
228 -1.153744299 1.209626515
229 -0.795660805 -1.153744299
230 1.830783777 -0.795660805
231 -3.169517413 1.830783777
232 4.660886528 -3.169517413
233 2.307925333 4.660886528
234 -0.676650766 2.307925333
235 -2.178114678 -0.676650766
236 -6.958444628 -2.178114678
237 -1.145474846 -6.958444628
238 1.920620582 -1.145474846
239 -1.568847509 1.920620582
240 -0.102918790 -1.568847509
241 -1.342321241 -0.102918790
242 1.198444309 -1.342321241
243 2.060247615 1.198444309
244 1.501591044 2.060247615
245 0.269234279 1.501591044
246 1.326556074 0.269234279
247 -2.317496234 1.326556074
248 1.342915834 -2.317496234
249 0.521133297 1.342915834
250 -0.649996427 0.521133297
251 -1.443616347 -0.649996427
252 0.774800495 -1.443616347
253 3.367264160 0.774800495
254 -1.123361527 3.367264160
255 0.368626554 -1.123361527
256 1.979420708 0.368626554
257 2.513401424 1.979420708
258 -0.953228086 2.513401424
259 -5.305242539 -0.953228086
260 1.538736966 -5.305242539
261 -3.658891436 1.538736966
262 -0.094270601 -3.658891436
263 1.462952724 -0.094270601
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/73k6g1289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/8dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/freestat/rcomp/tmp/9dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/freestat/rcomp/tmp/10dtn01289986122.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/freestat/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/11a3l91289986122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/122v2u1289986122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/13rdz61289986122.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/freestat/rcomp/tmp/14vegu1289986122.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/15gfei1289986122.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/freestat/rcomp/tmp/161xu51289986122.tab")
+ }
>
> try(system("convert tmp/1pa8p1289986122.ps tmp/1pa8p1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zk7s1289986122.ps tmp/2zk7s1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zk7s1289986122.ps tmp/3zk7s1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/4sbpd1289986122.ps tmp/4sbpd1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/5sbpd1289986122.ps tmp/5sbpd1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/6sbpd1289986122.ps tmp/6sbpd1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/73k6g1289986122.ps tmp/73k6g1289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dtn01289986122.ps tmp/8dtn01289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/9dtn01289986122.ps tmp/9dtn01289986122.png",intern=TRUE))
character(0)
> try(system("convert tmp/10dtn01289986122.ps tmp/10dtn01289986122.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
9.769 2.930 10.112