R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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Type 'q()' to quit R.
> x <- array(list(1
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+ ,263
+ ,0
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+ ,0
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+ ,0
+ ,11
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+ ,7
+ ,0
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+ ,0
+ ,264
+ ,0
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+ ,9
+ ,14
+ ,0
+ ,12
+ ,0
+ ,72
+ ,0)
+ ,dim=c(16
+ ,264)
+ ,dimnames=list(c('Pop'
+ ,'t'
+ ,'Pop_t'
+ ,'Connected'
+ ,'Connected_p'
+ ,'Separate'
+ ,'Separate_p'
+ ,'Learning'
+ ,'Learning_p'
+ ,'Software'
+ ,'Happiness'
+ ,'Happiness_p'
+ ,'Depression'
+ ,'Depression_p'
+ ,'Belonging'
+ ,'Belonging_p')
+ ,1:264))
> y <- array(NA,dim=c(16,264),dimnames=list(c('Pop','t','Pop_t','Connected','Connected_p','Separate','Separate_p','Learning','Learning_p','Software','Happiness','Happiness_p','Depression','Depression_p','Belonging','Belonging_p'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '9'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '9'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Learning_p Pop t Pop_t Connected Connected_p Separate Separate_p Learning
1 13 1 1 1 41 41 38 38 13
2 16 1 2 2 39 39 32 32 16
3 19 1 3 3 30 30 35 35 19
4 15 1 4 4 31 31 33 33 15
5 14 1 5 5 34 34 37 37 14
6 13 1 6 6 35 35 29 29 13
7 19 1 7 7 39 39 31 31 19
8 15 1 8 8 34 34 36 36 15
9 14 1 9 9 36 36 35 35 14
10 15 1 10 10 37 37 38 38 15
11 16 1 11 11 38 38 31 31 16
12 16 1 12 12 36 36 34 34 16
13 16 1 13 13 38 38 35 35 16
14 16 1 14 14 39 39 38 38 16
15 17 1 15 15 33 33 37 37 17
16 15 1 16 16 32 32 33 33 15
17 15 1 17 17 36 36 32 32 15
18 20 1 18 18 38 38 38 38 20
19 18 1 19 19 39 39 38 38 18
20 16 1 20 20 32 32 32 32 16
21 16 1 21 21 32 32 33 33 16
22 16 1 22 22 31 31 31 31 16
23 19 1 23 23 39 39 38 38 19
24 16 1 24 24 37 37 39 39 16
25 17 1 25 25 39 39 32 32 17
26 17 1 26 26 41 41 32 32 17
27 16 1 27 27 36 36 35 35 16
28 15 1 28 28 33 33 37 37 15
29 16 1 29 29 33 33 33 33 16
30 14 1 30 30 34 34 33 33 14
31 15 1 31 31 31 31 31 31 15
32 12 1 32 32 27 27 32 32 12
33 14 1 33 33 37 37 31 31 14
34 16 1 34 34 34 34 37 37 16
35 14 1 35 35 34 34 30 30 14
36 10 1 36 36 32 32 33 33 10
37 10 1 37 37 29 29 31 31 10
38 14 1 38 38 36 36 33 33 14
39 16 1 39 39 29 29 31 31 16
40 16 1 40 40 35 35 33 33 16
41 16 1 41 41 37 37 32 32 16
42 14 1 42 42 34 34 33 33 14
43 20 1 43 43 38 38 32 32 20
44 14 1 44 44 35 35 33 33 14
45 14 1 45 45 38 38 28 28 14
46 11 1 46 46 37 37 35 35 11
47 14 1 47 47 38 38 39 39 14
48 15 1 48 48 33 33 34 34 15
49 16 1 49 49 36 36 38 38 16
50 14 1 50 50 38 38 32 32 14
51 16 1 51 51 32 32 38 38 16
52 14 1 52 52 32 32 30 30 14
53 12 1 53 53 32 32 33 33 12
54 16 1 54 54 34 34 38 38 16
55 9 1 55 55 32 32 32 32 9
56 14 1 56 56 37 37 35 35 14
57 16 1 57 57 39 39 34 34 16
58 16 1 58 58 29 29 34 34 16
59 15 1 59 59 37 37 36 36 15
60 16 1 60 60 35 35 34 34 16
61 12 1 61 61 30 30 28 28 12
62 16 1 62 62 38 38 34 34 16
63 16 1 63 63 34 34 35 35 16
64 14 1 64 64 31 31 35 35 14
65 16 1 65 65 34 34 31 31 16
66 17 1 66 66 35 35 37 37 17
67 18 1 67 67 36 36 35 35 18
68 18 1 68 68 30 30 27 27 18
69 12 1 69 69 39 39 40 40 12
70 16 1 70 70 35 35 37 37 16
71 10 1 71 71 38 38 36 36 10
72 14 1 72 72 31 31 38 38 14
73 18 1 73 73 34 34 39 39 18
74 18 1 74 74 38 38 41 41 18
75 16 1 75 75 34 34 27 27 16
76 17 1 76 76 39 39 30 30 17
77 16 1 77 77 37 37 37 37 16
78 16 1 78 78 34 34 31 31 16
79 13 1 79 79 28 28 31 31 13
80 16 1 80 80 37 37 27 27 16
81 16 1 81 81 33 33 36 36 16
82 16 1 82 82 35 35 37 37 16
83 15 1 83 83 37 37 33 33 15
84 15 1 84 84 32 32 34 34 15
85 16 1 85 85 33 33 31 31 16
86 14 1 86 86 38 38 39 39 14
87 16 1 87 87 33 33 34 34 16
88 16 1 88 88 29 29 32 32 16
89 15 1 89 89 33 33 33 33 15
90 12 1 90 90 31 31 36 36 12
91 17 1 91 91 36 36 32 32 17
92 16 1 92 92 35 35 41 41 16
93 15 1 93 93 32 32 28 28 15
94 13 1 94 94 29 29 30 30 13
95 16 1 95 95 39 39 36 36 16
96 16 1 96 96 37 37 35 35 16
97 16 1 97 97 35 35 31 31 16
98 16 1 98 98 37 37 34 34 16
99 14 1 99 99 32 32 36 36 14
100 16 1 100 100 38 38 36 36 16
101 16 1 101 101 37 37 35 35 16
102 20 1 102 102 36 36 37 37 20
103 15 1 103 103 32 32 28 28 15
104 16 1 104 104 33 33 39 39 16
105 13 1 105 105 40 40 32 32 13
106 17 1 106 106 38 38 35 35 17
107 16 1 107 107 41 41 39 39 16
108 16 1 108 108 36 36 35 35 16
109 12 1 109 109 43 43 42 42 12
110 16 1 110 110 30 30 34 34 16
111 16 1 111 111 31 31 33 33 16
112 17 1 112 112 32 32 41 41 17
113 13 1 113 113 32 32 33 33 13
114 12 1 114 114 37 37 34 34 12
115 18 1 115 115 37 37 32 32 18
116 14 1 116 116 33 33 40 40 14
117 14 1 117 117 34 34 40 40 14
118 13 1 118 118 33 33 35 35 13
119 16 1 119 119 38 38 36 36 16
120 13 1 120 120 33 33 37 37 13
121 16 1 121 121 31 31 27 27 16
122 13 1 122 122 38 38 39 39 13
123 16 1 123 123 37 37 38 38 16
124 15 1 124 124 36 36 31 31 15
125 16 1 125 125 31 31 33 33 16
126 15 1 126 126 39 39 32 32 15
127 17 1 127 127 44 44 39 39 17
128 15 1 128 128 33 33 36 36 15
129 12 1 129 129 35 35 33 33 12
130 16 1 130 130 32 32 33 33 16
131 10 1 131 131 28 28 32 32 10
132 16 1 132 132 40 40 37 37 16
133 12 1 133 133 27 27 30 30 12
134 14 1 134 134 37 37 38 38 14
135 15 1 135 135 32 32 29 29 15
136 13 1 136 136 28 28 22 22 13
137 15 1 137 137 34 34 35 35 15
138 11 1 138 138 30 30 35 35 11
139 12 1 139 139 35 35 34 34 12
140 11 1 140 140 31 31 35 35 11
141 16 1 141 141 32 32 34 34 16
142 15 1 142 142 30 30 37 37 15
143 17 1 143 143 30 30 35 35 17
144 16 1 144 144 31 31 23 23 16
145 10 1 145 145 40 40 31 31 10
146 18 1 146 146 32 32 27 27 18
147 13 1 147 147 36 36 36 36 13
148 16 1 148 148 32 32 31 31 16
149 13 1 149 149 35 35 32 32 13
150 10 1 150 150 38 38 39 39 10
151 15 1 151 151 42 42 37 37 15
152 16 1 152 152 34 34 38 38 16
153 16 1 153 153 35 35 39 39 16
154 14 1 154 154 38 38 34 34 14
155 10 1 155 155 33 33 31 31 10
156 17 1 156 156 36 36 32 32 17
157 13 1 157 157 32 32 37 37 13
158 15 1 158 158 33 33 36 36 15
159 16 1 159 159 34 34 32 32 16
160 12 1 160 160 32 32 38 38 12
161 13 1 161 161 34 34 36 36 13
162 13 0 162 0 27 0 26 0 13
163 12 0 163 0 31 0 26 0 12
164 17 0 164 0 38 0 33 0 17
165 15 0 165 0 34 0 39 0 15
166 10 0 166 0 24 0 30 0 10
167 14 0 167 0 30 0 33 0 14
168 11 0 168 0 26 0 25 0 11
169 13 0 169 0 34 0 38 0 13
170 16 0 170 0 27 0 37 0 16
171 12 0 171 0 37 0 31 0 12
172 16 0 172 0 36 0 37 0 16
173 12 0 173 0 41 0 35 0 12
174 9 0 174 0 29 0 25 0 9
175 12 0 175 0 36 0 28 0 12
176 15 0 176 0 32 0 35 0 15
177 12 0 177 0 37 0 33 0 12
178 12 0 178 0 30 0 30 0 12
179 14 0 179 0 31 0 31 0 14
180 12 0 180 0 38 0 37 0 12
181 16 0 181 0 36 0 36 0 16
182 11 0 182 0 35 0 30 0 11
183 19 0 183 0 31 0 36 0 19
184 15 0 184 0 38 0 32 0 15
185 8 0 185 0 22 0 28 0 8
186 16 0 186 0 32 0 36 0 16
187 17 0 187 0 36 0 34 0 17
188 12 0 188 0 39 0 31 0 12
189 11 0 189 0 28 0 28 0 11
190 11 0 190 0 32 0 36 0 11
191 14 0 191 0 32 0 36 0 14
192 16 0 192 0 38 0 40 0 16
193 12 0 193 0 32 0 33 0 12
194 16 0 194 0 35 0 37 0 16
195 13 0 195 0 32 0 32 0 13
196 15 0 196 0 37 0 38 0 15
197 16 0 197 0 34 0 31 0 16
198 16 0 198 0 33 0 37 0 16
199 14 0 199 0 33 0 33 0 14
200 16 0 200 0 26 0 32 0 16
201 16 0 201 0 30 0 30 0 16
202 14 0 202 0 24 0 30 0 14
203 11 0 203 0 34 0 31 0 11
204 12 0 204 0 34 0 32 0 12
205 15 0 205 0 33 0 34 0 15
206 15 0 206 0 34 0 36 0 15
207 16 0 207 0 35 0 37 0 16
208 16 0 208 0 35 0 36 0 16
209 11 0 209 0 36 0 33 0 11
210 15 0 210 0 34 0 33 0 15
211 12 0 211 0 34 0 33 0 12
212 12 0 212 0 41 0 44 0 12
213 15 0 213 0 32 0 39 0 15
214 15 0 214 0 30 0 32 0 15
215 16 0 215 0 35 0 35 0 16
216 14 0 216 0 28 0 25 0 14
217 17 0 217 0 33 0 35 0 17
218 14 0 218 0 39 0 34 0 14
219 13 0 219 0 36 0 35 0 13
220 15 0 220 0 36 0 39 0 15
221 13 0 221 0 35 0 33 0 13
222 14 0 222 0 38 0 36 0 14
223 15 0 223 0 33 0 32 0 15
224 12 0 224 0 31 0 32 0 12
225 13 0 225 0 34 0 36 0 13
226 8 0 226 0 32 0 36 0 8
227 14 0 227 0 31 0 32 0 14
228 14 0 228 0 33 0 34 0 14
229 11 0 229 0 34 0 33 0 11
230 12 0 230 0 34 0 35 0 12
231 13 0 231 0 34 0 30 0 13
232 10 0 232 0 33 0 38 0 10
233 16 0 233 0 32 0 34 0 16
234 18 0 234 0 41 0 33 0 18
235 13 0 235 0 34 0 32 0 13
236 11 0 236 0 36 0 31 0 11
237 4 0 237 0 37 0 30 0 4
238 13 0 238 0 36 0 27 0 13
239 16 0 239 0 29 0 31 0 16
240 10 0 240 0 37 0 30 0 10
241 12 0 241 0 27 0 32 0 12
242 12 0 242 0 35 0 35 0 12
243 10 0 243 0 28 0 28 0 10
244 13 0 244 0 35 0 33 0 13
245 15 0 245 0 37 0 31 0 15
246 12 0 246 0 29 0 35 0 12
247 14 0 247 0 32 0 35 0 14
248 10 0 248 0 36 0 32 0 10
249 12 0 249 0 19 0 21 0 12
250 12 0 250 0 21 0 20 0 12
251 11 0 251 0 31 0 34 0 11
252 10 0 252 0 33 0 32 0 10
253 12 0 253 0 36 0 34 0 12
254 16 0 254 0 33 0 32 0 16
255 12 0 255 0 37 0 33 0 12
256 14 0 256 0 34 0 33 0 14
257 16 0 257 0 35 0 37 0 16
258 14 0 258 0 31 0 32 0 14
259 13 0 259 0 37 0 34 0 13
260 4 0 260 0 35 0 30 0 4
261 15 0 261 0 27 0 30 0 15
262 11 0 262 0 34 0 38 0 11
263 11 0 263 0 40 0 36 0 11
264 14 0 264 0 29 0 32 0 14
Software Happiness Happiness_p Depression Depression_p Belonging
1 12 14 14 12.0 12.0 53
2 11 18 18 11.0 11.0 83
3 15 11 11 14.0 14.0 66
4 6 12 12 12.0 12.0 67
5 13 16 16 21.0 21.0 76
6 10 18 18 12.0 12.0 78
7 12 14 14 22.0 22.0 53
8 14 14 14 11.0 11.0 80
9 12 15 15 10.0 10.0 74
10 9 15 15 13.0 13.0 76
11 10 17 17 10.0 10.0 79
12 12 19 19 8.0 8.0 54
13 12 10 10 15.0 15.0 67
14 11 16 16 14.0 14.0 54
15 15 18 18 10.0 10.0 87
16 12 14 14 14.0 14.0 58
17 10 14 14 14.0 14.0 75
18 12 17 17 11.0 11.0 88
19 11 14 14 10.0 10.0 64
20 12 16 16 13.0 13.0 57
21 11 18 18 9.5 9.5 66
22 12 11 11 14.0 14.0 68
23 13 14 14 12.0 12.0 54
24 11 12 12 14.0 14.0 56
25 12 17 17 11.0 11.0 86
26 13 9 9 9.0 9.0 80
27 10 16 16 11.0 11.0 76
28 14 14 14 15.0 15.0 69
29 12 15 15 14.0 14.0 78
30 10 11 11 13.0 13.0 67
31 12 16 16 9.0 9.0 80
32 8 13 13 15.0 15.0 54
33 10 17 17 10.0 10.0 71
34 12 15 15 11.0 11.0 84
35 12 14 14 13.0 13.0 74
36 7 16 16 8.0 8.0 71
37 9 9 9 20.0 20.0 63
38 12 15 15 12.0 12.0 71
39 10 17 17 10.0 10.0 76
40 10 13 13 10.0 10.0 69
41 10 15 15 9.0 9.0 74
42 12 16 16 14.0 14.0 75
43 15 16 16 8.0 8.0 54
44 10 12 12 14.0 14.0 52
45 10 15 15 11.0 11.0 69
46 12 11 11 13.0 13.0 68
47 13 15 15 9.0 9.0 65
48 11 15 15 11.0 11.0 75
49 11 17 17 15.0 15.0 74
50 12 13 13 11.0 11.0 75
51 14 16 16 10.0 10.0 72
52 10 14 14 14.0 14.0 67
53 12 11 11 18.0 18.0 63
54 13 12 12 14.0 14.0 62
55 5 12 12 11.0 11.0 63
56 6 15 15 14.5 14.5 76
57 12 16 16 13.0 13.0 74
58 12 15 15 9.0 9.0 67
59 11 12 12 10.0 10.0 73
60 10 12 12 15.0 15.0 70
61 7 8 8 20.0 20.0 53
62 12 13 13 12.0 12.0 77
63 14 11 11 12.0 12.0 80
64 11 14 14 14.0 14.0 52
65 12 15 15 13.0 13.0 54
66 13 10 10 11.0 11.0 80
67 14 11 11 17.0 17.0 66
68 11 12 12 12.0 12.0 73
69 12 15 15 13.0 13.0 63
70 12 15 15 14.0 14.0 69
71 8 14 14 13.0 13.0 67
72 11 16 16 15.0 15.0 54
73 14 15 15 13.0 13.0 81
74 14 15 15 10.0 10.0 69
75 12 13 13 11.0 11.0 84
76 9 12 12 19.0 19.0 80
77 13 17 17 13.0 13.0 70
78 11 13 13 17.0 17.0 69
79 12 15 15 13.0 13.0 77
80 12 13 13 9.0 9.0 54
81 12 15 15 11.0 11.0 79
82 12 15 15 9.0 9.0 71
83 12 16 16 12.0 12.0 73
84 11 15 15 12.0 12.0 72
85 10 14 14 13.0 13.0 77
86 9 15 15 13.0 13.0 75
87 12 14 14 12.0 12.0 69
88 12 13 13 15.0 15.0 54
89 12 7 7 22.0 22.0 70
90 9 17 17 13.0 13.0 73
91 15 13 13 15.0 15.0 54
92 12 15 15 13.0 13.0 77
93 12 14 14 15.0 15.0 82
94 12 13 13 12.5 12.5 80
95 10 16 16 11.0 11.0 80
96 13 12 12 16.0 16.0 69
97 9 14 14 11.0 11.0 78
98 12 17 17 11.0 11.0 81
99 10 15 15 10.0 10.0 76
100 14 17 17 10.0 10.0 76
101 11 12 12 16.0 16.0 73
102 15 16 16 12.0 12.0 85
103 11 11 11 11.0 11.0 66
104 11 15 15 16.0 16.0 79
105 12 9 9 19.0 19.0 68
106 12 16 16 11.0 11.0 76
107 12 15 15 16.0 16.0 71
108 11 10 10 15.0 15.0 54
109 7 10 10 24.0 24.0 46
110 12 15 15 14.0 14.0 85
111 14 11 11 15.0 15.0 74
112 11 13 13 11.0 11.0 88
113 11 14 14 15.0 15.0 38
114 10 18 18 12.0 12.0 76
115 13 16 16 10.0 10.0 86
116 13 14 14 14.0 14.0 54
117 8 14 14 13.0 13.0 67
118 11 14 14 9.0 9.0 69
119 12 14 14 15.0 15.0 90
120 11 12 12 15.0 15.0 54
121 13 14 14 14.0 14.0 76
122 12 15 15 11.0 11.0 89
123 14 15 15 8.0 8.0 76
124 13 15 15 11.0 11.0 73
125 15 13 13 11.0 11.0 79
126 10 17 17 8.0 8.0 90
127 11 17 17 10.0 10.0 74
128 9 19 19 11.0 11.0 81
129 11 15 15 13.0 13.0 72
130 10 13 13 11.0 11.0 71
131 11 9 9 20.0 20.0 66
132 8 15 15 10.0 10.0 77
133 11 15 15 15.0 15.0 65
134 12 15 15 12.0 12.0 74
135 12 16 16 14.0 14.0 85
136 9 11 11 23.0 23.0 54
137 11 14 14 14.0 14.0 63
138 10 11 11 16.0 16.0 54
139 8 15 15 11.0 11.0 64
140 9 13 13 12.0 12.0 69
141 8 15 15 10.0 10.0 54
142 9 16 16 14.0 14.0 84
143 15 14 14 12.0 12.0 86
144 11 15 15 12.0 12.0 77
145 8 16 16 11.0 11.0 89
146 13 16 16 12.0 12.0 76
147 12 11 11 13.0 13.0 60
148 12 12 12 11.0 11.0 75
149 9 9 9 19.0 19.0 73
150 7 16 16 12.0 12.0 85
151 13 13 13 17.0 17.0 79
152 9 16 16 9.0 9.0 71
153 6 12 12 12.0 12.0 72
154 8 9 9 19.0 19.0 69
155 8 13 13 18.0 18.0 78
156 15 13 13 15.0 15.0 54
157 6 14 14 14.0 14.0 69
158 9 19 19 11.0 11.0 81
159 11 13 13 9.0 9.0 84
160 8 12 12 18.0 18.0 84
161 8 13 13 16.0 16.0 69
162 10 10 0 24.0 0.0 66
163 8 14 0 14.0 0.0 81
164 14 16 0 20.0 0.0 82
165 10 10 0 18.0 0.0 72
166 8 11 0 23.0 0.0 54
167 11 14 0 12.0 0.0 78
168 12 12 0 14.0 0.0 74
169 12 9 0 16.0 0.0 82
170 12 9 0 18.0 0.0 73
171 5 11 0 20.0 0.0 55
172 12 16 0 12.0 0.0 72
173 10 9 0 12.0 0.0 78
174 7 13 0 17.0 0.0 59
175 12 16 0 13.0 0.0 72
176 11 13 0 9.0 0.0 78
177 8 9 0 16.0 0.0 68
178 9 12 0 18.0 0.0 69
179 10 16 0 10.0 0.0 67
180 9 11 0 14.0 0.0 74
181 12 14 0 11.0 0.0 54
182 6 13 0 9.0 0.0 67
183 15 15 0 11.0 0.0 70
184 12 14 0 10.0 0.0 80
185 12 16 0 11.0 0.0 89
186 12 13 0 19.0 0.0 76
187 11 14 0 14.0 0.0 74
188 7 15 0 12.0 0.0 87
189 7 13 0 14.0 0.0 54
190 5 11 0 21.0 0.0 61
191 12 11 0 13.0 0.0 38
192 12 14 0 10.0 0.0 75
193 3 15 0 15.0 0.0 69
194 11 11 0 16.0 0.0 62
195 10 15 0 14.0 0.0 72
196 12 12 0 12.0 0.0 70
197 9 14 0 19.0 0.0 79
198 12 14 0 15.0 0.0 87
199 9 8 0 19.0 0.0 62
200 12 13 0 13.0 0.0 77
201 12 9 0 17.0 0.0 69
202 10 15 0 12.0 0.0 69
203 9 17 0 11.0 0.0 75
204 12 13 0 14.0 0.0 54
205 8 15 0 11.0 0.0 72
206 11 15 0 13.0 0.0 74
207 11 14 0 12.0 0.0 85
208 12 16 0 15.0 0.0 52
209 10 13 0 14.0 0.0 70
210 10 16 0 12.0 0.0 84
211 12 9 0 17.0 0.0 64
212 12 16 0 11.0 0.0 84
213 11 11 0 18.0 0.0 87
214 8 10 0 13.0 0.0 79
215 12 11 0 17.0 0.0 67
216 10 15 0 13.0 0.0 65
217 11 17 0 11.0 0.0 85
218 10 14 0 12.0 0.0 83
219 8 8 0 22.0 0.0 61
220 12 15 0 14.0 0.0 82
221 12 11 0 12.0 0.0 76
222 10 16 0 12.0 0.0 58
223 12 10 0 17.0 0.0 72
224 9 15 0 9.0 0.0 72
225 9 9 0 21.0 0.0 38
226 6 16 0 10.0 0.0 78
227 10 19 0 11.0 0.0 54
228 9 12 0 12.0 0.0 63
229 9 8 0 23.0 0.0 66
230 9 11 0 13.0 0.0 70
231 6 14 0 12.0 0.0 71
232 10 9 0 16.0 0.0 67
233 6 15 0 9.0 0.0 58
234 14 13 0 17.0 0.0 72
235 10 16 0 9.0 0.0 72
236 10 11 0 14.0 0.0 70
237 6 12 0 17.0 0.0 76
238 12 13 0 13.0 0.0 50
239 12 10 0 11.0 0.0 72
240 7 11 0 12.0 0.0 72
241 8 12 0 10.0 0.0 88
242 11 8 0 19.0 0.0 53
243 3 12 0 16.0 0.0 58
244 6 12 0 16.0 0.0 66
245 10 15 0 14.0 0.0 82
246 8 11 0 20.0 0.0 69
247 9 13 0 15.0 0.0 68
248 9 14 0 23.0 0.0 44
249 8 10 0 20.0 0.0 56
250 9 12 0 16.0 0.0 53
251 7 15 0 14.0 0.0 70
252 7 13 0 17.0 0.0 78
253 6 13 0 11.0 0.0 71
254 9 13 0 13.0 0.0 72
255 10 12 0 17.0 0.0 68
256 11 12 0 15.0 0.0 67
257 12 9 0 21.0 0.0 75
258 8 9 0 18.0 0.0 62
259 11 15 0 15.0 0.0 67
260 3 10 0 8.0 0.0 83
261 11 14 0 12.0 0.0 64
262 12 15 0 12.0 0.0 68
263 7 7 0 22.0 0.0 62
264 9 14 0 12.0 0.0 72
Belonging_p
1 53
2 83
3 66
4 67
5 76
6 78
7 53
8 80
9 74
10 76
11 79
12 54
13 67
14 54
15 87
16 58
17 75
18 88
19 64
20 57
21 66
22 68
23 54
24 56
25 86
26 80
27 76
28 69
29 78
30 67
31 80
32 54
33 71
34 84
35 74
36 71
37 63
38 71
39 76
40 69
41 74
42 75
43 54
44 52
45 69
46 68
47 65
48 75
49 74
50 75
51 72
52 67
53 63
54 62
55 63
56 76
57 74
58 67
59 73
60 70
61 53
62 77
63 80
64 52
65 54
66 80
67 66
68 73
69 63
70 69
71 67
72 54
73 81
74 69
75 84
76 80
77 70
78 69
79 77
80 54
81 79
82 71
83 73
84 72
85 77
86 75
87 69
88 54
89 70
90 73
91 54
92 77
93 82
94 80
95 80
96 69
97 78
98 81
99 76
100 76
101 73
102 85
103 66
104 79
105 68
106 76
107 71
108 54
109 46
110 85
111 74
112 88
113 38
114 76
115 86
116 54
117 67
118 69
119 90
120 54
121 76
122 89
123 76
124 73
125 79
126 90
127 74
128 81
129 72
130 71
131 66
132 77
133 65
134 74
135 85
136 54
137 63
138 54
139 64
140 69
141 54
142 84
143 86
144 77
145 89
146 76
147 60
148 75
149 73
150 85
151 79
152 71
153 72
154 69
155 78
156 54
157 69
158 81
159 84
160 84
161 69
162 0
163 0
164 0
165 0
166 0
167 0
168 0
169 0
170 0
171 0
172 0
173 0
174 0
175 0
176 0
177 0
178 0
179 0
180 0
181 0
182 0
183 0
184 0
185 0
186 0
187 0
188 0
189 0
190 0
191 0
192 0
193 0
194 0
195 0
196 0
197 0
198 0
199 0
200 0
201 0
202 0
203 0
204 0
205 0
206 0
207 0
208 0
209 0
210 0
211 0
212 0
213 0
214 0
215 0
216 0
217 0
218 0
219 0
220 0
221 0
222 0
223 0
224 0
225 0
226 0
227 0
228 0
229 0
230 0
231 0
232 0
233 0
234 0
235 0
236 0
237 0
238 0
239 0
240 0
241 0
242 0
243 0
244 0
245 0
246 0
247 0
248 0
249 0
250 0
251 0
252 0
253 0
254 0
255 0
256 0
257 0
258 0
259 0
260 0
261 0
262 0
263 0
264 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop t Pop_t Connected
1.367e-16 -2.241e-16 0.000e+00 0.000e+00 0.000e+00
Connected_p Separate Separate_p Learning Software
0.000e+00 0.000e+00 0.000e+00 1.000e+00 -1.920e-18
Happiness Happiness_p Depression Depression_p Belonging
-8.890e-18 2.189e-18 -7.282e-18 1.445e-17 5.806e-19
Belonging_p
-7.821e-19
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.052e-15 -4.798e-17 8.780e-18 5.887e-17 5.184e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.367e-16 2.990e-16 4.570e-01 0.648
Pop -2.241e-16 3.739e-16 -5.990e-01 0.549
t 0.000e+00 5.675e-19 0.000e+00 1.000
Pop_t 0.000e+00 6.346e-19 0.000e+00 1.000
Connected 0.000e+00 4.713e-18 0.000e+00 1.000
Connected_p 0.000e+00 6.359e-18 0.000e+00 1.000
Separate 0.000e+00 5.351e-18 0.000e+00 1.000
Separate_p 0.000e+00 6.677e-18 0.000e+00 1.000
Learning 1.000e+00 5.719e-18 1.749e+17 <2e-16 ***
Software -1.920e-18 5.774e-18 -3.330e-01 0.740
Happiness -8.890e-18 8.063e-18 -1.103e+00 0.271
Happiness_p 2.189e-18 1.062e-17 2.060e-01 0.837
Depression -7.282e-18 5.605e-18 -1.299e+00 0.195
Depression_p 1.445e-17 7.616e-18 1.897e+00 0.059 .
Belonging 5.806e-19 1.717e-18 3.380e-01 0.735
Belonging_p -7.821e-19 2.219e-18 -3.520e-01 0.725
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.663e-16 on 248 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 3.825e+33 on 15 and 248 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,] 4.022883e-01 8.045767e-01 5.977117e-01
[2,] 5.553196e-01 8.893608e-01 4.446804e-01
[3,] 2.194066e-01 4.388131e-01 7.805934e-01
[4,] 9.210692e-01 1.578616e-01 7.893079e-02
[5,] 9.844252e-01 3.114960e-02 1.557480e-02
[6,] 9.803356e-01 3.932885e-02 1.966442e-02
[7,] 9.149896e-01 1.700208e-01 8.501039e-02
[8,] 3.249937e-04 6.499875e-04 9.996750e-01
[9,] 1.216195e-02 2.432390e-02 9.878380e-01
[10,] 1.000000e+00 3.717111e-20 1.858556e-20
[11,] 9.299524e-01 1.400952e-01 7.004759e-02
[12,] 8.188830e-03 1.637766e-02 9.918112e-01
[13,] 9.836951e-01 3.260987e-02 1.630493e-02
[14,] 2.277755e-01 4.555509e-01 7.722245e-01
[15,] 1.000000e+00 8.326056e-09 4.163028e-09
[16,] 8.456396e-09 1.691279e-08 1.000000e+00
[17,] 7.929278e-01 4.141445e-01 2.070722e-01
[18,] 7.506220e-02 1.501244e-01 9.249378e-01
[19,] 9.975154e-07 1.995031e-06 9.999990e-01
[20,] 3.008171e-01 6.016341e-01 6.991829e-01
[21,] 9.522188e-01 9.556244e-02 4.778122e-02
[22,] 3.345430e-09 6.690860e-09 1.000000e+00
[23,] 5.162266e-06 1.032453e-05 9.999948e-01
[24,] 1.000000e+00 6.210582e-15 3.105291e-15
[25,] 1.000000e+00 1.384139e-09 6.920693e-10
[26,] 5.599279e-01 8.801443e-01 4.400721e-01
[27,] 9.987771e-01 2.445896e-03 1.222948e-03
[28,] 9.996983e-01 6.033378e-04 3.016689e-04
[29,] 6.797926e-02 1.359585e-01 9.320207e-01
[30,] 1.000000e+00 3.856135e-09 1.928068e-09
[31,] 1.000000e+00 6.664668e-38 3.332334e-38
[32,] 9.744669e-01 5.106612e-02 2.553306e-02
[33,] 9.397793e-09 1.879559e-08 1.000000e+00
[34,] 9.541131e-01 9.177371e-02 4.588686e-02
[35,] 1.320366e-06 2.640732e-06 9.999987e-01
[36,] 2.245982e-01 4.491963e-01 7.754018e-01
[37,] 1.000000e+00 5.584051e-14 2.792026e-14
[38,] 3.435966e-03 6.871932e-03 9.965640e-01
[39,] 9.995640e-01 8.719120e-04 4.359560e-04
[40,] 6.056304e-01 7.887392e-01 3.943696e-01
[41,] 9.959327e-01 8.134572e-03 4.067286e-03
[42,] 4.903301e-05 9.806602e-05 9.999510e-01
[43,] 1.000000e+00 2.832231e-33 1.416116e-33
[44,] 9.999980e-01 4.079793e-06 2.039896e-06
[45,] 1.000000e+00 3.502892e-19 1.751446e-19
[46,] 7.067092e-14 1.413418e-13 1.000000e+00
[47,] 1.547733e-10 3.095467e-10 1.000000e+00
[48,] 2.472648e-24 4.945296e-24 1.000000e+00
[49,] 6.986658e-01 6.026684e-01 3.013342e-01
[50,] 1.158613e-05 2.317225e-05 9.999884e-01
[51,] 8.376230e-14 1.675246e-13 1.000000e+00
[52,] 3.741782e-11 7.483563e-11 1.000000e+00
[53,] 1.702057e-17 3.404114e-17 1.000000e+00
[54,] 9.999961e-01 7.824812e-06 3.912406e-06
[55,] 3.641109e-04 7.282219e-04 9.996359e-01
[56,] 1.000000e+00 4.574407e-09 2.287204e-09
[57,] 4.660775e-01 9.321550e-01 5.339225e-01
[58,] 9.996584e-01 6.832950e-04 3.416475e-04
[59,] 9.998607e-01 2.786898e-04 1.393449e-04
[60,] 2.683331e-23 5.366663e-23 1.000000e+00
[61,] 8.275274e-02 1.655055e-01 9.172473e-01
[62,] 1.294397e-04 2.588794e-04 9.998706e-01
[63,] 1.175520e-13 2.351039e-13 1.000000e+00
[64,] 8.281719e-01 3.436563e-01 1.718281e-01
[65,] 1.000000e+00 1.048128e-09 5.240639e-10
[66,] 1.350187e-03 2.700374e-03 9.986498e-01
[67,] 8.230704e-01 3.538593e-01 1.769296e-01
[68,] 5.276067e-09 1.055213e-08 1.000000e+00
[69,] 6.636486e-09 1.327297e-08 1.000000e+00
[70,] 9.999758e-01 4.836482e-05 2.418241e-05
[71,] 1.973531e-05 3.947062e-05 9.999803e-01
[72,] 4.757727e-01 9.515454e-01 5.242273e-01
[73,] 3.246971e-07 6.493942e-07 9.999997e-01
[74,] 1.307163e-07 2.614325e-07 9.999999e-01
[75,] 9.997183e-01 5.634509e-04 2.817254e-04
[76,] 2.243322e-12 4.486645e-12 1.000000e+00
[77,] 2.987845e-01 5.975690e-01 7.012155e-01
[78,] 1.034247e-01 2.068494e-01 8.965753e-01
[79,] 8.045869e-02 1.609174e-01 9.195413e-01
[80,] 1.000000e+00 2.030967e-10 1.015483e-10
[81,] 1.995635e-08 3.991270e-08 1.000000e+00
[82,] 9.994787e-01 1.042566e-03 5.212830e-04
[83,] 1.944798e-02 3.889596e-02 9.805520e-01
[84,] 5.639570e-32 1.127914e-31 1.000000e+00
[85,] 1.000000e+00 7.468610e-10 3.734305e-10
[86,] 1.000000e+00 2.051569e-10 1.025784e-10
[87,] 4.541580e-02 9.083160e-02 9.545842e-01
[88,] 5.139167e-02 1.027833e-01 9.486083e-01
[89,] 9.999999e-01 1.814902e-07 9.074512e-08
[90,] 2.460928e-09 4.921855e-09 1.000000e+00
[91,] 9.557675e-01 8.846496e-02 4.423248e-02
[92,] 8.854300e-38 1.770860e-37 1.000000e+00
[93,] 9.100433e-01 1.799133e-01 8.995666e-02
[94,] 4.022650e-09 8.045300e-09 1.000000e+00
[95,] 9.999500e-01 9.992845e-05 4.996423e-05
[96,] 3.859432e-08 7.718864e-08 1.000000e+00
[97,] 1.103432e-07 2.206864e-07 9.999999e-01
[98,] 8.550715e-01 2.898570e-01 1.449285e-01
[99,] 7.521457e-21 1.504291e-20 1.000000e+00
[100,] 8.819455e-04 1.763891e-03 9.991181e-01
[101,] 1.000000e+00 3.648587e-20 1.824294e-20
[102,] 9.058877e-09 1.811775e-08 1.000000e+00
[103,] 1.000000e+00 5.870094e-11 2.935047e-11
[104,] 9.011552e-15 1.802310e-14 1.000000e+00
[105,] 9.993038e-01 1.392469e-03 6.962344e-04
[106,] 4.887236e-31 9.774473e-31 1.000000e+00
[107,] 1.000000e+00 2.231284e-17 1.115642e-17
[108,] 1.000000e+00 1.972268e-21 9.861342e-22
[109,] 6.847396e-03 1.369479e-02 9.931526e-01
[110,] 6.700777e-01 6.598447e-01 3.299223e-01
[111,] 1.636037e-40 3.272074e-40 1.000000e+00
[112,] 3.997140e-28 7.994279e-28 1.000000e+00
[113,] 2.023686e-28 4.047372e-28 1.000000e+00
[114,] 3.028697e-23 6.057393e-23 1.000000e+00
[115,] 6.125358e-26 1.225072e-25 1.000000e+00
[116,] 2.545615e-08 5.091230e-08 1.000000e+00
[117,] 6.029380e-04 1.205876e-03 9.993971e-01
[118,] 7.511147e-03 1.502229e-02 9.924889e-01
[119,] 1.000000e+00 7.796265e-13 3.898132e-13
[120,] 1.000000e+00 1.801241e-10 9.006204e-11
[121,] 9.282199e-15 1.856440e-14 1.000000e+00
[122,] 1.037685e-07 2.075370e-07 9.999999e-01
[123,] 9.372306e-01 1.255388e-01 6.276938e-02
[124,] 6.387848e-01 7.224305e-01 3.612152e-01
[125,] 2.315699e-01 4.631398e-01 7.684301e-01
[126,] 9.660219e-01 6.795619e-02 3.397809e-02
[127,] 2.799549e-01 5.599098e-01 7.200451e-01
[128,] 1.657603e-42 3.315206e-42 1.000000e+00
[129,] 3.879697e-02 7.759394e-02 9.612030e-01
[130,] 2.025153e-21 4.050305e-21 1.000000e+00
[131,] 1.000000e+00 1.720587e-13 8.602935e-14
[132,] 7.493704e-01 5.012593e-01 2.506296e-01
[133,] 1.000000e+00 1.728113e-14 8.640564e-15
[134,] 2.053944e-04 4.107889e-04 9.997946e-01
[135,] 3.255358e-48 6.510716e-48 1.000000e+00
[136,] 9.858552e-01 2.828959e-02 1.414480e-02
[137,] 2.581429e-11 5.162858e-11 1.000000e+00
[138,] 1.000000e+00 1.490234e-26 7.451171e-27
[139,] 1.000000e+00 4.682260e-17 2.341130e-17
[140,] 9.785261e-01 4.294776e-02 2.147388e-02
[141,] 1.000000e+00 4.017633e-31 2.008817e-31
[142,] 2.131434e-04 4.262868e-04 9.997869e-01
[143,] 5.955052e-16 1.191010e-15 1.000000e+00
[144,] 1.646888e-13 3.293775e-13 1.000000e+00
[145,] 6.700176e-17 1.340035e-16 1.000000e+00
[146,] 3.838430e-13 7.676861e-13 1.000000e+00
[147,] 1.519940e-17 3.039880e-17 1.000000e+00
[148,] 1.280166e-25 2.560332e-25 1.000000e+00
[149,] 5.930881e-01 8.138237e-01 4.069119e-01
[150,] 2.603217e-13 5.206434e-13 1.000000e+00
[151,] 3.230016e-42 6.460032e-42 1.000000e+00
[152,] 1.000000e+00 2.630409e-08 1.315204e-08
[153,] 2.539861e-29 5.079722e-29 1.000000e+00
[154,] 3.479185e-35 6.958369e-35 1.000000e+00
[155,] 1.000000e+00 3.079551e-14 1.539776e-14
[156,] 8.211415e-90 1.642283e-89 1.000000e+00
[157,] 6.251328e-28 1.250266e-27 1.000000e+00
[158,] 7.436285e-16 1.487257e-15 1.000000e+00
[159,] 3.269021e-03 6.538042e-03 9.967310e-01
[160,] 3.596995e-10 7.193989e-10 1.000000e+00
[161,] 1.000000e+00 2.366007e-23 1.183003e-23
[162,] 1.915440e-02 3.830879e-02 9.808456e-01
[163,] 1.000000e+00 1.413038e-20 7.065189e-21
[164,] 5.760885e-24 1.152177e-23 1.000000e+00
[165,] 1.000000e+00 2.047337e-11 1.023669e-11
[166,] 2.356224e-32 4.712448e-32 1.000000e+00
[167,] 8.915170e-19 1.783034e-18 1.000000e+00
[168,] 1.428478e-01 2.856956e-01 8.571522e-01
[169,] 1.638025e-03 3.276049e-03 9.983620e-01
[170,] 5.576891e-65 1.115378e-64 1.000000e+00
[171,] 1.629098e-01 3.258196e-01 8.370902e-01
[172,] 1.217100e-09 2.434200e-09 1.000000e+00
[173,] 3.452175e-07 6.904350e-07 9.999997e-01
[174,] 1.461075e-09 2.922149e-09 1.000000e+00
[175,] 1.256992e-26 2.513983e-26 1.000000e+00
[176,] 9.830682e-02 1.966136e-01 9.016932e-01
[177,] 9.999961e-01 7.817620e-06 3.908810e-06
[178,] 1.000000e+00 3.377016e-20 1.688508e-20
[179,] 8.968381e-01 2.063239e-01 1.031619e-01
[180,] 1.659041e-02 3.318082e-02 9.834096e-01
[181,] 3.196883e-02 6.393767e-02 9.680312e-01
[182,] 9.999537e-01 9.263559e-05 4.631779e-05
[183,] 1.000000e+00 1.209036e-15 6.045181e-16
[184,] 9.997449e-01 5.102403e-04 2.551202e-04
[185,] 1.217885e-03 2.435771e-03 9.987821e-01
[186,] 2.583257e-01 5.166514e-01 7.416743e-01
[187,] 5.667517e-01 8.664966e-01 4.332483e-01
[188,] 7.264526e-01 5.470948e-01 2.735474e-01
[189,] 2.527123e-14 5.054245e-14 1.000000e+00
[190,] 9.489062e-01 1.021876e-01 5.109379e-02
[191,] 1.875736e-07 3.751472e-07 9.999998e-01
[192,] 6.024419e-51 1.204884e-50 1.000000e+00
[193,] 9.999999e-01 2.714981e-07 1.357490e-07
[194,] 1.116770e-13 2.233539e-13 1.000000e+00
[195,] 3.126067e-02 6.252135e-02 9.687393e-01
[196,] 9.999696e-01 6.080528e-05 3.040264e-05
[197,] 9.951930e-01 9.613950e-03 4.806975e-03
[198,] 2.092011e-17 4.184022e-17 1.000000e+00
[199,] 1.028641e-01 2.057283e-01 8.971359e-01
[200,] 1.688828e-14 3.377655e-14 1.000000e+00
[201,] 9.989540e-01 2.092065e-03 1.046032e-03
[202,] 9.873901e-01 2.521987e-02 1.260994e-02
[203,] 4.965420e-03 9.930840e-03 9.950346e-01
[204,] 8.640223e-07 1.728045e-06 9.999991e-01
[205,] 2.322668e-01 4.645337e-01 7.677332e-01
[206,] 4.594724e-28 9.189448e-28 1.000000e+00
[207,] 1.738428e-05 3.476857e-05 9.999826e-01
[208,] 9.999996e-01 7.921613e-07 3.960806e-07
[209,] 2.023497e-75 4.046993e-75 1.000000e+00
[210,] 1.799357e-02 3.598714e-02 9.820064e-01
[211,] 9.594394e-01 8.112113e-02 4.056057e-02
[212,] 1.006705e-02 2.013409e-02 9.899330e-01
[213,] 7.244755e-01 5.510489e-01 2.755245e-01
[214,] 9.999793e-01 4.142890e-05 2.071445e-05
[215,] 3.203920e-07 6.407840e-07 9.999997e-01
[216,] 3.348711e-02 6.697422e-02 9.665129e-01
[217,] 9.323183e-38 1.864637e-37 1.000000e+00
[218,] 1.761602e-36 3.523203e-36 1.000000e+00
[219,] 8.048616e-02 1.609723e-01 9.195138e-01
[220,] 6.968108e-21 1.393622e-20 1.000000e+00
[221,] 7.196959e-02 1.439392e-01 9.280304e-01
[222,] 4.671788e-06 9.343577e-06 9.999953e-01
[223,] 2.904904e-08 5.809809e-08 1.000000e+00
[224,] 1.735040e-03 3.470081e-03 9.982650e-01
[225,] 9.409084e-01 1.181831e-01 5.909155e-02
[226,] 5.090791e-28 1.018158e-27 1.000000e+00
[227,] 9.007922e-03 1.801584e-02 9.909921e-01
> postscript(file="/var/wessaorg/rcomp/tmp/14s1q1353265969.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2hfuh1353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3n4uk1353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4w02x1353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ccmj1353265969.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
-1.451452e-18 9.133823e-17 2.200808e-16 2.911314e-16 -1.837854e-17
6 7 8 9 10
1.475926e-16 -2.148149e-17 7.751997e-17 -2.051824e-15 9.624108e-17
11 12 13 14 15
7.853855e-17 1.075316e-16 -1.292168e-17 2.668347e-17 -6.493607e-18
16 17 18 19 20
1.325331e-17 4.246578e-17 1.406780e-16 8.792539e-18 -2.862648e-17
21 22 23 24 25
5.827482e-17 3.777356e-17 -1.523640e-16 2.402515e-18 3.741489e-17
26 27 28 29 30
-1.757159e-17 3.620994e-17 9.050173e-18 3.300782e-17 1.076788e-16
31 32 33 34 35
6.371981e-17 1.392665e-16 -3.959875e-17 1.577829e-17 3.491084e-17
36 37 38 39 40
-2.288376e-16 1.382989e-16 -1.314150e-18 -4.506700e-17 -3.240487e-17
41 42 43 44 45
3.159383e-17 -1.586445e-17 -1.131155e-16 -8.420504e-17 -3.765558e-17
46 47 48 49 50
1.716585e-16 7.781126e-17 3.425302e-17 -4.674611e-17 1.618711e-16
51 52 53 54 55
8.355259e-17 -4.735620e-18 2.047794e-16 3.405279e-17 -1.480367e-16
56 57 58 59 60
6.114896e-17 3.035835e-17 4.977059e-17 3.121056e-17 1.010100e-17
61 62 63 64 65
1.690644e-16 5.880917e-17 -2.059893e-17 -6.082733e-18 2.873648e-17
66 67 68 69 70
2.867860e-17 7.304358e-17 -1.623728e-16 2.335278e-16 -1.330228e-16
71 72 73 74 75
2.531622e-16 8.529097e-18 2.565353e-17 1.372980e-16 7.429323e-19
76 77 78 79 80
-1.238949e-16 8.478935e-17 4.569058e-17 1.059463e-16 5.215995e-17
81 82 83 84 85
1.613750e-17 5.946423e-17 2.196164e-17 5.548205e-17 8.926585e-18
86 87 88 89 90
1.756308e-17 5.138841e-17 2.037813e-17 -1.036637e-16 -4.217081e-17
91 92 93 94 95
7.271057e-17 3.761357e-17 -3.826149e-17 -1.402797e-17 -3.120354e-17
96 97 98 99 100
-1.280817e-16 5.904762e-17 -1.013066e-16 2.214399e-17 -7.508982e-17
101 102 103 104 105
5.361796e-19 -2.371560e-16 -3.836963e-17 -2.670133e-17 -3.670701e-17
106 107 108 109 110
1.820343e-16 -3.849167e-17 -8.145270e-17 -9.225925e-17 7.881979e-17
111 112 113 114 115
-6.861834e-17 -1.303802e-17 -5.864774e-17 6.042890e-17 -6.051065e-17
116 117 118 119 120
-3.793766e-17 -4.475255e-17 1.119790e-18 -1.838006e-17 -2.736411e-17
121 122 123 124 125
-6.000287e-17 8.729239e-17 -8.924864e-17 2.108273e-17 -6.396581e-17
126 127 128 129 130
1.011632e-17 9.822316e-18 4.969813e-17 -9.904157e-17 -4.681324e-17
131 132 133 134 135
-1.740686e-17 -1.108856e-16 9.157357e-17 -9.253348e-18 -5.154514e-17
136 137 138 139 140
-1.161521e-16 7.289978e-17 -1.124486e-16 6.255603e-17 -1.023481e-16
141 142 143 144 145
1.123310e-16 -7.340128e-17 -2.097031e-16 -1.091152e-16 5.183451e-16
146 147 148 149 150
-1.240251e-16 -3.385564e-17 1.586353e-16 -4.006358e-17 4.351555e-17
151 152 153 154 155
-3.438377e-17 7.681085e-17 1.344085e-16 -1.163269e-16 5.632983e-17
156 157 158 159 160
3.017029e-17 -4.477104e-17 -9.334308e-17 1.843169e-16 -1.060757e-17
161 162 163 164 165
-9.172173e-17 4.561522e-17 -2.587400e-17 -8.100749e-17 -5.583807e-17
166 167 168 169 170
1.585447e-16 -3.744442e-17 3.846217e-17 7.625998e-17 -1.176845e-16
171 172 173 174 175
-4.350008e-17 -1.364033e-17 1.297448e-16 -9.128042e-17 -4.668628e-17
176 177 178 179 180
-9.598007e-17 4.221394e-17 -1.523581e-18 -6.358051e-17 1.553733e-16
181 182 183 184 185
-8.771840e-17 3.084510e-17 4.149598e-16 -2.627659e-17 -1.487930e-16
186 187 188 189 190
8.767847e-18 -9.581150e-17 9.931192e-17 2.088668e-17 -9.518614e-17
191 192 193 194 195
-1.881661e-17 -2.353617e-17 -7.905655e-18 -7.700059e-17 1.983418e-18
196 197 198 199 200
-5.146941e-17 -2.353224e-16 5.844016e-17 -8.167794e-17 6.994253e-17
201 202 203 204 205
1.034772e-16 8.632087e-17 1.328795e-16 4.304986e-17 -1.555723e-16
206 207 208 209 210
2.727605e-17 4.044926e-17 1.079874e-17 1.184729e-16 -1.263964e-16
211 212 213 214 215
3.473244e-17 1.471143e-16 -6.358848e-17 1.234088e-16 -2.142736e-16
216 217 218 219 220
1.953464e-16 1.496324e-16 -1.310309e-17 -1.084206e-17 4.101520e-17
221 222 223 224 225
-2.658672e-17 -1.360241e-17 2.168632e-17 1.392044e-17 6.606831e-17
226 227 228 229 230
-1.780189e-16 3.816562e-17 2.734111e-18 -6.870039e-17 -9.516623e-17
231 232 233 234 235
-5.820784e-17 -1.968109e-16 -2.361045e-16 9.737317e-18 3.055773e-17
236 237 238 239 240
3.848272e-17 -2.184834e-16 -8.324494e-17 8.303884e-17 1.089404e-16
241 242 243 244 245
-3.593287e-17 4.041956e-17 2.224824e-17 -1.210895e-17 6.114846e-17
246 247 248 249 250
5.696316e-17 1.091730e-16 1.024556e-16 -7.640513e-17 6.260151e-17
251 252 253 254 255
-5.146820e-17 5.777125e-17 9.780750e-18 -7.577017e-17 3.719429e-17
256 257 258 259 260
-1.094809e-17 2.186704e-16 -1.181340e-16 7.480038e-18 -3.525372e-17
261 262 263 264
1.097143e-16 -4.238991e-17 2.628972e-17 -9.990015e-17
> postscript(file="/var/wessaorg/rcomp/tmp/6vcx01353265969.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 -1.451452e-18 NA
1 9.133823e-17 -1.451452e-18
2 2.200808e-16 9.133823e-17
3 2.911314e-16 2.200808e-16
4 -1.837854e-17 2.911314e-16
5 1.475926e-16 -1.837854e-17
6 -2.148149e-17 1.475926e-16
7 7.751997e-17 -2.148149e-17
8 -2.051824e-15 7.751997e-17
9 9.624108e-17 -2.051824e-15
10 7.853855e-17 9.624108e-17
11 1.075316e-16 7.853855e-17
12 -1.292168e-17 1.075316e-16
13 2.668347e-17 -1.292168e-17
14 -6.493607e-18 2.668347e-17
15 1.325331e-17 -6.493607e-18
16 4.246578e-17 1.325331e-17
17 1.406780e-16 4.246578e-17
18 8.792539e-18 1.406780e-16
19 -2.862648e-17 8.792539e-18
20 5.827482e-17 -2.862648e-17
21 3.777356e-17 5.827482e-17
22 -1.523640e-16 3.777356e-17
23 2.402515e-18 -1.523640e-16
24 3.741489e-17 2.402515e-18
25 -1.757159e-17 3.741489e-17
26 3.620994e-17 -1.757159e-17
27 9.050173e-18 3.620994e-17
28 3.300782e-17 9.050173e-18
29 1.076788e-16 3.300782e-17
30 6.371981e-17 1.076788e-16
31 1.392665e-16 6.371981e-17
32 -3.959875e-17 1.392665e-16
33 1.577829e-17 -3.959875e-17
34 3.491084e-17 1.577829e-17
35 -2.288376e-16 3.491084e-17
36 1.382989e-16 -2.288376e-16
37 -1.314150e-18 1.382989e-16
38 -4.506700e-17 -1.314150e-18
39 -3.240487e-17 -4.506700e-17
40 3.159383e-17 -3.240487e-17
41 -1.586445e-17 3.159383e-17
42 -1.131155e-16 -1.586445e-17
43 -8.420504e-17 -1.131155e-16
44 -3.765558e-17 -8.420504e-17
45 1.716585e-16 -3.765558e-17
46 7.781126e-17 1.716585e-16
47 3.425302e-17 7.781126e-17
48 -4.674611e-17 3.425302e-17
49 1.618711e-16 -4.674611e-17
50 8.355259e-17 1.618711e-16
51 -4.735620e-18 8.355259e-17
52 2.047794e-16 -4.735620e-18
53 3.405279e-17 2.047794e-16
54 -1.480367e-16 3.405279e-17
55 6.114896e-17 -1.480367e-16
56 3.035835e-17 6.114896e-17
57 4.977059e-17 3.035835e-17
58 3.121056e-17 4.977059e-17
59 1.010100e-17 3.121056e-17
60 1.690644e-16 1.010100e-17
61 5.880917e-17 1.690644e-16
62 -2.059893e-17 5.880917e-17
63 -6.082733e-18 -2.059893e-17
64 2.873648e-17 -6.082733e-18
65 2.867860e-17 2.873648e-17
66 7.304358e-17 2.867860e-17
67 -1.623728e-16 7.304358e-17
68 2.335278e-16 -1.623728e-16
69 -1.330228e-16 2.335278e-16
70 2.531622e-16 -1.330228e-16
71 8.529097e-18 2.531622e-16
72 2.565353e-17 8.529097e-18
73 1.372980e-16 2.565353e-17
74 7.429323e-19 1.372980e-16
75 -1.238949e-16 7.429323e-19
76 8.478935e-17 -1.238949e-16
77 4.569058e-17 8.478935e-17
78 1.059463e-16 4.569058e-17
79 5.215995e-17 1.059463e-16
80 1.613750e-17 5.215995e-17
81 5.946423e-17 1.613750e-17
82 2.196164e-17 5.946423e-17
83 5.548205e-17 2.196164e-17
84 8.926585e-18 5.548205e-17
85 1.756308e-17 8.926585e-18
86 5.138841e-17 1.756308e-17
87 2.037813e-17 5.138841e-17
88 -1.036637e-16 2.037813e-17
89 -4.217081e-17 -1.036637e-16
90 7.271057e-17 -4.217081e-17
91 3.761357e-17 7.271057e-17
92 -3.826149e-17 3.761357e-17
93 -1.402797e-17 -3.826149e-17
94 -3.120354e-17 -1.402797e-17
95 -1.280817e-16 -3.120354e-17
96 5.904762e-17 -1.280817e-16
97 -1.013066e-16 5.904762e-17
98 2.214399e-17 -1.013066e-16
99 -7.508982e-17 2.214399e-17
100 5.361796e-19 -7.508982e-17
101 -2.371560e-16 5.361796e-19
102 -3.836963e-17 -2.371560e-16
103 -2.670133e-17 -3.836963e-17
104 -3.670701e-17 -2.670133e-17
105 1.820343e-16 -3.670701e-17
106 -3.849167e-17 1.820343e-16
107 -8.145270e-17 -3.849167e-17
108 -9.225925e-17 -8.145270e-17
109 7.881979e-17 -9.225925e-17
110 -6.861834e-17 7.881979e-17
111 -1.303802e-17 -6.861834e-17
112 -5.864774e-17 -1.303802e-17
113 6.042890e-17 -5.864774e-17
114 -6.051065e-17 6.042890e-17
115 -3.793766e-17 -6.051065e-17
116 -4.475255e-17 -3.793766e-17
117 1.119790e-18 -4.475255e-17
118 -1.838006e-17 1.119790e-18
119 -2.736411e-17 -1.838006e-17
120 -6.000287e-17 -2.736411e-17
121 8.729239e-17 -6.000287e-17
122 -8.924864e-17 8.729239e-17
123 2.108273e-17 -8.924864e-17
124 -6.396581e-17 2.108273e-17
125 1.011632e-17 -6.396581e-17
126 9.822316e-18 1.011632e-17
127 4.969813e-17 9.822316e-18
128 -9.904157e-17 4.969813e-17
129 -4.681324e-17 -9.904157e-17
130 -1.740686e-17 -4.681324e-17
131 -1.108856e-16 -1.740686e-17
132 9.157357e-17 -1.108856e-16
133 -9.253348e-18 9.157357e-17
134 -5.154514e-17 -9.253348e-18
135 -1.161521e-16 -5.154514e-17
136 7.289978e-17 -1.161521e-16
137 -1.124486e-16 7.289978e-17
138 6.255603e-17 -1.124486e-16
139 -1.023481e-16 6.255603e-17
140 1.123310e-16 -1.023481e-16
141 -7.340128e-17 1.123310e-16
142 -2.097031e-16 -7.340128e-17
143 -1.091152e-16 -2.097031e-16
144 5.183451e-16 -1.091152e-16
145 -1.240251e-16 5.183451e-16
146 -3.385564e-17 -1.240251e-16
147 1.586353e-16 -3.385564e-17
148 -4.006358e-17 1.586353e-16
149 4.351555e-17 -4.006358e-17
150 -3.438377e-17 4.351555e-17
151 7.681085e-17 -3.438377e-17
152 1.344085e-16 7.681085e-17
153 -1.163269e-16 1.344085e-16
154 5.632983e-17 -1.163269e-16
155 3.017029e-17 5.632983e-17
156 -4.477104e-17 3.017029e-17
157 -9.334308e-17 -4.477104e-17
158 1.843169e-16 -9.334308e-17
159 -1.060757e-17 1.843169e-16
160 -9.172173e-17 -1.060757e-17
161 4.561522e-17 -9.172173e-17
162 -2.587400e-17 4.561522e-17
163 -8.100749e-17 -2.587400e-17
164 -5.583807e-17 -8.100749e-17
165 1.585447e-16 -5.583807e-17
166 -3.744442e-17 1.585447e-16
167 3.846217e-17 -3.744442e-17
168 7.625998e-17 3.846217e-17
169 -1.176845e-16 7.625998e-17
170 -4.350008e-17 -1.176845e-16
171 -1.364033e-17 -4.350008e-17
172 1.297448e-16 -1.364033e-17
173 -9.128042e-17 1.297448e-16
174 -4.668628e-17 -9.128042e-17
175 -9.598007e-17 -4.668628e-17
176 4.221394e-17 -9.598007e-17
177 -1.523581e-18 4.221394e-17
178 -6.358051e-17 -1.523581e-18
179 1.553733e-16 -6.358051e-17
180 -8.771840e-17 1.553733e-16
181 3.084510e-17 -8.771840e-17
182 4.149598e-16 3.084510e-17
183 -2.627659e-17 4.149598e-16
184 -1.487930e-16 -2.627659e-17
185 8.767847e-18 -1.487930e-16
186 -9.581150e-17 8.767847e-18
187 9.931192e-17 -9.581150e-17
188 2.088668e-17 9.931192e-17
189 -9.518614e-17 2.088668e-17
190 -1.881661e-17 -9.518614e-17
191 -2.353617e-17 -1.881661e-17
192 -7.905655e-18 -2.353617e-17
193 -7.700059e-17 -7.905655e-18
194 1.983418e-18 -7.700059e-17
195 -5.146941e-17 1.983418e-18
196 -2.353224e-16 -5.146941e-17
197 5.844016e-17 -2.353224e-16
198 -8.167794e-17 5.844016e-17
199 6.994253e-17 -8.167794e-17
200 1.034772e-16 6.994253e-17
201 8.632087e-17 1.034772e-16
202 1.328795e-16 8.632087e-17
203 4.304986e-17 1.328795e-16
204 -1.555723e-16 4.304986e-17
205 2.727605e-17 -1.555723e-16
206 4.044926e-17 2.727605e-17
207 1.079874e-17 4.044926e-17
208 1.184729e-16 1.079874e-17
209 -1.263964e-16 1.184729e-16
210 3.473244e-17 -1.263964e-16
211 1.471143e-16 3.473244e-17
212 -6.358848e-17 1.471143e-16
213 1.234088e-16 -6.358848e-17
214 -2.142736e-16 1.234088e-16
215 1.953464e-16 -2.142736e-16
216 1.496324e-16 1.953464e-16
217 -1.310309e-17 1.496324e-16
218 -1.084206e-17 -1.310309e-17
219 4.101520e-17 -1.084206e-17
220 -2.658672e-17 4.101520e-17
221 -1.360241e-17 -2.658672e-17
222 2.168632e-17 -1.360241e-17
223 1.392044e-17 2.168632e-17
224 6.606831e-17 1.392044e-17
225 -1.780189e-16 6.606831e-17
226 3.816562e-17 -1.780189e-16
227 2.734111e-18 3.816562e-17
228 -6.870039e-17 2.734111e-18
229 -9.516623e-17 -6.870039e-17
230 -5.820784e-17 -9.516623e-17
231 -1.968109e-16 -5.820784e-17
232 -2.361045e-16 -1.968109e-16
233 9.737317e-18 -2.361045e-16
234 3.055773e-17 9.737317e-18
235 3.848272e-17 3.055773e-17
236 -2.184834e-16 3.848272e-17
237 -8.324494e-17 -2.184834e-16
238 8.303884e-17 -8.324494e-17
239 1.089404e-16 8.303884e-17
240 -3.593287e-17 1.089404e-16
241 4.041956e-17 -3.593287e-17
242 2.224824e-17 4.041956e-17
243 -1.210895e-17 2.224824e-17
244 6.114846e-17 -1.210895e-17
245 5.696316e-17 6.114846e-17
246 1.091730e-16 5.696316e-17
247 1.024556e-16 1.091730e-16
248 -7.640513e-17 1.024556e-16
249 6.260151e-17 -7.640513e-17
250 -5.146820e-17 6.260151e-17
251 5.777125e-17 -5.146820e-17
252 9.780750e-18 5.777125e-17
253 -7.577017e-17 9.780750e-18
254 3.719429e-17 -7.577017e-17
255 -1.094809e-17 3.719429e-17
256 2.186704e-16 -1.094809e-17
257 -1.181340e-16 2.186704e-16
258 7.480038e-18 -1.181340e-16
259 -3.525372e-17 7.480038e-18
260 1.097143e-16 -3.525372e-17
261 -4.238991e-17 1.097143e-16
262 2.628972e-17 -4.238991e-17
263 -9.990015e-17 2.628972e-17
264 NA -9.990015e-17
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 9.133823e-17 -1.451452e-18
[2,] 2.200808e-16 9.133823e-17
[3,] 2.911314e-16 2.200808e-16
[4,] -1.837854e-17 2.911314e-16
[5,] 1.475926e-16 -1.837854e-17
[6,] -2.148149e-17 1.475926e-16
[7,] 7.751997e-17 -2.148149e-17
[8,] -2.051824e-15 7.751997e-17
[9,] 9.624108e-17 -2.051824e-15
[10,] 7.853855e-17 9.624108e-17
[11,] 1.075316e-16 7.853855e-17
[12,] -1.292168e-17 1.075316e-16
[13,] 2.668347e-17 -1.292168e-17
[14,] -6.493607e-18 2.668347e-17
[15,] 1.325331e-17 -6.493607e-18
[16,] 4.246578e-17 1.325331e-17
[17,] 1.406780e-16 4.246578e-17
[18,] 8.792539e-18 1.406780e-16
[19,] -2.862648e-17 8.792539e-18
[20,] 5.827482e-17 -2.862648e-17
[21,] 3.777356e-17 5.827482e-17
[22,] -1.523640e-16 3.777356e-17
[23,] 2.402515e-18 -1.523640e-16
[24,] 3.741489e-17 2.402515e-18
[25,] -1.757159e-17 3.741489e-17
[26,] 3.620994e-17 -1.757159e-17
[27,] 9.050173e-18 3.620994e-17
[28,] 3.300782e-17 9.050173e-18
[29,] 1.076788e-16 3.300782e-17
[30,] 6.371981e-17 1.076788e-16
[31,] 1.392665e-16 6.371981e-17
[32,] -3.959875e-17 1.392665e-16
[33,] 1.577829e-17 -3.959875e-17
[34,] 3.491084e-17 1.577829e-17
[35,] -2.288376e-16 3.491084e-17
[36,] 1.382989e-16 -2.288376e-16
[37,] -1.314150e-18 1.382989e-16
[38,] -4.506700e-17 -1.314150e-18
[39,] -3.240487e-17 -4.506700e-17
[40,] 3.159383e-17 -3.240487e-17
[41,] -1.586445e-17 3.159383e-17
[42,] -1.131155e-16 -1.586445e-17
[43,] -8.420504e-17 -1.131155e-16
[44,] -3.765558e-17 -8.420504e-17
[45,] 1.716585e-16 -3.765558e-17
[46,] 7.781126e-17 1.716585e-16
[47,] 3.425302e-17 7.781126e-17
[48,] -4.674611e-17 3.425302e-17
[49,] 1.618711e-16 -4.674611e-17
[50,] 8.355259e-17 1.618711e-16
[51,] -4.735620e-18 8.355259e-17
[52,] 2.047794e-16 -4.735620e-18
[53,] 3.405279e-17 2.047794e-16
[54,] -1.480367e-16 3.405279e-17
[55,] 6.114896e-17 -1.480367e-16
[56,] 3.035835e-17 6.114896e-17
[57,] 4.977059e-17 3.035835e-17
[58,] 3.121056e-17 4.977059e-17
[59,] 1.010100e-17 3.121056e-17
[60,] 1.690644e-16 1.010100e-17
[61,] 5.880917e-17 1.690644e-16
[62,] -2.059893e-17 5.880917e-17
[63,] -6.082733e-18 -2.059893e-17
[64,] 2.873648e-17 -6.082733e-18
[65,] 2.867860e-17 2.873648e-17
[66,] 7.304358e-17 2.867860e-17
[67,] -1.623728e-16 7.304358e-17
[68,] 2.335278e-16 -1.623728e-16
[69,] -1.330228e-16 2.335278e-16
[70,] 2.531622e-16 -1.330228e-16
[71,] 8.529097e-18 2.531622e-16
[72,] 2.565353e-17 8.529097e-18
[73,] 1.372980e-16 2.565353e-17
[74,] 7.429323e-19 1.372980e-16
[75,] -1.238949e-16 7.429323e-19
[76,] 8.478935e-17 -1.238949e-16
[77,] 4.569058e-17 8.478935e-17
[78,] 1.059463e-16 4.569058e-17
[79,] 5.215995e-17 1.059463e-16
[80,] 1.613750e-17 5.215995e-17
[81,] 5.946423e-17 1.613750e-17
[82,] 2.196164e-17 5.946423e-17
[83,] 5.548205e-17 2.196164e-17
[84,] 8.926585e-18 5.548205e-17
[85,] 1.756308e-17 8.926585e-18
[86,] 5.138841e-17 1.756308e-17
[87,] 2.037813e-17 5.138841e-17
[88,] -1.036637e-16 2.037813e-17
[89,] -4.217081e-17 -1.036637e-16
[90,] 7.271057e-17 -4.217081e-17
[91,] 3.761357e-17 7.271057e-17
[92,] -3.826149e-17 3.761357e-17
[93,] -1.402797e-17 -3.826149e-17
[94,] -3.120354e-17 -1.402797e-17
[95,] -1.280817e-16 -3.120354e-17
[96,] 5.904762e-17 -1.280817e-16
[97,] -1.013066e-16 5.904762e-17
[98,] 2.214399e-17 -1.013066e-16
[99,] -7.508982e-17 2.214399e-17
[100,] 5.361796e-19 -7.508982e-17
[101,] -2.371560e-16 5.361796e-19
[102,] -3.836963e-17 -2.371560e-16
[103,] -2.670133e-17 -3.836963e-17
[104,] -3.670701e-17 -2.670133e-17
[105,] 1.820343e-16 -3.670701e-17
[106,] -3.849167e-17 1.820343e-16
[107,] -8.145270e-17 -3.849167e-17
[108,] -9.225925e-17 -8.145270e-17
[109,] 7.881979e-17 -9.225925e-17
[110,] -6.861834e-17 7.881979e-17
[111,] -1.303802e-17 -6.861834e-17
[112,] -5.864774e-17 -1.303802e-17
[113,] 6.042890e-17 -5.864774e-17
[114,] -6.051065e-17 6.042890e-17
[115,] -3.793766e-17 -6.051065e-17
[116,] -4.475255e-17 -3.793766e-17
[117,] 1.119790e-18 -4.475255e-17
[118,] -1.838006e-17 1.119790e-18
[119,] -2.736411e-17 -1.838006e-17
[120,] -6.000287e-17 -2.736411e-17
[121,] 8.729239e-17 -6.000287e-17
[122,] -8.924864e-17 8.729239e-17
[123,] 2.108273e-17 -8.924864e-17
[124,] -6.396581e-17 2.108273e-17
[125,] 1.011632e-17 -6.396581e-17
[126,] 9.822316e-18 1.011632e-17
[127,] 4.969813e-17 9.822316e-18
[128,] -9.904157e-17 4.969813e-17
[129,] -4.681324e-17 -9.904157e-17
[130,] -1.740686e-17 -4.681324e-17
[131,] -1.108856e-16 -1.740686e-17
[132,] 9.157357e-17 -1.108856e-16
[133,] -9.253348e-18 9.157357e-17
[134,] -5.154514e-17 -9.253348e-18
[135,] -1.161521e-16 -5.154514e-17
[136,] 7.289978e-17 -1.161521e-16
[137,] -1.124486e-16 7.289978e-17
[138,] 6.255603e-17 -1.124486e-16
[139,] -1.023481e-16 6.255603e-17
[140,] 1.123310e-16 -1.023481e-16
[141,] -7.340128e-17 1.123310e-16
[142,] -2.097031e-16 -7.340128e-17
[143,] -1.091152e-16 -2.097031e-16
[144,] 5.183451e-16 -1.091152e-16
[145,] -1.240251e-16 5.183451e-16
[146,] -3.385564e-17 -1.240251e-16
[147,] 1.586353e-16 -3.385564e-17
[148,] -4.006358e-17 1.586353e-16
[149,] 4.351555e-17 -4.006358e-17
[150,] -3.438377e-17 4.351555e-17
[151,] 7.681085e-17 -3.438377e-17
[152,] 1.344085e-16 7.681085e-17
[153,] -1.163269e-16 1.344085e-16
[154,] 5.632983e-17 -1.163269e-16
[155,] 3.017029e-17 5.632983e-17
[156,] -4.477104e-17 3.017029e-17
[157,] -9.334308e-17 -4.477104e-17
[158,] 1.843169e-16 -9.334308e-17
[159,] -1.060757e-17 1.843169e-16
[160,] -9.172173e-17 -1.060757e-17
[161,] 4.561522e-17 -9.172173e-17
[162,] -2.587400e-17 4.561522e-17
[163,] -8.100749e-17 -2.587400e-17
[164,] -5.583807e-17 -8.100749e-17
[165,] 1.585447e-16 -5.583807e-17
[166,] -3.744442e-17 1.585447e-16
[167,] 3.846217e-17 -3.744442e-17
[168,] 7.625998e-17 3.846217e-17
[169,] -1.176845e-16 7.625998e-17
[170,] -4.350008e-17 -1.176845e-16
[171,] -1.364033e-17 -4.350008e-17
[172,] 1.297448e-16 -1.364033e-17
[173,] -9.128042e-17 1.297448e-16
[174,] -4.668628e-17 -9.128042e-17
[175,] -9.598007e-17 -4.668628e-17
[176,] 4.221394e-17 -9.598007e-17
[177,] -1.523581e-18 4.221394e-17
[178,] -6.358051e-17 -1.523581e-18
[179,] 1.553733e-16 -6.358051e-17
[180,] -8.771840e-17 1.553733e-16
[181,] 3.084510e-17 -8.771840e-17
[182,] 4.149598e-16 3.084510e-17
[183,] -2.627659e-17 4.149598e-16
[184,] -1.487930e-16 -2.627659e-17
[185,] 8.767847e-18 -1.487930e-16
[186,] -9.581150e-17 8.767847e-18
[187,] 9.931192e-17 -9.581150e-17
[188,] 2.088668e-17 9.931192e-17
[189,] -9.518614e-17 2.088668e-17
[190,] -1.881661e-17 -9.518614e-17
[191,] -2.353617e-17 -1.881661e-17
[192,] -7.905655e-18 -2.353617e-17
[193,] -7.700059e-17 -7.905655e-18
[194,] 1.983418e-18 -7.700059e-17
[195,] -5.146941e-17 1.983418e-18
[196,] -2.353224e-16 -5.146941e-17
[197,] 5.844016e-17 -2.353224e-16
[198,] -8.167794e-17 5.844016e-17
[199,] 6.994253e-17 -8.167794e-17
[200,] 1.034772e-16 6.994253e-17
[201,] 8.632087e-17 1.034772e-16
[202,] 1.328795e-16 8.632087e-17
[203,] 4.304986e-17 1.328795e-16
[204,] -1.555723e-16 4.304986e-17
[205,] 2.727605e-17 -1.555723e-16
[206,] 4.044926e-17 2.727605e-17
[207,] 1.079874e-17 4.044926e-17
[208,] 1.184729e-16 1.079874e-17
[209,] -1.263964e-16 1.184729e-16
[210,] 3.473244e-17 -1.263964e-16
[211,] 1.471143e-16 3.473244e-17
[212,] -6.358848e-17 1.471143e-16
[213,] 1.234088e-16 -6.358848e-17
[214,] -2.142736e-16 1.234088e-16
[215,] 1.953464e-16 -2.142736e-16
[216,] 1.496324e-16 1.953464e-16
[217,] -1.310309e-17 1.496324e-16
[218,] -1.084206e-17 -1.310309e-17
[219,] 4.101520e-17 -1.084206e-17
[220,] -2.658672e-17 4.101520e-17
[221,] -1.360241e-17 -2.658672e-17
[222,] 2.168632e-17 -1.360241e-17
[223,] 1.392044e-17 2.168632e-17
[224,] 6.606831e-17 1.392044e-17
[225,] -1.780189e-16 6.606831e-17
[226,] 3.816562e-17 -1.780189e-16
[227,] 2.734111e-18 3.816562e-17
[228,] -6.870039e-17 2.734111e-18
[229,] -9.516623e-17 -6.870039e-17
[230,] -5.820784e-17 -9.516623e-17
[231,] -1.968109e-16 -5.820784e-17
[232,] -2.361045e-16 -1.968109e-16
[233,] 9.737317e-18 -2.361045e-16
[234,] 3.055773e-17 9.737317e-18
[235,] 3.848272e-17 3.055773e-17
[236,] -2.184834e-16 3.848272e-17
[237,] -8.324494e-17 -2.184834e-16
[238,] 8.303884e-17 -8.324494e-17
[239,] 1.089404e-16 8.303884e-17
[240,] -3.593287e-17 1.089404e-16
[241,] 4.041956e-17 -3.593287e-17
[242,] 2.224824e-17 4.041956e-17
[243,] -1.210895e-17 2.224824e-17
[244,] 6.114846e-17 -1.210895e-17
[245,] 5.696316e-17 6.114846e-17
[246,] 1.091730e-16 5.696316e-17
[247,] 1.024556e-16 1.091730e-16
[248,] -7.640513e-17 1.024556e-16
[249,] 6.260151e-17 -7.640513e-17
[250,] -5.146820e-17 6.260151e-17
[251,] 5.777125e-17 -5.146820e-17
[252,] 9.780750e-18 5.777125e-17
[253,] -7.577017e-17 9.780750e-18
[254,] 3.719429e-17 -7.577017e-17
[255,] -1.094809e-17 3.719429e-17
[256,] 2.186704e-16 -1.094809e-17
[257,] -1.181340e-16 2.186704e-16
[258,] 7.480038e-18 -1.181340e-16
[259,] -3.525372e-17 7.480038e-18
[260,] 1.097143e-16 -3.525372e-17
[261,] -4.238991e-17 1.097143e-16
[262,] 2.628972e-17 -4.238991e-17
[263,] -9.990015e-17 2.628972e-17
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 9.133823e-17 -1.451452e-18
2 2.200808e-16 9.133823e-17
3 2.911314e-16 2.200808e-16
4 -1.837854e-17 2.911314e-16
5 1.475926e-16 -1.837854e-17
6 -2.148149e-17 1.475926e-16
7 7.751997e-17 -2.148149e-17
8 -2.051824e-15 7.751997e-17
9 9.624108e-17 -2.051824e-15
10 7.853855e-17 9.624108e-17
11 1.075316e-16 7.853855e-17
12 -1.292168e-17 1.075316e-16
13 2.668347e-17 -1.292168e-17
14 -6.493607e-18 2.668347e-17
15 1.325331e-17 -6.493607e-18
16 4.246578e-17 1.325331e-17
17 1.406780e-16 4.246578e-17
18 8.792539e-18 1.406780e-16
19 -2.862648e-17 8.792539e-18
20 5.827482e-17 -2.862648e-17
21 3.777356e-17 5.827482e-17
22 -1.523640e-16 3.777356e-17
23 2.402515e-18 -1.523640e-16
24 3.741489e-17 2.402515e-18
25 -1.757159e-17 3.741489e-17
26 3.620994e-17 -1.757159e-17
27 9.050173e-18 3.620994e-17
28 3.300782e-17 9.050173e-18
29 1.076788e-16 3.300782e-17
30 6.371981e-17 1.076788e-16
31 1.392665e-16 6.371981e-17
32 -3.959875e-17 1.392665e-16
33 1.577829e-17 -3.959875e-17
34 3.491084e-17 1.577829e-17
35 -2.288376e-16 3.491084e-17
36 1.382989e-16 -2.288376e-16
37 -1.314150e-18 1.382989e-16
38 -4.506700e-17 -1.314150e-18
39 -3.240487e-17 -4.506700e-17
40 3.159383e-17 -3.240487e-17
41 -1.586445e-17 3.159383e-17
42 -1.131155e-16 -1.586445e-17
43 -8.420504e-17 -1.131155e-16
44 -3.765558e-17 -8.420504e-17
45 1.716585e-16 -3.765558e-17
46 7.781126e-17 1.716585e-16
47 3.425302e-17 7.781126e-17
48 -4.674611e-17 3.425302e-17
49 1.618711e-16 -4.674611e-17
50 8.355259e-17 1.618711e-16
51 -4.735620e-18 8.355259e-17
52 2.047794e-16 -4.735620e-18
53 3.405279e-17 2.047794e-16
54 -1.480367e-16 3.405279e-17
55 6.114896e-17 -1.480367e-16
56 3.035835e-17 6.114896e-17
57 4.977059e-17 3.035835e-17
58 3.121056e-17 4.977059e-17
59 1.010100e-17 3.121056e-17
60 1.690644e-16 1.010100e-17
61 5.880917e-17 1.690644e-16
62 -2.059893e-17 5.880917e-17
63 -6.082733e-18 -2.059893e-17
64 2.873648e-17 -6.082733e-18
65 2.867860e-17 2.873648e-17
66 7.304358e-17 2.867860e-17
67 -1.623728e-16 7.304358e-17
68 2.335278e-16 -1.623728e-16
69 -1.330228e-16 2.335278e-16
70 2.531622e-16 -1.330228e-16
71 8.529097e-18 2.531622e-16
72 2.565353e-17 8.529097e-18
73 1.372980e-16 2.565353e-17
74 7.429323e-19 1.372980e-16
75 -1.238949e-16 7.429323e-19
76 8.478935e-17 -1.238949e-16
77 4.569058e-17 8.478935e-17
78 1.059463e-16 4.569058e-17
79 5.215995e-17 1.059463e-16
80 1.613750e-17 5.215995e-17
81 5.946423e-17 1.613750e-17
82 2.196164e-17 5.946423e-17
83 5.548205e-17 2.196164e-17
84 8.926585e-18 5.548205e-17
85 1.756308e-17 8.926585e-18
86 5.138841e-17 1.756308e-17
87 2.037813e-17 5.138841e-17
88 -1.036637e-16 2.037813e-17
89 -4.217081e-17 -1.036637e-16
90 7.271057e-17 -4.217081e-17
91 3.761357e-17 7.271057e-17
92 -3.826149e-17 3.761357e-17
93 -1.402797e-17 -3.826149e-17
94 -3.120354e-17 -1.402797e-17
95 -1.280817e-16 -3.120354e-17
96 5.904762e-17 -1.280817e-16
97 -1.013066e-16 5.904762e-17
98 2.214399e-17 -1.013066e-16
99 -7.508982e-17 2.214399e-17
100 5.361796e-19 -7.508982e-17
101 -2.371560e-16 5.361796e-19
102 -3.836963e-17 -2.371560e-16
103 -2.670133e-17 -3.836963e-17
104 -3.670701e-17 -2.670133e-17
105 1.820343e-16 -3.670701e-17
106 -3.849167e-17 1.820343e-16
107 -8.145270e-17 -3.849167e-17
108 -9.225925e-17 -8.145270e-17
109 7.881979e-17 -9.225925e-17
110 -6.861834e-17 7.881979e-17
111 -1.303802e-17 -6.861834e-17
112 -5.864774e-17 -1.303802e-17
113 6.042890e-17 -5.864774e-17
114 -6.051065e-17 6.042890e-17
115 -3.793766e-17 -6.051065e-17
116 -4.475255e-17 -3.793766e-17
117 1.119790e-18 -4.475255e-17
118 -1.838006e-17 1.119790e-18
119 -2.736411e-17 -1.838006e-17
120 -6.000287e-17 -2.736411e-17
121 8.729239e-17 -6.000287e-17
122 -8.924864e-17 8.729239e-17
123 2.108273e-17 -8.924864e-17
124 -6.396581e-17 2.108273e-17
125 1.011632e-17 -6.396581e-17
126 9.822316e-18 1.011632e-17
127 4.969813e-17 9.822316e-18
128 -9.904157e-17 4.969813e-17
129 -4.681324e-17 -9.904157e-17
130 -1.740686e-17 -4.681324e-17
131 -1.108856e-16 -1.740686e-17
132 9.157357e-17 -1.108856e-16
133 -9.253348e-18 9.157357e-17
134 -5.154514e-17 -9.253348e-18
135 -1.161521e-16 -5.154514e-17
136 7.289978e-17 -1.161521e-16
137 -1.124486e-16 7.289978e-17
138 6.255603e-17 -1.124486e-16
139 -1.023481e-16 6.255603e-17
140 1.123310e-16 -1.023481e-16
141 -7.340128e-17 1.123310e-16
142 -2.097031e-16 -7.340128e-17
143 -1.091152e-16 -2.097031e-16
144 5.183451e-16 -1.091152e-16
145 -1.240251e-16 5.183451e-16
146 -3.385564e-17 -1.240251e-16
147 1.586353e-16 -3.385564e-17
148 -4.006358e-17 1.586353e-16
149 4.351555e-17 -4.006358e-17
150 -3.438377e-17 4.351555e-17
151 7.681085e-17 -3.438377e-17
152 1.344085e-16 7.681085e-17
153 -1.163269e-16 1.344085e-16
154 5.632983e-17 -1.163269e-16
155 3.017029e-17 5.632983e-17
156 -4.477104e-17 3.017029e-17
157 -9.334308e-17 -4.477104e-17
158 1.843169e-16 -9.334308e-17
159 -1.060757e-17 1.843169e-16
160 -9.172173e-17 -1.060757e-17
161 4.561522e-17 -9.172173e-17
162 -2.587400e-17 4.561522e-17
163 -8.100749e-17 -2.587400e-17
164 -5.583807e-17 -8.100749e-17
165 1.585447e-16 -5.583807e-17
166 -3.744442e-17 1.585447e-16
167 3.846217e-17 -3.744442e-17
168 7.625998e-17 3.846217e-17
169 -1.176845e-16 7.625998e-17
170 -4.350008e-17 -1.176845e-16
171 -1.364033e-17 -4.350008e-17
172 1.297448e-16 -1.364033e-17
173 -9.128042e-17 1.297448e-16
174 -4.668628e-17 -9.128042e-17
175 -9.598007e-17 -4.668628e-17
176 4.221394e-17 -9.598007e-17
177 -1.523581e-18 4.221394e-17
178 -6.358051e-17 -1.523581e-18
179 1.553733e-16 -6.358051e-17
180 -8.771840e-17 1.553733e-16
181 3.084510e-17 -8.771840e-17
182 4.149598e-16 3.084510e-17
183 -2.627659e-17 4.149598e-16
184 -1.487930e-16 -2.627659e-17
185 8.767847e-18 -1.487930e-16
186 -9.581150e-17 8.767847e-18
187 9.931192e-17 -9.581150e-17
188 2.088668e-17 9.931192e-17
189 -9.518614e-17 2.088668e-17
190 -1.881661e-17 -9.518614e-17
191 -2.353617e-17 -1.881661e-17
192 -7.905655e-18 -2.353617e-17
193 -7.700059e-17 -7.905655e-18
194 1.983418e-18 -7.700059e-17
195 -5.146941e-17 1.983418e-18
196 -2.353224e-16 -5.146941e-17
197 5.844016e-17 -2.353224e-16
198 -8.167794e-17 5.844016e-17
199 6.994253e-17 -8.167794e-17
200 1.034772e-16 6.994253e-17
201 8.632087e-17 1.034772e-16
202 1.328795e-16 8.632087e-17
203 4.304986e-17 1.328795e-16
204 -1.555723e-16 4.304986e-17
205 2.727605e-17 -1.555723e-16
206 4.044926e-17 2.727605e-17
207 1.079874e-17 4.044926e-17
208 1.184729e-16 1.079874e-17
209 -1.263964e-16 1.184729e-16
210 3.473244e-17 -1.263964e-16
211 1.471143e-16 3.473244e-17
212 -6.358848e-17 1.471143e-16
213 1.234088e-16 -6.358848e-17
214 -2.142736e-16 1.234088e-16
215 1.953464e-16 -2.142736e-16
216 1.496324e-16 1.953464e-16
217 -1.310309e-17 1.496324e-16
218 -1.084206e-17 -1.310309e-17
219 4.101520e-17 -1.084206e-17
220 -2.658672e-17 4.101520e-17
221 -1.360241e-17 -2.658672e-17
222 2.168632e-17 -1.360241e-17
223 1.392044e-17 2.168632e-17
224 6.606831e-17 1.392044e-17
225 -1.780189e-16 6.606831e-17
226 3.816562e-17 -1.780189e-16
227 2.734111e-18 3.816562e-17
228 -6.870039e-17 2.734111e-18
229 -9.516623e-17 -6.870039e-17
230 -5.820784e-17 -9.516623e-17
231 -1.968109e-16 -5.820784e-17
232 -2.361045e-16 -1.968109e-16
233 9.737317e-18 -2.361045e-16
234 3.055773e-17 9.737317e-18
235 3.848272e-17 3.055773e-17
236 -2.184834e-16 3.848272e-17
237 -8.324494e-17 -2.184834e-16
238 8.303884e-17 -8.324494e-17
239 1.089404e-16 8.303884e-17
240 -3.593287e-17 1.089404e-16
241 4.041956e-17 -3.593287e-17
242 2.224824e-17 4.041956e-17
243 -1.210895e-17 2.224824e-17
244 6.114846e-17 -1.210895e-17
245 5.696316e-17 6.114846e-17
246 1.091730e-16 5.696316e-17
247 1.024556e-16 1.091730e-16
248 -7.640513e-17 1.024556e-16
249 6.260151e-17 -7.640513e-17
250 -5.146820e-17 6.260151e-17
251 5.777125e-17 -5.146820e-17
252 9.780750e-18 5.777125e-17
253 -7.577017e-17 9.780750e-18
254 3.719429e-17 -7.577017e-17
255 -1.094809e-17 3.719429e-17
256 2.186704e-16 -1.094809e-17
257 -1.181340e-16 2.186704e-16
258 7.480038e-18 -1.181340e-16
259 -3.525372e-17 7.480038e-18
260 1.097143e-16 -3.525372e-17
261 -4.238991e-17 1.097143e-16
262 2.628972e-17 -4.238991e-17
263 -9.990015e-17 2.628972e-17
> 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/7sk0g1353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8e1o01353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/96a1e1353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1027p11353265969.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/111ucn1353265969.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12p1mc1353265969.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/130lwf1353265969.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14s6tq1353265969.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15209r1353265969.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16pykn1353265969.tab")
+ }
>
> try(system("convert tmp/14s1q1353265969.ps tmp/14s1q1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/2hfuh1353265969.ps tmp/2hfuh1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/3n4uk1353265969.ps tmp/3n4uk1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/4w02x1353265969.ps tmp/4w02x1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ccmj1353265969.ps tmp/5ccmj1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/6vcx01353265969.ps tmp/6vcx01353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/7sk0g1353265969.ps tmp/7sk0g1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/8e1o01353265969.ps tmp/8e1o01353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/96a1e1353265969.ps tmp/96a1e1353265969.png",intern=TRUE))
character(0)
> try(system("convert tmp/1027p11353265969.ps tmp/1027p11353265969.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.295 1.263 15.561