R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
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+ ,0
+ ,0
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+ ,0
+ ,4
+ ,30
+ ,0
+ ,10
+ ,0
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+ ,0
+ ,15
+ ,30
+ ,0
+ ,14
+ ,0
+ ,0
+ ,262
+ ,0
+ ,11
+ ,38
+ ,0
+ ,15
+ ,0
+ ,0
+ ,263
+ ,0
+ ,11
+ ,36
+ ,0
+ ,7
+ ,0
+ ,0
+ ,264
+ ,0
+ ,14
+ ,32
+ ,0
+ ,14
+ ,0)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Pop'
+ ,'t'
+ ,'Pop_t'
+ ,'Doorzettingsvermogen'
+ ,'Zelfstandig'
+ ,'Zelfstandig_p'
+ ,'Stressbestendig'
+ ,'Stressbestendig_p')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Pop','t','Pop_t','Doorzettingsvermogen','Zelfstandig','Zelfstandig_p','Stressbestendig','Stressbestendig_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 = '4'
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Doorzettingsvermogen Pop t Pop_t Zelfstandig Zelfstandig_p
1 13 1 1 1 38 38
2 16 1 2 2 32 32
3 19 1 3 3 35 35
4 15 1 4 4 33 33
5 14 1 5 5 37 37
6 13 1 6 6 29 29
7 19 1 7 7 31 31
8 15 1 8 8 36 36
9 14 1 9 9 35 35
10 15 1 10 10 38 38
11 16 1 11 11 31 31
12 16 1 12 12 34 34
13 16 1 13 13 35 35
14 16 1 14 14 38 38
15 17 1 15 15 37 37
16 15 1 16 16 33 33
17 15 1 17 17 32 32
18 20 1 18 18 38 38
19 18 1 19 19 38 38
20 16 1 20 20 32 32
21 16 1 21 21 33 33
22 16 1 22 22 31 31
23 19 1 23 23 38 38
24 16 1 24 24 39 39
25 17 1 25 25 32 32
26 17 1 26 26 32 32
27 16 1 27 27 35 35
28 15 1 28 28 37 37
29 16 1 29 29 33 33
30 14 1 30 30 33 33
31 15 1 31 31 31 31
32 12 1 32 32 32 32
33 14 1 33 33 31 31
34 16 1 34 34 37 37
35 14 1 35 35 30 30
36 10 1 36 36 33 33
37 10 1 37 37 31 31
38 14 1 38 38 33 33
39 16 1 39 39 31 31
40 16 1 40 40 33 33
41 16 1 41 41 32 32
42 14 1 42 42 33 33
43 20 1 43 43 32 32
44 14 1 44 44 33 33
45 14 1 45 45 28 28
46 11 1 46 46 35 35
47 14 1 47 47 39 39
48 15 1 48 48 34 34
49 16 1 49 49 38 38
50 14 1 50 50 32 32
51 16 1 51 51 38 38
52 14 1 52 52 30 30
53 12 1 53 53 33 33
54 16 1 54 54 38 38
55 9 1 55 55 32 32
56 14 1 56 56 35 35
57 16 1 57 57 34 34
58 16 1 58 58 34 34
59 15 1 59 59 36 36
60 16 1 60 60 34 34
61 12 1 61 61 28 28
62 16 1 62 62 34 34
63 16 1 63 63 35 35
64 14 1 64 64 35 35
65 16 1 65 65 31 31
66 17 1 66 66 37 37
67 18 1 67 67 35 35
68 18 1 68 68 27 27
69 12 1 69 69 40 40
70 16 1 70 70 37 37
71 10 1 71 71 36 36
72 14 1 72 72 38 38
73 18 1 73 73 39 39
74 18 1 74 74 41 41
75 16 1 75 75 27 27
76 17 1 76 76 30 30
77 16 1 77 77 37 37
78 16 1 78 78 31 31
79 13 1 79 79 31 31
80 16 1 80 80 27 27
81 16 1 81 81 36 36
82 16 1 82 82 37 37
83 15 1 83 83 33 33
84 15 1 84 84 34 34
85 16 1 85 85 31 31
86 14 1 86 86 39 39
87 16 1 87 87 34 34
88 16 1 88 88 32 32
89 15 1 89 89 33 33
90 12 1 90 90 36 36
91 17 1 91 91 32 32
92 16 1 92 92 41 41
93 15 1 93 93 28 28
94 13 1 94 94 30 30
95 16 1 95 95 36 36
96 16 1 96 96 35 35
97 16 1 97 97 31 31
98 16 1 98 98 34 34
99 14 1 99 99 36 36
100 16 1 100 100 36 36
101 16 1 101 101 35 35
102 20 1 102 102 37 37
103 15 1 103 103 28 28
104 16 1 104 104 39 39
105 13 1 105 105 32 32
106 17 1 106 106 35 35
107 16 1 107 107 39 39
108 16 1 108 108 35 35
109 12 1 109 109 42 42
110 16 1 110 110 34 34
111 16 1 111 111 33 33
112 17 1 112 112 41 41
113 13 1 113 113 33 33
114 12 1 114 114 34 34
115 18 1 115 115 32 32
116 14 1 116 116 40 40
117 14 1 117 117 40 40
118 13 1 118 118 35 35
119 16 1 119 119 36 36
120 13 1 120 120 37 37
121 16 1 121 121 27 27
122 13 1 122 122 39 39
123 16 1 123 123 38 38
124 15 1 124 124 31 31
125 16 1 125 125 33 33
126 15 1 126 126 32 32
127 17 1 127 127 39 39
128 15 1 128 128 36 36
129 12 1 129 129 33 33
130 16 1 130 130 33 33
131 10 1 131 131 32 32
132 16 1 132 132 37 37
133 12 1 133 133 30 30
134 14 1 134 134 38 38
135 15 1 135 135 29 29
136 13 1 136 136 22 22
137 15 1 137 137 35 35
138 11 1 138 138 35 35
139 12 1 139 139 34 34
140 11 1 140 140 35 35
141 16 1 141 141 34 34
142 15 1 142 142 37 37
143 17 1 143 143 35 35
144 16 1 144 144 23 23
145 10 1 145 145 31 31
146 18 1 146 146 27 27
147 13 1 147 147 36 36
148 16 1 148 148 31 31
149 13 1 149 149 32 32
150 10 1 150 150 39 39
151 15 1 151 151 37 37
152 16 1 152 152 38 38
153 16 1 153 153 39 39
154 14 1 154 154 34 34
155 10 1 155 155 31 31
156 17 1 156 156 32 32
157 13 1 157 157 37 37
158 15 1 158 158 36 36
159 16 1 159 159 32 32
160 12 1 160 160 38 38
161 13 1 161 161 36 36
162 13 0 162 0 26 0
163 12 0 163 0 26 0
164 17 0 164 0 33 0
165 15 0 165 0 39 0
166 10 0 166 0 30 0
167 14 0 167 0 33 0
168 11 0 168 0 25 0
169 13 0 169 0 38 0
170 16 0 170 0 37 0
171 12 0 171 0 31 0
172 16 0 172 0 37 0
173 12 0 173 0 35 0
174 9 0 174 0 25 0
175 12 0 175 0 28 0
176 15 0 176 0 35 0
177 12 0 177 0 33 0
178 12 0 178 0 30 0
179 14 0 179 0 31 0
180 12 0 180 0 37 0
181 16 0 181 0 36 0
182 11 0 182 0 30 0
183 19 0 183 0 36 0
184 15 0 184 0 32 0
185 8 0 185 0 28 0
186 16 0 186 0 36 0
187 17 0 187 0 34 0
188 12 0 188 0 31 0
189 11 0 189 0 28 0
190 11 0 190 0 36 0
191 14 0 191 0 36 0
192 16 0 192 0 40 0
193 12 0 193 0 33 0
194 16 0 194 0 37 0
195 13 0 195 0 32 0
196 15 0 196 0 38 0
197 16 0 197 0 31 0
198 16 0 198 0 37 0
199 14 0 199 0 33 0
200 16 0 200 0 32 0
201 16 0 201 0 30 0
202 14 0 202 0 30 0
203 11 0 203 0 31 0
204 12 0 204 0 32 0
205 15 0 205 0 34 0
206 15 0 206 0 36 0
207 16 0 207 0 37 0
208 16 0 208 0 36 0
209 11 0 209 0 33 0
210 15 0 210 0 33 0
211 12 0 211 0 33 0
212 12 0 212 0 44 0
213 15 0 213 0 39 0
214 15 0 214 0 32 0
215 16 0 215 0 35 0
216 14 0 216 0 25 0
217 17 0 217 0 35 0
218 14 0 218 0 34 0
219 13 0 219 0 35 0
220 15 0 220 0 39 0
221 13 0 221 0 33 0
222 14 0 222 0 36 0
223 15 0 223 0 32 0
224 12 0 224 0 32 0
225 13 0 225 0 36 0
226 8 0 226 0 36 0
227 14 0 227 0 32 0
228 14 0 228 0 34 0
229 11 0 229 0 33 0
230 12 0 230 0 35 0
231 13 0 231 0 30 0
232 10 0 232 0 38 0
233 16 0 233 0 34 0
234 18 0 234 0 33 0
235 13 0 235 0 32 0
236 11 0 236 0 31 0
237 4 0 237 0 30 0
238 13 0 238 0 27 0
239 16 0 239 0 31 0
240 10 0 240 0 30 0
241 12 0 241 0 32 0
242 12 0 242 0 35 0
243 10 0 243 0 28 0
244 13 0 244 0 33 0
245 15 0 245 0 31 0
246 12 0 246 0 35 0
247 14 0 247 0 35 0
248 10 0 248 0 32 0
249 12 0 249 0 21 0
250 12 0 250 0 20 0
251 11 0 251 0 34 0
252 10 0 252 0 32 0
253 12 0 253 0 34 0
254 16 0 254 0 32 0
255 12 0 255 0 33 0
256 14 0 256 0 33 0
257 16 0 257 0 37 0
258 14 0 258 0 32 0
259 13 0 259 0 34 0
260 4 0 260 0 30 0
261 15 0 261 0 30 0
262 11 0 262 0 38 0
263 11 0 263 0 36 0
264 14 0 264 0 32 0
Stressbestendig Stressbestendig_p
1 14 14
2 18 18
3 11 11
4 12 12
5 16 16
6 18 18
7 14 14
8 14 14
9 15 15
10 15 15
11 17 17
12 19 19
13 10 10
14 16 16
15 18 18
16 14 14
17 14 14
18 17 17
19 14 14
20 16 16
21 18 18
22 11 11
23 14 14
24 12 12
25 17 17
26 9 9
27 16 16
28 14 14
29 15 15
30 11 11
31 16 16
32 13 13
33 17 17
34 15 15
35 14 14
36 16 16
37 9 9
38 15 15
39 17 17
40 13 13
41 15 15
42 16 16
43 16 16
44 12 12
45 15 15
46 11 11
47 15 15
48 15 15
49 17 17
50 13 13
51 16 16
52 14 14
53 11 11
54 12 12
55 12 12
56 15 15
57 16 16
58 15 15
59 12 12
60 12 12
61 8 8
62 13 13
63 11 11
64 14 14
65 15 15
66 10 10
67 11 11
68 12 12
69 15 15
70 15 15
71 14 14
72 16 16
73 15 15
74 15 15
75 13 13
76 12 12
77 17 17
78 13 13
79 15 15
80 13 13
81 15 15
82 15 15
83 16 16
84 15 15
85 14 14
86 15 15
87 14 14
88 13 13
89 7 7
90 17 17
91 13 13
92 15 15
93 14 14
94 13 13
95 16 16
96 12 12
97 14 14
98 17 17
99 15 15
100 17 17
101 12 12
102 16 16
103 11 11
104 15 15
105 9 9
106 16 16
107 15 15
108 10 10
109 10 10
110 15 15
111 11 11
112 13 13
113 14 14
114 18 18
115 16 16
116 14 14
117 14 14
118 14 14
119 14 14
120 12 12
121 14 14
122 15 15
123 15 15
124 15 15
125 13 13
126 17 17
127 17 17
128 19 19
129 15 15
130 13 13
131 9 9
132 15 15
133 15 15
134 15 15
135 16 16
136 11 11
137 14 14
138 11 11
139 15 15
140 13 13
141 15 15
142 16 16
143 14 14
144 15 15
145 16 16
146 16 16
147 11 11
148 12 12
149 9 9
150 16 16
151 13 13
152 16 16
153 12 12
154 9 9
155 13 13
156 13 13
157 14 14
158 19 19
159 13 13
160 12 12
161 13 13
162 10 0
163 14 0
164 16 0
165 10 0
166 11 0
167 14 0
168 12 0
169 9 0
170 9 0
171 11 0
172 16 0
173 9 0
174 13 0
175 16 0
176 13 0
177 9 0
178 12 0
179 16 0
180 11 0
181 14 0
182 13 0
183 15 0
184 14 0
185 16 0
186 13 0
187 14 0
188 15 0
189 13 0
190 11 0
191 11 0
192 14 0
193 15 0
194 11 0
195 15 0
196 12 0
197 14 0
198 14 0
199 8 0
200 13 0
201 9 0
202 15 0
203 17 0
204 13 0
205 15 0
206 15 0
207 14 0
208 16 0
209 13 0
210 16 0
211 9 0
212 16 0
213 11 0
214 10 0
215 11 0
216 15 0
217 17 0
218 14 0
219 8 0
220 15 0
221 11 0
222 16 0
223 10 0
224 15 0
225 9 0
226 16 0
227 19 0
228 12 0
229 8 0
230 11 0
231 14 0
232 9 0
233 15 0
234 13 0
235 16 0
236 11 0
237 12 0
238 13 0
239 10 0
240 11 0
241 12 0
242 8 0
243 12 0
244 12 0
245 15 0
246 11 0
247 13 0
248 14 0
249 10 0
250 12 0
251 15 0
252 13 0
253 13 0
254 13 0
255 12 0
256 12 0
257 9 0
258 9 0
259 15 0
260 10 0
261 14 0
262 15 0
263 7 0
264 14 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Pop t Pop_t
7.059653 5.739575 -0.013936 0.005345
Zelfstandig Zelfstandig_p Stressbestendig Stressbestendig_p
0.216451 -0.191550 0.156511 -0.013882
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.128 -1.244 0.265 1.366 5.024
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.059653 2.784902 2.535 0.011842 *
Pop 5.739575 3.372763 1.702 0.090018 .
t -0.013936 0.007309 -1.907 0.057686 .
Pop_t 0.005345 0.008215 0.651 0.515862
Zelfstandig 0.216451 0.056610 3.824 0.000165 ***
Zelfstandig_p -0.191550 0.075243 -2.546 0.011491 *
Stressbestendig 0.156511 0.086322 1.813 0.070987 .
Stressbestendig_p -0.013882 0.114746 -0.121 0.903800
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.197 on 256 degrees of freedom
Multiple R-squared: 0.2214, Adjusted R-squared: 0.2001
F-statistic: 10.4 on 7 and 256 DF, p-value: 1.748e-11
> 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,] 0.8504721314 0.2990557371 0.1495279
[2,] 0.8103210256 0.3793579489 0.1896790
[3,] 0.7524979525 0.4950040951 0.2475020
[4,] 0.6747179141 0.6505641719 0.3252821
[5,] 0.6346776511 0.7306446977 0.3653223
[6,] 0.5933333665 0.8133332670 0.4066666
[7,] 0.5315741374 0.9368517251 0.4684259
[8,] 0.7091620339 0.5816759321 0.2908380
[9,] 0.6438708833 0.7122582334 0.3561291
[10,] 0.5707961727 0.8584076545 0.4292038
[11,] 0.4928301609 0.9856603219 0.5071698
[12,] 0.4285214595 0.8570429190 0.5714785
[13,] 0.3973277295 0.7946554591 0.6026723
[14,] 0.3723786310 0.7447572621 0.6276214
[15,] 0.3054808136 0.6109616273 0.6945192
[16,] 0.2506433854 0.5012867708 0.7493566
[17,] 0.2124850413 0.4249700826 0.7875150
[18,] 0.2168872442 0.4337744883 0.7831128
[19,] 0.1754674861 0.3509349723 0.8245325
[20,] 0.2001414357 0.4002828714 0.7998586
[21,] 0.1682141973 0.3364283947 0.8317858
[22,] 0.2774001841 0.5548003682 0.7225998
[23,] 0.2482824383 0.4965648766 0.7517176
[24,] 0.2026604237 0.4053208473 0.7973396
[25,] 0.1716011407 0.3432022814 0.8283989
[26,] 0.4037720297 0.8075440593 0.5962280
[27,] 0.5536693212 0.8926613576 0.4463307
[28,] 0.5028538897 0.9942922206 0.4971461
[29,] 0.4833851474 0.9667702947 0.5166149
[30,] 0.4546121385 0.9092242770 0.5453879
[31,] 0.4242590791 0.8485181582 0.5757409
[32,] 0.3821457307 0.7642914614 0.6178543
[33,] 0.6046919857 0.7906160287 0.3953080
[34,] 0.5608704782 0.8782590436 0.4391295
[35,] 0.5142742900 0.9714514199 0.4857257
[36,] 0.6011257569 0.7977484862 0.3988742
[37,] 0.5726073863 0.8547852274 0.4273926
[38,] 0.5258538088 0.9482923825 0.4741462
[39,] 0.4797244314 0.9594488629 0.5202756
[40,] 0.4347816310 0.8695632619 0.5652184
[41,] 0.3915041019 0.7830082037 0.6084959
[42,] 0.3504324841 0.7008649682 0.6495675
[43,] 0.3463409918 0.6926819836 0.6536590
[44,] 0.3184884062 0.6369768125 0.6815116
[45,] 0.5046464763 0.9907070475 0.4953535
[46,] 0.4673376573 0.9346753145 0.5326623
[47,] 0.4421737554 0.8843475109 0.5578262
[48,] 0.4196798256 0.8393596512 0.5803202
[49,] 0.3824807177 0.7649614355 0.6175193
[50,] 0.3736247984 0.7472495967 0.6263752
[51,] 0.3518665537 0.7037331074 0.6481334
[52,] 0.3358064701 0.6716129402 0.6641935
[53,] 0.3218162512 0.6436325024 0.6781837
[54,] 0.2921024903 0.5842049807 0.7078975
[55,] 0.2791434455 0.5582868911 0.7208566
[56,] 0.2873402012 0.5746804024 0.7126598
[57,] 0.3403057003 0.6806114006 0.6596943
[58,] 0.4505757903 0.9011515806 0.5494242
[59,] 0.5295933600 0.9408132799 0.4704066
[60,] 0.4919856391 0.9839712782 0.5080144
[61,] 0.6632421052 0.6735157896 0.3367579
[62,] 0.6414679041 0.7170641918 0.3585321
[63,] 0.6586523070 0.6826953861 0.3413477
[64,] 0.6624003002 0.6751993995 0.3375997
[65,] 0.6530081405 0.6939837189 0.3469919
[66,] 0.6624136753 0.6751726494 0.3375863
[67,] 0.6261087198 0.7477825604 0.3738913
[68,] 0.5996325006 0.8007349988 0.4003675
[69,] 0.5972623121 0.8054753759 0.4027377
[70,] 0.5769891991 0.8460216017 0.4230108
[71,] 0.5409693015 0.9180613969 0.4590307
[72,] 0.5038712234 0.9922575532 0.4961288
[73,] 0.4671885632 0.9343771263 0.5328114
[74,] 0.4300779503 0.8601559007 0.5699220
[75,] 0.4003162939 0.8006325878 0.5996837
[76,] 0.3822321195 0.7644642390 0.6177679
[77,] 0.3510849820 0.7021699640 0.6489150
[78,] 0.3237355210 0.6474710421 0.6762645
[79,] 0.2934614018 0.5869228036 0.7065386
[80,] 0.3553928188 0.7107856377 0.6446072
[81,] 0.3504839251 0.7009678502 0.6495161
[82,] 0.3167980250 0.6335960499 0.6832020
[83,] 0.2850935605 0.5701871210 0.7149064
[84,] 0.2786554481 0.5573108962 0.7213446
[85,] 0.2497976184 0.4995952368 0.7502024
[86,] 0.2266046417 0.4532092834 0.7733954
[87,] 0.2043957423 0.4087914846 0.7956043
[88,] 0.1802259857 0.3604519715 0.8197740
[89,] 0.1666290955 0.3332581910 0.8333709
[90,] 0.1451678775 0.2903357550 0.8548321
[91,] 0.1289066673 0.2578133347 0.8710933
[92,] 0.1977177487 0.3954354975 0.8022823
[93,] 0.1736777647 0.3473555293 0.8263222
[94,] 0.1521837897 0.3043675794 0.8478162
[95,] 0.1402645083 0.2805290166 0.8597355
[96,] 0.1295175694 0.2590351387 0.8704824
[97,] 0.1126557914 0.2253115828 0.8873442
[98,] 0.1033996455 0.2067992909 0.8966004
[99,] 0.1161510176 0.2323020351 0.8838490
[100,] 0.1012865744 0.2025731489 0.8987134
[101,] 0.0930208589 0.1860417178 0.9069791
[102,] 0.0932409220 0.1864818439 0.9067591
[103,] 0.0903639934 0.1807279868 0.9096360
[104,] 0.1183260659 0.2366521319 0.8816739
[105,] 0.1304308798 0.2608617595 0.8695691
[106,] 0.1176913663 0.2353827327 0.8823086
[107,] 0.1052767924 0.2105535847 0.8947232
[108,] 0.1007011834 0.2014023667 0.8992988
[109,] 0.0905753009 0.1811506018 0.9094247
[110,] 0.0836390636 0.1672781271 0.9163609
[111,] 0.0753301582 0.1506603164 0.9246698
[112,] 0.0730679913 0.1461359826 0.9269320
[113,] 0.0646701368 0.1293402737 0.9353299
[114,] 0.0542375387 0.1084750774 0.9457625
[115,] 0.0505155358 0.1010310715 0.9494845
[116,] 0.0419269648 0.0838539297 0.9580730
[117,] 0.0415416630 0.0830833261 0.9584583
[118,] 0.0346906759 0.0693813518 0.9653093
[119,] 0.0378229825 0.0756459650 0.9621770
[120,] 0.0364412886 0.0728825771 0.9635587
[121,] 0.0500489860 0.1000979721 0.9499510
[122,] 0.0471186052 0.0942372103 0.9528814
[123,] 0.0493605857 0.0987211714 0.9506394
[124,] 0.0423332798 0.0846665597 0.9576667
[125,] 0.0350568130 0.0701136261 0.9649432
[126,] 0.0304370632 0.0608741263 0.9695629
[127,] 0.0260154629 0.0520309257 0.9739845
[128,] 0.0297512060 0.0595024120 0.9702488
[129,] 0.0313393934 0.0626787867 0.9686606
[130,] 0.0407387247 0.0814774494 0.9592613
[131,] 0.0357957257 0.0715914515 0.9642043
[132,] 0.0295287818 0.0590575636 0.9704712
[133,] 0.0332406311 0.0664812623 0.9667594
[134,] 0.0293694686 0.0587389371 0.9706305
[135,] 0.0600611006 0.1201222013 0.9399389
[136,] 0.0667351383 0.1334702765 0.9332649
[137,] 0.0577523607 0.1155047214 0.9422476
[138,] 0.0545259018 0.1090518035 0.9454741
[139,] 0.0461874267 0.0923748533 0.9538126
[140,] 0.0871642900 0.1743285800 0.9128357
[141,] 0.0744130531 0.1488261062 0.9255869
[142,] 0.0643307780 0.1286615561 0.9356692
[143,] 0.0627957888 0.1255915777 0.9372042
[144,] 0.0572561123 0.1145122247 0.9427439
[145,] 0.1031420603 0.2062841205 0.8968579
[146,] 0.0996538413 0.1993076826 0.9003462
[147,] 0.0875241509 0.1750483018 0.9124758
[148,] 0.0738746679 0.1477493359 0.9261253
[149,] 0.0655066085 0.1310132170 0.9344934
[150,] 0.0589767681 0.1179535362 0.9410232
[151,] 0.0501194740 0.1002389479 0.9498805
[152,] 0.0422905563 0.0845811125 0.9577094
[153,] 0.0348660863 0.0697321726 0.9651339
[154,] 0.0331351758 0.0662703517 0.9668648
[155,] 0.0269437568 0.0538875136 0.9730562
[156,] 0.0274617933 0.0549235865 0.9725382
[157,] 0.0232524907 0.0465049813 0.9767475
[158,] 0.0208127664 0.0416255329 0.9791872
[159,] 0.0175488967 0.0350977934 0.9824511
[160,] 0.0182211306 0.0364422611 0.9817789
[161,] 0.0150047860 0.0300095720 0.9849952
[162,] 0.0120715558 0.0241431117 0.9879284
[163,] 0.0103306586 0.0206613172 0.9896693
[164,] 0.0114317202 0.0228634403 0.9885683
[165,] 0.0096282091 0.0192564182 0.9903718
[166,] 0.0079853412 0.0159706823 0.9920147
[167,] 0.0067927159 0.0135854317 0.9932073
[168,] 0.0057150148 0.0114300296 0.9942850
[169,] 0.0044193531 0.0088387061 0.9955806
[170,] 0.0044149391 0.0088298782 0.9955851
[171,] 0.0037477057 0.0074954113 0.9962523
[172,] 0.0036919863 0.0073839726 0.9963080
[173,] 0.0067725871 0.0135451742 0.9932274
[174,] 0.0056127547 0.0112255094 0.9943872
[175,] 0.0176944705 0.0353889410 0.9823055
[176,] 0.0155322624 0.0310645247 0.9844677
[177,] 0.0173613553 0.0347227107 0.9826386
[178,] 0.0160883833 0.0321767666 0.9839116
[179,] 0.0163546277 0.0327092554 0.9836454
[180,] 0.0207215143 0.0414430286 0.9792785
[181,] 0.0167168401 0.0334336802 0.9832832
[182,] 0.0133021044 0.0266042088 0.9866979
[183,] 0.0135898157 0.0271796314 0.9864102
[184,] 0.0124375375 0.0248750750 0.9875625
[185,] 0.0105037155 0.0210074311 0.9894963
[186,] 0.0080483315 0.0160966631 0.9919517
[187,] 0.0086068857 0.0172137713 0.9913931
[188,] 0.0069791396 0.0139582792 0.9930209
[189,] 0.0059296555 0.0118593110 0.9940703
[190,] 0.0059029021 0.0118058042 0.9940971
[191,] 0.0079548384 0.0159096768 0.9920452
[192,] 0.0059745644 0.0119491288 0.9940254
[193,] 0.0078886047 0.0157772093 0.9921114
[194,] 0.0070692767 0.0141385535 0.9929307
[195,] 0.0053209735 0.0106419470 0.9946790
[196,] 0.0039501928 0.0079003856 0.9960498
[197,] 0.0032526237 0.0065052475 0.9967474
[198,] 0.0026381389 0.0052762779 0.9973619
[199,] 0.0029893913 0.0059787825 0.9970106
[200,] 0.0022152112 0.0044304224 0.9977848
[201,] 0.0016977687 0.0033955374 0.9983022
[202,] 0.0032421532 0.0064843064 0.9967578
[203,] 0.0023833939 0.0047667878 0.9976166
[204,] 0.0021059667 0.0042119335 0.9978940
[205,] 0.0021583158 0.0043166316 0.9978417
[206,] 0.0016966323 0.0033932646 0.9983034
[207,] 0.0019106983 0.0038213965 0.9980893
[208,] 0.0013600681 0.0027201362 0.9986399
[209,] 0.0009558665 0.0019117330 0.9990441
[210,] 0.0007202225 0.0014404450 0.9992798
[211,] 0.0004880823 0.0009761646 0.9995119
[212,] 0.0003348793 0.0006697585 0.9996651
[213,] 0.0003774766 0.0007549532 0.9996225
[214,] 0.0002631397 0.0005262794 0.9997369
[215,] 0.0001849144 0.0003698287 0.9998151
[216,] 0.0012846066 0.0025692132 0.9987154
[217,] 0.0008467921 0.0016935842 0.9991532
[218,] 0.0006007349 0.0012014698 0.9993993
[219,] 0.0004057126 0.0008114252 0.9995943
[220,] 0.0002647232 0.0005294464 0.9997353
[221,] 0.0001613984 0.0003227968 0.9998386
[222,] 0.0001886588 0.0003773176 0.9998113
[223,] 0.0001825643 0.0003651285 0.9998174
[224,] 0.0010847266 0.0021694533 0.9989153
[225,] 0.0007073145 0.0014146291 0.9992927
[226,] 0.0004418380 0.0008836761 0.9995582
[227,] 0.0231808429 0.0463616858 0.9768192
[228,] 0.0164974413 0.0329948826 0.9835026
[229,] 0.0296484638 0.0592969276 0.9703515
[230,] 0.0249586716 0.0499173432 0.9750413
[231,] 0.0166798293 0.0333596586 0.9833202
[232,] 0.0106868909 0.0213737818 0.9893131
[233,] 0.0092927888 0.0185855776 0.9907072
[234,] 0.0055905191 0.0111810383 0.9944095
[235,] 0.0047243655 0.0094487311 0.9952756
[236,] 0.0026975623 0.0053951246 0.9973024
[237,] 0.0017095973 0.0034191947 0.9982904
[238,] 0.0014690038 0.0029380075 0.9985310
[239,] 0.0007708824 0.0015417649 0.9992291
[240,] 0.0004115050 0.0008230100 0.9995885
[241,] 0.0002951439 0.0005902878 0.9997049
[242,] 0.0003362187 0.0006724374 0.9996638
[243,] 0.0002364059 0.0004728118 0.9997636
> postscript(file="/var/wessaorg/rcomp/tmp/15ddb1321987317.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/2jfr81321987317.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/36wos1321987317.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/43htc1321987317.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5op971321987317.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
-2.733665502 -0.146185050 3.786105280 -0.298130828 -1.959657311 -3.037117996
7 8 9 10 11 12
3.492186813 -0.623725410 -1.732862212 -0.798973048 0.098665573 -0.252702740
13 14 15 16 17 18
1.014646453 0.092763188 0.840997648 -0.480293382 -0.446801445 3.984499423
19 20 21 22 23 24
2.420976881 0.293714809 -0.007852117 1.048941680 3.455341855 0.724289882
25 26 27 28 29 30
1.194042288 2.343663437 0.279151433 -0.476801234 0.488764045 -0.932129759
31 32 33 34 35 36
-0.586880819 -3.175304054 -1.712327070 0.432117489 -1.242357675 -5.593725988
37 38 39 40 41 42
-4.536932191 -1.433914763 0.339220391 0.868525200 0.616759661 -1.542178527
43 44 45 46 47 48
4.491313410 -0.954481088 -1.249272592 -3.844471249 -1.505997732 -0.372903022
49 50 51 52 53 54
0.250827972 -1.020661671 0.410639197 -1.096306536 -2.734531158 1.006927880
55 56 57 58 59 60
-5.835076716 -1.329073767 0.561789432 0.713009413 0.099685485 1.158078115
61 62 63 64 65 66
-2.113411528 1.032631864 1.301579890 -1.117715081 0.847850198 2.420180972
67 68 69 70 71 72
3.335944864 3.401112917 -3.341891068 0.741402255 -5.082477070 -1.408944689
73 74 75 76 77 78
2.717374599 2.676164456 1.318622883 2.395140785 0.516283484 1.244793840
79 80 81 82 83 84
-2.031872393 1.361579101 0.860806627 0.844497177 -0.189937544 -0.063618255
85 86 87 88 89 90
1.162303806 -1.170939235 1.104784213 1.305805582 1.145268561 -3.347129657
91 92 93 94 95 96
2.331579312 0.830806839 0.305735835 -1.592845570 0.838455298 1.442462188
97 98 99 100 101 102
1.265398728 0.771401677 -0.984550990 0.738782778 1.485418405 4.873693309
103 104 105 106 107 108
0.819534484 0.983703148 -0.977628326 1.957859670 1.009476878 1.830814586
109 110 111 112 113 114
-2.334899024 1.159754076 1.763760965 2.287889185 -1.646942762 -3.233767165
115 116 117 118 119 120
3.109882942 -0.795473885 -0.786882642 -1.653787931 1.329902619 -1.401149355
121 122 123 124 125 126
1.571191346 -1.861654469 1.171837468 0.354733565 1.598780898 0.061757882
127 128 129 130 131 132
1.896044272 -0.305919881 -2.652111604 1.641737116 -3.754255995 1.274059353
133 134 135 136 137 138
-2.543044550 -0.733658854 0.356409892 -0.747550320 0.509445695 -3.054076847
139 140 141 142 143 144
-2.591099863 -3.322151836 1.426082624 0.217343050 2.560993156 1.725763982
145 146 147 148 149 150
-4.607479060 3.500714957 -1.001656349 1.988809624 -0.599613612 -4.763728389
151 152 153 154 155 156
0.722550456 1.278354791 1.832560294 0.393541219 -4.093680410 2.890010140
157 158 159 160 161 162
-1.368530822 -0.048182576 1.915783871 -2.082400308 -1.166636416 1.005198586
163 164 165 166 167 168
-0.606908951 2.578848954 0.233145519 -2.961370774 -0.066320127 -1.007754448
169 170 171 172 173 174
-1.338147273 1.892239983 -1.108140058 0.824535875 -1.633049177 -3.080647442
175 176 177 178 179 180
-1.185596795 0.782715943 -1.144402008 -0.950645797 0.220795738 -2.281418664
181 182 183 184 185 186
1.479435693 -2.051411449 4.350797383 1.387048387 -5.046233508 1.705628302
187 188 189 190 191 192
2.995955519 -1.497266337 -1.520955294 -2.925604450 0.088331879 0.766931599
193 194 195 196 197 198
-1.860486549 1.913689937 -0.616162964 0.568600701 2.784671587 1.499902352
199 200 201 202 203 204
1.318708189 2.766540612 3.839422661 0.914293191 -2.601243341 -1.177714073
205 206 207 208 209 210
1.090298468 0.671332942 1.625329310 1.542694633 -2.324483357 1.219920072
211 212 213 214 215 216
-0.670566834 -4.133167470 0.745578327 2.431182113 2.639254693 2.191656428
217 218 219 220 221 222
2.728061552 0.427981707 0.164532907 0.217088762 0.155774520 -0.262196766
223 224 225 226 227 228
2.556609071 -1.212009433 -0.124811014 -6.206451451 0.203755687 0.880366926
229 230 231 232 233 234
-1.263201952 -1.151700377 0.474957688 -3.460158568 2.480515670 5.023924859
235 236 237 238 239 240
-0.215220785 -1.202278696 -8.128402407 1.378375737 3.996041256 -1.930082454
241 242 243 244 245 246
-0.505558947 -0.514931534 -1.611882581 0.319799112 2.297104396 -0.928719119
247 248 249 250 251 252
0.772195277 -2.721026579 2.299913815 2.217279137 -2.268630414 -2.508770298
253 254 255 256 257 258
-0.927735824 3.519102359 -0.526901273 1.487035056 3.104700575 2.200891540
259 260 261 262 263 264
-0.157139785 -7.494844915 2.893047548 -2.981134508 -1.282208593 1.501954679
> postscript(file="/var/wessaorg/rcomp/tmp/61jlr1321987317.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 -2.733665502 NA
1 -0.146185050 -2.733665502
2 3.786105280 -0.146185050
3 -0.298130828 3.786105280
4 -1.959657311 -0.298130828
5 -3.037117996 -1.959657311
6 3.492186813 -3.037117996
7 -0.623725410 3.492186813
8 -1.732862212 -0.623725410
9 -0.798973048 -1.732862212
10 0.098665573 -0.798973048
11 -0.252702740 0.098665573
12 1.014646453 -0.252702740
13 0.092763188 1.014646453
14 0.840997648 0.092763188
15 -0.480293382 0.840997648
16 -0.446801445 -0.480293382
17 3.984499423 -0.446801445
18 2.420976881 3.984499423
19 0.293714809 2.420976881
20 -0.007852117 0.293714809
21 1.048941680 -0.007852117
22 3.455341855 1.048941680
23 0.724289882 3.455341855
24 1.194042288 0.724289882
25 2.343663437 1.194042288
26 0.279151433 2.343663437
27 -0.476801234 0.279151433
28 0.488764045 -0.476801234
29 -0.932129759 0.488764045
30 -0.586880819 -0.932129759
31 -3.175304054 -0.586880819
32 -1.712327070 -3.175304054
33 0.432117489 -1.712327070
34 -1.242357675 0.432117489
35 -5.593725988 -1.242357675
36 -4.536932191 -5.593725988
37 -1.433914763 -4.536932191
38 0.339220391 -1.433914763
39 0.868525200 0.339220391
40 0.616759661 0.868525200
41 -1.542178527 0.616759661
42 4.491313410 -1.542178527
43 -0.954481088 4.491313410
44 -1.249272592 -0.954481088
45 -3.844471249 -1.249272592
46 -1.505997732 -3.844471249
47 -0.372903022 -1.505997732
48 0.250827972 -0.372903022
49 -1.020661671 0.250827972
50 0.410639197 -1.020661671
51 -1.096306536 0.410639197
52 -2.734531158 -1.096306536
53 1.006927880 -2.734531158
54 -5.835076716 1.006927880
55 -1.329073767 -5.835076716
56 0.561789432 -1.329073767
57 0.713009413 0.561789432
58 0.099685485 0.713009413
59 1.158078115 0.099685485
60 -2.113411528 1.158078115
61 1.032631864 -2.113411528
62 1.301579890 1.032631864
63 -1.117715081 1.301579890
64 0.847850198 -1.117715081
65 2.420180972 0.847850198
66 3.335944864 2.420180972
67 3.401112917 3.335944864
68 -3.341891068 3.401112917
69 0.741402255 -3.341891068
70 -5.082477070 0.741402255
71 -1.408944689 -5.082477070
72 2.717374599 -1.408944689
73 2.676164456 2.717374599
74 1.318622883 2.676164456
75 2.395140785 1.318622883
76 0.516283484 2.395140785
77 1.244793840 0.516283484
78 -2.031872393 1.244793840
79 1.361579101 -2.031872393
80 0.860806627 1.361579101
81 0.844497177 0.860806627
82 -0.189937544 0.844497177
83 -0.063618255 -0.189937544
84 1.162303806 -0.063618255
85 -1.170939235 1.162303806
86 1.104784213 -1.170939235
87 1.305805582 1.104784213
88 1.145268561 1.305805582
89 -3.347129657 1.145268561
90 2.331579312 -3.347129657
91 0.830806839 2.331579312
92 0.305735835 0.830806839
93 -1.592845570 0.305735835
94 0.838455298 -1.592845570
95 1.442462188 0.838455298
96 1.265398728 1.442462188
97 0.771401677 1.265398728
98 -0.984550990 0.771401677
99 0.738782778 -0.984550990
100 1.485418405 0.738782778
101 4.873693309 1.485418405
102 0.819534484 4.873693309
103 0.983703148 0.819534484
104 -0.977628326 0.983703148
105 1.957859670 -0.977628326
106 1.009476878 1.957859670
107 1.830814586 1.009476878
108 -2.334899024 1.830814586
109 1.159754076 -2.334899024
110 1.763760965 1.159754076
111 2.287889185 1.763760965
112 -1.646942762 2.287889185
113 -3.233767165 -1.646942762
114 3.109882942 -3.233767165
115 -0.795473885 3.109882942
116 -0.786882642 -0.795473885
117 -1.653787931 -0.786882642
118 1.329902619 -1.653787931
119 -1.401149355 1.329902619
120 1.571191346 -1.401149355
121 -1.861654469 1.571191346
122 1.171837468 -1.861654469
123 0.354733565 1.171837468
124 1.598780898 0.354733565
125 0.061757882 1.598780898
126 1.896044272 0.061757882
127 -0.305919881 1.896044272
128 -2.652111604 -0.305919881
129 1.641737116 -2.652111604
130 -3.754255995 1.641737116
131 1.274059353 -3.754255995
132 -2.543044550 1.274059353
133 -0.733658854 -2.543044550
134 0.356409892 -0.733658854
135 -0.747550320 0.356409892
136 0.509445695 -0.747550320
137 -3.054076847 0.509445695
138 -2.591099863 -3.054076847
139 -3.322151836 -2.591099863
140 1.426082624 -3.322151836
141 0.217343050 1.426082624
142 2.560993156 0.217343050
143 1.725763982 2.560993156
144 -4.607479060 1.725763982
145 3.500714957 -4.607479060
146 -1.001656349 3.500714957
147 1.988809624 -1.001656349
148 -0.599613612 1.988809624
149 -4.763728389 -0.599613612
150 0.722550456 -4.763728389
151 1.278354791 0.722550456
152 1.832560294 1.278354791
153 0.393541219 1.832560294
154 -4.093680410 0.393541219
155 2.890010140 -4.093680410
156 -1.368530822 2.890010140
157 -0.048182576 -1.368530822
158 1.915783871 -0.048182576
159 -2.082400308 1.915783871
160 -1.166636416 -2.082400308
161 1.005198586 -1.166636416
162 -0.606908951 1.005198586
163 2.578848954 -0.606908951
164 0.233145519 2.578848954
165 -2.961370774 0.233145519
166 -0.066320127 -2.961370774
167 -1.007754448 -0.066320127
168 -1.338147273 -1.007754448
169 1.892239983 -1.338147273
170 -1.108140058 1.892239983
171 0.824535875 -1.108140058
172 -1.633049177 0.824535875
173 -3.080647442 -1.633049177
174 -1.185596795 -3.080647442
175 0.782715943 -1.185596795
176 -1.144402008 0.782715943
177 -0.950645797 -1.144402008
178 0.220795738 -0.950645797
179 -2.281418664 0.220795738
180 1.479435693 -2.281418664
181 -2.051411449 1.479435693
182 4.350797383 -2.051411449
183 1.387048387 4.350797383
184 -5.046233508 1.387048387
185 1.705628302 -5.046233508
186 2.995955519 1.705628302
187 -1.497266337 2.995955519
188 -1.520955294 -1.497266337
189 -2.925604450 -1.520955294
190 0.088331879 -2.925604450
191 0.766931599 0.088331879
192 -1.860486549 0.766931599
193 1.913689937 -1.860486549
194 -0.616162964 1.913689937
195 0.568600701 -0.616162964
196 2.784671587 0.568600701
197 1.499902352 2.784671587
198 1.318708189 1.499902352
199 2.766540612 1.318708189
200 3.839422661 2.766540612
201 0.914293191 3.839422661
202 -2.601243341 0.914293191
203 -1.177714073 -2.601243341
204 1.090298468 -1.177714073
205 0.671332942 1.090298468
206 1.625329310 0.671332942
207 1.542694633 1.625329310
208 -2.324483357 1.542694633
209 1.219920072 -2.324483357
210 -0.670566834 1.219920072
211 -4.133167470 -0.670566834
212 0.745578327 -4.133167470
213 2.431182113 0.745578327
214 2.639254693 2.431182113
215 2.191656428 2.639254693
216 2.728061552 2.191656428
217 0.427981707 2.728061552
218 0.164532907 0.427981707
219 0.217088762 0.164532907
220 0.155774520 0.217088762
221 -0.262196766 0.155774520
222 2.556609071 -0.262196766
223 -1.212009433 2.556609071
224 -0.124811014 -1.212009433
225 -6.206451451 -0.124811014
226 0.203755687 -6.206451451
227 0.880366926 0.203755687
228 -1.263201952 0.880366926
229 -1.151700377 -1.263201952
230 0.474957688 -1.151700377
231 -3.460158568 0.474957688
232 2.480515670 -3.460158568
233 5.023924859 2.480515670
234 -0.215220785 5.023924859
235 -1.202278696 -0.215220785
236 -8.128402407 -1.202278696
237 1.378375737 -8.128402407
238 3.996041256 1.378375737
239 -1.930082454 3.996041256
240 -0.505558947 -1.930082454
241 -0.514931534 -0.505558947
242 -1.611882581 -0.514931534
243 0.319799112 -1.611882581
244 2.297104396 0.319799112
245 -0.928719119 2.297104396
246 0.772195277 -0.928719119
247 -2.721026579 0.772195277
248 2.299913815 -2.721026579
249 2.217279137 2.299913815
250 -2.268630414 2.217279137
251 -2.508770298 -2.268630414
252 -0.927735824 -2.508770298
253 3.519102359 -0.927735824
254 -0.526901273 3.519102359
255 1.487035056 -0.526901273
256 3.104700575 1.487035056
257 2.200891540 3.104700575
258 -0.157139785 2.200891540
259 -7.494844915 -0.157139785
260 2.893047548 -7.494844915
261 -2.981134508 2.893047548
262 -1.282208593 -2.981134508
263 1.501954679 -1.282208593
264 NA 1.501954679
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.146185050 -2.733665502
[2,] 3.786105280 -0.146185050
[3,] -0.298130828 3.786105280
[4,] -1.959657311 -0.298130828
[5,] -3.037117996 -1.959657311
[6,] 3.492186813 -3.037117996
[7,] -0.623725410 3.492186813
[8,] -1.732862212 -0.623725410
[9,] -0.798973048 -1.732862212
[10,] 0.098665573 -0.798973048
[11,] -0.252702740 0.098665573
[12,] 1.014646453 -0.252702740
[13,] 0.092763188 1.014646453
[14,] 0.840997648 0.092763188
[15,] -0.480293382 0.840997648
[16,] -0.446801445 -0.480293382
[17,] 3.984499423 -0.446801445
[18,] 2.420976881 3.984499423
[19,] 0.293714809 2.420976881
[20,] -0.007852117 0.293714809
[21,] 1.048941680 -0.007852117
[22,] 3.455341855 1.048941680
[23,] 0.724289882 3.455341855
[24,] 1.194042288 0.724289882
[25,] 2.343663437 1.194042288
[26,] 0.279151433 2.343663437
[27,] -0.476801234 0.279151433
[28,] 0.488764045 -0.476801234
[29,] -0.932129759 0.488764045
[30,] -0.586880819 -0.932129759
[31,] -3.175304054 -0.586880819
[32,] -1.712327070 -3.175304054
[33,] 0.432117489 -1.712327070
[34,] -1.242357675 0.432117489
[35,] -5.593725988 -1.242357675
[36,] -4.536932191 -5.593725988
[37,] -1.433914763 -4.536932191
[38,] 0.339220391 -1.433914763
[39,] 0.868525200 0.339220391
[40,] 0.616759661 0.868525200
[41,] -1.542178527 0.616759661
[42,] 4.491313410 -1.542178527
[43,] -0.954481088 4.491313410
[44,] -1.249272592 -0.954481088
[45,] -3.844471249 -1.249272592
[46,] -1.505997732 -3.844471249
[47,] -0.372903022 -1.505997732
[48,] 0.250827972 -0.372903022
[49,] -1.020661671 0.250827972
[50,] 0.410639197 -1.020661671
[51,] -1.096306536 0.410639197
[52,] -2.734531158 -1.096306536
[53,] 1.006927880 -2.734531158
[54,] -5.835076716 1.006927880
[55,] -1.329073767 -5.835076716
[56,] 0.561789432 -1.329073767
[57,] 0.713009413 0.561789432
[58,] 0.099685485 0.713009413
[59,] 1.158078115 0.099685485
[60,] -2.113411528 1.158078115
[61,] 1.032631864 -2.113411528
[62,] 1.301579890 1.032631864
[63,] -1.117715081 1.301579890
[64,] 0.847850198 -1.117715081
[65,] 2.420180972 0.847850198
[66,] 3.335944864 2.420180972
[67,] 3.401112917 3.335944864
[68,] -3.341891068 3.401112917
[69,] 0.741402255 -3.341891068
[70,] -5.082477070 0.741402255
[71,] -1.408944689 -5.082477070
[72,] 2.717374599 -1.408944689
[73,] 2.676164456 2.717374599
[74,] 1.318622883 2.676164456
[75,] 2.395140785 1.318622883
[76,] 0.516283484 2.395140785
[77,] 1.244793840 0.516283484
[78,] -2.031872393 1.244793840
[79,] 1.361579101 -2.031872393
[80,] 0.860806627 1.361579101
[81,] 0.844497177 0.860806627
[82,] -0.189937544 0.844497177
[83,] -0.063618255 -0.189937544
[84,] 1.162303806 -0.063618255
[85,] -1.170939235 1.162303806
[86,] 1.104784213 -1.170939235
[87,] 1.305805582 1.104784213
[88,] 1.145268561 1.305805582
[89,] -3.347129657 1.145268561
[90,] 2.331579312 -3.347129657
[91,] 0.830806839 2.331579312
[92,] 0.305735835 0.830806839
[93,] -1.592845570 0.305735835
[94,] 0.838455298 -1.592845570
[95,] 1.442462188 0.838455298
[96,] 1.265398728 1.442462188
[97,] 0.771401677 1.265398728
[98,] -0.984550990 0.771401677
[99,] 0.738782778 -0.984550990
[100,] 1.485418405 0.738782778
[101,] 4.873693309 1.485418405
[102,] 0.819534484 4.873693309
[103,] 0.983703148 0.819534484
[104,] -0.977628326 0.983703148
[105,] 1.957859670 -0.977628326
[106,] 1.009476878 1.957859670
[107,] 1.830814586 1.009476878
[108,] -2.334899024 1.830814586
[109,] 1.159754076 -2.334899024
[110,] 1.763760965 1.159754076
[111,] 2.287889185 1.763760965
[112,] -1.646942762 2.287889185
[113,] -3.233767165 -1.646942762
[114,] 3.109882942 -3.233767165
[115,] -0.795473885 3.109882942
[116,] -0.786882642 -0.795473885
[117,] -1.653787931 -0.786882642
[118,] 1.329902619 -1.653787931
[119,] -1.401149355 1.329902619
[120,] 1.571191346 -1.401149355
[121,] -1.861654469 1.571191346
[122,] 1.171837468 -1.861654469
[123,] 0.354733565 1.171837468
[124,] 1.598780898 0.354733565
[125,] 0.061757882 1.598780898
[126,] 1.896044272 0.061757882
[127,] -0.305919881 1.896044272
[128,] -2.652111604 -0.305919881
[129,] 1.641737116 -2.652111604
[130,] -3.754255995 1.641737116
[131,] 1.274059353 -3.754255995
[132,] -2.543044550 1.274059353
[133,] -0.733658854 -2.543044550
[134,] 0.356409892 -0.733658854
[135,] -0.747550320 0.356409892
[136,] 0.509445695 -0.747550320
[137,] -3.054076847 0.509445695
[138,] -2.591099863 -3.054076847
[139,] -3.322151836 -2.591099863
[140,] 1.426082624 -3.322151836
[141,] 0.217343050 1.426082624
[142,] 2.560993156 0.217343050
[143,] 1.725763982 2.560993156
[144,] -4.607479060 1.725763982
[145,] 3.500714957 -4.607479060
[146,] -1.001656349 3.500714957
[147,] 1.988809624 -1.001656349
[148,] -0.599613612 1.988809624
[149,] -4.763728389 -0.599613612
[150,] 0.722550456 -4.763728389
[151,] 1.278354791 0.722550456
[152,] 1.832560294 1.278354791
[153,] 0.393541219 1.832560294
[154,] -4.093680410 0.393541219
[155,] 2.890010140 -4.093680410
[156,] -1.368530822 2.890010140
[157,] -0.048182576 -1.368530822
[158,] 1.915783871 -0.048182576
[159,] -2.082400308 1.915783871
[160,] -1.166636416 -2.082400308
[161,] 1.005198586 -1.166636416
[162,] -0.606908951 1.005198586
[163,] 2.578848954 -0.606908951
[164,] 0.233145519 2.578848954
[165,] -2.961370774 0.233145519
[166,] -0.066320127 -2.961370774
[167,] -1.007754448 -0.066320127
[168,] -1.338147273 -1.007754448
[169,] 1.892239983 -1.338147273
[170,] -1.108140058 1.892239983
[171,] 0.824535875 -1.108140058
[172,] -1.633049177 0.824535875
[173,] -3.080647442 -1.633049177
[174,] -1.185596795 -3.080647442
[175,] 0.782715943 -1.185596795
[176,] -1.144402008 0.782715943
[177,] -0.950645797 -1.144402008
[178,] 0.220795738 -0.950645797
[179,] -2.281418664 0.220795738
[180,] 1.479435693 -2.281418664
[181,] -2.051411449 1.479435693
[182,] 4.350797383 -2.051411449
[183,] 1.387048387 4.350797383
[184,] -5.046233508 1.387048387
[185,] 1.705628302 -5.046233508
[186,] 2.995955519 1.705628302
[187,] -1.497266337 2.995955519
[188,] -1.520955294 -1.497266337
[189,] -2.925604450 -1.520955294
[190,] 0.088331879 -2.925604450
[191,] 0.766931599 0.088331879
[192,] -1.860486549 0.766931599
[193,] 1.913689937 -1.860486549
[194,] -0.616162964 1.913689937
[195,] 0.568600701 -0.616162964
[196,] 2.784671587 0.568600701
[197,] 1.499902352 2.784671587
[198,] 1.318708189 1.499902352
[199,] 2.766540612 1.318708189
[200,] 3.839422661 2.766540612
[201,] 0.914293191 3.839422661
[202,] -2.601243341 0.914293191
[203,] -1.177714073 -2.601243341
[204,] 1.090298468 -1.177714073
[205,] 0.671332942 1.090298468
[206,] 1.625329310 0.671332942
[207,] 1.542694633 1.625329310
[208,] -2.324483357 1.542694633
[209,] 1.219920072 -2.324483357
[210,] -0.670566834 1.219920072
[211,] -4.133167470 -0.670566834
[212,] 0.745578327 -4.133167470
[213,] 2.431182113 0.745578327
[214,] 2.639254693 2.431182113
[215,] 2.191656428 2.639254693
[216,] 2.728061552 2.191656428
[217,] 0.427981707 2.728061552
[218,] 0.164532907 0.427981707
[219,] 0.217088762 0.164532907
[220,] 0.155774520 0.217088762
[221,] -0.262196766 0.155774520
[222,] 2.556609071 -0.262196766
[223,] -1.212009433 2.556609071
[224,] -0.124811014 -1.212009433
[225,] -6.206451451 -0.124811014
[226,] 0.203755687 -6.206451451
[227,] 0.880366926 0.203755687
[228,] -1.263201952 0.880366926
[229,] -1.151700377 -1.263201952
[230,] 0.474957688 -1.151700377
[231,] -3.460158568 0.474957688
[232,] 2.480515670 -3.460158568
[233,] 5.023924859 2.480515670
[234,] -0.215220785 5.023924859
[235,] -1.202278696 -0.215220785
[236,] -8.128402407 -1.202278696
[237,] 1.378375737 -8.128402407
[238,] 3.996041256 1.378375737
[239,] -1.930082454 3.996041256
[240,] -0.505558947 -1.930082454
[241,] -0.514931534 -0.505558947
[242,] -1.611882581 -0.514931534
[243,] 0.319799112 -1.611882581
[244,] 2.297104396 0.319799112
[245,] -0.928719119 2.297104396
[246,] 0.772195277 -0.928719119
[247,] -2.721026579 0.772195277
[248,] 2.299913815 -2.721026579
[249,] 2.217279137 2.299913815
[250,] -2.268630414 2.217279137
[251,] -2.508770298 -2.268630414
[252,] -0.927735824 -2.508770298
[253,] 3.519102359 -0.927735824
[254,] -0.526901273 3.519102359
[255,] 1.487035056 -0.526901273
[256,] 3.104700575 1.487035056
[257,] 2.200891540 3.104700575
[258,] -0.157139785 2.200891540
[259,] -7.494844915 -0.157139785
[260,] 2.893047548 -7.494844915
[261,] -2.981134508 2.893047548
[262,] -1.282208593 -2.981134508
[263,] 1.501954679 -1.282208593
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.146185050 -2.733665502
2 3.786105280 -0.146185050
3 -0.298130828 3.786105280
4 -1.959657311 -0.298130828
5 -3.037117996 -1.959657311
6 3.492186813 -3.037117996
7 -0.623725410 3.492186813
8 -1.732862212 -0.623725410
9 -0.798973048 -1.732862212
10 0.098665573 -0.798973048
11 -0.252702740 0.098665573
12 1.014646453 -0.252702740
13 0.092763188 1.014646453
14 0.840997648 0.092763188
15 -0.480293382 0.840997648
16 -0.446801445 -0.480293382
17 3.984499423 -0.446801445
18 2.420976881 3.984499423
19 0.293714809 2.420976881
20 -0.007852117 0.293714809
21 1.048941680 -0.007852117
22 3.455341855 1.048941680
23 0.724289882 3.455341855
24 1.194042288 0.724289882
25 2.343663437 1.194042288
26 0.279151433 2.343663437
27 -0.476801234 0.279151433
28 0.488764045 -0.476801234
29 -0.932129759 0.488764045
30 -0.586880819 -0.932129759
31 -3.175304054 -0.586880819
32 -1.712327070 -3.175304054
33 0.432117489 -1.712327070
34 -1.242357675 0.432117489
35 -5.593725988 -1.242357675
36 -4.536932191 -5.593725988
37 -1.433914763 -4.536932191
38 0.339220391 -1.433914763
39 0.868525200 0.339220391
40 0.616759661 0.868525200
41 -1.542178527 0.616759661
42 4.491313410 -1.542178527
43 -0.954481088 4.491313410
44 -1.249272592 -0.954481088
45 -3.844471249 -1.249272592
46 -1.505997732 -3.844471249
47 -0.372903022 -1.505997732
48 0.250827972 -0.372903022
49 -1.020661671 0.250827972
50 0.410639197 -1.020661671
51 -1.096306536 0.410639197
52 -2.734531158 -1.096306536
53 1.006927880 -2.734531158
54 -5.835076716 1.006927880
55 -1.329073767 -5.835076716
56 0.561789432 -1.329073767
57 0.713009413 0.561789432
58 0.099685485 0.713009413
59 1.158078115 0.099685485
60 -2.113411528 1.158078115
61 1.032631864 -2.113411528
62 1.301579890 1.032631864
63 -1.117715081 1.301579890
64 0.847850198 -1.117715081
65 2.420180972 0.847850198
66 3.335944864 2.420180972
67 3.401112917 3.335944864
68 -3.341891068 3.401112917
69 0.741402255 -3.341891068
70 -5.082477070 0.741402255
71 -1.408944689 -5.082477070
72 2.717374599 -1.408944689
73 2.676164456 2.717374599
74 1.318622883 2.676164456
75 2.395140785 1.318622883
76 0.516283484 2.395140785
77 1.244793840 0.516283484
78 -2.031872393 1.244793840
79 1.361579101 -2.031872393
80 0.860806627 1.361579101
81 0.844497177 0.860806627
82 -0.189937544 0.844497177
83 -0.063618255 -0.189937544
84 1.162303806 -0.063618255
85 -1.170939235 1.162303806
86 1.104784213 -1.170939235
87 1.305805582 1.104784213
88 1.145268561 1.305805582
89 -3.347129657 1.145268561
90 2.331579312 -3.347129657
91 0.830806839 2.331579312
92 0.305735835 0.830806839
93 -1.592845570 0.305735835
94 0.838455298 -1.592845570
95 1.442462188 0.838455298
96 1.265398728 1.442462188
97 0.771401677 1.265398728
98 -0.984550990 0.771401677
99 0.738782778 -0.984550990
100 1.485418405 0.738782778
101 4.873693309 1.485418405
102 0.819534484 4.873693309
103 0.983703148 0.819534484
104 -0.977628326 0.983703148
105 1.957859670 -0.977628326
106 1.009476878 1.957859670
107 1.830814586 1.009476878
108 -2.334899024 1.830814586
109 1.159754076 -2.334899024
110 1.763760965 1.159754076
111 2.287889185 1.763760965
112 -1.646942762 2.287889185
113 -3.233767165 -1.646942762
114 3.109882942 -3.233767165
115 -0.795473885 3.109882942
116 -0.786882642 -0.795473885
117 -1.653787931 -0.786882642
118 1.329902619 -1.653787931
119 -1.401149355 1.329902619
120 1.571191346 -1.401149355
121 -1.861654469 1.571191346
122 1.171837468 -1.861654469
123 0.354733565 1.171837468
124 1.598780898 0.354733565
125 0.061757882 1.598780898
126 1.896044272 0.061757882
127 -0.305919881 1.896044272
128 -2.652111604 -0.305919881
129 1.641737116 -2.652111604
130 -3.754255995 1.641737116
131 1.274059353 -3.754255995
132 -2.543044550 1.274059353
133 -0.733658854 -2.543044550
134 0.356409892 -0.733658854
135 -0.747550320 0.356409892
136 0.509445695 -0.747550320
137 -3.054076847 0.509445695
138 -2.591099863 -3.054076847
139 -3.322151836 -2.591099863
140 1.426082624 -3.322151836
141 0.217343050 1.426082624
142 2.560993156 0.217343050
143 1.725763982 2.560993156
144 -4.607479060 1.725763982
145 3.500714957 -4.607479060
146 -1.001656349 3.500714957
147 1.988809624 -1.001656349
148 -0.599613612 1.988809624
149 -4.763728389 -0.599613612
150 0.722550456 -4.763728389
151 1.278354791 0.722550456
152 1.832560294 1.278354791
153 0.393541219 1.832560294
154 -4.093680410 0.393541219
155 2.890010140 -4.093680410
156 -1.368530822 2.890010140
157 -0.048182576 -1.368530822
158 1.915783871 -0.048182576
159 -2.082400308 1.915783871
160 -1.166636416 -2.082400308
161 1.005198586 -1.166636416
162 -0.606908951 1.005198586
163 2.578848954 -0.606908951
164 0.233145519 2.578848954
165 -2.961370774 0.233145519
166 -0.066320127 -2.961370774
167 -1.007754448 -0.066320127
168 -1.338147273 -1.007754448
169 1.892239983 -1.338147273
170 -1.108140058 1.892239983
171 0.824535875 -1.108140058
172 -1.633049177 0.824535875
173 -3.080647442 -1.633049177
174 -1.185596795 -3.080647442
175 0.782715943 -1.185596795
176 -1.144402008 0.782715943
177 -0.950645797 -1.144402008
178 0.220795738 -0.950645797
179 -2.281418664 0.220795738
180 1.479435693 -2.281418664
181 -2.051411449 1.479435693
182 4.350797383 -2.051411449
183 1.387048387 4.350797383
184 -5.046233508 1.387048387
185 1.705628302 -5.046233508
186 2.995955519 1.705628302
187 -1.497266337 2.995955519
188 -1.520955294 -1.497266337
189 -2.925604450 -1.520955294
190 0.088331879 -2.925604450
191 0.766931599 0.088331879
192 -1.860486549 0.766931599
193 1.913689937 -1.860486549
194 -0.616162964 1.913689937
195 0.568600701 -0.616162964
196 2.784671587 0.568600701
197 1.499902352 2.784671587
198 1.318708189 1.499902352
199 2.766540612 1.318708189
200 3.839422661 2.766540612
201 0.914293191 3.839422661
202 -2.601243341 0.914293191
203 -1.177714073 -2.601243341
204 1.090298468 -1.177714073
205 0.671332942 1.090298468
206 1.625329310 0.671332942
207 1.542694633 1.625329310
208 -2.324483357 1.542694633
209 1.219920072 -2.324483357
210 -0.670566834 1.219920072
211 -4.133167470 -0.670566834
212 0.745578327 -4.133167470
213 2.431182113 0.745578327
214 2.639254693 2.431182113
215 2.191656428 2.639254693
216 2.728061552 2.191656428
217 0.427981707 2.728061552
218 0.164532907 0.427981707
219 0.217088762 0.164532907
220 0.155774520 0.217088762
221 -0.262196766 0.155774520
222 2.556609071 -0.262196766
223 -1.212009433 2.556609071
224 -0.124811014 -1.212009433
225 -6.206451451 -0.124811014
226 0.203755687 -6.206451451
227 0.880366926 0.203755687
228 -1.263201952 0.880366926
229 -1.151700377 -1.263201952
230 0.474957688 -1.151700377
231 -3.460158568 0.474957688
232 2.480515670 -3.460158568
233 5.023924859 2.480515670
234 -0.215220785 5.023924859
235 -1.202278696 -0.215220785
236 -8.128402407 -1.202278696
237 1.378375737 -8.128402407
238 3.996041256 1.378375737
239 -1.930082454 3.996041256
240 -0.505558947 -1.930082454
241 -0.514931534 -0.505558947
242 -1.611882581 -0.514931534
243 0.319799112 -1.611882581
244 2.297104396 0.319799112
245 -0.928719119 2.297104396
246 0.772195277 -0.928719119
247 -2.721026579 0.772195277
248 2.299913815 -2.721026579
249 2.217279137 2.299913815
250 -2.268630414 2.217279137
251 -2.508770298 -2.268630414
252 -0.927735824 -2.508770298
253 3.519102359 -0.927735824
254 -0.526901273 3.519102359
255 1.487035056 -0.526901273
256 3.104700575 1.487035056
257 2.200891540 3.104700575
258 -0.157139785 2.200891540
259 -7.494844915 -0.157139785
260 2.893047548 -7.494844915
261 -2.981134508 2.893047548
262 -1.282208593 -2.981134508
263 1.501954679 -1.282208593
> 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/76cow1321987317.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/8bcvh1321987317.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/9p8s81321987317.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/10njgu1321987317.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11g4mk1321987317.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12mgxb1321987317.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/1357j71321987317.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14q5yx1321987317.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/15inut1321987317.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/16zpap1321987317.tab")
+ }
>
> try(system("convert tmp/15ddb1321987317.ps tmp/15ddb1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jfr81321987317.ps tmp/2jfr81321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/36wos1321987317.ps tmp/36wos1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/43htc1321987317.ps tmp/43htc1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/5op971321987317.ps tmp/5op971321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/61jlr1321987317.ps tmp/61jlr1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/76cow1321987317.ps tmp/76cow1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/8bcvh1321987317.ps tmp/8bcvh1321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p8s81321987317.ps tmp/9p8s81321987317.png",intern=TRUE))
character(0)
> try(system("convert tmp/10njgu1321987317.ps tmp/10njgu1321987317.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.247 0.632 8.894