R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(41
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+ ,32
+ ,16
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+ ,12
+ ,10
+ ,12
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+ ,44
+ ,34
+ ,33
+ ,14
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
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+ ,75
+ ,43
+ ,31
+ ,32
+ ,14
+ ,8
+ ,9
+ ,18
+ ,62
+ ,40
+ ,37
+ ,34
+ ,13
+ ,11
+ ,15
+ ,15
+ ,67
+ ,41
+ ,35
+ ,30
+ ,4
+ ,3
+ ,10
+ ,8
+ ,83
+ ,52
+ ,27
+ ,30
+ ,15
+ ,11
+ ,14
+ ,12
+ ,64
+ ,38
+ ,34
+ ,38
+ ,11
+ ,12
+ ,15
+ ,12
+ ,68
+ ,41
+ ,40
+ ,36
+ ,11
+ ,7
+ ,7
+ ,22
+ ,62
+ ,39
+ ,29
+ ,32
+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43)
+ ,dim=c(8
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(8,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Belonging','Belonging_Final'),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 = '5'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Happiness Connected Separate Learning Software Depression Belonging
1 14 41 38 13 12 12.0 53
2 18 39 32 16 11 11.0 83
3 11 30 35 19 15 14.0 66
4 12 31 33 15 6 12.0 67
5 16 34 37 14 13 21.0 76
6 18 35 29 13 10 12.0 78
7 14 39 31 19 12 22.0 53
8 14 34 36 15 14 11.0 80
9 15 36 35 14 12 10.0 74
10 15 37 38 15 9 13.0 76
11 17 38 31 16 10 10.0 79
12 19 36 34 16 12 8.0 54
13 10 38 35 16 12 15.0 67
14 16 39 38 16 11 14.0 54
15 18 33 37 17 15 10.0 87
16 14 32 33 15 12 14.0 58
17 14 36 32 15 10 14.0 75
18 17 38 38 20 12 11.0 88
19 14 39 38 18 11 10.0 64
20 16 32 32 16 12 13.0 57
21 18 32 33 16 11 9.5 66
22 11 31 31 16 12 14.0 68
23 14 39 38 19 13 12.0 54
24 12 37 39 16 11 14.0 56
25 17 39 32 17 12 11.0 86
26 9 41 32 17 13 9.0 80
27 16 36 35 16 10 11.0 76
28 14 33 37 15 14 15.0 69
29 15 33 33 16 12 14.0 78
30 11 34 33 14 10 13.0 67
31 16 31 31 15 12 9.0 80
32 13 27 32 12 8 15.0 54
33 17 37 31 14 10 10.0 71
34 15 34 37 16 12 11.0 84
35 14 34 30 14 12 13.0 74
36 16 32 33 10 7 8.0 71
37 9 29 31 10 9 20.0 63
38 15 36 33 14 12 12.0 71
39 17 29 31 16 10 10.0 76
40 13 35 33 16 10 10.0 69
41 15 37 32 16 10 9.0 74
42 16 34 33 14 12 14.0 75
43 16 38 32 20 15 8.0 54
44 12 35 33 14 10 14.0 52
45 15 38 28 14 10 11.0 69
46 11 37 35 11 12 13.0 68
47 15 38 39 14 13 9.0 65
48 15 33 34 15 11 11.0 75
49 17 36 38 16 11 15.0 74
50 13 38 32 14 12 11.0 75
51 16 32 38 16 14 10.0 72
52 14 32 30 14 10 14.0 67
53 11 32 33 12 12 18.0 63
54 12 34 38 16 13 14.0 62
55 12 32 32 9 5 11.0 63
56 15 37 35 14 6 14.5 76
57 16 39 34 16 12 13.0 74
58 15 29 34 16 12 9.0 67
59 12 37 36 15 11 10.0 73
60 12 35 34 16 10 15.0 70
61 8 30 28 12 7 20.0 53
62 13 38 34 16 12 12.0 77
63 11 34 35 16 14 12.0 80
64 14 31 35 14 11 14.0 52
65 15 34 31 16 12 13.0 54
66 10 35 37 17 13 11.0 80
67 11 36 35 18 14 17.0 66
68 12 30 27 18 11 12.0 73
69 15 39 40 12 12 13.0 63
70 15 35 37 16 12 14.0 69
71 14 38 36 10 8 13.0 67
72 16 31 38 14 11 15.0 54
73 15 34 39 18 14 13.0 81
74 15 38 41 18 14 10.0 69
75 13 34 27 16 12 11.0 84
76 12 39 30 17 9 19.0 80
77 17 37 37 16 13 13.0 70
78 13 34 31 16 11 17.0 69
79 15 28 31 13 12 13.0 77
80 13 37 27 16 12 9.0 54
81 15 33 36 16 12 11.0 79
82 15 35 37 16 12 9.0 71
83 16 37 33 15 12 12.0 73
84 15 32 34 15 11 12.0 72
85 14 33 31 16 10 13.0 77
86 15 38 39 14 9 13.0 75
87 14 33 34 16 12 12.0 69
88 13 29 32 16 12 15.0 54
89 7 33 33 15 12 22.0 70
90 17 31 36 12 9 13.0 73
91 13 36 32 17 15 15.0 54
92 15 35 41 16 12 13.0 77
93 14 32 28 15 12 15.0 82
94 13 29 30 13 12 12.5 80
95 16 39 36 16 10 11.0 80
96 12 37 35 16 13 16.0 69
97 14 35 31 16 9 11.0 78
98 17 37 34 16 12 11.0 81
99 15 32 36 14 10 10.0 76
100 17 38 36 16 14 10.0 76
101 12 37 35 16 11 16.0 73
102 16 36 37 20 15 12.0 85
103 11 32 28 15 11 11.0 66
104 15 33 39 16 11 16.0 79
105 9 40 32 13 12 19.0 68
106 16 38 35 17 12 11.0 76
107 15 41 39 16 12 16.0 71
108 10 36 35 16 11 15.0 54
109 10 43 42 12 7 24.0 46
110 15 30 34 16 12 14.0 85
111 11 31 33 16 14 15.0 74
112 13 32 41 17 11 11.0 88
113 14 32 33 13 11 15.0 38
114 18 37 34 12 10 12.0 76
115 16 37 32 18 13 10.0 86
116 14 33 40 14 13 14.0 54
117 14 34 40 14 8 13.0 67
118 14 33 35 13 11 9.0 69
119 14 38 36 16 12 15.0 90
120 12 33 37 13 11 15.0 54
121 14 31 27 16 13 14.0 76
122 15 38 39 13 12 11.0 89
123 15 37 38 16 14 8.0 76
124 15 36 31 15 13 11.0 73
125 13 31 33 16 15 11.0 79
126 17 39 32 15 10 8.0 90
127 17 44 39 17 11 10.0 74
128 19 33 36 15 9 11.0 81
129 15 35 33 12 11 13.0 72
130 13 32 33 16 10 11.0 71
131 9 28 32 10 11 20.0 66
132 15 40 37 16 8 10.0 77
133 15 27 30 12 11 15.0 65
134 15 37 38 14 12 12.0 74
135 16 32 29 15 12 14.0 85
136 11 28 22 13 9 23.0 54
137 14 34 35 15 11 14.0 63
138 11 30 35 11 10 16.0 54
139 15 35 34 12 8 11.0 64
140 13 31 35 11 9 12.0 69
141 15 32 34 16 8 10.0 54
142 16 30 37 15 9 14.0 84
143 14 30 35 17 15 12.0 86
144 15 31 23 16 11 12.0 77
145 16 40 31 10 8 11.0 89
146 16 32 27 18 13 12.0 76
147 11 36 36 13 12 13.0 60
148 12 32 31 16 12 11.0 75
149 9 35 32 13 9 19.0 73
150 16 38 39 10 7 12.0 85
151 13 42 37 15 13 17.0 79
152 16 34 38 16 9 9.0 71
153 12 35 39 16 6 12.0 72
154 9 38 34 14 8 19.0 69
155 13 33 31 10 8 18.0 78
156 13 36 32 17 15 15.0 54
157 14 32 37 13 6 14.0 69
158 19 33 36 15 9 11.0 81
159 13 34 32 16 11 9.0 84
160 12 32 38 12 8 18.0 84
161 13 34 36 13 8 16.0 69
162 10 27 26 13 10 24.0 66
163 14 31 26 12 8 14.0 81
164 16 38 33 17 14 20.0 82
165 10 34 39 15 10 18.0 72
166 11 24 30 10 8 23.0 54
167 14 30 33 14 11 12.0 78
168 12 26 25 11 12 14.0 74
169 9 34 38 13 12 16.0 82
170 9 27 37 16 12 18.0 73
171 11 37 31 12 5 20.0 55
172 16 36 37 16 12 12.0 72
173 9 41 35 12 10 12.0 78
174 13 29 25 9 7 17.0 59
175 16 36 28 12 12 13.0 72
176 13 32 35 15 11 9.0 78
177 9 37 33 12 8 16.0 68
178 12 30 30 12 9 18.0 69
179 16 31 31 14 10 10.0 67
180 11 38 37 12 9 14.0 74
181 14 36 36 16 12 11.0 54
182 13 35 30 11 6 9.0 67
183 15 31 36 19 15 11.0 70
184 14 38 32 15 12 10.0 80
185 16 22 28 8 12 11.0 89
186 13 32 36 16 12 19.0 76
187 14 36 34 17 11 14.0 74
188 15 39 31 12 7 12.0 87
189 13 28 28 11 7 14.0 54
190 11 32 36 11 5 21.0 61
191 11 32 36 14 12 13.0 38
192 14 38 40 16 12 10.0 75
193 15 32 33 12 3 15.0 69
194 11 35 37 16 11 16.0 62
195 15 32 32 13 10 14.0 72
196 12 37 38 15 12 12.0 70
197 14 34 31 16 9 19.0 79
198 14 33 37 16 12 15.0 87
199 8 33 33 14 9 19.0 62
200 13 26 32 16 12 13.0 77
201 9 30 30 16 12 17.0 69
202 15 24 30 14 10 12.0 69
203 17 34 31 11 9 11.0 75
204 13 34 32 12 12 14.0 54
205 15 33 34 15 8 11.0 72
206 15 34 36 15 11 13.0 74
207 14 35 37 16 11 12.0 85
208 16 35 36 16 12 15.0 52
209 13 36 33 11 10 14.0 70
210 16 34 33 15 10 12.0 84
211 9 34 33 12 12 17.0 64
212 16 41 44 12 12 11.0 84
213 11 32 39 15 11 18.0 87
214 10 30 32 15 8 13.0 79
215 11 35 35 16 12 17.0 67
216 15 28 25 14 10 13.0 65
217 17 33 35 17 11 11.0 85
218 14 39 34 14 10 12.0 83
219 8 36 35 13 8 22.0 61
220 15 36 39 15 12 14.0 82
221 11 35 33 13 12 12.0 76
222 16 38 36 14 10 12.0 58
223 10 33 32 15 12 17.0 72
224 15 31 32 12 9 9.0 72
225 9 34 36 13 9 21.0 38
226 16 32 36 8 6 10.0 78
227 19 31 32 14 10 11.0 54
228 12 33 34 14 9 12.0 63
229 8 34 33 11 9 23.0 66
230 11 34 35 12 9 13.0 70
231 14 34 30 13 6 12.0 71
232 9 33 38 10 10 16.0 67
233 15 32 34 16 6 9.0 58
234 13 41 33 18 14 17.0 72
235 16 34 32 13 10 9.0 72
236 11 36 31 11 10 14.0 70
237 12 37 30 4 6 17.0 76
238 13 36 27 13 12 13.0 50
239 10 29 31 16 12 11.0 72
240 11 37 30 10 7 12.0 72
241 12 27 32 12 8 10.0 88
242 8 35 35 12 11 19.0 53
243 12 28 28 10 3 16.0 58
244 12 35 33 13 6 16.0 66
245 15 37 31 15 10 14.0 82
246 11 29 35 12 8 20.0 69
247 13 32 35 14 9 15.0 68
248 14 36 32 10 9 23.0 44
249 10 19 21 12 8 20.0 56
250 12 21 20 12 9 16.0 53
251 15 31 34 11 7 14.0 70
252 13 33 32 10 7 17.0 78
253 13 36 34 12 6 11.0 71
254 13 33 32 16 9 13.0 72
255 12 37 33 12 10 17.0 68
256 12 34 33 14 11 15.0 67
257 9 35 37 16 12 21.0 75
258 9 31 32 14 8 18.0 62
259 15 37 34 13 11 15.0 67
260 10 35 30 4 3 8.0 83
261 14 27 30 15 11 12.0 64
262 15 34 38 11 12 12.0 68
263 7 40 36 11 7 22.0 62
264 14 29 32 14 9 12.0 72
Belonging_Final
1 32
2 51
3 42
4 41
5 46
6 47
7 37
8 49
9 45
10 47
11 49
12 33
13 42
14 33
15 53
16 36
17 45
18 54
19 41
20 36
21 41
22 44
23 33
24 37
25 52
26 47
27 43
28 44
29 45
30 44
31 49
32 33
33 43
34 54
35 42
36 44
37 37
38 43
39 46
40 42
41 45
42 44
43 33
44 31
45 42
46 40
47 43
48 46
49 42
50 45
51 44
52 40
53 37
54 46
55 36
56 47
57 45
58 42
59 43
60 43
61 32
62 45
63 48
64 31
65 33
66 49
67 42
68 41
69 38
70 42
71 44
72 33
73 48
74 40
75 50
76 49
77 43
78 44
79 47
80 33
81 46
82 45
83 43
84 44
85 47
86 45
87 42
88 33
89 43
90 46
91 33
92 46
93 48
94 47
95 47
96 43
97 46
98 48
99 46
100 45
101 45
102 52
103 42
104 47
105 41
106 47
107 43
108 33
109 30
110 52
111 44
112 55
113 11
114 47
115 53
116 33
117 44
118 42
119 55
120 33
121 46
122 54
123 47
124 45
125 47
126 55
127 44
128 53
129 44
130 42
131 40
132 46
133 40
134 46
135 53
136 33
137 42
138 35
139 40
140 41
141 33
142 51
143 53
144 46
145 55
146 47
147 38
148 46
149 46
150 53
151 47
152 41
153 44
154 43
155 51
156 33
157 43
158 53
159 51
160 50
161 46
162 43
163 47
164 50
165 43
166 33
167 48
168 44
169 50
170 41
171 34
172 44
173 47
174 35
175 44
176 44
177 43
178 41
179 41
180 42
181 33
182 41
183 44
184 48
185 55
186 44
187 43
188 52
189 30
190 39
191 11
192 44
193 42
194 41
195 44
196 44
197 48
198 53
199 37
200 44
201 44
202 40
203 42
204 35
205 43
206 45
207 55
208 31
209 44
210 50
211 40
212 53
213 54
214 49
215 40
216 41
217 52
218 52
219 36
220 52
221 46
222 31
223 44
224 44
225 11
226 46
227 33
228 34
229 42
230 43
231 43
232 44
233 36
234 46
235 44
236 43
237 50
238 33
239 43
240 44
241 53
242 34
243 35
244 40
245 53
246 42
247 43
248 29
249 36
250 30
251 42
252 47
253 44
254 45
255 44
256 43
257 43
258 40
259 41
260 52
261 38
262 41
263 39
264 43
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning
14.769079 0.012793 0.010104 0.113394
Software Depression Belonging Belonging_Final
-0.007160 -0.378113 0.006582 0.025797
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7875 -1.4036 0.2378 1.2840 5.1812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.769079 1.854701 7.963 5.5e-14 ***
Connected 0.012793 0.037424 0.342 0.733
Separate 0.010104 0.038322 0.264 0.792
Learning 0.113394 0.066639 1.702 0.090 .
Software -0.007160 0.068898 -0.104 0.917
Depression -0.378113 0.039124 -9.664 < 2e-16 ***
Belonging 0.006582 0.040588 0.162 0.871
Belonging_Final 0.025797 0.060514 0.426 0.670
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.021 on 256 degrees of freedom
Multiple R-squared: 0.3635, Adjusted R-squared: 0.3461
F-statistic: 20.88 on 7 and 256 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.02376387 0.047527750 0.976236125
[2,] 0.70803167 0.583936656 0.291968328
[3,] 0.96351192 0.072976166 0.036488083
[4,] 0.93786869 0.124262630 0.062131315
[5,] 0.94136680 0.117266395 0.058633197
[6,] 0.91038750 0.179224993 0.089612496
[7,] 0.94729422 0.105411562 0.052705781
[8,] 0.92015433 0.159691346 0.079845673
[9,] 0.88459061 0.230818781 0.115409391
[10,] 0.88186913 0.236261738 0.118130869
[11,] 0.91253666 0.174926680 0.087463340
[12,] 0.91021562 0.179568756 0.089784378
[13,] 0.91123640 0.177527208 0.088763604
[14,] 0.88202641 0.235947174 0.117973587
[15,] 0.85868746 0.282625080 0.141312540
[16,] 0.99887026 0.002259474 0.001129737
[17,] 0.99822210 0.003555792 0.001777896
[18,] 0.99719969 0.005600625 0.002800313
[19,] 0.99574884 0.008502318 0.004251159
[20,] 0.99707308 0.005853833 0.002926916
[21,] 0.99558911 0.008821776 0.004410888
[22,] 0.99368850 0.012622995 0.006311497
[23,] 0.99241903 0.015161937 0.007580969
[24,] 0.98908703 0.021825942 0.010912971
[25,] 0.98658627 0.026827453 0.013413726
[26,] 0.98149726 0.037005480 0.018502740
[27,] 0.98736329 0.025273411 0.012636706
[28,] 0.98266632 0.034667360 0.017333680
[29,] 0.98021709 0.039565811 0.019782905
[30,] 0.98055229 0.038895417 0.019447708
[31,] 0.97458290 0.050834208 0.025417104
[32,] 0.97215931 0.055681386 0.027840693
[33,] 0.96346540 0.073069192 0.036534596
[34,] 0.95708694 0.085826124 0.042913062
[35,] 0.94503532 0.109929364 0.054964682
[36,] 0.95439957 0.091200860 0.045600430
[37,] 0.94203298 0.115934040 0.057967020
[38,] 0.92704497 0.145910056 0.072955028
[39,] 0.93839869 0.123202629 0.061601314
[40,] 0.93446890 0.131062192 0.065531096
[41,] 0.92028013 0.159439744 0.079719872
[42,] 0.90265927 0.194681465 0.097340733
[43,] 0.88862485 0.222750293 0.111375147
[44,] 0.87158760 0.256824794 0.128412397
[45,] 0.86426558 0.271468832 0.135734416
[46,] 0.84575063 0.308498743 0.154249372
[47,] 0.83229038 0.335419239 0.167709620
[48,] 0.80335567 0.393288665 0.196644333
[49,] 0.84584700 0.308306000 0.154153000
[50,] 0.84142147 0.317157061 0.158578530
[51,] 0.85985003 0.280299933 0.140149967
[52,] 0.85868335 0.282633307 0.141316653
[53,] 0.90964462 0.180710761 0.090355381
[54,] 0.89683995 0.206320098 0.103160049
[55,] 0.88650583 0.226988346 0.113494173
[56,] 0.95902047 0.081959051 0.040979525
[57,] 0.95749197 0.085016057 0.042508029
[58,] 0.96274696 0.074506072 0.037253036
[59,] 0.95773122 0.084537555 0.042268777
[60,] 0.95140057 0.097198862 0.048599431
[61,] 0.94092808 0.118143847 0.059071923
[62,] 0.95422986 0.091540281 0.045770141
[63,] 0.94418524 0.111629528 0.055814764
[64,] 0.93307154 0.133856926 0.066928463
[65,] 0.92813467 0.143730657 0.071865329
[66,] 0.91442282 0.171154351 0.085577176
[67,] 0.92644447 0.147111055 0.073555527
[68,] 0.91259216 0.174815679 0.087407839
[69,] 0.90379404 0.192411919 0.096205959
[70,] 0.89685212 0.206295761 0.103147881
[71,] 0.87842474 0.243150525 0.121575263
[72,] 0.85879001 0.282419973 0.141209986
[73,] 0.85102025 0.297959504 0.148979752
[74,] 0.83034852 0.339302958 0.169651479
[75,] 0.80484634 0.390307314 0.195153657
[76,] 0.78117203 0.437655931 0.218827966
[77,] 0.75242483 0.495150349 0.247575175
[78,] 0.72155716 0.556885682 0.278442841
[79,] 0.79527042 0.409459166 0.204729583
[80,] 0.82932732 0.341345353 0.170672676
[81,] 0.80476816 0.390463680 0.195231840
[82,] 0.78117336 0.437653272 0.218826636
[83,] 0.75750145 0.484997110 0.242498555
[84,] 0.73248205 0.535035909 0.267517955
[85,] 0.70593554 0.588128913 0.294064457
[86,] 0.68017554 0.639648915 0.319824457
[87,] 0.65232487 0.695350261 0.347675131
[88,] 0.65084932 0.698301364 0.349150682
[89,] 0.61683253 0.766334939 0.383167470
[90,] 0.60694900 0.786101993 0.393050996
[91,] 0.58245095 0.835098106 0.417549053
[92,] 0.55284558 0.894308841 0.447154421
[93,] 0.60113492 0.797730164 0.398865082
[94,] 0.58997712 0.820045763 0.410022881
[95,] 0.60527016 0.789459684 0.394729842
[96,] 0.57783737 0.844325268 0.422162634
[97,] 0.57014872 0.859702560 0.429851280
[98,] 0.61050953 0.778980937 0.389490468
[99,] 0.58290947 0.834181051 0.417090525
[100,] 0.55732631 0.885347386 0.442673693
[101,] 0.56227596 0.875448076 0.437724038
[102,] 0.59096558 0.818068848 0.409034424
[103,] 0.58869194 0.822616111 0.411308055
[104,] 0.67782427 0.644351456 0.322175728
[105,] 0.64675543 0.706489140 0.353244570
[106,] 0.62217245 0.755655101 0.377827550
[107,] 0.58918217 0.821635658 0.410817829
[108,] 0.56531466 0.869370672 0.434685336
[109,] 0.53126386 0.937472281 0.468736140
[110,] 0.50010213 0.999795732 0.499897866
[111,] 0.46950375 0.939007497 0.530496251
[112,] 0.43888110 0.877762204 0.561118898
[113,] 0.41229910 0.824598204 0.587700898
[114,] 0.37991033 0.759820665 0.620089668
[115,] 0.36869197 0.737383936 0.631308032
[116,] 0.33970405 0.679408093 0.660295954
[117,] 0.32702283 0.654045657 0.672977171
[118,] 0.42997381 0.859947620 0.570026190
[119,] 0.41334535 0.826690707 0.586654647
[120,] 0.40218049 0.804360972 0.597819514
[121,] 0.38664469 0.773289383 0.613355308
[122,] 0.35931460 0.718629197 0.640685401
[123,] 0.38009032 0.760180647 0.619909676
[124,] 0.35322850 0.706457005 0.646771498
[125,] 0.36488791 0.729775829 0.635112085
[126,] 0.35439428 0.708788567 0.645605717
[127,] 0.32529850 0.650597006 0.674701497
[128,] 0.30126943 0.602538850 0.698730575
[129,] 0.27580168 0.551603363 0.724198318
[130,] 0.25255562 0.505111240 0.747444380
[131,] 0.22559642 0.451192833 0.774403583
[132,] 0.23135643 0.462712857 0.768643571
[133,] 0.20677186 0.413543717 0.793228142
[134,] 0.18613738 0.372274756 0.813862622
[135,] 0.17431817 0.348636331 0.825681835
[136,] 0.16658201 0.333164017 0.833417991
[137,] 0.17502643 0.350052854 0.824973573
[138,] 0.19128501 0.382570015 0.808714992
[139,] 0.20765451 0.415309025 0.792345487
[140,] 0.20858321 0.417166413 0.791416793
[141,] 0.18817884 0.376357677 0.811821162
[142,] 0.16916402 0.338328031 0.830835984
[143,] 0.18289032 0.365780649 0.817109675
[144,] 0.19815066 0.396301323 0.801849338
[145,] 0.18402720 0.368054392 0.815972804
[146,] 0.16120591 0.322411815 0.838794093
[147,] 0.14364933 0.287298666 0.856350667
[148,] 0.22548567 0.450971340 0.774514330
[149,] 0.24348300 0.486966002 0.756516999
[150,] 0.22293695 0.445873895 0.777063053
[151,] 0.20000076 0.400001517 0.799999242
[152,] 0.17687470 0.353749396 0.823125302
[153,] 0.15645175 0.312903507 0.843548246
[154,] 0.26243010 0.524860201 0.737569899
[155,] 0.25898068 0.517961354 0.741019323
[156,] 0.25504235 0.510084703 0.744957649
[157,] 0.22677728 0.453554564 0.773222718
[158,] 0.20476681 0.409533617 0.795233192
[159,] 0.26149868 0.522997362 0.738501319
[160,] 0.28635710 0.572714208 0.713642896
[161,] 0.25879205 0.517584094 0.741207953
[162,] 0.25382213 0.507644257 0.746177871
[163,] 0.41889712 0.837794238 0.581102881
[164,] 0.40493794 0.809875884 0.595062058
[165,] 0.42743187 0.854863748 0.572568126
[166,] 0.43663151 0.873263027 0.563368487
[167,] 0.49466365 0.989327298 0.505336351
[168,] 0.46106406 0.922128111 0.538935945
[169,] 0.43839058 0.876781160 0.561609420
[170,] 0.43783811 0.875676216 0.562161892
[171,] 0.40054069 0.801081371 0.599459315
[172,] 0.39350097 0.787001933 0.606499034
[173,] 0.35646229 0.712924574 0.643537713
[174,] 0.32978812 0.659576238 0.670211881
[175,] 0.34497673 0.689953460 0.655023270
[176,] 0.33754393 0.675087861 0.662456070
[177,] 0.30348252 0.606965038 0.696517481
[178,] 0.27653914 0.553078286 0.723460857
[179,] 0.24580846 0.491616911 0.754191545
[180,] 0.22327533 0.446550663 0.776724669
[181,] 0.22713213 0.454264268 0.772867866
[182,] 0.20902730 0.418054603 0.790972699
[183,] 0.22693882 0.453877649 0.773061176
[184,] 0.21504533 0.430090666 0.784954667
[185,] 0.21447646 0.428952929 0.785523536
[186,] 0.22584942 0.451698845 0.774150577
[187,] 0.27297037 0.545940744 0.727029628
[188,] 0.25474602 0.509492047 0.745253977
[189,] 0.28826457 0.576529144 0.711735428
[190,] 0.25538386 0.510767720 0.744616140
[191,] 0.29298404 0.585968086 0.707015957
[192,] 0.27138385 0.542767699 0.728616150
[193,] 0.33314186 0.666283720 0.666858140
[194,] 0.29718240 0.594364809 0.702817595
[195,] 0.26300372 0.526007447 0.736996276
[196,] 0.24156488 0.483129760 0.758435120
[197,] 0.21019271 0.420385426 0.789807287
[198,] 0.22972796 0.459455914 0.770272043
[199,] 0.19782186 0.395643718 0.802178141
[200,] 0.21454198 0.429083958 0.785458021
[201,] 0.24256198 0.485123964 0.757438018
[202,] 0.22675831 0.453516621 0.773241689
[203,] 0.19932899 0.398657977 0.800671012
[204,] 0.24891491 0.497829821 0.751085089
[205,] 0.22149537 0.442990748 0.778504626
[206,] 0.20817150 0.416343001 0.791828499
[207,] 0.24119662 0.482393246 0.758803377
[208,] 0.21050901 0.421018029 0.789490985
[209,] 0.19649436 0.392988722 0.803505639
[210,] 0.20403861 0.408077223 0.795961388
[211,] 0.21221622 0.424432431 0.787783784
[212,] 0.21331402 0.426628045 0.786685977
[213,] 0.19721536 0.394430726 0.802784637
[214,] 0.16516702 0.330334049 0.834832975
[215,] 0.14952470 0.299049395 0.850475303
[216,] 0.17841431 0.356828625 0.821585688
[217,] 0.37058474 0.741169489 0.629415255
[218,] 0.34717964 0.694359281 0.652820360
[219,] 0.32012118 0.640242354 0.679878823
[220,] 0.30491756 0.609835111 0.695082445
[221,] 0.26225601 0.524512017 0.737743992
[222,] 0.30163407 0.603268143 0.698365929
[223,] 0.25691070 0.513821407 0.743089296
[224,] 0.21510054 0.430201084 0.784899458
[225,] 0.21432024 0.428640484 0.785679758
[226,] 0.19002633 0.380052666 0.809973667
[227,] 0.15913965 0.318279303 0.840860348
[228,] 0.12400491 0.248009824 0.875995088
[229,] 0.24104336 0.482086721 0.758956639
[230,] 0.23647155 0.472943094 0.763528453
[231,] 0.20434836 0.408696722 0.795651639
[232,] 0.40408733 0.808174661 0.595912669
[233,] 0.35993075 0.719861498 0.640069251
[234,] 0.30004034 0.600080675 0.699959662
[235,] 0.42984169 0.859683372 0.570158314
[236,] 0.34509233 0.690184651 0.654907674
[237,] 0.26796980 0.535939591 0.732030204
[238,] 0.41186821 0.823736418 0.588131791
[239,] 0.31888288 0.637765757 0.681117121
[240,] 0.23060127 0.461202548 0.769398726
[241,] 0.35622797 0.712455946 0.643772027
[242,] 0.87268770 0.254624592 0.127312296
[243,] 0.75032076 0.499358478 0.249679239
> postscript(file="/var/wessaorg/rcomp/tmp/1jn711384785375.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/2rd6w1384785375.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/39q0z1384785375.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4chrm1384785375.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5k8md1384785375.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.297272232 2.970430513 -2.777886146 -2.118345310 5.181167742 3.899145911
7 8 9 10 11 12
3.365364043 -0.799807956 0.048350402 0.939951789 1.685975746 3.516641002
13 14 15 16 17 18
-3.189996147 2.699362665 2.455879676 0.856268626 0.456817905 1.365883236
19 20 21 22 23 24
-1.312070670 2.381447645 2.852567832 -2.496318772 -0.382724632 -1.401507904
25 26 27 28 29 30
1.818654379 -6.787520704 1.223786299 0.916716260 1.366273537 -2.713968710
31 32 33 34 35 36
0.518545346 0.723709771 2.132993158 -0.092938809 0.336186343 0.826822306
37 38 39 40 41 42
-1.329684047 0.896123463 1.898248113 -2.049456608 -0.553350999 2.625810802
43 44 45 46 47 48
0.079167696 -0.914559958 0.567585786 -2.321446241 -0.277774854 0.322012908
49 50 51 52 53 54
3.752045706 -1.575392041 0.895702354 0.823230830 -0.349804047 -1.610370199
55 56 57 58 59 60
-1.670630397 1.629346951 1.927626240 -0.333432889 -3.036924392 -1.201375798
61 62 63 64 65 66
-2.358469944 -1.457438645 -3.499187874 1.123563379 1.463102654 -5.056652814
67 68 69 70 71 72
-1.614071109 -2.388799764 1.573556553 1.436898222 0.643803431 3.406606175
73 74 75 76 77 78
0.605139063 -0.315220989 -1.888709308 -0.040833937 3.007980905 0.585899216
79 80 81 82 83 84
1.367502266 -2.047310678 0.169244213 -0.544220533 1.756773326 0.784257953
85 86 87 88 89 90
-0.050963945 1.088624936 -0.263429386 0.273188377 -3.391182744 3.422641123
91 92 93 94 95 96
0.091724531 0.862527027 0.817375078 -0.843987896 1.045788121 -0.830891056
97 98 99 100 101 102
-0.820719633 2.073523136 0.036137148 1.787029461 -0.923132224 0.872505299
103 104 105 106 107 108
-3.442146007 1.996538531 -2.313421250 0.995937805 2.057197916 -2.853833832
109 110 111 112 113 114
0.943883171 1.167895836 -2.163584055 -2.280447204 2.230573108 3.949597176
115 116 117 118 119 120
0.334096197 0.997019657 0.200982798 -1.074849873 0.313157628 -0.495481523
121 122 123 124 125 126
0.447009750 0.142955189 -1.028205754 0.367227139 -1.779175015 0.793065641
127 128 129 130 131 132
1.584046637 4.067414174 1.474278058 -1.646128615 -1.409811845 -0.323999401
133 134 135 136 137 138
2.512419131 0.735673643 2.280427157 1.730625613 0.615623100 -0.890748655
139 140 141 142 143 144
0.842310595 -0.676660842 0.295397795 2.261876394 -0.722726467 0.710298759
145 146 147 148 149 150
1.483946466 1.425406260 -2.441297599 -2.741115974 -2.432831504 1.877573511
151 152 153 154 155 156
0.407437719 0.540177056 -2.453835041 -2.508253858 1.395872911 0.091724531
157 158 159 160 161 162
0.746701227 4.067414174 -2.728412372 0.097456678 0.424373475 0.751322816
163 164 165 166 167 168
0.816183918 4.316599106 -2.004534364 2.033986381 -0.209337194 -0.844250338
169 170 171 172 173 174
-3.755945030 -2.948837352 0.442590038 1.596540426 -5.124841637 1.773597050
175 176 177 178 179 180
2.519165539 -2.399674342 -3.386325624 0.541934872 1.287671396 -2.202292949
181 182 183 184 185 186
-0.369228865 -1.819966992 -0.013042418 -1.177198734 1.999958103 1.278278518
187 188 189 190 191 192
0.275158020 0.731459129 0.556845997 0.779064623 -1.662198497 -1.235327962
193 194 195 196 197 198
2.282941298 -1.742167441 1.780319452 -2.299799211 2.158799124 0.438357286
199 200 201 202 203 204
-3.226170497 -0.879806273 -3.345665264 1.156184851 2.881976877 0.233092713
205 206 207 208 209 210
0.397669468 1.077616320 -0.767157835 3.220772771 -0.041004800 1.527852966
211 212 213 214 215 216
-2.837475845 1.226155811 -1.354283028 -3.988373324 -1.343797628 1.534175911
217 218 219 220 221 222
1.864520965 -0.477834206 -2.011806126 1.173757339 -3.087989714 2.221033808
223 224 225 226 227 228
-2.310602991 0.008782471 -0.570968345 1.674697410 4.947619500 -1.812253810
229 230 231 232 233 234
-1.541643257 -2.508496724 0.022455110 -3.163782088 -0.200753348 0.199493857
235 236 237 238 239 240
0.864169982 -1.994999455 0.681697001 -0.155558301 -4.605600744 -2.700960451
241 242 243 244 245 246
-2.906575012 -2.894229715 0.242512548 -0.397898935 1.201679677 0.227475671
247 248 249 250 251 252
0.059689910 5.036429545 -0.262793951 0.390961756 2.042969767 1.103684959
253 254 255 256 257 258
-1.334062867 -0.983725275 -0.019690029 -0.924785950 -1.981599721 -2.653145179
259 260 261 262 263 264
2.191719821 -4.814878241 0.096075126 1.282710171 -2.937427071 -0.032284246
> postscript(file="/var/wessaorg/rcomp/tmp/6zaai1384785375.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 0.297272232 NA
1 2.970430513 0.297272232
2 -2.777886146 2.970430513
3 -2.118345310 -2.777886146
4 5.181167742 -2.118345310
5 3.899145911 5.181167742
6 3.365364043 3.899145911
7 -0.799807956 3.365364043
8 0.048350402 -0.799807956
9 0.939951789 0.048350402
10 1.685975746 0.939951789
11 3.516641002 1.685975746
12 -3.189996147 3.516641002
13 2.699362665 -3.189996147
14 2.455879676 2.699362665
15 0.856268626 2.455879676
16 0.456817905 0.856268626
17 1.365883236 0.456817905
18 -1.312070670 1.365883236
19 2.381447645 -1.312070670
20 2.852567832 2.381447645
21 -2.496318772 2.852567832
22 -0.382724632 -2.496318772
23 -1.401507904 -0.382724632
24 1.818654379 -1.401507904
25 -6.787520704 1.818654379
26 1.223786299 -6.787520704
27 0.916716260 1.223786299
28 1.366273537 0.916716260
29 -2.713968710 1.366273537
30 0.518545346 -2.713968710
31 0.723709771 0.518545346
32 2.132993158 0.723709771
33 -0.092938809 2.132993158
34 0.336186343 -0.092938809
35 0.826822306 0.336186343
36 -1.329684047 0.826822306
37 0.896123463 -1.329684047
38 1.898248113 0.896123463
39 -2.049456608 1.898248113
40 -0.553350999 -2.049456608
41 2.625810802 -0.553350999
42 0.079167696 2.625810802
43 -0.914559958 0.079167696
44 0.567585786 -0.914559958
45 -2.321446241 0.567585786
46 -0.277774854 -2.321446241
47 0.322012908 -0.277774854
48 3.752045706 0.322012908
49 -1.575392041 3.752045706
50 0.895702354 -1.575392041
51 0.823230830 0.895702354
52 -0.349804047 0.823230830
53 -1.610370199 -0.349804047
54 -1.670630397 -1.610370199
55 1.629346951 -1.670630397
56 1.927626240 1.629346951
57 -0.333432889 1.927626240
58 -3.036924392 -0.333432889
59 -1.201375798 -3.036924392
60 -2.358469944 -1.201375798
61 -1.457438645 -2.358469944
62 -3.499187874 -1.457438645
63 1.123563379 -3.499187874
64 1.463102654 1.123563379
65 -5.056652814 1.463102654
66 -1.614071109 -5.056652814
67 -2.388799764 -1.614071109
68 1.573556553 -2.388799764
69 1.436898222 1.573556553
70 0.643803431 1.436898222
71 3.406606175 0.643803431
72 0.605139063 3.406606175
73 -0.315220989 0.605139063
74 -1.888709308 -0.315220989
75 -0.040833937 -1.888709308
76 3.007980905 -0.040833937
77 0.585899216 3.007980905
78 1.367502266 0.585899216
79 -2.047310678 1.367502266
80 0.169244213 -2.047310678
81 -0.544220533 0.169244213
82 1.756773326 -0.544220533
83 0.784257953 1.756773326
84 -0.050963945 0.784257953
85 1.088624936 -0.050963945
86 -0.263429386 1.088624936
87 0.273188377 -0.263429386
88 -3.391182744 0.273188377
89 3.422641123 -3.391182744
90 0.091724531 3.422641123
91 0.862527027 0.091724531
92 0.817375078 0.862527027
93 -0.843987896 0.817375078
94 1.045788121 -0.843987896
95 -0.830891056 1.045788121
96 -0.820719633 -0.830891056
97 2.073523136 -0.820719633
98 0.036137148 2.073523136
99 1.787029461 0.036137148
100 -0.923132224 1.787029461
101 0.872505299 -0.923132224
102 -3.442146007 0.872505299
103 1.996538531 -3.442146007
104 -2.313421250 1.996538531
105 0.995937805 -2.313421250
106 2.057197916 0.995937805
107 -2.853833832 2.057197916
108 0.943883171 -2.853833832
109 1.167895836 0.943883171
110 -2.163584055 1.167895836
111 -2.280447204 -2.163584055
112 2.230573108 -2.280447204
113 3.949597176 2.230573108
114 0.334096197 3.949597176
115 0.997019657 0.334096197
116 0.200982798 0.997019657
117 -1.074849873 0.200982798
118 0.313157628 -1.074849873
119 -0.495481523 0.313157628
120 0.447009750 -0.495481523
121 0.142955189 0.447009750
122 -1.028205754 0.142955189
123 0.367227139 -1.028205754
124 -1.779175015 0.367227139
125 0.793065641 -1.779175015
126 1.584046637 0.793065641
127 4.067414174 1.584046637
128 1.474278058 4.067414174
129 -1.646128615 1.474278058
130 -1.409811845 -1.646128615
131 -0.323999401 -1.409811845
132 2.512419131 -0.323999401
133 0.735673643 2.512419131
134 2.280427157 0.735673643
135 1.730625613 2.280427157
136 0.615623100 1.730625613
137 -0.890748655 0.615623100
138 0.842310595 -0.890748655
139 -0.676660842 0.842310595
140 0.295397795 -0.676660842
141 2.261876394 0.295397795
142 -0.722726467 2.261876394
143 0.710298759 -0.722726467
144 1.483946466 0.710298759
145 1.425406260 1.483946466
146 -2.441297599 1.425406260
147 -2.741115974 -2.441297599
148 -2.432831504 -2.741115974
149 1.877573511 -2.432831504
150 0.407437719 1.877573511
151 0.540177056 0.407437719
152 -2.453835041 0.540177056
153 -2.508253858 -2.453835041
154 1.395872911 -2.508253858
155 0.091724531 1.395872911
156 0.746701227 0.091724531
157 4.067414174 0.746701227
158 -2.728412372 4.067414174
159 0.097456678 -2.728412372
160 0.424373475 0.097456678
161 0.751322816 0.424373475
162 0.816183918 0.751322816
163 4.316599106 0.816183918
164 -2.004534364 4.316599106
165 2.033986381 -2.004534364
166 -0.209337194 2.033986381
167 -0.844250338 -0.209337194
168 -3.755945030 -0.844250338
169 -2.948837352 -3.755945030
170 0.442590038 -2.948837352
171 1.596540426 0.442590038
172 -5.124841637 1.596540426
173 1.773597050 -5.124841637
174 2.519165539 1.773597050
175 -2.399674342 2.519165539
176 -3.386325624 -2.399674342
177 0.541934872 -3.386325624
178 1.287671396 0.541934872
179 -2.202292949 1.287671396
180 -0.369228865 -2.202292949
181 -1.819966992 -0.369228865
182 -0.013042418 -1.819966992
183 -1.177198734 -0.013042418
184 1.999958103 -1.177198734
185 1.278278518 1.999958103
186 0.275158020 1.278278518
187 0.731459129 0.275158020
188 0.556845997 0.731459129
189 0.779064623 0.556845997
190 -1.662198497 0.779064623
191 -1.235327962 -1.662198497
192 2.282941298 -1.235327962
193 -1.742167441 2.282941298
194 1.780319452 -1.742167441
195 -2.299799211 1.780319452
196 2.158799124 -2.299799211
197 0.438357286 2.158799124
198 -3.226170497 0.438357286
199 -0.879806273 -3.226170497
200 -3.345665264 -0.879806273
201 1.156184851 -3.345665264
202 2.881976877 1.156184851
203 0.233092713 2.881976877
204 0.397669468 0.233092713
205 1.077616320 0.397669468
206 -0.767157835 1.077616320
207 3.220772771 -0.767157835
208 -0.041004800 3.220772771
209 1.527852966 -0.041004800
210 -2.837475845 1.527852966
211 1.226155811 -2.837475845
212 -1.354283028 1.226155811
213 -3.988373324 -1.354283028
214 -1.343797628 -3.988373324
215 1.534175911 -1.343797628
216 1.864520965 1.534175911
217 -0.477834206 1.864520965
218 -2.011806126 -0.477834206
219 1.173757339 -2.011806126
220 -3.087989714 1.173757339
221 2.221033808 -3.087989714
222 -2.310602991 2.221033808
223 0.008782471 -2.310602991
224 -0.570968345 0.008782471
225 1.674697410 -0.570968345
226 4.947619500 1.674697410
227 -1.812253810 4.947619500
228 -1.541643257 -1.812253810
229 -2.508496724 -1.541643257
230 0.022455110 -2.508496724
231 -3.163782088 0.022455110
232 -0.200753348 -3.163782088
233 0.199493857 -0.200753348
234 0.864169982 0.199493857
235 -1.994999455 0.864169982
236 0.681697001 -1.994999455
237 -0.155558301 0.681697001
238 -4.605600744 -0.155558301
239 -2.700960451 -4.605600744
240 -2.906575012 -2.700960451
241 -2.894229715 -2.906575012
242 0.242512548 -2.894229715
243 -0.397898935 0.242512548
244 1.201679677 -0.397898935
245 0.227475671 1.201679677
246 0.059689910 0.227475671
247 5.036429545 0.059689910
248 -0.262793951 5.036429545
249 0.390961756 -0.262793951
250 2.042969767 0.390961756
251 1.103684959 2.042969767
252 -1.334062867 1.103684959
253 -0.983725275 -1.334062867
254 -0.019690029 -0.983725275
255 -0.924785950 -0.019690029
256 -1.981599721 -0.924785950
257 -2.653145179 -1.981599721
258 2.191719821 -2.653145179
259 -4.814878241 2.191719821
260 0.096075126 -4.814878241
261 1.282710171 0.096075126
262 -2.937427071 1.282710171
263 -0.032284246 -2.937427071
264 NA -0.032284246
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.970430513 0.297272232
[2,] -2.777886146 2.970430513
[3,] -2.118345310 -2.777886146
[4,] 5.181167742 -2.118345310
[5,] 3.899145911 5.181167742
[6,] 3.365364043 3.899145911
[7,] -0.799807956 3.365364043
[8,] 0.048350402 -0.799807956
[9,] 0.939951789 0.048350402
[10,] 1.685975746 0.939951789
[11,] 3.516641002 1.685975746
[12,] -3.189996147 3.516641002
[13,] 2.699362665 -3.189996147
[14,] 2.455879676 2.699362665
[15,] 0.856268626 2.455879676
[16,] 0.456817905 0.856268626
[17,] 1.365883236 0.456817905
[18,] -1.312070670 1.365883236
[19,] 2.381447645 -1.312070670
[20,] 2.852567832 2.381447645
[21,] -2.496318772 2.852567832
[22,] -0.382724632 -2.496318772
[23,] -1.401507904 -0.382724632
[24,] 1.818654379 -1.401507904
[25,] -6.787520704 1.818654379
[26,] 1.223786299 -6.787520704
[27,] 0.916716260 1.223786299
[28,] 1.366273537 0.916716260
[29,] -2.713968710 1.366273537
[30,] 0.518545346 -2.713968710
[31,] 0.723709771 0.518545346
[32,] 2.132993158 0.723709771
[33,] -0.092938809 2.132993158
[34,] 0.336186343 -0.092938809
[35,] 0.826822306 0.336186343
[36,] -1.329684047 0.826822306
[37,] 0.896123463 -1.329684047
[38,] 1.898248113 0.896123463
[39,] -2.049456608 1.898248113
[40,] -0.553350999 -2.049456608
[41,] 2.625810802 -0.553350999
[42,] 0.079167696 2.625810802
[43,] -0.914559958 0.079167696
[44,] 0.567585786 -0.914559958
[45,] -2.321446241 0.567585786
[46,] -0.277774854 -2.321446241
[47,] 0.322012908 -0.277774854
[48,] 3.752045706 0.322012908
[49,] -1.575392041 3.752045706
[50,] 0.895702354 -1.575392041
[51,] 0.823230830 0.895702354
[52,] -0.349804047 0.823230830
[53,] -1.610370199 -0.349804047
[54,] -1.670630397 -1.610370199
[55,] 1.629346951 -1.670630397
[56,] 1.927626240 1.629346951
[57,] -0.333432889 1.927626240
[58,] -3.036924392 -0.333432889
[59,] -1.201375798 -3.036924392
[60,] -2.358469944 -1.201375798
[61,] -1.457438645 -2.358469944
[62,] -3.499187874 -1.457438645
[63,] 1.123563379 -3.499187874
[64,] 1.463102654 1.123563379
[65,] -5.056652814 1.463102654
[66,] -1.614071109 -5.056652814
[67,] -2.388799764 -1.614071109
[68,] 1.573556553 -2.388799764
[69,] 1.436898222 1.573556553
[70,] 0.643803431 1.436898222
[71,] 3.406606175 0.643803431
[72,] 0.605139063 3.406606175
[73,] -0.315220989 0.605139063
[74,] -1.888709308 -0.315220989
[75,] -0.040833937 -1.888709308
[76,] 3.007980905 -0.040833937
[77,] 0.585899216 3.007980905
[78,] 1.367502266 0.585899216
[79,] -2.047310678 1.367502266
[80,] 0.169244213 -2.047310678
[81,] -0.544220533 0.169244213
[82,] 1.756773326 -0.544220533
[83,] 0.784257953 1.756773326
[84,] -0.050963945 0.784257953
[85,] 1.088624936 -0.050963945
[86,] -0.263429386 1.088624936
[87,] 0.273188377 -0.263429386
[88,] -3.391182744 0.273188377
[89,] 3.422641123 -3.391182744
[90,] 0.091724531 3.422641123
[91,] 0.862527027 0.091724531
[92,] 0.817375078 0.862527027
[93,] -0.843987896 0.817375078
[94,] 1.045788121 -0.843987896
[95,] -0.830891056 1.045788121
[96,] -0.820719633 -0.830891056
[97,] 2.073523136 -0.820719633
[98,] 0.036137148 2.073523136
[99,] 1.787029461 0.036137148
[100,] -0.923132224 1.787029461
[101,] 0.872505299 -0.923132224
[102,] -3.442146007 0.872505299
[103,] 1.996538531 -3.442146007
[104,] -2.313421250 1.996538531
[105,] 0.995937805 -2.313421250
[106,] 2.057197916 0.995937805
[107,] -2.853833832 2.057197916
[108,] 0.943883171 -2.853833832
[109,] 1.167895836 0.943883171
[110,] -2.163584055 1.167895836
[111,] -2.280447204 -2.163584055
[112,] 2.230573108 -2.280447204
[113,] 3.949597176 2.230573108
[114,] 0.334096197 3.949597176
[115,] 0.997019657 0.334096197
[116,] 0.200982798 0.997019657
[117,] -1.074849873 0.200982798
[118,] 0.313157628 -1.074849873
[119,] -0.495481523 0.313157628
[120,] 0.447009750 -0.495481523
[121,] 0.142955189 0.447009750
[122,] -1.028205754 0.142955189
[123,] 0.367227139 -1.028205754
[124,] -1.779175015 0.367227139
[125,] 0.793065641 -1.779175015
[126,] 1.584046637 0.793065641
[127,] 4.067414174 1.584046637
[128,] 1.474278058 4.067414174
[129,] -1.646128615 1.474278058
[130,] -1.409811845 -1.646128615
[131,] -0.323999401 -1.409811845
[132,] 2.512419131 -0.323999401
[133,] 0.735673643 2.512419131
[134,] 2.280427157 0.735673643
[135,] 1.730625613 2.280427157
[136,] 0.615623100 1.730625613
[137,] -0.890748655 0.615623100
[138,] 0.842310595 -0.890748655
[139,] -0.676660842 0.842310595
[140,] 0.295397795 -0.676660842
[141,] 2.261876394 0.295397795
[142,] -0.722726467 2.261876394
[143,] 0.710298759 -0.722726467
[144,] 1.483946466 0.710298759
[145,] 1.425406260 1.483946466
[146,] -2.441297599 1.425406260
[147,] -2.741115974 -2.441297599
[148,] -2.432831504 -2.741115974
[149,] 1.877573511 -2.432831504
[150,] 0.407437719 1.877573511
[151,] 0.540177056 0.407437719
[152,] -2.453835041 0.540177056
[153,] -2.508253858 -2.453835041
[154,] 1.395872911 -2.508253858
[155,] 0.091724531 1.395872911
[156,] 0.746701227 0.091724531
[157,] 4.067414174 0.746701227
[158,] -2.728412372 4.067414174
[159,] 0.097456678 -2.728412372
[160,] 0.424373475 0.097456678
[161,] 0.751322816 0.424373475
[162,] 0.816183918 0.751322816
[163,] 4.316599106 0.816183918
[164,] -2.004534364 4.316599106
[165,] 2.033986381 -2.004534364
[166,] -0.209337194 2.033986381
[167,] -0.844250338 -0.209337194
[168,] -3.755945030 -0.844250338
[169,] -2.948837352 -3.755945030
[170,] 0.442590038 -2.948837352
[171,] 1.596540426 0.442590038
[172,] -5.124841637 1.596540426
[173,] 1.773597050 -5.124841637
[174,] 2.519165539 1.773597050
[175,] -2.399674342 2.519165539
[176,] -3.386325624 -2.399674342
[177,] 0.541934872 -3.386325624
[178,] 1.287671396 0.541934872
[179,] -2.202292949 1.287671396
[180,] -0.369228865 -2.202292949
[181,] -1.819966992 -0.369228865
[182,] -0.013042418 -1.819966992
[183,] -1.177198734 -0.013042418
[184,] 1.999958103 -1.177198734
[185,] 1.278278518 1.999958103
[186,] 0.275158020 1.278278518
[187,] 0.731459129 0.275158020
[188,] 0.556845997 0.731459129
[189,] 0.779064623 0.556845997
[190,] -1.662198497 0.779064623
[191,] -1.235327962 -1.662198497
[192,] 2.282941298 -1.235327962
[193,] -1.742167441 2.282941298
[194,] 1.780319452 -1.742167441
[195,] -2.299799211 1.780319452
[196,] 2.158799124 -2.299799211
[197,] 0.438357286 2.158799124
[198,] -3.226170497 0.438357286
[199,] -0.879806273 -3.226170497
[200,] -3.345665264 -0.879806273
[201,] 1.156184851 -3.345665264
[202,] 2.881976877 1.156184851
[203,] 0.233092713 2.881976877
[204,] 0.397669468 0.233092713
[205,] 1.077616320 0.397669468
[206,] -0.767157835 1.077616320
[207,] 3.220772771 -0.767157835
[208,] -0.041004800 3.220772771
[209,] 1.527852966 -0.041004800
[210,] -2.837475845 1.527852966
[211,] 1.226155811 -2.837475845
[212,] -1.354283028 1.226155811
[213,] -3.988373324 -1.354283028
[214,] -1.343797628 -3.988373324
[215,] 1.534175911 -1.343797628
[216,] 1.864520965 1.534175911
[217,] -0.477834206 1.864520965
[218,] -2.011806126 -0.477834206
[219,] 1.173757339 -2.011806126
[220,] -3.087989714 1.173757339
[221,] 2.221033808 -3.087989714
[222,] -2.310602991 2.221033808
[223,] 0.008782471 -2.310602991
[224,] -0.570968345 0.008782471
[225,] 1.674697410 -0.570968345
[226,] 4.947619500 1.674697410
[227,] -1.812253810 4.947619500
[228,] -1.541643257 -1.812253810
[229,] -2.508496724 -1.541643257
[230,] 0.022455110 -2.508496724
[231,] -3.163782088 0.022455110
[232,] -0.200753348 -3.163782088
[233,] 0.199493857 -0.200753348
[234,] 0.864169982 0.199493857
[235,] -1.994999455 0.864169982
[236,] 0.681697001 -1.994999455
[237,] -0.155558301 0.681697001
[238,] -4.605600744 -0.155558301
[239,] -2.700960451 -4.605600744
[240,] -2.906575012 -2.700960451
[241,] -2.894229715 -2.906575012
[242,] 0.242512548 -2.894229715
[243,] -0.397898935 0.242512548
[244,] 1.201679677 -0.397898935
[245,] 0.227475671 1.201679677
[246,] 0.059689910 0.227475671
[247,] 5.036429545 0.059689910
[248,] -0.262793951 5.036429545
[249,] 0.390961756 -0.262793951
[250,] 2.042969767 0.390961756
[251,] 1.103684959 2.042969767
[252,] -1.334062867 1.103684959
[253,] -0.983725275 -1.334062867
[254,] -0.019690029 -0.983725275
[255,] -0.924785950 -0.019690029
[256,] -1.981599721 -0.924785950
[257,] -2.653145179 -1.981599721
[258,] 2.191719821 -2.653145179
[259,] -4.814878241 2.191719821
[260,] 0.096075126 -4.814878241
[261,] 1.282710171 0.096075126
[262,] -2.937427071 1.282710171
[263,] -0.032284246 -2.937427071
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.970430513 0.297272232
2 -2.777886146 2.970430513
3 -2.118345310 -2.777886146
4 5.181167742 -2.118345310
5 3.899145911 5.181167742
6 3.365364043 3.899145911
7 -0.799807956 3.365364043
8 0.048350402 -0.799807956
9 0.939951789 0.048350402
10 1.685975746 0.939951789
11 3.516641002 1.685975746
12 -3.189996147 3.516641002
13 2.699362665 -3.189996147
14 2.455879676 2.699362665
15 0.856268626 2.455879676
16 0.456817905 0.856268626
17 1.365883236 0.456817905
18 -1.312070670 1.365883236
19 2.381447645 -1.312070670
20 2.852567832 2.381447645
21 -2.496318772 2.852567832
22 -0.382724632 -2.496318772
23 -1.401507904 -0.382724632
24 1.818654379 -1.401507904
25 -6.787520704 1.818654379
26 1.223786299 -6.787520704
27 0.916716260 1.223786299
28 1.366273537 0.916716260
29 -2.713968710 1.366273537
30 0.518545346 -2.713968710
31 0.723709771 0.518545346
32 2.132993158 0.723709771
33 -0.092938809 2.132993158
34 0.336186343 -0.092938809
35 0.826822306 0.336186343
36 -1.329684047 0.826822306
37 0.896123463 -1.329684047
38 1.898248113 0.896123463
39 -2.049456608 1.898248113
40 -0.553350999 -2.049456608
41 2.625810802 -0.553350999
42 0.079167696 2.625810802
43 -0.914559958 0.079167696
44 0.567585786 -0.914559958
45 -2.321446241 0.567585786
46 -0.277774854 -2.321446241
47 0.322012908 -0.277774854
48 3.752045706 0.322012908
49 -1.575392041 3.752045706
50 0.895702354 -1.575392041
51 0.823230830 0.895702354
52 -0.349804047 0.823230830
53 -1.610370199 -0.349804047
54 -1.670630397 -1.610370199
55 1.629346951 -1.670630397
56 1.927626240 1.629346951
57 -0.333432889 1.927626240
58 -3.036924392 -0.333432889
59 -1.201375798 -3.036924392
60 -2.358469944 -1.201375798
61 -1.457438645 -2.358469944
62 -3.499187874 -1.457438645
63 1.123563379 -3.499187874
64 1.463102654 1.123563379
65 -5.056652814 1.463102654
66 -1.614071109 -5.056652814
67 -2.388799764 -1.614071109
68 1.573556553 -2.388799764
69 1.436898222 1.573556553
70 0.643803431 1.436898222
71 3.406606175 0.643803431
72 0.605139063 3.406606175
73 -0.315220989 0.605139063
74 -1.888709308 -0.315220989
75 -0.040833937 -1.888709308
76 3.007980905 -0.040833937
77 0.585899216 3.007980905
78 1.367502266 0.585899216
79 -2.047310678 1.367502266
80 0.169244213 -2.047310678
81 -0.544220533 0.169244213
82 1.756773326 -0.544220533
83 0.784257953 1.756773326
84 -0.050963945 0.784257953
85 1.088624936 -0.050963945
86 -0.263429386 1.088624936
87 0.273188377 -0.263429386
88 -3.391182744 0.273188377
89 3.422641123 -3.391182744
90 0.091724531 3.422641123
91 0.862527027 0.091724531
92 0.817375078 0.862527027
93 -0.843987896 0.817375078
94 1.045788121 -0.843987896
95 -0.830891056 1.045788121
96 -0.820719633 -0.830891056
97 2.073523136 -0.820719633
98 0.036137148 2.073523136
99 1.787029461 0.036137148
100 -0.923132224 1.787029461
101 0.872505299 -0.923132224
102 -3.442146007 0.872505299
103 1.996538531 -3.442146007
104 -2.313421250 1.996538531
105 0.995937805 -2.313421250
106 2.057197916 0.995937805
107 -2.853833832 2.057197916
108 0.943883171 -2.853833832
109 1.167895836 0.943883171
110 -2.163584055 1.167895836
111 -2.280447204 -2.163584055
112 2.230573108 -2.280447204
113 3.949597176 2.230573108
114 0.334096197 3.949597176
115 0.997019657 0.334096197
116 0.200982798 0.997019657
117 -1.074849873 0.200982798
118 0.313157628 -1.074849873
119 -0.495481523 0.313157628
120 0.447009750 -0.495481523
121 0.142955189 0.447009750
122 -1.028205754 0.142955189
123 0.367227139 -1.028205754
124 -1.779175015 0.367227139
125 0.793065641 -1.779175015
126 1.584046637 0.793065641
127 4.067414174 1.584046637
128 1.474278058 4.067414174
129 -1.646128615 1.474278058
130 -1.409811845 -1.646128615
131 -0.323999401 -1.409811845
132 2.512419131 -0.323999401
133 0.735673643 2.512419131
134 2.280427157 0.735673643
135 1.730625613 2.280427157
136 0.615623100 1.730625613
137 -0.890748655 0.615623100
138 0.842310595 -0.890748655
139 -0.676660842 0.842310595
140 0.295397795 -0.676660842
141 2.261876394 0.295397795
142 -0.722726467 2.261876394
143 0.710298759 -0.722726467
144 1.483946466 0.710298759
145 1.425406260 1.483946466
146 -2.441297599 1.425406260
147 -2.741115974 -2.441297599
148 -2.432831504 -2.741115974
149 1.877573511 -2.432831504
150 0.407437719 1.877573511
151 0.540177056 0.407437719
152 -2.453835041 0.540177056
153 -2.508253858 -2.453835041
154 1.395872911 -2.508253858
155 0.091724531 1.395872911
156 0.746701227 0.091724531
157 4.067414174 0.746701227
158 -2.728412372 4.067414174
159 0.097456678 -2.728412372
160 0.424373475 0.097456678
161 0.751322816 0.424373475
162 0.816183918 0.751322816
163 4.316599106 0.816183918
164 -2.004534364 4.316599106
165 2.033986381 -2.004534364
166 -0.209337194 2.033986381
167 -0.844250338 -0.209337194
168 -3.755945030 -0.844250338
169 -2.948837352 -3.755945030
170 0.442590038 -2.948837352
171 1.596540426 0.442590038
172 -5.124841637 1.596540426
173 1.773597050 -5.124841637
174 2.519165539 1.773597050
175 -2.399674342 2.519165539
176 -3.386325624 -2.399674342
177 0.541934872 -3.386325624
178 1.287671396 0.541934872
179 -2.202292949 1.287671396
180 -0.369228865 -2.202292949
181 -1.819966992 -0.369228865
182 -0.013042418 -1.819966992
183 -1.177198734 -0.013042418
184 1.999958103 -1.177198734
185 1.278278518 1.999958103
186 0.275158020 1.278278518
187 0.731459129 0.275158020
188 0.556845997 0.731459129
189 0.779064623 0.556845997
190 -1.662198497 0.779064623
191 -1.235327962 -1.662198497
192 2.282941298 -1.235327962
193 -1.742167441 2.282941298
194 1.780319452 -1.742167441
195 -2.299799211 1.780319452
196 2.158799124 -2.299799211
197 0.438357286 2.158799124
198 -3.226170497 0.438357286
199 -0.879806273 -3.226170497
200 -3.345665264 -0.879806273
201 1.156184851 -3.345665264
202 2.881976877 1.156184851
203 0.233092713 2.881976877
204 0.397669468 0.233092713
205 1.077616320 0.397669468
206 -0.767157835 1.077616320
207 3.220772771 -0.767157835
208 -0.041004800 3.220772771
209 1.527852966 -0.041004800
210 -2.837475845 1.527852966
211 1.226155811 -2.837475845
212 -1.354283028 1.226155811
213 -3.988373324 -1.354283028
214 -1.343797628 -3.988373324
215 1.534175911 -1.343797628
216 1.864520965 1.534175911
217 -0.477834206 1.864520965
218 -2.011806126 -0.477834206
219 1.173757339 -2.011806126
220 -3.087989714 1.173757339
221 2.221033808 -3.087989714
222 -2.310602991 2.221033808
223 0.008782471 -2.310602991
224 -0.570968345 0.008782471
225 1.674697410 -0.570968345
226 4.947619500 1.674697410
227 -1.812253810 4.947619500
228 -1.541643257 -1.812253810
229 -2.508496724 -1.541643257
230 0.022455110 -2.508496724
231 -3.163782088 0.022455110
232 -0.200753348 -3.163782088
233 0.199493857 -0.200753348
234 0.864169982 0.199493857
235 -1.994999455 0.864169982
236 0.681697001 -1.994999455
237 -0.155558301 0.681697001
238 -4.605600744 -0.155558301
239 -2.700960451 -4.605600744
240 -2.906575012 -2.700960451
241 -2.894229715 -2.906575012
242 0.242512548 -2.894229715
243 -0.397898935 0.242512548
244 1.201679677 -0.397898935
245 0.227475671 1.201679677
246 0.059689910 0.227475671
247 5.036429545 0.059689910
248 -0.262793951 5.036429545
249 0.390961756 -0.262793951
250 2.042969767 0.390961756
251 1.103684959 2.042969767
252 -1.334062867 1.103684959
253 -0.983725275 -1.334062867
254 -0.019690029 -0.983725275
255 -0.924785950 -0.019690029
256 -1.981599721 -0.924785950
257 -2.653145179 -1.981599721
258 2.191719821 -2.653145179
259 -4.814878241 2.191719821
260 0.096075126 -4.814878241
261 1.282710171 0.096075126
262 -2.937427071 1.282710171
263 -0.032284246 -2.937427071
> 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/72mik1384785375.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/804jm1384785375.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/9rum11384785375.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/1016cu1384785375.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11qv3x1384785375.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12e63d1384785375.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13mkwt1384785375.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14af0o1384785375.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/152vyr1384785375.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/16qeks1384785375.tab")
+ }
>
> try(system("convert tmp/1jn711384785375.ps tmp/1jn711384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/2rd6w1384785375.ps tmp/2rd6w1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/39q0z1384785375.ps tmp/39q0z1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/4chrm1384785375.ps tmp/4chrm1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/5k8md1384785375.ps tmp/5k8md1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/6zaai1384785375.ps tmp/6zaai1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/72mik1384785375.ps tmp/72mik1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/804jm1384785375.ps tmp/804jm1384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/9rum11384785375.ps tmp/9rum11384785375.png",intern=TRUE))
character(0)
> try(system("convert tmp/1016cu1384785375.ps tmp/1016cu1384785375.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.589 2.816 18.383