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(9
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+ ,43)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('month'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Belonging'
+ ,'Belonging_Final')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('month','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 = '7'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Depression month Connected Separate Learning Software Happiness Belonging
1 12.0 9 41 38 13 12 14 53
2 11.0 9 39 32 16 11 18 83
3 14.0 9 30 35 19 15 11 66
4 12.0 9 31 33 15 6 12 67
5 21.0 9 34 37 14 13 16 76
6 12.0 9 35 29 13 10 18 78
7 22.0 9 39 31 19 12 14 53
8 11.0 9 34 36 15 14 14 80
9 10.0 9 36 35 14 12 15 74
10 13.0 9 37 38 15 9 15 76
11 10.0 9 38 31 16 10 17 79
12 8.0 9 36 34 16 12 19 54
13 15.0 9 38 35 16 12 10 67
14 14.0 9 39 38 16 11 16 54
15 10.0 9 33 37 17 15 18 87
16 14.0 9 32 33 15 12 14 58
17 14.0 9 36 32 15 10 14 75
18 11.0 9 38 38 20 12 17 88
19 10.0 9 39 38 18 11 14 64
20 13.0 9 32 32 16 12 16 57
21 9.5 9 32 33 16 11 18 66
22 14.0 9 31 31 16 12 11 68
23 12.0 9 39 38 19 13 14 54
24 14.0 9 37 39 16 11 12 56
25 11.0 9 39 32 17 12 17 86
26 9.0 9 41 32 17 13 9 80
27 11.0 9 36 35 16 10 16 76
28 15.0 9 33 37 15 14 14 69
29 14.0 9 33 33 16 12 15 78
30 13.0 9 34 33 14 10 11 67
31 9.0 9 31 31 15 12 16 80
32 15.0 9 27 32 12 8 13 54
33 10.0 9 37 31 14 10 17 71
34 11.0 9 34 37 16 12 15 84
35 13.0 9 34 30 14 12 14 74
36 8.0 9 32 33 10 7 16 71
37 20.0 9 29 31 10 9 9 63
38 12.0 9 36 33 14 12 15 71
39 10.0 9 29 31 16 10 17 76
40 10.0 9 35 33 16 10 13 69
41 9.0 9 37 32 16 10 15 74
42 14.0 9 34 33 14 12 16 75
43 8.0 9 38 32 20 15 16 54
44 14.0 9 35 33 14 10 12 52
45 11.0 9 38 28 14 10 15 69
46 13.0 9 37 35 11 12 11 68
47 9.0 9 38 39 14 13 15 65
48 11.0 9 33 34 15 11 15 75
49 15.0 9 36 38 16 11 17 74
50 11.0 9 38 32 14 12 13 75
51 10.0 9 32 38 16 14 16 72
52 14.0 9 32 30 14 10 14 67
53 18.0 9 32 33 12 12 11 63
54 14.0 9 34 38 16 13 12 62
55 11.0 9 32 32 9 5 12 63
56 14.5 9 37 35 14 6 15 76
57 13.0 9 39 34 16 12 16 74
58 9.0 9 29 34 16 12 15 67
59 10.0 9 37 36 15 11 12 73
60 15.0 9 35 34 16 10 12 70
61 20.0 9 30 28 12 7 8 53
62 12.0 9 38 34 16 12 13 77
63 12.0 9 34 35 16 14 11 80
64 14.0 9 31 35 14 11 14 52
65 13.0 9 34 31 16 12 15 54
66 11.0 10 35 37 17 13 10 80
67 17.0 10 36 35 18 14 11 66
68 12.0 10 30 27 18 11 12 73
69 13.0 10 39 40 12 12 15 63
70 14.0 10 35 37 16 12 15 69
71 13.0 10 38 36 10 8 14 67
72 15.0 10 31 38 14 11 16 54
73 13.0 10 34 39 18 14 15 81
74 10.0 10 38 41 18 14 15 69
75 11.0 10 34 27 16 12 13 84
76 19.0 10 39 30 17 9 12 80
77 13.0 10 37 37 16 13 17 70
78 17.0 10 34 31 16 11 13 69
79 13.0 10 28 31 13 12 15 77
80 9.0 10 37 27 16 12 13 54
81 11.0 10 33 36 16 12 15 79
82 9.0 10 35 37 16 12 15 71
83 12.0 10 37 33 15 12 16 73
84 12.0 10 32 34 15 11 15 72
85 13.0 10 33 31 16 10 14 77
86 13.0 10 38 39 14 9 15 75
87 12.0 10 33 34 16 12 14 69
88 15.0 10 29 32 16 12 13 54
89 22.0 10 33 33 15 12 7 70
90 13.0 10 31 36 12 9 17 73
91 15.0 10 36 32 17 15 13 54
92 13.0 10 35 41 16 12 15 77
93 15.0 10 32 28 15 12 14 82
94 12.5 10 29 30 13 12 13 80
95 11.0 10 39 36 16 10 16 80
96 16.0 10 37 35 16 13 12 69
97 11.0 10 35 31 16 9 14 78
98 11.0 10 37 34 16 12 17 81
99 10.0 10 32 36 14 10 15 76
100 10.0 10 38 36 16 14 17 76
101 16.0 10 37 35 16 11 12 73
102 12.0 10 36 37 20 15 16 85
103 11.0 10 32 28 15 11 11 66
104 16.0 10 33 39 16 11 15 79
105 19.0 10 40 32 13 12 9 68
106 11.0 10 38 35 17 12 16 76
107 16.0 10 41 39 16 12 15 71
108 15.0 10 36 35 16 11 10 54
109 24.0 10 43 42 12 7 10 46
110 14.0 10 30 34 16 12 15 85
111 15.0 10 31 33 16 14 11 74
112 11.0 10 32 41 17 11 13 88
113 15.0 10 32 33 13 11 14 38
114 12.0 10 37 34 12 10 18 76
115 10.0 10 37 32 18 13 16 86
116 14.0 10 33 40 14 13 14 54
117 13.0 10 34 40 14 8 14 67
118 9.0 10 33 35 13 11 14 69
119 15.0 10 38 36 16 12 14 90
120 15.0 10 33 37 13 11 12 54
121 14.0 10 31 27 16 13 14 76
122 11.0 10 38 39 13 12 15 89
123 8.0 10 37 38 16 14 15 76
124 11.0 10 36 31 15 13 15 73
125 11.0 10 31 33 16 15 13 79
126 8.0 10 39 32 15 10 17 90
127 10.0 10 44 39 17 11 17 74
128 11.0 10 33 36 15 9 19 81
129 13.0 10 35 33 12 11 15 72
130 11.0 10 32 33 16 10 13 71
131 20.0 10 28 32 10 11 9 66
132 10.0 10 40 37 16 8 15 77
133 15.0 10 27 30 12 11 15 65
134 12.0 10 37 38 14 12 15 74
135 14.0 10 32 29 15 12 16 85
136 23.0 10 28 22 13 9 11 54
137 14.0 10 34 35 15 11 14 63
138 16.0 10 30 35 11 10 11 54
139 11.0 10 35 34 12 8 15 64
140 12.0 10 31 35 11 9 13 69
141 10.0 10 32 34 16 8 15 54
142 14.0 10 30 37 15 9 16 84
143 12.0 10 30 35 17 15 14 86
144 12.0 10 31 23 16 11 15 77
145 11.0 10 40 31 10 8 16 89
146 12.0 10 32 27 18 13 16 76
147 13.0 10 36 36 13 12 11 60
148 11.0 10 32 31 16 12 12 75
149 19.0 10 35 32 13 9 9 73
150 12.0 10 38 39 10 7 16 85
151 17.0 10 42 37 15 13 13 79
152 9.0 10 34 38 16 9 16 71
153 12.0 10 35 39 16 6 12 72
154 19.0 9 38 34 14 8 9 69
155 18.0 10 33 31 10 8 13 78
156 15.0 10 36 32 17 15 13 54
157 14.0 10 32 37 13 6 14 69
158 11.0 10 33 36 15 9 19 81
159 9.0 10 34 32 16 11 13 84
160 18.0 10 32 38 12 8 12 84
161 16.0 10 34 36 13 8 13 69
162 24.0 11 27 26 13 10 10 66
163 14.0 11 31 26 12 8 14 81
164 20.0 11 38 33 17 14 16 82
165 18.0 11 34 39 15 10 10 72
166 23.0 11 24 30 10 8 11 54
167 12.0 11 30 33 14 11 14 78
168 14.0 11 26 25 11 12 12 74
169 16.0 11 34 38 13 12 9 82
170 18.0 11 27 37 16 12 9 73
171 20.0 11 37 31 12 5 11 55
172 12.0 11 36 37 16 12 16 72
173 12.0 11 41 35 12 10 9 78
174 17.0 11 29 25 9 7 13 59
175 13.0 11 36 28 12 12 16 72
176 9.0 11 32 35 15 11 13 78
177 16.0 11 37 33 12 8 9 68
178 18.0 11 30 30 12 9 12 69
179 10.0 11 31 31 14 10 16 67
180 14.0 11 38 37 12 9 11 74
181 11.0 11 36 36 16 12 14 54
182 9.0 11 35 30 11 6 13 67
183 11.0 11 31 36 19 15 15 70
184 10.0 11 38 32 15 12 14 80
185 11.0 11 22 28 8 12 16 89
186 19.0 11 32 36 16 12 13 76
187 14.0 11 36 34 17 11 14 74
188 12.0 11 39 31 12 7 15 87
189 14.0 11 28 28 11 7 13 54
190 21.0 11 32 36 11 5 11 61
191 13.0 11 32 36 14 12 11 38
192 10.0 11 38 40 16 12 14 75
193 15.0 11 32 33 12 3 15 69
194 16.0 11 35 37 16 11 11 62
195 14.0 11 32 32 13 10 15 72
196 12.0 11 37 38 15 12 12 70
197 19.0 11 34 31 16 9 14 79
198 15.0 11 33 37 16 12 14 87
199 19.0 11 33 33 14 9 8 62
200 13.0 11 26 32 16 12 13 77
201 17.0 11 30 30 16 12 9 69
202 12.0 11 24 30 14 10 15 69
203 11.0 11 34 31 11 9 17 75
204 14.0 11 34 32 12 12 13 54
205 11.0 11 33 34 15 8 15 72
206 13.0 11 34 36 15 11 15 74
207 12.0 11 35 37 16 11 14 85
208 15.0 11 35 36 16 12 16 52
209 14.0 11 36 33 11 10 13 70
210 12.0 11 34 33 15 10 16 84
211 17.0 11 34 33 12 12 9 64
212 11.0 11 41 44 12 12 16 84
213 18.0 11 32 39 15 11 11 87
214 13.0 11 30 32 15 8 10 79
215 17.0 11 35 35 16 12 11 67
216 13.0 11 28 25 14 10 15 65
217 11.0 11 33 35 17 11 17 85
218 12.0 11 39 34 14 10 14 83
219 22.0 11 36 35 13 8 8 61
220 14.0 11 36 39 15 12 15 82
221 12.0 11 35 33 13 12 11 76
222 12.0 11 38 36 14 10 16 58
223 17.0 11 33 32 15 12 10 72
224 9.0 11 31 32 12 9 15 72
225 21.0 11 34 36 13 9 9 38
226 10.0 11 32 36 8 6 16 78
227 11.0 11 31 32 14 10 19 54
228 12.0 11 33 34 14 9 12 63
229 23.0 11 34 33 11 9 8 66
230 13.0 11 34 35 12 9 11 70
231 12.0 11 34 30 13 6 14 71
232 16.0 11 33 38 10 10 9 67
233 9.0 11 32 34 16 6 15 58
234 17.0 11 41 33 18 14 13 72
235 9.0 11 34 32 13 10 16 72
236 14.0 11 36 31 11 10 11 70
237 17.0 11 37 30 4 6 12 76
238 13.0 11 36 27 13 12 13 50
239 11.0 11 29 31 16 12 10 72
240 12.0 11 37 30 10 7 11 72
241 10.0 11 27 32 12 8 12 88
242 19.0 11 35 35 12 11 8 53
243 16.0 11 28 28 10 3 12 58
244 16.0 11 35 33 13 6 12 66
245 14.0 11 37 31 15 10 15 82
246 20.0 11 29 35 12 8 11 69
247 15.0 11 32 35 14 9 13 68
248 23.0 11 36 32 10 9 14 44
249 20.0 11 19 21 12 8 10 56
250 16.0 11 21 20 12 9 12 53
251 14.0 11 31 34 11 7 15 70
252 17.0 11 33 32 10 7 13 78
253 11.0 11 36 34 12 6 13 71
254 13.0 11 33 32 16 9 13 72
255 17.0 11 37 33 12 10 12 68
256 15.0 11 34 33 14 11 12 67
257 21.0 11 35 37 16 12 9 75
258 18.0 11 31 32 14 8 9 62
259 15.0 11 37 34 13 11 15 67
260 8.0 11 35 30 4 3 10 83
261 12.0 11 27 30 15 11 14 64
262 12.0 11 34 38 11 12 15 68
263 22.0 11 40 36 11 7 7 62
264 12.0 11 29 32 14 9 14 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) month Connected Separate
24.475536 0.440018 -0.027795 0.003352
Learning Software Happiness Belonging
-0.056755 -0.005343 -0.679487 -0.158205
Belonging_Final
0.155997
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-8.3864 -1.8147 -0.2068 1.5693 9.9688
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 24.475536 3.624457 6.753 9.74e-11 ***
month 0.440018 0.236568 1.860 0.06404 .
Connected -0.027795 0.051321 -0.542 0.58858
Separate 0.003352 0.052176 0.064 0.94883
Learning -0.056755 0.092273 -0.615 0.53905
Software -0.005343 0.094258 -0.057 0.95484
Happiness -0.679487 0.074290 -9.146 < 2e-16 ***
Belonging -0.158205 0.054863 -2.884 0.00427 **
Belonging_Final 0.155997 0.082197 1.898 0.05885 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.75 on 255 degrees of freedom
Multiple R-squared: 0.391, Adjusted R-squared: 0.3719
F-statistic: 20.46 on 8 and 255 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,] 0.97883524 0.04232951 0.02116476
[2,] 0.97328791 0.05342417 0.02671209
[3,] 0.97962883 0.04074234 0.02037117
[4,] 0.96454920 0.07090161 0.03545080
[5,] 0.94071213 0.11857575 0.05928787
[6,] 0.97156728 0.05686544 0.02843272
[7,] 0.95942653 0.08114694 0.04057347
[8,] 0.97472657 0.05054686 0.02527343
[9,] 0.96143024 0.07713951 0.03856976
[10,] 0.95313576 0.09372848 0.04686424
[11,] 0.94890480 0.10219041 0.05109520
[12,] 0.92809092 0.14381817 0.07190908
[13,] 0.91229705 0.17540590 0.08770295
[14,] 0.88194296 0.23611408 0.11805704
[15,] 0.87956090 0.24087819 0.12043910
[16,] 0.88794366 0.22411268 0.11205634
[17,] 0.85528880 0.28942239 0.14471120
[18,] 0.88172538 0.23654925 0.11827462
[19,] 0.87181825 0.25636351 0.12818175
[20,] 0.87516487 0.24967027 0.12483513
[21,] 0.84825204 0.30349593 0.15174796
[22,] 0.82073449 0.35853102 0.17926551
[23,] 0.79825923 0.40348154 0.20174077
[24,] 0.76844836 0.46310329 0.23155164
[25,] 0.79503140 0.40993721 0.20496860
[26,] 0.86792297 0.26415406 0.13207703
[27,] 0.83733128 0.32533744 0.16266872
[28,] 0.80692402 0.38615195 0.19307598
[29,] 0.80336490 0.39327020 0.19663510
[30,] 0.79037679 0.41924643 0.20962321
[31,] 0.78005594 0.43988811 0.21994406
[32,] 0.82538615 0.34922771 0.17461385
[33,] 0.79201074 0.41597853 0.20798926
[34,] 0.75714464 0.48571073 0.24285536
[35,] 0.73063609 0.53872782 0.26936391
[36,] 0.77972619 0.44054761 0.22027381
[37,] 0.74698989 0.50602022 0.25301011
[38,] 0.79929906 0.40140188 0.20070094
[39,] 0.77343525 0.45312951 0.22656475
[40,] 0.76093815 0.47812371 0.23906185
[41,] 0.72879137 0.54241727 0.27120863
[42,] 0.73288517 0.53422967 0.26711483
[43,] 0.69965026 0.60069947 0.30034974
[44,] 0.70619166 0.58761667 0.29380834
[45,] 0.72245711 0.55508578 0.27754289
[46,] 0.70201837 0.59596326 0.29798163
[47,] 0.73363061 0.53273879 0.26636939
[48,] 0.74524244 0.50951513 0.25475756
[49,] 0.72269432 0.55461137 0.27730568
[50,] 0.73433327 0.53133345 0.26566673
[51,] 0.69836249 0.60327501 0.30163751
[52,] 0.67401167 0.65197665 0.32598833
[53,] 0.63446063 0.73107873 0.36553937
[54,] 0.59378341 0.81243319 0.40621659
[55,] 0.58124984 0.83750032 0.41875016
[56,] 0.60421916 0.79156168 0.39578084
[57,] 0.57560751 0.84878497 0.42439249
[58,] 0.53482817 0.93034367 0.46517183
[59,] 0.50640996 0.98718009 0.49359004
[60,] 0.46859885 0.93719771 0.53140115
[61,] 0.43924014 0.87848028 0.56075986
[62,] 0.40681536 0.81363073 0.59318464
[63,] 0.39130528 0.78261057 0.60869472
[64,] 0.36445566 0.72891133 0.63554434
[65,] 0.49768366 0.99536733 0.50231634
[66,] 0.46666067 0.93332134 0.53333933
[67,] 0.45859272 0.91718544 0.54140728
[68,] 0.42054932 0.84109865 0.57945068
[69,] 0.55421433 0.89157135 0.44578567
[70,] 0.52111379 0.95777242 0.47888621
[71,] 0.55777970 0.88444059 0.44222030
[72,] 0.51952630 0.96094740 0.48047370
[73,] 0.48297254 0.96594508 0.51702746
[74,] 0.44400450 0.88800899 0.55599550
[75,] 0.40802914 0.81605828 0.59197086
[76,] 0.37815834 0.75631669 0.62184166
[77,] 0.34320172 0.68640345 0.65679828
[78,] 0.42014395 0.84028791 0.57985605
[79,] 0.38763391 0.77526783 0.61236609
[80,] 0.35488643 0.70977286 0.64511357
[81,] 0.32279191 0.64558382 0.67720809
[82,] 0.31462932 0.62925863 0.68537068
[83,] 0.28724435 0.57448869 0.71275565
[84,] 0.25619026 0.51238052 0.74380974
[85,] 0.23535909 0.47071818 0.76464091
[86,] 0.21812543 0.43625086 0.78187457
[87,] 0.19232397 0.38464793 0.80767603
[88,] 0.19014103 0.38028205 0.80985897
[89,] 0.16747992 0.33495983 0.83252008
[90,] 0.15372891 0.30745782 0.84627109
[91,] 0.13569223 0.27138446 0.86430777
[92,] 0.18190909 0.36381818 0.81809091
[93,] 0.20648277 0.41296553 0.79351723
[94,] 0.20765275 0.41530551 0.79234725
[95,] 0.18349471 0.36698941 0.81650529
[96,] 0.19987035 0.39974070 0.80012965
[97,] 0.18648962 0.37297924 0.81351038
[98,] 0.28849226 0.57698452 0.71150774
[99,] 0.27565367 0.55130733 0.72434633
[100,] 0.24630434 0.49260868 0.75369566
[101,] 0.23922145 0.47844291 0.76077855
[102,] 0.21730995 0.43461991 0.78269005
[103,] 0.19829708 0.39659417 0.80170292
[104,] 0.17608904 0.35217808 0.82391096
[105,] 0.15474714 0.30949429 0.84525286
[106,] 0.13944281 0.27888561 0.86055719
[107,] 0.18094912 0.36189824 0.81905088
[108,] 0.18362201 0.36724403 0.81637799
[109,] 0.16281364 0.32562727 0.83718636
[110,] 0.14625176 0.29250352 0.85374824
[111,] 0.12932417 0.25864834 0.87067583
[112,] 0.15955306 0.31910612 0.84044694
[113,] 0.14422110 0.28844220 0.85577890
[114,] 0.13669031 0.27338063 0.86330969
[115,] 0.12983554 0.25967109 0.87016446
[116,] 0.11458864 0.22917728 0.88541136
[117,] 0.10041994 0.20083988 0.89958006
[118,] 0.08548338 0.17096677 0.91451662
[119,] 0.08678502 0.17357005 0.91321498
[120,] 0.08610564 0.17221128 0.91389436
[121,] 0.08065253 0.16130506 0.91934747
[122,] 0.07172351 0.14344702 0.92827649
[123,] 0.06078615 0.12157231 0.93921385
[124,] 0.06023618 0.12047236 0.93976382
[125,] 0.11580236 0.23160471 0.88419764
[126,] 0.09955183 0.19910365 0.90044817
[127,] 0.08761903 0.17523806 0.91238097
[128,] 0.08590665 0.17181330 0.91409335
[129,] 0.08275186 0.16550371 0.91724814
[130,] 0.09776266 0.19552531 0.90223734
[131,] 0.09667314 0.19334628 0.90332686
[132,] 0.08252299 0.16504598 0.91747701
[133,] 0.06970751 0.13941502 0.93029249
[134,] 0.05903937 0.11807875 0.94096063
[135,] 0.04923968 0.09847936 0.95076032
[136,] 0.05646010 0.11292020 0.94353990
[137,] 0.06558482 0.13116964 0.93441518
[138,] 0.05972924 0.11945849 0.94027076
[139,] 0.04996089 0.09992179 0.95003911
[140,] 0.05830857 0.11661713 0.94169143
[141,] 0.05783011 0.11566021 0.94216989
[142,] 0.05915678 0.11831357 0.94084322
[143,] 0.05492974 0.10985948 0.94507026
[144,] 0.05845969 0.11691937 0.94154031
[145,] 0.04935380 0.09870760 0.95064620
[146,] 0.04092533 0.08185067 0.95907467
[147,] 0.03410417 0.06820834 0.96589583
[148,] 0.05008547 0.10017094 0.94991453
[149,] 0.05358259 0.10716519 0.94641741
[150,] 0.04490823 0.08981647 0.95509177
[151,] 0.09088860 0.18177720 0.90911140
[152,] 0.08214389 0.16428777 0.91785611
[153,] 0.29185982 0.58371963 0.70814018
[154,] 0.26834090 0.53668180 0.73165910
[155,] 0.34295208 0.68590417 0.65704792
[156,] 0.32457237 0.64914473 0.67542763
[157,] 0.30525869 0.61051737 0.69474131
[158,] 0.27611106 0.55222212 0.72388894
[159,] 0.25277546 0.50555092 0.74722454
[160,] 0.24910918 0.49821837 0.75089082
[161,] 0.22175091 0.44350182 0.77824909
[162,] 0.27682080 0.55364160 0.72317920
[163,] 0.26448913 0.52897827 0.73551087
[164,] 0.24601632 0.49203264 0.75398368
[165,] 0.29246608 0.58493216 0.70753392
[166,] 0.27444770 0.54889539 0.72555230
[167,] 0.28034917 0.56069833 0.71965083
[168,] 0.27803493 0.55606987 0.72196507
[169,] 0.25161288 0.50322576 0.74838712
[170,] 0.28696148 0.57392295 0.71303852
[171,] 0.40103335 0.80206671 0.59896665
[172,] 0.39042132 0.78084265 0.60957868
[173,] 0.38468707 0.76937414 0.61531293
[174,] 0.37042225 0.74084451 0.62957775
[175,] 0.48635136 0.97270272 0.51364864
[176,] 0.45126494 0.90252988 0.54873506
[177,] 0.41919997 0.83839995 0.58080003
[178,] 0.38630766 0.77261532 0.61369234
[179,] 0.41799309 0.83598617 0.58200691
[180,] 0.44971682 0.89943364 0.55028318
[181,] 0.47116725 0.94233451 0.52883275
[182,] 0.46016295 0.92032590 0.53983705
[183,] 0.42989058 0.85978116 0.57010942
[184,] 0.40128447 0.80256894 0.59871553
[185,] 0.44052069 0.88104139 0.55947931
[186,] 0.64143833 0.71712334 0.35856167
[187,] 0.64674659 0.70650681 0.35325341
[188,] 0.60629774 0.78740452 0.39370226
[189,] 0.56545686 0.86908627 0.43454314
[190,] 0.52421656 0.95156689 0.47578344
[191,] 0.48440879 0.96881757 0.51559121
[192,] 0.45992110 0.91984219 0.54007890
[193,] 0.45830152 0.91660303 0.54169848
[194,] 0.42396394 0.84792787 0.57603606
[195,] 0.38163620 0.76327240 0.61836380
[196,] 0.34362716 0.68725432 0.65637284
[197,] 0.30757243 0.61514487 0.69242757
[198,] 0.27019445 0.54038889 0.72980555
[199,] 0.26267857 0.52535715 0.73732143
[200,] 0.23957804 0.47915609 0.76042196
[201,] 0.20898643 0.41797286 0.79101357
[202,] 0.23136302 0.46272604 0.76863698
[203,] 0.21739042 0.43478085 0.78260958
[204,] 0.18639102 0.37278203 0.81360898
[205,] 0.15861011 0.31722022 0.84138989
[206,] 0.14949531 0.29899063 0.85050469
[207,] 0.12483581 0.24967162 0.87516419
[208,] 0.12564647 0.25129293 0.87435353
[209,] 0.11752290 0.23504580 0.88247710
[210,] 0.11797938 0.23595877 0.88202062
[211,] 0.09582724 0.19165448 0.90417276
[212,] 0.07711800 0.15423599 0.92288200
[213,] 0.07920926 0.15841852 0.92079074
[214,] 0.07043453 0.14086907 0.92956547
[215,] 0.05664095 0.11328190 0.94335905
[216,] 0.04323475 0.08646950 0.95676525
[217,] 0.04068835 0.08137670 0.95931165
[218,] 0.05539356 0.11078712 0.94460644
[219,] 0.05284088 0.10568176 0.94715912
[220,] 0.04006529 0.08013058 0.95993471
[221,] 0.04120584 0.08241167 0.95879416
[222,] 0.09159083 0.18318166 0.90840917
[223,] 0.09583202 0.19166404 0.90416798
[224,] 0.09120932 0.18241864 0.90879068
[225,] 0.06959991 0.13919981 0.93040009
[226,] 0.09167430 0.18334861 0.90832570
[227,] 0.12808777 0.25617555 0.87191223
[228,] 0.21529170 0.43058340 0.78470830
[229,] 0.21302002 0.42604004 0.78697998
[230,] 0.17227384 0.34454768 0.82772616
[231,] 0.21140140 0.42280280 0.78859860
[232,] 0.15897735 0.31795469 0.84102265
[233,] 0.11432055 0.22864110 0.88567945
[234,] 0.17345194 0.34690387 0.82654806
[235,] 0.18795056 0.37590113 0.81204944
[236,] 0.13137148 0.26274297 0.86862852
[237,] 0.16130773 0.32261546 0.83869227
[238,] 0.24423794 0.48847587 0.75576206
[239,] 0.16075173 0.32150347 0.83924827
[240,] 0.17568193 0.35136385 0.82431807
[241,] 0.79882032 0.40235935 0.20117968
> postscript(file="/var/wessaorg/rcomp/tmp/119wz1384693303.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/2xa2z1384693303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3v8ys1384693303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/45c611384693303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5s7af1384693303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
-1.71577203 1.91381741 -1.19667177 -2.44359290 9.96883665 2.47004903
7 8 9 10 11 12
7.81264756 -1.15986004 -1.81411223 1.24876835 -0.11628064 -2.27142971
13 14 15 16 17 18
-0.68188953 1.75474287 1.12924026 0.33137825 1.72073001 1.74182029
19 20 21 22 23 24
-3.15665074 0.59225522 -0.91360841 -1.33734910 -1.42327959 -1.32972691
25 26 27 28 29 30
1.61504554 -5.92916213 0.59660296 1.84872684 2.85553593 -2.54306936
31 32 33 34 35 36
-1.87819723 0.15980220 -0.58724128 -0.58482219 0.93556124 -3.81145473
37 38 39 40 41 42
3.19248153 0.02997163 -0.37305839 -3.41438576 -2.67343716 3.13069059
43 44 45 46 47 48
-4.00454728 -1.18089550 -1.06877741 -1.84377485 -3.87843482 -0.84052549
49 50 51 52 53 54
5.11096531 -1.94923638 -1.29207618 1.07384639 2.75767704 -1.85381936
55 56 57 58 59 60
-3.61115421 2.68603948 2.06562245 -3.53125631 -3.62292921 0.90498272
61 62 63 64 65 66
1.85160781 -0.52602020 -1.98221752 0.06553833 -0.03491300 -4.18521602
67 68 69 70 71 72
1.46798221 -1.74508468 0.05071304 1.50184975 -1.08120824 1.97885479
73 74 75 76 77 78
1.55402158 -1.99198194 -1.72630704 5.28702932 1.92396492 2.81785438
79 80 81 82 83 84
0.64278247 -5.73711254 -0.59232971 -3.64973209 0.67040095 -0.47095799
85 86 87 88 89 90
0.26184986 0.93022803 -1.22317180 0.02376809 3.96922170 1.51877624
91 92 93 94 95 96
0.29111756 1.13009142 2.83306782 -0.71043106 0.24544891 1.37502778
97 98 99 100 101 102
-1.37370147 0.88894375 -2.21893553 -0.40231111 1.68516623 1.42348670
103 104 105 106 107 108
-4.80602963 4.23627445 2.40818721 -0.34437150 3.82232822 -1.83552749
109 110 111 112 113 114
6.28953440 2.34423479 0.17584386 -1.92457791 1.50834009 1.69569729
115 116 117 118 119 120
-0.65394798 -0.32054733 -0.97877461 -4.40213263 3.20343591 -0.73690761
121 122 123 124 125 126
1.23348909 -0.29960599 -4.10777853 -1.33682857 -2.13680633 -1.78434125
127 128 129 130 131 132
-0.36528312 1.27726457 0.44551301 -2.62137892 2.73862608 -1.73889841
133 134 135 136 137 138
1.74976423 -0.39238758 2.88331911 6.48422240 -0.21005523 -0.92392385
139 140 141 142 143 144
-2.21551737 -2.10540952 -3.56194858 2.93867421 -0.26361219 0.07390243
145 146 147 148 149 150
0.11465108 0.57777207 -3.15507214 -3.27464576 2.26422108 0.70607827
151 152 153 154 155 156
4.08808953 -2.39343267 -2.41275436 2.66750451 3.76536111 0.29111756
157 158 159 160 161 162
0.38065609 1.27726457 -3.90440666 4.25335144 1.30280565 6.66734364
163 164 165 166 167 168
1.17811321 8.71423779 1.88107271 5.73060078 -1.37421635 -0.99130890
169 170 171 172 173 174
-0.40781966 1.55136463 3.18827269 -0.06826567 -4.43546806 1.66224062
175 176 177 178 179 180
0.73488020 -4.32407380 -1.50868902 2.82080540 -2.63435825 -1.02475878
181 182 183 184 185 186
-3.55560489 -5.74992634 -2.01349024 -2.76999678 -0.90775887 5.41826330
187 188 189 190 191 192
1.10663418 0.22710060 -1.27313755 4.14502576 -2.91809654 -2.90709178
193 194 195 196 197 198
1.71674585 -0.61289568 0.97687455 -3.13493623 6.00469649 2.45847156
199 200 201 202 203 204
0.80625174 -0.57689438 -0.44259717 -1.03265206 0.06254549 -1.81628986
205 206 207 208 209 210
-1.74321276 0.29832293 -1.11968509 1.77115938 -0.70419184 0.78458431
211 212 213 214 215 216
-0.73552919 -0.68529250 3.16741560 -3.04587979 1.34617174 -0.69352850
217 218 219 220 221 222
0.39463698 -0.96572126 3.81862694 1.52285832 -3.32953057 -0.32042424
223 224 225 226 227 228
0.73143005 -4.11301881 3.70573441 -2.02496054 -0.40793279 -2.85295524
229 230 231 232 233 234
4.51661425 -2.91805081 -1.66389889 -2.05365461 -4.84782480 2.85785769
235 236 237 238 239 240
-3.28804823 -1.90046547 1.24875793 -3.00800975 -5.16364565 -3.78169082
241 242 243 244 245 246
-4.14070220 -0.20353747 -0.17791904 0.67183370 1.41078519 3.93542354
247 248 249 250 251 252
0.18243489 7.14318308 1.90423434 -0.21113782 0.80842072 2.94063686
253 254 255 256 257 258
-3.51395609 -1.34537926 1.38446181 -0.58227712 4.77813977 -0.03983342
259 260 261 262 263 264
1.79145657 -8.38639710 -2.04568528 -1.25529876 2.81832756 -1.57858838
> postscript(file="/var/wessaorg/rcomp/tmp/6ru6h1384693303.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.71577203 NA
1 1.91381741 -1.71577203
2 -1.19667177 1.91381741
3 -2.44359290 -1.19667177
4 9.96883665 -2.44359290
5 2.47004903 9.96883665
6 7.81264756 2.47004903
7 -1.15986004 7.81264756
8 -1.81411223 -1.15986004
9 1.24876835 -1.81411223
10 -0.11628064 1.24876835
11 -2.27142971 -0.11628064
12 -0.68188953 -2.27142971
13 1.75474287 -0.68188953
14 1.12924026 1.75474287
15 0.33137825 1.12924026
16 1.72073001 0.33137825
17 1.74182029 1.72073001
18 -3.15665074 1.74182029
19 0.59225522 -3.15665074
20 -0.91360841 0.59225522
21 -1.33734910 -0.91360841
22 -1.42327959 -1.33734910
23 -1.32972691 -1.42327959
24 1.61504554 -1.32972691
25 -5.92916213 1.61504554
26 0.59660296 -5.92916213
27 1.84872684 0.59660296
28 2.85553593 1.84872684
29 -2.54306936 2.85553593
30 -1.87819723 -2.54306936
31 0.15980220 -1.87819723
32 -0.58724128 0.15980220
33 -0.58482219 -0.58724128
34 0.93556124 -0.58482219
35 -3.81145473 0.93556124
36 3.19248153 -3.81145473
37 0.02997163 3.19248153
38 -0.37305839 0.02997163
39 -3.41438576 -0.37305839
40 -2.67343716 -3.41438576
41 3.13069059 -2.67343716
42 -4.00454728 3.13069059
43 -1.18089550 -4.00454728
44 -1.06877741 -1.18089550
45 -1.84377485 -1.06877741
46 -3.87843482 -1.84377485
47 -0.84052549 -3.87843482
48 5.11096531 -0.84052549
49 -1.94923638 5.11096531
50 -1.29207618 -1.94923638
51 1.07384639 -1.29207618
52 2.75767704 1.07384639
53 -1.85381936 2.75767704
54 -3.61115421 -1.85381936
55 2.68603948 -3.61115421
56 2.06562245 2.68603948
57 -3.53125631 2.06562245
58 -3.62292921 -3.53125631
59 0.90498272 -3.62292921
60 1.85160781 0.90498272
61 -0.52602020 1.85160781
62 -1.98221752 -0.52602020
63 0.06553833 -1.98221752
64 -0.03491300 0.06553833
65 -4.18521602 -0.03491300
66 1.46798221 -4.18521602
67 -1.74508468 1.46798221
68 0.05071304 -1.74508468
69 1.50184975 0.05071304
70 -1.08120824 1.50184975
71 1.97885479 -1.08120824
72 1.55402158 1.97885479
73 -1.99198194 1.55402158
74 -1.72630704 -1.99198194
75 5.28702932 -1.72630704
76 1.92396492 5.28702932
77 2.81785438 1.92396492
78 0.64278247 2.81785438
79 -5.73711254 0.64278247
80 -0.59232971 -5.73711254
81 -3.64973209 -0.59232971
82 0.67040095 -3.64973209
83 -0.47095799 0.67040095
84 0.26184986 -0.47095799
85 0.93022803 0.26184986
86 -1.22317180 0.93022803
87 0.02376809 -1.22317180
88 3.96922170 0.02376809
89 1.51877624 3.96922170
90 0.29111756 1.51877624
91 1.13009142 0.29111756
92 2.83306782 1.13009142
93 -0.71043106 2.83306782
94 0.24544891 -0.71043106
95 1.37502778 0.24544891
96 -1.37370147 1.37502778
97 0.88894375 -1.37370147
98 -2.21893553 0.88894375
99 -0.40231111 -2.21893553
100 1.68516623 -0.40231111
101 1.42348670 1.68516623
102 -4.80602963 1.42348670
103 4.23627445 -4.80602963
104 2.40818721 4.23627445
105 -0.34437150 2.40818721
106 3.82232822 -0.34437150
107 -1.83552749 3.82232822
108 6.28953440 -1.83552749
109 2.34423479 6.28953440
110 0.17584386 2.34423479
111 -1.92457791 0.17584386
112 1.50834009 -1.92457791
113 1.69569729 1.50834009
114 -0.65394798 1.69569729
115 -0.32054733 -0.65394798
116 -0.97877461 -0.32054733
117 -4.40213263 -0.97877461
118 3.20343591 -4.40213263
119 -0.73690761 3.20343591
120 1.23348909 -0.73690761
121 -0.29960599 1.23348909
122 -4.10777853 -0.29960599
123 -1.33682857 -4.10777853
124 -2.13680633 -1.33682857
125 -1.78434125 -2.13680633
126 -0.36528312 -1.78434125
127 1.27726457 -0.36528312
128 0.44551301 1.27726457
129 -2.62137892 0.44551301
130 2.73862608 -2.62137892
131 -1.73889841 2.73862608
132 1.74976423 -1.73889841
133 -0.39238758 1.74976423
134 2.88331911 -0.39238758
135 6.48422240 2.88331911
136 -0.21005523 6.48422240
137 -0.92392385 -0.21005523
138 -2.21551737 -0.92392385
139 -2.10540952 -2.21551737
140 -3.56194858 -2.10540952
141 2.93867421 -3.56194858
142 -0.26361219 2.93867421
143 0.07390243 -0.26361219
144 0.11465108 0.07390243
145 0.57777207 0.11465108
146 -3.15507214 0.57777207
147 -3.27464576 -3.15507214
148 2.26422108 -3.27464576
149 0.70607827 2.26422108
150 4.08808953 0.70607827
151 -2.39343267 4.08808953
152 -2.41275436 -2.39343267
153 2.66750451 -2.41275436
154 3.76536111 2.66750451
155 0.29111756 3.76536111
156 0.38065609 0.29111756
157 1.27726457 0.38065609
158 -3.90440666 1.27726457
159 4.25335144 -3.90440666
160 1.30280565 4.25335144
161 6.66734364 1.30280565
162 1.17811321 6.66734364
163 8.71423779 1.17811321
164 1.88107271 8.71423779
165 5.73060078 1.88107271
166 -1.37421635 5.73060078
167 -0.99130890 -1.37421635
168 -0.40781966 -0.99130890
169 1.55136463 -0.40781966
170 3.18827269 1.55136463
171 -0.06826567 3.18827269
172 -4.43546806 -0.06826567
173 1.66224062 -4.43546806
174 0.73488020 1.66224062
175 -4.32407380 0.73488020
176 -1.50868902 -4.32407380
177 2.82080540 -1.50868902
178 -2.63435825 2.82080540
179 -1.02475878 -2.63435825
180 -3.55560489 -1.02475878
181 -5.74992634 -3.55560489
182 -2.01349024 -5.74992634
183 -2.76999678 -2.01349024
184 -0.90775887 -2.76999678
185 5.41826330 -0.90775887
186 1.10663418 5.41826330
187 0.22710060 1.10663418
188 -1.27313755 0.22710060
189 4.14502576 -1.27313755
190 -2.91809654 4.14502576
191 -2.90709178 -2.91809654
192 1.71674585 -2.90709178
193 -0.61289568 1.71674585
194 0.97687455 -0.61289568
195 -3.13493623 0.97687455
196 6.00469649 -3.13493623
197 2.45847156 6.00469649
198 0.80625174 2.45847156
199 -0.57689438 0.80625174
200 -0.44259717 -0.57689438
201 -1.03265206 -0.44259717
202 0.06254549 -1.03265206
203 -1.81628986 0.06254549
204 -1.74321276 -1.81628986
205 0.29832293 -1.74321276
206 -1.11968509 0.29832293
207 1.77115938 -1.11968509
208 -0.70419184 1.77115938
209 0.78458431 -0.70419184
210 -0.73552919 0.78458431
211 -0.68529250 -0.73552919
212 3.16741560 -0.68529250
213 -3.04587979 3.16741560
214 1.34617174 -3.04587979
215 -0.69352850 1.34617174
216 0.39463698 -0.69352850
217 -0.96572126 0.39463698
218 3.81862694 -0.96572126
219 1.52285832 3.81862694
220 -3.32953057 1.52285832
221 -0.32042424 -3.32953057
222 0.73143005 -0.32042424
223 -4.11301881 0.73143005
224 3.70573441 -4.11301881
225 -2.02496054 3.70573441
226 -0.40793279 -2.02496054
227 -2.85295524 -0.40793279
228 4.51661425 -2.85295524
229 -2.91805081 4.51661425
230 -1.66389889 -2.91805081
231 -2.05365461 -1.66389889
232 -4.84782480 -2.05365461
233 2.85785769 -4.84782480
234 -3.28804823 2.85785769
235 -1.90046547 -3.28804823
236 1.24875793 -1.90046547
237 -3.00800975 1.24875793
238 -5.16364565 -3.00800975
239 -3.78169082 -5.16364565
240 -4.14070220 -3.78169082
241 -0.20353747 -4.14070220
242 -0.17791904 -0.20353747
243 0.67183370 -0.17791904
244 1.41078519 0.67183370
245 3.93542354 1.41078519
246 0.18243489 3.93542354
247 7.14318308 0.18243489
248 1.90423434 7.14318308
249 -0.21113782 1.90423434
250 0.80842072 -0.21113782
251 2.94063686 0.80842072
252 -3.51395609 2.94063686
253 -1.34537926 -3.51395609
254 1.38446181 -1.34537926
255 -0.58227712 1.38446181
256 4.77813977 -0.58227712
257 -0.03983342 4.77813977
258 1.79145657 -0.03983342
259 -8.38639710 1.79145657
260 -2.04568528 -8.38639710
261 -1.25529876 -2.04568528
262 2.81832756 -1.25529876
263 -1.57858838 2.81832756
264 NA -1.57858838
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.91381741 -1.71577203
[2,] -1.19667177 1.91381741
[3,] -2.44359290 -1.19667177
[4,] 9.96883665 -2.44359290
[5,] 2.47004903 9.96883665
[6,] 7.81264756 2.47004903
[7,] -1.15986004 7.81264756
[8,] -1.81411223 -1.15986004
[9,] 1.24876835 -1.81411223
[10,] -0.11628064 1.24876835
[11,] -2.27142971 -0.11628064
[12,] -0.68188953 -2.27142971
[13,] 1.75474287 -0.68188953
[14,] 1.12924026 1.75474287
[15,] 0.33137825 1.12924026
[16,] 1.72073001 0.33137825
[17,] 1.74182029 1.72073001
[18,] -3.15665074 1.74182029
[19,] 0.59225522 -3.15665074
[20,] -0.91360841 0.59225522
[21,] -1.33734910 -0.91360841
[22,] -1.42327959 -1.33734910
[23,] -1.32972691 -1.42327959
[24,] 1.61504554 -1.32972691
[25,] -5.92916213 1.61504554
[26,] 0.59660296 -5.92916213
[27,] 1.84872684 0.59660296
[28,] 2.85553593 1.84872684
[29,] -2.54306936 2.85553593
[30,] -1.87819723 -2.54306936
[31,] 0.15980220 -1.87819723
[32,] -0.58724128 0.15980220
[33,] -0.58482219 -0.58724128
[34,] 0.93556124 -0.58482219
[35,] -3.81145473 0.93556124
[36,] 3.19248153 -3.81145473
[37,] 0.02997163 3.19248153
[38,] -0.37305839 0.02997163
[39,] -3.41438576 -0.37305839
[40,] -2.67343716 -3.41438576
[41,] 3.13069059 -2.67343716
[42,] -4.00454728 3.13069059
[43,] -1.18089550 -4.00454728
[44,] -1.06877741 -1.18089550
[45,] -1.84377485 -1.06877741
[46,] -3.87843482 -1.84377485
[47,] -0.84052549 -3.87843482
[48,] 5.11096531 -0.84052549
[49,] -1.94923638 5.11096531
[50,] -1.29207618 -1.94923638
[51,] 1.07384639 -1.29207618
[52,] 2.75767704 1.07384639
[53,] -1.85381936 2.75767704
[54,] -3.61115421 -1.85381936
[55,] 2.68603948 -3.61115421
[56,] 2.06562245 2.68603948
[57,] -3.53125631 2.06562245
[58,] -3.62292921 -3.53125631
[59,] 0.90498272 -3.62292921
[60,] 1.85160781 0.90498272
[61,] -0.52602020 1.85160781
[62,] -1.98221752 -0.52602020
[63,] 0.06553833 -1.98221752
[64,] -0.03491300 0.06553833
[65,] -4.18521602 -0.03491300
[66,] 1.46798221 -4.18521602
[67,] -1.74508468 1.46798221
[68,] 0.05071304 -1.74508468
[69,] 1.50184975 0.05071304
[70,] -1.08120824 1.50184975
[71,] 1.97885479 -1.08120824
[72,] 1.55402158 1.97885479
[73,] -1.99198194 1.55402158
[74,] -1.72630704 -1.99198194
[75,] 5.28702932 -1.72630704
[76,] 1.92396492 5.28702932
[77,] 2.81785438 1.92396492
[78,] 0.64278247 2.81785438
[79,] -5.73711254 0.64278247
[80,] -0.59232971 -5.73711254
[81,] -3.64973209 -0.59232971
[82,] 0.67040095 -3.64973209
[83,] -0.47095799 0.67040095
[84,] 0.26184986 -0.47095799
[85,] 0.93022803 0.26184986
[86,] -1.22317180 0.93022803
[87,] 0.02376809 -1.22317180
[88,] 3.96922170 0.02376809
[89,] 1.51877624 3.96922170
[90,] 0.29111756 1.51877624
[91,] 1.13009142 0.29111756
[92,] 2.83306782 1.13009142
[93,] -0.71043106 2.83306782
[94,] 0.24544891 -0.71043106
[95,] 1.37502778 0.24544891
[96,] -1.37370147 1.37502778
[97,] 0.88894375 -1.37370147
[98,] -2.21893553 0.88894375
[99,] -0.40231111 -2.21893553
[100,] 1.68516623 -0.40231111
[101,] 1.42348670 1.68516623
[102,] -4.80602963 1.42348670
[103,] 4.23627445 -4.80602963
[104,] 2.40818721 4.23627445
[105,] -0.34437150 2.40818721
[106,] 3.82232822 -0.34437150
[107,] -1.83552749 3.82232822
[108,] 6.28953440 -1.83552749
[109,] 2.34423479 6.28953440
[110,] 0.17584386 2.34423479
[111,] -1.92457791 0.17584386
[112,] 1.50834009 -1.92457791
[113,] 1.69569729 1.50834009
[114,] -0.65394798 1.69569729
[115,] -0.32054733 -0.65394798
[116,] -0.97877461 -0.32054733
[117,] -4.40213263 -0.97877461
[118,] 3.20343591 -4.40213263
[119,] -0.73690761 3.20343591
[120,] 1.23348909 -0.73690761
[121,] -0.29960599 1.23348909
[122,] -4.10777853 -0.29960599
[123,] -1.33682857 -4.10777853
[124,] -2.13680633 -1.33682857
[125,] -1.78434125 -2.13680633
[126,] -0.36528312 -1.78434125
[127,] 1.27726457 -0.36528312
[128,] 0.44551301 1.27726457
[129,] -2.62137892 0.44551301
[130,] 2.73862608 -2.62137892
[131,] -1.73889841 2.73862608
[132,] 1.74976423 -1.73889841
[133,] -0.39238758 1.74976423
[134,] 2.88331911 -0.39238758
[135,] 6.48422240 2.88331911
[136,] -0.21005523 6.48422240
[137,] -0.92392385 -0.21005523
[138,] -2.21551737 -0.92392385
[139,] -2.10540952 -2.21551737
[140,] -3.56194858 -2.10540952
[141,] 2.93867421 -3.56194858
[142,] -0.26361219 2.93867421
[143,] 0.07390243 -0.26361219
[144,] 0.11465108 0.07390243
[145,] 0.57777207 0.11465108
[146,] -3.15507214 0.57777207
[147,] -3.27464576 -3.15507214
[148,] 2.26422108 -3.27464576
[149,] 0.70607827 2.26422108
[150,] 4.08808953 0.70607827
[151,] -2.39343267 4.08808953
[152,] -2.41275436 -2.39343267
[153,] 2.66750451 -2.41275436
[154,] 3.76536111 2.66750451
[155,] 0.29111756 3.76536111
[156,] 0.38065609 0.29111756
[157,] 1.27726457 0.38065609
[158,] -3.90440666 1.27726457
[159,] 4.25335144 -3.90440666
[160,] 1.30280565 4.25335144
[161,] 6.66734364 1.30280565
[162,] 1.17811321 6.66734364
[163,] 8.71423779 1.17811321
[164,] 1.88107271 8.71423779
[165,] 5.73060078 1.88107271
[166,] -1.37421635 5.73060078
[167,] -0.99130890 -1.37421635
[168,] -0.40781966 -0.99130890
[169,] 1.55136463 -0.40781966
[170,] 3.18827269 1.55136463
[171,] -0.06826567 3.18827269
[172,] -4.43546806 -0.06826567
[173,] 1.66224062 -4.43546806
[174,] 0.73488020 1.66224062
[175,] -4.32407380 0.73488020
[176,] -1.50868902 -4.32407380
[177,] 2.82080540 -1.50868902
[178,] -2.63435825 2.82080540
[179,] -1.02475878 -2.63435825
[180,] -3.55560489 -1.02475878
[181,] -5.74992634 -3.55560489
[182,] -2.01349024 -5.74992634
[183,] -2.76999678 -2.01349024
[184,] -0.90775887 -2.76999678
[185,] 5.41826330 -0.90775887
[186,] 1.10663418 5.41826330
[187,] 0.22710060 1.10663418
[188,] -1.27313755 0.22710060
[189,] 4.14502576 -1.27313755
[190,] -2.91809654 4.14502576
[191,] -2.90709178 -2.91809654
[192,] 1.71674585 -2.90709178
[193,] -0.61289568 1.71674585
[194,] 0.97687455 -0.61289568
[195,] -3.13493623 0.97687455
[196,] 6.00469649 -3.13493623
[197,] 2.45847156 6.00469649
[198,] 0.80625174 2.45847156
[199,] -0.57689438 0.80625174
[200,] -0.44259717 -0.57689438
[201,] -1.03265206 -0.44259717
[202,] 0.06254549 -1.03265206
[203,] -1.81628986 0.06254549
[204,] -1.74321276 -1.81628986
[205,] 0.29832293 -1.74321276
[206,] -1.11968509 0.29832293
[207,] 1.77115938 -1.11968509
[208,] -0.70419184 1.77115938
[209,] 0.78458431 -0.70419184
[210,] -0.73552919 0.78458431
[211,] -0.68529250 -0.73552919
[212,] 3.16741560 -0.68529250
[213,] -3.04587979 3.16741560
[214,] 1.34617174 -3.04587979
[215,] -0.69352850 1.34617174
[216,] 0.39463698 -0.69352850
[217,] -0.96572126 0.39463698
[218,] 3.81862694 -0.96572126
[219,] 1.52285832 3.81862694
[220,] -3.32953057 1.52285832
[221,] -0.32042424 -3.32953057
[222,] 0.73143005 -0.32042424
[223,] -4.11301881 0.73143005
[224,] 3.70573441 -4.11301881
[225,] -2.02496054 3.70573441
[226,] -0.40793279 -2.02496054
[227,] -2.85295524 -0.40793279
[228,] 4.51661425 -2.85295524
[229,] -2.91805081 4.51661425
[230,] -1.66389889 -2.91805081
[231,] -2.05365461 -1.66389889
[232,] -4.84782480 -2.05365461
[233,] 2.85785769 -4.84782480
[234,] -3.28804823 2.85785769
[235,] -1.90046547 -3.28804823
[236,] 1.24875793 -1.90046547
[237,] -3.00800975 1.24875793
[238,] -5.16364565 -3.00800975
[239,] -3.78169082 -5.16364565
[240,] -4.14070220 -3.78169082
[241,] -0.20353747 -4.14070220
[242,] -0.17791904 -0.20353747
[243,] 0.67183370 -0.17791904
[244,] 1.41078519 0.67183370
[245,] 3.93542354 1.41078519
[246,] 0.18243489 3.93542354
[247,] 7.14318308 0.18243489
[248,] 1.90423434 7.14318308
[249,] -0.21113782 1.90423434
[250,] 0.80842072 -0.21113782
[251,] 2.94063686 0.80842072
[252,] -3.51395609 2.94063686
[253,] -1.34537926 -3.51395609
[254,] 1.38446181 -1.34537926
[255,] -0.58227712 1.38446181
[256,] 4.77813977 -0.58227712
[257,] -0.03983342 4.77813977
[258,] 1.79145657 -0.03983342
[259,] -8.38639710 1.79145657
[260,] -2.04568528 -8.38639710
[261,] -1.25529876 -2.04568528
[262,] 2.81832756 -1.25529876
[263,] -1.57858838 2.81832756
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.91381741 -1.71577203
2 -1.19667177 1.91381741
3 -2.44359290 -1.19667177
4 9.96883665 -2.44359290
5 2.47004903 9.96883665
6 7.81264756 2.47004903
7 -1.15986004 7.81264756
8 -1.81411223 -1.15986004
9 1.24876835 -1.81411223
10 -0.11628064 1.24876835
11 -2.27142971 -0.11628064
12 -0.68188953 -2.27142971
13 1.75474287 -0.68188953
14 1.12924026 1.75474287
15 0.33137825 1.12924026
16 1.72073001 0.33137825
17 1.74182029 1.72073001
18 -3.15665074 1.74182029
19 0.59225522 -3.15665074
20 -0.91360841 0.59225522
21 -1.33734910 -0.91360841
22 -1.42327959 -1.33734910
23 -1.32972691 -1.42327959
24 1.61504554 -1.32972691
25 -5.92916213 1.61504554
26 0.59660296 -5.92916213
27 1.84872684 0.59660296
28 2.85553593 1.84872684
29 -2.54306936 2.85553593
30 -1.87819723 -2.54306936
31 0.15980220 -1.87819723
32 -0.58724128 0.15980220
33 -0.58482219 -0.58724128
34 0.93556124 -0.58482219
35 -3.81145473 0.93556124
36 3.19248153 -3.81145473
37 0.02997163 3.19248153
38 -0.37305839 0.02997163
39 -3.41438576 -0.37305839
40 -2.67343716 -3.41438576
41 3.13069059 -2.67343716
42 -4.00454728 3.13069059
43 -1.18089550 -4.00454728
44 -1.06877741 -1.18089550
45 -1.84377485 -1.06877741
46 -3.87843482 -1.84377485
47 -0.84052549 -3.87843482
48 5.11096531 -0.84052549
49 -1.94923638 5.11096531
50 -1.29207618 -1.94923638
51 1.07384639 -1.29207618
52 2.75767704 1.07384639
53 -1.85381936 2.75767704
54 -3.61115421 -1.85381936
55 2.68603948 -3.61115421
56 2.06562245 2.68603948
57 -3.53125631 2.06562245
58 -3.62292921 -3.53125631
59 0.90498272 -3.62292921
60 1.85160781 0.90498272
61 -0.52602020 1.85160781
62 -1.98221752 -0.52602020
63 0.06553833 -1.98221752
64 -0.03491300 0.06553833
65 -4.18521602 -0.03491300
66 1.46798221 -4.18521602
67 -1.74508468 1.46798221
68 0.05071304 -1.74508468
69 1.50184975 0.05071304
70 -1.08120824 1.50184975
71 1.97885479 -1.08120824
72 1.55402158 1.97885479
73 -1.99198194 1.55402158
74 -1.72630704 -1.99198194
75 5.28702932 -1.72630704
76 1.92396492 5.28702932
77 2.81785438 1.92396492
78 0.64278247 2.81785438
79 -5.73711254 0.64278247
80 -0.59232971 -5.73711254
81 -3.64973209 -0.59232971
82 0.67040095 -3.64973209
83 -0.47095799 0.67040095
84 0.26184986 -0.47095799
85 0.93022803 0.26184986
86 -1.22317180 0.93022803
87 0.02376809 -1.22317180
88 3.96922170 0.02376809
89 1.51877624 3.96922170
90 0.29111756 1.51877624
91 1.13009142 0.29111756
92 2.83306782 1.13009142
93 -0.71043106 2.83306782
94 0.24544891 -0.71043106
95 1.37502778 0.24544891
96 -1.37370147 1.37502778
97 0.88894375 -1.37370147
98 -2.21893553 0.88894375
99 -0.40231111 -2.21893553
100 1.68516623 -0.40231111
101 1.42348670 1.68516623
102 -4.80602963 1.42348670
103 4.23627445 -4.80602963
104 2.40818721 4.23627445
105 -0.34437150 2.40818721
106 3.82232822 -0.34437150
107 -1.83552749 3.82232822
108 6.28953440 -1.83552749
109 2.34423479 6.28953440
110 0.17584386 2.34423479
111 -1.92457791 0.17584386
112 1.50834009 -1.92457791
113 1.69569729 1.50834009
114 -0.65394798 1.69569729
115 -0.32054733 -0.65394798
116 -0.97877461 -0.32054733
117 -4.40213263 -0.97877461
118 3.20343591 -4.40213263
119 -0.73690761 3.20343591
120 1.23348909 -0.73690761
121 -0.29960599 1.23348909
122 -4.10777853 -0.29960599
123 -1.33682857 -4.10777853
124 -2.13680633 -1.33682857
125 -1.78434125 -2.13680633
126 -0.36528312 -1.78434125
127 1.27726457 -0.36528312
128 0.44551301 1.27726457
129 -2.62137892 0.44551301
130 2.73862608 -2.62137892
131 -1.73889841 2.73862608
132 1.74976423 -1.73889841
133 -0.39238758 1.74976423
134 2.88331911 -0.39238758
135 6.48422240 2.88331911
136 -0.21005523 6.48422240
137 -0.92392385 -0.21005523
138 -2.21551737 -0.92392385
139 -2.10540952 -2.21551737
140 -3.56194858 -2.10540952
141 2.93867421 -3.56194858
142 -0.26361219 2.93867421
143 0.07390243 -0.26361219
144 0.11465108 0.07390243
145 0.57777207 0.11465108
146 -3.15507214 0.57777207
147 -3.27464576 -3.15507214
148 2.26422108 -3.27464576
149 0.70607827 2.26422108
150 4.08808953 0.70607827
151 -2.39343267 4.08808953
152 -2.41275436 -2.39343267
153 2.66750451 -2.41275436
154 3.76536111 2.66750451
155 0.29111756 3.76536111
156 0.38065609 0.29111756
157 1.27726457 0.38065609
158 -3.90440666 1.27726457
159 4.25335144 -3.90440666
160 1.30280565 4.25335144
161 6.66734364 1.30280565
162 1.17811321 6.66734364
163 8.71423779 1.17811321
164 1.88107271 8.71423779
165 5.73060078 1.88107271
166 -1.37421635 5.73060078
167 -0.99130890 -1.37421635
168 -0.40781966 -0.99130890
169 1.55136463 -0.40781966
170 3.18827269 1.55136463
171 -0.06826567 3.18827269
172 -4.43546806 -0.06826567
173 1.66224062 -4.43546806
174 0.73488020 1.66224062
175 -4.32407380 0.73488020
176 -1.50868902 -4.32407380
177 2.82080540 -1.50868902
178 -2.63435825 2.82080540
179 -1.02475878 -2.63435825
180 -3.55560489 -1.02475878
181 -5.74992634 -3.55560489
182 -2.01349024 -5.74992634
183 -2.76999678 -2.01349024
184 -0.90775887 -2.76999678
185 5.41826330 -0.90775887
186 1.10663418 5.41826330
187 0.22710060 1.10663418
188 -1.27313755 0.22710060
189 4.14502576 -1.27313755
190 -2.91809654 4.14502576
191 -2.90709178 -2.91809654
192 1.71674585 -2.90709178
193 -0.61289568 1.71674585
194 0.97687455 -0.61289568
195 -3.13493623 0.97687455
196 6.00469649 -3.13493623
197 2.45847156 6.00469649
198 0.80625174 2.45847156
199 -0.57689438 0.80625174
200 -0.44259717 -0.57689438
201 -1.03265206 -0.44259717
202 0.06254549 -1.03265206
203 -1.81628986 0.06254549
204 -1.74321276 -1.81628986
205 0.29832293 -1.74321276
206 -1.11968509 0.29832293
207 1.77115938 -1.11968509
208 -0.70419184 1.77115938
209 0.78458431 -0.70419184
210 -0.73552919 0.78458431
211 -0.68529250 -0.73552919
212 3.16741560 -0.68529250
213 -3.04587979 3.16741560
214 1.34617174 -3.04587979
215 -0.69352850 1.34617174
216 0.39463698 -0.69352850
217 -0.96572126 0.39463698
218 3.81862694 -0.96572126
219 1.52285832 3.81862694
220 -3.32953057 1.52285832
221 -0.32042424 -3.32953057
222 0.73143005 -0.32042424
223 -4.11301881 0.73143005
224 3.70573441 -4.11301881
225 -2.02496054 3.70573441
226 -0.40793279 -2.02496054
227 -2.85295524 -0.40793279
228 4.51661425 -2.85295524
229 -2.91805081 4.51661425
230 -1.66389889 -2.91805081
231 -2.05365461 -1.66389889
232 -4.84782480 -2.05365461
233 2.85785769 -4.84782480
234 -3.28804823 2.85785769
235 -1.90046547 -3.28804823
236 1.24875793 -1.90046547
237 -3.00800975 1.24875793
238 -5.16364565 -3.00800975
239 -3.78169082 -5.16364565
240 -4.14070220 -3.78169082
241 -0.20353747 -4.14070220
242 -0.17791904 -0.20353747
243 0.67183370 -0.17791904
244 1.41078519 0.67183370
245 3.93542354 1.41078519
246 0.18243489 3.93542354
247 7.14318308 0.18243489
248 1.90423434 7.14318308
249 -0.21113782 1.90423434
250 0.80842072 -0.21113782
251 2.94063686 0.80842072
252 -3.51395609 2.94063686
253 -1.34537926 -3.51395609
254 1.38446181 -1.34537926
255 -0.58227712 1.38446181
256 4.77813977 -0.58227712
257 -0.03983342 4.77813977
258 1.79145657 -0.03983342
259 -8.38639710 1.79145657
260 -2.04568528 -8.38639710
261 -1.25529876 -2.04568528
262 2.81832756 -1.25529876
263 -1.57858838 2.81832756
> 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/73gq71384693303.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/8gwui1384693303.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/9lozz1384693303.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/104lfh1384693303.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11fj8u1384693303.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12ik511384693303.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13hami1384693303.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/14svke1384693303.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/15jikm1384693303.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/16l7pj1384693303.tab")
+ }
>
> try(system("convert tmp/119wz1384693303.ps tmp/119wz1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xa2z1384693303.ps tmp/2xa2z1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/3v8ys1384693303.ps tmp/3v8ys1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/45c611384693303.ps tmp/45c611384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/5s7af1384693303.ps tmp/5s7af1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ru6h1384693303.ps tmp/6ru6h1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/73gq71384693303.ps tmp/73gq71384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/8gwui1384693303.ps tmp/8gwui1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/9lozz1384693303.ps tmp/9lozz1384693303.png",intern=TRUE))
character(0)
> try(system("convert tmp/104lfh1384693303.ps tmp/104lfh1384693303.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.806 3.103 19.898