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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+ ,33
+ ,32
+ ,16
+ ,9
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+ ,13
+ ,72
+ ,45
+ ,37
+ ,33
+ ,12
+ ,10
+ ,12
+ ,17
+ ,68
+ ,44
+ ,34
+ ,33
+ ,14
+ ,11
+ ,12
+ ,15
+ ,67
+ ,43
+ ,35
+ ,37
+ ,16
+ ,12
+ ,9
+ ,21
+ ,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 = '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
Belonging Connected Separate Learning Software Happiness Depression
1 53 41 38 13 12 14 12.0
2 83 39 32 16 11 18 11.0
3 66 30 35 19 15 11 14.0
4 67 31 33 15 6 12 12.0
5 76 34 37 14 13 16 21.0
6 78 35 29 13 10 18 12.0
7 53 39 31 19 12 14 22.0
8 80 34 36 15 14 14 11.0
9 74 36 35 14 12 15 10.0
10 76 37 38 15 9 15 13.0
11 79 38 31 16 10 17 10.0
12 54 36 34 16 12 19 8.0
13 67 38 35 16 12 10 15.0
14 54 39 38 16 11 16 14.0
15 87 33 37 17 15 18 10.0
16 58 32 33 15 12 14 14.0
17 75 36 32 15 10 14 14.0
18 88 38 38 20 12 17 11.0
19 64 39 38 18 11 14 10.0
20 57 32 32 16 12 16 13.0
21 66 32 33 16 11 18 9.5
22 68 31 31 16 12 11 14.0
23 54 39 38 19 13 14 12.0
24 56 37 39 16 11 12 14.0
25 86 39 32 17 12 17 11.0
26 80 41 32 17 13 9 9.0
27 76 36 35 16 10 16 11.0
28 69 33 37 15 14 14 15.0
29 78 33 33 16 12 15 14.0
30 67 34 33 14 10 11 13.0
31 80 31 31 15 12 16 9.0
32 54 27 32 12 8 13 15.0
33 71 37 31 14 10 17 10.0
34 84 34 37 16 12 15 11.0
35 74 34 30 14 12 14 13.0
36 71 32 33 10 7 16 8.0
37 63 29 31 10 9 9 20.0
38 71 36 33 14 12 15 12.0
39 76 29 31 16 10 17 10.0
40 69 35 33 16 10 13 10.0
41 74 37 32 16 10 15 9.0
42 75 34 33 14 12 16 14.0
43 54 38 32 20 15 16 8.0
44 52 35 33 14 10 12 14.0
45 69 38 28 14 10 15 11.0
46 68 37 35 11 12 11 13.0
47 65 38 39 14 13 15 9.0
48 75 33 34 15 11 15 11.0
49 74 36 38 16 11 17 15.0
50 75 38 32 14 12 13 11.0
51 72 32 38 16 14 16 10.0
52 67 32 30 14 10 14 14.0
53 63 32 33 12 12 11 18.0
54 62 34 38 16 13 12 14.0
55 63 32 32 9 5 12 11.0
56 76 37 35 14 6 15 14.5
57 74 39 34 16 12 16 13.0
58 67 29 34 16 12 15 9.0
59 73 37 36 15 11 12 10.0
60 70 35 34 16 10 12 15.0
61 53 30 28 12 7 8 20.0
62 77 38 34 16 12 13 12.0
63 80 34 35 16 14 11 12.0
64 52 31 35 14 11 14 14.0
65 54 34 31 16 12 15 13.0
66 80 35 37 17 13 10 11.0
67 66 36 35 18 14 11 17.0
68 73 30 27 18 11 12 12.0
69 63 39 40 12 12 15 13.0
70 69 35 37 16 12 15 14.0
71 67 38 36 10 8 14 13.0
72 54 31 38 14 11 16 15.0
73 81 34 39 18 14 15 13.0
74 69 38 41 18 14 15 10.0
75 84 34 27 16 12 13 11.0
76 80 39 30 17 9 12 19.0
77 70 37 37 16 13 17 13.0
78 69 34 31 16 11 13 17.0
79 77 28 31 13 12 15 13.0
80 54 37 27 16 12 13 9.0
81 79 33 36 16 12 15 11.0
82 71 35 37 16 12 15 9.0
83 73 37 33 15 12 16 12.0
84 72 32 34 15 11 15 12.0
85 77 33 31 16 10 14 13.0
86 75 38 39 14 9 15 13.0
87 69 33 34 16 12 14 12.0
88 54 29 32 16 12 13 15.0
89 70 33 33 15 12 7 22.0
90 73 31 36 12 9 17 13.0
91 54 36 32 17 15 13 15.0
92 77 35 41 16 12 15 13.0
93 82 32 28 15 12 14 15.0
94 80 29 30 13 12 13 12.5
95 80 39 36 16 10 16 11.0
96 69 37 35 16 13 12 16.0
97 78 35 31 16 9 14 11.0
98 81 37 34 16 12 17 11.0
99 76 32 36 14 10 15 10.0
100 76 38 36 16 14 17 10.0
101 73 37 35 16 11 12 16.0
102 85 36 37 20 15 16 12.0
103 66 32 28 15 11 11 11.0
104 79 33 39 16 11 15 16.0
105 68 40 32 13 12 9 19.0
106 76 38 35 17 12 16 11.0
107 71 41 39 16 12 15 16.0
108 54 36 35 16 11 10 15.0
109 46 43 42 12 7 10 24.0
110 85 30 34 16 12 15 14.0
111 74 31 33 16 14 11 15.0
112 88 32 41 17 11 13 11.0
113 38 32 33 13 11 14 15.0
114 76 37 34 12 10 18 12.0
115 86 37 32 18 13 16 10.0
116 54 33 40 14 13 14 14.0
117 67 34 40 14 8 14 13.0
118 69 33 35 13 11 14 9.0
119 90 38 36 16 12 14 15.0
120 54 33 37 13 11 12 15.0
121 76 31 27 16 13 14 14.0
122 89 38 39 13 12 15 11.0
123 76 37 38 16 14 15 8.0
124 73 36 31 15 13 15 11.0
125 79 31 33 16 15 13 11.0
126 90 39 32 15 10 17 8.0
127 74 44 39 17 11 17 10.0
128 81 33 36 15 9 19 11.0
129 72 35 33 12 11 15 13.0
130 71 32 33 16 10 13 11.0
131 66 28 32 10 11 9 20.0
132 77 40 37 16 8 15 10.0
133 65 27 30 12 11 15 15.0
134 74 37 38 14 12 15 12.0
135 85 32 29 15 12 16 14.0
136 54 28 22 13 9 11 23.0
137 63 34 35 15 11 14 14.0
138 54 30 35 11 10 11 16.0
139 64 35 34 12 8 15 11.0
140 69 31 35 11 9 13 12.0
141 54 32 34 16 8 15 10.0
142 84 30 37 15 9 16 14.0
143 86 30 35 17 15 14 12.0
144 77 31 23 16 11 15 12.0
145 89 40 31 10 8 16 11.0
146 76 32 27 18 13 16 12.0
147 60 36 36 13 12 11 13.0
148 75 32 31 16 12 12 11.0
149 73 35 32 13 9 9 19.0
150 85 38 39 10 7 16 12.0
151 79 42 37 15 13 13 17.0
152 71 34 38 16 9 16 9.0
153 72 35 39 16 6 12 12.0
154 69 38 34 14 8 9 19.0
155 78 33 31 10 8 13 18.0
156 54 36 32 17 15 13 15.0
157 69 32 37 13 6 14 14.0
158 81 33 36 15 9 19 11.0
159 84 34 32 16 11 13 9.0
160 84 32 38 12 8 12 18.0
161 69 34 36 13 8 13 16.0
162 66 27 26 13 10 10 24.0
163 81 31 26 12 8 14 14.0
164 82 38 33 17 14 16 20.0
165 72 34 39 15 10 10 18.0
166 54 24 30 10 8 11 23.0
167 78 30 33 14 11 14 12.0
168 74 26 25 11 12 12 14.0
169 82 34 38 13 12 9 16.0
170 73 27 37 16 12 9 18.0
171 55 37 31 12 5 11 20.0
172 72 36 37 16 12 16 12.0
173 78 41 35 12 10 9 12.0
174 59 29 25 9 7 13 17.0
175 72 36 28 12 12 16 13.0
176 78 32 35 15 11 13 9.0
177 68 37 33 12 8 9 16.0
178 69 30 30 12 9 12 18.0
179 67 31 31 14 10 16 10.0
180 74 38 37 12 9 11 14.0
181 54 36 36 16 12 14 11.0
182 67 35 30 11 6 13 9.0
183 70 31 36 19 15 15 11.0
184 80 38 32 15 12 14 10.0
185 89 22 28 8 12 16 11.0
186 76 32 36 16 12 13 19.0
187 74 36 34 17 11 14 14.0
188 87 39 31 12 7 15 12.0
189 54 28 28 11 7 13 14.0
190 61 32 36 11 5 11 21.0
191 38 32 36 14 12 11 13.0
192 75 38 40 16 12 14 10.0
193 69 32 33 12 3 15 15.0
194 62 35 37 16 11 11 16.0
195 72 32 32 13 10 15 14.0
196 70 37 38 15 12 12 12.0
197 79 34 31 16 9 14 19.0
198 87 33 37 16 12 14 15.0
199 62 33 33 14 9 8 19.0
200 77 26 32 16 12 13 13.0
201 69 30 30 16 12 9 17.0
202 69 24 30 14 10 15 12.0
203 75 34 31 11 9 17 11.0
204 54 34 32 12 12 13 14.0
205 72 33 34 15 8 15 11.0
206 74 34 36 15 11 15 13.0
207 85 35 37 16 11 14 12.0
208 52 35 36 16 12 16 15.0
209 70 36 33 11 10 13 14.0
210 84 34 33 15 10 16 12.0
211 64 34 33 12 12 9 17.0
212 84 41 44 12 12 16 11.0
213 87 32 39 15 11 11 18.0
214 79 30 32 15 8 10 13.0
215 67 35 35 16 12 11 17.0
216 65 28 25 14 10 15 13.0
217 85 33 35 17 11 17 11.0
218 83 39 34 14 10 14 12.0
219 61 36 35 13 8 8 22.0
220 82 36 39 15 12 15 14.0
221 76 35 33 13 12 11 12.0
222 58 38 36 14 10 16 12.0
223 72 33 32 15 12 10 17.0
224 72 31 32 12 9 15 9.0
225 38 34 36 13 9 9 21.0
226 78 32 36 8 6 16 10.0
227 54 31 32 14 10 19 11.0
228 63 33 34 14 9 12 12.0
229 66 34 33 11 9 8 23.0
230 70 34 35 12 9 11 13.0
231 71 34 30 13 6 14 12.0
232 67 33 38 10 10 9 16.0
233 58 32 34 16 6 15 9.0
234 72 41 33 18 14 13 17.0
235 72 34 32 13 10 16 9.0
236 70 36 31 11 10 11 14.0
237 76 37 30 4 6 12 17.0
238 50 36 27 13 12 13 13.0
239 72 29 31 16 12 10 11.0
240 72 37 30 10 7 11 12.0
241 88 27 32 12 8 12 10.0
242 53 35 35 12 11 8 19.0
243 58 28 28 10 3 12 16.0
244 66 35 33 13 6 12 16.0
245 82 37 31 15 10 15 14.0
246 69 29 35 12 8 11 20.0
247 68 32 35 14 9 13 15.0
248 44 36 32 10 9 14 23.0
249 56 19 21 12 8 10 20.0
250 53 21 20 12 9 12 16.0
251 70 31 34 11 7 15 14.0
252 78 33 32 10 7 13 17.0
253 71 36 34 12 6 13 11.0
254 72 33 32 16 9 13 13.0
255 68 37 33 12 10 12 17.0
256 67 34 33 14 11 12 15.0
257 75 35 37 16 12 9 21.0
258 62 31 32 14 8 9 18.0
259 67 37 34 13 11 15 15.0
260 83 35 30 4 3 10 8.0
261 64 27 30 15 11 14 12.0
262 68 34 38 11 12 15 12.0
263 62 40 36 11 7 7 22.0
264 72 29 32 14 9 14 12.0
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
13.150708 -0.061872 -0.001457 0.043248
Software Happiness Depression Belonging_Final
0.038522 0.015605 -0.185419 1.414653
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-12.8499 -2.0657 -0.0533 1.8748 14.2887
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.150708 3.082276 4.267 2.8e-05 ***
Connected -0.061872 0.057508 -1.076 0.28299
Separate -0.001457 0.059015 -0.025 0.98032
Learning 0.043248 0.103152 0.419 0.67538
Software 0.038522 0.106063 0.363 0.71676
Happiness 0.015605 0.096232 0.162 0.87131
Depression -0.185419 0.069419 -2.671 0.00805 **
Belonging_Final 1.414653 0.029512 47.935 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.111 on 256 degrees of freedom
Multiple R-squared: 0.9128, Adjusted R-squared: 0.9104
F-statistic: 382.9 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,] 0.0192763363 0.0385526727 0.98072366
[2,] 0.0128615952 0.0257231904 0.98713840
[3,] 0.0070218875 0.0140437749 0.99297811
[4,] 0.0049457176 0.0098914352 0.99505428
[5,] 0.0014595185 0.0029190370 0.99854048
[6,] 0.0004723618 0.0009447235 0.99952764
[7,] 0.0016099736 0.0032199473 0.99839003
[8,] 0.0014706189 0.0029412378 0.99852938
[9,] 0.0010665478 0.0021330957 0.99893345
[10,] 0.0004797196 0.0009594392 0.99952028
[11,] 0.0002533639 0.0005067278 0.99974664
[12,] 0.0002401004 0.0004802009 0.99975990
[13,] 0.0006151371 0.0012302741 0.99938486
[14,] 0.0024415869 0.0048831739 0.99755841
[15,] 0.0019090917 0.0038181835 0.99809091
[16,] 0.0052850962 0.0105701923 0.99471490
[17,] 0.0487418498 0.0974836996 0.95125815
[18,] 0.0421936730 0.0843873461 0.95780633
[19,] 0.1196380908 0.2392761816 0.88036191
[20,] 0.1862155371 0.3724310741 0.81378446
[21,] 0.1491097248 0.2982194495 0.85089028
[22,] 0.1252926664 0.2505853327 0.87470733
[23,] 0.0959086893 0.1918173786 0.90409131
[24,] 0.1350836222 0.2701672444 0.86491638
[25,] 0.2186041098 0.4372082196 0.78139589
[26,] 0.2195013808 0.4390027616 0.78049862
[27,] 0.2302851974 0.4605703948 0.76971480
[28,] 0.1888286157 0.3776572314 0.81117138
[29,] 0.1577227028 0.3154454055 0.84227730
[30,] 0.1282745409 0.2565490818 0.87172546
[31,] 0.1012535369 0.2025070737 0.89874646
[32,] 0.0977970582 0.1955941165 0.90220294
[33,] 0.0815751022 0.1631502044 0.91842490
[34,] 0.0681424243 0.1362848485 0.93185758
[35,] 0.0554098690 0.1108197380 0.94459013
[36,] 0.0438017427 0.0876034854 0.95619826
[37,] 0.1000709301 0.2001418602 0.89992907
[38,] 0.0793788634 0.1587577268 0.92062114
[39,] 0.1720964953 0.3441929905 0.82790350
[40,] 0.1443179266 0.2886358532 0.85568207
[41,] 0.1199044015 0.2398088029 0.88009560
[42,] 0.0997208242 0.1994416484 0.90027918
[43,] 0.0888621989 0.1777243978 0.91113780
[44,] 0.6288757185 0.7422485631 0.37112428
[45,] 0.6053472377 0.7893055246 0.39465276
[46,] 0.5624924895 0.8750150210 0.43750751
[47,] 0.5180271340 0.9639457320 0.48197287
[48,] 0.4926482178 0.9852964356 0.50735178
[49,] 0.5004374628 0.9991250744 0.49956254
[50,] 0.4610191745 0.9220383491 0.53898083
[51,] 0.4180791169 0.8361582338 0.58192088
[52,] 0.4414447065 0.8828894131 0.55855529
[53,] 0.4328487890 0.8656975780 0.56715121
[54,] 0.4039411020 0.8078822039 0.59605890
[55,] 0.3753789378 0.7507578755 0.62462106
[56,] 0.3494528491 0.6989056983 0.65054715
[57,] 0.3228650907 0.6457301814 0.67713491
[58,] 0.4137663285 0.8275326570 0.58623367
[59,] 0.3779513861 0.7559027721 0.62204861
[60,] 0.3472800829 0.6945601657 0.65271992
[61,] 0.4369866619 0.8739733237 0.56301334
[62,] 0.4115720863 0.8231441726 0.58842791
[63,] 0.4486367636 0.8972735271 0.55136324
[64,] 0.4921193782 0.9842387564 0.50788062
[65,] 0.4662089696 0.9324179392 0.53379103
[66,] 0.4363636258 0.8727272517 0.56363637
[67,] 0.3989540764 0.7979081529 0.60104592
[68,] 0.3897552284 0.7795104568 0.61024477
[69,] 0.3533921025 0.7067842050 0.64660790
[70,] 0.3482417881 0.6964835761 0.65175821
[71,] 0.3705568357 0.7411136713 0.62944316
[72,] 0.3695886646 0.7391773292 0.63041134
[73,] 0.3490500017 0.6981000034 0.65095000
[74,] 0.3141592141 0.6283184281 0.68584079
[75,] 0.2804565955 0.5609131910 0.71954340
[76,] 0.2630706875 0.5261413749 0.73692931
[77,] 0.2339670490 0.4679340980 0.76603295
[78,] 0.2171305150 0.4342610301 0.78286948
[79,] 0.1933820262 0.3867640525 0.80661797
[80,] 0.1828396707 0.3656793415 0.81716033
[81,] 0.1685550974 0.3371101948 0.83144490
[82,] 0.1647799073 0.3295598147 0.83522009
[83,] 0.1728974590 0.3457949180 0.82710254
[84,] 0.1660067745 0.3320135491 0.83399323
[85,] 0.1717865583 0.3435731167 0.82821344
[86,] 0.1514518417 0.3029036835 0.84854816
[87,] 0.1438955249 0.2877910498 0.85610448
[88,] 0.1352456700 0.2704913399 0.86475433
[89,] 0.1161246390 0.2322492780 0.88387536
[90,] 0.1046325616 0.2092651232 0.89536744
[91,] 0.0885495385 0.1770990770 0.91145046
[92,] 0.0752374600 0.1504749200 0.92476254
[93,] 0.0850365089 0.1700730178 0.91496349
[94,] 0.0863400616 0.1726801233 0.91365994
[95,] 0.0750388844 0.1500777688 0.92496112
[96,] 0.0640865562 0.1281731123 0.93591344
[97,] 0.0548080388 0.1096160776 0.94519196
[98,] 0.0496008080 0.0992016159 0.95039919
[99,] 0.0483037638 0.0966075276 0.95169624
[100,] 0.0407369015 0.0814738031 0.95926310
[101,] 0.0366994300 0.0733988599 0.96330057
[102,] 0.0300582389 0.0601164778 0.96994176
[103,] 0.5939062595 0.8121874811 0.40609374
[104,] 0.5657827355 0.8684345290 0.43421726
[105,] 0.5305183937 0.9389632125 0.46948161
[106,] 0.5169743083 0.9660513834 0.48302569
[107,] 0.5643641412 0.8712717176 0.43563586
[108,] 0.5333582261 0.9332835477 0.46664177
[109,] 0.5232142114 0.9535715773 0.47678579
[110,] 0.5072740031 0.9854519938 0.49272600
[111,] 0.4761350295 0.9522700589 0.52386497
[112,] 0.4606243685 0.9212487370 0.53937563
[113,] 0.4329459558 0.8658919115 0.56705404
[114,] 0.4052329787 0.8104659574 0.59476702
[115,] 0.3809425450 0.7618850901 0.61905745
[116,] 0.3578847331 0.7157694662 0.64211527
[117,] 0.3390530184 0.6781060368 0.66094698
[118,] 0.3768056796 0.7536113591 0.62319432
[119,] 0.3450833391 0.6901666782 0.65491666
[120,] 0.3215138093 0.6430276186 0.67848619
[121,] 0.2906310854 0.5812621708 0.70936891
[122,] 0.2760211311 0.5520422621 0.72397887
[123,] 0.2563516715 0.5127033429 0.74364833
[124,] 0.2321045795 0.4642091589 0.76789542
[125,] 0.2112898969 0.4225797938 0.78871010
[126,] 0.1909574293 0.3819148586 0.80904257
[127,] 0.2726355089 0.5452710178 0.72736449
[128,] 0.3256961791 0.6513923582 0.67430382
[129,] 0.3181253189 0.6362506379 0.68187468
[130,] 0.2911545237 0.5823090474 0.70884548
[131,] 0.3005159758 0.6010319515 0.69948402
[132,] 0.2831109855 0.5662219709 0.71688901
[133,] 0.2536890061 0.5073780122 0.74631099
[134,] 0.2414193370 0.4828386741 0.75858066
[135,] 0.2337804406 0.4675608812 0.76621956
[136,] 0.2116569847 0.4233139694 0.78834302
[137,] 0.2289199045 0.4578398089 0.77108010
[138,] 0.2029886922 0.4059773843 0.79701131
[139,] 0.1790590660 0.3581181321 0.82094093
[140,] 0.1568907476 0.3137814952 0.84310925
[141,] 0.1689160747 0.3378321495 0.83108393
[142,] 0.1640994725 0.3281989450 0.83590053
[143,] 0.1487639222 0.2975278444 0.85123608
[144,] 0.1286568971 0.2573137942 0.87134310
[145,] 0.1272686314 0.2545372628 0.87273137
[146,] 0.1211623724 0.2423247448 0.87883763
[147,] 0.1099727633 0.2199455266 0.89002724
[148,] 0.1292675647 0.2585351294 0.87073244
[149,] 0.1141822864 0.2283645728 0.88581771
[150,] 0.1330402968 0.2660805936 0.86695970
[151,] 0.1771450979 0.3542901957 0.82285490
[152,] 0.1680970976 0.3361941951 0.83190290
[153,] 0.2292338083 0.4584676166 0.77076619
[154,] 0.2470620254 0.4941240507 0.75293797
[155,] 0.2346635684 0.4693271368 0.76533643
[156,] 0.2133960731 0.4267921462 0.78660393
[157,] 0.1875002519 0.3750005037 0.81249975
[158,] 0.1796468007 0.3592936014 0.82035320
[159,] 0.1637722942 0.3275445884 0.83622771
[160,] 0.2102419925 0.4204839850 0.78975801
[161,] 0.1871361766 0.3742723532 0.81286382
[162,] 0.1644025770 0.3288051540 0.83559742
[163,] 0.1541051416 0.3082102831 0.84589486
[164,] 0.1350836011 0.2701672021 0.86491640
[165,] 0.1235259144 0.2470518287 0.87647409
[166,] 0.1490322193 0.2980644387 0.85096778
[167,] 0.1329008851 0.2658017701 0.86709911
[168,] 0.1246181119 0.2492362237 0.87538189
[169,] 0.1109478415 0.2218956829 0.88905216
[170,] 0.1430415889 0.2860831778 0.85695841
[171,] 0.1601108675 0.3202217350 0.83988913
[172,] 0.1413423833 0.2826847666 0.85865762
[173,] 0.1526758298 0.3053516596 0.84732417
[174,] 0.1467170235 0.2934340470 0.85328298
[175,] 0.1416470786 0.2832941572 0.85835292
[176,] 0.1758155266 0.3516310532 0.82418447
[177,] 0.1829770697 0.3659541394 0.81702293
[178,] 0.2536735665 0.5073471330 0.74632643
[179,] 0.2313091218 0.4626182436 0.76869088
[180,] 0.2392887423 0.4785774846 0.76071126
[181,] 0.6040597010 0.7918805980 0.39594030
[182,] 0.5787621845 0.8424756311 0.42123782
[183,] 0.5457304190 0.9085391620 0.45426958
[184,] 0.6611339615 0.6777320770 0.33886604
[185,] 0.6235005654 0.7529988692 0.37649943
[186,] 0.6129345076 0.7741309847 0.38706549
[187,] 0.6244030965 0.7511938070 0.37559690
[188,] 0.6135628656 0.7728742689 0.38643713
[189,] 0.5734759436 0.8530481129 0.42652406
[190,] 0.6155093551 0.7689812899 0.38449064
[191,] 0.5873450479 0.8253099043 0.41265495
[192,] 0.5594772806 0.8810454387 0.44052272
[193,] 0.7264946006 0.5470107987 0.27350540
[194,] 0.7722031935 0.4555936130 0.22779681
[195,] 0.7378447584 0.5243104831 0.26215524
[196,] 0.7005318491 0.5989363018 0.29946815
[197,] 0.6869423245 0.6261153509 0.31305768
[198,] 0.6760001774 0.6479996452 0.32399982
[199,] 0.6373674794 0.7252650412 0.36263252
[200,] 0.6937096281 0.6125807438 0.30629037
[201,] 0.6606848298 0.6786303404 0.33931517
[202,] 0.6248201047 0.7503597906 0.37517990
[203,] 0.5877934815 0.8244130370 0.41220652
[204,] 0.5407811985 0.9184376031 0.45921880
[205,] 0.4968756584 0.9937513169 0.50312434
[206,] 0.4599062575 0.9198125150 0.54009374
[207,] 0.4402676528 0.8805353057 0.55973235
[208,] 0.4113686800 0.8227373600 0.58863132
[209,] 0.3713252916 0.7426505831 0.62867471
[210,] 0.3259516010 0.6519032021 0.67404840
[211,] 0.2973228897 0.5946457795 0.70267711
[212,] 0.3021345636 0.6042691273 0.69786544
[213,] 0.2720480166 0.5440960332 0.72795198
[214,] 0.2309280994 0.4618561989 0.76907190
[215,] 0.9294098461 0.1411803078 0.07059015
[216,] 0.9229448388 0.1541103225 0.07705516
[217,] 0.9073885904 0.1852228192 0.09261141
[218,] 0.9722831739 0.0554336521 0.02771683
[219,] 0.9611810044 0.0776379911 0.03881900
[220,] 0.9461729570 0.1076540860 0.05382704
[221,] 0.9321823584 0.1356352832 0.06781764
[222,] 0.9704148979 0.0591702042 0.02958510
[223,] 0.9705570790 0.0588858420 0.02944292
[224,] 0.9580791753 0.0838416493 0.04192082
[225,] 0.9403120943 0.1193758114 0.05968791
[226,] 0.9284855749 0.1430288501 0.07151443
[227,] 0.9096094588 0.1807810824 0.09039054
[228,] 0.8973777522 0.2052444955 0.10262225
[229,] 0.8620038543 0.2759922915 0.13799615
[230,] 0.8495297647 0.3009404706 0.15047024
[231,] 0.8008479515 0.3983040970 0.19915205
[232,] 0.7858609256 0.4282781488 0.21413907
[233,] 0.7243269750 0.5513460500 0.27567302
[234,] 0.6875655821 0.6248688357 0.31243442
[235,] 0.6184730892 0.7630538217 0.38152691
[236,] 0.5279207787 0.9441584425 0.47207922
[237,] 0.5104545759 0.9790908482 0.48954542
[238,] 0.4720646161 0.9441292322 0.52793538
[239,] 0.6418320112 0.7163359776 0.35816799
[240,] 0.6870762896 0.6258474207 0.31292371
[241,] 0.5722246744 0.8555506513 0.42777533
[242,] 0.4287774902 0.8575549804 0.57122251
[243,] 0.2808834340 0.5617668681 0.71911657
> postscript(file="/var/fisher/rcomp/tmp/1ybqk1384974640.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/fisher/rcomp/tmp/2gnm61384974640.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3f1uo1384974640.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4rwez1384974640.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5l47g1384974640.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
-1.84539385 0.80465505 -3.63428643 -1.02743117 2.47069937 1.56509174
7 8 9 10 11 12
-7.45789611 0.32053014 0.02069610 -0.11379070 -0.56066120 -3.52467984
13 14 15 16 17 18
-1.69297687 -2.13538340 1.22864805 -2.78379646 1.80740251 1.31165841
19 20 21 22 23 24
-4.24956996 -4.04513017 -2.75859258 -4.16223944 -2.68179993 -5.85386307
25 26 27 28 29 30
2.32383718 3.23632271 5.01036433 -2.92494320 4.48734724 -4.99558898
31 32 33 34 35 36
-0.19737597 -2.36580032 -0.04812031 -2.73308601 4.70548770 -1.83589619
37 38 39 40 41 42
2.13536456 0.21792625 0.12644890 -0.77837431 -0.11667462 3.03476323
43 44 45 46 47 48
-3.64559284 -1.37341507 -0.35933800 1.90425286 -6.24436677 -0.40033678
49 50 51 52 53 54
5.11693929 1.35669760 -0.98691203 0.67351227 1.71978569 -12.84985575
55 56 57 58 59 60
2.21883788 0.31878029 0.65901266 -3.44182189 1.95541902 -0.24886876
61 62 63 64 65 66
-0.72771782 3.45853537 1.92271142 -1.68772028 -2.66327988 0.39830415
67 68 69 70 71 72
-2.62502644 4.57960138 -0.24107842 -0.13912162 -4.54050835 -2.35844475
73 74 75 76 77 78
2.96504464 1.97641202 2.94216375 2.24182506 -0.68518091 -2.41305288
79 80 81 82 83 84
0.29009134 -3.19396005 3.52080774 -3.31017682 2.22094573 -0.44748378
85 86 87 88 89 90
0.56235631 1.82209207 -0.62247101 -2.56913501 0.96809299 -1.97475014
91 92 93 94 95 96
-2.29484397 2.02267604 4.41850295 3.28900602 3.53882615 -1.05381368
97 98 99 100 101 102
2.76843646 2.90486662 0.43705576 1.95114853 0.19392423 1.10122148
103 104 105 106 107 108
-3.74992125 3.07614463 1.72807440 -0.64479454 1.19121071 -2.04632339
109 110 111 112 113 114
-3.36320795 1.40061814 1.94905046 -0.23917121 12.89296203 -0.26063073
115 116 117 118 119 120
0.53385588 -2.46303999 -4.95515966 -1.00900640 2.85557422 -2.13049109
121 122 123 124 125 126
0.91729049 2.64706008 -1.27674429 -0.88148272 1.89368337 1.68716002
127 128 129 130 131 132
1.81372262 -4.28536775 0.05183824 1.22142884 0.75394739 1.92403719
133 134 135 136 137 138
-1.41805943 -0.95687474 0.13006703 -0.88570509 -6.10653350 -4.82228560
139 140 141 142 143 144
-2.54336638 1.01730442 -3.18482335 1.96285098 0.47337510 1.60206235
145 146 147 148 149 150
1.61272067 -0.92403412 -3.41335212 -0.50153577 -0.53898474 0.55388031
151 152 153 154 155 156
3.81291186 2.38798045 -0.05840677 -0.11122182 -2.81702292 -2.29484397
157 158 159 160 161 162
-1.36291139 -4.28536775 1.20247961 4.47506659 -5.17518328 -2.92577515
163 164 165 166 167 168
4.86678114 2.70006291 2.32725997 -0.95327097 -0.17244388 1.72029234
169 170 171 172 173 174
2.07745099 5.61586027 -1.08931828 -0.29299863 2.12875687 0.45754241
175 176 177 178 179 180
0.05229823 5.02892475 -1.64431319 2.03301956 -1.57444221 5.39747031
181 182 183 184 185 186
-2.88748417 -1.18318542 -3.01893974 1.86846711 0.42706084 4.80280538
187 188 189 190 191 192
3.51460488 3.94785729 1.83055227 -2.23599013 12.49153910 2.49548755
193 194 195 196 197 198
0.37454100 -5.25268951 0.04545822 -2.12400280 2.36061325 2.37697679
199 200 201 202 203 204
1.04296085 4.31322868 -2.63810125 1.79209250 5.53460117 -5.10150826
205 206 207 208 209 210
0.95918727 0.44994100 -2.84632098 -1.40958260 -1.58789129 3.20980316
211 212 213 214 215 216
-1.55463872 -0.71774217 1.58820784 -0.26839817 1.30594664 -3.19693837
217 218 219 220 221 222
0.99549800 -0.23422791 2.28417176 -1.17761610 1.01776227 4.38331500
223 224 225 226 227 228
0.57807232 -0.86174074 14.28866052 2.83502563 -3.11715696 4.92802252
229 230 231 232 233 234
-1.09701048 -0.45300431 0.37979431 -4.28971661 -3.53715762 -2.00840123
235 236 237 238 239 240
-0.77349897 -0.14494326 -2.98962264 -6.38441117 0.58801605 0.28879352
241 242 243 244 245 246
2.42965050 -3.57697011 -0.65893355 0.46288296 -2.46501015 0.98874535
247 248 249 250 251 252
-2.32361493 -4.63461655 -4.14685285 0.65194283 1.01786733 2.69614815
253 254 255 256 257 258
-1.03185275 -1.55275434 -2.99740446 -3.26422399 5.83778894 -3.48870116
259 260 261 262 263 264
-0.25141119 -0.46467152 -0.25915062 0.06051706 -0.57021922 0.91453491
> postscript(file="/var/fisher/rcomp/tmp/6efle1384974640.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 -1.84539385 NA
1 0.80465505 -1.84539385
2 -3.63428643 0.80465505
3 -1.02743117 -3.63428643
4 2.47069937 -1.02743117
5 1.56509174 2.47069937
6 -7.45789611 1.56509174
7 0.32053014 -7.45789611
8 0.02069610 0.32053014
9 -0.11379070 0.02069610
10 -0.56066120 -0.11379070
11 -3.52467984 -0.56066120
12 -1.69297687 -3.52467984
13 -2.13538340 -1.69297687
14 1.22864805 -2.13538340
15 -2.78379646 1.22864805
16 1.80740251 -2.78379646
17 1.31165841 1.80740251
18 -4.24956996 1.31165841
19 -4.04513017 -4.24956996
20 -2.75859258 -4.04513017
21 -4.16223944 -2.75859258
22 -2.68179993 -4.16223944
23 -5.85386307 -2.68179993
24 2.32383718 -5.85386307
25 3.23632271 2.32383718
26 5.01036433 3.23632271
27 -2.92494320 5.01036433
28 4.48734724 -2.92494320
29 -4.99558898 4.48734724
30 -0.19737597 -4.99558898
31 -2.36580032 -0.19737597
32 -0.04812031 -2.36580032
33 -2.73308601 -0.04812031
34 4.70548770 -2.73308601
35 -1.83589619 4.70548770
36 2.13536456 -1.83589619
37 0.21792625 2.13536456
38 0.12644890 0.21792625
39 -0.77837431 0.12644890
40 -0.11667462 -0.77837431
41 3.03476323 -0.11667462
42 -3.64559284 3.03476323
43 -1.37341507 -3.64559284
44 -0.35933800 -1.37341507
45 1.90425286 -0.35933800
46 -6.24436677 1.90425286
47 -0.40033678 -6.24436677
48 5.11693929 -0.40033678
49 1.35669760 5.11693929
50 -0.98691203 1.35669760
51 0.67351227 -0.98691203
52 1.71978569 0.67351227
53 -12.84985575 1.71978569
54 2.21883788 -12.84985575
55 0.31878029 2.21883788
56 0.65901266 0.31878029
57 -3.44182189 0.65901266
58 1.95541902 -3.44182189
59 -0.24886876 1.95541902
60 -0.72771782 -0.24886876
61 3.45853537 -0.72771782
62 1.92271142 3.45853537
63 -1.68772028 1.92271142
64 -2.66327988 -1.68772028
65 0.39830415 -2.66327988
66 -2.62502644 0.39830415
67 4.57960138 -2.62502644
68 -0.24107842 4.57960138
69 -0.13912162 -0.24107842
70 -4.54050835 -0.13912162
71 -2.35844475 -4.54050835
72 2.96504464 -2.35844475
73 1.97641202 2.96504464
74 2.94216375 1.97641202
75 2.24182506 2.94216375
76 -0.68518091 2.24182506
77 -2.41305288 -0.68518091
78 0.29009134 -2.41305288
79 -3.19396005 0.29009134
80 3.52080774 -3.19396005
81 -3.31017682 3.52080774
82 2.22094573 -3.31017682
83 -0.44748378 2.22094573
84 0.56235631 -0.44748378
85 1.82209207 0.56235631
86 -0.62247101 1.82209207
87 -2.56913501 -0.62247101
88 0.96809299 -2.56913501
89 -1.97475014 0.96809299
90 -2.29484397 -1.97475014
91 2.02267604 -2.29484397
92 4.41850295 2.02267604
93 3.28900602 4.41850295
94 3.53882615 3.28900602
95 -1.05381368 3.53882615
96 2.76843646 -1.05381368
97 2.90486662 2.76843646
98 0.43705576 2.90486662
99 1.95114853 0.43705576
100 0.19392423 1.95114853
101 1.10122148 0.19392423
102 -3.74992125 1.10122148
103 3.07614463 -3.74992125
104 1.72807440 3.07614463
105 -0.64479454 1.72807440
106 1.19121071 -0.64479454
107 -2.04632339 1.19121071
108 -3.36320795 -2.04632339
109 1.40061814 -3.36320795
110 1.94905046 1.40061814
111 -0.23917121 1.94905046
112 12.89296203 -0.23917121
113 -0.26063073 12.89296203
114 0.53385588 -0.26063073
115 -2.46303999 0.53385588
116 -4.95515966 -2.46303999
117 -1.00900640 -4.95515966
118 2.85557422 -1.00900640
119 -2.13049109 2.85557422
120 0.91729049 -2.13049109
121 2.64706008 0.91729049
122 -1.27674429 2.64706008
123 -0.88148272 -1.27674429
124 1.89368337 -0.88148272
125 1.68716002 1.89368337
126 1.81372262 1.68716002
127 -4.28536775 1.81372262
128 0.05183824 -4.28536775
129 1.22142884 0.05183824
130 0.75394739 1.22142884
131 1.92403719 0.75394739
132 -1.41805943 1.92403719
133 -0.95687474 -1.41805943
134 0.13006703 -0.95687474
135 -0.88570509 0.13006703
136 -6.10653350 -0.88570509
137 -4.82228560 -6.10653350
138 -2.54336638 -4.82228560
139 1.01730442 -2.54336638
140 -3.18482335 1.01730442
141 1.96285098 -3.18482335
142 0.47337510 1.96285098
143 1.60206235 0.47337510
144 1.61272067 1.60206235
145 -0.92403412 1.61272067
146 -3.41335212 -0.92403412
147 -0.50153577 -3.41335212
148 -0.53898474 -0.50153577
149 0.55388031 -0.53898474
150 3.81291186 0.55388031
151 2.38798045 3.81291186
152 -0.05840677 2.38798045
153 -0.11122182 -0.05840677
154 -2.81702292 -0.11122182
155 -2.29484397 -2.81702292
156 -1.36291139 -2.29484397
157 -4.28536775 -1.36291139
158 1.20247961 -4.28536775
159 4.47506659 1.20247961
160 -5.17518328 4.47506659
161 -2.92577515 -5.17518328
162 4.86678114 -2.92577515
163 2.70006291 4.86678114
164 2.32725997 2.70006291
165 -0.95327097 2.32725997
166 -0.17244388 -0.95327097
167 1.72029234 -0.17244388
168 2.07745099 1.72029234
169 5.61586027 2.07745099
170 -1.08931828 5.61586027
171 -0.29299863 -1.08931828
172 2.12875687 -0.29299863
173 0.45754241 2.12875687
174 0.05229823 0.45754241
175 5.02892475 0.05229823
176 -1.64431319 5.02892475
177 2.03301956 -1.64431319
178 -1.57444221 2.03301956
179 5.39747031 -1.57444221
180 -2.88748417 5.39747031
181 -1.18318542 -2.88748417
182 -3.01893974 -1.18318542
183 1.86846711 -3.01893974
184 0.42706084 1.86846711
185 4.80280538 0.42706084
186 3.51460488 4.80280538
187 3.94785729 3.51460488
188 1.83055227 3.94785729
189 -2.23599013 1.83055227
190 12.49153910 -2.23599013
191 2.49548755 12.49153910
192 0.37454100 2.49548755
193 -5.25268951 0.37454100
194 0.04545822 -5.25268951
195 -2.12400280 0.04545822
196 2.36061325 -2.12400280
197 2.37697679 2.36061325
198 1.04296085 2.37697679
199 4.31322868 1.04296085
200 -2.63810125 4.31322868
201 1.79209250 -2.63810125
202 5.53460117 1.79209250
203 -5.10150826 5.53460117
204 0.95918727 -5.10150826
205 0.44994100 0.95918727
206 -2.84632098 0.44994100
207 -1.40958260 -2.84632098
208 -1.58789129 -1.40958260
209 3.20980316 -1.58789129
210 -1.55463872 3.20980316
211 -0.71774217 -1.55463872
212 1.58820784 -0.71774217
213 -0.26839817 1.58820784
214 1.30594664 -0.26839817
215 -3.19693837 1.30594664
216 0.99549800 -3.19693837
217 -0.23422791 0.99549800
218 2.28417176 -0.23422791
219 -1.17761610 2.28417176
220 1.01776227 -1.17761610
221 4.38331500 1.01776227
222 0.57807232 4.38331500
223 -0.86174074 0.57807232
224 14.28866052 -0.86174074
225 2.83502563 14.28866052
226 -3.11715696 2.83502563
227 4.92802252 -3.11715696
228 -1.09701048 4.92802252
229 -0.45300431 -1.09701048
230 0.37979431 -0.45300431
231 -4.28971661 0.37979431
232 -3.53715762 -4.28971661
233 -2.00840123 -3.53715762
234 -0.77349897 -2.00840123
235 -0.14494326 -0.77349897
236 -2.98962264 -0.14494326
237 -6.38441117 -2.98962264
238 0.58801605 -6.38441117
239 0.28879352 0.58801605
240 2.42965050 0.28879352
241 -3.57697011 2.42965050
242 -0.65893355 -3.57697011
243 0.46288296 -0.65893355
244 -2.46501015 0.46288296
245 0.98874535 -2.46501015
246 -2.32361493 0.98874535
247 -4.63461655 -2.32361493
248 -4.14685285 -4.63461655
249 0.65194283 -4.14685285
250 1.01786733 0.65194283
251 2.69614815 1.01786733
252 -1.03185275 2.69614815
253 -1.55275434 -1.03185275
254 -2.99740446 -1.55275434
255 -3.26422399 -2.99740446
256 5.83778894 -3.26422399
257 -3.48870116 5.83778894
258 -0.25141119 -3.48870116
259 -0.46467152 -0.25141119
260 -0.25915062 -0.46467152
261 0.06051706 -0.25915062
262 -0.57021922 0.06051706
263 0.91453491 -0.57021922
264 NA 0.91453491
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.80465505 -1.84539385
[2,] -3.63428643 0.80465505
[3,] -1.02743117 -3.63428643
[4,] 2.47069937 -1.02743117
[5,] 1.56509174 2.47069937
[6,] -7.45789611 1.56509174
[7,] 0.32053014 -7.45789611
[8,] 0.02069610 0.32053014
[9,] -0.11379070 0.02069610
[10,] -0.56066120 -0.11379070
[11,] -3.52467984 -0.56066120
[12,] -1.69297687 -3.52467984
[13,] -2.13538340 -1.69297687
[14,] 1.22864805 -2.13538340
[15,] -2.78379646 1.22864805
[16,] 1.80740251 -2.78379646
[17,] 1.31165841 1.80740251
[18,] -4.24956996 1.31165841
[19,] -4.04513017 -4.24956996
[20,] -2.75859258 -4.04513017
[21,] -4.16223944 -2.75859258
[22,] -2.68179993 -4.16223944
[23,] -5.85386307 -2.68179993
[24,] 2.32383718 -5.85386307
[25,] 3.23632271 2.32383718
[26,] 5.01036433 3.23632271
[27,] -2.92494320 5.01036433
[28,] 4.48734724 -2.92494320
[29,] -4.99558898 4.48734724
[30,] -0.19737597 -4.99558898
[31,] -2.36580032 -0.19737597
[32,] -0.04812031 -2.36580032
[33,] -2.73308601 -0.04812031
[34,] 4.70548770 -2.73308601
[35,] -1.83589619 4.70548770
[36,] 2.13536456 -1.83589619
[37,] 0.21792625 2.13536456
[38,] 0.12644890 0.21792625
[39,] -0.77837431 0.12644890
[40,] -0.11667462 -0.77837431
[41,] 3.03476323 -0.11667462
[42,] -3.64559284 3.03476323
[43,] -1.37341507 -3.64559284
[44,] -0.35933800 -1.37341507
[45,] 1.90425286 -0.35933800
[46,] -6.24436677 1.90425286
[47,] -0.40033678 -6.24436677
[48,] 5.11693929 -0.40033678
[49,] 1.35669760 5.11693929
[50,] -0.98691203 1.35669760
[51,] 0.67351227 -0.98691203
[52,] 1.71978569 0.67351227
[53,] -12.84985575 1.71978569
[54,] 2.21883788 -12.84985575
[55,] 0.31878029 2.21883788
[56,] 0.65901266 0.31878029
[57,] -3.44182189 0.65901266
[58,] 1.95541902 -3.44182189
[59,] -0.24886876 1.95541902
[60,] -0.72771782 -0.24886876
[61,] 3.45853537 -0.72771782
[62,] 1.92271142 3.45853537
[63,] -1.68772028 1.92271142
[64,] -2.66327988 -1.68772028
[65,] 0.39830415 -2.66327988
[66,] -2.62502644 0.39830415
[67,] 4.57960138 -2.62502644
[68,] -0.24107842 4.57960138
[69,] -0.13912162 -0.24107842
[70,] -4.54050835 -0.13912162
[71,] -2.35844475 -4.54050835
[72,] 2.96504464 -2.35844475
[73,] 1.97641202 2.96504464
[74,] 2.94216375 1.97641202
[75,] 2.24182506 2.94216375
[76,] -0.68518091 2.24182506
[77,] -2.41305288 -0.68518091
[78,] 0.29009134 -2.41305288
[79,] -3.19396005 0.29009134
[80,] 3.52080774 -3.19396005
[81,] -3.31017682 3.52080774
[82,] 2.22094573 -3.31017682
[83,] -0.44748378 2.22094573
[84,] 0.56235631 -0.44748378
[85,] 1.82209207 0.56235631
[86,] -0.62247101 1.82209207
[87,] -2.56913501 -0.62247101
[88,] 0.96809299 -2.56913501
[89,] -1.97475014 0.96809299
[90,] -2.29484397 -1.97475014
[91,] 2.02267604 -2.29484397
[92,] 4.41850295 2.02267604
[93,] 3.28900602 4.41850295
[94,] 3.53882615 3.28900602
[95,] -1.05381368 3.53882615
[96,] 2.76843646 -1.05381368
[97,] 2.90486662 2.76843646
[98,] 0.43705576 2.90486662
[99,] 1.95114853 0.43705576
[100,] 0.19392423 1.95114853
[101,] 1.10122148 0.19392423
[102,] -3.74992125 1.10122148
[103,] 3.07614463 -3.74992125
[104,] 1.72807440 3.07614463
[105,] -0.64479454 1.72807440
[106,] 1.19121071 -0.64479454
[107,] -2.04632339 1.19121071
[108,] -3.36320795 -2.04632339
[109,] 1.40061814 -3.36320795
[110,] 1.94905046 1.40061814
[111,] -0.23917121 1.94905046
[112,] 12.89296203 -0.23917121
[113,] -0.26063073 12.89296203
[114,] 0.53385588 -0.26063073
[115,] -2.46303999 0.53385588
[116,] -4.95515966 -2.46303999
[117,] -1.00900640 -4.95515966
[118,] 2.85557422 -1.00900640
[119,] -2.13049109 2.85557422
[120,] 0.91729049 -2.13049109
[121,] 2.64706008 0.91729049
[122,] -1.27674429 2.64706008
[123,] -0.88148272 -1.27674429
[124,] 1.89368337 -0.88148272
[125,] 1.68716002 1.89368337
[126,] 1.81372262 1.68716002
[127,] -4.28536775 1.81372262
[128,] 0.05183824 -4.28536775
[129,] 1.22142884 0.05183824
[130,] 0.75394739 1.22142884
[131,] 1.92403719 0.75394739
[132,] -1.41805943 1.92403719
[133,] -0.95687474 -1.41805943
[134,] 0.13006703 -0.95687474
[135,] -0.88570509 0.13006703
[136,] -6.10653350 -0.88570509
[137,] -4.82228560 -6.10653350
[138,] -2.54336638 -4.82228560
[139,] 1.01730442 -2.54336638
[140,] -3.18482335 1.01730442
[141,] 1.96285098 -3.18482335
[142,] 0.47337510 1.96285098
[143,] 1.60206235 0.47337510
[144,] 1.61272067 1.60206235
[145,] -0.92403412 1.61272067
[146,] -3.41335212 -0.92403412
[147,] -0.50153577 -3.41335212
[148,] -0.53898474 -0.50153577
[149,] 0.55388031 -0.53898474
[150,] 3.81291186 0.55388031
[151,] 2.38798045 3.81291186
[152,] -0.05840677 2.38798045
[153,] -0.11122182 -0.05840677
[154,] -2.81702292 -0.11122182
[155,] -2.29484397 -2.81702292
[156,] -1.36291139 -2.29484397
[157,] -4.28536775 -1.36291139
[158,] 1.20247961 -4.28536775
[159,] 4.47506659 1.20247961
[160,] -5.17518328 4.47506659
[161,] -2.92577515 -5.17518328
[162,] 4.86678114 -2.92577515
[163,] 2.70006291 4.86678114
[164,] 2.32725997 2.70006291
[165,] -0.95327097 2.32725997
[166,] -0.17244388 -0.95327097
[167,] 1.72029234 -0.17244388
[168,] 2.07745099 1.72029234
[169,] 5.61586027 2.07745099
[170,] -1.08931828 5.61586027
[171,] -0.29299863 -1.08931828
[172,] 2.12875687 -0.29299863
[173,] 0.45754241 2.12875687
[174,] 0.05229823 0.45754241
[175,] 5.02892475 0.05229823
[176,] -1.64431319 5.02892475
[177,] 2.03301956 -1.64431319
[178,] -1.57444221 2.03301956
[179,] 5.39747031 -1.57444221
[180,] -2.88748417 5.39747031
[181,] -1.18318542 -2.88748417
[182,] -3.01893974 -1.18318542
[183,] 1.86846711 -3.01893974
[184,] 0.42706084 1.86846711
[185,] 4.80280538 0.42706084
[186,] 3.51460488 4.80280538
[187,] 3.94785729 3.51460488
[188,] 1.83055227 3.94785729
[189,] -2.23599013 1.83055227
[190,] 12.49153910 -2.23599013
[191,] 2.49548755 12.49153910
[192,] 0.37454100 2.49548755
[193,] -5.25268951 0.37454100
[194,] 0.04545822 -5.25268951
[195,] -2.12400280 0.04545822
[196,] 2.36061325 -2.12400280
[197,] 2.37697679 2.36061325
[198,] 1.04296085 2.37697679
[199,] 4.31322868 1.04296085
[200,] -2.63810125 4.31322868
[201,] 1.79209250 -2.63810125
[202,] 5.53460117 1.79209250
[203,] -5.10150826 5.53460117
[204,] 0.95918727 -5.10150826
[205,] 0.44994100 0.95918727
[206,] -2.84632098 0.44994100
[207,] -1.40958260 -2.84632098
[208,] -1.58789129 -1.40958260
[209,] 3.20980316 -1.58789129
[210,] -1.55463872 3.20980316
[211,] -0.71774217 -1.55463872
[212,] 1.58820784 -0.71774217
[213,] -0.26839817 1.58820784
[214,] 1.30594664 -0.26839817
[215,] -3.19693837 1.30594664
[216,] 0.99549800 -3.19693837
[217,] -0.23422791 0.99549800
[218,] 2.28417176 -0.23422791
[219,] -1.17761610 2.28417176
[220,] 1.01776227 -1.17761610
[221,] 4.38331500 1.01776227
[222,] 0.57807232 4.38331500
[223,] -0.86174074 0.57807232
[224,] 14.28866052 -0.86174074
[225,] 2.83502563 14.28866052
[226,] -3.11715696 2.83502563
[227,] 4.92802252 -3.11715696
[228,] -1.09701048 4.92802252
[229,] -0.45300431 -1.09701048
[230,] 0.37979431 -0.45300431
[231,] -4.28971661 0.37979431
[232,] -3.53715762 -4.28971661
[233,] -2.00840123 -3.53715762
[234,] -0.77349897 -2.00840123
[235,] -0.14494326 -0.77349897
[236,] -2.98962264 -0.14494326
[237,] -6.38441117 -2.98962264
[238,] 0.58801605 -6.38441117
[239,] 0.28879352 0.58801605
[240,] 2.42965050 0.28879352
[241,] -3.57697011 2.42965050
[242,] -0.65893355 -3.57697011
[243,] 0.46288296 -0.65893355
[244,] -2.46501015 0.46288296
[245,] 0.98874535 -2.46501015
[246,] -2.32361493 0.98874535
[247,] -4.63461655 -2.32361493
[248,] -4.14685285 -4.63461655
[249,] 0.65194283 -4.14685285
[250,] 1.01786733 0.65194283
[251,] 2.69614815 1.01786733
[252,] -1.03185275 2.69614815
[253,] -1.55275434 -1.03185275
[254,] -2.99740446 -1.55275434
[255,] -3.26422399 -2.99740446
[256,] 5.83778894 -3.26422399
[257,] -3.48870116 5.83778894
[258,] -0.25141119 -3.48870116
[259,] -0.46467152 -0.25141119
[260,] -0.25915062 -0.46467152
[261,] 0.06051706 -0.25915062
[262,] -0.57021922 0.06051706
[263,] 0.91453491 -0.57021922
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.80465505 -1.84539385
2 -3.63428643 0.80465505
3 -1.02743117 -3.63428643
4 2.47069937 -1.02743117
5 1.56509174 2.47069937
6 -7.45789611 1.56509174
7 0.32053014 -7.45789611
8 0.02069610 0.32053014
9 -0.11379070 0.02069610
10 -0.56066120 -0.11379070
11 -3.52467984 -0.56066120
12 -1.69297687 -3.52467984
13 -2.13538340 -1.69297687
14 1.22864805 -2.13538340
15 -2.78379646 1.22864805
16 1.80740251 -2.78379646
17 1.31165841 1.80740251
18 -4.24956996 1.31165841
19 -4.04513017 -4.24956996
20 -2.75859258 -4.04513017
21 -4.16223944 -2.75859258
22 -2.68179993 -4.16223944
23 -5.85386307 -2.68179993
24 2.32383718 -5.85386307
25 3.23632271 2.32383718
26 5.01036433 3.23632271
27 -2.92494320 5.01036433
28 4.48734724 -2.92494320
29 -4.99558898 4.48734724
30 -0.19737597 -4.99558898
31 -2.36580032 -0.19737597
32 -0.04812031 -2.36580032
33 -2.73308601 -0.04812031
34 4.70548770 -2.73308601
35 -1.83589619 4.70548770
36 2.13536456 -1.83589619
37 0.21792625 2.13536456
38 0.12644890 0.21792625
39 -0.77837431 0.12644890
40 -0.11667462 -0.77837431
41 3.03476323 -0.11667462
42 -3.64559284 3.03476323
43 -1.37341507 -3.64559284
44 -0.35933800 -1.37341507
45 1.90425286 -0.35933800
46 -6.24436677 1.90425286
47 -0.40033678 -6.24436677
48 5.11693929 -0.40033678
49 1.35669760 5.11693929
50 -0.98691203 1.35669760
51 0.67351227 -0.98691203
52 1.71978569 0.67351227
53 -12.84985575 1.71978569
54 2.21883788 -12.84985575
55 0.31878029 2.21883788
56 0.65901266 0.31878029
57 -3.44182189 0.65901266
58 1.95541902 -3.44182189
59 -0.24886876 1.95541902
60 -0.72771782 -0.24886876
61 3.45853537 -0.72771782
62 1.92271142 3.45853537
63 -1.68772028 1.92271142
64 -2.66327988 -1.68772028
65 0.39830415 -2.66327988
66 -2.62502644 0.39830415
67 4.57960138 -2.62502644
68 -0.24107842 4.57960138
69 -0.13912162 -0.24107842
70 -4.54050835 -0.13912162
71 -2.35844475 -4.54050835
72 2.96504464 -2.35844475
73 1.97641202 2.96504464
74 2.94216375 1.97641202
75 2.24182506 2.94216375
76 -0.68518091 2.24182506
77 -2.41305288 -0.68518091
78 0.29009134 -2.41305288
79 -3.19396005 0.29009134
80 3.52080774 -3.19396005
81 -3.31017682 3.52080774
82 2.22094573 -3.31017682
83 -0.44748378 2.22094573
84 0.56235631 -0.44748378
85 1.82209207 0.56235631
86 -0.62247101 1.82209207
87 -2.56913501 -0.62247101
88 0.96809299 -2.56913501
89 -1.97475014 0.96809299
90 -2.29484397 -1.97475014
91 2.02267604 -2.29484397
92 4.41850295 2.02267604
93 3.28900602 4.41850295
94 3.53882615 3.28900602
95 -1.05381368 3.53882615
96 2.76843646 -1.05381368
97 2.90486662 2.76843646
98 0.43705576 2.90486662
99 1.95114853 0.43705576
100 0.19392423 1.95114853
101 1.10122148 0.19392423
102 -3.74992125 1.10122148
103 3.07614463 -3.74992125
104 1.72807440 3.07614463
105 -0.64479454 1.72807440
106 1.19121071 -0.64479454
107 -2.04632339 1.19121071
108 -3.36320795 -2.04632339
109 1.40061814 -3.36320795
110 1.94905046 1.40061814
111 -0.23917121 1.94905046
112 12.89296203 -0.23917121
113 -0.26063073 12.89296203
114 0.53385588 -0.26063073
115 -2.46303999 0.53385588
116 -4.95515966 -2.46303999
117 -1.00900640 -4.95515966
118 2.85557422 -1.00900640
119 -2.13049109 2.85557422
120 0.91729049 -2.13049109
121 2.64706008 0.91729049
122 -1.27674429 2.64706008
123 -0.88148272 -1.27674429
124 1.89368337 -0.88148272
125 1.68716002 1.89368337
126 1.81372262 1.68716002
127 -4.28536775 1.81372262
128 0.05183824 -4.28536775
129 1.22142884 0.05183824
130 0.75394739 1.22142884
131 1.92403719 0.75394739
132 -1.41805943 1.92403719
133 -0.95687474 -1.41805943
134 0.13006703 -0.95687474
135 -0.88570509 0.13006703
136 -6.10653350 -0.88570509
137 -4.82228560 -6.10653350
138 -2.54336638 -4.82228560
139 1.01730442 -2.54336638
140 -3.18482335 1.01730442
141 1.96285098 -3.18482335
142 0.47337510 1.96285098
143 1.60206235 0.47337510
144 1.61272067 1.60206235
145 -0.92403412 1.61272067
146 -3.41335212 -0.92403412
147 -0.50153577 -3.41335212
148 -0.53898474 -0.50153577
149 0.55388031 -0.53898474
150 3.81291186 0.55388031
151 2.38798045 3.81291186
152 -0.05840677 2.38798045
153 -0.11122182 -0.05840677
154 -2.81702292 -0.11122182
155 -2.29484397 -2.81702292
156 -1.36291139 -2.29484397
157 -4.28536775 -1.36291139
158 1.20247961 -4.28536775
159 4.47506659 1.20247961
160 -5.17518328 4.47506659
161 -2.92577515 -5.17518328
162 4.86678114 -2.92577515
163 2.70006291 4.86678114
164 2.32725997 2.70006291
165 -0.95327097 2.32725997
166 -0.17244388 -0.95327097
167 1.72029234 -0.17244388
168 2.07745099 1.72029234
169 5.61586027 2.07745099
170 -1.08931828 5.61586027
171 -0.29299863 -1.08931828
172 2.12875687 -0.29299863
173 0.45754241 2.12875687
174 0.05229823 0.45754241
175 5.02892475 0.05229823
176 -1.64431319 5.02892475
177 2.03301956 -1.64431319
178 -1.57444221 2.03301956
179 5.39747031 -1.57444221
180 -2.88748417 5.39747031
181 -1.18318542 -2.88748417
182 -3.01893974 -1.18318542
183 1.86846711 -3.01893974
184 0.42706084 1.86846711
185 4.80280538 0.42706084
186 3.51460488 4.80280538
187 3.94785729 3.51460488
188 1.83055227 3.94785729
189 -2.23599013 1.83055227
190 12.49153910 -2.23599013
191 2.49548755 12.49153910
192 0.37454100 2.49548755
193 -5.25268951 0.37454100
194 0.04545822 -5.25268951
195 -2.12400280 0.04545822
196 2.36061325 -2.12400280
197 2.37697679 2.36061325
198 1.04296085 2.37697679
199 4.31322868 1.04296085
200 -2.63810125 4.31322868
201 1.79209250 -2.63810125
202 5.53460117 1.79209250
203 -5.10150826 5.53460117
204 0.95918727 -5.10150826
205 0.44994100 0.95918727
206 -2.84632098 0.44994100
207 -1.40958260 -2.84632098
208 -1.58789129 -1.40958260
209 3.20980316 -1.58789129
210 -1.55463872 3.20980316
211 -0.71774217 -1.55463872
212 1.58820784 -0.71774217
213 -0.26839817 1.58820784
214 1.30594664 -0.26839817
215 -3.19693837 1.30594664
216 0.99549800 -3.19693837
217 -0.23422791 0.99549800
218 2.28417176 -0.23422791
219 -1.17761610 2.28417176
220 1.01776227 -1.17761610
221 4.38331500 1.01776227
222 0.57807232 4.38331500
223 -0.86174074 0.57807232
224 14.28866052 -0.86174074
225 2.83502563 14.28866052
226 -3.11715696 2.83502563
227 4.92802252 -3.11715696
228 -1.09701048 4.92802252
229 -0.45300431 -1.09701048
230 0.37979431 -0.45300431
231 -4.28971661 0.37979431
232 -3.53715762 -4.28971661
233 -2.00840123 -3.53715762
234 -0.77349897 -2.00840123
235 -0.14494326 -0.77349897
236 -2.98962264 -0.14494326
237 -6.38441117 -2.98962264
238 0.58801605 -6.38441117
239 0.28879352 0.58801605
240 2.42965050 0.28879352
241 -3.57697011 2.42965050
242 -0.65893355 -3.57697011
243 0.46288296 -0.65893355
244 -2.46501015 0.46288296
245 0.98874535 -2.46501015
246 -2.32361493 0.98874535
247 -4.63461655 -2.32361493
248 -4.14685285 -4.63461655
249 0.65194283 -4.14685285
250 1.01786733 0.65194283
251 2.69614815 1.01786733
252 -1.03185275 2.69614815
253 -1.55275434 -1.03185275
254 -2.99740446 -1.55275434
255 -3.26422399 -2.99740446
256 5.83778894 -3.26422399
257 -3.48870116 5.83778894
258 -0.25141119 -3.48870116
259 -0.46467152 -0.25141119
260 -0.25915062 -0.46467152
261 0.06051706 -0.25915062
262 -0.57021922 0.06051706
263 0.91453491 -0.57021922
> 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/fisher/rcomp/tmp/7rys61384974640.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/fisher/rcomp/tmp/8p1ld1384974640.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/fisher/rcomp/tmp/9d2w11384974640.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/fisher/rcomp/tmp/10fq941384974640.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/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11q1j21384974640.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12ghat1384974640.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13fwrd1384974640.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14aga31384974640.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/fisher/rcomp/tmp/15w4yb1384974640.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/fisher/rcomp/tmp/16ykdq1384974640.tab")
+ }
>
> try(system("convert tmp/1ybqk1384974640.ps tmp/1ybqk1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gnm61384974640.ps tmp/2gnm61384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/3f1uo1384974640.ps tmp/3f1uo1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/4rwez1384974640.ps tmp/4rwez1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/5l47g1384974640.ps tmp/5l47g1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/6efle1384974640.ps tmp/6efle1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rys61384974640.ps tmp/7rys61384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/8p1ld1384974640.ps tmp/8p1ld1384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/9d2w11384974640.ps tmp/9d2w11384974640.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fq941384974640.ps tmp/10fq941384974640.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.576 1.774 13.346