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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+ ,12
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+ ,11)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Software'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Month')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Connected','Separate','Learning','Software','Happiness','Depression','Sport1','Sport2','Month'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '9'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '9'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> 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
Month Connected Separate Learning Software Happiness Depression Sport1
1 9 41 38 13 12 14 12.0 53
2 9 39 32 16 11 18 11.0 83
3 9 30 35 19 15 11 14.0 66
4 9 31 33 15 6 12 12.0 67
5 9 34 37 14 13 16 21.0 76
6 9 35 29 13 10 18 12.0 78
7 9 39 31 19 12 14 22.0 53
8 9 34 36 15 14 14 11.0 80
9 9 36 35 14 12 15 10.0 74
10 9 37 38 15 9 15 13.0 76
11 9 38 31 16 10 17 10.0 79
12 9 36 34 16 12 19 8.0 54
13 9 38 35 16 12 10 15.0 67
14 9 39 38 16 11 16 14.0 54
15 9 33 37 17 15 18 10.0 87
16 9 32 33 15 12 14 14.0 58
17 9 36 32 15 10 14 14.0 75
18 9 38 38 20 12 17 11.0 88
19 9 39 38 18 11 14 10.0 64
20 9 32 32 16 12 16 13.0 57
21 9 32 33 16 11 18 9.5 66
22 9 31 31 16 12 11 14.0 68
23 9 39 38 19 13 14 12.0 54
24 9 37 39 16 11 12 14.0 56
25 9 39 32 17 12 17 11.0 86
26 9 41 32 17 13 9 9.0 80
27 9 36 35 16 10 16 11.0 76
28 9 33 37 15 14 14 15.0 69
29 9 33 33 16 12 15 14.0 78
30 9 34 33 14 10 11 13.0 67
31 9 31 31 15 12 16 9.0 80
32 9 27 32 12 8 13 15.0 54
33 9 37 31 14 10 17 10.0 71
34 9 34 37 16 12 15 11.0 84
35 9 34 30 14 12 14 13.0 74
36 9 32 33 10 7 16 8.0 71
37 9 29 31 10 9 9 20.0 63
38 9 36 33 14 12 15 12.0 71
39 9 29 31 16 10 17 10.0 76
40 9 35 33 16 10 13 10.0 69
41 9 37 32 16 10 15 9.0 74
42 9 34 33 14 12 16 14.0 75
43 9 38 32 20 15 16 8.0 54
44 9 35 33 14 10 12 14.0 52
45 9 38 28 14 10 15 11.0 69
46 9 37 35 11 12 11 13.0 68
47 9 38 39 14 13 15 9.0 65
48 9 33 34 15 11 15 11.0 75
49 9 36 38 16 11 17 15.0 74
50 9 38 32 14 12 13 11.0 75
51 9 32 38 16 14 16 10.0 72
52 9 32 30 14 10 14 14.0 67
53 9 32 33 12 12 11 18.0 63
54 9 34 38 16 13 12 14.0 62
55 9 32 32 9 5 12 11.0 63
56 9 37 35 14 6 15 14.5 76
57 9 39 34 16 12 16 13.0 74
58 9 29 34 16 12 15 9.0 67
59 9 37 36 15 11 12 10.0 73
60 9 35 34 16 10 12 15.0 70
61 9 30 28 12 7 8 20.0 53
62 9 38 34 16 12 13 12.0 77
63 9 34 35 16 14 11 12.0 80
64 9 31 35 14 11 14 14.0 52
65 9 34 31 16 12 15 13.0 54
66 10 35 37 17 13 10 11.0 80
67 10 36 35 18 14 11 17.0 66
68 10 30 27 18 11 12 12.0 73
69 10 39 40 12 12 15 13.0 63
70 10 35 37 16 12 15 14.0 69
71 10 38 36 10 8 14 13.0 67
72 10 31 38 14 11 16 15.0 54
73 10 34 39 18 14 15 13.0 81
74 10 38 41 18 14 15 10.0 69
75 10 34 27 16 12 13 11.0 84
76 10 39 30 17 9 12 19.0 80
77 10 37 37 16 13 17 13.0 70
78 10 34 31 16 11 13 17.0 69
79 10 28 31 13 12 15 13.0 77
80 10 37 27 16 12 13 9.0 54
81 10 33 36 16 12 15 11.0 79
82 10 35 37 16 12 15 9.0 71
83 10 37 33 15 12 16 12.0 73
84 10 32 34 15 11 15 12.0 72
85 10 33 31 16 10 14 13.0 77
86 10 38 39 14 9 15 13.0 75
87 10 33 34 16 12 14 12.0 69
88 10 29 32 16 12 13 15.0 54
89 10 33 33 15 12 7 22.0 70
90 10 31 36 12 9 17 13.0 73
91 10 36 32 17 15 13 15.0 54
92 10 35 41 16 12 15 13.0 77
93 10 32 28 15 12 14 15.0 82
94 10 29 30 13 12 13 12.5 80
95 10 39 36 16 10 16 11.0 80
96 10 37 35 16 13 12 16.0 69
97 10 35 31 16 9 14 11.0 78
98 10 37 34 16 12 17 11.0 81
99 10 32 36 14 10 15 10.0 76
100 10 38 36 16 14 17 10.0 76
101 10 37 35 16 11 12 16.0 73
102 10 36 37 20 15 16 12.0 85
103 10 32 28 15 11 11 11.0 66
104 10 33 39 16 11 15 16.0 79
105 10 40 32 13 12 9 19.0 68
106 10 38 35 17 12 16 11.0 76
107 10 41 39 16 12 15 16.0 71
108 10 36 35 16 11 10 15.0 54
109 10 43 42 12 7 10 24.0 46
110 10 30 34 16 12 15 14.0 85
111 10 31 33 16 14 11 15.0 74
112 10 32 41 17 11 13 11.0 88
113 10 32 33 13 11 14 15.0 38
114 10 37 34 12 10 18 12.0 76
115 10 37 32 18 13 16 10.0 86
116 10 33 40 14 13 14 14.0 54
117 10 34 40 14 8 14 13.0 67
118 10 33 35 13 11 14 9.0 69
119 10 38 36 16 12 14 15.0 90
120 10 33 37 13 11 12 15.0 54
121 10 31 27 16 13 14 14.0 76
122 10 38 39 13 12 15 11.0 89
123 10 37 38 16 14 15 8.0 76
124 10 36 31 15 13 15 11.0 73
125 10 31 33 16 15 13 11.0 79
126 10 39 32 15 10 17 8.0 90
127 10 44 39 17 11 17 10.0 74
128 10 33 36 15 9 19 11.0 81
129 10 35 33 12 11 15 13.0 72
130 10 32 33 16 10 13 11.0 71
131 10 28 32 10 11 9 20.0 66
132 10 40 37 16 8 15 10.0 77
133 10 27 30 12 11 15 15.0 65
134 10 37 38 14 12 15 12.0 74
135 10 32 29 15 12 16 14.0 85
136 10 28 22 13 9 11 23.0 54
137 10 34 35 15 11 14 14.0 63
138 10 30 35 11 10 11 16.0 54
139 10 35 34 12 8 15 11.0 64
140 10 31 35 11 9 13 12.0 69
141 10 32 34 16 8 15 10.0 54
142 10 30 37 15 9 16 14.0 84
143 10 30 35 17 15 14 12.0 86
144 10 31 23 16 11 15 12.0 77
145 10 40 31 10 8 16 11.0 89
146 10 32 27 18 13 16 12.0 76
147 10 36 36 13 12 11 13.0 60
148 10 32 31 16 12 12 11.0 75
149 10 35 32 13 9 9 19.0 73
150 10 38 39 10 7 16 12.0 85
151 10 42 37 15 13 13 17.0 79
152 10 34 38 16 9 16 9.0 71
153 10 35 39 16 6 12 12.0 72
154 9 38 34 14 8 9 19.0 69
155 10 33 31 10 8 13 18.0 78
156 10 36 32 17 15 13 15.0 54
157 10 32 37 13 6 14 14.0 69
158 10 33 36 15 9 19 11.0 81
159 10 34 32 16 11 13 9.0 84
160 10 32 38 12 8 12 18.0 84
161 10 34 36 13 8 13 16.0 69
162 11 27 26 13 10 10 24.0 66
163 11 31 26 12 8 14 14.0 81
164 11 38 33 17 14 16 20.0 82
165 11 34 39 15 10 10 18.0 72
166 11 24 30 10 8 11 23.0 54
167 11 30 33 14 11 14 12.0 78
168 11 26 25 11 12 12 14.0 74
169 11 34 38 13 12 9 16.0 82
170 11 27 37 16 12 9 18.0 73
171 11 37 31 12 5 11 20.0 55
172 11 36 37 16 12 16 12.0 72
173 11 41 35 12 10 9 12.0 78
174 11 29 25 9 7 13 17.0 59
175 11 36 28 12 12 16 13.0 72
176 11 32 35 15 11 13 9.0 78
177 11 37 33 12 8 9 16.0 68
178 11 30 30 12 9 12 18.0 69
179 11 31 31 14 10 16 10.0 67
180 11 38 37 12 9 11 14.0 74
181 11 36 36 16 12 14 11.0 54
182 11 35 30 11 6 13 9.0 67
183 11 31 36 19 15 15 11.0 70
184 11 38 32 15 12 14 10.0 80
185 11 22 28 8 12 16 11.0 89
186 11 32 36 16 12 13 19.0 76
187 11 36 34 17 11 14 14.0 74
188 11 39 31 12 7 15 12.0 87
189 11 28 28 11 7 13 14.0 54
190 11 32 36 11 5 11 21.0 61
191 11 32 36 14 12 11 13.0 38
192 11 38 40 16 12 14 10.0 75
193 11 32 33 12 3 15 15.0 69
194 11 35 37 16 11 11 16.0 62
195 11 32 32 13 10 15 14.0 72
196 11 37 38 15 12 12 12.0 70
197 11 34 31 16 9 14 19.0 79
198 11 33 37 16 12 14 15.0 87
199 11 33 33 14 9 8 19.0 62
200 11 26 32 16 12 13 13.0 77
201 11 30 30 16 12 9 17.0 69
202 11 24 30 14 10 15 12.0 69
203 11 34 31 11 9 17 11.0 75
204 11 34 32 12 12 13 14.0 54
205 11 33 34 15 8 15 11.0 72
206 11 34 36 15 11 15 13.0 74
207 11 35 37 16 11 14 12.0 85
208 11 35 36 16 12 16 15.0 52
209 11 36 33 11 10 13 14.0 70
210 11 34 33 15 10 16 12.0 84
211 11 34 33 12 12 9 17.0 64
212 11 41 44 12 12 16 11.0 84
213 11 32 39 15 11 11 18.0 87
214 11 30 32 15 8 10 13.0 79
215 11 35 35 16 12 11 17.0 67
216 11 28 25 14 10 15 13.0 65
217 11 33 35 17 11 17 11.0 85
218 11 39 34 14 10 14 12.0 83
219 11 36 35 13 8 8 22.0 61
220 11 36 39 15 12 15 14.0 82
221 11 35 33 13 12 11 12.0 76
222 11 38 36 14 10 16 12.0 58
223 11 33 32 15 12 10 17.0 72
224 11 31 32 12 9 15 9.0 72
225 11 34 36 13 9 9 21.0 38
226 11 32 36 8 6 16 10.0 78
227 11 31 32 14 10 19 11.0 54
228 11 33 34 14 9 12 12.0 63
229 11 34 33 11 9 8 23.0 66
230 11 34 35 12 9 11 13.0 70
231 11 34 30 13 6 14 12.0 71
232 11 33 38 10 10 9 16.0 67
233 11 32 34 16 6 15 9.0 58
234 11 41 33 18 14 13 17.0 72
235 11 34 32 13 10 16 9.0 72
236 11 36 31 11 10 11 14.0 70
237 11 37 30 4 6 12 17.0 76
238 11 36 27 13 12 13 13.0 50
239 11 29 31 16 12 10 11.0 72
240 11 37 30 10 7 11 12.0 72
241 11 27 32 12 8 12 10.0 88
242 11 35 35 12 11 8 19.0 53
243 11 28 28 10 3 12 16.0 58
244 11 35 33 13 6 12 16.0 66
245 11 37 31 15 10 15 14.0 82
246 11 29 35 12 8 11 20.0 69
247 11 32 35 14 9 13 15.0 68
248 11 36 32 10 9 14 23.0 44
249 11 19 21 12 8 10 20.0 56
250 11 21 20 12 9 12 16.0 53
251 11 31 34 11 7 15 14.0 70
252 11 33 32 10 7 13 17.0 78
253 11 36 34 12 6 13 11.0 71
254 11 33 32 16 9 13 13.0 72
255 11 37 33 12 10 12 17.0 68
256 11 34 33 14 11 12 15.0 67
257 11 35 37 16 12 9 21.0 75
258 11 31 32 14 8 9 18.0 62
259 11 37 34 13 11 15 15.0 67
260 11 35 30 4 3 10 8.0 83
261 11 27 30 15 11 14 12.0 64
262 11 34 38 11 12 15 12.0 68
263 11 40 36 11 7 7 22.0 62
264 11 29 32 14 9 14 12.0 72
Sport2
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 Software Happiness
11.527235 -0.027200 0.005873 -0.060821 -0.040524 -0.040982
Depression Sport1 Sport2
0.030420 0.031672 -0.033894
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.92843 -0.56761 0.04587 0.52781 1.42236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 11.527235 0.741299 15.550 <2e-16 ***
Connected -0.027200 0.013394 -2.031 0.0433 *
Separate 0.005873 0.013714 0.428 0.6688
Learning -0.060821 0.023979 -2.536 0.0118 *
Software -0.040524 0.024654 -1.644 0.1015
Happiness -0.040982 0.022364 -1.833 0.0680 .
Depression 0.030420 0.016355 1.860 0.0640 .
Sport1 0.031672 0.014524 2.181 0.0301 *
Sport2 -0.033894 0.021661 -1.565 0.1189
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.723 on 255 degrees of freedom
Multiple R-squared: 0.1863, Adjusted R-squared: 0.1608
F-statistic: 7.297 on 8 and 255 DF, p-value: 9.977e-09
> 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,] 5.501798e-46 1.100360e-45 1.000000e+00
[2,] 8.398019e-61 1.679604e-60 1.000000e+00
[3,] 5.127738e-73 1.025548e-72 1.000000e+00
[4,] 1.965026e-87 3.930052e-87 1.000000e+00
[5,] 0.000000e+00 0.000000e+00 1.000000e+00
[6,] 4.229113e-117 8.458226e-117 1.000000e+00
[7,] 5.553751e-133 1.110750e-132 1.000000e+00
[8,] 2.026859e-148 4.053717e-148 1.000000e+00
[9,] 2.297650e-170 4.595300e-170 1.000000e+00
[10,] 3.160242e-180 6.320483e-180 1.000000e+00
[11,] 4.984404e-193 9.968808e-193 1.000000e+00
[12,] 7.322067e-205 1.464413e-204 1.000000e+00
[13,] 3.985024e-227 7.970049e-227 1.000000e+00
[14,] 5.849423e-241 1.169885e-240 1.000000e+00
[15,] 6.776939e-259 1.355388e-258 1.000000e+00
[16,] 1.665720e-269 3.331440e-269 1.000000e+00
[17,] 6.795864e-283 1.359173e-282 1.000000e+00
[18,] 7.846032e-308 1.569206e-307 1.000000e+00
[19,] 1.423067e-304 2.846133e-304 1.000000e+00
[20,] 0.000000e+00 0.000000e+00 1.000000e+00
[21,] 0.000000e+00 0.000000e+00 1.000000e+00
[22,] 0.000000e+00 0.000000e+00 1.000000e+00
[23,] 0.000000e+00 0.000000e+00 1.000000e+00
[24,] 0.000000e+00 0.000000e+00 1.000000e+00
[25,] 0.000000e+00 0.000000e+00 1.000000e+00
[26,] 0.000000e+00 0.000000e+00 1.000000e+00
[27,] 0.000000e+00 0.000000e+00 1.000000e+00
[28,] 0.000000e+00 0.000000e+00 1.000000e+00
[29,] 0.000000e+00 0.000000e+00 1.000000e+00
[30,] 0.000000e+00 0.000000e+00 1.000000e+00
[31,] 0.000000e+00 0.000000e+00 1.000000e+00
[32,] 0.000000e+00 0.000000e+00 1.000000e+00
[33,] 0.000000e+00 0.000000e+00 1.000000e+00
[34,] 0.000000e+00 0.000000e+00 1.000000e+00
[35,] 0.000000e+00 0.000000e+00 1.000000e+00
[36,] 0.000000e+00 0.000000e+00 1.000000e+00
[37,] 0.000000e+00 0.000000e+00 1.000000e+00
[38,] 0.000000e+00 0.000000e+00 1.000000e+00
[39,] 0.000000e+00 0.000000e+00 1.000000e+00
[40,] 0.000000e+00 0.000000e+00 1.000000e+00
[41,] 0.000000e+00 0.000000e+00 1.000000e+00
[42,] 0.000000e+00 0.000000e+00 1.000000e+00
[43,] 0.000000e+00 0.000000e+00 1.000000e+00
[44,] 0.000000e+00 0.000000e+00 1.000000e+00
[45,] 0.000000e+00 0.000000e+00 1.000000e+00
[46,] 0.000000e+00 0.000000e+00 1.000000e+00
[47,] 0.000000e+00 0.000000e+00 1.000000e+00
[48,] 0.000000e+00 0.000000e+00 1.000000e+00
[49,] 0.000000e+00 0.000000e+00 1.000000e+00
[50,] 0.000000e+00 0.000000e+00 1.000000e+00
[51,] 0.000000e+00 0.000000e+00 1.000000e+00
[52,] 0.000000e+00 0.000000e+00 1.000000e+00
[53,] 0.000000e+00 0.000000e+00 1.000000e+00
[54,] 0.000000e+00 0.000000e+00 1.000000e+00
[55,] 1.657093e-22 3.314185e-22 1.000000e+00
[56,] 8.789394e-16 1.757879e-15 1.000000e+00
[57,] 7.052436e-13 1.410487e-12 1.000000e+00
[58,] 3.081861e-09 6.163723e-09 1.000000e+00
[59,] 1.388778e-07 2.777556e-07 9.999999e-01
[60,] 5.864321e-06 1.172864e-05 9.999941e-01
[61,] 4.087776e-05 8.175553e-05 9.999591e-01
[62,] 1.090740e-04 2.181480e-04 9.998909e-01
[63,] 1.838451e-04 3.676901e-04 9.998162e-01
[64,] 1.165781e-03 2.331561e-03 9.988342e-01
[65,] 2.716139e-03 5.432278e-03 9.972839e-01
[66,] 5.400638e-03 1.080128e-02 9.945994e-01
[67,] 1.057698e-02 2.115395e-02 9.894230e-01
[68,] 2.080093e-02 4.160187e-02 9.791991e-01
[69,] 4.523868e-02 9.047736e-02 9.547613e-01
[70,] 5.962291e-02 1.192458e-01 9.403771e-01
[71,] 8.439665e-02 1.687933e-01 9.156034e-01
[72,] 1.085604e-01 2.171208e-01 8.914396e-01
[73,] 1.386207e-01 2.772413e-01 8.613793e-01
[74,] 1.695353e-01 3.390705e-01 8.304647e-01
[75,] 1.977445e-01 3.954890e-01 8.022555e-01
[76,] 2.266214e-01 4.532427e-01 7.733786e-01
[77,] 2.592716e-01 5.185432e-01 7.407284e-01
[78,] 2.719825e-01 5.439650e-01 7.280175e-01
[79,] 3.158243e-01 6.316486e-01 6.841757e-01
[80,] 3.398584e-01 6.797168e-01 6.601416e-01
[81,] 3.420702e-01 6.841403e-01 6.579298e-01
[82,] 3.598930e-01 7.197860e-01 6.401070e-01
[83,] 3.843087e-01 7.686175e-01 6.156913e-01
[84,] 4.075718e-01 8.151436e-01 5.924282e-01
[85,] 4.186576e-01 8.373152e-01 5.813424e-01
[86,] 4.498580e-01 8.997159e-01 5.501420e-01
[87,] 4.606780e-01 9.213559e-01 5.393220e-01
[88,] 4.861602e-01 9.723204e-01 5.138398e-01
[89,] 4.922159e-01 9.844319e-01 5.077841e-01
[90,] 5.016692e-01 9.966615e-01 4.983308e-01
[91,] 4.812996e-01 9.625992e-01 5.187004e-01
[92,] 5.357212e-01 9.285576e-01 4.642788e-01
[93,] 5.245632e-01 9.508735e-01 4.754368e-01
[94,] 5.459710e-01 9.080580e-01 4.540290e-01
[95,] 5.563529e-01 8.872942e-01 4.436471e-01
[96,] 5.508456e-01 8.983087e-01 4.491544e-01
[97,] 5.759777e-01 8.480445e-01 4.240223e-01
[98,] 5.913391e-01 8.173218e-01 4.086609e-01
[99,] 5.883401e-01 8.233199e-01 4.116599e-01
[100,] 5.919584e-01 8.160831e-01 4.080416e-01
[101,] 5.818597e-01 8.362806e-01 4.181403e-01
[102,] 6.221285e-01 7.557430e-01 3.778715e-01
[103,] 6.543990e-01 6.912020e-01 3.456010e-01
[104,] 6.587712e-01 6.824576e-01 3.412288e-01
[105,] 6.720250e-01 6.559500e-01 3.279750e-01
[106,] 6.899354e-01 6.201292e-01 3.100646e-01
[107,] 7.207618e-01 5.584764e-01 2.792382e-01
[108,] 7.133089e-01 5.733821e-01 2.866911e-01
[109,] 7.398811e-01 5.202378e-01 2.601189e-01
[110,] 7.582116e-01 4.835767e-01 2.417884e-01
[111,] 7.591886e-01 4.816228e-01 2.408114e-01
[112,] 7.669133e-01 4.661733e-01 2.330867e-01
[113,] 7.883151e-01 4.233698e-01 2.116849e-01
[114,] 8.012210e-01 3.975581e-01 1.987790e-01
[115,] 8.155738e-01 3.688524e-01 1.844262e-01
[116,] 8.213861e-01 3.572278e-01 1.786139e-01
[117,] 8.301231e-01 3.397538e-01 1.698769e-01
[118,] 8.515656e-01 2.968687e-01 1.484344e-01
[119,] 8.705382e-01 2.589235e-01 1.294618e-01
[120,] 8.976373e-01 2.047253e-01 1.023627e-01
[121,] 9.056920e-01 1.886160e-01 9.430802e-02
[122,] 9.260119e-01 1.479762e-01 7.398812e-02
[123,] 9.346981e-01 1.306038e-01 6.530189e-02
[124,] 9.429541e-01 1.140918e-01 5.704591e-02
[125,] 9.620825e-01 7.583492e-02 3.791746e-02
[126,] 9.688336e-01 6.233278e-02 3.116639e-02
[127,] 9.794460e-01 4.110792e-02 2.055396e-02
[128,] 9.859039e-01 2.819218e-02 1.409609e-02
[129,] 9.913463e-01 1.730739e-02 8.653693e-03
[130,] 9.947767e-01 1.044668e-02 5.223340e-03
[131,] 9.957914e-01 8.417259e-03 4.208629e-03
[132,] 9.971112e-01 5.777533e-03 2.888766e-03
[133,] 9.983630e-01 3.273934e-03 1.636967e-03
[134,] 9.989846e-01 2.030734e-03 1.015367e-03
[135,] 9.995196e-01 9.607605e-04 4.803802e-04
[136,] 9.998080e-01 3.840376e-04 1.920188e-04
[137,] 9.999337e-01 1.326340e-04 6.631699e-05
[138,] 9.999722e-01 5.561713e-05 2.780857e-05
[139,] 9.999854e-01 2.912076e-05 1.456038e-05
[140,] 9.999957e-01 8.647283e-06 4.323641e-06
[141,] 9.999987e-01 2.556692e-06 1.278346e-06
[142,] 9.999995e-01 1.011533e-06 5.057663e-07
[143,] 1.000000e+00 2.005300e-12 1.002650e-12
[144,] 1.000000e+00 3.181967e-14 1.590983e-14
[145,] 1.000000e+00 1.411088e-17 7.055441e-18
[146,] 1.000000e+00 4.122462e-20 2.061231e-20
[147,] 1.000000e+00 1.041918e-23 5.209590e-24
[148,] 1.000000e+00 5.185886e-31 2.592943e-31
[149,] 1.000000e+00 9.102657e-43 4.551328e-43
[150,] 1.000000e+00 0.000000e+00 0.000000e+00
[151,] 1.000000e+00 0.000000e+00 0.000000e+00
[152,] 1.000000e+00 0.000000e+00 0.000000e+00
[153,] 1.000000e+00 0.000000e+00 0.000000e+00
[154,] 1.000000e+00 0.000000e+00 0.000000e+00
[155,] 1.000000e+00 0.000000e+00 0.000000e+00
[156,] 1.000000e+00 0.000000e+00 0.000000e+00
[157,] 1.000000e+00 0.000000e+00 0.000000e+00
[158,] 1.000000e+00 0.000000e+00 0.000000e+00
[159,] 1.000000e+00 0.000000e+00 0.000000e+00
[160,] 1.000000e+00 0.000000e+00 0.000000e+00
[161,] 1.000000e+00 0.000000e+00 0.000000e+00
[162,] 1.000000e+00 0.000000e+00 0.000000e+00
[163,] 1.000000e+00 0.000000e+00 0.000000e+00
[164,] 1.000000e+00 0.000000e+00 0.000000e+00
[165,] 1.000000e+00 0.000000e+00 0.000000e+00
[166,] 1.000000e+00 0.000000e+00 0.000000e+00
[167,] 1.000000e+00 0.000000e+00 0.000000e+00
[168,] 1.000000e+00 0.000000e+00 0.000000e+00
[169,] 1.000000e+00 0.000000e+00 0.000000e+00
[170,] 1.000000e+00 0.000000e+00 0.000000e+00
[171,] 1.000000e+00 0.000000e+00 0.000000e+00
[172,] 1.000000e+00 0.000000e+00 0.000000e+00
[173,] 1.000000e+00 0.000000e+00 0.000000e+00
[174,] 1.000000e+00 0.000000e+00 0.000000e+00
[175,] 1.000000e+00 0.000000e+00 0.000000e+00
[176,] 1.000000e+00 0.000000e+00 0.000000e+00
[177,] 1.000000e+00 0.000000e+00 0.000000e+00
[178,] 1.000000e+00 0.000000e+00 0.000000e+00
[179,] 1.000000e+00 0.000000e+00 0.000000e+00
[180,] 1.000000e+00 0.000000e+00 0.000000e+00
[181,] 1.000000e+00 0.000000e+00 0.000000e+00
[182,] 1.000000e+00 0.000000e+00 0.000000e+00
[183,] 1.000000e+00 0.000000e+00 0.000000e+00
[184,] 1.000000e+00 0.000000e+00 0.000000e+00
[185,] 1.000000e+00 0.000000e+00 0.000000e+00
[186,] 1.000000e+00 0.000000e+00 0.000000e+00
[187,] 1.000000e+00 0.000000e+00 0.000000e+00
[188,] 1.000000e+00 0.000000e+00 0.000000e+00
[189,] 1.000000e+00 0.000000e+00 0.000000e+00
[190,] 1.000000e+00 0.000000e+00 0.000000e+00
[191,] 1.000000e+00 0.000000e+00 0.000000e+00
[192,] 1.000000e+00 0.000000e+00 0.000000e+00
[193,] 1.000000e+00 0.000000e+00 0.000000e+00
[194,] 1.000000e+00 0.000000e+00 0.000000e+00
[195,] 1.000000e+00 0.000000e+00 0.000000e+00
[196,] 1.000000e+00 0.000000e+00 0.000000e+00
[197,] 1.000000e+00 0.000000e+00 0.000000e+00
[198,] 1.000000e+00 0.000000e+00 0.000000e+00
[199,] 1.000000e+00 0.000000e+00 0.000000e+00
[200,] 1.000000e+00 0.000000e+00 0.000000e+00
[201,] 1.000000e+00 0.000000e+00 0.000000e+00
[202,] 1.000000e+00 0.000000e+00 0.000000e+00
[203,] 1.000000e+00 0.000000e+00 0.000000e+00
[204,] 1.000000e+00 0.000000e+00 0.000000e+00
[205,] 1.000000e+00 0.000000e+00 0.000000e+00
[206,] 1.000000e+00 0.000000e+00 0.000000e+00
[207,] 1.000000e+00 0.000000e+00 0.000000e+00
[208,] 1.000000e+00 0.000000e+00 0.000000e+00
[209,] 1.000000e+00 0.000000e+00 0.000000e+00
[210,] 1.000000e+00 0.000000e+00 0.000000e+00
[211,] 1.000000e+00 0.000000e+00 0.000000e+00
[212,] 1.000000e+00 0.000000e+00 0.000000e+00
[213,] 1.000000e+00 0.000000e+00 0.000000e+00
[214,] 1.000000e+00 0.000000e+00 0.000000e+00
[215,] 1.000000e+00 0.000000e+00 0.000000e+00
[216,] 1.000000e+00 0.000000e+00 0.000000e+00
[217,] 1.000000e+00 0.000000e+00 0.000000e+00
[218,] 1.000000e+00 0.000000e+00 0.000000e+00
[219,] 1.000000e+00 0.000000e+00 0.000000e+00
[220,] 1.000000e+00 0.000000e+00 0.000000e+00
[221,] 1.000000e+00 0.000000e+00 0.000000e+00
[222,] 1.000000e+00 0.000000e+00 0.000000e+00
[223,] 1.000000e+00 2.615994e-304 1.307997e-304
[224,] 1.000000e+00 9.060369e-301 4.530184e-301
[225,] 1.000000e+00 1.200780e-276 6.003898e-277
[226,] 1.000000e+00 5.305450e-268 2.652725e-268
[227,] 1.000000e+00 5.080012e-250 2.540006e-250
[228,] 1.000000e+00 2.693651e-230 1.346825e-230
[229,] 1.000000e+00 1.024321e-233 5.121605e-234
[230,] 1.000000e+00 4.117342e-210 2.058671e-210
[231,] 1.000000e+00 1.145126e-189 5.725628e-190
[232,] 1.000000e+00 9.695847e-185 4.847923e-185
[233,] 1.000000e+00 6.141936e-170 3.070968e-170
[234,] 1.000000e+00 1.069516e-148 5.347579e-149
[235,] 1.000000e+00 9.396416e-145 4.698208e-145
[236,] 1.000000e+00 1.345826e-118 6.729130e-119
[237,] 1.000000e+00 0.000000e+00 0.000000e+00
[238,] 1.000000e+00 2.356749e-89 1.178375e-89
[239,] 1.000000e+00 1.088438e-72 5.442192e-73
[240,] 1.000000e+00 6.665253e-64 3.332627e-64
[241,] 1.000000e+00 1.037767e-44 5.188834e-45
> postscript(file="/var/wessaorg/rcomp/tmp/1q4zz1383229955.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/258jt1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3w0rn1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/49enu1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5mhyv1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 264
Frequency = 1
1 2 3 4 5 6
-0.743583147 -0.732641459 -0.795255531 -1.328049996 -1.272533588 -1.054442693
7 8 9 10 11 12
-0.526678938 -0.968083658 -0.923817875 -1.061805932 -0.746152220 -0.344813461
13 14 15 16 17 18
-0.984764343 -0.632699153 -0.730772959 -0.921002104 -1.120761185 -0.608935883
19 20 21 22 23 24
-0.516911981 -0.710250742 -0.683793500 -1.044153146 -0.390312699 -0.784669716
25 26 27 28 29 30
-0.733402327 -0.884931237 -1.003795106 -0.943911997 -1.120401242 -1.114895931
31 32 33 34 35 36
-0.958559889 -1.442081705 -0.844978921 -0.910420649 -1.182777537 -1.303827447
37 38 39 40 41 42
-1.928432905 -0.945683065 -0.997617326 -0.923961774 -0.807983648 -1.112737422
43 44 45 46 47 48
-0.036758914 -1.042670695 -0.883093237 -1.313706251 -0.604702642 -1.007446376
49 50 51 52 53 54
-1.032139595 -0.995855450 -0.777115363 -1.194726515 -1.472561071 -0.664336685
55 56 57 58 59 60
-1.712785916 -1.272209023 -0.764981907 -0.836257782 -1.041256443 -1.120697564
61 62 63 64 65 66
-1.736747248 -0.979725555 -1.088650750 -1.040729010 -0.697624662 -0.029567823
67 68 69 70 71 72
0.175338741 -0.124964009 0.026651412 0.093875692 -0.225099835 -0.002361359
73 74 75 76 77 78
0.111334005 0.408565425 -0.069229266 -0.203149050 0.303405985 -0.044046117
79 80 81 82 83 84
-0.297236280 0.447185475 -0.044538072 0.284315126 0.119975076 -0.037837639
85 86 87 88 89 90
-0.100803451 -0.137415762 0.076953936 0.017696803 -0.566851521 -0.252634390
91 92 93 94 95 96
0.390489088 -0.017000058 -0.275466066 -0.425934655 0.080818333 0.050652638
97 98 99 100 101 102
-0.091652981 0.162416186 -0.148989532 0.346017664 -0.089296537 0.419935190
103 104 105 106 107 108
-0.013860173 -0.220889420 -0.323437547 0.328049802 0.155037718 0.027006806
109 110 111 112 113 114
-0.351170416 -0.192322929 -0.195307234 -0.142776698 -0.327491673 -0.026884719
115 116 117 118 119 120
0.336874568 0.069795745 -0.114109450 -0.060644392 -0.114551840 -0.166837794
121 122 123 124 125 126
-0.042781286 -0.154191888 0.353735804 0.202271723 -0.007817388 0.130162139
127 128 129 130 131 132
0.460298384 0.110912516 -0.163247286 -0.099327149 -0.873793626 0.071659023
133 134 135 136 137 138
-0.337937754 0.058815772 -0.094501261 -0.580132413 0.126130088 -0.402471217
139 140 141 142 143 144
-0.112047814 -0.483871197 0.159521708 -0.353574234 0.006276261 -0.030185377
145 146 147 148 149 150
-0.322487329 0.282759759 -0.039546339 -0.038628726 -0.569900683 -0.435915753
151 152 153 154 155 156
-0.056500934 0.035076281 -0.250348429 -1.454742534 -0.635956254 0.390489088
157 158 159 160 161 162
-0.420417641 0.110912516 -0.044384268 -0.847538309 -0.279237480 0.297167446
163 164 165 166 167 168
0.392721485 1.058707785 0.525344878 0.041095393 0.757374198 0.401929374
169 170 171 172 173 174
0.431016766 0.348116708 0.482375569 1.195669569 0.643857263 0.279029744
175 176 177 178 179 180
0.974824866 0.775551121 0.525222664 0.355611156 1.009740390 0.488329865
181 182 183 184 185 186
1.347267812 0.687329805 1.422359694 1.079688043 0.245985511 0.630163434
187 188 189 190 191 192
0.993542042 0.621690514 0.436004648 0.205203918 0.694127420 1.116308895
193 194 195 196 197 198
0.397350791 1.016395389 0.750902558 1.055591590 0.673899574 0.770804007
199 200 201 202 203 204
0.433014713 0.641306196 0.729621974 0.626151447 0.659430354 1.008622158
205 206 207 208 209 210
0.864317322 0.944944725 1.007074630 1.275907310 0.713568211 0.846189203
211 212 213 214 215 216
0.600304829 1.002671588 0.450199192 0.510369333 0.845988783 0.894481230
217 218 219 220 221 222
1.076926579 0.932991488 0.308040826 0.975707571 0.745687262 1.056044534
223 224 225 226 227 228
0.684619542 0.774459926 0.240807643 0.301627594 1.236982479 0.670658319
229 230 231 232 233 234
0.198851346 0.582280638 0.672590865 0.412028917 1.083887053 1.350591397
235 236 237 238 239 240
0.998386805 0.609456122 0.051633431 1.242531192 0.791141834 0.491527182
241 242 243 244 245 246
0.270056693 0.618443982 0.154046420 0.535205126 1.002739981 0.190592322
247 248 249 250 251 252
0.733990843 0.700367704 0.168212476 0.364308218 0.464299756 0.212490660
253 254 255 256 257 258
0.666009446 0.902391410 0.732690581 0.871875140 0.478898941 0.517049106
259 260 261 262 263 264
0.941939518 -0.086437767 0.858687982 0.815317390 0.277829880 0.675564041
> postscript(file="/var/wessaorg/rcomp/tmp/6eev91383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 264
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.743583147 NA
1 -0.732641459 -0.743583147
2 -0.795255531 -0.732641459
3 -1.328049996 -0.795255531
4 -1.272533588 -1.328049996
5 -1.054442693 -1.272533588
6 -0.526678938 -1.054442693
7 -0.968083658 -0.526678938
8 -0.923817875 -0.968083658
9 -1.061805932 -0.923817875
10 -0.746152220 -1.061805932
11 -0.344813461 -0.746152220
12 -0.984764343 -0.344813461
13 -0.632699153 -0.984764343
14 -0.730772959 -0.632699153
15 -0.921002104 -0.730772959
16 -1.120761185 -0.921002104
17 -0.608935883 -1.120761185
18 -0.516911981 -0.608935883
19 -0.710250742 -0.516911981
20 -0.683793500 -0.710250742
21 -1.044153146 -0.683793500
22 -0.390312699 -1.044153146
23 -0.784669716 -0.390312699
24 -0.733402327 -0.784669716
25 -0.884931237 -0.733402327
26 -1.003795106 -0.884931237
27 -0.943911997 -1.003795106
28 -1.120401242 -0.943911997
29 -1.114895931 -1.120401242
30 -0.958559889 -1.114895931
31 -1.442081705 -0.958559889
32 -0.844978921 -1.442081705
33 -0.910420649 -0.844978921
34 -1.182777537 -0.910420649
35 -1.303827447 -1.182777537
36 -1.928432905 -1.303827447
37 -0.945683065 -1.928432905
38 -0.997617326 -0.945683065
39 -0.923961774 -0.997617326
40 -0.807983648 -0.923961774
41 -1.112737422 -0.807983648
42 -0.036758914 -1.112737422
43 -1.042670695 -0.036758914
44 -0.883093237 -1.042670695
45 -1.313706251 -0.883093237
46 -0.604702642 -1.313706251
47 -1.007446376 -0.604702642
48 -1.032139595 -1.007446376
49 -0.995855450 -1.032139595
50 -0.777115363 -0.995855450
51 -1.194726515 -0.777115363
52 -1.472561071 -1.194726515
53 -0.664336685 -1.472561071
54 -1.712785916 -0.664336685
55 -1.272209023 -1.712785916
56 -0.764981907 -1.272209023
57 -0.836257782 -0.764981907
58 -1.041256443 -0.836257782
59 -1.120697564 -1.041256443
60 -1.736747248 -1.120697564
61 -0.979725555 -1.736747248
62 -1.088650750 -0.979725555
63 -1.040729010 -1.088650750
64 -0.697624662 -1.040729010
65 -0.029567823 -0.697624662
66 0.175338741 -0.029567823
67 -0.124964009 0.175338741
68 0.026651412 -0.124964009
69 0.093875692 0.026651412
70 -0.225099835 0.093875692
71 -0.002361359 -0.225099835
72 0.111334005 -0.002361359
73 0.408565425 0.111334005
74 -0.069229266 0.408565425
75 -0.203149050 -0.069229266
76 0.303405985 -0.203149050
77 -0.044046117 0.303405985
78 -0.297236280 -0.044046117
79 0.447185475 -0.297236280
80 -0.044538072 0.447185475
81 0.284315126 -0.044538072
82 0.119975076 0.284315126
83 -0.037837639 0.119975076
84 -0.100803451 -0.037837639
85 -0.137415762 -0.100803451
86 0.076953936 -0.137415762
87 0.017696803 0.076953936
88 -0.566851521 0.017696803
89 -0.252634390 -0.566851521
90 0.390489088 -0.252634390
91 -0.017000058 0.390489088
92 -0.275466066 -0.017000058
93 -0.425934655 -0.275466066
94 0.080818333 -0.425934655
95 0.050652638 0.080818333
96 -0.091652981 0.050652638
97 0.162416186 -0.091652981
98 -0.148989532 0.162416186
99 0.346017664 -0.148989532
100 -0.089296537 0.346017664
101 0.419935190 -0.089296537
102 -0.013860173 0.419935190
103 -0.220889420 -0.013860173
104 -0.323437547 -0.220889420
105 0.328049802 -0.323437547
106 0.155037718 0.328049802
107 0.027006806 0.155037718
108 -0.351170416 0.027006806
109 -0.192322929 -0.351170416
110 -0.195307234 -0.192322929
111 -0.142776698 -0.195307234
112 -0.327491673 -0.142776698
113 -0.026884719 -0.327491673
114 0.336874568 -0.026884719
115 0.069795745 0.336874568
116 -0.114109450 0.069795745
117 -0.060644392 -0.114109450
118 -0.114551840 -0.060644392
119 -0.166837794 -0.114551840
120 -0.042781286 -0.166837794
121 -0.154191888 -0.042781286
122 0.353735804 -0.154191888
123 0.202271723 0.353735804
124 -0.007817388 0.202271723
125 0.130162139 -0.007817388
126 0.460298384 0.130162139
127 0.110912516 0.460298384
128 -0.163247286 0.110912516
129 -0.099327149 -0.163247286
130 -0.873793626 -0.099327149
131 0.071659023 -0.873793626
132 -0.337937754 0.071659023
133 0.058815772 -0.337937754
134 -0.094501261 0.058815772
135 -0.580132413 -0.094501261
136 0.126130088 -0.580132413
137 -0.402471217 0.126130088
138 -0.112047814 -0.402471217
139 -0.483871197 -0.112047814
140 0.159521708 -0.483871197
141 -0.353574234 0.159521708
142 0.006276261 -0.353574234
143 -0.030185377 0.006276261
144 -0.322487329 -0.030185377
145 0.282759759 -0.322487329
146 -0.039546339 0.282759759
147 -0.038628726 -0.039546339
148 -0.569900683 -0.038628726
149 -0.435915753 -0.569900683
150 -0.056500934 -0.435915753
151 0.035076281 -0.056500934
152 -0.250348429 0.035076281
153 -1.454742534 -0.250348429
154 -0.635956254 -1.454742534
155 0.390489088 -0.635956254
156 -0.420417641 0.390489088
157 0.110912516 -0.420417641
158 -0.044384268 0.110912516
159 -0.847538309 -0.044384268
160 -0.279237480 -0.847538309
161 0.297167446 -0.279237480
162 0.392721485 0.297167446
163 1.058707785 0.392721485
164 0.525344878 1.058707785
165 0.041095393 0.525344878
166 0.757374198 0.041095393
167 0.401929374 0.757374198
168 0.431016766 0.401929374
169 0.348116708 0.431016766
170 0.482375569 0.348116708
171 1.195669569 0.482375569
172 0.643857263 1.195669569
173 0.279029744 0.643857263
174 0.974824866 0.279029744
175 0.775551121 0.974824866
176 0.525222664 0.775551121
177 0.355611156 0.525222664
178 1.009740390 0.355611156
179 0.488329865 1.009740390
180 1.347267812 0.488329865
181 0.687329805 1.347267812
182 1.422359694 0.687329805
183 1.079688043 1.422359694
184 0.245985511 1.079688043
185 0.630163434 0.245985511
186 0.993542042 0.630163434
187 0.621690514 0.993542042
188 0.436004648 0.621690514
189 0.205203918 0.436004648
190 0.694127420 0.205203918
191 1.116308895 0.694127420
192 0.397350791 1.116308895
193 1.016395389 0.397350791
194 0.750902558 1.016395389
195 1.055591590 0.750902558
196 0.673899574 1.055591590
197 0.770804007 0.673899574
198 0.433014713 0.770804007
199 0.641306196 0.433014713
200 0.729621974 0.641306196
201 0.626151447 0.729621974
202 0.659430354 0.626151447
203 1.008622158 0.659430354
204 0.864317322 1.008622158
205 0.944944725 0.864317322
206 1.007074630 0.944944725
207 1.275907310 1.007074630
208 0.713568211 1.275907310
209 0.846189203 0.713568211
210 0.600304829 0.846189203
211 1.002671588 0.600304829
212 0.450199192 1.002671588
213 0.510369333 0.450199192
214 0.845988783 0.510369333
215 0.894481230 0.845988783
216 1.076926579 0.894481230
217 0.932991488 1.076926579
218 0.308040826 0.932991488
219 0.975707571 0.308040826
220 0.745687262 0.975707571
221 1.056044534 0.745687262
222 0.684619542 1.056044534
223 0.774459926 0.684619542
224 0.240807643 0.774459926
225 0.301627594 0.240807643
226 1.236982479 0.301627594
227 0.670658319 1.236982479
228 0.198851346 0.670658319
229 0.582280638 0.198851346
230 0.672590865 0.582280638
231 0.412028917 0.672590865
232 1.083887053 0.412028917
233 1.350591397 1.083887053
234 0.998386805 1.350591397
235 0.609456122 0.998386805
236 0.051633431 0.609456122
237 1.242531192 0.051633431
238 0.791141834 1.242531192
239 0.491527182 0.791141834
240 0.270056693 0.491527182
241 0.618443982 0.270056693
242 0.154046420 0.618443982
243 0.535205126 0.154046420
244 1.002739981 0.535205126
245 0.190592322 1.002739981
246 0.733990843 0.190592322
247 0.700367704 0.733990843
248 0.168212476 0.700367704
249 0.364308218 0.168212476
250 0.464299756 0.364308218
251 0.212490660 0.464299756
252 0.666009446 0.212490660
253 0.902391410 0.666009446
254 0.732690581 0.902391410
255 0.871875140 0.732690581
256 0.478898941 0.871875140
257 0.517049106 0.478898941
258 0.941939518 0.517049106
259 -0.086437767 0.941939518
260 0.858687982 -0.086437767
261 0.815317390 0.858687982
262 0.277829880 0.815317390
263 0.675564041 0.277829880
264 NA 0.675564041
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.732641459 -0.743583147
[2,] -0.795255531 -0.732641459
[3,] -1.328049996 -0.795255531
[4,] -1.272533588 -1.328049996
[5,] -1.054442693 -1.272533588
[6,] -0.526678938 -1.054442693
[7,] -0.968083658 -0.526678938
[8,] -0.923817875 -0.968083658
[9,] -1.061805932 -0.923817875
[10,] -0.746152220 -1.061805932
[11,] -0.344813461 -0.746152220
[12,] -0.984764343 -0.344813461
[13,] -0.632699153 -0.984764343
[14,] -0.730772959 -0.632699153
[15,] -0.921002104 -0.730772959
[16,] -1.120761185 -0.921002104
[17,] -0.608935883 -1.120761185
[18,] -0.516911981 -0.608935883
[19,] -0.710250742 -0.516911981
[20,] -0.683793500 -0.710250742
[21,] -1.044153146 -0.683793500
[22,] -0.390312699 -1.044153146
[23,] -0.784669716 -0.390312699
[24,] -0.733402327 -0.784669716
[25,] -0.884931237 -0.733402327
[26,] -1.003795106 -0.884931237
[27,] -0.943911997 -1.003795106
[28,] -1.120401242 -0.943911997
[29,] -1.114895931 -1.120401242
[30,] -0.958559889 -1.114895931
[31,] -1.442081705 -0.958559889
[32,] -0.844978921 -1.442081705
[33,] -0.910420649 -0.844978921
[34,] -1.182777537 -0.910420649
[35,] -1.303827447 -1.182777537
[36,] -1.928432905 -1.303827447
[37,] -0.945683065 -1.928432905
[38,] -0.997617326 -0.945683065
[39,] -0.923961774 -0.997617326
[40,] -0.807983648 -0.923961774
[41,] -1.112737422 -0.807983648
[42,] -0.036758914 -1.112737422
[43,] -1.042670695 -0.036758914
[44,] -0.883093237 -1.042670695
[45,] -1.313706251 -0.883093237
[46,] -0.604702642 -1.313706251
[47,] -1.007446376 -0.604702642
[48,] -1.032139595 -1.007446376
[49,] -0.995855450 -1.032139595
[50,] -0.777115363 -0.995855450
[51,] -1.194726515 -0.777115363
[52,] -1.472561071 -1.194726515
[53,] -0.664336685 -1.472561071
[54,] -1.712785916 -0.664336685
[55,] -1.272209023 -1.712785916
[56,] -0.764981907 -1.272209023
[57,] -0.836257782 -0.764981907
[58,] -1.041256443 -0.836257782
[59,] -1.120697564 -1.041256443
[60,] -1.736747248 -1.120697564
[61,] -0.979725555 -1.736747248
[62,] -1.088650750 -0.979725555
[63,] -1.040729010 -1.088650750
[64,] -0.697624662 -1.040729010
[65,] -0.029567823 -0.697624662
[66,] 0.175338741 -0.029567823
[67,] -0.124964009 0.175338741
[68,] 0.026651412 -0.124964009
[69,] 0.093875692 0.026651412
[70,] -0.225099835 0.093875692
[71,] -0.002361359 -0.225099835
[72,] 0.111334005 -0.002361359
[73,] 0.408565425 0.111334005
[74,] -0.069229266 0.408565425
[75,] -0.203149050 -0.069229266
[76,] 0.303405985 -0.203149050
[77,] -0.044046117 0.303405985
[78,] -0.297236280 -0.044046117
[79,] 0.447185475 -0.297236280
[80,] -0.044538072 0.447185475
[81,] 0.284315126 -0.044538072
[82,] 0.119975076 0.284315126
[83,] -0.037837639 0.119975076
[84,] -0.100803451 -0.037837639
[85,] -0.137415762 -0.100803451
[86,] 0.076953936 -0.137415762
[87,] 0.017696803 0.076953936
[88,] -0.566851521 0.017696803
[89,] -0.252634390 -0.566851521
[90,] 0.390489088 -0.252634390
[91,] -0.017000058 0.390489088
[92,] -0.275466066 -0.017000058
[93,] -0.425934655 -0.275466066
[94,] 0.080818333 -0.425934655
[95,] 0.050652638 0.080818333
[96,] -0.091652981 0.050652638
[97,] 0.162416186 -0.091652981
[98,] -0.148989532 0.162416186
[99,] 0.346017664 -0.148989532
[100,] -0.089296537 0.346017664
[101,] 0.419935190 -0.089296537
[102,] -0.013860173 0.419935190
[103,] -0.220889420 -0.013860173
[104,] -0.323437547 -0.220889420
[105,] 0.328049802 -0.323437547
[106,] 0.155037718 0.328049802
[107,] 0.027006806 0.155037718
[108,] -0.351170416 0.027006806
[109,] -0.192322929 -0.351170416
[110,] -0.195307234 -0.192322929
[111,] -0.142776698 -0.195307234
[112,] -0.327491673 -0.142776698
[113,] -0.026884719 -0.327491673
[114,] 0.336874568 -0.026884719
[115,] 0.069795745 0.336874568
[116,] -0.114109450 0.069795745
[117,] -0.060644392 -0.114109450
[118,] -0.114551840 -0.060644392
[119,] -0.166837794 -0.114551840
[120,] -0.042781286 -0.166837794
[121,] -0.154191888 -0.042781286
[122,] 0.353735804 -0.154191888
[123,] 0.202271723 0.353735804
[124,] -0.007817388 0.202271723
[125,] 0.130162139 -0.007817388
[126,] 0.460298384 0.130162139
[127,] 0.110912516 0.460298384
[128,] -0.163247286 0.110912516
[129,] -0.099327149 -0.163247286
[130,] -0.873793626 -0.099327149
[131,] 0.071659023 -0.873793626
[132,] -0.337937754 0.071659023
[133,] 0.058815772 -0.337937754
[134,] -0.094501261 0.058815772
[135,] -0.580132413 -0.094501261
[136,] 0.126130088 -0.580132413
[137,] -0.402471217 0.126130088
[138,] -0.112047814 -0.402471217
[139,] -0.483871197 -0.112047814
[140,] 0.159521708 -0.483871197
[141,] -0.353574234 0.159521708
[142,] 0.006276261 -0.353574234
[143,] -0.030185377 0.006276261
[144,] -0.322487329 -0.030185377
[145,] 0.282759759 -0.322487329
[146,] -0.039546339 0.282759759
[147,] -0.038628726 -0.039546339
[148,] -0.569900683 -0.038628726
[149,] -0.435915753 -0.569900683
[150,] -0.056500934 -0.435915753
[151,] 0.035076281 -0.056500934
[152,] -0.250348429 0.035076281
[153,] -1.454742534 -0.250348429
[154,] -0.635956254 -1.454742534
[155,] 0.390489088 -0.635956254
[156,] -0.420417641 0.390489088
[157,] 0.110912516 -0.420417641
[158,] -0.044384268 0.110912516
[159,] -0.847538309 -0.044384268
[160,] -0.279237480 -0.847538309
[161,] 0.297167446 -0.279237480
[162,] 0.392721485 0.297167446
[163,] 1.058707785 0.392721485
[164,] 0.525344878 1.058707785
[165,] 0.041095393 0.525344878
[166,] 0.757374198 0.041095393
[167,] 0.401929374 0.757374198
[168,] 0.431016766 0.401929374
[169,] 0.348116708 0.431016766
[170,] 0.482375569 0.348116708
[171,] 1.195669569 0.482375569
[172,] 0.643857263 1.195669569
[173,] 0.279029744 0.643857263
[174,] 0.974824866 0.279029744
[175,] 0.775551121 0.974824866
[176,] 0.525222664 0.775551121
[177,] 0.355611156 0.525222664
[178,] 1.009740390 0.355611156
[179,] 0.488329865 1.009740390
[180,] 1.347267812 0.488329865
[181,] 0.687329805 1.347267812
[182,] 1.422359694 0.687329805
[183,] 1.079688043 1.422359694
[184,] 0.245985511 1.079688043
[185,] 0.630163434 0.245985511
[186,] 0.993542042 0.630163434
[187,] 0.621690514 0.993542042
[188,] 0.436004648 0.621690514
[189,] 0.205203918 0.436004648
[190,] 0.694127420 0.205203918
[191,] 1.116308895 0.694127420
[192,] 0.397350791 1.116308895
[193,] 1.016395389 0.397350791
[194,] 0.750902558 1.016395389
[195,] 1.055591590 0.750902558
[196,] 0.673899574 1.055591590
[197,] 0.770804007 0.673899574
[198,] 0.433014713 0.770804007
[199,] 0.641306196 0.433014713
[200,] 0.729621974 0.641306196
[201,] 0.626151447 0.729621974
[202,] 0.659430354 0.626151447
[203,] 1.008622158 0.659430354
[204,] 0.864317322 1.008622158
[205,] 0.944944725 0.864317322
[206,] 1.007074630 0.944944725
[207,] 1.275907310 1.007074630
[208,] 0.713568211 1.275907310
[209,] 0.846189203 0.713568211
[210,] 0.600304829 0.846189203
[211,] 1.002671588 0.600304829
[212,] 0.450199192 1.002671588
[213,] 0.510369333 0.450199192
[214,] 0.845988783 0.510369333
[215,] 0.894481230 0.845988783
[216,] 1.076926579 0.894481230
[217,] 0.932991488 1.076926579
[218,] 0.308040826 0.932991488
[219,] 0.975707571 0.308040826
[220,] 0.745687262 0.975707571
[221,] 1.056044534 0.745687262
[222,] 0.684619542 1.056044534
[223,] 0.774459926 0.684619542
[224,] 0.240807643 0.774459926
[225,] 0.301627594 0.240807643
[226,] 1.236982479 0.301627594
[227,] 0.670658319 1.236982479
[228,] 0.198851346 0.670658319
[229,] 0.582280638 0.198851346
[230,] 0.672590865 0.582280638
[231,] 0.412028917 0.672590865
[232,] 1.083887053 0.412028917
[233,] 1.350591397 1.083887053
[234,] 0.998386805 1.350591397
[235,] 0.609456122 0.998386805
[236,] 0.051633431 0.609456122
[237,] 1.242531192 0.051633431
[238,] 0.791141834 1.242531192
[239,] 0.491527182 0.791141834
[240,] 0.270056693 0.491527182
[241,] 0.618443982 0.270056693
[242,] 0.154046420 0.618443982
[243,] 0.535205126 0.154046420
[244,] 1.002739981 0.535205126
[245,] 0.190592322 1.002739981
[246,] 0.733990843 0.190592322
[247,] 0.700367704 0.733990843
[248,] 0.168212476 0.700367704
[249,] 0.364308218 0.168212476
[250,] 0.464299756 0.364308218
[251,] 0.212490660 0.464299756
[252,] 0.666009446 0.212490660
[253,] 0.902391410 0.666009446
[254,] 0.732690581 0.902391410
[255,] 0.871875140 0.732690581
[256,] 0.478898941 0.871875140
[257,] 0.517049106 0.478898941
[258,] 0.941939518 0.517049106
[259,] -0.086437767 0.941939518
[260,] 0.858687982 -0.086437767
[261,] 0.815317390 0.858687982
[262,] 0.277829880 0.815317390
[263,] 0.675564041 0.277829880
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.732641459 -0.743583147
2 -0.795255531 -0.732641459
3 -1.328049996 -0.795255531
4 -1.272533588 -1.328049996
5 -1.054442693 -1.272533588
6 -0.526678938 -1.054442693
7 -0.968083658 -0.526678938
8 -0.923817875 -0.968083658
9 -1.061805932 -0.923817875
10 -0.746152220 -1.061805932
11 -0.344813461 -0.746152220
12 -0.984764343 -0.344813461
13 -0.632699153 -0.984764343
14 -0.730772959 -0.632699153
15 -0.921002104 -0.730772959
16 -1.120761185 -0.921002104
17 -0.608935883 -1.120761185
18 -0.516911981 -0.608935883
19 -0.710250742 -0.516911981
20 -0.683793500 -0.710250742
21 -1.044153146 -0.683793500
22 -0.390312699 -1.044153146
23 -0.784669716 -0.390312699
24 -0.733402327 -0.784669716
25 -0.884931237 -0.733402327
26 -1.003795106 -0.884931237
27 -0.943911997 -1.003795106
28 -1.120401242 -0.943911997
29 -1.114895931 -1.120401242
30 -0.958559889 -1.114895931
31 -1.442081705 -0.958559889
32 -0.844978921 -1.442081705
33 -0.910420649 -0.844978921
34 -1.182777537 -0.910420649
35 -1.303827447 -1.182777537
36 -1.928432905 -1.303827447
37 -0.945683065 -1.928432905
38 -0.997617326 -0.945683065
39 -0.923961774 -0.997617326
40 -0.807983648 -0.923961774
41 -1.112737422 -0.807983648
42 -0.036758914 -1.112737422
43 -1.042670695 -0.036758914
44 -0.883093237 -1.042670695
45 -1.313706251 -0.883093237
46 -0.604702642 -1.313706251
47 -1.007446376 -0.604702642
48 -1.032139595 -1.007446376
49 -0.995855450 -1.032139595
50 -0.777115363 -0.995855450
51 -1.194726515 -0.777115363
52 -1.472561071 -1.194726515
53 -0.664336685 -1.472561071
54 -1.712785916 -0.664336685
55 -1.272209023 -1.712785916
56 -0.764981907 -1.272209023
57 -0.836257782 -0.764981907
58 -1.041256443 -0.836257782
59 -1.120697564 -1.041256443
60 -1.736747248 -1.120697564
61 -0.979725555 -1.736747248
62 -1.088650750 -0.979725555
63 -1.040729010 -1.088650750
64 -0.697624662 -1.040729010
65 -0.029567823 -0.697624662
66 0.175338741 -0.029567823
67 -0.124964009 0.175338741
68 0.026651412 -0.124964009
69 0.093875692 0.026651412
70 -0.225099835 0.093875692
71 -0.002361359 -0.225099835
72 0.111334005 -0.002361359
73 0.408565425 0.111334005
74 -0.069229266 0.408565425
75 -0.203149050 -0.069229266
76 0.303405985 -0.203149050
77 -0.044046117 0.303405985
78 -0.297236280 -0.044046117
79 0.447185475 -0.297236280
80 -0.044538072 0.447185475
81 0.284315126 -0.044538072
82 0.119975076 0.284315126
83 -0.037837639 0.119975076
84 -0.100803451 -0.037837639
85 -0.137415762 -0.100803451
86 0.076953936 -0.137415762
87 0.017696803 0.076953936
88 -0.566851521 0.017696803
89 -0.252634390 -0.566851521
90 0.390489088 -0.252634390
91 -0.017000058 0.390489088
92 -0.275466066 -0.017000058
93 -0.425934655 -0.275466066
94 0.080818333 -0.425934655
95 0.050652638 0.080818333
96 -0.091652981 0.050652638
97 0.162416186 -0.091652981
98 -0.148989532 0.162416186
99 0.346017664 -0.148989532
100 -0.089296537 0.346017664
101 0.419935190 -0.089296537
102 -0.013860173 0.419935190
103 -0.220889420 -0.013860173
104 -0.323437547 -0.220889420
105 0.328049802 -0.323437547
106 0.155037718 0.328049802
107 0.027006806 0.155037718
108 -0.351170416 0.027006806
109 -0.192322929 -0.351170416
110 -0.195307234 -0.192322929
111 -0.142776698 -0.195307234
112 -0.327491673 -0.142776698
113 -0.026884719 -0.327491673
114 0.336874568 -0.026884719
115 0.069795745 0.336874568
116 -0.114109450 0.069795745
117 -0.060644392 -0.114109450
118 -0.114551840 -0.060644392
119 -0.166837794 -0.114551840
120 -0.042781286 -0.166837794
121 -0.154191888 -0.042781286
122 0.353735804 -0.154191888
123 0.202271723 0.353735804
124 -0.007817388 0.202271723
125 0.130162139 -0.007817388
126 0.460298384 0.130162139
127 0.110912516 0.460298384
128 -0.163247286 0.110912516
129 -0.099327149 -0.163247286
130 -0.873793626 -0.099327149
131 0.071659023 -0.873793626
132 -0.337937754 0.071659023
133 0.058815772 -0.337937754
134 -0.094501261 0.058815772
135 -0.580132413 -0.094501261
136 0.126130088 -0.580132413
137 -0.402471217 0.126130088
138 -0.112047814 -0.402471217
139 -0.483871197 -0.112047814
140 0.159521708 -0.483871197
141 -0.353574234 0.159521708
142 0.006276261 -0.353574234
143 -0.030185377 0.006276261
144 -0.322487329 -0.030185377
145 0.282759759 -0.322487329
146 -0.039546339 0.282759759
147 -0.038628726 -0.039546339
148 -0.569900683 -0.038628726
149 -0.435915753 -0.569900683
150 -0.056500934 -0.435915753
151 0.035076281 -0.056500934
152 -0.250348429 0.035076281
153 -1.454742534 -0.250348429
154 -0.635956254 -1.454742534
155 0.390489088 -0.635956254
156 -0.420417641 0.390489088
157 0.110912516 -0.420417641
158 -0.044384268 0.110912516
159 -0.847538309 -0.044384268
160 -0.279237480 -0.847538309
161 0.297167446 -0.279237480
162 0.392721485 0.297167446
163 1.058707785 0.392721485
164 0.525344878 1.058707785
165 0.041095393 0.525344878
166 0.757374198 0.041095393
167 0.401929374 0.757374198
168 0.431016766 0.401929374
169 0.348116708 0.431016766
170 0.482375569 0.348116708
171 1.195669569 0.482375569
172 0.643857263 1.195669569
173 0.279029744 0.643857263
174 0.974824866 0.279029744
175 0.775551121 0.974824866
176 0.525222664 0.775551121
177 0.355611156 0.525222664
178 1.009740390 0.355611156
179 0.488329865 1.009740390
180 1.347267812 0.488329865
181 0.687329805 1.347267812
182 1.422359694 0.687329805
183 1.079688043 1.422359694
184 0.245985511 1.079688043
185 0.630163434 0.245985511
186 0.993542042 0.630163434
187 0.621690514 0.993542042
188 0.436004648 0.621690514
189 0.205203918 0.436004648
190 0.694127420 0.205203918
191 1.116308895 0.694127420
192 0.397350791 1.116308895
193 1.016395389 0.397350791
194 0.750902558 1.016395389
195 1.055591590 0.750902558
196 0.673899574 1.055591590
197 0.770804007 0.673899574
198 0.433014713 0.770804007
199 0.641306196 0.433014713
200 0.729621974 0.641306196
201 0.626151447 0.729621974
202 0.659430354 0.626151447
203 1.008622158 0.659430354
204 0.864317322 1.008622158
205 0.944944725 0.864317322
206 1.007074630 0.944944725
207 1.275907310 1.007074630
208 0.713568211 1.275907310
209 0.846189203 0.713568211
210 0.600304829 0.846189203
211 1.002671588 0.600304829
212 0.450199192 1.002671588
213 0.510369333 0.450199192
214 0.845988783 0.510369333
215 0.894481230 0.845988783
216 1.076926579 0.894481230
217 0.932991488 1.076926579
218 0.308040826 0.932991488
219 0.975707571 0.308040826
220 0.745687262 0.975707571
221 1.056044534 0.745687262
222 0.684619542 1.056044534
223 0.774459926 0.684619542
224 0.240807643 0.774459926
225 0.301627594 0.240807643
226 1.236982479 0.301627594
227 0.670658319 1.236982479
228 0.198851346 0.670658319
229 0.582280638 0.198851346
230 0.672590865 0.582280638
231 0.412028917 0.672590865
232 1.083887053 0.412028917
233 1.350591397 1.083887053
234 0.998386805 1.350591397
235 0.609456122 0.998386805
236 0.051633431 0.609456122
237 1.242531192 0.051633431
238 0.791141834 1.242531192
239 0.491527182 0.791141834
240 0.270056693 0.491527182
241 0.618443982 0.270056693
242 0.154046420 0.618443982
243 0.535205126 0.154046420
244 1.002739981 0.535205126
245 0.190592322 1.002739981
246 0.733990843 0.190592322
247 0.700367704 0.733990843
248 0.168212476 0.700367704
249 0.364308218 0.168212476
250 0.464299756 0.364308218
251 0.212490660 0.464299756
252 0.666009446 0.212490660
253 0.902391410 0.666009446
254 0.732690581 0.902391410
255 0.871875140 0.732690581
256 0.478898941 0.871875140
257 0.517049106 0.478898941
258 0.941939518 0.517049106
259 -0.086437767 0.941939518
260 0.858687982 -0.086437767
261 0.815317390 0.858687982
262 0.277829880 0.815317390
263 0.675564041 0.277829880
> 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/70u2r1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8l0zf1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9h1mp1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10o8nh1383229955.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$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/118br21383229955.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$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/127y0y1383229955.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, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/133ht21383229955.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,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14s8yl1383229955.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,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15dius1383229955.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,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ 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,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ 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,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ 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/16i5jm1383229955.tab")
+ }
>
> try(system("convert tmp/1q4zz1383229955.ps tmp/1q4zz1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/258jt1383229955.ps tmp/258jt1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/3w0rn1383229955.ps tmp/3w0rn1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/49enu1383229955.ps tmp/49enu1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/5mhyv1383229955.ps tmp/5mhyv1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/6eev91383229955.ps tmp/6eev91383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/70u2r1383229955.ps tmp/70u2r1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/8l0zf1383229955.ps tmp/8l0zf1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/9h1mp1383229955.ps tmp/9h1mp1383229955.png",intern=TRUE))
character(0)
> try(system("convert tmp/10o8nh1383229955.ps tmp/10o8nh1383229955.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
17.593 3.188 20.765