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(14
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+ ,14)
+ ,dim=c(9
+ ,264)
+ ,dimnames=list(c('Happiness'
+ ,'Month'
+ ,'Software'
+ ,'Connected'
+ ,'Separate'
+ ,'Depression'
+ ,'Sport1'
+ ,'Sport2'
+ ,'Learning')
+ ,1:264))
> y <- array(NA,dim=c(9,264),dimnames=list(c('Happiness','Month','Software','Connected','Separate','Depression','Sport1','Sport2','Learning'),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 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'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
Happiness Month Software Connected Separate Depression Sport1 Sport2
1 14 9 12 41 38 12.0 53 32
2 18 9 11 39 32 11.0 83 51
3 11 9 15 30 35 14.0 66 42
4 12 9 6 31 33 12.0 67 41
5 16 9 13 34 37 21.0 76 46
6 18 9 10 35 29 12.0 78 47
7 14 9 12 39 31 22.0 53 37
8 14 9 14 34 36 11.0 80 49
9 15 9 12 36 35 10.0 74 45
10 15 9 9 37 38 13.0 76 47
11 17 9 10 38 31 10.0 79 49
12 19 9 12 36 34 8.0 54 33
13 10 9 12 38 35 15.0 67 42
14 16 9 11 39 38 14.0 54 33
15 18 9 15 33 37 10.0 87 53
16 14 9 12 32 33 14.0 58 36
17 14 9 10 36 32 14.0 75 45
18 17 9 12 38 38 11.0 88 54
19 14 9 11 39 38 10.0 64 41
20 16 9 12 32 32 13.0 57 36
21 18 9 11 32 33 9.5 66 41
22 11 9 12 31 31 14.0 68 44
23 14 9 13 39 38 12.0 54 33
24 12 9 11 37 39 14.0 56 37
25 17 9 12 39 32 11.0 86 52
26 9 9 13 41 32 9.0 80 47
27 16 9 10 36 35 11.0 76 43
28 14 9 14 33 37 15.0 69 44
29 15 9 12 33 33 14.0 78 45
30 11 9 10 34 33 13.0 67 44
31 16 9 12 31 31 9.0 80 49
32 13 9 8 27 32 15.0 54 33
33 17 9 10 37 31 10.0 71 43
34 15 9 12 34 37 11.0 84 54
35 14 9 12 34 30 13.0 74 42
36 16 9 7 32 33 8.0 71 44
37 9 9 9 29 31 20.0 63 37
38 15 9 12 36 33 12.0 71 43
39 17 9 10 29 31 10.0 76 46
40 13 9 10 35 33 10.0 69 42
41 15 9 10 37 32 9.0 74 45
42 16 9 12 34 33 14.0 75 44
43 16 9 15 38 32 8.0 54 33
44 12 9 10 35 33 14.0 52 31
45 15 9 10 38 28 11.0 69 42
46 11 9 12 37 35 13.0 68 40
47 15 9 13 38 39 9.0 65 43
48 15 9 11 33 34 11.0 75 46
49 17 9 11 36 38 15.0 74 42
50 13 9 12 38 32 11.0 75 45
51 16 9 14 32 38 10.0 72 44
52 14 9 10 32 30 14.0 67 40
53 11 9 12 32 33 18.0 63 37
54 12 9 13 34 38 14.0 62 46
55 12 9 5 32 32 11.0 63 36
56 15 9 6 37 35 14.5 76 47
57 16 9 12 39 34 13.0 74 45
58 15 9 12 29 34 9.0 67 42
59 12 9 11 37 36 10.0 73 43
60 12 9 10 35 34 15.0 70 43
61 8 9 7 30 28 20.0 53 32
62 13 9 12 38 34 12.0 77 45
63 11 9 14 34 35 12.0 80 48
64 14 9 11 31 35 14.0 52 31
65 15 9 12 34 31 13.0 54 33
66 10 10 13 35 37 11.0 80 49
67 11 10 14 36 35 17.0 66 42
68 12 10 11 30 27 12.0 73 41
69 15 10 12 39 40 13.0 63 38
70 15 10 12 35 37 14.0 69 42
71 14 10 8 38 36 13.0 67 44
72 16 10 11 31 38 15.0 54 33
73 15 10 14 34 39 13.0 81 48
74 15 10 14 38 41 10.0 69 40
75 13 10 12 34 27 11.0 84 50
76 12 10 9 39 30 19.0 80 49
77 17 10 13 37 37 13.0 70 43
78 13 10 11 34 31 17.0 69 44
79 15 10 12 28 31 13.0 77 47
80 13 10 12 37 27 9.0 54 33
81 15 10 12 33 36 11.0 79 46
82 15 10 12 35 37 9.0 71 45
83 16 10 12 37 33 12.0 73 43
84 15 10 11 32 34 12.0 72 44
85 14 10 10 33 31 13.0 77 47
86 15 10 9 38 39 13.0 75 45
87 14 10 12 33 34 12.0 69 42
88 13 10 12 29 32 15.0 54 33
89 7 10 12 33 33 22.0 70 43
90 17 10 9 31 36 13.0 73 46
91 13 10 15 36 32 15.0 54 33
92 15 10 12 35 41 13.0 77 46
93 14 10 12 32 28 15.0 82 48
94 13 10 12 29 30 12.5 80 47
95 16 10 10 39 36 11.0 80 47
96 12 10 13 37 35 16.0 69 43
97 14 10 9 35 31 11.0 78 46
98 17 10 12 37 34 11.0 81 48
99 15 10 10 32 36 10.0 76 46
100 17 10 14 38 36 10.0 76 45
101 12 10 11 37 35 16.0 73 45
102 16 10 15 36 37 12.0 85 52
103 11 10 11 32 28 11.0 66 42
104 15 10 11 33 39 16.0 79 47
105 9 10 12 40 32 19.0 68 41
106 16 10 12 38 35 11.0 76 47
107 15 10 12 41 39 16.0 71 43
108 10 10 11 36 35 15.0 54 33
109 10 10 7 43 42 24.0 46 30
110 15 10 12 30 34 14.0 85 52
111 11 10 14 31 33 15.0 74 44
112 13 10 11 32 41 11.0 88 55
113 14 10 11 32 33 15.0 38 11
114 18 10 10 37 34 12.0 76 47
115 16 10 13 37 32 10.0 86 53
116 14 10 13 33 40 14.0 54 33
117 14 10 8 34 40 13.0 67 44
118 14 10 11 33 35 9.0 69 42
119 14 10 12 38 36 15.0 90 55
120 12 10 11 33 37 15.0 54 33
121 14 10 13 31 27 14.0 76 46
122 15 10 12 38 39 11.0 89 54
123 15 10 14 37 38 8.0 76 47
124 15 10 13 36 31 11.0 73 45
125 13 10 15 31 33 11.0 79 47
126 17 10 10 39 32 8.0 90 55
127 17 10 11 44 39 10.0 74 44
128 19 10 9 33 36 11.0 81 53
129 15 10 11 35 33 13.0 72 44
130 13 10 10 32 33 11.0 71 42
131 9 10 11 28 32 20.0 66 40
132 15 10 8 40 37 10.0 77 46
133 15 10 11 27 30 15.0 65 40
134 15 10 12 37 38 12.0 74 46
135 16 10 12 32 29 14.0 85 53
136 11 10 9 28 22 23.0 54 33
137 14 10 11 34 35 14.0 63 42
138 11 10 10 30 35 16.0 54 35
139 15 10 8 35 34 11.0 64 40
140 13 10 9 31 35 12.0 69 41
141 15 10 8 32 34 10.0 54 33
142 16 10 9 30 37 14.0 84 51
143 14 10 15 30 35 12.0 86 53
144 15 10 11 31 23 12.0 77 46
145 16 10 8 40 31 11.0 89 55
146 16 10 13 32 27 12.0 76 47
147 11 10 12 36 36 13.0 60 38
148 12 10 12 32 31 11.0 75 46
149 9 10 9 35 32 19.0 73 46
150 16 10 7 38 39 12.0 85 53
151 13 10 13 42 37 17.0 79 47
152 16 10 9 34 38 9.0 71 41
153 12 10 6 35 39 12.0 72 44
154 9 9 8 38 34 19.0 69 43
155 13 10 8 33 31 18.0 78 51
156 13 10 15 36 32 15.0 54 33
157 14 10 6 32 37 14.0 69 43
158 19 10 9 33 36 11.0 81 53
159 13 10 11 34 32 9.0 84 51
160 12 10 8 32 38 18.0 84 50
161 13 10 8 34 36 16.0 69 46
162 10 11 10 27 26 24.0 66 43
163 14 11 8 31 26 14.0 81 47
164 16 11 14 38 33 20.0 82 50
165 10 11 10 34 39 18.0 72 43
166 11 11 8 24 30 23.0 54 33
167 14 11 11 30 33 12.0 78 48
168 12 11 12 26 25 14.0 74 44
169 9 11 12 34 38 16.0 82 50
170 9 11 12 27 37 18.0 73 41
171 11 11 5 37 31 20.0 55 34
172 16 11 12 36 37 12.0 72 44
173 9 11 10 41 35 12.0 78 47
174 13 11 7 29 25 17.0 59 35
175 16 11 12 36 28 13.0 72 44
176 13 11 11 32 35 9.0 78 44
177 9 11 8 37 33 16.0 68 43
178 12 11 9 30 30 18.0 69 41
179 16 11 10 31 31 10.0 67 41
180 11 11 9 38 37 14.0 74 42
181 14 11 12 36 36 11.0 54 33
182 13 11 6 35 30 9.0 67 41
183 15 11 15 31 36 11.0 70 44
184 14 11 12 38 32 10.0 80 48
185 16 11 12 22 28 11.0 89 55
186 13 11 12 32 36 19.0 76 44
187 14 11 11 36 34 14.0 74 43
188 15 11 7 39 31 12.0 87 52
189 13 11 7 28 28 14.0 54 30
190 11 11 5 32 36 21.0 61 39
191 11 11 12 32 36 13.0 38 11
192 14 11 12 38 40 10.0 75 44
193 15 11 3 32 33 15.0 69 42
194 11 11 11 35 37 16.0 62 41
195 15 11 10 32 32 14.0 72 44
196 12 11 12 37 38 12.0 70 44
197 14 11 9 34 31 19.0 79 48
198 14 11 12 33 37 15.0 87 53
199 8 11 9 33 33 19.0 62 37
200 13 11 12 26 32 13.0 77 44
201 9 11 12 30 30 17.0 69 44
202 15 11 10 24 30 12.0 69 40
203 17 11 9 34 31 11.0 75 42
204 13 11 12 34 32 14.0 54 35
205 15 11 8 33 34 11.0 72 43
206 15 11 11 34 36 13.0 74 45
207 14 11 11 35 37 12.0 85 55
208 16 11 12 35 36 15.0 52 31
209 13 11 10 36 33 14.0 70 44
210 16 11 10 34 33 12.0 84 50
211 9 11 12 34 33 17.0 64 40
212 16 11 12 41 44 11.0 84 53
213 11 11 11 32 39 18.0 87 54
214 10 11 8 30 32 13.0 79 49
215 11 11 12 35 35 17.0 67 40
216 15 11 10 28 25 13.0 65 41
217 17 11 11 33 35 11.0 85 52
218 14 11 10 39 34 12.0 83 52
219 8 11 8 36 35 22.0 61 36
220 15 11 12 36 39 14.0 82 52
221 11 11 12 35 33 12.0 76 46
222 16 11 10 38 36 12.0 58 31
223 10 11 12 33 32 17.0 72 44
224 15 11 9 31 32 9.0 72 44
225 9 11 9 34 36 21.0 38 11
226 16 11 6 32 36 10.0 78 46
227 19 11 10 31 32 11.0 54 33
228 12 11 9 33 34 12.0 63 34
229 8 11 9 34 33 23.0 66 42
230 11 11 9 34 35 13.0 70 43
231 14 11 6 34 30 12.0 71 43
232 9 11 10 33 38 16.0 67 44
233 15 11 6 32 34 9.0 58 36
234 13 11 14 41 33 17.0 72 46
235 16 11 10 34 32 9.0 72 44
236 11 11 10 36 31 14.0 70 43
237 12 11 6 37 30 17.0 76 50
238 13 11 12 36 27 13.0 50 33
239 10 11 12 29 31 11.0 72 43
240 11 11 7 37 30 12.0 72 44
241 12 11 8 27 32 10.0 88 53
242 8 11 11 35 35 19.0 53 34
243 12 11 3 28 28 16.0 58 35
244 12 11 6 35 33 16.0 66 40
245 15 11 10 37 31 14.0 82 53
246 11 11 8 29 35 20.0 69 42
247 13 11 9 32 35 15.0 68 43
248 14 11 9 36 32 23.0 44 29
249 10 11 8 19 21 20.0 56 36
250 12 11 9 21 20 16.0 53 30
251 15 11 7 31 34 14.0 70 42
252 13 11 7 33 32 17.0 78 47
253 13 11 6 36 34 11.0 71 44
254 13 11 9 33 32 13.0 72 45
255 12 11 10 37 33 17.0 68 44
256 12 11 11 34 33 15.0 67 43
257 9 11 12 35 37 21.0 75 43
258 9 11 8 31 32 18.0 62 40
259 15 11 11 37 34 15.0 67 41
260 10 11 3 35 30 8.0 83 52
261 14 11 11 27 30 12.0 64 38
262 15 11 12 34 38 12.0 68 41
263 7 11 7 40 36 22.0 62 39
264 14 11 9 29 32 12.0 72 43
Learning
1 13
2 16
3 19
4 15
5 14
6 13
7 19
8 15
9 14
10 15
11 16
12 16
13 16
14 16
15 17
16 15
17 15
18 20
19 18
20 16
21 16
22 16
23 19
24 16
25 17
26 17
27 16
28 15
29 16
30 14
31 15
32 12
33 14
34 16
35 14
36 10
37 10
38 14
39 16
40 16
41 16
42 14
43 20
44 14
45 14
46 11
47 14
48 15
49 16
50 14
51 16
52 14
53 12
54 16
55 9
56 14
57 16
58 16
59 15
60 16
61 12
62 16
63 16
64 14
65 16
66 17
67 18
68 18
69 12
70 16
71 10
72 14
73 18
74 18
75 16
76 17
77 16
78 16
79 13
80 16
81 16
82 16
83 15
84 15
85 16
86 14
87 16
88 16
89 15
90 12
91 17
92 16
93 15
94 13
95 16
96 16
97 16
98 16
99 14
100 16
101 16
102 20
103 15
104 16
105 13
106 17
107 16
108 16
109 12
110 16
111 16
112 17
113 13
114 12
115 18
116 14
117 14
118 13
119 16
120 13
121 16
122 13
123 16
124 15
125 16
126 15
127 17
128 15
129 12
130 16
131 10
132 16
133 12
134 14
135 15
136 13
137 15
138 11
139 12
140 11
141 16
142 15
143 17
144 16
145 10
146 18
147 13
148 16
149 13
150 10
151 15
152 16
153 16
154 14
155 10
156 17
157 13
158 15
159 16
160 12
161 13
162 13
163 12
164 17
165 15
166 10
167 14
168 11
169 13
170 16
171 12
172 16
173 12
174 9
175 12
176 15
177 12
178 12
179 14
180 12
181 16
182 11
183 19
184 15
185 8
186 16
187 17
188 12
189 11
190 11
191 14
192 16
193 12
194 16
195 13
196 15
197 16
198 16
199 14
200 16
201 16
202 14
203 11
204 12
205 15
206 15
207 16
208 16
209 11
210 15
211 12
212 12
213 15
214 15
215 16
216 14
217 17
218 14
219 13
220 15
221 13
222 14
223 15
224 12
225 13
226 8
227 14
228 14
229 11
230 12
231 13
232 10
233 16
234 18
235 13
236 11
237 4
238 13
239 16
240 10
241 12
242 12
243 10
244 13
245 15
246 12
247 14
248 10
249 12
250 12
251 11
252 10
253 12
254 16
255 12
256 14
257 16
258 14
259 13
260 4
261 15
262 11
263 11
264 14
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Month Software Connected Separate Depression
18.23313 -0.31716 -0.01992 0.00400 0.01184 -0.36355
Sport1 Sport2 Learning
0.01654 0.01471 0.09263
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.9800 -1.4786 0.2648 1.3454 5.2756
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.23313 2.64231 6.900 4.09e-11 ***
Month -0.31716 0.17307 -1.833 0.068 .
Software -0.01992 0.06893 -0.289 0.773
Connected 0.00400 0.03756 0.106 0.915
Separate 0.01184 0.03816 0.310 0.757
Depression -0.36355 0.03975 -9.146 < 2e-16 ***
Sport1 0.01654 0.04077 0.406 0.685
Sport2 0.01471 0.06054 0.243 0.808
Learning 0.09263 0.06729 1.376 0.170
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.011 on 255 degrees of freedom
Multiple R-squared: 0.3717, Adjusted R-squared: 0.352
F-statistic: 18.86 on 8 and 255 DF, p-value: < 2.2e-16
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 0.82460254 0.350794915 0.1753974574
[2,] 0.98177869 0.036442611 0.0182213057
[3,] 0.96504148 0.069917039 0.0349585197
[4,] 0.96541569 0.069168623 0.0345843115
[5,] 0.94298289 0.114034227 0.0570171136
[6,] 0.96685730 0.066285403 0.0331427014
[7,] 0.94671120 0.106577596 0.0532887979
[8,] 0.91913844 0.161723112 0.0808615559
[9,] 0.91530866 0.169382674 0.0846913368
[10,] 0.93742454 0.125150921 0.0625754605
[11,] 0.93506165 0.129876706 0.0649383528
[12,] 0.93504809 0.129903819 0.0649519094
[13,] 0.91127448 0.177451047 0.0887255237
[14,] 0.89091390 0.218172190 0.1090860952
[15,] 0.99930546 0.001389074 0.0006945371
[16,] 0.99886753 0.002264937 0.0011324684
[17,] 0.99815801 0.003683980 0.0018419900
[18,] 0.99710989 0.005780212 0.0028901059
[19,] 0.99804940 0.003901199 0.0019505996
[20,] 0.99698455 0.006030894 0.0030154470
[21,] 0.99557577 0.008848459 0.0044242293
[22,] 0.99454828 0.010903447 0.0054517236
[23,] 0.99200536 0.015989280 0.0079946398
[24,] 0.99001614 0.019967718 0.0099838592
[25,] 0.98593405 0.028131893 0.0140659464
[26,] 0.99057337 0.018853255 0.0094266277
[27,] 0.98678891 0.026422178 0.0132110892
[28,] 0.98461318 0.030773633 0.0153868167
[29,] 0.98495988 0.030080240 0.0150401202
[30,] 0.98013200 0.039735993 0.0198679965
[31,] 0.97772030 0.044559408 0.0222797039
[32,] 0.97037164 0.059256716 0.0296283580
[33,] 0.96503817 0.069923668 0.0349618338
[34,] 0.95454033 0.090919340 0.0454596698
[35,] 0.96291685 0.074166306 0.0370831531
[36,] 0.95238105 0.095237899 0.0476189493
[37,] 0.93926874 0.121462512 0.0607312558
[38,] 0.94781574 0.104368517 0.0521842586
[39,] 0.94461704 0.110765922 0.0553829612
[40,] 0.93173759 0.136524817 0.0682624087
[41,] 0.91553914 0.168921719 0.0844608595
[42,] 0.90286536 0.194269271 0.0971346356
[43,] 0.88793767 0.224124658 0.1120623289
[44,] 0.88281147 0.234377065 0.1171885325
[45,] 0.86443785 0.271124295 0.1355621475
[46,] 0.85051081 0.298978380 0.1494891901
[47,] 0.82372083 0.352558333 0.1762791663
[48,] 0.86521402 0.269571969 0.1347859846
[49,] 0.86221526 0.275569487 0.1377847437
[50,] 0.88288273 0.234234543 0.1171172713
[51,] 0.88375862 0.232482752 0.1162413761
[52,] 0.93166225 0.136675501 0.0683377507
[53,] 0.92015959 0.159680819 0.0798404097
[54,] 0.90976376 0.180472476 0.0902362380
[55,] 0.92083912 0.158321768 0.0791608841
[56,] 0.92002485 0.159950301 0.0799751504
[57,] 0.91123338 0.177533234 0.0887666170
[58,] 0.94193349 0.116133022 0.0580665108
[59,] 0.94638503 0.107229943 0.0536149713
[60,] 0.94109031 0.117819377 0.0589096883
[61,] 0.96106860 0.077862801 0.0389314006
[62,] 0.95258829 0.094823413 0.0474117065
[63,] 0.94199888 0.116002230 0.0580011152
[64,] 0.93514354 0.129712919 0.0648564594
[65,] 0.92158273 0.156834543 0.0784172717
[66,] 0.93761911 0.124761790 0.0623808950
[67,] 0.92604431 0.147911374 0.0739556869
[68,] 0.91967293 0.160654137 0.0803270684
[69,] 0.91299405 0.174011903 0.0870059515
[70,] 0.89644268 0.207114644 0.1035573222
[71,] 0.87848624 0.243027521 0.1215137605
[72,] 0.87294021 0.254119585 0.1270597925
[73,] 0.85461548 0.290769030 0.1453845151
[74,] 0.83157435 0.336851305 0.1684256523
[75,] 0.80897935 0.382041293 0.1910206464
[76,] 0.78215508 0.435689832 0.2178449160
[77,] 0.75317508 0.493649831 0.2468249157
[78,] 0.82166002 0.356679952 0.1783399761
[79,] 0.85255530 0.294889402 0.1474447010
[80,] 0.83044853 0.339102935 0.1695514676
[81,] 0.80768569 0.384628623 0.1923143116
[82,] 0.78551257 0.428974852 0.2144874262
[83,] 0.76297866 0.474042685 0.2370213427
[84,] 0.73731310 0.525373799 0.2626868993
[85,] 0.71274507 0.574509857 0.2872549284
[86,] 0.68661652 0.626766964 0.3133834821
[87,] 0.68443938 0.631121234 0.3155606168
[88,] 0.65093070 0.698138593 0.3490692964
[89,] 0.64028022 0.719439553 0.3597197767
[90,] 0.61665524 0.766689528 0.3833447642
[91,] 0.58697877 0.826042453 0.4130212266
[92,] 0.63776445 0.724471096 0.3622355478
[93,] 0.62325153 0.753496949 0.3767484747
[94,] 0.64061840 0.718763191 0.3593815956
[95,] 0.61390596 0.772188078 0.3860940389
[96,] 0.60380510 0.792389809 0.3961949045
[97,] 0.64442522 0.711149559 0.3555747794
[98,] 0.61357638 0.772847243 0.3864236213
[99,] 0.58655288 0.826894232 0.4134471162
[100,] 0.59418224 0.811635519 0.4058177593
[101,] 0.62160393 0.756792143 0.3783960717
[102,] 0.60943067 0.781138661 0.3905693304
[103,] 0.69620274 0.607594516 0.3037972581
[104,] 0.66637751 0.667244973 0.3336224866
[105,] 0.63969174 0.720616512 0.3603082558
[106,] 0.60579634 0.788407327 0.3942036633
[107,] 0.58184100 0.836317995 0.4181589974
[108,] 0.54676649 0.906467017 0.4532335085
[109,] 0.51477507 0.970449869 0.4852249346
[110,] 0.48555768 0.971115367 0.5144423167
[111,] 0.45267523 0.905350470 0.5473247651
[112,] 0.42555738 0.851114769 0.5744426155
[113,] 0.39361932 0.787238641 0.6063806793
[114,] 0.38530708 0.770614160 0.6146929199
[115,] 0.35565148 0.711302967 0.6443485166
[116,] 0.33941733 0.678834665 0.6605826675
[117,] 0.44132040 0.882640793 0.5586796036
[118,] 0.42195992 0.843919834 0.5780400828
[119,] 0.41233503 0.824670057 0.5876649717
[120,] 0.40021580 0.800431592 0.5997842041
[121,] 0.37101750 0.742035008 0.6289824961
[122,] 0.38819006 0.776380116 0.6118099422
[123,] 0.35879096 0.717581913 0.6412090436
[124,] 0.36799120 0.735982394 0.6320088031
[125,] 0.35643048 0.712860956 0.6435695219
[126,] 0.32660325 0.653206496 0.6733967518
[127,] 0.30299698 0.605993965 0.6970030175
[128,] 0.27633683 0.552673668 0.7236631658
[129,] 0.25307621 0.506152410 0.7469237948
[130,] 0.22573470 0.451469410 0.7742652951
[131,] 0.22955344 0.459106874 0.7704465631
[132,] 0.20504520 0.410090393 0.7949548035
[133,] 0.18457405 0.369148107 0.8154259466
[134,] 0.17243497 0.344869944 0.8275650279
[135,] 0.16516695 0.330333910 0.8348330452
[136,] 0.17273666 0.345473326 0.8272633368
[137,] 0.19015697 0.380313932 0.8098430339
[138,] 0.20965255 0.419305094 0.7903474532
[139,] 0.20628888 0.412577754 0.7937111232
[140,] 0.18459972 0.369199437 0.8154002815
[141,] 0.16500152 0.330003044 0.8349984782
[142,] 0.17843110 0.356862209 0.8215688954
[143,] 0.21410996 0.428219920 0.7858900398
[144,] 0.19274163 0.385483267 0.8072583665
[145,] 0.17266805 0.345336091 0.8273319546
[146,] 0.15056344 0.301126885 0.8494365575
[147,] 0.22043756 0.440875125 0.7795624373
[148,] 0.24626443 0.492528858 0.7537355711
[149,] 0.22066391 0.441327811 0.7793360943
[150,] 0.19421072 0.388421438 0.8057892808
[151,] 0.17171078 0.343421555 0.8282892225
[152,] 0.15171805 0.303436107 0.8482819464
[153,] 0.25498616 0.509972320 0.7450138398
[154,] 0.25177566 0.503551330 0.7482243352
[155,] 0.24759027 0.495180546 0.7524097269
[156,] 0.21972282 0.439445646 0.7802771770
[157,] 0.19798106 0.395962128 0.8020189362
[158,] 0.25185487 0.503709743 0.7481451285
[159,] 0.27419672 0.548393448 0.7258032762
[160,] 0.24703219 0.494064383 0.7529678085
[161,] 0.24340237 0.486804733 0.7565976335
[162,] 0.39703967 0.794079333 0.6029603333
[163,] 0.38549907 0.770998136 0.6145009321
[164,] 0.41081256 0.821625114 0.5891874428
[165,] 0.41655984 0.833119679 0.5834401607
[166,] 0.46761205 0.935224099 0.5323879503
[167,] 0.43450398 0.869007950 0.5654960248
[168,] 0.41478804 0.829576071 0.5852119643
[169,] 0.41060704 0.821214070 0.5893929650
[170,] 0.37358477 0.747169549 0.6264152255
[171,] 0.36289262 0.725785240 0.6371073802
[172,] 0.32737716 0.654754327 0.6726228366
[173,] 0.29948678 0.598973560 0.7005132198
[174,] 0.31517934 0.630358682 0.6848206589
[175,] 0.30911166 0.618223315 0.6908883427
[176,] 0.27682538 0.553650753 0.7231746233
[177,] 0.25156541 0.503130813 0.7484345933
[178,] 0.22285336 0.445706728 0.7771466360
[179,] 0.20164522 0.403290431 0.7983547843
[180,] 0.20416034 0.408320674 0.7958396628
[181,] 0.18576745 0.371534892 0.8142325539
[182,] 0.20382062 0.407641239 0.7961793803
[183,] 0.19012770 0.380255393 0.8098723033
[184,] 0.19095041 0.381900817 0.8090495915
[185,] 0.19842502 0.396850050 0.8015749752
[186,] 0.24369838 0.487396751 0.7563016244
[187,] 0.22649338 0.452986752 0.7735066241
[188,] 0.25455267 0.509105342 0.7454473288
[189,] 0.22288322 0.445766446 0.7771167771
[190,] 0.25315563 0.506311252 0.7468443738
[191,] 0.23415719 0.468314380 0.7658428102
[192,] 0.29447638 0.588952769 0.7055236154
[193,] 0.26102792 0.522055843 0.7389720786
[194,] 0.22933729 0.458674585 0.7706627076
[195,] 0.21036207 0.420724143 0.7896379283
[196,] 0.18042222 0.360844445 0.8195777776
[197,] 0.20187471 0.403749415 0.7981252924
[198,] 0.17205051 0.344101020 0.8279494901
[199,] 0.18787476 0.375749513 0.8121252436
[200,] 0.21136682 0.422733649 0.7886331755
[201,] 0.19730576 0.394611529 0.8026942354
[202,] 0.17130298 0.342605967 0.8286970167
[203,] 0.21350295 0.427005898 0.7864970512
[204,] 0.18726391 0.374527823 0.8127360886
[205,] 0.17629427 0.352588541 0.8237057295
[206,] 0.20725219 0.414504379 0.7927478104
[207,] 0.17852098 0.357041960 0.8214790200
[208,] 0.16476572 0.329531446 0.8352342769
[209,] 0.17178783 0.343575662 0.8282121690
[210,] 0.17670130 0.353402602 0.8232986991
[211,] 0.17901974 0.358039488 0.8209802562
[212,] 0.16308715 0.326174298 0.8369128511
[213,] 0.13451950 0.269038998 0.8654805010
[214,] 0.12055133 0.241102665 0.8794486674
[215,] 0.14535176 0.290703523 0.8546482385
[216,] 0.32359679 0.647193586 0.6764032072
[217,] 0.29978998 0.599579960 0.7002100199
[218,] 0.27325745 0.546514902 0.7267425489
[219,] 0.25707409 0.514148173 0.7429259133
[220,] 0.21744740 0.434894805 0.7825525973
[221,] 0.25076743 0.501534851 0.7492325745
[222,] 0.20947399 0.418947980 0.7905260099
[223,] 0.17236111 0.344722221 0.8276388893
[224,] 0.17119339 0.342386779 0.8288066107
[225,] 0.14873472 0.297469448 0.8512652761
[226,] 0.12202841 0.244056811 0.8779715947
[227,] 0.09261159 0.185223189 0.9073884056
[228,] 0.18708260 0.374165192 0.8129174042
[229,] 0.18088934 0.361778673 0.8191106637
[230,] 0.15213725 0.304274494 0.8478627532
[231,] 0.32297309 0.645946182 0.6770269088
[232,] 0.28059134 0.561182680 0.7194086598
[233,] 0.22563355 0.451267101 0.7743664495
[234,] 0.33828189 0.676563785 0.6617181076
[235,] 0.25891682 0.517833638 0.7410831808
[236,] 0.19049257 0.380985140 0.8095074301
[237,] 0.30930723 0.618614455 0.6906927726
[238,] 0.22349567 0.446991346 0.7765043271
[239,] 0.14819907 0.296398143 0.8518009285
[240,] 0.23588960 0.471779199 0.7641104003
[241,] 0.74366858 0.512662836 0.2563314178
> postscript(file="/var/fisher/rcomp/tmp/1e0ur1384814247.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/28o0z1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3grck1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4fiyh1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5f9901384814247.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
0.057570895 2.699453610 -2.994004983 -2.512019814 4.710221511 3.514033929
7 8 9 10 11 12
3.154577548 -1.096452731 -0.245279298 0.590968237 1.427409142 3.361569277
13 14 15 16 17 18
-3.460863617 2.463607162 2.192183555 0.553030617 0.095415706 1.154997217
19 20 21 22 23 24
-1.458961871 2.125227892 2.598615701 -2.795038658 -0.501542866 -1.632159554
25 26 27 28 29 30
1.562407011 -6.979963929 0.889512361 0.605426369 0.993164341 -3.032296593
31 32 33 34 35 36
0.207785128 0.256927380 1.837271981 -0.380482913 -0.043317198 0.402548846
37 38 39 40 41 42
-1.924029114 0.584539520 1.557166847 -2.315864528 -0.802421369 2.238760749
43 44 45 46 47 48
0.066480087 -1.233369459 0.280123399 -2.707931515 -0.465953879 -0.001697775
49 50 51 52 53 54
3.375919469 -1.870763924 0.637587394 0.433606878 -0.812299781 -1.800141809
55 56 57 58 59 60
-2.192148486 1.204670436 1.659946561 -0.594329288 -3.327698726 -1.541204519
61 62 63 64 65 66
-2.878647113 -1.749227881 -3.798985421 0.778878062 1.222824134 -5.000303974
67 68 69 70 71 72
-1.537480282 -2.397384016 1.561556225 1.447995270 0.564041802 3.361577970
73 74 75 76 77 78
0.632584504 -0.181541719 -1.886116743 -0.104734623 3.065112272 0.564313846
79 80 81 82 83 84
1.255454915 -1.878868737 0.152918520 -0.446972364 1.771990388 0.762063407
85 86 87 88 89 90
-0.082272680 1.030891672 -0.235598342 0.275250237 -3.526888518 3.298027044
91 92 93 94 95 96
0.214381145 0.845924053 0.719383028 -0.968108584 1.057827525 -0.804025921
97 98 99 100 101 102
-0.839120844 2.098083833 -0.011586594 1.873545693 -0.939454892 0.994352505
103 104 105 106 107 108
-3.401800729 1.900529361 -2.385933823 1.087037944 2.079630629 -2.808173961
109 110 111 112 113 114
0.820236111 1.091717659 -2.197405972 -2.296089363 2.097711608 3.889733229
115 116 117 118 119 120
0.436597885 1.006196993 0.162179068 -1.080113094 0.272755534 -0.541956071
121 122 123 124 125 126
0.427630832 0.092193022 -0.902649017 0.426607091 -1.758528582 0.824039997
127 128 129 130 131 132
1.709446916 4.049723078 1.403339237 -1.656235076 -1.668622404 -0.297060416
133 134 135 136 137 138
2.372581065 0.744765525 2.220813687 1.524134058 0.647625045 -1.006819799
139 140 141 142 143 144
0.795824724 -0.821331809 0.342152648 2.120335577 -0.711341826 0.691492557
145 146 147 148 149 150
1.362376857 1.496559797 -2.422108121 -2.717738405 -2.581961067 1.714912168
151 152 153 154 155 156
0.384221789 0.544280716 -2.501341313 -2.937042546 1.176027211 0.214381145
157 158 159 160 161 162
0.603654463 4.049723078 -2.707025373 -0.172618220 0.330293529 0.835807721
163 164 165 166 167 168
0.930132001 4.596275080 -1.811859129 2.020582461 0.033595175 -0.705799426
169 170 171 172 173 174
-3.570422282 -2.800098303 0.589829113 1.955011151 -4.854020670 1.839041759
175 176 177 178 179 180
2.795600067 -2.122506786 -3.175728546 0.647677622 1.591186885 -2.018787044
181 182 183 184 185 186
0.062874463 -1.578315081 0.438247576 -0.819459881 2.051980136 1.461528170
187 188 189 190 191 192
0.586696694 0.919129071 0.687892806 0.834021472 -1.420441315 -0.865218747
193 194 195 196 197 198
2.379292629 -1.397159207 1.995337579 -1.935111244 2.344475220 0.677130329
199 200 201 202 203 204
-3.046900114 -0.664971632 -3.070768708 1.339749027 3.053663958 0.549961015
205 206 207 208 209 210
0.666630376 1.363311302 -0.437779036 3.583580104 0.185845919 1.776374106
211 212 213 214 215 216
-2.610199433 1.528228917 -1.193893261 -3.774661728 -1.058014170 1.797931329
217 218 219 220 221 222
2.181847530 -0.175712516 -1.887957268 1.467959524 -2.811347118 2.527103377
223 224 225 226 227 228
-2.063433360 0.254298676 -0.487171489 1.748594834 5.275635645 -1.575989741
229 230 231 232 233 234
-1.458536514 -2.291213016 0.235484457 -2.991978560 0.145625383 0.625259013
235 236 237 238 239 240
1.169589225 -1.775771145 0.689212499 0.240549100 -4.294815622 -2.509958764
241 242 243 244 245 246
-2.783143074 -2.660462537 0.288218245 -0.222979532 1.504092618 0.284967842
247 248 249 250 251 252
0.291709105 5.193096754 -0.206027296 0.501425253 2.163674025 1.156733462
253 254 255 256 257 258
-1.105488804 -0.684733208 0.212948555 -0.636238625 -1.803953544 -2.454670378
259 260 261 262 263 264
2.461980478 -4.779709067 0.367170754 1.524626282 -2.811128745 0.182399589
> postscript(file="/var/fisher/rcomp/tmp/6ym491384814247.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 0.057570895 NA
1 2.699453610 0.057570895
2 -2.994004983 2.699453610
3 -2.512019814 -2.994004983
4 4.710221511 -2.512019814
5 3.514033929 4.710221511
6 3.154577548 3.514033929
7 -1.096452731 3.154577548
8 -0.245279298 -1.096452731
9 0.590968237 -0.245279298
10 1.427409142 0.590968237
11 3.361569277 1.427409142
12 -3.460863617 3.361569277
13 2.463607162 -3.460863617
14 2.192183555 2.463607162
15 0.553030617 2.192183555
16 0.095415706 0.553030617
17 1.154997217 0.095415706
18 -1.458961871 1.154997217
19 2.125227892 -1.458961871
20 2.598615701 2.125227892
21 -2.795038658 2.598615701
22 -0.501542866 -2.795038658
23 -1.632159554 -0.501542866
24 1.562407011 -1.632159554
25 -6.979963929 1.562407011
26 0.889512361 -6.979963929
27 0.605426369 0.889512361
28 0.993164341 0.605426369
29 -3.032296593 0.993164341
30 0.207785128 -3.032296593
31 0.256927380 0.207785128
32 1.837271981 0.256927380
33 -0.380482913 1.837271981
34 -0.043317198 -0.380482913
35 0.402548846 -0.043317198
36 -1.924029114 0.402548846
37 0.584539520 -1.924029114
38 1.557166847 0.584539520
39 -2.315864528 1.557166847
40 -0.802421369 -2.315864528
41 2.238760749 -0.802421369
42 0.066480087 2.238760749
43 -1.233369459 0.066480087
44 0.280123399 -1.233369459
45 -2.707931515 0.280123399
46 -0.465953879 -2.707931515
47 -0.001697775 -0.465953879
48 3.375919469 -0.001697775
49 -1.870763924 3.375919469
50 0.637587394 -1.870763924
51 0.433606878 0.637587394
52 -0.812299781 0.433606878
53 -1.800141809 -0.812299781
54 -2.192148486 -1.800141809
55 1.204670436 -2.192148486
56 1.659946561 1.204670436
57 -0.594329288 1.659946561
58 -3.327698726 -0.594329288
59 -1.541204519 -3.327698726
60 -2.878647113 -1.541204519
61 -1.749227881 -2.878647113
62 -3.798985421 -1.749227881
63 0.778878062 -3.798985421
64 1.222824134 0.778878062
65 -5.000303974 1.222824134
66 -1.537480282 -5.000303974
67 -2.397384016 -1.537480282
68 1.561556225 -2.397384016
69 1.447995270 1.561556225
70 0.564041802 1.447995270
71 3.361577970 0.564041802
72 0.632584504 3.361577970
73 -0.181541719 0.632584504
74 -1.886116743 -0.181541719
75 -0.104734623 -1.886116743
76 3.065112272 -0.104734623
77 0.564313846 3.065112272
78 1.255454915 0.564313846
79 -1.878868737 1.255454915
80 0.152918520 -1.878868737
81 -0.446972364 0.152918520
82 1.771990388 -0.446972364
83 0.762063407 1.771990388
84 -0.082272680 0.762063407
85 1.030891672 -0.082272680
86 -0.235598342 1.030891672
87 0.275250237 -0.235598342
88 -3.526888518 0.275250237
89 3.298027044 -3.526888518
90 0.214381145 3.298027044
91 0.845924053 0.214381145
92 0.719383028 0.845924053
93 -0.968108584 0.719383028
94 1.057827525 -0.968108584
95 -0.804025921 1.057827525
96 -0.839120844 -0.804025921
97 2.098083833 -0.839120844
98 -0.011586594 2.098083833
99 1.873545693 -0.011586594
100 -0.939454892 1.873545693
101 0.994352505 -0.939454892
102 -3.401800729 0.994352505
103 1.900529361 -3.401800729
104 -2.385933823 1.900529361
105 1.087037944 -2.385933823
106 2.079630629 1.087037944
107 -2.808173961 2.079630629
108 0.820236111 -2.808173961
109 1.091717659 0.820236111
110 -2.197405972 1.091717659
111 -2.296089363 -2.197405972
112 2.097711608 -2.296089363
113 3.889733229 2.097711608
114 0.436597885 3.889733229
115 1.006196993 0.436597885
116 0.162179068 1.006196993
117 -1.080113094 0.162179068
118 0.272755534 -1.080113094
119 -0.541956071 0.272755534
120 0.427630832 -0.541956071
121 0.092193022 0.427630832
122 -0.902649017 0.092193022
123 0.426607091 -0.902649017
124 -1.758528582 0.426607091
125 0.824039997 -1.758528582
126 1.709446916 0.824039997
127 4.049723078 1.709446916
128 1.403339237 4.049723078
129 -1.656235076 1.403339237
130 -1.668622404 -1.656235076
131 -0.297060416 -1.668622404
132 2.372581065 -0.297060416
133 0.744765525 2.372581065
134 2.220813687 0.744765525
135 1.524134058 2.220813687
136 0.647625045 1.524134058
137 -1.006819799 0.647625045
138 0.795824724 -1.006819799
139 -0.821331809 0.795824724
140 0.342152648 -0.821331809
141 2.120335577 0.342152648
142 -0.711341826 2.120335577
143 0.691492557 -0.711341826
144 1.362376857 0.691492557
145 1.496559797 1.362376857
146 -2.422108121 1.496559797
147 -2.717738405 -2.422108121
148 -2.581961067 -2.717738405
149 1.714912168 -2.581961067
150 0.384221789 1.714912168
151 0.544280716 0.384221789
152 -2.501341313 0.544280716
153 -2.937042546 -2.501341313
154 1.176027211 -2.937042546
155 0.214381145 1.176027211
156 0.603654463 0.214381145
157 4.049723078 0.603654463
158 -2.707025373 4.049723078
159 -0.172618220 -2.707025373
160 0.330293529 -0.172618220
161 0.835807721 0.330293529
162 0.930132001 0.835807721
163 4.596275080 0.930132001
164 -1.811859129 4.596275080
165 2.020582461 -1.811859129
166 0.033595175 2.020582461
167 -0.705799426 0.033595175
168 -3.570422282 -0.705799426
169 -2.800098303 -3.570422282
170 0.589829113 -2.800098303
171 1.955011151 0.589829113
172 -4.854020670 1.955011151
173 1.839041759 -4.854020670
174 2.795600067 1.839041759
175 -2.122506786 2.795600067
176 -3.175728546 -2.122506786
177 0.647677622 -3.175728546
178 1.591186885 0.647677622
179 -2.018787044 1.591186885
180 0.062874463 -2.018787044
181 -1.578315081 0.062874463
182 0.438247576 -1.578315081
183 -0.819459881 0.438247576
184 2.051980136 -0.819459881
185 1.461528170 2.051980136
186 0.586696694 1.461528170
187 0.919129071 0.586696694
188 0.687892806 0.919129071
189 0.834021472 0.687892806
190 -1.420441315 0.834021472
191 -0.865218747 -1.420441315
192 2.379292629 -0.865218747
193 -1.397159207 2.379292629
194 1.995337579 -1.397159207
195 -1.935111244 1.995337579
196 2.344475220 -1.935111244
197 0.677130329 2.344475220
198 -3.046900114 0.677130329
199 -0.664971632 -3.046900114
200 -3.070768708 -0.664971632
201 1.339749027 -3.070768708
202 3.053663958 1.339749027
203 0.549961015 3.053663958
204 0.666630376 0.549961015
205 1.363311302 0.666630376
206 -0.437779036 1.363311302
207 3.583580104 -0.437779036
208 0.185845919 3.583580104
209 1.776374106 0.185845919
210 -2.610199433 1.776374106
211 1.528228917 -2.610199433
212 -1.193893261 1.528228917
213 -3.774661728 -1.193893261
214 -1.058014170 -3.774661728
215 1.797931329 -1.058014170
216 2.181847530 1.797931329
217 -0.175712516 2.181847530
218 -1.887957268 -0.175712516
219 1.467959524 -1.887957268
220 -2.811347118 1.467959524
221 2.527103377 -2.811347118
222 -2.063433360 2.527103377
223 0.254298676 -2.063433360
224 -0.487171489 0.254298676
225 1.748594834 -0.487171489
226 5.275635645 1.748594834
227 -1.575989741 5.275635645
228 -1.458536514 -1.575989741
229 -2.291213016 -1.458536514
230 0.235484457 -2.291213016
231 -2.991978560 0.235484457
232 0.145625383 -2.991978560
233 0.625259013 0.145625383
234 1.169589225 0.625259013
235 -1.775771145 1.169589225
236 0.689212499 -1.775771145
237 0.240549100 0.689212499
238 -4.294815622 0.240549100
239 -2.509958764 -4.294815622
240 -2.783143074 -2.509958764
241 -2.660462537 -2.783143074
242 0.288218245 -2.660462537
243 -0.222979532 0.288218245
244 1.504092618 -0.222979532
245 0.284967842 1.504092618
246 0.291709105 0.284967842
247 5.193096754 0.291709105
248 -0.206027296 5.193096754
249 0.501425253 -0.206027296
250 2.163674025 0.501425253
251 1.156733462 2.163674025
252 -1.105488804 1.156733462
253 -0.684733208 -1.105488804
254 0.212948555 -0.684733208
255 -0.636238625 0.212948555
256 -1.803953544 -0.636238625
257 -2.454670378 -1.803953544
258 2.461980478 -2.454670378
259 -4.779709067 2.461980478
260 0.367170754 -4.779709067
261 1.524626282 0.367170754
262 -2.811128745 1.524626282
263 0.182399589 -2.811128745
264 NA 0.182399589
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.699453610 0.057570895
[2,] -2.994004983 2.699453610
[3,] -2.512019814 -2.994004983
[4,] 4.710221511 -2.512019814
[5,] 3.514033929 4.710221511
[6,] 3.154577548 3.514033929
[7,] -1.096452731 3.154577548
[8,] -0.245279298 -1.096452731
[9,] 0.590968237 -0.245279298
[10,] 1.427409142 0.590968237
[11,] 3.361569277 1.427409142
[12,] -3.460863617 3.361569277
[13,] 2.463607162 -3.460863617
[14,] 2.192183555 2.463607162
[15,] 0.553030617 2.192183555
[16,] 0.095415706 0.553030617
[17,] 1.154997217 0.095415706
[18,] -1.458961871 1.154997217
[19,] 2.125227892 -1.458961871
[20,] 2.598615701 2.125227892
[21,] -2.795038658 2.598615701
[22,] -0.501542866 -2.795038658
[23,] -1.632159554 -0.501542866
[24,] 1.562407011 -1.632159554
[25,] -6.979963929 1.562407011
[26,] 0.889512361 -6.979963929
[27,] 0.605426369 0.889512361
[28,] 0.993164341 0.605426369
[29,] -3.032296593 0.993164341
[30,] 0.207785128 -3.032296593
[31,] 0.256927380 0.207785128
[32,] 1.837271981 0.256927380
[33,] -0.380482913 1.837271981
[34,] -0.043317198 -0.380482913
[35,] 0.402548846 -0.043317198
[36,] -1.924029114 0.402548846
[37,] 0.584539520 -1.924029114
[38,] 1.557166847 0.584539520
[39,] -2.315864528 1.557166847
[40,] -0.802421369 -2.315864528
[41,] 2.238760749 -0.802421369
[42,] 0.066480087 2.238760749
[43,] -1.233369459 0.066480087
[44,] 0.280123399 -1.233369459
[45,] -2.707931515 0.280123399
[46,] -0.465953879 -2.707931515
[47,] -0.001697775 -0.465953879
[48,] 3.375919469 -0.001697775
[49,] -1.870763924 3.375919469
[50,] 0.637587394 -1.870763924
[51,] 0.433606878 0.637587394
[52,] -0.812299781 0.433606878
[53,] -1.800141809 -0.812299781
[54,] -2.192148486 -1.800141809
[55,] 1.204670436 -2.192148486
[56,] 1.659946561 1.204670436
[57,] -0.594329288 1.659946561
[58,] -3.327698726 -0.594329288
[59,] -1.541204519 -3.327698726
[60,] -2.878647113 -1.541204519
[61,] -1.749227881 -2.878647113
[62,] -3.798985421 -1.749227881
[63,] 0.778878062 -3.798985421
[64,] 1.222824134 0.778878062
[65,] -5.000303974 1.222824134
[66,] -1.537480282 -5.000303974
[67,] -2.397384016 -1.537480282
[68,] 1.561556225 -2.397384016
[69,] 1.447995270 1.561556225
[70,] 0.564041802 1.447995270
[71,] 3.361577970 0.564041802
[72,] 0.632584504 3.361577970
[73,] -0.181541719 0.632584504
[74,] -1.886116743 -0.181541719
[75,] -0.104734623 -1.886116743
[76,] 3.065112272 -0.104734623
[77,] 0.564313846 3.065112272
[78,] 1.255454915 0.564313846
[79,] -1.878868737 1.255454915
[80,] 0.152918520 -1.878868737
[81,] -0.446972364 0.152918520
[82,] 1.771990388 -0.446972364
[83,] 0.762063407 1.771990388
[84,] -0.082272680 0.762063407
[85,] 1.030891672 -0.082272680
[86,] -0.235598342 1.030891672
[87,] 0.275250237 -0.235598342
[88,] -3.526888518 0.275250237
[89,] 3.298027044 -3.526888518
[90,] 0.214381145 3.298027044
[91,] 0.845924053 0.214381145
[92,] 0.719383028 0.845924053
[93,] -0.968108584 0.719383028
[94,] 1.057827525 -0.968108584
[95,] -0.804025921 1.057827525
[96,] -0.839120844 -0.804025921
[97,] 2.098083833 -0.839120844
[98,] -0.011586594 2.098083833
[99,] 1.873545693 -0.011586594
[100,] -0.939454892 1.873545693
[101,] 0.994352505 -0.939454892
[102,] -3.401800729 0.994352505
[103,] 1.900529361 -3.401800729
[104,] -2.385933823 1.900529361
[105,] 1.087037944 -2.385933823
[106,] 2.079630629 1.087037944
[107,] -2.808173961 2.079630629
[108,] 0.820236111 -2.808173961
[109,] 1.091717659 0.820236111
[110,] -2.197405972 1.091717659
[111,] -2.296089363 -2.197405972
[112,] 2.097711608 -2.296089363
[113,] 3.889733229 2.097711608
[114,] 0.436597885 3.889733229
[115,] 1.006196993 0.436597885
[116,] 0.162179068 1.006196993
[117,] -1.080113094 0.162179068
[118,] 0.272755534 -1.080113094
[119,] -0.541956071 0.272755534
[120,] 0.427630832 -0.541956071
[121,] 0.092193022 0.427630832
[122,] -0.902649017 0.092193022
[123,] 0.426607091 -0.902649017
[124,] -1.758528582 0.426607091
[125,] 0.824039997 -1.758528582
[126,] 1.709446916 0.824039997
[127,] 4.049723078 1.709446916
[128,] 1.403339237 4.049723078
[129,] -1.656235076 1.403339237
[130,] -1.668622404 -1.656235076
[131,] -0.297060416 -1.668622404
[132,] 2.372581065 -0.297060416
[133,] 0.744765525 2.372581065
[134,] 2.220813687 0.744765525
[135,] 1.524134058 2.220813687
[136,] 0.647625045 1.524134058
[137,] -1.006819799 0.647625045
[138,] 0.795824724 -1.006819799
[139,] -0.821331809 0.795824724
[140,] 0.342152648 -0.821331809
[141,] 2.120335577 0.342152648
[142,] -0.711341826 2.120335577
[143,] 0.691492557 -0.711341826
[144,] 1.362376857 0.691492557
[145,] 1.496559797 1.362376857
[146,] -2.422108121 1.496559797
[147,] -2.717738405 -2.422108121
[148,] -2.581961067 -2.717738405
[149,] 1.714912168 -2.581961067
[150,] 0.384221789 1.714912168
[151,] 0.544280716 0.384221789
[152,] -2.501341313 0.544280716
[153,] -2.937042546 -2.501341313
[154,] 1.176027211 -2.937042546
[155,] 0.214381145 1.176027211
[156,] 0.603654463 0.214381145
[157,] 4.049723078 0.603654463
[158,] -2.707025373 4.049723078
[159,] -0.172618220 -2.707025373
[160,] 0.330293529 -0.172618220
[161,] 0.835807721 0.330293529
[162,] 0.930132001 0.835807721
[163,] 4.596275080 0.930132001
[164,] -1.811859129 4.596275080
[165,] 2.020582461 -1.811859129
[166,] 0.033595175 2.020582461
[167,] -0.705799426 0.033595175
[168,] -3.570422282 -0.705799426
[169,] -2.800098303 -3.570422282
[170,] 0.589829113 -2.800098303
[171,] 1.955011151 0.589829113
[172,] -4.854020670 1.955011151
[173,] 1.839041759 -4.854020670
[174,] 2.795600067 1.839041759
[175,] -2.122506786 2.795600067
[176,] -3.175728546 -2.122506786
[177,] 0.647677622 -3.175728546
[178,] 1.591186885 0.647677622
[179,] -2.018787044 1.591186885
[180,] 0.062874463 -2.018787044
[181,] -1.578315081 0.062874463
[182,] 0.438247576 -1.578315081
[183,] -0.819459881 0.438247576
[184,] 2.051980136 -0.819459881
[185,] 1.461528170 2.051980136
[186,] 0.586696694 1.461528170
[187,] 0.919129071 0.586696694
[188,] 0.687892806 0.919129071
[189,] 0.834021472 0.687892806
[190,] -1.420441315 0.834021472
[191,] -0.865218747 -1.420441315
[192,] 2.379292629 -0.865218747
[193,] -1.397159207 2.379292629
[194,] 1.995337579 -1.397159207
[195,] -1.935111244 1.995337579
[196,] 2.344475220 -1.935111244
[197,] 0.677130329 2.344475220
[198,] -3.046900114 0.677130329
[199,] -0.664971632 -3.046900114
[200,] -3.070768708 -0.664971632
[201,] 1.339749027 -3.070768708
[202,] 3.053663958 1.339749027
[203,] 0.549961015 3.053663958
[204,] 0.666630376 0.549961015
[205,] 1.363311302 0.666630376
[206,] -0.437779036 1.363311302
[207,] 3.583580104 -0.437779036
[208,] 0.185845919 3.583580104
[209,] 1.776374106 0.185845919
[210,] -2.610199433 1.776374106
[211,] 1.528228917 -2.610199433
[212,] -1.193893261 1.528228917
[213,] -3.774661728 -1.193893261
[214,] -1.058014170 -3.774661728
[215,] 1.797931329 -1.058014170
[216,] 2.181847530 1.797931329
[217,] -0.175712516 2.181847530
[218,] -1.887957268 -0.175712516
[219,] 1.467959524 -1.887957268
[220,] -2.811347118 1.467959524
[221,] 2.527103377 -2.811347118
[222,] -2.063433360 2.527103377
[223,] 0.254298676 -2.063433360
[224,] -0.487171489 0.254298676
[225,] 1.748594834 -0.487171489
[226,] 5.275635645 1.748594834
[227,] -1.575989741 5.275635645
[228,] -1.458536514 -1.575989741
[229,] -2.291213016 -1.458536514
[230,] 0.235484457 -2.291213016
[231,] -2.991978560 0.235484457
[232,] 0.145625383 -2.991978560
[233,] 0.625259013 0.145625383
[234,] 1.169589225 0.625259013
[235,] -1.775771145 1.169589225
[236,] 0.689212499 -1.775771145
[237,] 0.240549100 0.689212499
[238,] -4.294815622 0.240549100
[239,] -2.509958764 -4.294815622
[240,] -2.783143074 -2.509958764
[241,] -2.660462537 -2.783143074
[242,] 0.288218245 -2.660462537
[243,] -0.222979532 0.288218245
[244,] 1.504092618 -0.222979532
[245,] 0.284967842 1.504092618
[246,] 0.291709105 0.284967842
[247,] 5.193096754 0.291709105
[248,] -0.206027296 5.193096754
[249,] 0.501425253 -0.206027296
[250,] 2.163674025 0.501425253
[251,] 1.156733462 2.163674025
[252,] -1.105488804 1.156733462
[253,] -0.684733208 -1.105488804
[254,] 0.212948555 -0.684733208
[255,] -0.636238625 0.212948555
[256,] -1.803953544 -0.636238625
[257,] -2.454670378 -1.803953544
[258,] 2.461980478 -2.454670378
[259,] -4.779709067 2.461980478
[260,] 0.367170754 -4.779709067
[261,] 1.524626282 0.367170754
[262,] -2.811128745 1.524626282
[263,] 0.182399589 -2.811128745
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.699453610 0.057570895
2 -2.994004983 2.699453610
3 -2.512019814 -2.994004983
4 4.710221511 -2.512019814
5 3.514033929 4.710221511
6 3.154577548 3.514033929
7 -1.096452731 3.154577548
8 -0.245279298 -1.096452731
9 0.590968237 -0.245279298
10 1.427409142 0.590968237
11 3.361569277 1.427409142
12 -3.460863617 3.361569277
13 2.463607162 -3.460863617
14 2.192183555 2.463607162
15 0.553030617 2.192183555
16 0.095415706 0.553030617
17 1.154997217 0.095415706
18 -1.458961871 1.154997217
19 2.125227892 -1.458961871
20 2.598615701 2.125227892
21 -2.795038658 2.598615701
22 -0.501542866 -2.795038658
23 -1.632159554 -0.501542866
24 1.562407011 -1.632159554
25 -6.979963929 1.562407011
26 0.889512361 -6.979963929
27 0.605426369 0.889512361
28 0.993164341 0.605426369
29 -3.032296593 0.993164341
30 0.207785128 -3.032296593
31 0.256927380 0.207785128
32 1.837271981 0.256927380
33 -0.380482913 1.837271981
34 -0.043317198 -0.380482913
35 0.402548846 -0.043317198
36 -1.924029114 0.402548846
37 0.584539520 -1.924029114
38 1.557166847 0.584539520
39 -2.315864528 1.557166847
40 -0.802421369 -2.315864528
41 2.238760749 -0.802421369
42 0.066480087 2.238760749
43 -1.233369459 0.066480087
44 0.280123399 -1.233369459
45 -2.707931515 0.280123399
46 -0.465953879 -2.707931515
47 -0.001697775 -0.465953879
48 3.375919469 -0.001697775
49 -1.870763924 3.375919469
50 0.637587394 -1.870763924
51 0.433606878 0.637587394
52 -0.812299781 0.433606878
53 -1.800141809 -0.812299781
54 -2.192148486 -1.800141809
55 1.204670436 -2.192148486
56 1.659946561 1.204670436
57 -0.594329288 1.659946561
58 -3.327698726 -0.594329288
59 -1.541204519 -3.327698726
60 -2.878647113 -1.541204519
61 -1.749227881 -2.878647113
62 -3.798985421 -1.749227881
63 0.778878062 -3.798985421
64 1.222824134 0.778878062
65 -5.000303974 1.222824134
66 -1.537480282 -5.000303974
67 -2.397384016 -1.537480282
68 1.561556225 -2.397384016
69 1.447995270 1.561556225
70 0.564041802 1.447995270
71 3.361577970 0.564041802
72 0.632584504 3.361577970
73 -0.181541719 0.632584504
74 -1.886116743 -0.181541719
75 -0.104734623 -1.886116743
76 3.065112272 -0.104734623
77 0.564313846 3.065112272
78 1.255454915 0.564313846
79 -1.878868737 1.255454915
80 0.152918520 -1.878868737
81 -0.446972364 0.152918520
82 1.771990388 -0.446972364
83 0.762063407 1.771990388
84 -0.082272680 0.762063407
85 1.030891672 -0.082272680
86 -0.235598342 1.030891672
87 0.275250237 -0.235598342
88 -3.526888518 0.275250237
89 3.298027044 -3.526888518
90 0.214381145 3.298027044
91 0.845924053 0.214381145
92 0.719383028 0.845924053
93 -0.968108584 0.719383028
94 1.057827525 -0.968108584
95 -0.804025921 1.057827525
96 -0.839120844 -0.804025921
97 2.098083833 -0.839120844
98 -0.011586594 2.098083833
99 1.873545693 -0.011586594
100 -0.939454892 1.873545693
101 0.994352505 -0.939454892
102 -3.401800729 0.994352505
103 1.900529361 -3.401800729
104 -2.385933823 1.900529361
105 1.087037944 -2.385933823
106 2.079630629 1.087037944
107 -2.808173961 2.079630629
108 0.820236111 -2.808173961
109 1.091717659 0.820236111
110 -2.197405972 1.091717659
111 -2.296089363 -2.197405972
112 2.097711608 -2.296089363
113 3.889733229 2.097711608
114 0.436597885 3.889733229
115 1.006196993 0.436597885
116 0.162179068 1.006196993
117 -1.080113094 0.162179068
118 0.272755534 -1.080113094
119 -0.541956071 0.272755534
120 0.427630832 -0.541956071
121 0.092193022 0.427630832
122 -0.902649017 0.092193022
123 0.426607091 -0.902649017
124 -1.758528582 0.426607091
125 0.824039997 -1.758528582
126 1.709446916 0.824039997
127 4.049723078 1.709446916
128 1.403339237 4.049723078
129 -1.656235076 1.403339237
130 -1.668622404 -1.656235076
131 -0.297060416 -1.668622404
132 2.372581065 -0.297060416
133 0.744765525 2.372581065
134 2.220813687 0.744765525
135 1.524134058 2.220813687
136 0.647625045 1.524134058
137 -1.006819799 0.647625045
138 0.795824724 -1.006819799
139 -0.821331809 0.795824724
140 0.342152648 -0.821331809
141 2.120335577 0.342152648
142 -0.711341826 2.120335577
143 0.691492557 -0.711341826
144 1.362376857 0.691492557
145 1.496559797 1.362376857
146 -2.422108121 1.496559797
147 -2.717738405 -2.422108121
148 -2.581961067 -2.717738405
149 1.714912168 -2.581961067
150 0.384221789 1.714912168
151 0.544280716 0.384221789
152 -2.501341313 0.544280716
153 -2.937042546 -2.501341313
154 1.176027211 -2.937042546
155 0.214381145 1.176027211
156 0.603654463 0.214381145
157 4.049723078 0.603654463
158 -2.707025373 4.049723078
159 -0.172618220 -2.707025373
160 0.330293529 -0.172618220
161 0.835807721 0.330293529
162 0.930132001 0.835807721
163 4.596275080 0.930132001
164 -1.811859129 4.596275080
165 2.020582461 -1.811859129
166 0.033595175 2.020582461
167 -0.705799426 0.033595175
168 -3.570422282 -0.705799426
169 -2.800098303 -3.570422282
170 0.589829113 -2.800098303
171 1.955011151 0.589829113
172 -4.854020670 1.955011151
173 1.839041759 -4.854020670
174 2.795600067 1.839041759
175 -2.122506786 2.795600067
176 -3.175728546 -2.122506786
177 0.647677622 -3.175728546
178 1.591186885 0.647677622
179 -2.018787044 1.591186885
180 0.062874463 -2.018787044
181 -1.578315081 0.062874463
182 0.438247576 -1.578315081
183 -0.819459881 0.438247576
184 2.051980136 -0.819459881
185 1.461528170 2.051980136
186 0.586696694 1.461528170
187 0.919129071 0.586696694
188 0.687892806 0.919129071
189 0.834021472 0.687892806
190 -1.420441315 0.834021472
191 -0.865218747 -1.420441315
192 2.379292629 -0.865218747
193 -1.397159207 2.379292629
194 1.995337579 -1.397159207
195 -1.935111244 1.995337579
196 2.344475220 -1.935111244
197 0.677130329 2.344475220
198 -3.046900114 0.677130329
199 -0.664971632 -3.046900114
200 -3.070768708 -0.664971632
201 1.339749027 -3.070768708
202 3.053663958 1.339749027
203 0.549961015 3.053663958
204 0.666630376 0.549961015
205 1.363311302 0.666630376
206 -0.437779036 1.363311302
207 3.583580104 -0.437779036
208 0.185845919 3.583580104
209 1.776374106 0.185845919
210 -2.610199433 1.776374106
211 1.528228917 -2.610199433
212 -1.193893261 1.528228917
213 -3.774661728 -1.193893261
214 -1.058014170 -3.774661728
215 1.797931329 -1.058014170
216 2.181847530 1.797931329
217 -0.175712516 2.181847530
218 -1.887957268 -0.175712516
219 1.467959524 -1.887957268
220 -2.811347118 1.467959524
221 2.527103377 -2.811347118
222 -2.063433360 2.527103377
223 0.254298676 -2.063433360
224 -0.487171489 0.254298676
225 1.748594834 -0.487171489
226 5.275635645 1.748594834
227 -1.575989741 5.275635645
228 -1.458536514 -1.575989741
229 -2.291213016 -1.458536514
230 0.235484457 -2.291213016
231 -2.991978560 0.235484457
232 0.145625383 -2.991978560
233 0.625259013 0.145625383
234 1.169589225 0.625259013
235 -1.775771145 1.169589225
236 0.689212499 -1.775771145
237 0.240549100 0.689212499
238 -4.294815622 0.240549100
239 -2.509958764 -4.294815622
240 -2.783143074 -2.509958764
241 -2.660462537 -2.783143074
242 0.288218245 -2.660462537
243 -0.222979532 0.288218245
244 1.504092618 -0.222979532
245 0.284967842 1.504092618
246 0.291709105 0.284967842
247 5.193096754 0.291709105
248 -0.206027296 5.193096754
249 0.501425253 -0.206027296
250 2.163674025 0.501425253
251 1.156733462 2.163674025
252 -1.105488804 1.156733462
253 -0.684733208 -1.105488804
254 0.212948555 -0.684733208
255 -0.636238625 0.212948555
256 -1.803953544 -0.636238625
257 -2.454670378 -1.803953544
258 2.461980478 -2.454670378
259 -4.779709067 2.461980478
260 0.367170754 -4.779709067
261 1.524626282 0.367170754
262 -2.811128745 1.524626282
263 0.182399589 -2.811128745
> 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/7b0ep1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/84cm31384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9site1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/10624u1384814247.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, 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/fisher/rcomp/tmp/11n44x1384814247.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/fisher/rcomp/tmp/12vccc1384814247.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/fisher/rcomp/tmp/138ywx1384814247.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/fisher/rcomp/tmp/14hnl01384814247.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/fisher/rcomp/tmp/153jqq1384814247.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/fisher/rcomp/tmp/16ungo1384814247.tab")
+ }
>
> try(system("convert tmp/1e0ur1384814247.ps tmp/1e0ur1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/28o0z1384814247.ps tmp/28o0z1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/3grck1384814247.ps tmp/3grck1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/4fiyh1384814247.ps tmp/4fiyh1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/5f9901384814247.ps tmp/5f9901384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/6ym491384814247.ps tmp/6ym491384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/7b0ep1384814247.ps tmp/7b0ep1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/84cm31384814247.ps tmp/84cm31384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/9site1384814247.ps tmp/9site1384814247.png",intern=TRUE))
character(0)
> try(system("convert tmp/10624u1384814247.ps tmp/10624u1384814247.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
11.754 1.794 13.555