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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+ ,4
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+ ,30
+ ,15
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+ ,64
+ ,38
+ ,11
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+ ,11
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+ ,41
+ ,11
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+ ,11
+ ,7
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+ ,14
+ ,9
+ ,14
+ ,12
+ ,72
+ ,43
+ ,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 = 'Include Monthly Dummies'
> par1 = '3'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '3'
> #'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
Learning Connected Separate Software Happiness Depression Sport1 Sport2
1 13 41 38 12 14 12.0 53 32
2 16 39 32 11 18 11.0 83 51
3 19 30 35 15 11 14.0 66 42
4 15 31 33 6 12 12.0 67 41
5 14 34 37 13 16 21.0 76 46
6 13 35 29 10 18 12.0 78 47
7 19 39 31 12 14 22.0 53 37
8 15 34 36 14 14 11.0 80 49
9 14 36 35 12 15 10.0 74 45
10 15 37 38 9 15 13.0 76 47
11 16 38 31 10 17 10.0 79 49
12 16 36 34 12 19 8.0 54 33
13 16 38 35 12 10 15.0 67 42
14 16 39 38 11 16 14.0 54 33
15 17 33 37 15 18 10.0 87 53
16 15 32 33 12 14 14.0 58 36
17 15 36 32 10 14 14.0 75 45
18 20 38 38 12 17 11.0 88 54
19 18 39 38 11 14 10.0 64 41
20 16 32 32 12 16 13.0 57 36
21 16 32 33 11 18 9.5 66 41
22 16 31 31 12 11 14.0 68 44
23 19 39 38 13 14 12.0 54 33
24 16 37 39 11 12 14.0 56 37
25 17 39 32 12 17 11.0 86 52
26 17 41 32 13 9 9.0 80 47
27 16 36 35 10 16 11.0 76 43
28 15 33 37 14 14 15.0 69 44
29 16 33 33 12 15 14.0 78 45
30 14 34 33 10 11 13.0 67 44
31 15 31 31 12 16 9.0 80 49
32 12 27 32 8 13 15.0 54 33
33 14 37 31 10 17 10.0 71 43
34 16 34 37 12 15 11.0 84 54
35 14 34 30 12 14 13.0 74 42
36 10 32 33 7 16 8.0 71 44
37 10 29 31 9 9 20.0 63 37
38 14 36 33 12 15 12.0 71 43
39 16 29 31 10 17 10.0 76 46
40 16 35 33 10 13 10.0 69 42
41 16 37 32 10 15 9.0 74 45
42 14 34 33 12 16 14.0 75 44
43 20 38 32 15 16 8.0 54 33
44 14 35 33 10 12 14.0 52 31
45 14 38 28 10 15 11.0 69 42
46 11 37 35 12 11 13.0 68 40
47 14 38 39 13 15 9.0 65 43
48 15 33 34 11 15 11.0 75 46
49 16 36 38 11 17 15.0 74 42
50 14 38 32 12 13 11.0 75 45
51 16 32 38 14 16 10.0 72 44
52 14 32 30 10 14 14.0 67 40
53 12 32 33 12 11 18.0 63 37
54 16 34 38 13 12 14.0 62 46
55 9 32 32 5 12 11.0 63 36
56 14 37 35 6 15 14.5 76 47
57 16 39 34 12 16 13.0 74 45
58 16 29 34 12 15 9.0 67 42
59 15 37 36 11 12 10.0 73 43
60 16 35 34 10 12 15.0 70 43
61 12 30 28 7 8 20.0 53 32
62 16 38 34 12 13 12.0 77 45
63 16 34 35 14 11 12.0 80 48
64 14 31 35 11 14 14.0 52 31
65 16 34 31 12 15 13.0 54 33
66 17 35 37 13 10 11.0 80 49
67 18 36 35 14 11 17.0 66 42
68 18 30 27 11 12 12.0 73 41
69 12 39 40 12 15 13.0 63 38
70 16 35 37 12 15 14.0 69 42
71 10 38 36 8 14 13.0 67 44
72 14 31 38 11 16 15.0 54 33
73 18 34 39 14 15 13.0 81 48
74 18 38 41 14 15 10.0 69 40
75 16 34 27 12 13 11.0 84 50
76 17 39 30 9 12 19.0 80 49
77 16 37 37 13 17 13.0 70 43
78 16 34 31 11 13 17.0 69 44
79 13 28 31 12 15 13.0 77 47
80 16 37 27 12 13 9.0 54 33
81 16 33 36 12 15 11.0 79 46
82 16 35 37 12 15 9.0 71 45
83 15 37 33 12 16 12.0 73 43
84 15 32 34 11 15 12.0 72 44
85 16 33 31 10 14 13.0 77 47
86 14 38 39 9 15 13.0 75 45
87 16 33 34 12 14 12.0 69 42
88 16 29 32 12 13 15.0 54 33
89 15 33 33 12 7 22.0 70 43
90 12 31 36 9 17 13.0 73 46
91 17 36 32 15 13 15.0 54 33
92 16 35 41 12 15 13.0 77 46
93 15 32 28 12 14 15.0 82 48
94 13 29 30 12 13 12.5 80 47
95 16 39 36 10 16 11.0 80 47
96 16 37 35 13 12 16.0 69 43
97 16 35 31 9 14 11.0 78 46
98 16 37 34 12 17 11.0 81 48
99 14 32 36 10 15 10.0 76 46
100 16 38 36 14 17 10.0 76 45
101 16 37 35 11 12 16.0 73 45
102 20 36 37 15 16 12.0 85 52
103 15 32 28 11 11 11.0 66 42
104 16 33 39 11 15 16.0 79 47
105 13 40 32 12 9 19.0 68 41
106 17 38 35 12 16 11.0 76 47
107 16 41 39 12 15 16.0 71 43
108 16 36 35 11 10 15.0 54 33
109 12 43 42 7 10 24.0 46 30
110 16 30 34 12 15 14.0 85 52
111 16 31 33 14 11 15.0 74 44
112 17 32 41 11 13 11.0 88 55
113 13 32 33 11 14 15.0 38 11
114 12 37 34 10 18 12.0 76 47
115 18 37 32 13 16 10.0 86 53
116 14 33 40 13 14 14.0 54 33
117 14 34 40 8 14 13.0 67 44
118 13 33 35 11 14 9.0 69 42
119 16 38 36 12 14 15.0 90 55
120 13 33 37 11 12 15.0 54 33
121 16 31 27 13 14 14.0 76 46
122 13 38 39 12 15 11.0 89 54
123 16 37 38 14 15 8.0 76 47
124 15 36 31 13 15 11.0 73 45
125 16 31 33 15 13 11.0 79 47
126 15 39 32 10 17 8.0 90 55
127 17 44 39 11 17 10.0 74 44
128 15 33 36 9 19 11.0 81 53
129 12 35 33 11 15 13.0 72 44
130 16 32 33 10 13 11.0 71 42
131 10 28 32 11 9 20.0 66 40
132 16 40 37 8 15 10.0 77 46
133 12 27 30 11 15 15.0 65 40
134 14 37 38 12 15 12.0 74 46
135 15 32 29 12 16 14.0 85 53
136 13 28 22 9 11 23.0 54 33
137 15 34 35 11 14 14.0 63 42
138 11 30 35 10 11 16.0 54 35
139 12 35 34 8 15 11.0 64 40
140 11 31 35 9 13 12.0 69 41
141 16 32 34 8 15 10.0 54 33
142 15 30 37 9 16 14.0 84 51
143 17 30 35 15 14 12.0 86 53
144 16 31 23 11 15 12.0 77 46
145 10 40 31 8 16 11.0 89 55
146 18 32 27 13 16 12.0 76 47
147 13 36 36 12 11 13.0 60 38
148 16 32 31 12 12 11.0 75 46
149 13 35 32 9 9 19.0 73 46
150 10 38 39 7 16 12.0 85 53
151 15 42 37 13 13 17.0 79 47
152 16 34 38 9 16 9.0 71 41
153 16 35 39 6 12 12.0 72 44
154 14 38 34 8 9 19.0 69 43
155 10 33 31 8 13 18.0 78 51
156 17 36 32 15 13 15.0 54 33
157 13 32 37 6 14 14.0 69 43
158 15 33 36 9 19 11.0 81 53
159 16 34 32 11 13 9.0 84 51
160 12 32 38 8 12 18.0 84 50
161 13 34 36 8 13 16.0 69 46
162 13 27 26 10 10 24.0 66 43
163 12 31 26 8 14 14.0 81 47
164 17 38 33 14 16 20.0 82 50
165 15 34 39 10 10 18.0 72 43
166 10 24 30 8 11 23.0 54 33
167 14 30 33 11 14 12.0 78 48
168 11 26 25 12 12 14.0 74 44
169 13 34 38 12 9 16.0 82 50
170 16 27 37 12 9 18.0 73 41
171 12 37 31 5 11 20.0 55 34
172 16 36 37 12 16 12.0 72 44
173 12 41 35 10 9 12.0 78 47
174 9 29 25 7 13 17.0 59 35
175 12 36 28 12 16 13.0 72 44
176 15 32 35 11 13 9.0 78 44
177 12 37 33 8 9 16.0 68 43
178 12 30 30 9 12 18.0 69 41
179 14 31 31 10 16 10.0 67 41
180 12 38 37 9 11 14.0 74 42
181 16 36 36 12 14 11.0 54 33
182 11 35 30 6 13 9.0 67 41
183 19 31 36 15 15 11.0 70 44
184 15 38 32 12 14 10.0 80 48
185 8 22 28 12 16 11.0 89 55
186 16 32 36 12 13 19.0 76 44
187 17 36 34 11 14 14.0 74 43
188 12 39 31 7 15 12.0 87 52
189 11 28 28 7 13 14.0 54 30
190 11 32 36 5 11 21.0 61 39
191 14 32 36 12 11 13.0 38 11
192 16 38 40 12 14 10.0 75 44
193 12 32 33 3 15 15.0 69 42
194 16 35 37 11 11 16.0 62 41
195 13 32 32 10 15 14.0 72 44
196 15 37 38 12 12 12.0 70 44
197 16 34 31 9 14 19.0 79 48
198 16 33 37 12 14 15.0 87 53
199 14 33 33 9 8 19.0 62 37
200 16 26 32 12 13 13.0 77 44
201 16 30 30 12 9 17.0 69 44
202 14 24 30 10 15 12.0 69 40
203 11 34 31 9 17 11.0 75 42
204 12 34 32 12 13 14.0 54 35
205 15 33 34 8 15 11.0 72 43
206 15 34 36 11 15 13.0 74 45
207 16 35 37 11 14 12.0 85 55
208 16 35 36 12 16 15.0 52 31
209 11 36 33 10 13 14.0 70 44
210 15 34 33 10 16 12.0 84 50
211 12 34 33 12 9 17.0 64 40
212 12 41 44 12 16 11.0 84 53
213 15 32 39 11 11 18.0 87 54
214 15 30 32 8 10 13.0 79 49
215 16 35 35 12 11 17.0 67 40
216 14 28 25 10 15 13.0 65 41
217 17 33 35 11 17 11.0 85 52
218 14 39 34 10 14 12.0 83 52
219 13 36 35 8 8 22.0 61 36
220 15 36 39 12 15 14.0 82 52
221 13 35 33 12 11 12.0 76 46
222 14 38 36 10 16 12.0 58 31
223 15 33 32 12 10 17.0 72 44
224 12 31 32 9 15 9.0 72 44
225 13 34 36 9 9 21.0 38 11
226 8 32 36 6 16 10.0 78 46
227 14 31 32 10 19 11.0 54 33
228 14 33 34 9 12 12.0 63 34
229 11 34 33 9 8 23.0 66 42
230 12 34 35 9 11 13.0 70 43
231 13 34 30 6 14 12.0 71 43
232 10 33 38 10 9 16.0 67 44
233 16 32 34 6 15 9.0 58 36
234 18 41 33 14 13 17.0 72 46
235 13 34 32 10 16 9.0 72 44
236 11 36 31 10 11 14.0 70 43
237 4 37 30 6 12 17.0 76 50
238 13 36 27 12 13 13.0 50 33
239 16 29 31 12 10 11.0 72 43
240 10 37 30 7 11 12.0 72 44
241 12 27 32 8 12 10.0 88 53
242 12 35 35 11 8 19.0 53 34
243 10 28 28 3 12 16.0 58 35
244 13 35 33 6 12 16.0 66 40
245 15 37 31 10 15 14.0 82 53
246 12 29 35 8 11 20.0 69 42
247 14 32 35 9 13 15.0 68 43
248 10 36 32 9 14 23.0 44 29
249 12 19 21 8 10 20.0 56 36
250 12 21 20 9 12 16.0 53 30
251 11 31 34 7 15 14.0 70 42
252 10 33 32 7 13 17.0 78 47
253 12 36 34 6 13 11.0 71 44
254 16 33 32 9 13 13.0 72 45
255 12 37 33 10 12 17.0 68 44
256 14 34 33 11 12 15.0 67 43
257 16 35 37 12 9 21.0 75 43
258 14 31 32 8 9 18.0 62 40
259 13 37 34 11 15 15.0 67 41
260 4 35 30 3 10 8.0 83 52
261 15 27 30 11 14 12.0 64 38
262 11 34 38 12 15 12.0 68 41
263 11 40 36 7 7 22.0 62 39
264 14 29 32 9 14 12.0 72 43
Month M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 9 1 0 0 0 0 0 0 0 0 0 0
2 9 0 1 0 0 0 0 0 0 0 0 0
3 9 0 0 1 0 0 0 0 0 0 0 0
4 9 0 0 0 1 0 0 0 0 0 0 0
5 9 0 0 0 0 1 0 0 0 0 0 0
6 9 0 0 0 0 0 1 0 0 0 0 0
7 9 0 0 0 0 0 0 1 0 0 0 0
8 9 0 0 0 0 0 0 0 1 0 0 0
9 9 0 0 0 0 0 0 0 0 1 0 0
10 9 0 0 0 0 0 0 0 0 0 1 0
11 9 0 0 0 0 0 0 0 0 0 0 1
12 9 0 0 0 0 0 0 0 0 0 0 0
13 9 1 0 0 0 0 0 0 0 0 0 0
14 9 0 1 0 0 0 0 0 0 0 0 0
15 9 0 0 1 0 0 0 0 0 0 0 0
16 9 0 0 0 1 0 0 0 0 0 0 0
17 9 0 0 0 0 1 0 0 0 0 0 0
18 9 0 0 0 0 0 1 0 0 0 0 0
19 9 0 0 0 0 0 0 1 0 0 0 0
20 9 0 0 0 0 0 0 0 1 0 0 0
21 9 0 0 0 0 0 0 0 0 1 0 0
22 9 0 0 0 0 0 0 0 0 0 1 0
23 9 0 0 0 0 0 0 0 0 0 0 1
24 9 0 0 0 0 0 0 0 0 0 0 0
25 9 1 0 0 0 0 0 0 0 0 0 0
26 9 0 1 0 0 0 0 0 0 0 0 0
27 9 0 0 1 0 0 0 0 0 0 0 0
28 9 0 0 0 1 0 0 0 0 0 0 0
29 9 0 0 0 0 1 0 0 0 0 0 0
30 9 0 0 0 0 0 1 0 0 0 0 0
31 9 0 0 0 0 0 0 1 0 0 0 0
32 9 0 0 0 0 0 0 0 1 0 0 0
33 9 0 0 0 0 0 0 0 0 1 0 0
34 9 0 0 0 0 0 0 0 0 0 1 0
35 9 0 0 0 0 0 0 0 0 0 0 1
36 9 0 0 0 0 0 0 0 0 0 0 0
37 9 1 0 0 0 0 0 0 0 0 0 0
38 9 0 1 0 0 0 0 0 0 0 0 0
39 9 0 0 1 0 0 0 0 0 0 0 0
40 9 0 0 0 1 0 0 0 0 0 0 0
41 9 0 0 0 0 1 0 0 0 0 0 0
42 9 0 0 0 0 0 1 0 0 0 0 0
43 9 0 0 0 0 0 0 1 0 0 0 0
44 9 0 0 0 0 0 0 0 1 0 0 0
45 9 0 0 0 0 0 0 0 0 1 0 0
46 9 0 0 0 0 0 0 0 0 0 1 0
47 9 0 0 0 0 0 0 0 0 0 0 1
48 9 0 0 0 0 0 0 0 0 0 0 0
49 9 1 0 0 0 0 0 0 0 0 0 0
50 9 0 1 0 0 0 0 0 0 0 0 0
51 9 0 0 1 0 0 0 0 0 0 0 0
52 9 0 0 0 1 0 0 0 0 0 0 0
53 9 0 0 0 0 1 0 0 0 0 0 0
54 9 0 0 0 0 0 1 0 0 0 0 0
55 9 0 0 0 0 0 0 1 0 0 0 0
56 9 0 0 0 0 0 0 0 1 0 0 0
57 9 0 0 0 0 0 0 0 0 1 0 0
58 9 0 0 0 0 0 0 0 0 0 1 0
59 9 0 0 0 0 0 0 0 0 0 0 1
60 9 0 0 0 0 0 0 0 0 0 0 0
61 9 1 0 0 0 0 0 0 0 0 0 0
62 9 0 1 0 0 0 0 0 0 0 0 0
63 9 0 0 1 0 0 0 0 0 0 0 0
64 9 0 0 0 1 0 0 0 0 0 0 0
65 9 0 0 0 0 1 0 0 0 0 0 0
66 10 0 0 0 0 0 1 0 0 0 0 0
67 10 0 0 0 0 0 0 1 0 0 0 0
68 10 0 0 0 0 0 0 0 1 0 0 0
69 10 0 0 0 0 0 0 0 0 1 0 0
70 10 0 0 0 0 0 0 0 0 0 1 0
71 10 0 0 0 0 0 0 0 0 0 0 1
72 10 0 0 0 0 0 0 0 0 0 0 0
73 10 1 0 0 0 0 0 0 0 0 0 0
74 10 0 1 0 0 0 0 0 0 0 0 0
75 10 0 0 1 0 0 0 0 0 0 0 0
76 10 0 0 0 1 0 0 0 0 0 0 0
77 10 0 0 0 0 1 0 0 0 0 0 0
78 10 0 0 0 0 0 1 0 0 0 0 0
79 10 0 0 0 0 0 0 1 0 0 0 0
80 10 0 0 0 0 0 0 0 1 0 0 0
81 10 0 0 0 0 0 0 0 0 1 0 0
82 10 0 0 0 0 0 0 0 0 0 1 0
83 10 0 0 0 0 0 0 0 0 0 0 1
84 10 0 0 0 0 0 0 0 0 0 0 0
85 10 1 0 0 0 0 0 0 0 0 0 0
86 10 0 1 0 0 0 0 0 0 0 0 0
87 10 0 0 1 0 0 0 0 0 0 0 0
88 10 0 0 0 1 0 0 0 0 0 0 0
89 10 0 0 0 0 1 0 0 0 0 0 0
90 10 0 0 0 0 0 1 0 0 0 0 0
91 10 0 0 0 0 0 0 1 0 0 0 0
92 10 0 0 0 0 0 0 0 1 0 0 0
93 10 0 0 0 0 0 0 0 0 1 0 0
94 10 0 0 0 0 0 0 0 0 0 1 0
95 10 0 0 0 0 0 0 0 0 0 0 1
96 10 0 0 0 0 0 0 0 0 0 0 0
97 10 1 0 0 0 0 0 0 0 0 0 0
98 10 0 1 0 0 0 0 0 0 0 0 0
99 10 0 0 1 0 0 0 0 0 0 0 0
100 10 0 0 0 1 0 0 0 0 0 0 0
101 10 0 0 0 0 1 0 0 0 0 0 0
102 10 0 0 0 0 0 1 0 0 0 0 0
103 10 0 0 0 0 0 0 1 0 0 0 0
104 10 0 0 0 0 0 0 0 1 0 0 0
105 10 0 0 0 0 0 0 0 0 1 0 0
106 10 0 0 0 0 0 0 0 0 0 1 0
107 10 0 0 0 0 0 0 0 0 0 0 1
108 10 0 0 0 0 0 0 0 0 0 0 0
109 10 1 0 0 0 0 0 0 0 0 0 0
110 10 0 1 0 0 0 0 0 0 0 0 0
111 10 0 0 1 0 0 0 0 0 0 0 0
112 10 0 0 0 1 0 0 0 0 0 0 0
113 10 0 0 0 0 1 0 0 0 0 0 0
114 10 0 0 0 0 0 1 0 0 0 0 0
115 10 0 0 0 0 0 0 1 0 0 0 0
116 10 0 0 0 0 0 0 0 1 0 0 0
117 10 0 0 0 0 0 0 0 0 1 0 0
118 10 0 0 0 0 0 0 0 0 0 1 0
119 10 0 0 0 0 0 0 0 0 0 0 1
120 10 0 0 0 0 0 0 0 0 0 0 0
121 10 1 0 0 0 0 0 0 0 0 0 0
122 10 0 1 0 0 0 0 0 0 0 0 0
123 10 0 0 1 0 0 0 0 0 0 0 0
124 10 0 0 0 1 0 0 0 0 0 0 0
125 10 0 0 0 0 1 0 0 0 0 0 0
126 10 0 0 0 0 0 1 0 0 0 0 0
127 10 0 0 0 0 0 0 1 0 0 0 0
128 10 0 0 0 0 0 0 0 1 0 0 0
129 10 0 0 0 0 0 0 0 0 1 0 0
130 10 0 0 0 0 0 0 0 0 0 1 0
131 10 0 0 0 0 0 0 0 0 0 0 1
132 10 0 0 0 0 0 0 0 0 0 0 0
133 10 1 0 0 0 0 0 0 0 0 0 0
134 10 0 1 0 0 0 0 0 0 0 0 0
135 10 0 0 1 0 0 0 0 0 0 0 0
136 10 0 0 0 1 0 0 0 0 0 0 0
137 10 0 0 0 0 1 0 0 0 0 0 0
138 10 0 0 0 0 0 1 0 0 0 0 0
139 10 0 0 0 0 0 0 1 0 0 0 0
140 10 0 0 0 0 0 0 0 1 0 0 0
141 10 0 0 0 0 0 0 0 0 1 0 0
142 10 0 0 0 0 0 0 0 0 0 1 0
143 10 0 0 0 0 0 0 0 0 0 0 1
144 10 0 0 0 0 0 0 0 0 0 0 0
145 10 1 0 0 0 0 0 0 0 0 0 0
146 10 0 1 0 0 0 0 0 0 0 0 0
147 10 0 0 1 0 0 0 0 0 0 0 0
148 10 0 0 0 1 0 0 0 0 0 0 0
149 10 0 0 0 0 1 0 0 0 0 0 0
150 10 0 0 0 0 0 1 0 0 0 0 0
151 10 0 0 0 0 0 0 1 0 0 0 0
152 10 0 0 0 0 0 0 0 1 0 0 0
153 10 0 0 0 0 0 0 0 0 1 0 0
154 9 0 0 0 0 0 0 0 0 0 1 0
155 10 0 0 0 0 0 0 0 0 0 0 1
156 10 0 0 0 0 0 0 0 0 0 0 0
157 10 1 0 0 0 0 0 0 0 0 0 0
158 10 0 1 0 0 0 0 0 0 0 0 0
159 10 0 0 1 0 0 0 0 0 0 0 0
160 10 0 0 0 1 0 0 0 0 0 0 0
161 10 0 0 0 0 1 0 0 0 0 0 0
162 11 0 0 0 0 0 1 0 0 0 0 0
163 11 0 0 0 0 0 0 1 0 0 0 0
164 11 0 0 0 0 0 0 0 1 0 0 0
165 11 0 0 0 0 0 0 0 0 1 0 0
166 11 0 0 0 0 0 0 0 0 0 1 0
167 11 0 0 0 0 0 0 0 0 0 0 1
168 11 0 0 0 0 0 0 0 0 0 0 0
169 11 1 0 0 0 0 0 0 0 0 0 0
170 11 0 1 0 0 0 0 0 0 0 0 0
171 11 0 0 1 0 0 0 0 0 0 0 0
172 11 0 0 0 1 0 0 0 0 0 0 0
173 11 0 0 0 0 1 0 0 0 0 0 0
174 11 0 0 0 0 0 1 0 0 0 0 0
175 11 0 0 0 0 0 0 1 0 0 0 0
176 11 0 0 0 0 0 0 0 1 0 0 0
177 11 0 0 0 0 0 0 0 0 1 0 0
178 11 0 0 0 0 0 0 0 0 0 1 0
179 11 0 0 0 0 0 0 0 0 0 0 1
180 11 0 0 0 0 0 0 0 0 0 0 0
181 11 1 0 0 0 0 0 0 0 0 0 0
182 11 0 1 0 0 0 0 0 0 0 0 0
183 11 0 0 1 0 0 0 0 0 0 0 0
184 11 0 0 0 1 0 0 0 0 0 0 0
185 11 0 0 0 0 1 0 0 0 0 0 0
186 11 0 0 0 0 0 1 0 0 0 0 0
187 11 0 0 0 0 0 0 1 0 0 0 0
188 11 0 0 0 0 0 0 0 1 0 0 0
189 11 0 0 0 0 0 0 0 0 1 0 0
190 11 0 0 0 0 0 0 0 0 0 1 0
191 11 0 0 0 0 0 0 0 0 0 0 1
192 11 0 0 0 0 0 0 0 0 0 0 0
193 11 1 0 0 0 0 0 0 0 0 0 0
194 11 0 1 0 0 0 0 0 0 0 0 0
195 11 0 0 1 0 0 0 0 0 0 0 0
196 11 0 0 0 1 0 0 0 0 0 0 0
197 11 0 0 0 0 1 0 0 0 0 0 0
198 11 0 0 0 0 0 1 0 0 0 0 0
199 11 0 0 0 0 0 0 1 0 0 0 0
200 11 0 0 0 0 0 0 0 1 0 0 0
201 11 0 0 0 0 0 0 0 0 1 0 0
202 11 0 0 0 0 0 0 0 0 0 1 0
203 11 0 0 0 0 0 0 0 0 0 0 1
204 11 0 0 0 0 0 0 0 0 0 0 0
205 11 1 0 0 0 0 0 0 0 0 0 0
206 11 0 1 0 0 0 0 0 0 0 0 0
207 11 0 0 1 0 0 0 0 0 0 0 0
208 11 0 0 0 1 0 0 0 0 0 0 0
209 11 0 0 0 0 1 0 0 0 0 0 0
210 11 0 0 0 0 0 1 0 0 0 0 0
211 11 0 0 0 0 0 0 1 0 0 0 0
212 11 0 0 0 0 0 0 0 1 0 0 0
213 11 0 0 0 0 0 0 0 0 1 0 0
214 11 0 0 0 0 0 0 0 0 0 1 0
215 11 0 0 0 0 0 0 0 0 0 0 1
216 11 0 0 0 0 0 0 0 0 0 0 0
217 11 1 0 0 0 0 0 0 0 0 0 0
218 11 0 1 0 0 0 0 0 0 0 0 0
219 11 0 0 1 0 0 0 0 0 0 0 0
220 11 0 0 0 1 0 0 0 0 0 0 0
221 11 0 0 0 0 1 0 0 0 0 0 0
222 11 0 0 0 0 0 1 0 0 0 0 0
223 11 0 0 0 0 0 0 1 0 0 0 0
224 11 0 0 0 0 0 0 0 1 0 0 0
225 11 0 0 0 0 0 0 0 0 1 0 0
226 11 0 0 0 0 0 0 0 0 0 1 0
227 11 0 0 0 0 0 0 0 0 0 0 1
228 11 0 0 0 0 0 0 0 0 0 0 0
229 11 1 0 0 0 0 0 0 0 0 0 0
230 11 0 1 0 0 0 0 0 0 0 0 0
231 11 0 0 1 0 0 0 0 0 0 0 0
232 11 0 0 0 1 0 0 0 0 0 0 0
233 11 0 0 0 0 1 0 0 0 0 0 0
234 11 0 0 0 0 0 1 0 0 0 0 0
235 11 0 0 0 0 0 0 1 0 0 0 0
236 11 0 0 0 0 0 0 0 1 0 0 0
237 11 0 0 0 0 0 0 0 0 1 0 0
238 11 0 0 0 0 0 0 0 0 0 1 0
239 11 0 0 0 0 0 0 0 0 0 0 1
240 11 0 0 0 0 0 0 0 0 0 0 0
241 11 1 0 0 0 0 0 0 0 0 0 0
242 11 0 1 0 0 0 0 0 0 0 0 0
243 11 0 0 1 0 0 0 0 0 0 0 0
244 11 0 0 0 1 0 0 0 0 0 0 0
245 11 0 0 0 0 1 0 0 0 0 0 0
246 11 0 0 0 0 0 1 0 0 0 0 0
247 11 0 0 0 0 0 0 1 0 0 0 0
248 11 0 0 0 0 0 0 0 1 0 0 0
249 11 0 0 0 0 0 0 0 0 1 0 0
250 11 0 0 0 0 0 0 0 0 0 1 0
251 11 0 0 0 0 0 0 0 0 0 0 1
252 11 0 0 0 0 0 0 0 0 0 0 0
253 11 1 0 0 0 0 0 0 0 0 0 0
254 11 0 1 0 0 0 0 0 0 0 0 0
255 11 0 0 1 0 0 0 0 0 0 0 0
256 11 0 0 0 1 0 0 0 0 0 0 0
257 11 0 0 0 0 1 0 0 0 0 0 0
258 11 0 0 0 0 0 1 0 0 0 0 0
259 11 0 0 0 0 0 0 1 0 0 0 0
260 11 0 0 0 0 0 0 0 1 0 0 0
261 11 0 0 0 0 0 0 0 0 1 0 0
262 11 0 0 0 0 0 0 0 0 0 1 0
263 11 0 0 0 0 0 0 0 0 0 0 1
264 11 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Software Happiness Depression
8.39956 0.02986 0.04789 0.56480 0.08481 -0.02627
Sport1 Sport2 Month M1 M2 M3
0.03567 -0.04699 -0.40474 0.26762 0.30555 0.52864
M4 M5 M6 M7 M8 M9
0.48116 -0.09501 0.22482 0.43191 -0.28801 -0.01357
M10 M11
-0.16161 -0.32734
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7972 -1.3287 0.3752 1.1548 4.7617
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 8.39956 2.69965 3.111 0.00208 **
Connected 0.02986 0.03642 0.820 0.41306
Separate 0.04789 0.03623 1.322 0.18740
Software 0.56480 0.05471 10.323 < 2e-16 ***
Happiness 0.08481 0.05896 1.438 0.15161
Depression -0.02627 0.04411 -0.595 0.55206
Sport1 0.03567 0.03866 0.923 0.35703
Sport2 -0.04699 0.05746 -0.818 0.41430
Month -0.40474 0.16248 -2.491 0.01341 *
M1 0.26762 0.57880 0.462 0.64424
M2 0.30555 0.57719 0.529 0.59703
M3 0.52864 0.57517 0.919 0.35895
M4 0.48116 0.57277 0.840 0.40170
M5 -0.09501 0.57941 -0.164 0.86989
M6 0.22482 0.58368 0.385 0.70044
M7 0.43191 0.57485 0.751 0.45316
M8 -0.28801 0.57204 -0.503 0.61508
M9 -0.01357 0.57173 -0.024 0.98109
M10 -0.16161 0.57608 -0.281 0.77931
M11 -0.32734 0.57197 -0.572 0.56764
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.884 on 244 degrees of freedom
Multiple R-squared: 0.4543, Adjusted R-squared: 0.4118
F-statistic: 10.69 on 19 and 244 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,] 0.401863086 0.80372617 0.5981369
[2,] 0.781489882 0.43702024 0.2185101
[3,] 0.859235099 0.28152980 0.1407649
[4,] 0.829169802 0.34166040 0.1708302
[5,] 0.799357144 0.40128571 0.2006429
[6,] 0.751174180 0.49765164 0.2488258
[7,] 0.700382681 0.59923464 0.2996173
[8,] 0.724740375 0.55051925 0.2752596
[9,] 0.769170261 0.46165948 0.2308297
[10,] 0.706840955 0.58631809 0.2931590
[11,] 0.642444226 0.71511155 0.3575558
[12,] 0.569307294 0.86138541 0.4306927
[13,] 0.514305627 0.97138875 0.4856944
[14,] 0.598570319 0.80285936 0.4014297
[15,] 0.543593690 0.91281262 0.4564063
[16,] 0.503792730 0.99241454 0.4962073
[17,] 0.442825924 0.88565185 0.5571741
[18,] 0.379906721 0.75981344 0.6200933
[19,] 0.323665453 0.64733091 0.6763345
[20,] 0.273253468 0.54650694 0.7267465
[21,] 0.241763897 0.48352779 0.7582361
[22,] 0.198505814 0.39701163 0.8014942
[23,] 0.164418485 0.32883697 0.8355815
[24,] 0.306762303 0.61352461 0.6932377
[25,] 0.547798326 0.90440335 0.4522017
[26,] 0.515410206 0.96917959 0.4845898
[27,] 0.550911742 0.89817652 0.4490883
[28,] 0.525350911 0.94929818 0.4746491
[29,] 0.506837919 0.98632416 0.4931621
[30,] 0.456114321 0.91222864 0.5438857
[31,] 0.464201387 0.92840277 0.5357986
[32,] 0.421740072 0.84348014 0.5782599
[33,] 0.503066533 0.99386693 0.4969335
[34,] 0.457952215 0.91590443 0.5420478
[35,] 0.407396087 0.81479217 0.5926039
[36,] 0.410249788 0.82049958 0.5897502
[37,] 0.367855444 0.73571089 0.6321446
[38,] 0.382112447 0.76422489 0.6178876
[39,] 0.358333632 0.71666726 0.6416664
[40,] 0.327670645 0.65534129 0.6723294
[41,] 0.300051597 0.60010319 0.6999484
[42,] 0.265490472 0.53098094 0.7345095
[43,] 0.232502665 0.46500533 0.7674973
[44,] 0.198892576 0.39778515 0.8011074
[45,] 0.178592839 0.35718568 0.8214072
[46,] 0.221706096 0.44341219 0.7782939
[47,] 0.364426126 0.72885225 0.6355739
[48,] 0.323087967 0.64617593 0.6769120
[49,] 0.483124998 0.96625000 0.5168750
[50,] 0.442239188 0.88447838 0.5577608
[51,] 0.437335200 0.87467040 0.5626648
[52,] 0.418940325 0.83788065 0.5810597
[53,] 0.393562138 0.78712428 0.6064379
[54,] 0.398692642 0.79738528 0.6013074
[55,] 0.362999962 0.72599992 0.6370000
[56,] 0.334548002 0.66909600 0.6654520
[57,] 0.365395884 0.73079177 0.6346041
[58,] 0.357529358 0.71505872 0.6424706
[59,] 0.352711813 0.70542363 0.6472882
[60,] 0.315467571 0.63093514 0.6845324
[61,] 0.279494366 0.55898873 0.7205056
[62,] 0.245023651 0.49004730 0.7549763
[63,] 0.233621369 0.46724274 0.7663786
[64,] 0.203341583 0.40668317 0.7966584
[65,] 0.175353817 0.35070763 0.8246462
[66,] 0.153851320 0.30770264 0.8461487
[67,] 0.133270058 0.26654012 0.8667299
[68,] 0.131700163 0.26340033 0.8682998
[69,] 0.114884993 0.22976999 0.8851150
[70,] 0.096885687 0.19377137 0.9031143
[71,] 0.081030121 0.16206024 0.9189699
[72,] 0.077291271 0.15458254 0.9227087
[73,] 0.072029138 0.14405828 0.9279709
[74,] 0.060554657 0.12110931 0.9394453
[75,] 0.059433675 0.11886735 0.9405663
[76,] 0.048391841 0.09678368 0.9516082
[77,] 0.041879169 0.08375834 0.9581208
[78,] 0.046762916 0.09352583 0.9532371
[79,] 0.040797882 0.08159576 0.9592021
[80,] 0.045147771 0.09029554 0.9548522
[81,] 0.038144196 0.07628839 0.9618558
[82,] 0.032889701 0.06577940 0.9671103
[83,] 0.033303156 0.06660631 0.9666968
[84,] 0.029007405 0.05801481 0.9709926
[85,] 0.023547957 0.04709591 0.9764520
[86,] 0.022606555 0.04521311 0.9773934
[87,] 0.018856398 0.03771280 0.9811436
[88,] 0.015573171 0.03114634 0.9844268
[89,] 0.012299113 0.02459823 0.9877009
[90,] 0.010757054 0.02151411 0.9892429
[91,] 0.010396937 0.02079387 0.9896031
[92,] 0.017475248 0.03495050 0.9825248
[93,] 0.015414082 0.03082816 0.9845859
[94,] 0.014770351 0.02954070 0.9852296
[95,] 0.013477999 0.02695600 0.9865220
[96,] 0.012408077 0.02481615 0.9875919
[97,] 0.010172569 0.02034514 0.9898274
[98,] 0.008934081 0.01786816 0.9910659
[99,] 0.006981368 0.01396274 0.9930186
[100,] 0.012440838 0.02488168 0.9875592
[101,] 0.012085494 0.02417099 0.9879145
[102,] 0.012794159 0.02558832 0.9872058
[103,] 0.010528210 0.02105642 0.9894718
[104,] 0.008475238 0.01695048 0.9915248
[105,] 0.006900249 0.01380050 0.9930998
[106,] 0.005953541 0.01190708 0.9940465
[107,] 0.007696845 0.01539369 0.9923032
[108,] 0.008961818 0.01792364 0.9910382
[109,] 0.013248457 0.02649691 0.9867515
[110,] 0.014173844 0.02834769 0.9858262
[111,] 0.016517012 0.03303402 0.9834830
[112,] 0.017038091 0.03407618 0.9829619
[113,] 0.014593282 0.02918656 0.9854067
[114,] 0.011518654 0.02303731 0.9884813
[115,] 0.009012627 0.01802525 0.9909874
[116,] 0.010845537 0.02169107 0.9891545
[117,] 0.010332365 0.02066473 0.9896676
[118,] 0.012400543 0.02480109 0.9875995
[119,] 0.024168432 0.04833686 0.9758316
[120,] 0.022313275 0.04462655 0.9776867
[121,] 0.018582662 0.03716532 0.9814173
[122,] 0.017942273 0.03588455 0.9820577
[123,] 0.041964965 0.08392993 0.9580350
[124,] 0.043104732 0.08620946 0.9568953
[125,] 0.057380004 0.11476001 0.9426200
[126,] 0.047937497 0.09587499 0.9520625
[127,] 0.039290472 0.07858094 0.9607095
[128,] 0.062827898 0.12565580 0.9371721
[129,] 0.062173519 0.12434704 0.9378265
[130,] 0.061763953 0.12352791 0.9382360
[131,] 0.115033111 0.23006622 0.8849669
[132,] 0.109260345 0.21852069 0.8907397
[133,] 0.111977344 0.22395469 0.8880227
[134,] 0.096112980 0.19222596 0.9038870
[135,] 0.085077730 0.17015546 0.9149223
[136,] 0.072649397 0.14529879 0.9273506
[137,] 0.062312761 0.12462552 0.9376872
[138,] 0.056018647 0.11203729 0.9439814
[139,] 0.045895846 0.09179169 0.9541042
[140,] 0.038151846 0.07630369 0.9618482
[141,] 0.031846116 0.06369223 0.9681539
[142,] 0.031407051 0.06281410 0.9685929
[143,] 0.029471913 0.05894383 0.9705281
[144,] 0.025598968 0.05119794 0.9744010
[145,] 0.020366055 0.04073211 0.9796339
[146,] 0.028134815 0.05626963 0.9718652
[147,] 0.030651643 0.06130329 0.9693484
[148,] 0.029687276 0.05937455 0.9703127
[149,] 0.025564274 0.05112855 0.9744357
[150,] 0.020584780 0.04116956 0.9794152
[151,] 0.019723195 0.03944639 0.9802768
[152,] 0.024383039 0.04876608 0.9756170
[153,] 0.032712747 0.06542549 0.9672873
[154,] 0.029906715 0.05981343 0.9700933
[155,] 0.025153467 0.05030693 0.9748465
[156,] 0.019802099 0.03960420 0.9801979
[157,] 0.016196334 0.03239267 0.9838037
[158,] 0.013430212 0.02686042 0.9865698
[159,] 0.010791180 0.02158236 0.9892088
[160,] 0.008401125 0.01680225 0.9915989
[161,] 0.007996332 0.01599266 0.9920037
[162,] 0.006229830 0.01245966 0.9937702
[163,] 0.176900693 0.35380139 0.8230993
[164,] 0.163037548 0.32607510 0.8369625
[165,] 0.184777005 0.36955401 0.8152230
[166,] 0.208690870 0.41738174 0.7913091
[167,] 0.179535676 0.35907135 0.8204643
[168,] 0.178797788 0.35759558 0.8212022
[169,] 0.179788549 0.35957710 0.8202115
[170,] 0.168504076 0.33700815 0.8314959
[171,] 0.176375636 0.35275127 0.8236244
[172,] 0.172624047 0.34524809 0.8273760
[173,] 0.174223514 0.34844703 0.8257765
[174,] 0.149251056 0.29850211 0.8507489
[175,] 0.172403279 0.34480656 0.8275967
[176,] 0.149327714 0.29865543 0.8506723
[177,] 0.145986148 0.29197230 0.8540139
[178,] 0.133080346 0.26616069 0.8669197
[179,] 0.150572722 0.30114544 0.8494273
[180,] 0.126989049 0.25397810 0.8730110
[181,] 0.161645285 0.32329057 0.8383547
[182,] 0.165998257 0.33199651 0.8340017
[183,] 0.161402276 0.32280455 0.8385977
[184,] 0.134036763 0.26807353 0.8659632
[185,] 0.114251355 0.22850271 0.8857486
[186,] 0.093178610 0.18635722 0.9068214
[187,] 0.134156300 0.26831260 0.8658437
[188,] 0.111103520 0.22220704 0.8888965
[189,] 0.115699436 0.23139887 0.8843006
[190,] 0.117124925 0.23424985 0.8828751
[191,] 0.158669325 0.31733865 0.8413307
[192,] 0.545094787 0.90981043 0.4549052
[193,] 0.528509082 0.94298184 0.4714909
[194,] 0.481091102 0.96218220 0.5189089
[195,] 0.494761822 0.98952364 0.5052382
[196,] 0.451968758 0.90393752 0.5480312
[197,] 0.423207154 0.84641431 0.5767928
[198,] 0.393362125 0.78672425 0.6066379
[199,] 0.608552301 0.78289540 0.3914477
[200,] 0.702704376 0.59459125 0.2972956
[201,] 0.661005649 0.67798870 0.3389944
[202,] 0.621610147 0.75677971 0.3783899
[203,] 0.592072197 0.81585561 0.4079278
[204,] 0.569067138 0.86186572 0.4309329
[205,] 0.665026470 0.66994706 0.3349735
[206,] 0.604363032 0.79127394 0.3956370
[207,] 0.533896906 0.93220619 0.4661031
[208,] 0.483195333 0.96639067 0.5168047
[209,] 0.431866959 0.86373392 0.5681330
[210,] 0.464926846 0.92985369 0.5350732
[211,] 0.411466917 0.82293383 0.5885331
[212,] 0.379647403 0.75929481 0.6203526
[213,] 0.346156102 0.69231220 0.6538439
[214,] 0.308590837 0.61718167 0.6914092
[215,] 0.433883353 0.86776671 0.5661166
[216,] 0.333025370 0.66605074 0.6669746
[217,] 0.266599689 0.53319938 0.7334003
[218,] 0.191604017 0.38320803 0.8083960
[219,] 0.106409705 0.21281941 0.8935903
> postscript(file="/var/wessaorg/rcomp/tmp/1xrb01383426154.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2lf0n1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3r1mg1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/42f0v1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5hj7k1383426154.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
-3.10551938 0.22553480 1.72427125 2.70091997 -2.14660437 -1.84913310
7 8 9 10 11 12
3.62278888 -1.56533000 -1.80698386 0.96337007 1.60822724 -0.01482221
13 14 15 16 17 18
0.51623508 0.37531714 -1.39207148 -0.74872393 0.70196517 3.53135556
19 20 21 22 23 24
2.33265526 0.90812698 0.80292269 1.29328184 2.99566564 1.14851257
25 26 27 28 29 30
0.72343904 0.66599160 0.55656694 -2.08999696 0.42222427 -0.13951216
31 32 33 34 35 36
-1.04874626 -0.41034121 -0.67222924 0.39742079 -1.17136084 -2.85861870
37 38 39 40 41 42
-3.20512245 -1.96472540 0.98704324 1.16056054 1.49162016 -1.95224805
43 44 45 46 47 48
2.14933867 0.20918335 -0.33816886 -4.29166559 -2.10851249 -0.08068675
49 50 51 52 53 54
0.15373897 -1.88192324 -1.56349178 -0.60853179 -2.94442905 0.14039537
55 56 57 58 59 60
-2.78550928 1.96726920 0.14533395 0.68044687 0.18995739 1.82129419
61 62 63 64 65 66
0.24489720 -0.02278317 -1.10038741 -1.27074800 0.75416298 1.15284432
67 68 69 70 71 72
1.69019163 4.15400054 -3.58900418 0.82234807 -2.57054530 -0.64925021
73 74 75 76 77 78
1.02517482 0.74537627 0.57251273 3.41209356 -0.05334478 1.66033335
79 80 81 82 83 84
-2.35150207 1.51842689 0.53433821 0.76061187 -0.11311296 0.39322136
85 86 87 88 89 90
1.87790771 -0.23510489 0.36776208 0.90628994 0.90696471 -1.85296515
91 92 93 94 95 96
0.05210216 0.63347518 0.12419343 -1.69048160 1.72506727 0.48594430
97 98 99 100 101 102
2.24779538 0.04459058 -0.76765941 -1.37514739 1.66184851 2.47341129
103 104 105 106 107 108
0.68168199 1.40824064 -1.60665626 1.65016916 0.74131091 1.85396679
109 110 111 112 113 114
-0.31786904 0.54729005 -0.40538875 1.66624998 -1.63795914 -2.67224549
115 116 117 118 119 120
1.56431580 -1.50301430 1.04354622 -1.50388383 0.91918311 -1.32185273
121 122 123 124 125 126
0.44974710 -3.05858387 -1.27751890 -1.20825845 -0.65863099 0.22003628
127 128 129 130 131 132
1.07002445 1.42152670 -2.60862945 2.25257219 -3.31913149 2.56814670
133 134 135 136 137 138
-2.39295272 -1.79537691 -0.53387796 0.48927383 0.74503115 -2.59145859
139 140 141 142 143 144
-1.36270188 -2.07152488 3.17389927 1.46905726 0.48144883 1.86553051
145 146 147 148 149 150
-3.47582634 2.20679027 -2.40382034 0.70604700 0.37507182 -3.11667860
151 152 153 154 155 156
-1.41836121 2.29064322 4.15617512 1.41810143 -2.02910332 0.48401446
157 158 159 160 161 162
1.00331745 0.82796986 0.89229232 -1.31920221 0.50281052 0.57669890
163 164 165 166 167 168
-0.56926861 1.31084412 1.61180088 -1.16212999 0.29163044 -2.92102534
169 170 171 172 173 174
-1.74664237 1.42305354 1.33845769 0.40406727 -1.42309888 -2.30520789
175 176 177 178 179 180
-3.08937197 0.91484911 0.11414258 -0.28143342 0.76369470 -1.16882610
181 182 183 184 185 186
0.93408431 -0.45337233 1.98924116 -0.39653821 -6.28651821 1.12335810
187 188 189 190 191 192
2.26562293 0.12066217 -0.31602097 0.98571256 -0.50750287 0.69188146
193 194 195 196 197 198
2.18851999 1.91920628 -1.01755452 -0.26312107 3.31347632 0.88620453
199 200 201 202 203 204
1.31903132 1.81363837 2.24521282 0.87389988 -2.05800013 -2.28941693
205 206 207 208 209 210
2.12163762 0.33881564 1.17397218 0.66324289 -2.32028200 0.89516386
211 212 213 214 215 216
-2.47297321 -3.34277766 1.00368392 3.24501673 1.88400466 1.04825822
217 218 219 220 221 222
2.16886386 -0.08349894 0.66923185 -0.53520259 -1.42301381 -0.33323238
223 224 225 226 227 228
0.42254795 -0.73758649 0.19318822 -3.56960139 0.57549388 1.00330913
229 230 231 232 233 234
-1.34921037 -1.09574502 1.29868135 -3.54756173 4.76168884 2.05280562
235 236 237 238 239 240
-1.19669944 -1.90886660 -6.79718620 -0.92561334 2.14452766 -1.56132750
241 242 243 244 245 246
-0.47614959 -1.65963590 0.63057482 1.48471651 1.57084939 -0.12834291
247 248 249 250 251 252
0.79187948 -2.14029184 1.34578077 0.46760497 -0.55571665 -1.64900715
253 254 255 256 257 258
0.41393374 2.93081364 -1.73883707 -0.23042916 1.68616739 2.22841716
259 260 261 262 263 264
-1.66704656 -4.99115348 1.24066094 -3.85480453 0.11277433 1.15075394
> postscript(file="/var/wessaorg/rcomp/tmp/66b041383426154.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 -3.10551938 NA
1 0.22553480 -3.10551938
2 1.72427125 0.22553480
3 2.70091997 1.72427125
4 -2.14660437 2.70091997
5 -1.84913310 -2.14660437
6 3.62278888 -1.84913310
7 -1.56533000 3.62278888
8 -1.80698386 -1.56533000
9 0.96337007 -1.80698386
10 1.60822724 0.96337007
11 -0.01482221 1.60822724
12 0.51623508 -0.01482221
13 0.37531714 0.51623508
14 -1.39207148 0.37531714
15 -0.74872393 -1.39207148
16 0.70196517 -0.74872393
17 3.53135556 0.70196517
18 2.33265526 3.53135556
19 0.90812698 2.33265526
20 0.80292269 0.90812698
21 1.29328184 0.80292269
22 2.99566564 1.29328184
23 1.14851257 2.99566564
24 0.72343904 1.14851257
25 0.66599160 0.72343904
26 0.55656694 0.66599160
27 -2.08999696 0.55656694
28 0.42222427 -2.08999696
29 -0.13951216 0.42222427
30 -1.04874626 -0.13951216
31 -0.41034121 -1.04874626
32 -0.67222924 -0.41034121
33 0.39742079 -0.67222924
34 -1.17136084 0.39742079
35 -2.85861870 -1.17136084
36 -3.20512245 -2.85861870
37 -1.96472540 -3.20512245
38 0.98704324 -1.96472540
39 1.16056054 0.98704324
40 1.49162016 1.16056054
41 -1.95224805 1.49162016
42 2.14933867 -1.95224805
43 0.20918335 2.14933867
44 -0.33816886 0.20918335
45 -4.29166559 -0.33816886
46 -2.10851249 -4.29166559
47 -0.08068675 -2.10851249
48 0.15373897 -0.08068675
49 -1.88192324 0.15373897
50 -1.56349178 -1.88192324
51 -0.60853179 -1.56349178
52 -2.94442905 -0.60853179
53 0.14039537 -2.94442905
54 -2.78550928 0.14039537
55 1.96726920 -2.78550928
56 0.14533395 1.96726920
57 0.68044687 0.14533395
58 0.18995739 0.68044687
59 1.82129419 0.18995739
60 0.24489720 1.82129419
61 -0.02278317 0.24489720
62 -1.10038741 -0.02278317
63 -1.27074800 -1.10038741
64 0.75416298 -1.27074800
65 1.15284432 0.75416298
66 1.69019163 1.15284432
67 4.15400054 1.69019163
68 -3.58900418 4.15400054
69 0.82234807 -3.58900418
70 -2.57054530 0.82234807
71 -0.64925021 -2.57054530
72 1.02517482 -0.64925021
73 0.74537627 1.02517482
74 0.57251273 0.74537627
75 3.41209356 0.57251273
76 -0.05334478 3.41209356
77 1.66033335 -0.05334478
78 -2.35150207 1.66033335
79 1.51842689 -2.35150207
80 0.53433821 1.51842689
81 0.76061187 0.53433821
82 -0.11311296 0.76061187
83 0.39322136 -0.11311296
84 1.87790771 0.39322136
85 -0.23510489 1.87790771
86 0.36776208 -0.23510489
87 0.90628994 0.36776208
88 0.90696471 0.90628994
89 -1.85296515 0.90696471
90 0.05210216 -1.85296515
91 0.63347518 0.05210216
92 0.12419343 0.63347518
93 -1.69048160 0.12419343
94 1.72506727 -1.69048160
95 0.48594430 1.72506727
96 2.24779538 0.48594430
97 0.04459058 2.24779538
98 -0.76765941 0.04459058
99 -1.37514739 -0.76765941
100 1.66184851 -1.37514739
101 2.47341129 1.66184851
102 0.68168199 2.47341129
103 1.40824064 0.68168199
104 -1.60665626 1.40824064
105 1.65016916 -1.60665626
106 0.74131091 1.65016916
107 1.85396679 0.74131091
108 -0.31786904 1.85396679
109 0.54729005 -0.31786904
110 -0.40538875 0.54729005
111 1.66624998 -0.40538875
112 -1.63795914 1.66624998
113 -2.67224549 -1.63795914
114 1.56431580 -2.67224549
115 -1.50301430 1.56431580
116 1.04354622 -1.50301430
117 -1.50388383 1.04354622
118 0.91918311 -1.50388383
119 -1.32185273 0.91918311
120 0.44974710 -1.32185273
121 -3.05858387 0.44974710
122 -1.27751890 -3.05858387
123 -1.20825845 -1.27751890
124 -0.65863099 -1.20825845
125 0.22003628 -0.65863099
126 1.07002445 0.22003628
127 1.42152670 1.07002445
128 -2.60862945 1.42152670
129 2.25257219 -2.60862945
130 -3.31913149 2.25257219
131 2.56814670 -3.31913149
132 -2.39295272 2.56814670
133 -1.79537691 -2.39295272
134 -0.53387796 -1.79537691
135 0.48927383 -0.53387796
136 0.74503115 0.48927383
137 -2.59145859 0.74503115
138 -1.36270188 -2.59145859
139 -2.07152488 -1.36270188
140 3.17389927 -2.07152488
141 1.46905726 3.17389927
142 0.48144883 1.46905726
143 1.86553051 0.48144883
144 -3.47582634 1.86553051
145 2.20679027 -3.47582634
146 -2.40382034 2.20679027
147 0.70604700 -2.40382034
148 0.37507182 0.70604700
149 -3.11667860 0.37507182
150 -1.41836121 -3.11667860
151 2.29064322 -1.41836121
152 4.15617512 2.29064322
153 1.41810143 4.15617512
154 -2.02910332 1.41810143
155 0.48401446 -2.02910332
156 1.00331745 0.48401446
157 0.82796986 1.00331745
158 0.89229232 0.82796986
159 -1.31920221 0.89229232
160 0.50281052 -1.31920221
161 0.57669890 0.50281052
162 -0.56926861 0.57669890
163 1.31084412 -0.56926861
164 1.61180088 1.31084412
165 -1.16212999 1.61180088
166 0.29163044 -1.16212999
167 -2.92102534 0.29163044
168 -1.74664237 -2.92102534
169 1.42305354 -1.74664237
170 1.33845769 1.42305354
171 0.40406727 1.33845769
172 -1.42309888 0.40406727
173 -2.30520789 -1.42309888
174 -3.08937197 -2.30520789
175 0.91484911 -3.08937197
176 0.11414258 0.91484911
177 -0.28143342 0.11414258
178 0.76369470 -0.28143342
179 -1.16882610 0.76369470
180 0.93408431 -1.16882610
181 -0.45337233 0.93408431
182 1.98924116 -0.45337233
183 -0.39653821 1.98924116
184 -6.28651821 -0.39653821
185 1.12335810 -6.28651821
186 2.26562293 1.12335810
187 0.12066217 2.26562293
188 -0.31602097 0.12066217
189 0.98571256 -0.31602097
190 -0.50750287 0.98571256
191 0.69188146 -0.50750287
192 2.18851999 0.69188146
193 1.91920628 2.18851999
194 -1.01755452 1.91920628
195 -0.26312107 -1.01755452
196 3.31347632 -0.26312107
197 0.88620453 3.31347632
198 1.31903132 0.88620453
199 1.81363837 1.31903132
200 2.24521282 1.81363837
201 0.87389988 2.24521282
202 -2.05800013 0.87389988
203 -2.28941693 -2.05800013
204 2.12163762 -2.28941693
205 0.33881564 2.12163762
206 1.17397218 0.33881564
207 0.66324289 1.17397218
208 -2.32028200 0.66324289
209 0.89516386 -2.32028200
210 -2.47297321 0.89516386
211 -3.34277766 -2.47297321
212 1.00368392 -3.34277766
213 3.24501673 1.00368392
214 1.88400466 3.24501673
215 1.04825822 1.88400466
216 2.16886386 1.04825822
217 -0.08349894 2.16886386
218 0.66923185 -0.08349894
219 -0.53520259 0.66923185
220 -1.42301381 -0.53520259
221 -0.33323238 -1.42301381
222 0.42254795 -0.33323238
223 -0.73758649 0.42254795
224 0.19318822 -0.73758649
225 -3.56960139 0.19318822
226 0.57549388 -3.56960139
227 1.00330913 0.57549388
228 -1.34921037 1.00330913
229 -1.09574502 -1.34921037
230 1.29868135 -1.09574502
231 -3.54756173 1.29868135
232 4.76168884 -3.54756173
233 2.05280562 4.76168884
234 -1.19669944 2.05280562
235 -1.90886660 -1.19669944
236 -6.79718620 -1.90886660
237 -0.92561334 -6.79718620
238 2.14452766 -0.92561334
239 -1.56132750 2.14452766
240 -0.47614959 -1.56132750
241 -1.65963590 -0.47614959
242 0.63057482 -1.65963590
243 1.48471651 0.63057482
244 1.57084939 1.48471651
245 -0.12834291 1.57084939
246 0.79187948 -0.12834291
247 -2.14029184 0.79187948
248 1.34578077 -2.14029184
249 0.46760497 1.34578077
250 -0.55571665 0.46760497
251 -1.64900715 -0.55571665
252 0.41393374 -1.64900715
253 2.93081364 0.41393374
254 -1.73883707 2.93081364
255 -0.23042916 -1.73883707
256 1.68616739 -0.23042916
257 2.22841716 1.68616739
258 -1.66704656 2.22841716
259 -4.99115348 -1.66704656
260 1.24066094 -4.99115348
261 -3.85480453 1.24066094
262 0.11277433 -3.85480453
263 1.15075394 0.11277433
264 NA 1.15075394
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.22553480 -3.10551938
[2,] 1.72427125 0.22553480
[3,] 2.70091997 1.72427125
[4,] -2.14660437 2.70091997
[5,] -1.84913310 -2.14660437
[6,] 3.62278888 -1.84913310
[7,] -1.56533000 3.62278888
[8,] -1.80698386 -1.56533000
[9,] 0.96337007 -1.80698386
[10,] 1.60822724 0.96337007
[11,] -0.01482221 1.60822724
[12,] 0.51623508 -0.01482221
[13,] 0.37531714 0.51623508
[14,] -1.39207148 0.37531714
[15,] -0.74872393 -1.39207148
[16,] 0.70196517 -0.74872393
[17,] 3.53135556 0.70196517
[18,] 2.33265526 3.53135556
[19,] 0.90812698 2.33265526
[20,] 0.80292269 0.90812698
[21,] 1.29328184 0.80292269
[22,] 2.99566564 1.29328184
[23,] 1.14851257 2.99566564
[24,] 0.72343904 1.14851257
[25,] 0.66599160 0.72343904
[26,] 0.55656694 0.66599160
[27,] -2.08999696 0.55656694
[28,] 0.42222427 -2.08999696
[29,] -0.13951216 0.42222427
[30,] -1.04874626 -0.13951216
[31,] -0.41034121 -1.04874626
[32,] -0.67222924 -0.41034121
[33,] 0.39742079 -0.67222924
[34,] -1.17136084 0.39742079
[35,] -2.85861870 -1.17136084
[36,] -3.20512245 -2.85861870
[37,] -1.96472540 -3.20512245
[38,] 0.98704324 -1.96472540
[39,] 1.16056054 0.98704324
[40,] 1.49162016 1.16056054
[41,] -1.95224805 1.49162016
[42,] 2.14933867 -1.95224805
[43,] 0.20918335 2.14933867
[44,] -0.33816886 0.20918335
[45,] -4.29166559 -0.33816886
[46,] -2.10851249 -4.29166559
[47,] -0.08068675 -2.10851249
[48,] 0.15373897 -0.08068675
[49,] -1.88192324 0.15373897
[50,] -1.56349178 -1.88192324
[51,] -0.60853179 -1.56349178
[52,] -2.94442905 -0.60853179
[53,] 0.14039537 -2.94442905
[54,] -2.78550928 0.14039537
[55,] 1.96726920 -2.78550928
[56,] 0.14533395 1.96726920
[57,] 0.68044687 0.14533395
[58,] 0.18995739 0.68044687
[59,] 1.82129419 0.18995739
[60,] 0.24489720 1.82129419
[61,] -0.02278317 0.24489720
[62,] -1.10038741 -0.02278317
[63,] -1.27074800 -1.10038741
[64,] 0.75416298 -1.27074800
[65,] 1.15284432 0.75416298
[66,] 1.69019163 1.15284432
[67,] 4.15400054 1.69019163
[68,] -3.58900418 4.15400054
[69,] 0.82234807 -3.58900418
[70,] -2.57054530 0.82234807
[71,] -0.64925021 -2.57054530
[72,] 1.02517482 -0.64925021
[73,] 0.74537627 1.02517482
[74,] 0.57251273 0.74537627
[75,] 3.41209356 0.57251273
[76,] -0.05334478 3.41209356
[77,] 1.66033335 -0.05334478
[78,] -2.35150207 1.66033335
[79,] 1.51842689 -2.35150207
[80,] 0.53433821 1.51842689
[81,] 0.76061187 0.53433821
[82,] -0.11311296 0.76061187
[83,] 0.39322136 -0.11311296
[84,] 1.87790771 0.39322136
[85,] -0.23510489 1.87790771
[86,] 0.36776208 -0.23510489
[87,] 0.90628994 0.36776208
[88,] 0.90696471 0.90628994
[89,] -1.85296515 0.90696471
[90,] 0.05210216 -1.85296515
[91,] 0.63347518 0.05210216
[92,] 0.12419343 0.63347518
[93,] -1.69048160 0.12419343
[94,] 1.72506727 -1.69048160
[95,] 0.48594430 1.72506727
[96,] 2.24779538 0.48594430
[97,] 0.04459058 2.24779538
[98,] -0.76765941 0.04459058
[99,] -1.37514739 -0.76765941
[100,] 1.66184851 -1.37514739
[101,] 2.47341129 1.66184851
[102,] 0.68168199 2.47341129
[103,] 1.40824064 0.68168199
[104,] -1.60665626 1.40824064
[105,] 1.65016916 -1.60665626
[106,] 0.74131091 1.65016916
[107,] 1.85396679 0.74131091
[108,] -0.31786904 1.85396679
[109,] 0.54729005 -0.31786904
[110,] -0.40538875 0.54729005
[111,] 1.66624998 -0.40538875
[112,] -1.63795914 1.66624998
[113,] -2.67224549 -1.63795914
[114,] 1.56431580 -2.67224549
[115,] -1.50301430 1.56431580
[116,] 1.04354622 -1.50301430
[117,] -1.50388383 1.04354622
[118,] 0.91918311 -1.50388383
[119,] -1.32185273 0.91918311
[120,] 0.44974710 -1.32185273
[121,] -3.05858387 0.44974710
[122,] -1.27751890 -3.05858387
[123,] -1.20825845 -1.27751890
[124,] -0.65863099 -1.20825845
[125,] 0.22003628 -0.65863099
[126,] 1.07002445 0.22003628
[127,] 1.42152670 1.07002445
[128,] -2.60862945 1.42152670
[129,] 2.25257219 -2.60862945
[130,] -3.31913149 2.25257219
[131,] 2.56814670 -3.31913149
[132,] -2.39295272 2.56814670
[133,] -1.79537691 -2.39295272
[134,] -0.53387796 -1.79537691
[135,] 0.48927383 -0.53387796
[136,] 0.74503115 0.48927383
[137,] -2.59145859 0.74503115
[138,] -1.36270188 -2.59145859
[139,] -2.07152488 -1.36270188
[140,] 3.17389927 -2.07152488
[141,] 1.46905726 3.17389927
[142,] 0.48144883 1.46905726
[143,] 1.86553051 0.48144883
[144,] -3.47582634 1.86553051
[145,] 2.20679027 -3.47582634
[146,] -2.40382034 2.20679027
[147,] 0.70604700 -2.40382034
[148,] 0.37507182 0.70604700
[149,] -3.11667860 0.37507182
[150,] -1.41836121 -3.11667860
[151,] 2.29064322 -1.41836121
[152,] 4.15617512 2.29064322
[153,] 1.41810143 4.15617512
[154,] -2.02910332 1.41810143
[155,] 0.48401446 -2.02910332
[156,] 1.00331745 0.48401446
[157,] 0.82796986 1.00331745
[158,] 0.89229232 0.82796986
[159,] -1.31920221 0.89229232
[160,] 0.50281052 -1.31920221
[161,] 0.57669890 0.50281052
[162,] -0.56926861 0.57669890
[163,] 1.31084412 -0.56926861
[164,] 1.61180088 1.31084412
[165,] -1.16212999 1.61180088
[166,] 0.29163044 -1.16212999
[167,] -2.92102534 0.29163044
[168,] -1.74664237 -2.92102534
[169,] 1.42305354 -1.74664237
[170,] 1.33845769 1.42305354
[171,] 0.40406727 1.33845769
[172,] -1.42309888 0.40406727
[173,] -2.30520789 -1.42309888
[174,] -3.08937197 -2.30520789
[175,] 0.91484911 -3.08937197
[176,] 0.11414258 0.91484911
[177,] -0.28143342 0.11414258
[178,] 0.76369470 -0.28143342
[179,] -1.16882610 0.76369470
[180,] 0.93408431 -1.16882610
[181,] -0.45337233 0.93408431
[182,] 1.98924116 -0.45337233
[183,] -0.39653821 1.98924116
[184,] -6.28651821 -0.39653821
[185,] 1.12335810 -6.28651821
[186,] 2.26562293 1.12335810
[187,] 0.12066217 2.26562293
[188,] -0.31602097 0.12066217
[189,] 0.98571256 -0.31602097
[190,] -0.50750287 0.98571256
[191,] 0.69188146 -0.50750287
[192,] 2.18851999 0.69188146
[193,] 1.91920628 2.18851999
[194,] -1.01755452 1.91920628
[195,] -0.26312107 -1.01755452
[196,] 3.31347632 -0.26312107
[197,] 0.88620453 3.31347632
[198,] 1.31903132 0.88620453
[199,] 1.81363837 1.31903132
[200,] 2.24521282 1.81363837
[201,] 0.87389988 2.24521282
[202,] -2.05800013 0.87389988
[203,] -2.28941693 -2.05800013
[204,] 2.12163762 -2.28941693
[205,] 0.33881564 2.12163762
[206,] 1.17397218 0.33881564
[207,] 0.66324289 1.17397218
[208,] -2.32028200 0.66324289
[209,] 0.89516386 -2.32028200
[210,] -2.47297321 0.89516386
[211,] -3.34277766 -2.47297321
[212,] 1.00368392 -3.34277766
[213,] 3.24501673 1.00368392
[214,] 1.88400466 3.24501673
[215,] 1.04825822 1.88400466
[216,] 2.16886386 1.04825822
[217,] -0.08349894 2.16886386
[218,] 0.66923185 -0.08349894
[219,] -0.53520259 0.66923185
[220,] -1.42301381 -0.53520259
[221,] -0.33323238 -1.42301381
[222,] 0.42254795 -0.33323238
[223,] -0.73758649 0.42254795
[224,] 0.19318822 -0.73758649
[225,] -3.56960139 0.19318822
[226,] 0.57549388 -3.56960139
[227,] 1.00330913 0.57549388
[228,] -1.34921037 1.00330913
[229,] -1.09574502 -1.34921037
[230,] 1.29868135 -1.09574502
[231,] -3.54756173 1.29868135
[232,] 4.76168884 -3.54756173
[233,] 2.05280562 4.76168884
[234,] -1.19669944 2.05280562
[235,] -1.90886660 -1.19669944
[236,] -6.79718620 -1.90886660
[237,] -0.92561334 -6.79718620
[238,] 2.14452766 -0.92561334
[239,] -1.56132750 2.14452766
[240,] -0.47614959 -1.56132750
[241,] -1.65963590 -0.47614959
[242,] 0.63057482 -1.65963590
[243,] 1.48471651 0.63057482
[244,] 1.57084939 1.48471651
[245,] -0.12834291 1.57084939
[246,] 0.79187948 -0.12834291
[247,] -2.14029184 0.79187948
[248,] 1.34578077 -2.14029184
[249,] 0.46760497 1.34578077
[250,] -0.55571665 0.46760497
[251,] -1.64900715 -0.55571665
[252,] 0.41393374 -1.64900715
[253,] 2.93081364 0.41393374
[254,] -1.73883707 2.93081364
[255,] -0.23042916 -1.73883707
[256,] 1.68616739 -0.23042916
[257,] 2.22841716 1.68616739
[258,] -1.66704656 2.22841716
[259,] -4.99115348 -1.66704656
[260,] 1.24066094 -4.99115348
[261,] -3.85480453 1.24066094
[262,] 0.11277433 -3.85480453
[263,] 1.15075394 0.11277433
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.22553480 -3.10551938
2 1.72427125 0.22553480
3 2.70091997 1.72427125
4 -2.14660437 2.70091997
5 -1.84913310 -2.14660437
6 3.62278888 -1.84913310
7 -1.56533000 3.62278888
8 -1.80698386 -1.56533000
9 0.96337007 -1.80698386
10 1.60822724 0.96337007
11 -0.01482221 1.60822724
12 0.51623508 -0.01482221
13 0.37531714 0.51623508
14 -1.39207148 0.37531714
15 -0.74872393 -1.39207148
16 0.70196517 -0.74872393
17 3.53135556 0.70196517
18 2.33265526 3.53135556
19 0.90812698 2.33265526
20 0.80292269 0.90812698
21 1.29328184 0.80292269
22 2.99566564 1.29328184
23 1.14851257 2.99566564
24 0.72343904 1.14851257
25 0.66599160 0.72343904
26 0.55656694 0.66599160
27 -2.08999696 0.55656694
28 0.42222427 -2.08999696
29 -0.13951216 0.42222427
30 -1.04874626 -0.13951216
31 -0.41034121 -1.04874626
32 -0.67222924 -0.41034121
33 0.39742079 -0.67222924
34 -1.17136084 0.39742079
35 -2.85861870 -1.17136084
36 -3.20512245 -2.85861870
37 -1.96472540 -3.20512245
38 0.98704324 -1.96472540
39 1.16056054 0.98704324
40 1.49162016 1.16056054
41 -1.95224805 1.49162016
42 2.14933867 -1.95224805
43 0.20918335 2.14933867
44 -0.33816886 0.20918335
45 -4.29166559 -0.33816886
46 -2.10851249 -4.29166559
47 -0.08068675 -2.10851249
48 0.15373897 -0.08068675
49 -1.88192324 0.15373897
50 -1.56349178 -1.88192324
51 -0.60853179 -1.56349178
52 -2.94442905 -0.60853179
53 0.14039537 -2.94442905
54 -2.78550928 0.14039537
55 1.96726920 -2.78550928
56 0.14533395 1.96726920
57 0.68044687 0.14533395
58 0.18995739 0.68044687
59 1.82129419 0.18995739
60 0.24489720 1.82129419
61 -0.02278317 0.24489720
62 -1.10038741 -0.02278317
63 -1.27074800 -1.10038741
64 0.75416298 -1.27074800
65 1.15284432 0.75416298
66 1.69019163 1.15284432
67 4.15400054 1.69019163
68 -3.58900418 4.15400054
69 0.82234807 -3.58900418
70 -2.57054530 0.82234807
71 -0.64925021 -2.57054530
72 1.02517482 -0.64925021
73 0.74537627 1.02517482
74 0.57251273 0.74537627
75 3.41209356 0.57251273
76 -0.05334478 3.41209356
77 1.66033335 -0.05334478
78 -2.35150207 1.66033335
79 1.51842689 -2.35150207
80 0.53433821 1.51842689
81 0.76061187 0.53433821
82 -0.11311296 0.76061187
83 0.39322136 -0.11311296
84 1.87790771 0.39322136
85 -0.23510489 1.87790771
86 0.36776208 -0.23510489
87 0.90628994 0.36776208
88 0.90696471 0.90628994
89 -1.85296515 0.90696471
90 0.05210216 -1.85296515
91 0.63347518 0.05210216
92 0.12419343 0.63347518
93 -1.69048160 0.12419343
94 1.72506727 -1.69048160
95 0.48594430 1.72506727
96 2.24779538 0.48594430
97 0.04459058 2.24779538
98 -0.76765941 0.04459058
99 -1.37514739 -0.76765941
100 1.66184851 -1.37514739
101 2.47341129 1.66184851
102 0.68168199 2.47341129
103 1.40824064 0.68168199
104 -1.60665626 1.40824064
105 1.65016916 -1.60665626
106 0.74131091 1.65016916
107 1.85396679 0.74131091
108 -0.31786904 1.85396679
109 0.54729005 -0.31786904
110 -0.40538875 0.54729005
111 1.66624998 -0.40538875
112 -1.63795914 1.66624998
113 -2.67224549 -1.63795914
114 1.56431580 -2.67224549
115 -1.50301430 1.56431580
116 1.04354622 -1.50301430
117 -1.50388383 1.04354622
118 0.91918311 -1.50388383
119 -1.32185273 0.91918311
120 0.44974710 -1.32185273
121 -3.05858387 0.44974710
122 -1.27751890 -3.05858387
123 -1.20825845 -1.27751890
124 -0.65863099 -1.20825845
125 0.22003628 -0.65863099
126 1.07002445 0.22003628
127 1.42152670 1.07002445
128 -2.60862945 1.42152670
129 2.25257219 -2.60862945
130 -3.31913149 2.25257219
131 2.56814670 -3.31913149
132 -2.39295272 2.56814670
133 -1.79537691 -2.39295272
134 -0.53387796 -1.79537691
135 0.48927383 -0.53387796
136 0.74503115 0.48927383
137 -2.59145859 0.74503115
138 -1.36270188 -2.59145859
139 -2.07152488 -1.36270188
140 3.17389927 -2.07152488
141 1.46905726 3.17389927
142 0.48144883 1.46905726
143 1.86553051 0.48144883
144 -3.47582634 1.86553051
145 2.20679027 -3.47582634
146 -2.40382034 2.20679027
147 0.70604700 -2.40382034
148 0.37507182 0.70604700
149 -3.11667860 0.37507182
150 -1.41836121 -3.11667860
151 2.29064322 -1.41836121
152 4.15617512 2.29064322
153 1.41810143 4.15617512
154 -2.02910332 1.41810143
155 0.48401446 -2.02910332
156 1.00331745 0.48401446
157 0.82796986 1.00331745
158 0.89229232 0.82796986
159 -1.31920221 0.89229232
160 0.50281052 -1.31920221
161 0.57669890 0.50281052
162 -0.56926861 0.57669890
163 1.31084412 -0.56926861
164 1.61180088 1.31084412
165 -1.16212999 1.61180088
166 0.29163044 -1.16212999
167 -2.92102534 0.29163044
168 -1.74664237 -2.92102534
169 1.42305354 -1.74664237
170 1.33845769 1.42305354
171 0.40406727 1.33845769
172 -1.42309888 0.40406727
173 -2.30520789 -1.42309888
174 -3.08937197 -2.30520789
175 0.91484911 -3.08937197
176 0.11414258 0.91484911
177 -0.28143342 0.11414258
178 0.76369470 -0.28143342
179 -1.16882610 0.76369470
180 0.93408431 -1.16882610
181 -0.45337233 0.93408431
182 1.98924116 -0.45337233
183 -0.39653821 1.98924116
184 -6.28651821 -0.39653821
185 1.12335810 -6.28651821
186 2.26562293 1.12335810
187 0.12066217 2.26562293
188 -0.31602097 0.12066217
189 0.98571256 -0.31602097
190 -0.50750287 0.98571256
191 0.69188146 -0.50750287
192 2.18851999 0.69188146
193 1.91920628 2.18851999
194 -1.01755452 1.91920628
195 -0.26312107 -1.01755452
196 3.31347632 -0.26312107
197 0.88620453 3.31347632
198 1.31903132 0.88620453
199 1.81363837 1.31903132
200 2.24521282 1.81363837
201 0.87389988 2.24521282
202 -2.05800013 0.87389988
203 -2.28941693 -2.05800013
204 2.12163762 -2.28941693
205 0.33881564 2.12163762
206 1.17397218 0.33881564
207 0.66324289 1.17397218
208 -2.32028200 0.66324289
209 0.89516386 -2.32028200
210 -2.47297321 0.89516386
211 -3.34277766 -2.47297321
212 1.00368392 -3.34277766
213 3.24501673 1.00368392
214 1.88400466 3.24501673
215 1.04825822 1.88400466
216 2.16886386 1.04825822
217 -0.08349894 2.16886386
218 0.66923185 -0.08349894
219 -0.53520259 0.66923185
220 -1.42301381 -0.53520259
221 -0.33323238 -1.42301381
222 0.42254795 -0.33323238
223 -0.73758649 0.42254795
224 0.19318822 -0.73758649
225 -3.56960139 0.19318822
226 0.57549388 -3.56960139
227 1.00330913 0.57549388
228 -1.34921037 1.00330913
229 -1.09574502 -1.34921037
230 1.29868135 -1.09574502
231 -3.54756173 1.29868135
232 4.76168884 -3.54756173
233 2.05280562 4.76168884
234 -1.19669944 2.05280562
235 -1.90886660 -1.19669944
236 -6.79718620 -1.90886660
237 -0.92561334 -6.79718620
238 2.14452766 -0.92561334
239 -1.56132750 2.14452766
240 -0.47614959 -1.56132750
241 -1.65963590 -0.47614959
242 0.63057482 -1.65963590
243 1.48471651 0.63057482
244 1.57084939 1.48471651
245 -0.12834291 1.57084939
246 0.79187948 -0.12834291
247 -2.14029184 0.79187948
248 1.34578077 -2.14029184
249 0.46760497 1.34578077
250 -0.55571665 0.46760497
251 -1.64900715 -0.55571665
252 0.41393374 -1.64900715
253 2.93081364 0.41393374
254 -1.73883707 2.93081364
255 -0.23042916 -1.73883707
256 1.68616739 -0.23042916
257 2.22841716 1.68616739
258 -1.66704656 2.22841716
259 -4.99115348 -1.66704656
260 1.24066094 -4.99115348
261 -3.85480453 1.24066094
262 0.11277433 -3.85480453
263 1.15075394 0.11277433
> 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/7dhnc1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8csi91383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/943qv1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10pxcf1383426154.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11tkhi1383426154.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/126gw11383426154.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/13olba1383426155.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/14ri4p1383426155.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/15yf961383426155.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/16uu3w1383426155.tab")
+ }
>
> try(system("convert tmp/1xrb01383426154.ps tmp/1xrb01383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lf0n1383426154.ps tmp/2lf0n1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/3r1mg1383426154.ps tmp/3r1mg1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/42f0v1383426154.ps tmp/42f0v1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hj7k1383426154.ps tmp/5hj7k1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/66b041383426154.ps tmp/66b041383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/7dhnc1383426154.ps tmp/7dhnc1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/8csi91383426154.ps tmp/8csi91383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/943qv1383426154.ps tmp/943qv1383426154.png",intern=TRUE))
character(0)
> try(system("convert tmp/10pxcf1383426154.ps tmp/10pxcf1383426154.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
18.901 3.085 21.962