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
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+ ,11
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+ ,11
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+ ,41
+ ,11
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+ ,7
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+ ,14
+ ,9
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+ ,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 = '5'
> par3 <- 'No Linear Trend'
> par2 <- 'Include Monthly Dummies'
> par1 <- '5'
> #'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 Connected Separate Learning Software Depression Sport1 Sport2
1 14 41 38 13 12 12.0 53 32
2 18 39 32 16 11 11.0 83 51
3 11 30 35 19 15 14.0 66 42
4 12 31 33 15 6 12.0 67 41
5 16 34 37 14 13 21.0 76 46
6 18 35 29 13 10 12.0 78 47
7 14 39 31 19 12 22.0 53 37
8 14 34 36 15 14 11.0 80 49
9 15 36 35 14 12 10.0 74 45
10 15 37 38 15 9 13.0 76 47
11 17 38 31 16 10 10.0 79 49
12 19 36 34 16 12 8.0 54 33
13 10 38 35 16 12 15.0 67 42
14 16 39 38 16 11 14.0 54 33
15 18 33 37 17 15 10.0 87 53
16 14 32 33 15 12 14.0 58 36
17 14 36 32 15 10 14.0 75 45
18 17 38 38 20 12 11.0 88 54
19 14 39 38 18 11 10.0 64 41
20 16 32 32 16 12 13.0 57 36
21 18 32 33 16 11 9.5 66 41
22 11 31 31 16 12 14.0 68 44
23 14 39 38 19 13 12.0 54 33
24 12 37 39 16 11 14.0 56 37
25 17 39 32 17 12 11.0 86 52
26 9 41 32 17 13 9.0 80 47
27 16 36 35 16 10 11.0 76 43
28 14 33 37 15 14 15.0 69 44
29 15 33 33 16 12 14.0 78 45
30 11 34 33 14 10 13.0 67 44
31 16 31 31 15 12 9.0 80 49
32 13 27 32 12 8 15.0 54 33
33 17 37 31 14 10 10.0 71 43
34 15 34 37 16 12 11.0 84 54
35 14 34 30 14 12 13.0 74 42
36 16 32 33 10 7 8.0 71 44
37 9 29 31 10 9 20.0 63 37
38 15 36 33 14 12 12.0 71 43
39 17 29 31 16 10 10.0 76 46
40 13 35 33 16 10 10.0 69 42
41 15 37 32 16 10 9.0 74 45
42 16 34 33 14 12 14.0 75 44
43 16 38 32 20 15 8.0 54 33
44 12 35 33 14 10 14.0 52 31
45 15 38 28 14 10 11.0 69 42
46 11 37 35 11 12 13.0 68 40
47 15 38 39 14 13 9.0 65 43
48 15 33 34 15 11 11.0 75 46
49 17 36 38 16 11 15.0 74 42
50 13 38 32 14 12 11.0 75 45
51 16 32 38 16 14 10.0 72 44
52 14 32 30 14 10 14.0 67 40
53 11 32 33 12 12 18.0 63 37
54 12 34 38 16 13 14.0 62 46
55 12 32 32 9 5 11.0 63 36
56 15 37 35 14 6 14.5 76 47
57 16 39 34 16 12 13.0 74 45
58 15 29 34 16 12 9.0 67 42
59 12 37 36 15 11 10.0 73 43
60 12 35 34 16 10 15.0 70 43
61 8 30 28 12 7 20.0 53 32
62 13 38 34 16 12 12.0 77 45
63 11 34 35 16 14 12.0 80 48
64 14 31 35 14 11 14.0 52 31
65 15 34 31 16 12 13.0 54 33
66 10 35 37 17 13 11.0 80 49
67 11 36 35 18 14 17.0 66 42
68 12 30 27 18 11 12.0 73 41
69 15 39 40 12 12 13.0 63 38
70 15 35 37 16 12 14.0 69 42
71 14 38 36 10 8 13.0 67 44
72 16 31 38 14 11 15.0 54 33
73 15 34 39 18 14 13.0 81 48
74 15 38 41 18 14 10.0 69 40
75 13 34 27 16 12 11.0 84 50
76 12 39 30 17 9 19.0 80 49
77 17 37 37 16 13 13.0 70 43
78 13 34 31 16 11 17.0 69 44
79 15 28 31 13 12 13.0 77 47
80 13 37 27 16 12 9.0 54 33
81 15 33 36 16 12 11.0 79 46
82 15 35 37 16 12 9.0 71 45
83 16 37 33 15 12 12.0 73 43
84 15 32 34 15 11 12.0 72 44
85 14 33 31 16 10 13.0 77 47
86 15 38 39 14 9 13.0 75 45
87 14 33 34 16 12 12.0 69 42
88 13 29 32 16 12 15.0 54 33
89 7 33 33 15 12 22.0 70 43
90 17 31 36 12 9 13.0 73 46
91 13 36 32 17 15 15.0 54 33
92 15 35 41 16 12 13.0 77 46
93 14 32 28 15 12 15.0 82 48
94 13 29 30 13 12 12.5 80 47
95 16 39 36 16 10 11.0 80 47
96 12 37 35 16 13 16.0 69 43
97 14 35 31 16 9 11.0 78 46
98 17 37 34 16 12 11.0 81 48
99 15 32 36 14 10 10.0 76 46
100 17 38 36 16 14 10.0 76 45
101 12 37 35 16 11 16.0 73 45
102 16 36 37 20 15 12.0 85 52
103 11 32 28 15 11 11.0 66 42
104 15 33 39 16 11 16.0 79 47
105 9 40 32 13 12 19.0 68 41
106 16 38 35 17 12 11.0 76 47
107 15 41 39 16 12 16.0 71 43
108 10 36 35 16 11 15.0 54 33
109 10 43 42 12 7 24.0 46 30
110 15 30 34 16 12 14.0 85 52
111 11 31 33 16 14 15.0 74 44
112 13 32 41 17 11 11.0 88 55
113 14 32 33 13 11 15.0 38 11
114 18 37 34 12 10 12.0 76 47
115 16 37 32 18 13 10.0 86 53
116 14 33 40 14 13 14.0 54 33
117 14 34 40 14 8 13.0 67 44
118 14 33 35 13 11 9.0 69 42
119 14 38 36 16 12 15.0 90 55
120 12 33 37 13 11 15.0 54 33
121 14 31 27 16 13 14.0 76 46
122 15 38 39 13 12 11.0 89 54
123 15 37 38 16 14 8.0 76 47
124 15 36 31 15 13 11.0 73 45
125 13 31 33 16 15 11.0 79 47
126 17 39 32 15 10 8.0 90 55
127 17 44 39 17 11 10.0 74 44
128 19 33 36 15 9 11.0 81 53
129 15 35 33 12 11 13.0 72 44
130 13 32 33 16 10 11.0 71 42
131 9 28 32 10 11 20.0 66 40
132 15 40 37 16 8 10.0 77 46
133 15 27 30 12 11 15.0 65 40
134 15 37 38 14 12 12.0 74 46
135 16 32 29 15 12 14.0 85 53
136 11 28 22 13 9 23.0 54 33
137 14 34 35 15 11 14.0 63 42
138 11 30 35 11 10 16.0 54 35
139 15 35 34 12 8 11.0 64 40
140 13 31 35 11 9 12.0 69 41
141 15 32 34 16 8 10.0 54 33
142 16 30 37 15 9 14.0 84 51
143 14 30 35 17 15 12.0 86 53
144 15 31 23 16 11 12.0 77 46
145 16 40 31 10 8 11.0 89 55
146 16 32 27 18 13 12.0 76 47
147 11 36 36 13 12 13.0 60 38
148 12 32 31 16 12 11.0 75 46
149 9 35 32 13 9 19.0 73 46
150 16 38 39 10 7 12.0 85 53
151 13 42 37 15 13 17.0 79 47
152 16 34 38 16 9 9.0 71 41
153 12 35 39 16 6 12.0 72 44
154 9 38 34 14 8 19.0 69 43
155 13 33 31 10 8 18.0 78 51
156 13 36 32 17 15 15.0 54 33
157 14 32 37 13 6 14.0 69 43
158 19 33 36 15 9 11.0 81 53
159 13 34 32 16 11 9.0 84 51
160 12 32 38 12 8 18.0 84 50
161 13 34 36 13 8 16.0 69 46
162 10 27 26 13 10 24.0 66 43
163 14 31 26 12 8 14.0 81 47
164 16 38 33 17 14 20.0 82 50
165 10 34 39 15 10 18.0 72 43
166 11 24 30 10 8 23.0 54 33
167 14 30 33 14 11 12.0 78 48
168 12 26 25 11 12 14.0 74 44
169 9 34 38 13 12 16.0 82 50
170 9 27 37 16 12 18.0 73 41
171 11 37 31 12 5 20.0 55 34
172 16 36 37 16 12 12.0 72 44
173 9 41 35 12 10 12.0 78 47
174 13 29 25 9 7 17.0 59 35
175 16 36 28 12 12 13.0 72 44
176 13 32 35 15 11 9.0 78 44
177 9 37 33 12 8 16.0 68 43
178 12 30 30 12 9 18.0 69 41
179 16 31 31 14 10 10.0 67 41
180 11 38 37 12 9 14.0 74 42
181 14 36 36 16 12 11.0 54 33
182 13 35 30 11 6 9.0 67 41
183 15 31 36 19 15 11.0 70 44
184 14 38 32 15 12 10.0 80 48
185 16 22 28 8 12 11.0 89 55
186 13 32 36 16 12 19.0 76 44
187 14 36 34 17 11 14.0 74 43
188 15 39 31 12 7 12.0 87 52
189 13 28 28 11 7 14.0 54 30
190 11 32 36 11 5 21.0 61 39
191 11 32 36 14 12 13.0 38 11
192 14 38 40 16 12 10.0 75 44
193 15 32 33 12 3 15.0 69 42
194 11 35 37 16 11 16.0 62 41
195 15 32 32 13 10 14.0 72 44
196 12 37 38 15 12 12.0 70 44
197 14 34 31 16 9 19.0 79 48
198 14 33 37 16 12 15.0 87 53
199 8 33 33 14 9 19.0 62 37
200 13 26 32 16 12 13.0 77 44
201 9 30 30 16 12 17.0 69 44
202 15 24 30 14 10 12.0 69 40
203 17 34 31 11 9 11.0 75 42
204 13 34 32 12 12 14.0 54 35
205 15 33 34 15 8 11.0 72 43
206 15 34 36 15 11 13.0 74 45
207 14 35 37 16 11 12.0 85 55
208 16 35 36 16 12 15.0 52 31
209 13 36 33 11 10 14.0 70 44
210 16 34 33 15 10 12.0 84 50
211 9 34 33 12 12 17.0 64 40
212 16 41 44 12 12 11.0 84 53
213 11 32 39 15 11 18.0 87 54
214 10 30 32 15 8 13.0 79 49
215 11 35 35 16 12 17.0 67 40
216 15 28 25 14 10 13.0 65 41
217 17 33 35 17 11 11.0 85 52
218 14 39 34 14 10 12.0 83 52
219 8 36 35 13 8 22.0 61 36
220 15 36 39 15 12 14.0 82 52
221 11 35 33 13 12 12.0 76 46
222 16 38 36 14 10 12.0 58 31
223 10 33 32 15 12 17.0 72 44
224 15 31 32 12 9 9.0 72 44
225 9 34 36 13 9 21.0 38 11
226 16 32 36 8 6 10.0 78 46
227 19 31 32 14 10 11.0 54 33
228 12 33 34 14 9 12.0 63 34
229 8 34 33 11 9 23.0 66 42
230 11 34 35 12 9 13.0 70 43
231 14 34 30 13 6 12.0 71 43
232 9 33 38 10 10 16.0 67 44
233 15 32 34 16 6 9.0 58 36
234 13 41 33 18 14 17.0 72 46
235 16 34 32 13 10 9.0 72 44
236 11 36 31 11 10 14.0 70 43
237 12 37 30 4 6 17.0 76 50
238 13 36 27 13 12 13.0 50 33
239 10 29 31 16 12 11.0 72 43
240 11 37 30 10 7 12.0 72 44
241 12 27 32 12 8 10.0 88 53
242 8 35 35 12 11 19.0 53 34
243 12 28 28 10 3 16.0 58 35
244 12 35 33 13 6 16.0 66 40
245 15 37 31 15 10 14.0 82 53
246 11 29 35 12 8 20.0 69 42
247 13 32 35 14 9 15.0 68 43
248 14 36 32 10 9 23.0 44 29
249 10 19 21 12 8 20.0 56 36
250 12 21 20 12 9 16.0 53 30
251 15 31 34 11 7 14.0 70 42
252 13 33 32 10 7 17.0 78 47
253 13 36 34 12 6 11.0 71 44
254 13 33 32 16 9 13.0 72 45
255 12 37 33 12 10 17.0 68 44
256 12 34 33 14 11 15.0 67 43
257 9 35 37 16 12 21.0 75 43
258 9 31 32 14 8 18.0 62 40
259 15 37 34 13 11 15.0 67 41
260 10 35 30 4 3 8.0 83 52
261 14 27 30 15 11 12.0 64 38
262 15 34 38 11 12 12.0 68 41
263 7 40 36 11 7 22.0 62 39
264 14 29 32 14 9 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 Learning Software Depression
18.393283 0.001458 0.010191 0.099140 -0.021295 -0.369643
Sport1 Sport2 Month M1 M2 M3
0.015998 0.012711 -0.320030 0.074351 -0.070315 -0.240352
M4 M5 M6 M7 M8 M9
-0.010567 0.125367 0.829632 0.141309 0.485995 0.020226
M10 M11
-0.011904 0.328975
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.7877 -1.4755 0.2724 1.2475 5.0344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 18.393283 2.733409 6.729 1.21e-10 ***
Connected 0.001458 0.039430 0.037 0.9705
Separate 0.010191 0.039305 0.259 0.7956
Learning 0.099140 0.068926 1.438 0.1516
Software -0.021295 0.070896 -0.300 0.7642
Depression -0.369643 0.041453 -8.917 < 2e-16 ***
Sport1 0.015998 0.041856 0.382 0.7026
Sport2 0.012711 0.062203 0.204 0.8383
Month -0.320030 0.176714 -1.811 0.0714 .
M1 0.074351 0.626069 0.119 0.9056
M2 -0.070315 0.624406 -0.113 0.9104
M3 -0.240352 0.622767 -0.386 0.6999
M4 -0.010567 0.620183 -0.017 0.9864
M5 0.125367 0.626455 0.200 0.8416
M6 0.829632 0.629042 1.319 0.1884
M7 0.141309 0.622185 0.227 0.8205
M8 0.485995 0.618034 0.786 0.4324
M9 0.020226 0.618161 0.033 0.9739
M10 -0.011904 0.622970 -0.019 0.9848
M11 0.328975 0.618478 0.532 0.5953
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.037 on 244 degrees of freedom
Multiple R-squared: 0.3837, Adjusted R-squared: 0.3357
F-statistic: 7.995 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,] 0.982985341 0.0340293175 1.701466e-02
[2,] 0.976637216 0.0467255677 2.336278e-02
[3,] 0.956644112 0.0867117766 4.335589e-02
[4,] 0.999970737 0.0000585258 2.926290e-05
[5,] 0.999933032 0.0001339365 6.696823e-05
[6,] 0.999879033 0.0002419346 1.209673e-04
[7,] 0.999747130 0.0005057402 2.528701e-04
[8,] 0.999888444 0.0002231116 1.115558e-04
[9,] 0.999808437 0.0003831259 1.915630e-04
[10,] 0.999684699 0.0006306013 3.153007e-04
[11,] 0.999446208 0.0011075843 5.537922e-04
[12,] 0.999181101 0.0016377984 8.188992e-04
[13,] 0.999067989 0.0018640223 9.320111e-04
[14,] 0.998421359 0.0031572820 1.578641e-03
[15,] 0.998001158 0.0039976849 1.998842e-03
[16,] 0.997110640 0.0057787203 2.889360e-03
[17,] 0.996723380 0.0065532401 3.276620e-03
[18,] 0.995859711 0.0082805771 4.140289e-03
[19,] 0.994077529 0.0118449418 5.922471e-03
[20,] 0.991687764 0.0166244724 8.312236e-03
[21,] 0.987876429 0.0242471422 1.212357e-02
[22,] 0.986233090 0.0275338204 1.376691e-02
[23,] 0.982438120 0.0351237593 1.756188e-02
[24,] 0.981883547 0.0362329056 1.811645e-02
[25,] 0.974949879 0.0501002417 2.505012e-02
[26,] 0.968221833 0.0635563346 3.177817e-02
[27,] 0.975573157 0.0488536867 2.442684e-02
[28,] 0.969984522 0.0600309558 3.001548e-02
[29,] 0.961169404 0.0776611913 3.883060e-02
[30,] 0.951356165 0.0972876698 4.864383e-02
[31,] 0.945506932 0.1089861360 5.449307e-02
[32,] 0.936900162 0.1261996769 6.309984e-02
[33,] 0.935235125 0.1295297510 6.476488e-02
[34,] 0.918681091 0.1626378186 8.131891e-02
[35,] 0.904776061 0.1904478781 9.522394e-02
[36,] 0.890774896 0.2184502077 1.092251e-01
[37,] 0.920586634 0.1588267319 7.941337e-02
[38,] 0.931950570 0.1360988595 6.804943e-02
[39,] 0.934725761 0.1305484770 6.527424e-02
[40,] 0.927901488 0.1441970248 7.209851e-02
[41,] 0.957900583 0.0841988344 4.209942e-02
[42,] 0.949959538 0.1000809239 5.004046e-02
[43,] 0.940076301 0.1198473984 5.992370e-02
[44,] 0.953628361 0.0927432784 4.637164e-02
[45,] 0.953785527 0.0924289454 4.621447e-02
[46,] 0.949156969 0.1016860625 5.084303e-02
[47,] 0.957376775 0.0852464508 4.262323e-02
[48,] 0.970494835 0.0590103295 2.950516e-02
[49,] 0.971414963 0.0571700744 2.858504e-02
[50,] 0.980517616 0.0389647683 1.948238e-02
[51,] 0.976257400 0.0474852007 2.374260e-02
[52,] 0.969707992 0.0605840160 3.029201e-02
[53,] 0.963572442 0.0728551160 3.642756e-02
[54,] 0.954250089 0.0914998223 4.574991e-02
[55,] 0.960445537 0.0791089265 3.955446e-02
[56,] 0.952979849 0.0940403028 4.702015e-02
[57,] 0.947854076 0.1042918476 5.214592e-02
[58,] 0.946116620 0.1077667601 5.388338e-02
[59,] 0.938725409 0.1225491813 6.127459e-02
[60,] 0.926511307 0.1469773863 7.348869e-02
[61,] 0.922382091 0.1552358184 7.761791e-02
[62,] 0.908098257 0.1838034857 9.190174e-02
[63,] 0.891624874 0.2167502513 1.083751e-01
[64,] 0.876601559 0.2467968823 1.233984e-01
[65,] 0.854630342 0.2907393157 1.453697e-01
[66,] 0.831533823 0.3369323541 1.684662e-01
[67,] 0.902779906 0.1944401872 9.722009e-02
[68,] 0.916585502 0.1668289952 8.341450e-02
[69,] 0.900615360 0.1987692804 9.938464e-02
[70,] 0.882097980 0.2358040392 1.179020e-01
[71,] 0.863887717 0.2722245664 1.361123e-01
[72,] 0.843732420 0.3125351602 1.562676e-01
[73,] 0.821049951 0.3579000986 1.789500e-01
[74,] 0.801707739 0.3965845228 1.982923e-01
[75,] 0.778017061 0.4439658781 2.219829e-01
[76,] 0.784337730 0.4313245409 2.156623e-01
[77,] 0.754787319 0.4904253627 2.452127e-01
[78,] 0.748856635 0.5022867301 2.511434e-01
[79,] 0.731231732 0.5375365362 2.687683e-01
[80,] 0.698767448 0.6024651049 3.012326e-01
[81,] 0.749537061 0.5009258778 2.504629e-01
[82,] 0.727504147 0.5449917051 2.724959e-01
[83,] 0.747080190 0.5058396196 2.529198e-01
[84,] 0.731108780 0.5377824395 2.688912e-01
[85,] 0.718340582 0.5633188362 2.816594e-01
[86,] 0.749113010 0.5017739792 2.508870e-01
[87,] 0.720842918 0.5583141638 2.791571e-01
[88,] 0.698154787 0.6036904258 3.018452e-01
[89,] 0.688064804 0.6238703926 3.119352e-01
[90,] 0.716598272 0.5668034558 2.834017e-01
[91,] 0.709082080 0.5818358393 2.909179e-01
[92,] 0.756775404 0.4864491915 2.432246e-01
[93,] 0.728424913 0.5431501734 2.715751e-01
[94,] 0.697220432 0.6055591359 3.027796e-01
[95,] 0.670435201 0.6591295984 3.295648e-01
[96,] 0.642806369 0.7143872622 3.571936e-01
[97,] 0.607125459 0.7857490812 3.928745e-01
[98,] 0.573945465 0.8521090708 4.260545e-01
[99,] 0.548761504 0.9024769916 4.512385e-01
[100,] 0.515581288 0.9688374249 4.844187e-01
[101,] 0.480225993 0.9604519865 5.197740e-01
[102,] 0.448117430 0.8962348592 5.518826e-01
[103,] 0.438810499 0.8776209989 5.611895e-01
[104,] 0.402341219 0.8046824379 5.976588e-01
[105,] 0.379526377 0.7590527537 6.204736e-01
[106,] 0.455281565 0.9105631305 5.447184e-01
[107,] 0.442193740 0.8843874807 5.578063e-01
[108,] 0.428601685 0.8572033709 5.713983e-01
[109,] 0.414032355 0.8280647097 5.859676e-01
[110,] 0.382850059 0.7657001187 6.171499e-01
[111,] 0.402033347 0.8040666942 5.979667e-01
[112,] 0.376532953 0.7530659061 6.234670e-01
[113,] 0.405389314 0.8107786287 5.946107e-01
[114,] 0.398527138 0.7970542760 6.014729e-01
[115,] 0.366827808 0.7336556160 6.331722e-01
[116,] 0.355746205 0.7114924105 6.442538e-01
[117,] 0.323575994 0.6471519876 6.764240e-01
[118,] 0.303278511 0.6065570215 6.967215e-01
[119,] 0.274646527 0.5492930535 7.253535e-01
[120,] 0.279366145 0.5587322905 7.206339e-01
[121,] 0.252583779 0.5051675582 7.474162e-01
[122,] 0.229559275 0.4591185497 7.704407e-01
[123,] 0.218154245 0.4363084908 7.818458e-01
[124,] 0.216172663 0.4323453263 7.838273e-01
[125,] 0.213208149 0.4264162976 7.867919e-01
[126,] 0.231300893 0.4626017858 7.686991e-01
[127,] 0.247280848 0.4945616961 7.527192e-01
[128,] 0.227087738 0.4541754752 7.729123e-01
[129,] 0.203338294 0.4066765888 7.966617e-01
[130,] 0.177862828 0.3557256553 8.221372e-01
[131,] 0.189852394 0.3797047888 8.101476e-01
[132,] 0.223666348 0.4473326955 7.763337e-01
[133,] 0.199794706 0.3995894129 8.002053e-01
[134,] 0.176631135 0.3532622707 8.233689e-01
[135,] 0.152925174 0.3058503476 8.470748e-01
[136,] 0.244137244 0.4882744880 7.558628e-01
[137,] 0.258946012 0.5178920233 7.410540e-01
[138,] 0.230067020 0.4601340406 7.699330e-01
[139,] 0.201544180 0.4030883596 7.984558e-01
[140,] 0.176610042 0.3532200844 8.233900e-01
[141,] 0.155867662 0.3117353245 8.441323e-01
[142,] 0.244516565 0.4890331290 7.554834e-01
[143,] 0.236812001 0.4736240030 7.631880e-01
[144,] 0.232576541 0.4651530822 7.674235e-01
[145,] 0.204071781 0.4081435623 7.959282e-01
[146,] 0.181029238 0.3620584765 8.189708e-01
[147,] 0.226768426 0.4535368529 7.732316e-01
[148,] 0.230640195 0.4612803890 7.693598e-01
[149,] 0.204675473 0.4093509450 7.953245e-01
[150,] 0.201035771 0.4020715427 7.989642e-01
[151,] 0.343232763 0.6864655260 6.567672e-01
[152,] 0.312629803 0.6252596062 6.873702e-01
[153,] 0.336668058 0.6733361163 6.633319e-01
[154,] 0.347032043 0.6940640870 6.529680e-01
[155,] 0.377552685 0.7551053694 6.224473e-01
[156,] 0.344069419 0.6881388371 6.559306e-01
[157,] 0.319706149 0.6394122990 6.802939e-01
[158,] 0.305793210 0.6115864195 6.942068e-01
[159,] 0.273515393 0.5470307857 7.264846e-01
[160,] 0.248748896 0.4974977913 7.512511e-01
[161,] 0.219007607 0.4380152142 7.809924e-01
[162,] 0.191982384 0.3839647686 8.080176e-01
[163,] 0.202278404 0.4045568084 7.977216e-01
[164,] 0.182489135 0.3649782709 8.175109e-01
[165,] 0.159256873 0.3185137453 8.407431e-01
[166,] 0.138663222 0.2773264447 8.613368e-01
[167,] 0.118628346 0.2372566926 8.813717e-01
[168,] 0.101561628 0.2031232567 8.984384e-01
[169,] 0.105030118 0.2100602352 8.949699e-01
[170,] 0.090696743 0.1813934862 9.093033e-01
[171,] 0.101365121 0.2027302415 8.986349e-01
[172,] 0.086798244 0.1735964881 9.132018e-01
[173,] 0.084914396 0.1698287923 9.150856e-01
[174,] 0.086754890 0.1735097809 9.132451e-01
[175,] 0.116987145 0.2339742908 8.830129e-01
[176,] 0.097877740 0.1957554803 9.021223e-01
[177,] 0.105596311 0.2111926214 8.944037e-01
[178,] 0.087694921 0.1753898425 9.123051e-01
[179,] 0.104336938 0.2086738766 8.956631e-01
[180,] 0.093497786 0.1869955729 9.065022e-01
[181,] 0.125403506 0.2508070127 8.745965e-01
[182,] 0.106316142 0.2126322844 8.936839e-01
[183,] 0.086903218 0.1738064355 9.130968e-01
[184,] 0.089868928 0.1797378560 9.101311e-01
[185,] 0.072604245 0.1452084905 9.273958e-01
[186,] 0.084202933 0.1684058661 9.157971e-01
[187,] 0.068231242 0.1364624831 9.317688e-01
[188,] 0.066596768 0.1331935364 9.334032e-01
[189,] 0.077540638 0.1550812769 9.224594e-01
[190,] 0.063636334 0.1272726690 9.363637e-01
[191,] 0.050380129 0.1007602575 9.496199e-01
[192,] 0.093583845 0.1871676909 9.064162e-01
[193,] 0.079079408 0.1581588160 9.209206e-01
[194,] 0.074577806 0.1491556112 9.254222e-01
[195,] 0.083287282 0.1665745644 9.167127e-01
[196,] 0.078081305 0.1561626107 9.219187e-01
[197,] 0.074978776 0.1499575530 9.250212e-01
[198,] 0.079146732 0.1582934646 9.208533e-01
[199,] 0.072218864 0.1444377284 9.277811e-01
[200,] 0.079917833 0.1598356667 9.200822e-01
[201,] 0.086140909 0.1722818189 9.138591e-01
[202,] 0.065628873 0.1312577470 9.343711e-01
[203,] 0.049489273 0.0989785464 9.505107e-01
[204,] 0.045747580 0.0914951609 9.542524e-01
[205,] 0.229471115 0.4589422300 7.705289e-01
[206,] 0.195344784 0.3906895672 8.046552e-01
[207,] 0.160295889 0.3205917780 8.397041e-01
[208,] 0.124290050 0.2485801008 8.757099e-01
[209,] 0.089930436 0.1798608718 9.100696e-01
[210,] 0.083508764 0.1670175281 9.164912e-01
[211,] 0.056698384 0.1133967673 9.433016e-01
[212,] 0.050995551 0.1019911015 9.490044e-01
[213,] 0.037544210 0.0750884205 9.624558e-01
[214,] 0.024200308 0.0484006151 9.757997e-01
[215,] 0.019614387 0.0392287749 9.803856e-01
[216,] 0.011064808 0.0221296153 9.889352e-01
[217,] 0.018648233 0.0372964660 9.813518e-01
[218,] 0.011244872 0.0224897433 9.887551e-01
[219,] 0.005136776 0.0102735527 9.948632e-01
> postscript(file="/var/wessaorg/rcomp/tmp/1apje1384528558.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/24twa1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3b95p1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wn7q1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5ford1384528558.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
0.113345191 2.912263087 -2.652093742 -2.400617679 4.785759875 2.825328162
7 8 9 10 11 12
3.158677656 -1.441079200 -0.134294721 0.754290474 1.223099798 3.431050156
13 14 15 16 17 18
-3.391281643 2.652789307 2.567074419 0.672516286 0.111981993 0.459236186
19 20 21 22 23 24
-1.497357187 1.733359805 2.666353594 -2.665115345 -0.740619152 -1.507639717
25 26 27 28 29 30
1.629045363 -6.787653422 1.248481081 0.764858006 1.001619728 -3.729362126
31 32 33 34 35 36
0.177062764 -0.122551207 1.935839022 -0.222647694 -0.242109055 0.523655604
37 38 39 40 41 42
-1.830669312 0.789331377 1.891679466 -2.204407001 -0.820834313 1.554883743
43 44 45 46 47 48
-0.025471268 -1.612326471 0.379304832 -2.537728406 -0.665667696 0.120996622
49 50 51 52 53 54
3.447778671 -1.662452035 0.990558733 0.564813561 -0.780126788 -2.490502268
55 56 57 58 59 60
-2.212309004 0.876686236 1.782169763 -0.499568203 -3.533708624 -1.405658942
61 62 63 64 65 66
-2.818887425 -1.543468976 -3.421327481 0.890981636 1.187388910 -5.695911345
67 68 69 70 71 72
-1.535700023 -2.801482788 1.702570675 1.597356599 0.408890009 3.482095397
73 74 75 76 77 78
0.698597977 0.001784239 -1.521414848 0.081759567 3.080110393 -0.119360287
79 80 81 82 83 84
1.251737724 -2.295391648 0.258578901 -0.320987945 1.577475774 0.885544360
85 86 87 88 89 90
-0.028607173 1.261641589 0.120008562 0.379740605 -3.468656875 2.620044217
91 92 93 94 95 96
0.182398824 0.510219737 0.845863867 -0.819132667 0.869781317 -0.649211916
97 98 99 100 101 102
-0.795392436 2.306250632 0.354658879 2.015731928 -0.906583303 0.299317365
103 104 105 106 107 108
-3.442848960 1.576446483 -2.216763213 1.229752108 1.921923465 -2.692903874
109 110 111 112 113 114
0.955488104 1.310552673 -1.820777787 -2.138942088 2.040975317 3.222622097
115 116 117 118 119 120
0.424834399 0.645747184 0.286145618 -0.951432968 0.130732523 -0.411489666
121 122 123 124 125 126
0.477310564 0.347006654 -0.538115191 0.565088876 -1.761899085 0.138431494
127 128 129 130 131 132
1.706214578 3.707094738 1.538199125 -1.541018518 -1.817534001 -0.164420143
133 134 135 136 137 138
2.428432185 0.970819461 2.615058891 1.673794206 0.655761401 -1.695155650
139 140 141 142 143 144
0.782584198 -1.169083570 0.390795515 2.285534021 -0.902180472 0.794552846
145 146 147 148 149 150
1.480485931 1.670240316 -2.042856745 -2.594219985 -2.522041814 1.084355506
151 152 153 154 155 156
0.439762302 0.159338992 -2.395626434 -2.747867110 1.050397354 0.323707748
157 158 159 160 161 162
0.672419446 4.263404769 -2.345662656 0.038499219 0.372420842 0.186169040
163 164 165 166 167 168
0.937960503 4.307520466 -1.659249733 2.195591556 -0.157984970 -0.568808071
169 170 171 172 173 174
-3.450558057 -2.585245671 0.995085617 2.101888344 -4.801105311 1.152293692
175 176 177 178 179 180
2.807938297 -2.495531377 -3.022938272 0.819978533 1.365314764 -1.846206518
181 182 183 184 185 186
0.085305572 -1.388437769 0.777972077 -0.669047478 2.089783665 0.801222844
187 188 189 190 191 192
0.580151053 0.610526634 0.768918982 1.032213787 -1.690308163 -0.729450762
193 194 195 196 197 198
2.450824759 -1.181512335 2.382581405 -1.778624174 2.390804801 0.020622613
199 200 201 202 203 204
-3.033990384 -1.039482286 -2.952605253 1.446594397 2.866012387 0.683408402
205 206 207 208 209 210
0.708948404 1.577525705 -0.035959253 3.707673157 0.231114620 1.093678153
211 212 213 214 215 216
-2.582699095 1.247237824 -1.014831062 -3.629004727 -1.236762817 1.900738290
217 218 219 220 221 222
2.241985343 0.065859009 -1.461602661 1.658263682 -2.783816388 1.813871358
223 224 225 226 227 228
-2.047300366 -0.112677125 -0.392074165 1.923908879 5.034430576 -1.468240816
229 230 231 232 233 234
-1.320043843 -2.068036065 0.604291430 -2.793109799 0.091325958 -0.037735333
235 236 237 238 239 240
1.149787868 -2.096419703 0.910851727 0.363983207 -4.523230102 -2.350428813
241 242 243 244 245 246
-2.717220694 -2.422678514 0.655198454 -0.060829061 1.547100383 -0.365774367
247 248 249 250 251 252
0.296258697 4.891929593 -0.114864834 0.591521227 1.986145650 1.349115964
253 254 255 256 257 258
-1.037306929 -0.489984030 0.637161349 -0.475811806 -1.741084010 -3.138275095
259 260 261 262 263 264
2.482307423 -5.080082315 0.437656068 1.693778794 -2.924098566 0.299593850
> postscript(file="/var/wessaorg/rcomp/tmp/65aj11384528558.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 0.113345191 NA
1 2.912263087 0.113345191
2 -2.652093742 2.912263087
3 -2.400617679 -2.652093742
4 4.785759875 -2.400617679
5 2.825328162 4.785759875
6 3.158677656 2.825328162
7 -1.441079200 3.158677656
8 -0.134294721 -1.441079200
9 0.754290474 -0.134294721
10 1.223099798 0.754290474
11 3.431050156 1.223099798
12 -3.391281643 3.431050156
13 2.652789307 -3.391281643
14 2.567074419 2.652789307
15 0.672516286 2.567074419
16 0.111981993 0.672516286
17 0.459236186 0.111981993
18 -1.497357187 0.459236186
19 1.733359805 -1.497357187
20 2.666353594 1.733359805
21 -2.665115345 2.666353594
22 -0.740619152 -2.665115345
23 -1.507639717 -0.740619152
24 1.629045363 -1.507639717
25 -6.787653422 1.629045363
26 1.248481081 -6.787653422
27 0.764858006 1.248481081
28 1.001619728 0.764858006
29 -3.729362126 1.001619728
30 0.177062764 -3.729362126
31 -0.122551207 0.177062764
32 1.935839022 -0.122551207
33 -0.222647694 1.935839022
34 -0.242109055 -0.222647694
35 0.523655604 -0.242109055
36 -1.830669312 0.523655604
37 0.789331377 -1.830669312
38 1.891679466 0.789331377
39 -2.204407001 1.891679466
40 -0.820834313 -2.204407001
41 1.554883743 -0.820834313
42 -0.025471268 1.554883743
43 -1.612326471 -0.025471268
44 0.379304832 -1.612326471
45 -2.537728406 0.379304832
46 -0.665667696 -2.537728406
47 0.120996622 -0.665667696
48 3.447778671 0.120996622
49 -1.662452035 3.447778671
50 0.990558733 -1.662452035
51 0.564813561 0.990558733
52 -0.780126788 0.564813561
53 -2.490502268 -0.780126788
54 -2.212309004 -2.490502268
55 0.876686236 -2.212309004
56 1.782169763 0.876686236
57 -0.499568203 1.782169763
58 -3.533708624 -0.499568203
59 -1.405658942 -3.533708624
60 -2.818887425 -1.405658942
61 -1.543468976 -2.818887425
62 -3.421327481 -1.543468976
63 0.890981636 -3.421327481
64 1.187388910 0.890981636
65 -5.695911345 1.187388910
66 -1.535700023 -5.695911345
67 -2.801482788 -1.535700023
68 1.702570675 -2.801482788
69 1.597356599 1.702570675
70 0.408890009 1.597356599
71 3.482095397 0.408890009
72 0.698597977 3.482095397
73 0.001784239 0.698597977
74 -1.521414848 0.001784239
75 0.081759567 -1.521414848
76 3.080110393 0.081759567
77 -0.119360287 3.080110393
78 1.251737724 -0.119360287
79 -2.295391648 1.251737724
80 0.258578901 -2.295391648
81 -0.320987945 0.258578901
82 1.577475774 -0.320987945
83 0.885544360 1.577475774
84 -0.028607173 0.885544360
85 1.261641589 -0.028607173
86 0.120008562 1.261641589
87 0.379740605 0.120008562
88 -3.468656875 0.379740605
89 2.620044217 -3.468656875
90 0.182398824 2.620044217
91 0.510219737 0.182398824
92 0.845863867 0.510219737
93 -0.819132667 0.845863867
94 0.869781317 -0.819132667
95 -0.649211916 0.869781317
96 -0.795392436 -0.649211916
97 2.306250632 -0.795392436
98 0.354658879 2.306250632
99 2.015731928 0.354658879
100 -0.906583303 2.015731928
101 0.299317365 -0.906583303
102 -3.442848960 0.299317365
103 1.576446483 -3.442848960
104 -2.216763213 1.576446483
105 1.229752108 -2.216763213
106 1.921923465 1.229752108
107 -2.692903874 1.921923465
108 0.955488104 -2.692903874
109 1.310552673 0.955488104
110 -1.820777787 1.310552673
111 -2.138942088 -1.820777787
112 2.040975317 -2.138942088
113 3.222622097 2.040975317
114 0.424834399 3.222622097
115 0.645747184 0.424834399
116 0.286145618 0.645747184
117 -0.951432968 0.286145618
118 0.130732523 -0.951432968
119 -0.411489666 0.130732523
120 0.477310564 -0.411489666
121 0.347006654 0.477310564
122 -0.538115191 0.347006654
123 0.565088876 -0.538115191
124 -1.761899085 0.565088876
125 0.138431494 -1.761899085
126 1.706214578 0.138431494
127 3.707094738 1.706214578
128 1.538199125 3.707094738
129 -1.541018518 1.538199125
130 -1.817534001 -1.541018518
131 -0.164420143 -1.817534001
132 2.428432185 -0.164420143
133 0.970819461 2.428432185
134 2.615058891 0.970819461
135 1.673794206 2.615058891
136 0.655761401 1.673794206
137 -1.695155650 0.655761401
138 0.782584198 -1.695155650
139 -1.169083570 0.782584198
140 0.390795515 -1.169083570
141 2.285534021 0.390795515
142 -0.902180472 2.285534021
143 0.794552846 -0.902180472
144 1.480485931 0.794552846
145 1.670240316 1.480485931
146 -2.042856745 1.670240316
147 -2.594219985 -2.042856745
148 -2.522041814 -2.594219985
149 1.084355506 -2.522041814
150 0.439762302 1.084355506
151 0.159338992 0.439762302
152 -2.395626434 0.159338992
153 -2.747867110 -2.395626434
154 1.050397354 -2.747867110
155 0.323707748 1.050397354
156 0.672419446 0.323707748
157 4.263404769 0.672419446
158 -2.345662656 4.263404769
159 0.038499219 -2.345662656
160 0.372420842 0.038499219
161 0.186169040 0.372420842
162 0.937960503 0.186169040
163 4.307520466 0.937960503
164 -1.659249733 4.307520466
165 2.195591556 -1.659249733
166 -0.157984970 2.195591556
167 -0.568808071 -0.157984970
168 -3.450558057 -0.568808071
169 -2.585245671 -3.450558057
170 0.995085617 -2.585245671
171 2.101888344 0.995085617
172 -4.801105311 2.101888344
173 1.152293692 -4.801105311
174 2.807938297 1.152293692
175 -2.495531377 2.807938297
176 -3.022938272 -2.495531377
177 0.819978533 -3.022938272
178 1.365314764 0.819978533
179 -1.846206518 1.365314764
180 0.085305572 -1.846206518
181 -1.388437769 0.085305572
182 0.777972077 -1.388437769
183 -0.669047478 0.777972077
184 2.089783665 -0.669047478
185 0.801222844 2.089783665
186 0.580151053 0.801222844
187 0.610526634 0.580151053
188 0.768918982 0.610526634
189 1.032213787 0.768918982
190 -1.690308163 1.032213787
191 -0.729450762 -1.690308163
192 2.450824759 -0.729450762
193 -1.181512335 2.450824759
194 2.382581405 -1.181512335
195 -1.778624174 2.382581405
196 2.390804801 -1.778624174
197 0.020622613 2.390804801
198 -3.033990384 0.020622613
199 -1.039482286 -3.033990384
200 -2.952605253 -1.039482286
201 1.446594397 -2.952605253
202 2.866012387 1.446594397
203 0.683408402 2.866012387
204 0.708948404 0.683408402
205 1.577525705 0.708948404
206 -0.035959253 1.577525705
207 3.707673157 -0.035959253
208 0.231114620 3.707673157
209 1.093678153 0.231114620
210 -2.582699095 1.093678153
211 1.247237824 -2.582699095
212 -1.014831062 1.247237824
213 -3.629004727 -1.014831062
214 -1.236762817 -3.629004727
215 1.900738290 -1.236762817
216 2.241985343 1.900738290
217 0.065859009 2.241985343
218 -1.461602661 0.065859009
219 1.658263682 -1.461602661
220 -2.783816388 1.658263682
221 1.813871358 -2.783816388
222 -2.047300366 1.813871358
223 -0.112677125 -2.047300366
224 -0.392074165 -0.112677125
225 1.923908879 -0.392074165
226 5.034430576 1.923908879
227 -1.468240816 5.034430576
228 -1.320043843 -1.468240816
229 -2.068036065 -1.320043843
230 0.604291430 -2.068036065
231 -2.793109799 0.604291430
232 0.091325958 -2.793109799
233 -0.037735333 0.091325958
234 1.149787868 -0.037735333
235 -2.096419703 1.149787868
236 0.910851727 -2.096419703
237 0.363983207 0.910851727
238 -4.523230102 0.363983207
239 -2.350428813 -4.523230102
240 -2.717220694 -2.350428813
241 -2.422678514 -2.717220694
242 0.655198454 -2.422678514
243 -0.060829061 0.655198454
244 1.547100383 -0.060829061
245 -0.365774367 1.547100383
246 0.296258697 -0.365774367
247 4.891929593 0.296258697
248 -0.114864834 4.891929593
249 0.591521227 -0.114864834
250 1.986145650 0.591521227
251 1.349115964 1.986145650
252 -1.037306929 1.349115964
253 -0.489984030 -1.037306929
254 0.637161349 -0.489984030
255 -0.475811806 0.637161349
256 -1.741084010 -0.475811806
257 -3.138275095 -1.741084010
258 2.482307423 -3.138275095
259 -5.080082315 2.482307423
260 0.437656068 -5.080082315
261 1.693778794 0.437656068
262 -2.924098566 1.693778794
263 0.299593850 -2.924098566
264 NA 0.299593850
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.912263087 0.113345191
[2,] -2.652093742 2.912263087
[3,] -2.400617679 -2.652093742
[4,] 4.785759875 -2.400617679
[5,] 2.825328162 4.785759875
[6,] 3.158677656 2.825328162
[7,] -1.441079200 3.158677656
[8,] -0.134294721 -1.441079200
[9,] 0.754290474 -0.134294721
[10,] 1.223099798 0.754290474
[11,] 3.431050156 1.223099798
[12,] -3.391281643 3.431050156
[13,] 2.652789307 -3.391281643
[14,] 2.567074419 2.652789307
[15,] 0.672516286 2.567074419
[16,] 0.111981993 0.672516286
[17,] 0.459236186 0.111981993
[18,] -1.497357187 0.459236186
[19,] 1.733359805 -1.497357187
[20,] 2.666353594 1.733359805
[21,] -2.665115345 2.666353594
[22,] -0.740619152 -2.665115345
[23,] -1.507639717 -0.740619152
[24,] 1.629045363 -1.507639717
[25,] -6.787653422 1.629045363
[26,] 1.248481081 -6.787653422
[27,] 0.764858006 1.248481081
[28,] 1.001619728 0.764858006
[29,] -3.729362126 1.001619728
[30,] 0.177062764 -3.729362126
[31,] -0.122551207 0.177062764
[32,] 1.935839022 -0.122551207
[33,] -0.222647694 1.935839022
[34,] -0.242109055 -0.222647694
[35,] 0.523655604 -0.242109055
[36,] -1.830669312 0.523655604
[37,] 0.789331377 -1.830669312
[38,] 1.891679466 0.789331377
[39,] -2.204407001 1.891679466
[40,] -0.820834313 -2.204407001
[41,] 1.554883743 -0.820834313
[42,] -0.025471268 1.554883743
[43,] -1.612326471 -0.025471268
[44,] 0.379304832 -1.612326471
[45,] -2.537728406 0.379304832
[46,] -0.665667696 -2.537728406
[47,] 0.120996622 -0.665667696
[48,] 3.447778671 0.120996622
[49,] -1.662452035 3.447778671
[50,] 0.990558733 -1.662452035
[51,] 0.564813561 0.990558733
[52,] -0.780126788 0.564813561
[53,] -2.490502268 -0.780126788
[54,] -2.212309004 -2.490502268
[55,] 0.876686236 -2.212309004
[56,] 1.782169763 0.876686236
[57,] -0.499568203 1.782169763
[58,] -3.533708624 -0.499568203
[59,] -1.405658942 -3.533708624
[60,] -2.818887425 -1.405658942
[61,] -1.543468976 -2.818887425
[62,] -3.421327481 -1.543468976
[63,] 0.890981636 -3.421327481
[64,] 1.187388910 0.890981636
[65,] -5.695911345 1.187388910
[66,] -1.535700023 -5.695911345
[67,] -2.801482788 -1.535700023
[68,] 1.702570675 -2.801482788
[69,] 1.597356599 1.702570675
[70,] 0.408890009 1.597356599
[71,] 3.482095397 0.408890009
[72,] 0.698597977 3.482095397
[73,] 0.001784239 0.698597977
[74,] -1.521414848 0.001784239
[75,] 0.081759567 -1.521414848
[76,] 3.080110393 0.081759567
[77,] -0.119360287 3.080110393
[78,] 1.251737724 -0.119360287
[79,] -2.295391648 1.251737724
[80,] 0.258578901 -2.295391648
[81,] -0.320987945 0.258578901
[82,] 1.577475774 -0.320987945
[83,] 0.885544360 1.577475774
[84,] -0.028607173 0.885544360
[85,] 1.261641589 -0.028607173
[86,] 0.120008562 1.261641589
[87,] 0.379740605 0.120008562
[88,] -3.468656875 0.379740605
[89,] 2.620044217 -3.468656875
[90,] 0.182398824 2.620044217
[91,] 0.510219737 0.182398824
[92,] 0.845863867 0.510219737
[93,] -0.819132667 0.845863867
[94,] 0.869781317 -0.819132667
[95,] -0.649211916 0.869781317
[96,] -0.795392436 -0.649211916
[97,] 2.306250632 -0.795392436
[98,] 0.354658879 2.306250632
[99,] 2.015731928 0.354658879
[100,] -0.906583303 2.015731928
[101,] 0.299317365 -0.906583303
[102,] -3.442848960 0.299317365
[103,] 1.576446483 -3.442848960
[104,] -2.216763213 1.576446483
[105,] 1.229752108 -2.216763213
[106,] 1.921923465 1.229752108
[107,] -2.692903874 1.921923465
[108,] 0.955488104 -2.692903874
[109,] 1.310552673 0.955488104
[110,] -1.820777787 1.310552673
[111,] -2.138942088 -1.820777787
[112,] 2.040975317 -2.138942088
[113,] 3.222622097 2.040975317
[114,] 0.424834399 3.222622097
[115,] 0.645747184 0.424834399
[116,] 0.286145618 0.645747184
[117,] -0.951432968 0.286145618
[118,] 0.130732523 -0.951432968
[119,] -0.411489666 0.130732523
[120,] 0.477310564 -0.411489666
[121,] 0.347006654 0.477310564
[122,] -0.538115191 0.347006654
[123,] 0.565088876 -0.538115191
[124,] -1.761899085 0.565088876
[125,] 0.138431494 -1.761899085
[126,] 1.706214578 0.138431494
[127,] 3.707094738 1.706214578
[128,] 1.538199125 3.707094738
[129,] -1.541018518 1.538199125
[130,] -1.817534001 -1.541018518
[131,] -0.164420143 -1.817534001
[132,] 2.428432185 -0.164420143
[133,] 0.970819461 2.428432185
[134,] 2.615058891 0.970819461
[135,] 1.673794206 2.615058891
[136,] 0.655761401 1.673794206
[137,] -1.695155650 0.655761401
[138,] 0.782584198 -1.695155650
[139,] -1.169083570 0.782584198
[140,] 0.390795515 -1.169083570
[141,] 2.285534021 0.390795515
[142,] -0.902180472 2.285534021
[143,] 0.794552846 -0.902180472
[144,] 1.480485931 0.794552846
[145,] 1.670240316 1.480485931
[146,] -2.042856745 1.670240316
[147,] -2.594219985 -2.042856745
[148,] -2.522041814 -2.594219985
[149,] 1.084355506 -2.522041814
[150,] 0.439762302 1.084355506
[151,] 0.159338992 0.439762302
[152,] -2.395626434 0.159338992
[153,] -2.747867110 -2.395626434
[154,] 1.050397354 -2.747867110
[155,] 0.323707748 1.050397354
[156,] 0.672419446 0.323707748
[157,] 4.263404769 0.672419446
[158,] -2.345662656 4.263404769
[159,] 0.038499219 -2.345662656
[160,] 0.372420842 0.038499219
[161,] 0.186169040 0.372420842
[162,] 0.937960503 0.186169040
[163,] 4.307520466 0.937960503
[164,] -1.659249733 4.307520466
[165,] 2.195591556 -1.659249733
[166,] -0.157984970 2.195591556
[167,] -0.568808071 -0.157984970
[168,] -3.450558057 -0.568808071
[169,] -2.585245671 -3.450558057
[170,] 0.995085617 -2.585245671
[171,] 2.101888344 0.995085617
[172,] -4.801105311 2.101888344
[173,] 1.152293692 -4.801105311
[174,] 2.807938297 1.152293692
[175,] -2.495531377 2.807938297
[176,] -3.022938272 -2.495531377
[177,] 0.819978533 -3.022938272
[178,] 1.365314764 0.819978533
[179,] -1.846206518 1.365314764
[180,] 0.085305572 -1.846206518
[181,] -1.388437769 0.085305572
[182,] 0.777972077 -1.388437769
[183,] -0.669047478 0.777972077
[184,] 2.089783665 -0.669047478
[185,] 0.801222844 2.089783665
[186,] 0.580151053 0.801222844
[187,] 0.610526634 0.580151053
[188,] 0.768918982 0.610526634
[189,] 1.032213787 0.768918982
[190,] -1.690308163 1.032213787
[191,] -0.729450762 -1.690308163
[192,] 2.450824759 -0.729450762
[193,] -1.181512335 2.450824759
[194,] 2.382581405 -1.181512335
[195,] -1.778624174 2.382581405
[196,] 2.390804801 -1.778624174
[197,] 0.020622613 2.390804801
[198,] -3.033990384 0.020622613
[199,] -1.039482286 -3.033990384
[200,] -2.952605253 -1.039482286
[201,] 1.446594397 -2.952605253
[202,] 2.866012387 1.446594397
[203,] 0.683408402 2.866012387
[204,] 0.708948404 0.683408402
[205,] 1.577525705 0.708948404
[206,] -0.035959253 1.577525705
[207,] 3.707673157 -0.035959253
[208,] 0.231114620 3.707673157
[209,] 1.093678153 0.231114620
[210,] -2.582699095 1.093678153
[211,] 1.247237824 -2.582699095
[212,] -1.014831062 1.247237824
[213,] -3.629004727 -1.014831062
[214,] -1.236762817 -3.629004727
[215,] 1.900738290 -1.236762817
[216,] 2.241985343 1.900738290
[217,] 0.065859009 2.241985343
[218,] -1.461602661 0.065859009
[219,] 1.658263682 -1.461602661
[220,] -2.783816388 1.658263682
[221,] 1.813871358 -2.783816388
[222,] -2.047300366 1.813871358
[223,] -0.112677125 -2.047300366
[224,] -0.392074165 -0.112677125
[225,] 1.923908879 -0.392074165
[226,] 5.034430576 1.923908879
[227,] -1.468240816 5.034430576
[228,] -1.320043843 -1.468240816
[229,] -2.068036065 -1.320043843
[230,] 0.604291430 -2.068036065
[231,] -2.793109799 0.604291430
[232,] 0.091325958 -2.793109799
[233,] -0.037735333 0.091325958
[234,] 1.149787868 -0.037735333
[235,] -2.096419703 1.149787868
[236,] 0.910851727 -2.096419703
[237,] 0.363983207 0.910851727
[238,] -4.523230102 0.363983207
[239,] -2.350428813 -4.523230102
[240,] -2.717220694 -2.350428813
[241,] -2.422678514 -2.717220694
[242,] 0.655198454 -2.422678514
[243,] -0.060829061 0.655198454
[244,] 1.547100383 -0.060829061
[245,] -0.365774367 1.547100383
[246,] 0.296258697 -0.365774367
[247,] 4.891929593 0.296258697
[248,] -0.114864834 4.891929593
[249,] 0.591521227 -0.114864834
[250,] 1.986145650 0.591521227
[251,] 1.349115964 1.986145650
[252,] -1.037306929 1.349115964
[253,] -0.489984030 -1.037306929
[254,] 0.637161349 -0.489984030
[255,] -0.475811806 0.637161349
[256,] -1.741084010 -0.475811806
[257,] -3.138275095 -1.741084010
[258,] 2.482307423 -3.138275095
[259,] -5.080082315 2.482307423
[260,] 0.437656068 -5.080082315
[261,] 1.693778794 0.437656068
[262,] -2.924098566 1.693778794
[263,] 0.299593850 -2.924098566
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.912263087 0.113345191
2 -2.652093742 2.912263087
3 -2.400617679 -2.652093742
4 4.785759875 -2.400617679
5 2.825328162 4.785759875
6 3.158677656 2.825328162
7 -1.441079200 3.158677656
8 -0.134294721 -1.441079200
9 0.754290474 -0.134294721
10 1.223099798 0.754290474
11 3.431050156 1.223099798
12 -3.391281643 3.431050156
13 2.652789307 -3.391281643
14 2.567074419 2.652789307
15 0.672516286 2.567074419
16 0.111981993 0.672516286
17 0.459236186 0.111981993
18 -1.497357187 0.459236186
19 1.733359805 -1.497357187
20 2.666353594 1.733359805
21 -2.665115345 2.666353594
22 -0.740619152 -2.665115345
23 -1.507639717 -0.740619152
24 1.629045363 -1.507639717
25 -6.787653422 1.629045363
26 1.248481081 -6.787653422
27 0.764858006 1.248481081
28 1.001619728 0.764858006
29 -3.729362126 1.001619728
30 0.177062764 -3.729362126
31 -0.122551207 0.177062764
32 1.935839022 -0.122551207
33 -0.222647694 1.935839022
34 -0.242109055 -0.222647694
35 0.523655604 -0.242109055
36 -1.830669312 0.523655604
37 0.789331377 -1.830669312
38 1.891679466 0.789331377
39 -2.204407001 1.891679466
40 -0.820834313 -2.204407001
41 1.554883743 -0.820834313
42 -0.025471268 1.554883743
43 -1.612326471 -0.025471268
44 0.379304832 -1.612326471
45 -2.537728406 0.379304832
46 -0.665667696 -2.537728406
47 0.120996622 -0.665667696
48 3.447778671 0.120996622
49 -1.662452035 3.447778671
50 0.990558733 -1.662452035
51 0.564813561 0.990558733
52 -0.780126788 0.564813561
53 -2.490502268 -0.780126788
54 -2.212309004 -2.490502268
55 0.876686236 -2.212309004
56 1.782169763 0.876686236
57 -0.499568203 1.782169763
58 -3.533708624 -0.499568203
59 -1.405658942 -3.533708624
60 -2.818887425 -1.405658942
61 -1.543468976 -2.818887425
62 -3.421327481 -1.543468976
63 0.890981636 -3.421327481
64 1.187388910 0.890981636
65 -5.695911345 1.187388910
66 -1.535700023 -5.695911345
67 -2.801482788 -1.535700023
68 1.702570675 -2.801482788
69 1.597356599 1.702570675
70 0.408890009 1.597356599
71 3.482095397 0.408890009
72 0.698597977 3.482095397
73 0.001784239 0.698597977
74 -1.521414848 0.001784239
75 0.081759567 -1.521414848
76 3.080110393 0.081759567
77 -0.119360287 3.080110393
78 1.251737724 -0.119360287
79 -2.295391648 1.251737724
80 0.258578901 -2.295391648
81 -0.320987945 0.258578901
82 1.577475774 -0.320987945
83 0.885544360 1.577475774
84 -0.028607173 0.885544360
85 1.261641589 -0.028607173
86 0.120008562 1.261641589
87 0.379740605 0.120008562
88 -3.468656875 0.379740605
89 2.620044217 -3.468656875
90 0.182398824 2.620044217
91 0.510219737 0.182398824
92 0.845863867 0.510219737
93 -0.819132667 0.845863867
94 0.869781317 -0.819132667
95 -0.649211916 0.869781317
96 -0.795392436 -0.649211916
97 2.306250632 -0.795392436
98 0.354658879 2.306250632
99 2.015731928 0.354658879
100 -0.906583303 2.015731928
101 0.299317365 -0.906583303
102 -3.442848960 0.299317365
103 1.576446483 -3.442848960
104 -2.216763213 1.576446483
105 1.229752108 -2.216763213
106 1.921923465 1.229752108
107 -2.692903874 1.921923465
108 0.955488104 -2.692903874
109 1.310552673 0.955488104
110 -1.820777787 1.310552673
111 -2.138942088 -1.820777787
112 2.040975317 -2.138942088
113 3.222622097 2.040975317
114 0.424834399 3.222622097
115 0.645747184 0.424834399
116 0.286145618 0.645747184
117 -0.951432968 0.286145618
118 0.130732523 -0.951432968
119 -0.411489666 0.130732523
120 0.477310564 -0.411489666
121 0.347006654 0.477310564
122 -0.538115191 0.347006654
123 0.565088876 -0.538115191
124 -1.761899085 0.565088876
125 0.138431494 -1.761899085
126 1.706214578 0.138431494
127 3.707094738 1.706214578
128 1.538199125 3.707094738
129 -1.541018518 1.538199125
130 -1.817534001 -1.541018518
131 -0.164420143 -1.817534001
132 2.428432185 -0.164420143
133 0.970819461 2.428432185
134 2.615058891 0.970819461
135 1.673794206 2.615058891
136 0.655761401 1.673794206
137 -1.695155650 0.655761401
138 0.782584198 -1.695155650
139 -1.169083570 0.782584198
140 0.390795515 -1.169083570
141 2.285534021 0.390795515
142 -0.902180472 2.285534021
143 0.794552846 -0.902180472
144 1.480485931 0.794552846
145 1.670240316 1.480485931
146 -2.042856745 1.670240316
147 -2.594219985 -2.042856745
148 -2.522041814 -2.594219985
149 1.084355506 -2.522041814
150 0.439762302 1.084355506
151 0.159338992 0.439762302
152 -2.395626434 0.159338992
153 -2.747867110 -2.395626434
154 1.050397354 -2.747867110
155 0.323707748 1.050397354
156 0.672419446 0.323707748
157 4.263404769 0.672419446
158 -2.345662656 4.263404769
159 0.038499219 -2.345662656
160 0.372420842 0.038499219
161 0.186169040 0.372420842
162 0.937960503 0.186169040
163 4.307520466 0.937960503
164 -1.659249733 4.307520466
165 2.195591556 -1.659249733
166 -0.157984970 2.195591556
167 -0.568808071 -0.157984970
168 -3.450558057 -0.568808071
169 -2.585245671 -3.450558057
170 0.995085617 -2.585245671
171 2.101888344 0.995085617
172 -4.801105311 2.101888344
173 1.152293692 -4.801105311
174 2.807938297 1.152293692
175 -2.495531377 2.807938297
176 -3.022938272 -2.495531377
177 0.819978533 -3.022938272
178 1.365314764 0.819978533
179 -1.846206518 1.365314764
180 0.085305572 -1.846206518
181 -1.388437769 0.085305572
182 0.777972077 -1.388437769
183 -0.669047478 0.777972077
184 2.089783665 -0.669047478
185 0.801222844 2.089783665
186 0.580151053 0.801222844
187 0.610526634 0.580151053
188 0.768918982 0.610526634
189 1.032213787 0.768918982
190 -1.690308163 1.032213787
191 -0.729450762 -1.690308163
192 2.450824759 -0.729450762
193 -1.181512335 2.450824759
194 2.382581405 -1.181512335
195 -1.778624174 2.382581405
196 2.390804801 -1.778624174
197 0.020622613 2.390804801
198 -3.033990384 0.020622613
199 -1.039482286 -3.033990384
200 -2.952605253 -1.039482286
201 1.446594397 -2.952605253
202 2.866012387 1.446594397
203 0.683408402 2.866012387
204 0.708948404 0.683408402
205 1.577525705 0.708948404
206 -0.035959253 1.577525705
207 3.707673157 -0.035959253
208 0.231114620 3.707673157
209 1.093678153 0.231114620
210 -2.582699095 1.093678153
211 1.247237824 -2.582699095
212 -1.014831062 1.247237824
213 -3.629004727 -1.014831062
214 -1.236762817 -3.629004727
215 1.900738290 -1.236762817
216 2.241985343 1.900738290
217 0.065859009 2.241985343
218 -1.461602661 0.065859009
219 1.658263682 -1.461602661
220 -2.783816388 1.658263682
221 1.813871358 -2.783816388
222 -2.047300366 1.813871358
223 -0.112677125 -2.047300366
224 -0.392074165 -0.112677125
225 1.923908879 -0.392074165
226 5.034430576 1.923908879
227 -1.468240816 5.034430576
228 -1.320043843 -1.468240816
229 -2.068036065 -1.320043843
230 0.604291430 -2.068036065
231 -2.793109799 0.604291430
232 0.091325958 -2.793109799
233 -0.037735333 0.091325958
234 1.149787868 -0.037735333
235 -2.096419703 1.149787868
236 0.910851727 -2.096419703
237 0.363983207 0.910851727
238 -4.523230102 0.363983207
239 -2.350428813 -4.523230102
240 -2.717220694 -2.350428813
241 -2.422678514 -2.717220694
242 0.655198454 -2.422678514
243 -0.060829061 0.655198454
244 1.547100383 -0.060829061
245 -0.365774367 1.547100383
246 0.296258697 -0.365774367
247 4.891929593 0.296258697
248 -0.114864834 4.891929593
249 0.591521227 -0.114864834
250 1.986145650 0.591521227
251 1.349115964 1.986145650
252 -1.037306929 1.349115964
253 -0.489984030 -1.037306929
254 0.637161349 -0.489984030
255 -0.475811806 0.637161349
256 -1.741084010 -0.475811806
257 -3.138275095 -1.741084010
258 2.482307423 -3.138275095
259 -5.080082315 2.482307423
260 0.437656068 -5.080082315
261 1.693778794 0.437656068
262 -2.924098566 1.693778794
263 0.299593850 -2.924098566
> 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/7v1tn1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/80a0a1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9fx1a1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10j90s1384528558.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11qqv01384528558.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/125e721384528558.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/13bwu01384528558.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/14d4ve1384528558.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/15buor1384528558.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/16r36i1384528558.tab")
+ }
>
> try(system("convert tmp/1apje1384528558.ps tmp/1apje1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/24twa1384528558.ps tmp/24twa1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/3b95p1384528558.ps tmp/3b95p1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wn7q1384528558.ps tmp/4wn7q1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ford1384528558.ps tmp/5ford1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/65aj11384528558.ps tmp/65aj11384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v1tn1384528558.ps tmp/7v1tn1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/80a0a1384528558.ps tmp/80a0a1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/9fx1a1384528558.ps tmp/9fx1a1384528558.png",intern=TRUE))
character(0)
> try(system("convert tmp/10j90s1384528558.ps tmp/10j90s1384528558.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
19.420 3.439 22.929