R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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> x <- array(list(12
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+ ,12
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+ ,16
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+ ,75
+ ,8
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+ ,14
+ ,9
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+ ,62
+ ,11
+ ,37
+ ,34
+ ,13
+ ,15
+ ,15
+ ,67
+ ,3
+ ,35
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+ ,4
+ ,10
+ ,8
+ ,83
+ ,11
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+ ,30
+ ,15
+ ,14
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+ ,12
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+ ,11
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+ ,11
+ ,7
+ ,22
+ ,62
+ ,9
+ ,29
+ ,32
+ ,14
+ ,14
+ ,12
+ ,72)
+ ,dim=c(7
+ ,264)
+ ,dimnames=list(c('Software'
+ ,'Connected'
+ ,'Separate'
+ ,'Learning'
+ ,'Happiness'
+ ,'Depression'
+ ,'Sport1')
+ ,1:264))
> y <- array(NA,dim=c(7,264),dimnames=list(c('Software','Connected','Separate','Learning','Happiness','Depression','Sport1'),1:264))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Software Connected Separate Learning Happiness Depression Sport1
1 12 41 38 13 14 12.0 53
2 11 39 32 16 18 11.0 83
3 15 30 35 19 11 14.0 66
4 6 31 33 15 12 12.0 67
5 13 34 37 14 16 21.0 76
6 10 35 29 13 18 12.0 78
7 12 39 31 19 14 22.0 53
8 14 34 36 15 14 11.0 80
9 12 36 35 14 15 10.0 74
10 9 37 38 15 15 13.0 76
11 10 38 31 16 17 10.0 79
12 12 36 34 16 19 8.0 54
13 12 38 35 16 10 15.0 67
14 11 39 38 16 16 14.0 54
15 15 33 37 17 18 10.0 87
16 12 32 33 15 14 14.0 58
17 10 36 32 15 14 14.0 75
18 12 38 38 20 17 11.0 88
19 11 39 38 18 14 10.0 64
20 12 32 32 16 16 13.0 57
21 11 32 33 16 18 9.5 66
22 12 31 31 16 11 14.0 68
23 13 39 38 19 14 12.0 54
24 11 37 39 16 12 14.0 56
25 12 39 32 17 17 11.0 86
26 13 41 32 17 9 9.0 80
27 10 36 35 16 16 11.0 76
28 14 33 37 15 14 15.0 69
29 12 33 33 16 15 14.0 78
30 10 34 33 14 11 13.0 67
31 12 31 31 15 16 9.0 80
32 8 27 32 12 13 15.0 54
33 10 37 31 14 17 10.0 71
34 12 34 37 16 15 11.0 84
35 12 34 30 14 14 13.0 74
36 7 32 33 10 16 8.0 71
37 9 29 31 10 9 20.0 63
38 12 36 33 14 15 12.0 71
39 10 29 31 16 17 10.0 76
40 10 35 33 16 13 10.0 69
41 10 37 32 16 15 9.0 74
42 12 34 33 14 16 14.0 75
43 15 38 32 20 16 8.0 54
44 10 35 33 14 12 14.0 52
45 10 38 28 14 15 11.0 69
46 12 37 35 11 11 13.0 68
47 13 38 39 14 15 9.0 65
48 11 33 34 15 15 11.0 75
49 11 36 38 16 17 15.0 74
50 12 38 32 14 13 11.0 75
51 14 32 38 16 16 10.0 72
52 10 32 30 14 14 14.0 67
53 12 32 33 12 11 18.0 63
54 13 34 38 16 12 14.0 62
55 5 32 32 9 12 11.0 63
56 6 37 35 14 15 14.5 76
57 12 39 34 16 16 13.0 74
58 12 29 34 16 15 9.0 67
59 11 37 36 15 12 10.0 73
60 10 35 34 16 12 15.0 70
61 7 30 28 12 8 20.0 53
62 12 38 34 16 13 12.0 77
63 14 34 35 16 11 12.0 80
64 11 31 35 14 14 14.0 52
65 12 34 31 16 15 13.0 54
66 13 35 37 17 10 11.0 80
67 14 36 35 18 11 17.0 66
68 11 30 27 18 12 12.0 73
69 12 39 40 12 15 13.0 63
70 12 35 37 16 15 14.0 69
71 8 38 36 10 14 13.0 67
72 11 31 38 14 16 15.0 54
73 14 34 39 18 15 13.0 81
74 14 38 41 18 15 10.0 69
75 12 34 27 16 13 11.0 84
76 9 39 30 17 12 19.0 80
77 13 37 37 16 17 13.0 70
78 11 34 31 16 13 17.0 69
79 12 28 31 13 15 13.0 77
80 12 37 27 16 13 9.0 54
81 12 33 36 16 15 11.0 79
82 12 35 37 16 15 9.0 71
83 12 37 33 15 16 12.0 73
84 11 32 34 15 15 12.0 72
85 10 33 31 16 14 13.0 77
86 9 38 39 14 15 13.0 75
87 12 33 34 16 14 12.0 69
88 12 29 32 16 13 15.0 54
89 12 33 33 15 7 22.0 70
90 9 31 36 12 17 13.0 73
91 15 36 32 17 13 15.0 54
92 12 35 41 16 15 13.0 77
93 12 32 28 15 14 15.0 82
94 12 29 30 13 13 12.5 80
95 10 39 36 16 16 11.0 80
96 13 37 35 16 12 16.0 69
97 9 35 31 16 14 11.0 78
98 12 37 34 16 17 11.0 81
99 10 32 36 14 15 10.0 76
100 14 38 36 16 17 10.0 76
101 11 37 35 16 12 16.0 73
102 15 36 37 20 16 12.0 85
103 11 32 28 15 11 11.0 66
104 11 33 39 16 15 16.0 79
105 12 40 32 13 9 19.0 68
106 12 38 35 17 16 11.0 76
107 12 41 39 16 15 16.0 71
108 11 36 35 16 10 15.0 54
109 7 43 42 12 10 24.0 46
110 12 30 34 16 15 14.0 85
111 14 31 33 16 11 15.0 74
112 11 32 41 17 13 11.0 88
113 11 32 33 13 14 15.0 38
114 10 37 34 12 18 12.0 76
115 13 37 32 18 16 10.0 86
116 13 33 40 14 14 14.0 54
117 8 34 40 14 14 13.0 67
118 11 33 35 13 14 9.0 69
119 12 38 36 16 14 15.0 90
120 11 33 37 13 12 15.0 54
121 13 31 27 16 14 14.0 76
122 12 38 39 13 15 11.0 89
123 14 37 38 16 15 8.0 76
124 13 36 31 15 15 11.0 73
125 15 31 33 16 13 11.0 79
126 10 39 32 15 17 8.0 90
127 11 44 39 17 17 10.0 74
128 9 33 36 15 19 11.0 81
129 11 35 33 12 15 13.0 72
130 10 32 33 16 13 11.0 71
131 11 28 32 10 9 20.0 66
132 8 40 37 16 15 10.0 77
133 11 27 30 12 15 15.0 65
134 12 37 38 14 15 12.0 74
135 12 32 29 15 16 14.0 85
136 9 28 22 13 11 23.0 54
137 11 34 35 15 14 14.0 63
138 10 30 35 11 11 16.0 54
139 8 35 34 12 15 11.0 64
140 9 31 35 11 13 12.0 69
141 8 32 34 16 15 10.0 54
142 9 30 37 15 16 14.0 84
143 15 30 35 17 14 12.0 86
144 11 31 23 16 15 12.0 77
145 8 40 31 10 16 11.0 89
146 13 32 27 18 16 12.0 76
147 12 36 36 13 11 13.0 60
148 12 32 31 16 12 11.0 75
149 9 35 32 13 9 19.0 73
150 7 38 39 10 16 12.0 85
151 13 42 37 15 13 17.0 79
152 9 34 38 16 16 9.0 71
153 6 35 39 16 12 12.0 72
154 8 38 34 14 9 19.0 69
155 8 33 31 10 13 18.0 78
156 15 36 32 17 13 15.0 54
157 6 32 37 13 14 14.0 69
158 9 33 36 15 19 11.0 81
159 11 34 32 16 13 9.0 84
160 8 32 38 12 12 18.0 84
161 8 34 36 13 13 16.0 69
162 10 27 26 13 10 24.0 66
163 8 31 26 12 14 14.0 81
164 14 38 33 17 16 20.0 82
165 10 34 39 15 10 18.0 72
166 8 24 30 10 11 23.0 54
167 11 30 33 14 14 12.0 78
168 12 26 25 11 12 14.0 74
169 12 34 38 13 9 16.0 82
170 12 27 37 16 9 18.0 73
171 5 37 31 12 11 20.0 55
172 12 36 37 16 16 12.0 72
173 10 41 35 12 9 12.0 78
174 7 29 25 9 13 17.0 59
175 12 36 28 12 16 13.0 72
176 11 32 35 15 13 9.0 78
177 8 37 33 12 9 16.0 68
178 9 30 30 12 12 18.0 69
179 10 31 31 14 16 10.0 67
180 9 38 37 12 11 14.0 74
181 12 36 36 16 14 11.0 54
182 6 35 30 11 13 9.0 67
183 15 31 36 19 15 11.0 70
184 12 38 32 15 14 10.0 80
185 12 22 28 8 16 11.0 89
186 12 32 36 16 13 19.0 76
187 11 36 34 17 14 14.0 74
188 7 39 31 12 15 12.0 87
189 7 28 28 11 13 14.0 54
190 5 32 36 11 11 21.0 61
191 12 32 36 14 11 13.0 38
192 12 38 40 16 14 10.0 75
193 3 32 33 12 15 15.0 69
194 11 35 37 16 11 16.0 62
195 10 32 32 13 15 14.0 72
196 12 37 38 15 12 12.0 70
197 9 34 31 16 14 19.0 79
198 12 33 37 16 14 15.0 87
199 9 33 33 14 8 19.0 62
200 12 26 32 16 13 13.0 77
201 12 30 30 16 9 17.0 69
202 10 24 30 14 15 12.0 69
203 9 34 31 11 17 11.0 75
204 12 34 32 12 13 14.0 54
205 8 33 34 15 15 11.0 72
206 11 34 36 15 15 13.0 74
207 11 35 37 16 14 12.0 85
208 12 35 36 16 16 15.0 52
209 10 36 33 11 13 14.0 70
210 10 34 33 15 16 12.0 84
211 12 34 33 12 9 17.0 64
212 12 41 44 12 16 11.0 84
213 11 32 39 15 11 18.0 87
214 8 30 32 15 10 13.0 79
215 12 35 35 16 11 17.0 67
216 10 28 25 14 15 13.0 65
217 11 33 35 17 17 11.0 85
218 10 39 34 14 14 12.0 83
219 8 36 35 13 8 22.0 61
220 12 36 39 15 15 14.0 82
221 12 35 33 13 11 12.0 76
222 10 38 36 14 16 12.0 58
223 12 33 32 15 10 17.0 72
224 9 31 32 12 15 9.0 72
225 9 34 36 13 9 21.0 38
226 6 32 36 8 16 10.0 78
227 10 31 32 14 19 11.0 54
228 9 33 34 14 12 12.0 63
229 9 34 33 11 8 23.0 66
230 9 34 35 12 11 13.0 70
231 6 34 30 13 14 12.0 71
232 10 33 38 10 9 16.0 67
233 6 32 34 16 15 9.0 58
234 14 41 33 18 13 17.0 72
235 10 34 32 13 16 9.0 72
236 10 36 31 11 11 14.0 70
237 6 37 30 4 12 17.0 76
238 12 36 27 13 13 13.0 50
239 12 29 31 16 10 11.0 72
240 7 37 30 10 11 12.0 72
241 8 27 32 12 12 10.0 88
242 11 35 35 12 8 19.0 53
243 3 28 28 10 12 16.0 58
244 6 35 33 13 12 16.0 66
245 10 37 31 15 15 14.0 82
246 8 29 35 12 11 20.0 69
247 9 32 35 14 13 15.0 68
248 9 36 32 10 14 23.0 44
249 8 19 21 12 10 20.0 56
250 9 21 20 12 12 16.0 53
251 7 31 34 11 15 14.0 70
252 7 33 32 10 13 17.0 78
253 6 36 34 12 13 11.0 71
254 9 33 32 16 13 13.0 72
255 10 37 33 12 12 17.0 68
256 11 34 33 14 12 15.0 67
257 12 35 37 16 9 21.0 75
258 8 31 32 14 9 18.0 62
259 11 37 34 13 15 15.0 67
260 3 35 30 4 10 8.0 83
261 11 27 30 15 14 12.0 64
262 12 34 38 11 15 12.0 68
263 7 40 36 11 7 22.0 62
264 9 29 32 14 14 12.0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Learning Happiness Depression
1.395131 -0.011421 0.037437 0.575114 -0.006285 -0.011501
Sport1
0.004317
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-6.197 -1.141 0.222 1.165 5.050
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.395131 1.854636 0.752 0.453
Connected -0.011421 0.033756 -0.338 0.735
Separate 0.037437 0.034623 1.081 0.281
Learning 0.575114 0.048922 11.756 <2e-16 ***
Happiness -0.006285 0.056571 -0.111 0.912
Depression -0.011501 0.041139 -0.280 0.780
Sport1 0.004317 0.011633 0.371 0.711
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.83 on 257 degrees of freedom
Multiple R-squared: 0.3923, Adjusted R-squared: 0.3781
F-statistic: 27.65 on 6 and 257 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,] 0.991268779 0.017462442 0.008731221
[2,] 0.981525574 0.036948852 0.018474426
[3,] 0.979296891 0.041406219 0.020703109
[4,] 0.966132099 0.067735801 0.033867901
[5,] 0.958347366 0.083305269 0.041652634
[6,] 0.940788970 0.118422060 0.059211030
[7,] 0.923470303 0.153059393 0.076529697
[8,] 0.888365931 0.223268137 0.111634069
[9,] 0.899312158 0.201375684 0.100687842
[10,] 0.872953839 0.254092322 0.127046161
[11,] 0.827681499 0.344637003 0.172318501
[12,] 0.785956183 0.428087634 0.214043817
[13,] 0.742130332 0.515739337 0.257869668
[14,] 0.684093121 0.631813758 0.315906879
[15,] 0.638522833 0.722954334 0.361477167
[16,] 0.586737514 0.826524973 0.413262486
[17,] 0.619904690 0.760190620 0.380095310
[18,] 0.607287926 0.785424148 0.392712074
[19,] 0.620853591 0.758292817 0.379146409
[20,] 0.558935775 0.882128451 0.441064225
[21,] 0.510536948 0.978926104 0.489463052
[22,] 0.460772446 0.921544891 0.539227554
[23,] 0.479857704 0.959715408 0.520142296
[24,] 0.420565507 0.841131014 0.579434493
[25,] 0.366497673 0.732995347 0.633502327
[26,] 0.354384684 0.708769368 0.645615316
[27,] 0.350904791 0.701809581 0.649095209
[28,] 0.301877986 0.603755972 0.698122014
[29,] 0.287635124 0.575270247 0.712364876
[30,] 0.267896633 0.535793265 0.732103367
[31,] 0.243566601 0.487133202 0.756433399
[32,] 0.216404156 0.432808312 0.783595844
[33,] 0.194572114 0.389144227 0.805427886
[34,] 0.241202868 0.482405735 0.758797132
[35,] 0.203264408 0.406528816 0.796735592
[36,] 0.168906159 0.337812318 0.831093841
[37,] 0.211500962 0.423001923 0.788499038
[38,] 0.215764593 0.431529185 0.784235407
[39,] 0.181413601 0.362827201 0.818586399
[40,] 0.166604725 0.333209449 0.833395275
[41,] 0.153932718 0.307865436 0.846067282
[42,] 0.161574362 0.323148725 0.838425638
[43,] 0.134348572 0.268697144 0.865651428
[44,] 0.142190720 0.284381440 0.857809280
[45,] 0.122175194 0.244350388 0.877824806
[46,] 0.188880112 0.377760224 0.811119888
[47,] 0.446405790 0.892811580 0.553594210
[48,] 0.404572917 0.809145835 0.595427083
[49,] 0.363285749 0.726571497 0.636714251
[50,] 0.324077306 0.648154613 0.675922694
[51,] 0.320786277 0.641572554 0.679213723
[52,] 0.324999421 0.649998843 0.675000579
[53,] 0.288134239 0.576268478 0.711865761
[54,] 0.298687584 0.597375168 0.701312416
[55,] 0.264278016 0.528556031 0.735721984
[56,] 0.240571453 0.481142906 0.759428547
[57,] 0.209703337 0.419406674 0.790296663
[58,] 0.191906780 0.383813561 0.808093220
[59,] 0.172138308 0.344276616 0.827861692
[60,] 0.172004934 0.344009869 0.827995066
[61,] 0.147249409 0.294498817 0.852750591
[62,] 0.130317965 0.260635929 0.869682035
[63,] 0.110687421 0.221374843 0.889312579
[64,] 0.094408247 0.188816493 0.905591753
[65,] 0.080215474 0.160430949 0.919784526
[66,] 0.071934916 0.143869832 0.928065084
[67,] 0.091960448 0.183920896 0.908039552
[68,] 0.082146495 0.164292990 0.917853505
[69,] 0.067987450 0.135974900 0.932012550
[70,] 0.072600625 0.145201249 0.927399375
[71,] 0.069634364 0.139268728 0.930365636
[72,] 0.057584351 0.115168701 0.942415649
[73,] 0.047431351 0.094862701 0.952568649
[74,] 0.041434237 0.082868474 0.958565763
[75,] 0.033601081 0.067202161 0.966398919
[76,] 0.030860699 0.061721398 0.969139301
[77,] 0.034972411 0.069944821 0.965027589
[78,] 0.028238152 0.056476304 0.971761848
[79,] 0.022947123 0.045894245 0.977052877
[80,] 0.019455394 0.038910789 0.980544606
[81,] 0.016381346 0.032762692 0.983618654
[82,] 0.027467749 0.054935498 0.972532251
[83,] 0.022631269 0.045262537 0.977368731
[84,] 0.020891896 0.041783793 0.979108104
[85,] 0.022844750 0.045689501 0.977155250
[86,] 0.022470306 0.044940611 0.977529694
[87,] 0.020291085 0.040582170 0.979708915
[88,] 0.024725716 0.049451433 0.975274284
[89,] 0.020050184 0.040100369 0.979949816
[90,] 0.016946935 0.033893871 0.983053065
[91,] 0.020222618 0.040445236 0.979777382
[92,] 0.016696410 0.033392819 0.983303590
[93,] 0.014157250 0.028314499 0.985842750
[94,] 0.011170372 0.022340744 0.988829628
[95,] 0.009788994 0.019577989 0.990211006
[96,] 0.011108058 0.022216116 0.988891942
[97,] 0.008681064 0.017362127 0.991318936
[98,] 0.006815909 0.013631817 0.993184091
[99,] 0.005561521 0.011123042 0.994438479
[100,] 0.007929448 0.015858896 0.992070552
[101,] 0.006174415 0.012348830 0.993825585
[102,] 0.007240335 0.014480669 0.992759665
[103,] 0.007725485 0.015450970 0.992274515
[104,] 0.006783295 0.013566590 0.993216705
[105,] 0.005454108 0.010908216 0.994545892
[106,] 0.004242899 0.008485798 0.995757101
[107,] 0.005018980 0.010037960 0.994981020
[108,] 0.007705404 0.015410809 0.992294596
[109,] 0.006456500 0.012913000 0.993543500
[110,] 0.005026309 0.010052618 0.994973691
[111,] 0.004237028 0.008474056 0.995762972
[112,] 0.004052275 0.008104550 0.995947725
[113,] 0.004095839 0.008191678 0.995904161
[114,] 0.004831857 0.009663714 0.995168143
[115,] 0.005441794 0.010883588 0.994558206
[116,] 0.009958992 0.019917984 0.990041008
[117,] 0.008365212 0.016730424 0.991634788
[118,] 0.007082221 0.014164441 0.992917779
[119,] 0.007894556 0.015789113 0.992105444
[120,] 0.007718997 0.015437993 0.992281003
[121,] 0.007556431 0.015112861 0.992443569
[122,] 0.009543817 0.019087635 0.990456183
[123,] 0.018656527 0.037313053 0.981343473
[124,] 0.018116370 0.036232739 0.981883630
[125,] 0.016918674 0.033837347 0.983081326
[126,] 0.014600547 0.029201094 0.985399453
[127,] 0.011962856 0.023925711 0.988037144
[128,] 0.009568034 0.019136069 0.990431966
[129,] 0.008540865 0.017081729 0.991459135
[130,] 0.007736819 0.015473638 0.992263181
[131,] 0.006281496 0.012562991 0.993718504
[132,] 0.011890246 0.023780492 0.988109754
[133,] 0.013811609 0.027623219 0.986188391
[134,] 0.018302834 0.036605668 0.981697166
[135,] 0.014694937 0.029389875 0.985305063
[136,] 0.011695990 0.023391980 0.988304010
[137,] 0.009472918 0.018945837 0.990527082
[138,] 0.010420549 0.020841098 0.989579451
[139,] 0.008413164 0.016826329 0.991586836
[140,] 0.007153696 0.014307391 0.992846304
[141,] 0.006545279 0.013090557 0.993454721
[142,] 0.006954427 0.013908855 0.993045573
[143,] 0.008935380 0.017870761 0.991064620
[144,] 0.056261293 0.112522585 0.943738707
[145,] 0.064157799 0.128315598 0.935842201
[146,] 0.054136372 0.108272744 0.945863628
[147,] 0.077626358 0.155252717 0.922373642
[148,] 0.136416609 0.272833217 0.863583391
[149,] 0.140144871 0.280289743 0.859855129
[150,] 0.122361038 0.244722075 0.877638962
[151,] 0.117531845 0.235063691 0.882468155
[152,] 0.118314795 0.236629589 0.881685205
[153,] 0.103449053 0.206898106 0.896550947
[154,] 0.092908916 0.185817833 0.907091084
[155,] 0.098258652 0.196517303 0.901741348
[156,] 0.088611052 0.177222104 0.911388948
[157,] 0.075717256 0.151434513 0.924282744
[158,] 0.064695321 0.129390642 0.935304679
[159,] 0.105692084 0.211384168 0.894307916
[160,] 0.107560317 0.215120633 0.892439683
[161,] 0.093102332 0.186204664 0.906897668
[162,] 0.159909433 0.319818867 0.840090567
[163,] 0.139471776 0.278943552 0.860528224
[164,] 0.122882285 0.245764570 0.877117715
[165,] 0.105847222 0.211694443 0.894152778
[166,] 0.142827749 0.285655497 0.857172251
[167,] 0.123186145 0.246372290 0.876813855
[168,] 0.111154577 0.222309154 0.888845423
[169,] 0.095163314 0.190326628 0.904836686
[170,] 0.080746456 0.161492913 0.919253544
[171,] 0.067816394 0.135632788 0.932183606
[172,] 0.057218697 0.114437394 0.942781303
[173,] 0.065535833 0.131071666 0.934464167
[174,] 0.067163100 0.134326200 0.932836900
[175,] 0.061769321 0.123538643 0.938230679
[176,] 0.235618402 0.471236804 0.764381598
[177,] 0.214810671 0.429621343 0.785189329
[178,] 0.193182326 0.386364652 0.806817674
[179,] 0.197255759 0.394511518 0.802744241
[180,] 0.182998140 0.365996280 0.817001860
[181,] 0.263453019 0.526906038 0.736546981
[182,] 0.252469684 0.504939369 0.747530316
[183,] 0.222156772 0.444313544 0.777843228
[184,] 0.590104284 0.819791433 0.409895716
[185,] 0.552636838 0.894726324 0.447363162
[186,] 0.515698872 0.968602255 0.484301128
[187,] 0.487034996 0.974069992 0.512965004
[188,] 0.511029274 0.977941453 0.488970726
[189,] 0.473987892 0.947975783 0.526012108
[190,] 0.449313668 0.898627337 0.550686332
[191,] 0.438380317 0.876760635 0.561619683
[192,] 0.418443254 0.836886508 0.581556746
[193,] 0.394083448 0.788166896 0.605916552
[194,] 0.357546315 0.715092630 0.642453685
[195,] 0.429859820 0.859719640 0.570140180
[196,] 0.467482397 0.934964793 0.532517603
[197,] 0.425050349 0.850100697 0.574949651
[198,] 0.383873817 0.767747634 0.616126183
[199,] 0.346418110 0.692836220 0.653581890
[200,] 0.329166558 0.658333116 0.670833442
[201,] 0.294054337 0.588108674 0.705945663
[202,] 0.367832001 0.735664002 0.632167999
[203,] 0.392079338 0.784158677 0.607920662
[204,] 0.350444702 0.700889404 0.649555298
[205,] 0.370095369 0.740190739 0.629904631
[206,] 0.333511192 0.667022383 0.666488808
[207,] 0.297786692 0.595573383 0.702213308
[208,] 0.262537903 0.525075806 0.737462097
[209,] 0.225764597 0.451529195 0.774235403
[210,] 0.227121478 0.454242956 0.772878522
[211,] 0.204751431 0.409502862 0.795248569
[212,] 0.245075424 0.490150847 0.754924576
[213,] 0.207323091 0.414646181 0.792676909
[214,] 0.197863011 0.395726023 0.802136989
[215,] 0.172611854 0.345223708 0.827388146
[216,] 0.148257253 0.296514506 0.851742747
[217,] 0.122387929 0.244775857 0.877612071
[218,] 0.099617774 0.199235548 0.900382226
[219,] 0.081201753 0.162403505 0.918798247
[220,] 0.062877894 0.125755787 0.937122106
[221,] 0.048477096 0.096954192 0.951522904
[222,] 0.073598717 0.147197434 0.926401283
[223,] 0.091232327 0.182464654 0.908767673
[224,] 0.317088010 0.634176021 0.682911990
[225,] 0.284796132 0.569592265 0.715203868
[226,] 0.235955367 0.471910734 0.764044633
[227,] 0.233273096 0.466546192 0.766726904
[228,] 0.279624450 0.559248901 0.720375550
[229,] 0.299245345 0.598490690 0.700754655
[230,] 0.292456633 0.584913266 0.707543367
[231,] 0.244282265 0.488564530 0.755717735
[232,] 0.203003135 0.406006269 0.796996865
[233,] 0.250879993 0.501759987 0.749120007
[234,] 0.546701207 0.906597586 0.453298793
[235,] 0.702275939 0.595448121 0.297724061
[236,] 0.623954123 0.752091755 0.376045877
[237,] 0.566851636 0.866296728 0.433148364
[238,] 0.519139389 0.961721223 0.480860611
[239,] 0.460639160 0.921278319 0.539360840
[240,] 0.364403178 0.728806355 0.635596822
[241,] 0.336297485 0.672594971 0.663702515
[242,] 0.473977329 0.947954658 0.526022671
[243,] 0.688350043 0.623299914 0.311649957
[244,] 0.681808046 0.636383908 0.318191954
[245,] 0.709763682 0.580472635 0.290236318
> postscript(file="/var/wessaorg/rcomp/tmp/1j02x1384947333.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2xwpi1384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/32tyc1384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/407x91384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5iyp51384947333.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
2.171260493 -0.468165481 1.655282880 -4.978999071 2.570425220 0.356886891
7 8 9 10 11 12
-0.925200420 2.887905049 1.543983215 -2.106152088 -1.442668254 0.519665228
13 14 15 16 17 18
0.472893904 -0.545669719 2.672240373 1.106843880 -0.883418758 -2.032533318
19 20 21 22 23 24
-1.797637452 0.574552698 -0.529417565 0.533160650 -0.306582709 -0.639722132
25 26 27 28 29 30
-0.062514561 0.912948257 -1.597092754 2.932533573 0.463101119 -0.364405536
31 32 33 34 35 36
1.030394747 -1.165000609 -0.269327568 0.264371930 1.736542845 -1.130138008
37 38 39 40 41 42
1.039020019 1.654808759 -1.532509265 -1.533778000 -1.494013153 1.643985441
43 44 45 46 47 48
1.298071200 -0.270447628 -0.138030872 3.316009434 2.444427728 -0.020773613
49 50 51 52 53 54
-0.648482833 1.673750929 2.250677554 -0.244581798 2.837750176 1.337550666
55 56 57 58 59 60
-2.473690530 -4.401476099 0.506242787 0.369960047 -0.071684359 -1.524313357
61 62 63 64 65 66
-1.950593118 0.451516091 2.342874147 0.621563073 0.641497578 0.686521884
67 68 69 70 71 72
1.333426318 -1.517040722 2.623276044 0.375046283 -0.111721021 0.524688926
73 74 75 76 77 78
1.075221460 1.063331416 0.626172287 -2.901159163 1.394641098 -0.389820687
79 80 81 82 83 84
2.199026962 0.766937043 0.311971406 0.308909546 1.088767382 -0.007743981
85 86 87 88 89 90
-1.475493629 -1.552737108 0.435228650 0.557385916 1.114475418 -0.348943646
91 92 93 94 95 96
3.062220665 0.179263657 1.201927718 2.216614951 -1.617532988 1.476909528
97 98 99 100 101 102
-2.479969174 0.436466639 -0.547772275 2.383096942 -0.540357446 1.000227216
103 104 105 106 107 108
0.206138450 -0.742836365 2.368790403 -0.149364250 0.383067576 -0.493830927
109 110 111 112 113 114
-2.237445460 0.361183160 2.443886824 -1.513169274 1.354907389 0.776291836
115 116 117 118 119 120
0.321743442 2.448587050 -2.607610018 1.088631674 0.361311216 1.134943003
121 122 123 124 125 126
1.667229294 1.938941175 2.261230670 2.134434642 3.388870217 -0.964055498
127 128 129 130 131 132
-1.227167090 -2.096408667 1.800799403 -1.565174585 2.977211532 -3.648384020
133 134 135 136 137 138
1.874958936 1.466094743 1.152609541 -0.274887797 0.033228647 1.330997051
139 140 141 142 143 144
-1.225104955 0.244234257 -3.528157890 -2.165412268 2.715029261 -0.204055867
145 146 147 148 149 150
-0.007093437 0.517990967 2.151457140 0.486147599 -0.709899560 -1.300664365
151 152 153 154 155 156
2.008873822 -2.733663854 -5.754633827 -2.308356845 0.022091983 3.062220665
157 158 159 160 161 162
-3.940160339 -2.096408667 -0.584014046 -1.433801618 -1.863164390 0.517357798
163 164 165 166 167 168
-1.016461444 2.003115151 -1.134506894 0.105273133 0.549779204 3.556630806
169 170 171 172 173 174
1.980704509 0.274702784 -3.972734316 0.356800856 0.719342641 -0.153339501
175 176 177 178 179 180
3.005690541 -0.118153110 -1.162298307 -0.092396908 -0.326872943 -0.336957135
181 182 183 184 185 186
0.447868888 -2.548764175 1.602637620 1.071837423 5.049863088 0.392935876
187 188 189 190 191 192
-1.104204015 -2.154893391 -1.440217989 -3.656311414 1.625626510 0.218811093
193 194 195 196 197 198
-6.197512814 -0.596874634 0.240359319 0.889393158 -2.403701968 0.279718194
199 200 201 202 203 204
-1.304093734 0.400835766 0.576791903 -0.361301722 0.415984341 2.903447485
205 206 207 208 209 210
-3.007823382 -0.056908334 -0.723307770 0.503653436 1.394899072 -0.992980544
211 212 213 214 215 216
2.832205571 2.389002455 -0.215815691 -3.005853297 0.467916312 -0.099664068
217 218 219 220 221 222
-1.239036332 -0.406450279 -1.730771314 0.830589845 2.171778364 -0.372257241
223 224 225 226 227 228
1.104630254 -0.253451186 -0.696981510 -1.099436946 -0.277837067 -1.401212612
229 230 231 232 233 234
0.461405121 -0.302001793 -3.686893581 1.759376394 -5.556925514 1.452075825
235 236 237 238 239 240
0.211983423 1.457203375 1.546745928 2.544127348 0.452264372 -0.950459163
241 242 243 244 245 246
-1.375557996 1.832953430 -4.865654525 -3.732766745 -0.858492852 -1.274286746
247 248 249 250 251 252
-1.430867781 1.229476110 -0.814548330 0.225248517 -1.687074469 -1.026845674
253 254 255 256 257 258
-3.256470621 -2.497631773 0.868056909 0.664880619 0.391941238 -2.294715020
259 260 261 262 263 264
1.255675919 -1.622389334 0.113146894 3.183073438 -1.582896936 -1.398304577
> postscript(file="/var/wessaorg/rcomp/tmp/6h9271384947333.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 2.171260493 NA
1 -0.468165481 2.171260493
2 1.655282880 -0.468165481
3 -4.978999071 1.655282880
4 2.570425220 -4.978999071
5 0.356886891 2.570425220
6 -0.925200420 0.356886891
7 2.887905049 -0.925200420
8 1.543983215 2.887905049
9 -2.106152088 1.543983215
10 -1.442668254 -2.106152088
11 0.519665228 -1.442668254
12 0.472893904 0.519665228
13 -0.545669719 0.472893904
14 2.672240373 -0.545669719
15 1.106843880 2.672240373
16 -0.883418758 1.106843880
17 -2.032533318 -0.883418758
18 -1.797637452 -2.032533318
19 0.574552698 -1.797637452
20 -0.529417565 0.574552698
21 0.533160650 -0.529417565
22 -0.306582709 0.533160650
23 -0.639722132 -0.306582709
24 -0.062514561 -0.639722132
25 0.912948257 -0.062514561
26 -1.597092754 0.912948257
27 2.932533573 -1.597092754
28 0.463101119 2.932533573
29 -0.364405536 0.463101119
30 1.030394747 -0.364405536
31 -1.165000609 1.030394747
32 -0.269327568 -1.165000609
33 0.264371930 -0.269327568
34 1.736542845 0.264371930
35 -1.130138008 1.736542845
36 1.039020019 -1.130138008
37 1.654808759 1.039020019
38 -1.532509265 1.654808759
39 -1.533778000 -1.532509265
40 -1.494013153 -1.533778000
41 1.643985441 -1.494013153
42 1.298071200 1.643985441
43 -0.270447628 1.298071200
44 -0.138030872 -0.270447628
45 3.316009434 -0.138030872
46 2.444427728 3.316009434
47 -0.020773613 2.444427728
48 -0.648482833 -0.020773613
49 1.673750929 -0.648482833
50 2.250677554 1.673750929
51 -0.244581798 2.250677554
52 2.837750176 -0.244581798
53 1.337550666 2.837750176
54 -2.473690530 1.337550666
55 -4.401476099 -2.473690530
56 0.506242787 -4.401476099
57 0.369960047 0.506242787
58 -0.071684359 0.369960047
59 -1.524313357 -0.071684359
60 -1.950593118 -1.524313357
61 0.451516091 -1.950593118
62 2.342874147 0.451516091
63 0.621563073 2.342874147
64 0.641497578 0.621563073
65 0.686521884 0.641497578
66 1.333426318 0.686521884
67 -1.517040722 1.333426318
68 2.623276044 -1.517040722
69 0.375046283 2.623276044
70 -0.111721021 0.375046283
71 0.524688926 -0.111721021
72 1.075221460 0.524688926
73 1.063331416 1.075221460
74 0.626172287 1.063331416
75 -2.901159163 0.626172287
76 1.394641098 -2.901159163
77 -0.389820687 1.394641098
78 2.199026962 -0.389820687
79 0.766937043 2.199026962
80 0.311971406 0.766937043
81 0.308909546 0.311971406
82 1.088767382 0.308909546
83 -0.007743981 1.088767382
84 -1.475493629 -0.007743981
85 -1.552737108 -1.475493629
86 0.435228650 -1.552737108
87 0.557385916 0.435228650
88 1.114475418 0.557385916
89 -0.348943646 1.114475418
90 3.062220665 -0.348943646
91 0.179263657 3.062220665
92 1.201927718 0.179263657
93 2.216614951 1.201927718
94 -1.617532988 2.216614951
95 1.476909528 -1.617532988
96 -2.479969174 1.476909528
97 0.436466639 -2.479969174
98 -0.547772275 0.436466639
99 2.383096942 -0.547772275
100 -0.540357446 2.383096942
101 1.000227216 -0.540357446
102 0.206138450 1.000227216
103 -0.742836365 0.206138450
104 2.368790403 -0.742836365
105 -0.149364250 2.368790403
106 0.383067576 -0.149364250
107 -0.493830927 0.383067576
108 -2.237445460 -0.493830927
109 0.361183160 -2.237445460
110 2.443886824 0.361183160
111 -1.513169274 2.443886824
112 1.354907389 -1.513169274
113 0.776291836 1.354907389
114 0.321743442 0.776291836
115 2.448587050 0.321743442
116 -2.607610018 2.448587050
117 1.088631674 -2.607610018
118 0.361311216 1.088631674
119 1.134943003 0.361311216
120 1.667229294 1.134943003
121 1.938941175 1.667229294
122 2.261230670 1.938941175
123 2.134434642 2.261230670
124 3.388870217 2.134434642
125 -0.964055498 3.388870217
126 -1.227167090 -0.964055498
127 -2.096408667 -1.227167090
128 1.800799403 -2.096408667
129 -1.565174585 1.800799403
130 2.977211532 -1.565174585
131 -3.648384020 2.977211532
132 1.874958936 -3.648384020
133 1.466094743 1.874958936
134 1.152609541 1.466094743
135 -0.274887797 1.152609541
136 0.033228647 -0.274887797
137 1.330997051 0.033228647
138 -1.225104955 1.330997051
139 0.244234257 -1.225104955
140 -3.528157890 0.244234257
141 -2.165412268 -3.528157890
142 2.715029261 -2.165412268
143 -0.204055867 2.715029261
144 -0.007093437 -0.204055867
145 0.517990967 -0.007093437
146 2.151457140 0.517990967
147 0.486147599 2.151457140
148 -0.709899560 0.486147599
149 -1.300664365 -0.709899560
150 2.008873822 -1.300664365
151 -2.733663854 2.008873822
152 -5.754633827 -2.733663854
153 -2.308356845 -5.754633827
154 0.022091983 -2.308356845
155 3.062220665 0.022091983
156 -3.940160339 3.062220665
157 -2.096408667 -3.940160339
158 -0.584014046 -2.096408667
159 -1.433801618 -0.584014046
160 -1.863164390 -1.433801618
161 0.517357798 -1.863164390
162 -1.016461444 0.517357798
163 2.003115151 -1.016461444
164 -1.134506894 2.003115151
165 0.105273133 -1.134506894
166 0.549779204 0.105273133
167 3.556630806 0.549779204
168 1.980704509 3.556630806
169 0.274702784 1.980704509
170 -3.972734316 0.274702784
171 0.356800856 -3.972734316
172 0.719342641 0.356800856
173 -0.153339501 0.719342641
174 3.005690541 -0.153339501
175 -0.118153110 3.005690541
176 -1.162298307 -0.118153110
177 -0.092396908 -1.162298307
178 -0.326872943 -0.092396908
179 -0.336957135 -0.326872943
180 0.447868888 -0.336957135
181 -2.548764175 0.447868888
182 1.602637620 -2.548764175
183 1.071837423 1.602637620
184 5.049863088 1.071837423
185 0.392935876 5.049863088
186 -1.104204015 0.392935876
187 -2.154893391 -1.104204015
188 -1.440217989 -2.154893391
189 -3.656311414 -1.440217989
190 1.625626510 -3.656311414
191 0.218811093 1.625626510
192 -6.197512814 0.218811093
193 -0.596874634 -6.197512814
194 0.240359319 -0.596874634
195 0.889393158 0.240359319
196 -2.403701968 0.889393158
197 0.279718194 -2.403701968
198 -1.304093734 0.279718194
199 0.400835766 -1.304093734
200 0.576791903 0.400835766
201 -0.361301722 0.576791903
202 0.415984341 -0.361301722
203 2.903447485 0.415984341
204 -3.007823382 2.903447485
205 -0.056908334 -3.007823382
206 -0.723307770 -0.056908334
207 0.503653436 -0.723307770
208 1.394899072 0.503653436
209 -0.992980544 1.394899072
210 2.832205571 -0.992980544
211 2.389002455 2.832205571
212 -0.215815691 2.389002455
213 -3.005853297 -0.215815691
214 0.467916312 -3.005853297
215 -0.099664068 0.467916312
216 -1.239036332 -0.099664068
217 -0.406450279 -1.239036332
218 -1.730771314 -0.406450279
219 0.830589845 -1.730771314
220 2.171778364 0.830589845
221 -0.372257241 2.171778364
222 1.104630254 -0.372257241
223 -0.253451186 1.104630254
224 -0.696981510 -0.253451186
225 -1.099436946 -0.696981510
226 -0.277837067 -1.099436946
227 -1.401212612 -0.277837067
228 0.461405121 -1.401212612
229 -0.302001793 0.461405121
230 -3.686893581 -0.302001793
231 1.759376394 -3.686893581
232 -5.556925514 1.759376394
233 1.452075825 -5.556925514
234 0.211983423 1.452075825
235 1.457203375 0.211983423
236 1.546745928 1.457203375
237 2.544127348 1.546745928
238 0.452264372 2.544127348
239 -0.950459163 0.452264372
240 -1.375557996 -0.950459163
241 1.832953430 -1.375557996
242 -4.865654525 1.832953430
243 -3.732766745 -4.865654525
244 -0.858492852 -3.732766745
245 -1.274286746 -0.858492852
246 -1.430867781 -1.274286746
247 1.229476110 -1.430867781
248 -0.814548330 1.229476110
249 0.225248517 -0.814548330
250 -1.687074469 0.225248517
251 -1.026845674 -1.687074469
252 -3.256470621 -1.026845674
253 -2.497631773 -3.256470621
254 0.868056909 -2.497631773
255 0.664880619 0.868056909
256 0.391941238 0.664880619
257 -2.294715020 0.391941238
258 1.255675919 -2.294715020
259 -1.622389334 1.255675919
260 0.113146894 -1.622389334
261 3.183073438 0.113146894
262 -1.582896936 3.183073438
263 -1.398304577 -1.582896936
264 NA -1.398304577
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.468165481 2.171260493
[2,] 1.655282880 -0.468165481
[3,] -4.978999071 1.655282880
[4,] 2.570425220 -4.978999071
[5,] 0.356886891 2.570425220
[6,] -0.925200420 0.356886891
[7,] 2.887905049 -0.925200420
[8,] 1.543983215 2.887905049
[9,] -2.106152088 1.543983215
[10,] -1.442668254 -2.106152088
[11,] 0.519665228 -1.442668254
[12,] 0.472893904 0.519665228
[13,] -0.545669719 0.472893904
[14,] 2.672240373 -0.545669719
[15,] 1.106843880 2.672240373
[16,] -0.883418758 1.106843880
[17,] -2.032533318 -0.883418758
[18,] -1.797637452 -2.032533318
[19,] 0.574552698 -1.797637452
[20,] -0.529417565 0.574552698
[21,] 0.533160650 -0.529417565
[22,] -0.306582709 0.533160650
[23,] -0.639722132 -0.306582709
[24,] -0.062514561 -0.639722132
[25,] 0.912948257 -0.062514561
[26,] -1.597092754 0.912948257
[27,] 2.932533573 -1.597092754
[28,] 0.463101119 2.932533573
[29,] -0.364405536 0.463101119
[30,] 1.030394747 -0.364405536
[31,] -1.165000609 1.030394747
[32,] -0.269327568 -1.165000609
[33,] 0.264371930 -0.269327568
[34,] 1.736542845 0.264371930
[35,] -1.130138008 1.736542845
[36,] 1.039020019 -1.130138008
[37,] 1.654808759 1.039020019
[38,] -1.532509265 1.654808759
[39,] -1.533778000 -1.532509265
[40,] -1.494013153 -1.533778000
[41,] 1.643985441 -1.494013153
[42,] 1.298071200 1.643985441
[43,] -0.270447628 1.298071200
[44,] -0.138030872 -0.270447628
[45,] 3.316009434 -0.138030872
[46,] 2.444427728 3.316009434
[47,] -0.020773613 2.444427728
[48,] -0.648482833 -0.020773613
[49,] 1.673750929 -0.648482833
[50,] 2.250677554 1.673750929
[51,] -0.244581798 2.250677554
[52,] 2.837750176 -0.244581798
[53,] 1.337550666 2.837750176
[54,] -2.473690530 1.337550666
[55,] -4.401476099 -2.473690530
[56,] 0.506242787 -4.401476099
[57,] 0.369960047 0.506242787
[58,] -0.071684359 0.369960047
[59,] -1.524313357 -0.071684359
[60,] -1.950593118 -1.524313357
[61,] 0.451516091 -1.950593118
[62,] 2.342874147 0.451516091
[63,] 0.621563073 2.342874147
[64,] 0.641497578 0.621563073
[65,] 0.686521884 0.641497578
[66,] 1.333426318 0.686521884
[67,] -1.517040722 1.333426318
[68,] 2.623276044 -1.517040722
[69,] 0.375046283 2.623276044
[70,] -0.111721021 0.375046283
[71,] 0.524688926 -0.111721021
[72,] 1.075221460 0.524688926
[73,] 1.063331416 1.075221460
[74,] 0.626172287 1.063331416
[75,] -2.901159163 0.626172287
[76,] 1.394641098 -2.901159163
[77,] -0.389820687 1.394641098
[78,] 2.199026962 -0.389820687
[79,] 0.766937043 2.199026962
[80,] 0.311971406 0.766937043
[81,] 0.308909546 0.311971406
[82,] 1.088767382 0.308909546
[83,] -0.007743981 1.088767382
[84,] -1.475493629 -0.007743981
[85,] -1.552737108 -1.475493629
[86,] 0.435228650 -1.552737108
[87,] 0.557385916 0.435228650
[88,] 1.114475418 0.557385916
[89,] -0.348943646 1.114475418
[90,] 3.062220665 -0.348943646
[91,] 0.179263657 3.062220665
[92,] 1.201927718 0.179263657
[93,] 2.216614951 1.201927718
[94,] -1.617532988 2.216614951
[95,] 1.476909528 -1.617532988
[96,] -2.479969174 1.476909528
[97,] 0.436466639 -2.479969174
[98,] -0.547772275 0.436466639
[99,] 2.383096942 -0.547772275
[100,] -0.540357446 2.383096942
[101,] 1.000227216 -0.540357446
[102,] 0.206138450 1.000227216
[103,] -0.742836365 0.206138450
[104,] 2.368790403 -0.742836365
[105,] -0.149364250 2.368790403
[106,] 0.383067576 -0.149364250
[107,] -0.493830927 0.383067576
[108,] -2.237445460 -0.493830927
[109,] 0.361183160 -2.237445460
[110,] 2.443886824 0.361183160
[111,] -1.513169274 2.443886824
[112,] 1.354907389 -1.513169274
[113,] 0.776291836 1.354907389
[114,] 0.321743442 0.776291836
[115,] 2.448587050 0.321743442
[116,] -2.607610018 2.448587050
[117,] 1.088631674 -2.607610018
[118,] 0.361311216 1.088631674
[119,] 1.134943003 0.361311216
[120,] 1.667229294 1.134943003
[121,] 1.938941175 1.667229294
[122,] 2.261230670 1.938941175
[123,] 2.134434642 2.261230670
[124,] 3.388870217 2.134434642
[125,] -0.964055498 3.388870217
[126,] -1.227167090 -0.964055498
[127,] -2.096408667 -1.227167090
[128,] 1.800799403 -2.096408667
[129,] -1.565174585 1.800799403
[130,] 2.977211532 -1.565174585
[131,] -3.648384020 2.977211532
[132,] 1.874958936 -3.648384020
[133,] 1.466094743 1.874958936
[134,] 1.152609541 1.466094743
[135,] -0.274887797 1.152609541
[136,] 0.033228647 -0.274887797
[137,] 1.330997051 0.033228647
[138,] -1.225104955 1.330997051
[139,] 0.244234257 -1.225104955
[140,] -3.528157890 0.244234257
[141,] -2.165412268 -3.528157890
[142,] 2.715029261 -2.165412268
[143,] -0.204055867 2.715029261
[144,] -0.007093437 -0.204055867
[145,] 0.517990967 -0.007093437
[146,] 2.151457140 0.517990967
[147,] 0.486147599 2.151457140
[148,] -0.709899560 0.486147599
[149,] -1.300664365 -0.709899560
[150,] 2.008873822 -1.300664365
[151,] -2.733663854 2.008873822
[152,] -5.754633827 -2.733663854
[153,] -2.308356845 -5.754633827
[154,] 0.022091983 -2.308356845
[155,] 3.062220665 0.022091983
[156,] -3.940160339 3.062220665
[157,] -2.096408667 -3.940160339
[158,] -0.584014046 -2.096408667
[159,] -1.433801618 -0.584014046
[160,] -1.863164390 -1.433801618
[161,] 0.517357798 -1.863164390
[162,] -1.016461444 0.517357798
[163,] 2.003115151 -1.016461444
[164,] -1.134506894 2.003115151
[165,] 0.105273133 -1.134506894
[166,] 0.549779204 0.105273133
[167,] 3.556630806 0.549779204
[168,] 1.980704509 3.556630806
[169,] 0.274702784 1.980704509
[170,] -3.972734316 0.274702784
[171,] 0.356800856 -3.972734316
[172,] 0.719342641 0.356800856
[173,] -0.153339501 0.719342641
[174,] 3.005690541 -0.153339501
[175,] -0.118153110 3.005690541
[176,] -1.162298307 -0.118153110
[177,] -0.092396908 -1.162298307
[178,] -0.326872943 -0.092396908
[179,] -0.336957135 -0.326872943
[180,] 0.447868888 -0.336957135
[181,] -2.548764175 0.447868888
[182,] 1.602637620 -2.548764175
[183,] 1.071837423 1.602637620
[184,] 5.049863088 1.071837423
[185,] 0.392935876 5.049863088
[186,] -1.104204015 0.392935876
[187,] -2.154893391 -1.104204015
[188,] -1.440217989 -2.154893391
[189,] -3.656311414 -1.440217989
[190,] 1.625626510 -3.656311414
[191,] 0.218811093 1.625626510
[192,] -6.197512814 0.218811093
[193,] -0.596874634 -6.197512814
[194,] 0.240359319 -0.596874634
[195,] 0.889393158 0.240359319
[196,] -2.403701968 0.889393158
[197,] 0.279718194 -2.403701968
[198,] -1.304093734 0.279718194
[199,] 0.400835766 -1.304093734
[200,] 0.576791903 0.400835766
[201,] -0.361301722 0.576791903
[202,] 0.415984341 -0.361301722
[203,] 2.903447485 0.415984341
[204,] -3.007823382 2.903447485
[205,] -0.056908334 -3.007823382
[206,] -0.723307770 -0.056908334
[207,] 0.503653436 -0.723307770
[208,] 1.394899072 0.503653436
[209,] -0.992980544 1.394899072
[210,] 2.832205571 -0.992980544
[211,] 2.389002455 2.832205571
[212,] -0.215815691 2.389002455
[213,] -3.005853297 -0.215815691
[214,] 0.467916312 -3.005853297
[215,] -0.099664068 0.467916312
[216,] -1.239036332 -0.099664068
[217,] -0.406450279 -1.239036332
[218,] -1.730771314 -0.406450279
[219,] 0.830589845 -1.730771314
[220,] 2.171778364 0.830589845
[221,] -0.372257241 2.171778364
[222,] 1.104630254 -0.372257241
[223,] -0.253451186 1.104630254
[224,] -0.696981510 -0.253451186
[225,] -1.099436946 -0.696981510
[226,] -0.277837067 -1.099436946
[227,] -1.401212612 -0.277837067
[228,] 0.461405121 -1.401212612
[229,] -0.302001793 0.461405121
[230,] -3.686893581 -0.302001793
[231,] 1.759376394 -3.686893581
[232,] -5.556925514 1.759376394
[233,] 1.452075825 -5.556925514
[234,] 0.211983423 1.452075825
[235,] 1.457203375 0.211983423
[236,] 1.546745928 1.457203375
[237,] 2.544127348 1.546745928
[238,] 0.452264372 2.544127348
[239,] -0.950459163 0.452264372
[240,] -1.375557996 -0.950459163
[241,] 1.832953430 -1.375557996
[242,] -4.865654525 1.832953430
[243,] -3.732766745 -4.865654525
[244,] -0.858492852 -3.732766745
[245,] -1.274286746 -0.858492852
[246,] -1.430867781 -1.274286746
[247,] 1.229476110 -1.430867781
[248,] -0.814548330 1.229476110
[249,] 0.225248517 -0.814548330
[250,] -1.687074469 0.225248517
[251,] -1.026845674 -1.687074469
[252,] -3.256470621 -1.026845674
[253,] -2.497631773 -3.256470621
[254,] 0.868056909 -2.497631773
[255,] 0.664880619 0.868056909
[256,] 0.391941238 0.664880619
[257,] -2.294715020 0.391941238
[258,] 1.255675919 -2.294715020
[259,] -1.622389334 1.255675919
[260,] 0.113146894 -1.622389334
[261,] 3.183073438 0.113146894
[262,] -1.582896936 3.183073438
[263,] -1.398304577 -1.582896936
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.468165481 2.171260493
2 1.655282880 -0.468165481
3 -4.978999071 1.655282880
4 2.570425220 -4.978999071
5 0.356886891 2.570425220
6 -0.925200420 0.356886891
7 2.887905049 -0.925200420
8 1.543983215 2.887905049
9 -2.106152088 1.543983215
10 -1.442668254 -2.106152088
11 0.519665228 -1.442668254
12 0.472893904 0.519665228
13 -0.545669719 0.472893904
14 2.672240373 -0.545669719
15 1.106843880 2.672240373
16 -0.883418758 1.106843880
17 -2.032533318 -0.883418758
18 -1.797637452 -2.032533318
19 0.574552698 -1.797637452
20 -0.529417565 0.574552698
21 0.533160650 -0.529417565
22 -0.306582709 0.533160650
23 -0.639722132 -0.306582709
24 -0.062514561 -0.639722132
25 0.912948257 -0.062514561
26 -1.597092754 0.912948257
27 2.932533573 -1.597092754
28 0.463101119 2.932533573
29 -0.364405536 0.463101119
30 1.030394747 -0.364405536
31 -1.165000609 1.030394747
32 -0.269327568 -1.165000609
33 0.264371930 -0.269327568
34 1.736542845 0.264371930
35 -1.130138008 1.736542845
36 1.039020019 -1.130138008
37 1.654808759 1.039020019
38 -1.532509265 1.654808759
39 -1.533778000 -1.532509265
40 -1.494013153 -1.533778000
41 1.643985441 -1.494013153
42 1.298071200 1.643985441
43 -0.270447628 1.298071200
44 -0.138030872 -0.270447628
45 3.316009434 -0.138030872
46 2.444427728 3.316009434
47 -0.020773613 2.444427728
48 -0.648482833 -0.020773613
49 1.673750929 -0.648482833
50 2.250677554 1.673750929
51 -0.244581798 2.250677554
52 2.837750176 -0.244581798
53 1.337550666 2.837750176
54 -2.473690530 1.337550666
55 -4.401476099 -2.473690530
56 0.506242787 -4.401476099
57 0.369960047 0.506242787
58 -0.071684359 0.369960047
59 -1.524313357 -0.071684359
60 -1.950593118 -1.524313357
61 0.451516091 -1.950593118
62 2.342874147 0.451516091
63 0.621563073 2.342874147
64 0.641497578 0.621563073
65 0.686521884 0.641497578
66 1.333426318 0.686521884
67 -1.517040722 1.333426318
68 2.623276044 -1.517040722
69 0.375046283 2.623276044
70 -0.111721021 0.375046283
71 0.524688926 -0.111721021
72 1.075221460 0.524688926
73 1.063331416 1.075221460
74 0.626172287 1.063331416
75 -2.901159163 0.626172287
76 1.394641098 -2.901159163
77 -0.389820687 1.394641098
78 2.199026962 -0.389820687
79 0.766937043 2.199026962
80 0.311971406 0.766937043
81 0.308909546 0.311971406
82 1.088767382 0.308909546
83 -0.007743981 1.088767382
84 -1.475493629 -0.007743981
85 -1.552737108 -1.475493629
86 0.435228650 -1.552737108
87 0.557385916 0.435228650
88 1.114475418 0.557385916
89 -0.348943646 1.114475418
90 3.062220665 -0.348943646
91 0.179263657 3.062220665
92 1.201927718 0.179263657
93 2.216614951 1.201927718
94 -1.617532988 2.216614951
95 1.476909528 -1.617532988
96 -2.479969174 1.476909528
97 0.436466639 -2.479969174
98 -0.547772275 0.436466639
99 2.383096942 -0.547772275
100 -0.540357446 2.383096942
101 1.000227216 -0.540357446
102 0.206138450 1.000227216
103 -0.742836365 0.206138450
104 2.368790403 -0.742836365
105 -0.149364250 2.368790403
106 0.383067576 -0.149364250
107 -0.493830927 0.383067576
108 -2.237445460 -0.493830927
109 0.361183160 -2.237445460
110 2.443886824 0.361183160
111 -1.513169274 2.443886824
112 1.354907389 -1.513169274
113 0.776291836 1.354907389
114 0.321743442 0.776291836
115 2.448587050 0.321743442
116 -2.607610018 2.448587050
117 1.088631674 -2.607610018
118 0.361311216 1.088631674
119 1.134943003 0.361311216
120 1.667229294 1.134943003
121 1.938941175 1.667229294
122 2.261230670 1.938941175
123 2.134434642 2.261230670
124 3.388870217 2.134434642
125 -0.964055498 3.388870217
126 -1.227167090 -0.964055498
127 -2.096408667 -1.227167090
128 1.800799403 -2.096408667
129 -1.565174585 1.800799403
130 2.977211532 -1.565174585
131 -3.648384020 2.977211532
132 1.874958936 -3.648384020
133 1.466094743 1.874958936
134 1.152609541 1.466094743
135 -0.274887797 1.152609541
136 0.033228647 -0.274887797
137 1.330997051 0.033228647
138 -1.225104955 1.330997051
139 0.244234257 -1.225104955
140 -3.528157890 0.244234257
141 -2.165412268 -3.528157890
142 2.715029261 -2.165412268
143 -0.204055867 2.715029261
144 -0.007093437 -0.204055867
145 0.517990967 -0.007093437
146 2.151457140 0.517990967
147 0.486147599 2.151457140
148 -0.709899560 0.486147599
149 -1.300664365 -0.709899560
150 2.008873822 -1.300664365
151 -2.733663854 2.008873822
152 -5.754633827 -2.733663854
153 -2.308356845 -5.754633827
154 0.022091983 -2.308356845
155 3.062220665 0.022091983
156 -3.940160339 3.062220665
157 -2.096408667 -3.940160339
158 -0.584014046 -2.096408667
159 -1.433801618 -0.584014046
160 -1.863164390 -1.433801618
161 0.517357798 -1.863164390
162 -1.016461444 0.517357798
163 2.003115151 -1.016461444
164 -1.134506894 2.003115151
165 0.105273133 -1.134506894
166 0.549779204 0.105273133
167 3.556630806 0.549779204
168 1.980704509 3.556630806
169 0.274702784 1.980704509
170 -3.972734316 0.274702784
171 0.356800856 -3.972734316
172 0.719342641 0.356800856
173 -0.153339501 0.719342641
174 3.005690541 -0.153339501
175 -0.118153110 3.005690541
176 -1.162298307 -0.118153110
177 -0.092396908 -1.162298307
178 -0.326872943 -0.092396908
179 -0.336957135 -0.326872943
180 0.447868888 -0.336957135
181 -2.548764175 0.447868888
182 1.602637620 -2.548764175
183 1.071837423 1.602637620
184 5.049863088 1.071837423
185 0.392935876 5.049863088
186 -1.104204015 0.392935876
187 -2.154893391 -1.104204015
188 -1.440217989 -2.154893391
189 -3.656311414 -1.440217989
190 1.625626510 -3.656311414
191 0.218811093 1.625626510
192 -6.197512814 0.218811093
193 -0.596874634 -6.197512814
194 0.240359319 -0.596874634
195 0.889393158 0.240359319
196 -2.403701968 0.889393158
197 0.279718194 -2.403701968
198 -1.304093734 0.279718194
199 0.400835766 -1.304093734
200 0.576791903 0.400835766
201 -0.361301722 0.576791903
202 0.415984341 -0.361301722
203 2.903447485 0.415984341
204 -3.007823382 2.903447485
205 -0.056908334 -3.007823382
206 -0.723307770 -0.056908334
207 0.503653436 -0.723307770
208 1.394899072 0.503653436
209 -0.992980544 1.394899072
210 2.832205571 -0.992980544
211 2.389002455 2.832205571
212 -0.215815691 2.389002455
213 -3.005853297 -0.215815691
214 0.467916312 -3.005853297
215 -0.099664068 0.467916312
216 -1.239036332 -0.099664068
217 -0.406450279 -1.239036332
218 -1.730771314 -0.406450279
219 0.830589845 -1.730771314
220 2.171778364 0.830589845
221 -0.372257241 2.171778364
222 1.104630254 -0.372257241
223 -0.253451186 1.104630254
224 -0.696981510 -0.253451186
225 -1.099436946 -0.696981510
226 -0.277837067 -1.099436946
227 -1.401212612 -0.277837067
228 0.461405121 -1.401212612
229 -0.302001793 0.461405121
230 -3.686893581 -0.302001793
231 1.759376394 -3.686893581
232 -5.556925514 1.759376394
233 1.452075825 -5.556925514
234 0.211983423 1.452075825
235 1.457203375 0.211983423
236 1.546745928 1.457203375
237 2.544127348 1.546745928
238 0.452264372 2.544127348
239 -0.950459163 0.452264372
240 -1.375557996 -0.950459163
241 1.832953430 -1.375557996
242 -4.865654525 1.832953430
243 -3.732766745 -4.865654525
244 -0.858492852 -3.732766745
245 -1.274286746 -0.858492852
246 -1.430867781 -1.274286746
247 1.229476110 -1.430867781
248 -0.814548330 1.229476110
249 0.225248517 -0.814548330
250 -1.687074469 0.225248517
251 -1.026845674 -1.687074469
252 -3.256470621 -1.026845674
253 -2.497631773 -3.256470621
254 0.868056909 -2.497631773
255 0.664880619 0.868056909
256 0.391941238 0.664880619
257 -2.294715020 0.391941238
258 1.255675919 -2.294715020
259 -1.622389334 1.255675919
260 0.113146894 -1.622389334
261 3.183073438 0.113146894
262 -1.582896936 3.183073438
263 -1.398304577 -1.582896936
> 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/73d9x1384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8axba1384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9p4g51384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/103e8c1384947333.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11gjf31384947333.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/1210fg1384947333.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/13b0f51384947333.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/14q2vr1384947333.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/15ch8t1384947333.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/16p4iz1384947333.tab")
+ }
>
> try(system("convert tmp/1j02x1384947333.ps tmp/1j02x1384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xwpi1384947333.ps tmp/2xwpi1384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/32tyc1384947333.ps tmp/32tyc1384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/407x91384947333.ps tmp/407x91384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/5iyp51384947333.ps tmp/5iyp51384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/6h9271384947333.ps tmp/6h9271384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/73d9x1384947333.ps tmp/73d9x1384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/8axba1384947333.ps tmp/8axba1384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/9p4g51384947333.ps tmp/9p4g51384947333.png",intern=TRUE))
character(0)
> try(system("convert tmp/103e8c1384947333.ps tmp/103e8c1384947333.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.707 2.576 18.262