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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Type 'q()' to quit R.
> x <- array(list(41
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+ ,72)
+ ,dim=c(5
+ ,264)
+ ,dimnames=list(c('Connected'
+ ,'Separate'
+ ,'Computer'
+ ,'Depression'
+ ,'Sport
')
+ ,1:264))
> y <- array(NA,dim=c(5,264),dimnames=list(c('Connected','Separate','Computer','Depression','Sport
'),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 = '3'
> 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
Computer Connected Separate Depression Sport\r\r
1 12 41 38 12.0 53
2 11 39 32 11.0 83
3 15 30 35 14.0 66
4 6 31 33 12.0 67
5 13 34 37 21.0 76
6 10 35 29 12.0 78
7 12 39 31 22.0 53
8 14 34 36 11.0 80
9 12 36 35 10.0 74
10 9 37 38 13.0 76
11 10 38 31 10.0 79
12 12 36 34 8.0 54
13 12 38 35 15.0 67
14 11 39 38 14.0 54
15 15 33 37 10.0 87
16 12 32 33 14.0 58
17 10 36 32 14.0 75
18 12 38 38 11.0 88
19 11 39 38 10.0 64
20 12 32 32 13.0 57
21 11 32 33 9.5 66
22 12 31 31 14.0 68
23 13 39 38 12.0 54
24 11 37 39 14.0 56
25 12 39 32 11.0 86
26 13 41 32 9.0 80
27 10 36 35 11.0 76
28 14 33 37 15.0 69
29 12 33 33 14.0 78
30 10 34 33 13.0 67
31 12 31 31 9.0 80
32 8 27 32 15.0 54
33 10 37 31 10.0 71
34 12 34 37 11.0 84
35 12 34 30 13.0 74
36 7 32 33 8.0 71
37 9 29 31 20.0 63
38 12 36 33 12.0 71
39 10 29 31 10.0 76
40 10 35 33 10.0 69
41 10 37 32 9.0 74
42 12 34 33 14.0 75
43 15 38 32 8.0 54
44 10 35 33 14.0 52
45 10 38 28 11.0 69
46 12 37 35 13.0 68
47 13 38 39 9.0 65
48 11 33 34 11.0 75
49 11 36 38 15.0 74
50 12 38 32 11.0 75
51 14 32 38 10.0 72
52 10 32 30 14.0 67
53 12 32 33 18.0 63
54 13 34 38 14.0 62
55 5 32 32 11.0 63
56 6 37 35 14.5 76
57 12 39 34 13.0 74
58 12 29 34 9.0 67
59 11 37 36 10.0 73
60 10 35 34 15.0 70
61 7 30 28 20.0 53
62 12 38 34 12.0 77
63 14 34 35 12.0 80
64 11 31 35 14.0 52
65 12 34 31 13.0 54
66 13 35 37 11.0 80
67 14 36 35 17.0 66
68 11 30 27 12.0 73
69 12 39 40 13.0 63
70 12 35 37 14.0 69
71 8 38 36 13.0 67
72 11 31 38 15.0 54
73 14 34 39 13.0 81
74 14 38 41 10.0 69
75 12 34 27 11.0 84
76 9 39 30 19.0 80
77 13 37 37 13.0 70
78 11 34 31 17.0 69
79 12 28 31 13.0 77
80 12 37 27 9.0 54
81 12 33 36 11.0 79
82 12 35 37 9.0 71
83 12 37 33 12.0 73
84 11 32 34 12.0 72
85 10 33 31 13.0 77
86 9 38 39 13.0 75
87 12 33 34 12.0 69
88 12 29 32 15.0 54
89 12 33 33 22.0 70
90 9 31 36 13.0 73
91 15 36 32 15.0 54
92 12 35 41 13.0 77
93 12 32 28 15.0 82
94 12 29 30 12.5 80
95 10 39 36 11.0 80
96 13 37 35 16.0 69
97 9 35 31 11.0 78
98 12 37 34 11.0 81
99 10 32 36 10.0 76
100 14 38 36 10.0 76
101 11 37 35 16.0 73
102 15 36 37 12.0 85
103 11 32 28 11.0 66
104 11 33 39 16.0 79
105 12 40 32 19.0 68
106 12 38 35 11.0 76
107 12 41 39 16.0 71
108 11 36 35 15.0 54
109 7 43 42 24.0 46
110 12 30 34 14.0 85
111 14 31 33 15.0 74
112 11 32 41 11.0 88
113 11 32 33 15.0 38
114 10 37 34 12.0 76
115 13 37 32 10.0 86
116 13 33 40 14.0 54
117 8 34 40 13.0 67
118 11 33 35 9.0 69
119 12 38 36 15.0 90
120 11 33 37 15.0 54
121 13 31 27 14.0 76
122 12 38 39 11.0 89
123 14 37 38 8.0 76
124 13 36 31 11.0 73
125 15 31 33 11.0 79
126 10 39 32 8.0 90
127 11 44 39 10.0 74
128 9 33 36 11.0 81
129 11 35 33 13.0 72
130 10 32 33 11.0 71
131 11 28 32 20.0 66
132 8 40 37 10.0 77
133 11 27 30 15.0 65
134 12 37 38 12.0 74
135 12 32 29 14.0 85
136 9 28 22 23.0 54
137 11 34 35 14.0 63
138 10 30 35 16.0 54
139 8 35 34 11.0 64
140 9 31 35 12.0 69
141 8 32 34 10.0 54
142 9 30 37 14.0 84
143 15 30 35 12.0 86
144 11 31 23 12.0 77
145 8 40 31 11.0 89
146 13 32 27 12.0 76
147 12 36 36 13.0 60
148 12 32 31 11.0 75
149 9 35 32 19.0 73
150 7 38 39 12.0 85
151 13 42 37 17.0 79
152 9 34 38 9.0 71
153 6 35 39 12.0 72
154 8 38 34 19.0 69
155 8 33 31 18.0 78
156 15 36 32 15.0 54
157 6 32 37 14.0 69
158 9 33 36 11.0 81
159 11 34 32 9.0 84
160 8 32 38 18.0 84
161 8 34 36 16.0 69
162 10 27 26 24.0 66
163 8 31 26 14.0 81
164 14 38 33 20.0 82
165 10 34 39 18.0 72
166 8 24 30 23.0 54
167 11 30 33 12.0 78
168 12 26 25 14.0 74
169 12 34 38 16.0 82
170 12 27 37 18.0 73
171 5 37 31 20.0 55
172 12 36 37 12.0 72
173 10 41 35 12.0 78
174 7 29 25 17.0 59
175 12 36 28 13.0 72
176 11 32 35 9.0 78
177 8 37 33 16.0 68
178 9 30 30 18.0 69
179 10 31 31 10.0 67
180 9 38 37 14.0 74
181 12 36 36 11.0 54
182 6 35 30 9.0 67
183 15 31 36 11.0 70
184 12 38 32 10.0 80
185 12 22 28 11.0 89
186 12 32 36 19.0 76
187 11 36 34 14.0 74
188 7 39 31 12.0 87
189 7 28 28 14.0 54
190 5 32 36 21.0 61
191 12 32 36 13.0 38
192 12 38 40 10.0 75
193 3 32 33 15.0 69
194 11 35 37 16.0 62
195 10 32 32 14.0 72
196 12 37 38 12.0 70
197 9 34 31 19.0 79
198 12 33 37 15.0 87
199 9 33 33 19.0 62
200 12 26 32 13.0 77
201 12 30 30 17.0 69
202 10 24 30 12.0 69
203 9 34 31 11.0 75
204 12 34 32 14.0 54
205 8 33 34 11.0 72
206 11 34 36 13.0 74
207 11 35 37 12.0 85
208 12 35 36 15.0 52
209 10 36 33 14.0 70
210 10 34 33 12.0 84
211 12 34 33 17.0 64
212 12 41 44 11.0 84
213 11 32 39 18.0 87
214 8 30 32 13.0 79
215 12 35 35 17.0 67
216 10 28 25 13.0 65
217 11 33 35 11.0 85
218 10 39 34 12.0 83
219 8 36 35 22.0 61
220 12 36 39 14.0 82
221 12 35 33 12.0 76
222 10 38 36 12.0 58
223 12 33 32 17.0 72
224 9 31 32 9.0 72
225 9 34 36 21.0 38
226 6 32 36 10.0 78
227 10 31 32 11.0 54
228 9 33 34 12.0 63
229 9 34 33 23.0 66
230 9 34 35 13.0 70
231 6 34 30 12.0 71
232 10 33 38 16.0 67
233 6 32 34 9.0 58
234 14 41 33 17.0 72
235 10 34 32 9.0 72
236 10 36 31 14.0 70
237 6 37 30 17.0 76
238 12 36 27 13.0 50
239 12 29 31 11.0 72
240 7 37 30 12.0 72
241 8 27 32 10.0 88
242 11 35 35 19.0 53
243 3 28 28 16.0 58
244 6 35 33 16.0 66
245 10 37 31 14.0 82
246 8 29 35 20.0 69
247 9 32 35 15.0 68
248 9 36 32 23.0 44
249 8 19 21 20.0 56
250 9 21 20 16.0 53
251 7 31 34 14.0 70
252 7 33 32 17.0 78
253 6 36 34 11.0 71
254 9 33 32 13.0 72
255 10 37 33 17.0 68
256 11 34 33 15.0 67
257 12 35 37 21.0 75
258 8 31 32 18.0 62
259 11 37 34 15.0 67
260 3 35 30 8.0 83
261 11 27 30 12.0 64
262 12 34 38 12.0 68
263 7 40 36 22.0 62
264 9 29 32 12.0 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Connected Separate Depression `Sport\\r\\r`
6.78261 0.02467 0.09624 -0.08404 0.01163
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-7.8264 -1.1101 0.4004 1.5313 4.8819
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.78261 1.97302 3.438 0.000683 ***
Connected 0.02467 0.04161 0.593 0.553802
Separate 0.09624 0.04242 2.269 0.024104 *
Depression -0.08404 0.04288 -1.960 0.051105 .
`Sport\\r\\r` 0.01163 0.01429 0.814 0.416546
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.265 on 259 degrees of freedom
Multiple R-squared: 0.06105, Adjusted R-squared: 0.04655
F-statistic: 4.21 on 4 and 259 DF, p-value: 0.002551
> 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,] 0.937815603 0.124368794 0.06218440
[2,] 0.881275279 0.237449443 0.11872472
[3,] 0.932026526 0.135946947 0.06797347
[4,] 0.885673873 0.228652254 0.11432613
[5,] 0.844268642 0.311462716 0.15573136
[6,] 0.776852444 0.446295112 0.22314756
[7,] 0.717361382 0.565277235 0.28263862
[8,] 0.745703050 0.508593899 0.25429695
[9,] 0.677085193 0.645829613 0.32291481
[10,] 0.612466273 0.775067453 0.38753373
[11,] 0.533319003 0.933361995 0.46668100
[12,] 0.460676046 0.921352091 0.53932395
[13,] 0.394208333 0.788416666 0.60579167
[14,] 0.323756936 0.647513871 0.67624306
[15,] 0.265478587 0.530957174 0.73452141
[16,] 0.227153912 0.454307823 0.77284609
[17,] 0.199414134 0.398828268 0.80058587
[18,] 0.168805444 0.337610889 0.83119456
[19,] 0.174069916 0.348139832 0.82593008
[20,] 0.159738924 0.319477847 0.84026108
[21,] 0.149067514 0.298135029 0.85093249
[22,] 0.115650177 0.231300353 0.88434982
[23,] 0.099838669 0.199677338 0.90016133
[24,] 0.077876487 0.155752973 0.92212351
[25,] 0.107231889 0.214463779 0.89276811
[26,] 0.084447608 0.168895216 0.91555239
[27,] 0.064736847 0.129473695 0.93526315
[28,] 0.053347588 0.106695175 0.94665241
[29,] 0.101428994 0.202857988 0.89857101
[30,] 0.097488543 0.194977085 0.90251146
[31,] 0.079205409 0.158410818 0.92079459
[32,] 0.061195457 0.122390914 0.93880454
[33,] 0.048326483 0.096652965 0.95167352
[34,] 0.037734184 0.075468367 0.96226582
[35,] 0.029092701 0.058185401 0.97090730
[36,] 0.083789262 0.167578524 0.91621074
[37,] 0.068428497 0.136856995 0.93157150
[38,] 0.053624662 0.107249324 0.94637534
[39,] 0.041829631 0.083659263 0.95817037
[40,] 0.033303223 0.066606446 0.96669678
[41,] 0.025072793 0.050145585 0.97492721
[42,] 0.020801080 0.041602161 0.97919892
[43,] 0.016079057 0.032158113 0.98392094
[44,] 0.017045798 0.034091597 0.98295420
[45,] 0.012604828 0.025209656 0.98739517
[46,] 0.010284885 0.020569771 0.98971511
[47,] 0.008346938 0.016693876 0.99165306
[48,] 0.047026334 0.094052668 0.95297367
[49,] 0.163786558 0.327573116 0.83621344
[50,] 0.139834397 0.279668793 0.86016560
[51,] 0.123486770 0.246973539 0.87651323
[52,] 0.104492079 0.208984158 0.89550792
[53,] 0.090546203 0.181092406 0.90945380
[54,] 0.090289617 0.180579233 0.90971038
[55,] 0.075263323 0.150526646 0.92473668
[56,] 0.081720418 0.163440835 0.91827958
[57,] 0.066963536 0.133927073 0.93303646
[58,] 0.064231876 0.128463753 0.93576812
[59,] 0.054962661 0.109925323 0.94503734
[60,] 0.064688428 0.129376855 0.93531157
[61,] 0.058055332 0.116110664 0.94194467
[62,] 0.048488004 0.096976008 0.95151200
[63,] 0.039602807 0.079205613 0.96039719
[64,] 0.058645949 0.117291897 0.94135405
[65,] 0.048230506 0.096461012 0.95176949
[66,] 0.045838376 0.091676753 0.95416162
[67,] 0.041799720 0.083599439 0.95820028
[68,] 0.039472716 0.078945431 0.96052728
[69,] 0.035474585 0.070949170 0.96452542
[70,] 0.031331794 0.062663588 0.96866821
[71,] 0.025523412 0.051046825 0.97447659
[72,] 0.022426304 0.044852608 0.97757370
[73,] 0.023548796 0.047097592 0.97645120
[74,] 0.018892292 0.037784584 0.98110771
[75,] 0.015070231 0.030140463 0.98492977
[76,] 0.012358497 0.024716993 0.98764150
[77,] 0.009622408 0.019244815 0.99037759
[78,] 0.007598145 0.015196289 0.99240186
[79,] 0.010557875 0.021115750 0.98944212
[80,] 0.008649349 0.017298697 0.99135065
[81,] 0.008014194 0.016028388 0.99198581
[82,] 0.007156955 0.014313911 0.99284304
[83,] 0.007766487 0.015532974 0.99223351
[84,] 0.019184883 0.038369767 0.98081512
[85,] 0.015532687 0.031065374 0.98446731
[86,] 0.014379231 0.028758461 0.98562077
[87,] 0.012656098 0.025312197 0.98734390
[88,] 0.011557519 0.023115038 0.98844248
[89,] 0.011245581 0.022491161 0.98875442
[90,] 0.010613016 0.021226033 0.98938698
[91,] 0.008581514 0.017163028 0.99141849
[92,] 0.007474239 0.014948477 0.99252576
[93,] 0.008416523 0.016833047 0.99158348
[94,] 0.006662102 0.013324204 0.99333790
[95,] 0.009951717 0.019903434 0.99004828
[96,] 0.008093153 0.016186306 0.99190685
[97,] 0.006592753 0.013185505 0.99340725
[98,] 0.005807625 0.011615249 0.99419238
[99,] 0.004682661 0.009365322 0.99531734
[100,] 0.003805077 0.007610154 0.99619492
[101,] 0.002998795 0.005997589 0.99700121
[102,] 0.006573553 0.013147106 0.99342645
[103,] 0.005369137 0.010738273 0.99463086
[104,] 0.007639772 0.015279544 0.99236023
[105,] 0.006567361 0.013134722 0.99343264
[106,] 0.005409907 0.010819815 0.99459009
[107,] 0.004569433 0.009138866 0.99543057
[108,] 0.004364746 0.008729492 0.99563525
[109,] 0.004237967 0.008475935 0.99576203
[110,] 0.006570378 0.013140755 0.99342962
[111,] 0.005216234 0.010432468 0.99478377
[112,] 0.004217112 0.008434223 0.99578289
[113,] 0.003315609 0.006631217 0.99668439
[114,] 0.003991209 0.007982418 0.99600879
[115,] 0.003156725 0.006313449 0.99684328
[116,] 0.003591507 0.007183013 0.99640849
[117,] 0.003867891 0.007735782 0.99613211
[118,] 0.007760072 0.015520144 0.99223993
[119,] 0.006972025 0.013944051 0.99302797
[120,] 0.005701529 0.011403058 0.99429847
[121,] 0.006120940 0.012241880 0.99387906
[122,] 0.004924683 0.009849366 0.99507532
[123,] 0.004079971 0.008159943 0.99592003
[124,] 0.003284650 0.006569299 0.99671535
[125,] 0.004749504 0.009499008 0.99525050
[126,] 0.003874250 0.007748500 0.99612575
[127,] 0.003197066 0.006394131 0.99680293
[128,] 0.002865112 0.005730224 0.99713489
[129,] 0.002248618 0.004497235 0.99775138
[130,] 0.001765557 0.003531114 0.99823444
[131,] 0.001379236 0.002758472 0.99862076
[132,] 0.001686921 0.003373842 0.99831308
[133,] 0.001608050 0.003216100 0.99839195
[134,] 0.001810802 0.003621605 0.99818920
[135,] 0.001861730 0.003723460 0.99813827
[136,] 0.003708558 0.007417115 0.99629144
[137,] 0.003225244 0.006450489 0.99677476
[138,] 0.004004372 0.008008745 0.99599563
[139,] 0.005158501 0.010317002 0.99484150
[140,] 0.004530314 0.009060628 0.99546969
[141,] 0.004177927 0.008355855 0.99582207
[142,] 0.003614010 0.007228019 0.99638599
[143,] 0.007692470 0.015384940 0.99230753
[144,] 0.008071644 0.016143287 0.99192836
[145,] 0.008041245 0.016082490 0.99195876
[146,] 0.022053616 0.044107231 0.97794638
[147,] 0.022564428 0.045128855 0.97743557
[148,] 0.022739856 0.045479712 0.97726014
[149,] 0.054365352 0.108730703 0.94563465
[150,] 0.101202140 0.202404279 0.89879786
[151,] 0.097722413 0.195444827 0.90227759
[152,] 0.085612734 0.171225468 0.91438727
[153,] 0.095326851 0.190653701 0.90467315
[154,] 0.099868753 0.199737506 0.90013125
[155,] 0.089914940 0.179829880 0.91008506
[156,] 0.086422190 0.172844381 0.91357781
[157,] 0.129816371 0.259632743 0.87018363
[158,] 0.113578082 0.227156165 0.88642192
[159,] 0.103409270 0.206818541 0.89659073
[160,] 0.089470140 0.178940280 0.91052986
[161,] 0.102949009 0.205898018 0.89705099
[162,] 0.091919112 0.183838224 0.90808089
[163,] 0.083131901 0.166263803 0.91686810
[164,] 0.133989991 0.267979981 0.86601001
[165,] 0.121319710 0.242639419 0.87868029
[166,] 0.106259193 0.212518386 0.89374081
[167,] 0.104587733 0.209175466 0.89541227
[168,] 0.118008368 0.236016736 0.88199163
[169,] 0.102056497 0.204112995 0.89794350
[170,] 0.098345164 0.196690328 0.90165484
[171,] 0.084962901 0.169925802 0.91503710
[172,] 0.073050393 0.146100785 0.92694961
[173,] 0.067052654 0.134105309 0.93294735
[174,] 0.060669932 0.121339864 0.93933007
[175,] 0.087312733 0.174625465 0.91268727
[176,] 0.138286828 0.276573657 0.86171317
[177,] 0.139268220 0.278536439 0.86073178
[178,] 0.152784309 0.305568618 0.84721569
[179,] 0.147081995 0.294163990 0.85291800
[180,] 0.131847237 0.263694475 0.86815276
[181,] 0.144916482 0.289832963 0.85508352
[182,] 0.146412509 0.292825018 0.85358749
[183,] 0.261831819 0.523663638 0.73816818
[184,] 0.245331878 0.490663756 0.75466812
[185,] 0.223167498 0.446334997 0.77683250
[186,] 0.537738798 0.924522404 0.46226120
[187,] 0.500177433 0.999645133 0.49982257
[188,] 0.463236739 0.926473479 0.53676326
[189,] 0.442172485 0.884344970 0.55782751
[190,] 0.405146206 0.810292412 0.59485379
[191,] 0.388709734 0.777419468 0.61129027
[192,] 0.354196081 0.708392162 0.64580392
[193,] 0.366404821 0.732809641 0.63359518
[194,] 0.391019569 0.782039139 0.60898043
[195,] 0.365626130 0.731252260 0.63437387
[196,] 0.333864771 0.667729541 0.66613523
[197,] 0.341406310 0.682812620 0.65859369
[198,] 0.331349639 0.662699279 0.66865036
[199,] 0.302236785 0.604473570 0.69776322
[200,] 0.274223307 0.548446614 0.72577669
[201,] 0.266164351 0.532328702 0.73383565
[202,] 0.234325340 0.468650680 0.76567466
[203,] 0.207130366 0.414260732 0.79286963
[204,] 0.213708952 0.427417903 0.78629105
[205,] 0.190794235 0.381588470 0.80920576
[206,] 0.168262547 0.336525095 0.83173745
[207,] 0.152282331 0.304564663 0.84771767
[208,] 0.154289958 0.308579915 0.84571004
[209,] 0.144163040 0.288326080 0.85583696
[210,] 0.134804371 0.269608743 0.86519563
[211,] 0.115102142 0.230204283 0.88489786
[212,] 0.106401333 0.212802665 0.89359867
[213,] 0.107283703 0.214567406 0.89271630
[214,] 0.125715164 0.251430328 0.87428484
[215,] 0.103940398 0.207880795 0.89605960
[216,] 0.123724590 0.247449179 0.87627541
[217,] 0.105846649 0.211693299 0.89415335
[218,] 0.093810454 0.187620909 0.90618955
[219,] 0.115441426 0.230882853 0.88455857
[220,] 0.094858472 0.189716944 0.90514153
[221,] 0.076642138 0.153284275 0.92335786
[222,] 0.059924572 0.119849143 0.94007543
[223,] 0.047070957 0.094141914 0.95292904
[224,] 0.052862461 0.105724922 0.94713754
[225,] 0.040244957 0.080489914 0.95975504
[226,] 0.065333783 0.130667566 0.93466622
[227,] 0.182930181 0.365860361 0.81706982
[228,] 0.153646578 0.307293157 0.84635342
[229,] 0.140310017 0.280620035 0.85968998
[230,] 0.124893029 0.249786059 0.87510697
[231,] 0.180514193 0.361028386 0.81948581
[232,] 0.237779544 0.475559088 0.76222046
[233,] 0.201033740 0.402067481 0.79896626
[234,] 0.163637226 0.327274453 0.83636277
[235,] 0.137605647 0.275211294 0.86239435
[236,] 0.388115482 0.776230964 0.61188452
[237,] 0.427907849 0.855815697 0.57209215
[238,] 0.547185561 0.905628879 0.45281444
[239,] 0.594370082 0.811259835 0.40562992
[240,] 0.543570095 0.912859811 0.45642991
[241,] 0.501541747 0.996916506 0.49845825
[242,] 0.439784414 0.879568828 0.56021559
[243,] 0.355726973 0.711453946 0.64427303
[244,] 0.466202303 0.932404606 0.53379770
[245,] 0.380236106 0.760472212 0.61976389
[246,] 0.395106996 0.790213992 0.60489300
[247,] 0.290410000 0.580819999 0.70959000
[248,] 0.281661895 0.563323791 0.71833810
[249,] 0.262324049 0.524648097 0.73767595
> postscript(file="/var/fisher/rcomp/tmp/10rbd1384895112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2jmk51384895112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/3uzlm1384895112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4y59b1384895112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51vyr1384895112.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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
0.94066136 0.13450602 4.51763510 -4.49424946 2.69840760 -0.33589403
7 8 9 10 11 12
2.50405918 2.90777509 0.94042715 -2.14412891 -0.78209282 1.10121414
13 14 15 16 17 18
1.39267834 0.14644163 3.67075152 1.75382736 -0.44633187 0.52356180
19 20 21 22 23 24
-0.30600671 1.77766623 0.28262278 1.85467741 1.97837100 0.07627708
25 26 27 28 29 30
1.09961390 1.95198717 -0.99879894 3.30028083 1.49654305 -0.48422466
31 32 33 34 35 36
1.29493234 -1.89602017 -0.66437700 0.76500879 1.72309082 -3.90158373
37 38 39 40 41 42
-0.53361682 1.33587686 -0.52516917 -0.78426219 -0.87954791 1.50676500
43 44 45 46 47 48
4.24436075 -0.25039890 -0.29302001 1.23764717 1.52675396 0.18308575
49 50 51 52 53 54
0.07187331 1.25222185 2.77363883 -0.06211858 2.03181509 2.17674682
55 56 57 58 59 60
-5.46018865 -4.72934551 1.21476607 1.20674146 -0.16885578 -0.47195979
61 62 63 64 65 66
-2.15324949 1.12050880 3.08805388 0.65579483 1.85946148 1.78686145
67 68 69 70 71 72
3.62172002 1.03809731 0.76524300 1.16690517 -2.87163576 0.42783830
73 74 75 76 77 78
2.77548459 2.37177950 1.72744354 -0.96583238 2.02189881 1.02114214
79 80 81 82 83 84
1.73997624 1.83428361 0.94407597 0.72346718 1.28794527 0.32668336
85 86 87 88 89 90
-0.38337461 -2.25341184 1.33690531 2.05463949 2.26187123 -1.76872881
91 92 93 94 95 96
4.88194830 0.60485030 2.03994308 1.73463977 -1.21557576 2.47812241
97 98 99 100 101 102
-1.61241629 1.01462082 -1.08039705 2.77158192 0.43159959 3.78807306
103 104 105 106 107 108
0.88989313 0.07552213 1.95657898 0.95186071 0.97120642 0.59321788
109 110 111 112 113 114
-3.40381414 1.39289513 3.67644153 -0.61714760 1.07047682 -0.84319032
115 116 117 118 119 120
2.06491892 2.10197570 -3.15792898 -0.01144411 1.02892860 0.47474144
121 122 123 124 125 126
3.14660565 0.41568762 2.43569451 2.42106708 4.28214673 -1.19901488
127 128 129 130 131 132
-0.64190811 -2.07918544 0.43295164 -0.64947778 1.35991776 -3.38563260
133 134 135 136 137 138
1.16852900 0.79509719 1.82477217 0.71402694 0.45384654 -0.17472579
139 140 141 142 143 144
-2.73831681 -1.70999783 -2.63203455 -1.88420458 4.11695032 1.35187821
145 146 147 148 149 150
-2.86370492 2.95386485 1.25911953 1.49648634 -0.97822370 -4.45375423
151 152 153 154 155 156
2.13001286 -2.34810612 -5.22854453 -2.19819833 -1.97482874 4.88194830
157 158 159 160 161 162
-4.75908431 -2.07918544 0.07815553 -2.69364713 -2.54411055 1.29819004
163 164 165 166 167 168
-1.81530441 3.83088126 -0.69966246 -0.95724018 0.40248293 2.48570487
169 170 171 172 173 174
1.11220331 1.65388497 -4.63793252 0.93927225 -1.06137589 -2.16173909
175 176 177 178 179 180
1.88949884 -0.09145031 -2.31775993 -0.69989839 -0.46983315 -1.96525887
181 182 183 184 185 186
1.16083314 -4.55630567 4.09809268 1.11003299 1.86908857 1.67592079
187 188 189 190 191 192
0.37281189 -3.73173802 -2.61975175 -4.98154797 1.61367577 0.39823873
193 194 195 196 197 198
-7.29007510 0.41639076 -0.31275906 0.84162002 -0.92709430 1.09092810
199 200 201 202 203 204
-0.89718906 1.69307311 2.21606630 -0.05608926 -1.55285400 1.84725333
205 206 207 208 209 210
-2.78202213 0.14562997 -0.18725677 1.54490599 -0.48442180 -0.76598200
211 212 213 214 215 216
1.88680873 -0.08138673 0.17521727 -2.42886899 1.63475949 0.45700558
217 218 219 220 221 222
-0.02946480 -0.97394562 -1.89994986 0.79854886 1.30239349 -0.85099471
223 224 225 226 227 228
1.91467671 -1.70826547 -0.76338205 -5.10365847 -0.33084211 -1.59331045
229 230 231 232 233 234
-0.63224079 -1.71160373 -4.32605238 -0.68866591 -4.76259269 3.62107188
235 236 237 238 239 240
-0.78227598 -0.29193486 -4.03803985 2.24161787 1.60538897 -3.41169360
241 242 243 244 245 246
-2.71164079 0.96566002 -6.49820395 -4.24515817 -0.45617351 -1.98837496
247 248 249 250 251 252
-1.47093134 -0.32946210 -0.24306542 0.50258857 -3.45731443 -3.15510753
253 254 255 256 257 258
-4.84440193 -1.42146455 -0.23372461 0.68384597 1.68536814 -1.83564056
259 260 261 262 263 264
0.51359199 -7.82643230 0.92805376 0.93889195 -3.10650473 -1.40681918
> postscript(file="/var/fisher/rcomp/tmp/6hun01384895112.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 0.94066136 NA
1 0.13450602 0.94066136
2 4.51763510 0.13450602
3 -4.49424946 4.51763510
4 2.69840760 -4.49424946
5 -0.33589403 2.69840760
6 2.50405918 -0.33589403
7 2.90777509 2.50405918
8 0.94042715 2.90777509
9 -2.14412891 0.94042715
10 -0.78209282 -2.14412891
11 1.10121414 -0.78209282
12 1.39267834 1.10121414
13 0.14644163 1.39267834
14 3.67075152 0.14644163
15 1.75382736 3.67075152
16 -0.44633187 1.75382736
17 0.52356180 -0.44633187
18 -0.30600671 0.52356180
19 1.77766623 -0.30600671
20 0.28262278 1.77766623
21 1.85467741 0.28262278
22 1.97837100 1.85467741
23 0.07627708 1.97837100
24 1.09961390 0.07627708
25 1.95198717 1.09961390
26 -0.99879894 1.95198717
27 3.30028083 -0.99879894
28 1.49654305 3.30028083
29 -0.48422466 1.49654305
30 1.29493234 -0.48422466
31 -1.89602017 1.29493234
32 -0.66437700 -1.89602017
33 0.76500879 -0.66437700
34 1.72309082 0.76500879
35 -3.90158373 1.72309082
36 -0.53361682 -3.90158373
37 1.33587686 -0.53361682
38 -0.52516917 1.33587686
39 -0.78426219 -0.52516917
40 -0.87954791 -0.78426219
41 1.50676500 -0.87954791
42 4.24436075 1.50676500
43 -0.25039890 4.24436075
44 -0.29302001 -0.25039890
45 1.23764717 -0.29302001
46 1.52675396 1.23764717
47 0.18308575 1.52675396
48 0.07187331 0.18308575
49 1.25222185 0.07187331
50 2.77363883 1.25222185
51 -0.06211858 2.77363883
52 2.03181509 -0.06211858
53 2.17674682 2.03181509
54 -5.46018865 2.17674682
55 -4.72934551 -5.46018865
56 1.21476607 -4.72934551
57 1.20674146 1.21476607
58 -0.16885578 1.20674146
59 -0.47195979 -0.16885578
60 -2.15324949 -0.47195979
61 1.12050880 -2.15324949
62 3.08805388 1.12050880
63 0.65579483 3.08805388
64 1.85946148 0.65579483
65 1.78686145 1.85946148
66 3.62172002 1.78686145
67 1.03809731 3.62172002
68 0.76524300 1.03809731
69 1.16690517 0.76524300
70 -2.87163576 1.16690517
71 0.42783830 -2.87163576
72 2.77548459 0.42783830
73 2.37177950 2.77548459
74 1.72744354 2.37177950
75 -0.96583238 1.72744354
76 2.02189881 -0.96583238
77 1.02114214 2.02189881
78 1.73997624 1.02114214
79 1.83428361 1.73997624
80 0.94407597 1.83428361
81 0.72346718 0.94407597
82 1.28794527 0.72346718
83 0.32668336 1.28794527
84 -0.38337461 0.32668336
85 -2.25341184 -0.38337461
86 1.33690531 -2.25341184
87 2.05463949 1.33690531
88 2.26187123 2.05463949
89 -1.76872881 2.26187123
90 4.88194830 -1.76872881
91 0.60485030 4.88194830
92 2.03994308 0.60485030
93 1.73463977 2.03994308
94 -1.21557576 1.73463977
95 2.47812241 -1.21557576
96 -1.61241629 2.47812241
97 1.01462082 -1.61241629
98 -1.08039705 1.01462082
99 2.77158192 -1.08039705
100 0.43159959 2.77158192
101 3.78807306 0.43159959
102 0.88989313 3.78807306
103 0.07552213 0.88989313
104 1.95657898 0.07552213
105 0.95186071 1.95657898
106 0.97120642 0.95186071
107 0.59321788 0.97120642
108 -3.40381414 0.59321788
109 1.39289513 -3.40381414
110 3.67644153 1.39289513
111 -0.61714760 3.67644153
112 1.07047682 -0.61714760
113 -0.84319032 1.07047682
114 2.06491892 -0.84319032
115 2.10197570 2.06491892
116 -3.15792898 2.10197570
117 -0.01144411 -3.15792898
118 1.02892860 -0.01144411
119 0.47474144 1.02892860
120 3.14660565 0.47474144
121 0.41568762 3.14660565
122 2.43569451 0.41568762
123 2.42106708 2.43569451
124 4.28214673 2.42106708
125 -1.19901488 4.28214673
126 -0.64190811 -1.19901488
127 -2.07918544 -0.64190811
128 0.43295164 -2.07918544
129 -0.64947778 0.43295164
130 1.35991776 -0.64947778
131 -3.38563260 1.35991776
132 1.16852900 -3.38563260
133 0.79509719 1.16852900
134 1.82477217 0.79509719
135 0.71402694 1.82477217
136 0.45384654 0.71402694
137 -0.17472579 0.45384654
138 -2.73831681 -0.17472579
139 -1.70999783 -2.73831681
140 -2.63203455 -1.70999783
141 -1.88420458 -2.63203455
142 4.11695032 -1.88420458
143 1.35187821 4.11695032
144 -2.86370492 1.35187821
145 2.95386485 -2.86370492
146 1.25911953 2.95386485
147 1.49648634 1.25911953
148 -0.97822370 1.49648634
149 -4.45375423 -0.97822370
150 2.13001286 -4.45375423
151 -2.34810612 2.13001286
152 -5.22854453 -2.34810612
153 -2.19819833 -5.22854453
154 -1.97482874 -2.19819833
155 4.88194830 -1.97482874
156 -4.75908431 4.88194830
157 -2.07918544 -4.75908431
158 0.07815553 -2.07918544
159 -2.69364713 0.07815553
160 -2.54411055 -2.69364713
161 1.29819004 -2.54411055
162 -1.81530441 1.29819004
163 3.83088126 -1.81530441
164 -0.69966246 3.83088126
165 -0.95724018 -0.69966246
166 0.40248293 -0.95724018
167 2.48570487 0.40248293
168 1.11220331 2.48570487
169 1.65388497 1.11220331
170 -4.63793252 1.65388497
171 0.93927225 -4.63793252
172 -1.06137589 0.93927225
173 -2.16173909 -1.06137589
174 1.88949884 -2.16173909
175 -0.09145031 1.88949884
176 -2.31775993 -0.09145031
177 -0.69989839 -2.31775993
178 -0.46983315 -0.69989839
179 -1.96525887 -0.46983315
180 1.16083314 -1.96525887
181 -4.55630567 1.16083314
182 4.09809268 -4.55630567
183 1.11003299 4.09809268
184 1.86908857 1.11003299
185 1.67592079 1.86908857
186 0.37281189 1.67592079
187 -3.73173802 0.37281189
188 -2.61975175 -3.73173802
189 -4.98154797 -2.61975175
190 1.61367577 -4.98154797
191 0.39823873 1.61367577
192 -7.29007510 0.39823873
193 0.41639076 -7.29007510
194 -0.31275906 0.41639076
195 0.84162002 -0.31275906
196 -0.92709430 0.84162002
197 1.09092810 -0.92709430
198 -0.89718906 1.09092810
199 1.69307311 -0.89718906
200 2.21606630 1.69307311
201 -0.05608926 2.21606630
202 -1.55285400 -0.05608926
203 1.84725333 -1.55285400
204 -2.78202213 1.84725333
205 0.14562997 -2.78202213
206 -0.18725677 0.14562997
207 1.54490599 -0.18725677
208 -0.48442180 1.54490599
209 -0.76598200 -0.48442180
210 1.88680873 -0.76598200
211 -0.08138673 1.88680873
212 0.17521727 -0.08138673
213 -2.42886899 0.17521727
214 1.63475949 -2.42886899
215 0.45700558 1.63475949
216 -0.02946480 0.45700558
217 -0.97394562 -0.02946480
218 -1.89994986 -0.97394562
219 0.79854886 -1.89994986
220 1.30239349 0.79854886
221 -0.85099471 1.30239349
222 1.91467671 -0.85099471
223 -1.70826547 1.91467671
224 -0.76338205 -1.70826547
225 -5.10365847 -0.76338205
226 -0.33084211 -5.10365847
227 -1.59331045 -0.33084211
228 -0.63224079 -1.59331045
229 -1.71160373 -0.63224079
230 -4.32605238 -1.71160373
231 -0.68866591 -4.32605238
232 -4.76259269 -0.68866591
233 3.62107188 -4.76259269
234 -0.78227598 3.62107188
235 -0.29193486 -0.78227598
236 -4.03803985 -0.29193486
237 2.24161787 -4.03803985
238 1.60538897 2.24161787
239 -3.41169360 1.60538897
240 -2.71164079 -3.41169360
241 0.96566002 -2.71164079
242 -6.49820395 0.96566002
243 -4.24515817 -6.49820395
244 -0.45617351 -4.24515817
245 -1.98837496 -0.45617351
246 -1.47093134 -1.98837496
247 -0.32946210 -1.47093134
248 -0.24306542 -0.32946210
249 0.50258857 -0.24306542
250 -3.45731443 0.50258857
251 -3.15510753 -3.45731443
252 -4.84440193 -3.15510753
253 -1.42146455 -4.84440193
254 -0.23372461 -1.42146455
255 0.68384597 -0.23372461
256 1.68536814 0.68384597
257 -1.83564056 1.68536814
258 0.51359199 -1.83564056
259 -7.82643230 0.51359199
260 0.92805376 -7.82643230
261 0.93889195 0.92805376
262 -3.10650473 0.93889195
263 -1.40681918 -3.10650473
264 NA -1.40681918
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.13450602 0.94066136
[2,] 4.51763510 0.13450602
[3,] -4.49424946 4.51763510
[4,] 2.69840760 -4.49424946
[5,] -0.33589403 2.69840760
[6,] 2.50405918 -0.33589403
[7,] 2.90777509 2.50405918
[8,] 0.94042715 2.90777509
[9,] -2.14412891 0.94042715
[10,] -0.78209282 -2.14412891
[11,] 1.10121414 -0.78209282
[12,] 1.39267834 1.10121414
[13,] 0.14644163 1.39267834
[14,] 3.67075152 0.14644163
[15,] 1.75382736 3.67075152
[16,] -0.44633187 1.75382736
[17,] 0.52356180 -0.44633187
[18,] -0.30600671 0.52356180
[19,] 1.77766623 -0.30600671
[20,] 0.28262278 1.77766623
[21,] 1.85467741 0.28262278
[22,] 1.97837100 1.85467741
[23,] 0.07627708 1.97837100
[24,] 1.09961390 0.07627708
[25,] 1.95198717 1.09961390
[26,] -0.99879894 1.95198717
[27,] 3.30028083 -0.99879894
[28,] 1.49654305 3.30028083
[29,] -0.48422466 1.49654305
[30,] 1.29493234 -0.48422466
[31,] -1.89602017 1.29493234
[32,] -0.66437700 -1.89602017
[33,] 0.76500879 -0.66437700
[34,] 1.72309082 0.76500879
[35,] -3.90158373 1.72309082
[36,] -0.53361682 -3.90158373
[37,] 1.33587686 -0.53361682
[38,] -0.52516917 1.33587686
[39,] -0.78426219 -0.52516917
[40,] -0.87954791 -0.78426219
[41,] 1.50676500 -0.87954791
[42,] 4.24436075 1.50676500
[43,] -0.25039890 4.24436075
[44,] -0.29302001 -0.25039890
[45,] 1.23764717 -0.29302001
[46,] 1.52675396 1.23764717
[47,] 0.18308575 1.52675396
[48,] 0.07187331 0.18308575
[49,] 1.25222185 0.07187331
[50,] 2.77363883 1.25222185
[51,] -0.06211858 2.77363883
[52,] 2.03181509 -0.06211858
[53,] 2.17674682 2.03181509
[54,] -5.46018865 2.17674682
[55,] -4.72934551 -5.46018865
[56,] 1.21476607 -4.72934551
[57,] 1.20674146 1.21476607
[58,] -0.16885578 1.20674146
[59,] -0.47195979 -0.16885578
[60,] -2.15324949 -0.47195979
[61,] 1.12050880 -2.15324949
[62,] 3.08805388 1.12050880
[63,] 0.65579483 3.08805388
[64,] 1.85946148 0.65579483
[65,] 1.78686145 1.85946148
[66,] 3.62172002 1.78686145
[67,] 1.03809731 3.62172002
[68,] 0.76524300 1.03809731
[69,] 1.16690517 0.76524300
[70,] -2.87163576 1.16690517
[71,] 0.42783830 -2.87163576
[72,] 2.77548459 0.42783830
[73,] 2.37177950 2.77548459
[74,] 1.72744354 2.37177950
[75,] -0.96583238 1.72744354
[76,] 2.02189881 -0.96583238
[77,] 1.02114214 2.02189881
[78,] 1.73997624 1.02114214
[79,] 1.83428361 1.73997624
[80,] 0.94407597 1.83428361
[81,] 0.72346718 0.94407597
[82,] 1.28794527 0.72346718
[83,] 0.32668336 1.28794527
[84,] -0.38337461 0.32668336
[85,] -2.25341184 -0.38337461
[86,] 1.33690531 -2.25341184
[87,] 2.05463949 1.33690531
[88,] 2.26187123 2.05463949
[89,] -1.76872881 2.26187123
[90,] 4.88194830 -1.76872881
[91,] 0.60485030 4.88194830
[92,] 2.03994308 0.60485030
[93,] 1.73463977 2.03994308
[94,] -1.21557576 1.73463977
[95,] 2.47812241 -1.21557576
[96,] -1.61241629 2.47812241
[97,] 1.01462082 -1.61241629
[98,] -1.08039705 1.01462082
[99,] 2.77158192 -1.08039705
[100,] 0.43159959 2.77158192
[101,] 3.78807306 0.43159959
[102,] 0.88989313 3.78807306
[103,] 0.07552213 0.88989313
[104,] 1.95657898 0.07552213
[105,] 0.95186071 1.95657898
[106,] 0.97120642 0.95186071
[107,] 0.59321788 0.97120642
[108,] -3.40381414 0.59321788
[109,] 1.39289513 -3.40381414
[110,] 3.67644153 1.39289513
[111,] -0.61714760 3.67644153
[112,] 1.07047682 -0.61714760
[113,] -0.84319032 1.07047682
[114,] 2.06491892 -0.84319032
[115,] 2.10197570 2.06491892
[116,] -3.15792898 2.10197570
[117,] -0.01144411 -3.15792898
[118,] 1.02892860 -0.01144411
[119,] 0.47474144 1.02892860
[120,] 3.14660565 0.47474144
[121,] 0.41568762 3.14660565
[122,] 2.43569451 0.41568762
[123,] 2.42106708 2.43569451
[124,] 4.28214673 2.42106708
[125,] -1.19901488 4.28214673
[126,] -0.64190811 -1.19901488
[127,] -2.07918544 -0.64190811
[128,] 0.43295164 -2.07918544
[129,] -0.64947778 0.43295164
[130,] 1.35991776 -0.64947778
[131,] -3.38563260 1.35991776
[132,] 1.16852900 -3.38563260
[133,] 0.79509719 1.16852900
[134,] 1.82477217 0.79509719
[135,] 0.71402694 1.82477217
[136,] 0.45384654 0.71402694
[137,] -0.17472579 0.45384654
[138,] -2.73831681 -0.17472579
[139,] -1.70999783 -2.73831681
[140,] -2.63203455 -1.70999783
[141,] -1.88420458 -2.63203455
[142,] 4.11695032 -1.88420458
[143,] 1.35187821 4.11695032
[144,] -2.86370492 1.35187821
[145,] 2.95386485 -2.86370492
[146,] 1.25911953 2.95386485
[147,] 1.49648634 1.25911953
[148,] -0.97822370 1.49648634
[149,] -4.45375423 -0.97822370
[150,] 2.13001286 -4.45375423
[151,] -2.34810612 2.13001286
[152,] -5.22854453 -2.34810612
[153,] -2.19819833 -5.22854453
[154,] -1.97482874 -2.19819833
[155,] 4.88194830 -1.97482874
[156,] -4.75908431 4.88194830
[157,] -2.07918544 -4.75908431
[158,] 0.07815553 -2.07918544
[159,] -2.69364713 0.07815553
[160,] -2.54411055 -2.69364713
[161,] 1.29819004 -2.54411055
[162,] -1.81530441 1.29819004
[163,] 3.83088126 -1.81530441
[164,] -0.69966246 3.83088126
[165,] -0.95724018 -0.69966246
[166,] 0.40248293 -0.95724018
[167,] 2.48570487 0.40248293
[168,] 1.11220331 2.48570487
[169,] 1.65388497 1.11220331
[170,] -4.63793252 1.65388497
[171,] 0.93927225 -4.63793252
[172,] -1.06137589 0.93927225
[173,] -2.16173909 -1.06137589
[174,] 1.88949884 -2.16173909
[175,] -0.09145031 1.88949884
[176,] -2.31775993 -0.09145031
[177,] -0.69989839 -2.31775993
[178,] -0.46983315 -0.69989839
[179,] -1.96525887 -0.46983315
[180,] 1.16083314 -1.96525887
[181,] -4.55630567 1.16083314
[182,] 4.09809268 -4.55630567
[183,] 1.11003299 4.09809268
[184,] 1.86908857 1.11003299
[185,] 1.67592079 1.86908857
[186,] 0.37281189 1.67592079
[187,] -3.73173802 0.37281189
[188,] -2.61975175 -3.73173802
[189,] -4.98154797 -2.61975175
[190,] 1.61367577 -4.98154797
[191,] 0.39823873 1.61367577
[192,] -7.29007510 0.39823873
[193,] 0.41639076 -7.29007510
[194,] -0.31275906 0.41639076
[195,] 0.84162002 -0.31275906
[196,] -0.92709430 0.84162002
[197,] 1.09092810 -0.92709430
[198,] -0.89718906 1.09092810
[199,] 1.69307311 -0.89718906
[200,] 2.21606630 1.69307311
[201,] -0.05608926 2.21606630
[202,] -1.55285400 -0.05608926
[203,] 1.84725333 -1.55285400
[204,] -2.78202213 1.84725333
[205,] 0.14562997 -2.78202213
[206,] -0.18725677 0.14562997
[207,] 1.54490599 -0.18725677
[208,] -0.48442180 1.54490599
[209,] -0.76598200 -0.48442180
[210,] 1.88680873 -0.76598200
[211,] -0.08138673 1.88680873
[212,] 0.17521727 -0.08138673
[213,] -2.42886899 0.17521727
[214,] 1.63475949 -2.42886899
[215,] 0.45700558 1.63475949
[216,] -0.02946480 0.45700558
[217,] -0.97394562 -0.02946480
[218,] -1.89994986 -0.97394562
[219,] 0.79854886 -1.89994986
[220,] 1.30239349 0.79854886
[221,] -0.85099471 1.30239349
[222,] 1.91467671 -0.85099471
[223,] -1.70826547 1.91467671
[224,] -0.76338205 -1.70826547
[225,] -5.10365847 -0.76338205
[226,] -0.33084211 -5.10365847
[227,] -1.59331045 -0.33084211
[228,] -0.63224079 -1.59331045
[229,] -1.71160373 -0.63224079
[230,] -4.32605238 -1.71160373
[231,] -0.68866591 -4.32605238
[232,] -4.76259269 -0.68866591
[233,] 3.62107188 -4.76259269
[234,] -0.78227598 3.62107188
[235,] -0.29193486 -0.78227598
[236,] -4.03803985 -0.29193486
[237,] 2.24161787 -4.03803985
[238,] 1.60538897 2.24161787
[239,] -3.41169360 1.60538897
[240,] -2.71164079 -3.41169360
[241,] 0.96566002 -2.71164079
[242,] -6.49820395 0.96566002
[243,] -4.24515817 -6.49820395
[244,] -0.45617351 -4.24515817
[245,] -1.98837496 -0.45617351
[246,] -1.47093134 -1.98837496
[247,] -0.32946210 -1.47093134
[248,] -0.24306542 -0.32946210
[249,] 0.50258857 -0.24306542
[250,] -3.45731443 0.50258857
[251,] -3.15510753 -3.45731443
[252,] -4.84440193 -3.15510753
[253,] -1.42146455 -4.84440193
[254,] -0.23372461 -1.42146455
[255,] 0.68384597 -0.23372461
[256,] 1.68536814 0.68384597
[257,] -1.83564056 1.68536814
[258,] 0.51359199 -1.83564056
[259,] -7.82643230 0.51359199
[260,] 0.92805376 -7.82643230
[261,] 0.93889195 0.92805376
[262,] -3.10650473 0.93889195
[263,] -1.40681918 -3.10650473
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.13450602 0.94066136
2 4.51763510 0.13450602
3 -4.49424946 4.51763510
4 2.69840760 -4.49424946
5 -0.33589403 2.69840760
6 2.50405918 -0.33589403
7 2.90777509 2.50405918
8 0.94042715 2.90777509
9 -2.14412891 0.94042715
10 -0.78209282 -2.14412891
11 1.10121414 -0.78209282
12 1.39267834 1.10121414
13 0.14644163 1.39267834
14 3.67075152 0.14644163
15 1.75382736 3.67075152
16 -0.44633187 1.75382736
17 0.52356180 -0.44633187
18 -0.30600671 0.52356180
19 1.77766623 -0.30600671
20 0.28262278 1.77766623
21 1.85467741 0.28262278
22 1.97837100 1.85467741
23 0.07627708 1.97837100
24 1.09961390 0.07627708
25 1.95198717 1.09961390
26 -0.99879894 1.95198717
27 3.30028083 -0.99879894
28 1.49654305 3.30028083
29 -0.48422466 1.49654305
30 1.29493234 -0.48422466
31 -1.89602017 1.29493234
32 -0.66437700 -1.89602017
33 0.76500879 -0.66437700
34 1.72309082 0.76500879
35 -3.90158373 1.72309082
36 -0.53361682 -3.90158373
37 1.33587686 -0.53361682
38 -0.52516917 1.33587686
39 -0.78426219 -0.52516917
40 -0.87954791 -0.78426219
41 1.50676500 -0.87954791
42 4.24436075 1.50676500
43 -0.25039890 4.24436075
44 -0.29302001 -0.25039890
45 1.23764717 -0.29302001
46 1.52675396 1.23764717
47 0.18308575 1.52675396
48 0.07187331 0.18308575
49 1.25222185 0.07187331
50 2.77363883 1.25222185
51 -0.06211858 2.77363883
52 2.03181509 -0.06211858
53 2.17674682 2.03181509
54 -5.46018865 2.17674682
55 -4.72934551 -5.46018865
56 1.21476607 -4.72934551
57 1.20674146 1.21476607
58 -0.16885578 1.20674146
59 -0.47195979 -0.16885578
60 -2.15324949 -0.47195979
61 1.12050880 -2.15324949
62 3.08805388 1.12050880
63 0.65579483 3.08805388
64 1.85946148 0.65579483
65 1.78686145 1.85946148
66 3.62172002 1.78686145
67 1.03809731 3.62172002
68 0.76524300 1.03809731
69 1.16690517 0.76524300
70 -2.87163576 1.16690517
71 0.42783830 -2.87163576
72 2.77548459 0.42783830
73 2.37177950 2.77548459
74 1.72744354 2.37177950
75 -0.96583238 1.72744354
76 2.02189881 -0.96583238
77 1.02114214 2.02189881
78 1.73997624 1.02114214
79 1.83428361 1.73997624
80 0.94407597 1.83428361
81 0.72346718 0.94407597
82 1.28794527 0.72346718
83 0.32668336 1.28794527
84 -0.38337461 0.32668336
85 -2.25341184 -0.38337461
86 1.33690531 -2.25341184
87 2.05463949 1.33690531
88 2.26187123 2.05463949
89 -1.76872881 2.26187123
90 4.88194830 -1.76872881
91 0.60485030 4.88194830
92 2.03994308 0.60485030
93 1.73463977 2.03994308
94 -1.21557576 1.73463977
95 2.47812241 -1.21557576
96 -1.61241629 2.47812241
97 1.01462082 -1.61241629
98 -1.08039705 1.01462082
99 2.77158192 -1.08039705
100 0.43159959 2.77158192
101 3.78807306 0.43159959
102 0.88989313 3.78807306
103 0.07552213 0.88989313
104 1.95657898 0.07552213
105 0.95186071 1.95657898
106 0.97120642 0.95186071
107 0.59321788 0.97120642
108 -3.40381414 0.59321788
109 1.39289513 -3.40381414
110 3.67644153 1.39289513
111 -0.61714760 3.67644153
112 1.07047682 -0.61714760
113 -0.84319032 1.07047682
114 2.06491892 -0.84319032
115 2.10197570 2.06491892
116 -3.15792898 2.10197570
117 -0.01144411 -3.15792898
118 1.02892860 -0.01144411
119 0.47474144 1.02892860
120 3.14660565 0.47474144
121 0.41568762 3.14660565
122 2.43569451 0.41568762
123 2.42106708 2.43569451
124 4.28214673 2.42106708
125 -1.19901488 4.28214673
126 -0.64190811 -1.19901488
127 -2.07918544 -0.64190811
128 0.43295164 -2.07918544
129 -0.64947778 0.43295164
130 1.35991776 -0.64947778
131 -3.38563260 1.35991776
132 1.16852900 -3.38563260
133 0.79509719 1.16852900
134 1.82477217 0.79509719
135 0.71402694 1.82477217
136 0.45384654 0.71402694
137 -0.17472579 0.45384654
138 -2.73831681 -0.17472579
139 -1.70999783 -2.73831681
140 -2.63203455 -1.70999783
141 -1.88420458 -2.63203455
142 4.11695032 -1.88420458
143 1.35187821 4.11695032
144 -2.86370492 1.35187821
145 2.95386485 -2.86370492
146 1.25911953 2.95386485
147 1.49648634 1.25911953
148 -0.97822370 1.49648634
149 -4.45375423 -0.97822370
150 2.13001286 -4.45375423
151 -2.34810612 2.13001286
152 -5.22854453 -2.34810612
153 -2.19819833 -5.22854453
154 -1.97482874 -2.19819833
155 4.88194830 -1.97482874
156 -4.75908431 4.88194830
157 -2.07918544 -4.75908431
158 0.07815553 -2.07918544
159 -2.69364713 0.07815553
160 -2.54411055 -2.69364713
161 1.29819004 -2.54411055
162 -1.81530441 1.29819004
163 3.83088126 -1.81530441
164 -0.69966246 3.83088126
165 -0.95724018 -0.69966246
166 0.40248293 -0.95724018
167 2.48570487 0.40248293
168 1.11220331 2.48570487
169 1.65388497 1.11220331
170 -4.63793252 1.65388497
171 0.93927225 -4.63793252
172 -1.06137589 0.93927225
173 -2.16173909 -1.06137589
174 1.88949884 -2.16173909
175 -0.09145031 1.88949884
176 -2.31775993 -0.09145031
177 -0.69989839 -2.31775993
178 -0.46983315 -0.69989839
179 -1.96525887 -0.46983315
180 1.16083314 -1.96525887
181 -4.55630567 1.16083314
182 4.09809268 -4.55630567
183 1.11003299 4.09809268
184 1.86908857 1.11003299
185 1.67592079 1.86908857
186 0.37281189 1.67592079
187 -3.73173802 0.37281189
188 -2.61975175 -3.73173802
189 -4.98154797 -2.61975175
190 1.61367577 -4.98154797
191 0.39823873 1.61367577
192 -7.29007510 0.39823873
193 0.41639076 -7.29007510
194 -0.31275906 0.41639076
195 0.84162002 -0.31275906
196 -0.92709430 0.84162002
197 1.09092810 -0.92709430
198 -0.89718906 1.09092810
199 1.69307311 -0.89718906
200 2.21606630 1.69307311
201 -0.05608926 2.21606630
202 -1.55285400 -0.05608926
203 1.84725333 -1.55285400
204 -2.78202213 1.84725333
205 0.14562997 -2.78202213
206 -0.18725677 0.14562997
207 1.54490599 -0.18725677
208 -0.48442180 1.54490599
209 -0.76598200 -0.48442180
210 1.88680873 -0.76598200
211 -0.08138673 1.88680873
212 0.17521727 -0.08138673
213 -2.42886899 0.17521727
214 1.63475949 -2.42886899
215 0.45700558 1.63475949
216 -0.02946480 0.45700558
217 -0.97394562 -0.02946480
218 -1.89994986 -0.97394562
219 0.79854886 -1.89994986
220 1.30239349 0.79854886
221 -0.85099471 1.30239349
222 1.91467671 -0.85099471
223 -1.70826547 1.91467671
224 -0.76338205 -1.70826547
225 -5.10365847 -0.76338205
226 -0.33084211 -5.10365847
227 -1.59331045 -0.33084211
228 -0.63224079 -1.59331045
229 -1.71160373 -0.63224079
230 -4.32605238 -1.71160373
231 -0.68866591 -4.32605238
232 -4.76259269 -0.68866591
233 3.62107188 -4.76259269
234 -0.78227598 3.62107188
235 -0.29193486 -0.78227598
236 -4.03803985 -0.29193486
237 2.24161787 -4.03803985
238 1.60538897 2.24161787
239 -3.41169360 1.60538897
240 -2.71164079 -3.41169360
241 0.96566002 -2.71164079
242 -6.49820395 0.96566002
243 -4.24515817 -6.49820395
244 -0.45617351 -4.24515817
245 -1.98837496 -0.45617351
246 -1.47093134 -1.98837496
247 -0.32946210 -1.47093134
248 -0.24306542 -0.32946210
249 0.50258857 -0.24306542
250 -3.45731443 0.50258857
251 -3.15510753 -3.45731443
252 -4.84440193 -3.15510753
253 -1.42146455 -4.84440193
254 -0.23372461 -1.42146455
255 0.68384597 -0.23372461
256 1.68536814 0.68384597
257 -1.83564056 1.68536814
258 0.51359199 -1.83564056
259 -7.82643230 0.51359199
260 0.92805376 -7.82643230
261 0.93889195 0.92805376
262 -3.10650473 0.93889195
263 -1.40681918 -3.10650473
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/7ovxd1384895113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/8dhyo1384895113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/933ye1384895113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/105guh1384895113.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/112d6b1384895113.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12m87s1384895113.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> 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, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/133ou11384895113.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/14omrs1384895113.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,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15ndt21384895113.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,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ 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,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ 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,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/16rg7o1384895113.tab")
+ }
>
> try(system("convert tmp/10rbd1384895112.ps tmp/10rbd1384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/2jmk51384895112.ps tmp/2jmk51384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/3uzlm1384895112.ps tmp/3uzlm1384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/4y59b1384895112.ps tmp/4y59b1384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/51vyr1384895112.ps tmp/51vyr1384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hun01384895112.ps tmp/6hun01384895112.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ovxd1384895113.ps tmp/7ovxd1384895113.png",intern=TRUE))
character(0)
> try(system("convert tmp/8dhyo1384895113.ps tmp/8dhyo1384895113.png",intern=TRUE))
character(0)
> try(system("convert tmp/933ye1384895113.ps tmp/933ye1384895113.png",intern=TRUE))
character(0)
> try(system("convert tmp/105guh1384895113.ps tmp/105guh1384895113.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.118 2.275 14.448