R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)
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+ ,15
+ ,16
+ ,30884
+ ,757
+ ,62792
+ ,35
+ ,28
+ ,72)
+ ,dim=c(6
+ ,241)
+ ,dimnames=list(c('totsize'
+ ,'pageviews'
+ ,'time_in_rfc'
+ ,'logins'
+ ,'blogged_computations'
+ ,'tothyperlinks')
+ ,1:241))
> y <- array(NA,dim=c(6,241),dimnames=list(c('totsize','pageviews','time_in_rfc','logins','blogged_computations','tothyperlinks'),1:241))
> 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 = '5'
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) 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
blogged_computations totsize pageviews time_in_rfc logins tothyperlinks
1 79 112285 1418 210907 56 144
2 58 84786 869 120982 56 103
3 121 119182 3201 385534 92 150
4 43 116174 1583 149061 44 84
5 102 133824 1706 230964 53 151
6 82 99645 1036 135473 41 138
7 101 99052 1929 215147 58 124
8 50 65553 1220 153935 33 73
9 81 85323 2352 225548 112 116
10 94 117478 1677 210767 60 119
11 44 74007 1579 170266 62 129
12 107 101494 2452 294424 77 175
13 33 31081 865 106408 30 41
14 42 22996 1793 96560 76 47
15 56 60578 1324 149112 56 80
16 59 79892 1383 152871 58 73
17 91 82875 1831 183167 66 127
18 27 23789 1112 103597 43 26
19 105 149193 2474 235800 94 190
20 67 106671 1496 143246 103 116
21 114 92945 1833 187681 62 143
22 69 83737 1403 167488 45 113
23 105 69094 1425 143756 46 120
24 88 95536 1840 243199 75 134
25 67 95364 1054 130585 46 91
26 124 102860 1626 182079 63 181
27 110 115929 2888 265318 117 138
28 130 162901 2845 310839 92 254
29 93 109825 1982 225060 93 87
30 39 37510 1391 144966 144 51
31 28 192565 874 99466 50 56
32 28 44332 1105 102010 53 26
33 44 32334 1988 99923 66 36
34 116 91413 2395 317394 86 195
35 12 44339 620 22648 19 24
36 18 14116 449 31414 19 39
37 32 92696 1204 128423 64 37
38 25 94785 1138 97839 38 77
39 129 105547 2833 328107 65 153
40 59 71220 1002 158015 29 79
41 36 51009 1417 120445 118 63
42 113 135777 3261 324598 110 134
43 47 51513 1587 131069 67 69
44 92 74163 1424 204271 42 119
45 50 33416 946 116048 64 63
46 111 102372 1641 195838 67 197
47 120 103772 2312 254488 83 140
48 131 130115 1900 224330 83 167
49 45 24874 1254 135781 31 32
50 58 45549 1597 81240 66 13
51 0 4143 628 31774 23 0
52 21 28207 617 51567 30 30
53 50 45833 1656 102538 57 51
54 12 28394 1212 99373 63 25
55 21 18632 1143 86230 44 25
56 8 2325 435 30837 19 8
57 37 21792 830 64175 42 46
58 29 26263 652 59382 49 47
59 32 23686 707 119308 30 37
60 35 49303 954 76702 49 51
61 17 20055 733 84105 20 34
62 60 83123 1530 176508 54 98
63 69 57635 1439 165446 33 80
64 78 66198 1764 237213 84 130
65 44 57793 1373 133131 55 60
66 158 97668 4041 324799 154 140
67 77 101481 2152 236785 119 91
68 80 67654 2242 344297 75 119
69 123 69112 2515 174724 92 123
70 73 82753 2147 174415 100 90
71 105 72654 1638 223632 73 113
72 47 30727 1222 124817 40 56
73 84 79215 2662 325107 99 96
74 0 1423 186 7176 17 0
75 96 83122 2527 265769 146 126
76 57 39992 2702 175824 107 70
77 39 49810 1179 111665 34 57
78 76 100708 4308 362301 119 68
79 76 72260 1438 168809 66 102
80 8 5950 496 24188 24 7
81 79 115762 2253 329267 259 148
82 101 143558 2144 244052 68 137
83 94 117105 4691 341570 168 135
84 123 105195 1973 256462 105 181
85 41 95260 1226 196553 57 107
86 72 55183 1389 174184 53 94
87 75 73511 2269 187559 121 106
88 22 22618 893 73566 32 26
89 73 225920 1502 182999 88 54
90 62 61370 1420 152299 53 78
91 118 106117 2970 346485 90 121
92 100 84651 1644 193339 78 145
93 24 15986 1654 122774 45 27
94 46 26706 937 112611 41 48
95 57 89691 3004 286468 144 68
96 135 126846 2547 148446 91 150
97 33 51715 1468 140344 53 65
98 98 55801 2445 220516 62 97
99 58 111813 1964 243060 63 121
100 68 120293 1381 162765 32 99
101 131 161647 1659 232138 62 188
102 37 24266 1290 85574 34 40
103 118 129838 1904 232317 54 178
104 81 87771 1559 164709 109 176
105 51 44418 2146 220801 75 66
106 40 35232 1590 92661 61 39
107 56 40909 1590 133328 55 66
108 27 13294 1210 61361 77 27
109 83 140867 1281 100750 72 58
110 59 61056 1272 101523 42 77
111 133 101338 1944 243511 71 130
112 12 1168 391 22938 10 11
113 106 65567 1605 152474 65 101
114 71 40735 1386 132487 41 120
115 4 855 387 21054 16 4
116 62 97068 1742 209641 42 89
117 14 10288 800 46698 45 14
118 60 65622 1684 131698 65 78
119 98 76643 2699 244749 95 106
120 100 93815 2158 272458 65 132
121 45 34553 1421 108043 62 40
122 136 213688 2922 351067 95 220
123 63 91721 2186 229242 247 95
124 14 111194 1035 84207 29 12
125 41 83305 1926 250047 81 55
126 91 98952 3352 299775 95 103
127 41 37238 2035 173260 63 16
128 25 21399 961 92499 32 21
129 29 34988 1335 74408 67 36
130 47 64466 1645 181633 70 96
131 37 28579 1161 81437 38 36
132 26 38084 979 65745 53 50
133 38 27717 675 56653 45 30
134 23 32928 1241 158399 39 30
135 30 19499 1049 73624 24 33
136 18 36874 1081 91899 35 37
137 28 48259 1688 139526 151 83
138 12 29156 705 86678 40 19
139 27 45588 1597 150580 77 41
140 41 45097 982 99611 35 54
141 26 25139 532 31706 13 26
142 27 27975 882 89806 42 20
143 10 5752 285 19764 12 10
144 10 20154 642 64187 27 12
145 17 19540 894 72535 14 27
146 108 101193 2172 179321 89 135
147 49 38361 901 123185 40 61
148 0 68504 463 52746 25 39
149 1 22807 371 33170 18 5
150 20 17140 1192 101645 63 28
151 86 71701 1495 173326 88 82
152 104 80444 2187 258873 60 131
153 63 53855 1491 180083 66 84
154 115 114789 1882 202925 61 150
155 83 97500 1289 132943 40 110
156 105 77873 1812 221698 45 115
157 114 90183 1731 260561 75 127
158 38 61542 807 84853 31 27
159 30 27570 829 101011 34 35
160 71 55813 1940 215641 46 64
161 59 55461 1499 167542 66 84
162 106 70106 2747 269651 67 105
163 34 71570 2099 116408 61 40
164 20 33032 918 78800 42 21
165 115 139077 3373 277965 89 154
166 85 71595 1713 150629 44 116
167 21 32551 744 65029 17 21
168 92 120733 2694 233328 132 230
169 75 73107 1769 206161 71 71
170 128 132068 3148 311473 112 147
171 55 46821 2084 177939 82 64
172 56 87011 1954 207176 70 105
173 118 78664 1268 119016 52 81
174 77 70054 1943 182192 52 89
175 66 74011 1762 194979 62 84
176 116 93133 1857 275541 63 110
177 99 62133 1441 135649 46 96
178 53 43836 1416 120221 37 51
179 30 38692 1317 145790 63 38
180 49 56622 870 80953 25 59
181 75 67267 2008 241066 82 58
182 68 41140 1885 204713 71 74
183 81 138599 1369 182613 39 152
184 13 43750 602 43287 14 49
185 74 40652 1743 155754 61 73
186 109 85872 2014 201940 38 94
187 151 89275 2143 235454 73 120
188 37 32387 2072 125930 75 65
189 54 120662 1401 224549 50 98
190 27 21233 834 82316 32 25
191 0 13497 761 41566 35 2
192 23 25162 530 61857 25 31
193 7 16563 1050 91735 35 15
194 64 110681 1606 184510 49 83
195 29 29011 1502 79863 37 24
196 16 8773 568 38214 34 16
197 48 83209 1459 151101 32 56
198 46 86687 1111 172494 52 144
199 130 103487 1955 250579 83 143
200 25 23517 1060 98866 18 50
201 32 56926 956 85439 33 39
202 95 115168 3604 351619 139 169
203 70 51633 1701 165543 65 119
204 19 75345 1249 141722 94 75
205 135 123969 1369 104389 45 89
206 27 27142 1577 136084 30 40
207 87 135400 2201 199476 70 125
208 4 6023 207 14688 10 5
209 28 51776 1463 87186 54 47
210 16 21152 742 50090 20 20
211 22 11342 676 46455 20 34
212 16 16380 620 38395 31 34
213 32 16734 736 52164 52 32
214 23 30143 812 70551 31 43
215 29 41369 1051 84856 29 41
216 21 35944 945 85709 44 37
217 18 36278 554 34662 25 33
218 13 3895 222 19349 11 14
219 13 14483 608 62088 38 11
220 16 13127 459 40151 29 14
221 2 5839 578 27634 20 3
222 42 24069 826 76990 27 40
223 5 3738 509 37460 20 5
224 37 18625 717 54157 19 38
225 17 36341 637 49862 37 32
226 38 24548 857 84337 26 41
227 17 25659 1461 103425 67 49
228 20 28904 672 70344 28 21
229 7 2781 778 43410 19 1
230 46 29236 1141 104838 49 44
231 24 19546 680 62215 27 26
232 40 22818 1090 69304 30 21
233 3 32689 616 53117 22 4
234 37 22197 1145 86680 31 43
235 28 25272 888 77945 20 32
236 19 82206 849 89113 39 20
237 29 32073 1182 91005 29 34
238 8 5444 528 40248 16 6
239 15 36944 947 50857 21 24
240 15 8019 819 56613 19 16
241 28 30884 757 62792 35 72
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) totsize pageviews time_in_rfc logins
-4.869e-01 7.841e-05 1.370e-02 5.617e-05 -1.481e-01
tothyperlinks
4.342e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-47.289 -8.038 -0.347 7.055 69.177
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.869e-01 2.355e+00 -0.207 0.83642
totsize 7.841e-05 4.296e-05 1.825 0.06925 .
pageviews 1.370e-02 3.371e-03 4.064 6.59e-05 ***
time_in_rfc 5.617e-05 3.470e-05 1.619 0.10688
logins -1.481e-01 4.506e-02 -3.287 0.00117 **
tothyperlinks 4.342e-01 3.980e-02 10.908 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 16.25 on 235 degrees of freedom
Multiple R-squared: 0.819, Adjusted R-squared: 0.8152
F-statistic: 212.7 on 5 and 235 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,] 0.241354881 4.827098e-01 7.586451e-01
[2,] 0.378488900 7.569778e-01 6.215111e-01
[3,] 0.713317560 5.733649e-01 2.866824e-01
[4,] 0.601459438 7.970811e-01 3.985406e-01
[5,] 0.505353053 9.892939e-01 4.946469e-01
[6,] 0.510040223 9.799196e-01 4.899598e-01
[7,] 0.407990193 8.159804e-01 5.920098e-01
[8,] 0.316392149 6.327843e-01 6.836079e-01
[9,] 0.311516006 6.230320e-01 6.884840e-01
[10,] 0.235494374 4.709887e-01 7.645056e-01
[11,] 0.186373201 3.727464e-01 8.136268e-01
[12,] 0.134529095 2.690582e-01 8.654709e-01
[13,] 0.282830038 5.656601e-01 7.171700e-01
[14,] 0.223204034 4.464081e-01 7.767960e-01
[15,] 0.393790210 7.875804e-01 6.062098e-01
[16,] 0.326104216 6.522084e-01 6.738958e-01
[17,] 0.285550773 5.711015e-01 7.144492e-01
[18,] 0.279332527 5.586651e-01 7.206675e-01
[19,] 0.254707083 5.094142e-01 7.452929e-01
[20,] 0.368878242 7.377565e-01 6.311218e-01
[21,] 0.430650466 8.613009e-01 5.693495e-01
[22,] 0.375034631 7.500693e-01 6.249654e-01
[23,] 0.352858237 7.057165e-01 6.471418e-01
[24,] 0.300927889 6.018558e-01 6.990721e-01
[25,] 0.250868881 5.017378e-01 7.491311e-01
[26,] 0.227353244 4.547065e-01 7.726468e-01
[27,] 0.201781188 4.035624e-01 7.982188e-01
[28,] 0.173266001 3.465320e-01 8.267340e-01
[29,] 0.141374804 2.827496e-01 8.586252e-01
[30,] 0.199747701 3.994954e-01 8.002523e-01
[31,] 0.180380798 3.607616e-01 8.196192e-01
[32,] 0.147107643 2.942153e-01 8.528924e-01
[33,] 0.123592169 2.471843e-01 8.764078e-01
[34,] 0.098652194 1.973044e-01 9.013478e-01
[35,] 0.079980982 1.599620e-01 9.200190e-01
[36,] 0.070021296 1.400426e-01 9.299787e-01
[37,] 0.056937187 1.138744e-01 9.430628e-01
[38,] 0.044432502 8.886500e-02 9.555675e-01
[39,] 0.054636153 1.092723e-01 9.453638e-01
[40,] 0.096782506 1.935650e-01 9.032175e-01
[41,] 0.079637875 1.592757e-01 9.203621e-01
[42,] 0.156898340 3.137967e-01 8.431017e-01
[43,] 0.142718819 2.854376e-01 8.572812e-01
[44,] 0.117477770 2.349555e-01 8.825222e-01
[45,] 0.096563439 1.931269e-01 9.034366e-01
[46,] 0.102886635 2.057733e-01 8.971134e-01
[47,] 0.087882135 1.757643e-01 9.121179e-01
[48,] 0.070783670 1.415673e-01 9.292163e-01
[49,] 0.058325230 1.166505e-01 9.416748e-01
[50,] 0.045966435 9.193287e-02 9.540336e-01
[51,] 0.035935814 7.187163e-02 9.640642e-01
[52,] 0.027719319 5.543864e-02 9.722807e-01
[53,] 0.024925341 4.985068e-02 9.750747e-01
[54,] 0.021462896 4.292579e-02 9.785371e-01
[55,] 0.017143879 3.428776e-02 9.828561e-01
[56,] 0.014760377 2.952075e-02 9.852396e-01
[57,] 0.011325148 2.265030e-02 9.886749e-01
[58,] 0.027242612 5.448522e-02 9.727574e-01
[59,] 0.021146081 4.229216e-02 9.788539e-01
[60,] 0.023341806 4.668361e-02 9.766582e-01
[61,] 0.041069687 8.213937e-02 9.589303e-01
[62,] 0.032878116 6.575623e-02 9.671219e-01
[63,] 0.054407911 1.088158e-01 9.455921e-01
[64,] 0.043743897 8.748779e-02 9.562561e-01
[65,] 0.036952308 7.390462e-02 9.630477e-01
[66,] 0.029176400 5.835280e-02 9.708236e-01
[67,] 0.023479134 4.695827e-02 9.765209e-01
[68,] 0.026961304 5.392261e-02 9.730387e-01
[69,] 0.021877842 4.375568e-02 9.781222e-01
[70,] 0.035488123 7.097625e-02 9.645119e-01
[71,] 0.029512757 5.902551e-02 9.704872e-01
[72,] 0.023248272 4.649654e-02 9.767517e-01
[73,] 0.021260643 4.252129e-02 9.787394e-01
[74,] 0.016995392 3.399078e-02 9.830046e-01
[75,] 0.041766451 8.353290e-02 9.582335e-01
[76,] 0.036734187 7.346837e-02 9.632658e-01
[77,] 0.063240196 1.264804e-01 9.367598e-01
[78,] 0.053255787 1.065116e-01 9.467442e-01
[79,] 0.043565258 8.713052e-02 9.564347e-01
[80,] 0.035383589 7.076718e-02 9.646164e-01
[81,] 0.047120125 9.424025e-02 9.528799e-01
[82,] 0.038673254 7.734651e-02 9.613267e-01
[83,] 0.035731598 7.146320e-02 9.642684e-01
[84,] 0.030834130 6.166826e-02 9.691659e-01
[85,] 0.027859527 5.571905e-02 9.721405e-01
[86,] 0.024339273 4.867855e-02 9.756607e-01
[87,] 0.023922008 4.784402e-02 9.760780e-01
[88,] 0.042330608 8.466122e-02 9.576694e-01
[89,] 0.045893824 9.178765e-02 9.541062e-01
[90,] 0.045660463 9.132093e-02 9.543395e-01
[91,] 0.084584451 1.691689e-01 9.154155e-01
[92,] 0.073539740 1.470795e-01 9.264603e-01
[93,] 0.067680023 1.353600e-01 9.323200e-01
[94,] 0.056094462 1.121889e-01 9.439055e-01
[95,] 0.046253039 9.250608e-02 9.537470e-01
[96,] 0.049686811 9.937362e-02 9.503132e-01
[97,] 0.044570145 8.914029e-02 9.554299e-01
[98,] 0.036693363 7.338673e-02 9.633066e-01
[99,] 0.029992479 5.998496e-02 9.700075e-01
[100,] 0.025078252 5.015650e-02 9.749217e-01
[101,] 0.054478346 1.089567e-01 9.455217e-01
[102,] 0.045450827 9.090165e-02 9.545492e-01
[103,] 0.111513131 2.230263e-01 8.884869e-01
[104,] 0.094884489 1.897690e-01 9.051155e-01
[105,] 0.176141520 3.522830e-01 8.238585e-01
[106,] 0.154795939 3.095919e-01 8.452041e-01
[107,] 0.133754132 2.675083e-01 8.662459e-01
[108,] 0.127788759 2.555775e-01 8.722112e-01
[109,] 0.109610025 2.192201e-01 8.903900e-01
[110,] 0.093432756 1.868655e-01 9.065672e-01
[111,] 0.084384274 1.687685e-01 9.156157e-01
[112,] 0.071581627 1.431633e-01 9.284184e-01
[113,] 0.063638576 1.272772e-01 9.363614e-01
[114,] 0.075762567 1.515251e-01 9.242374e-01
[115,] 0.080049883 1.600998e-01 9.199501e-01
[116,] 0.077992620 1.559852e-01 9.220074e-01
[117,] 0.082646587 1.652932e-01 9.173534e-01
[118,] 0.074597354 1.491947e-01 9.254026e-01
[119,] 0.062907946 1.258159e-01 9.370921e-01
[120,] 0.052160828 1.043217e-01 9.478392e-01
[121,] 0.044305148 8.861030e-02 9.556949e-01
[122,] 0.051895489 1.037910e-01 9.481045e-01
[123,] 0.043745219 8.749044e-02 9.562548e-01
[124,] 0.037443621 7.488724e-02 9.625564e-01
[125,] 0.040734756 8.146951e-02 9.592652e-01
[126,] 0.039697826 7.939565e-02 9.603022e-01
[127,] 0.032309972 6.461994e-02 9.676900e-01
[128,] 0.031742233 6.348447e-02 9.682578e-01
[129,] 0.034365526 6.873105e-02 9.656345e-01
[130,] 0.028823591 5.764718e-02 9.711764e-01
[131,] 0.025936624 5.187325e-02 9.740634e-01
[132,] 0.020758303 4.151661e-02 9.792417e-01
[133,] 0.017091775 3.418355e-02 9.829082e-01
[134,] 0.013915703 2.783141e-02 9.860843e-01
[135,] 0.010994610 2.198922e-02 9.890054e-01
[136,] 0.008735669 1.747134e-02 9.912643e-01
[137,] 0.007595886 1.519177e-02 9.924041e-01
[138,] 0.008347780 1.669556e-02 9.916522e-01
[139,] 0.006682754 1.336551e-02 9.933172e-01
[140,] 0.011366292 2.273258e-02 9.886337e-01
[141,] 0.009308018 1.861604e-02 9.906920e-01
[142,] 0.007302337 1.460467e-02 9.926977e-01
[143,] 0.013829203 2.765841e-02 9.861708e-01
[144,] 0.011189615 2.237923e-02 9.888104e-01
[145,] 0.008711265 1.742253e-02 9.912887e-01
[146,] 0.007755533 1.551107e-02 9.922445e-01
[147,] 0.006316189 1.263238e-02 9.936838e-01
[148,] 0.006249553 1.249911e-02 9.937504e-01
[149,] 0.008303685 1.660737e-02 9.916963e-01
[150,] 0.006949733 1.389947e-02 9.930503e-01
[151,] 0.005289043 1.057809e-02 9.947110e-01
[152,] 0.004212219 8.424439e-03 9.957878e-01
[153,] 0.003152885 6.305770e-03 9.968471e-01
[154,] 0.002607274 5.214548e-03 9.973927e-01
[155,] 0.002424740 4.849479e-03 9.975753e-01
[156,] 0.001782183 3.564366e-03 9.982178e-01
[157,] 0.001615323 3.230646e-03 9.983847e-01
[158,] 0.001193407 2.386813e-03 9.988066e-01
[159,] 0.000877157 1.754314e-03 9.991228e-01
[160,] 0.003771586 7.543172e-03 9.962284e-01
[161,] 0.003324596 6.649192e-03 9.966754e-01
[162,] 0.002657275 5.314550e-03 9.973427e-01
[163,] 0.001947135 3.894270e-03 9.980529e-01
[164,] 0.002857660 5.715320e-03 9.971423e-01
[165,] 0.061337528 1.226751e-01 9.386625e-01
[166,] 0.050027025 1.000541e-01 9.499730e-01
[167,] 0.040622716 8.124543e-02 9.593773e-01
[168,] 0.056032621 1.120652e-01 9.439674e-01
[169,] 0.098075021 1.961500e-01 9.019250e-01
[170,] 0.083043494 1.660870e-01 9.169565e-01
[171,] 0.068914894 1.378298e-01 9.310851e-01
[172,] 0.057734246 1.154685e-01 9.422658e-01
[173,] 0.057963753 1.159275e-01 9.420362e-01
[174,] 0.051173226 1.023465e-01 9.488268e-01
[175,] 0.064216198 1.284324e-01 9.357838e-01
[176,] 0.082182737 1.643655e-01 9.178173e-01
[177,] 0.091803287 1.836066e-01 9.081967e-01
[178,] 0.116498622 2.329972e-01 8.835014e-01
[179,] 0.778317811 4.433644e-01 2.216822e-01
[180,] 0.760204205 4.795916e-01 2.397958e-01
[181,] 0.763263304 4.734734e-01 2.367367e-01
[182,] 0.739592018 5.208160e-01 2.604080e-01
[183,] 0.706064255 5.878715e-01 2.939357e-01
[184,] 0.664583335 6.708333e-01 3.354167e-01
[185,] 0.630708415 7.385832e-01 3.692916e-01
[186,] 0.592002998 8.159940e-01 4.079970e-01
[187,] 0.543776334 9.124473e-01 4.562237e-01
[188,] 0.506371266 9.872575e-01 4.936287e-01
[189,] 0.475005332 9.500107e-01 5.249947e-01
[190,] 0.849381983 3.012360e-01 1.506180e-01
[191,] 0.944892552 1.102149e-01 5.510745e-02
[192,] 0.943789891 1.124202e-01 5.621011e-02
[193,] 0.928995043 1.420099e-01 7.100496e-02
[194,] 0.923523323 1.529534e-01 7.647668e-02
[195,] 0.902320216 1.953596e-01 9.767978e-02
[196,] 0.916836772 1.663265e-01 8.316323e-02
[197,] 0.999997131 5.738331e-06 2.869166e-06
[198,] 0.999999055 1.890371e-06 9.451856e-07
[199,] 0.999998175 3.650219e-06 1.825109e-06
[200,] 0.999995870 8.260732e-06 4.130366e-06
[201,] 0.999991267 1.746639e-05 8.733193e-06
[202,] 0.999980732 3.853531e-05 1.926766e-05
[203,] 0.999958469 8.306292e-05 4.153146e-05
[204,] 0.999913390 1.732205e-04 8.661024e-05
[205,] 0.999980977 3.804530e-05 1.902265e-05
[206,] 0.999967565 6.486985e-05 3.243492e-05
[207,] 0.999936083 1.278348e-04 6.391740e-05
[208,] 0.999882485 2.350296e-04 1.175148e-04
[209,] 0.999767807 4.643851e-04 2.321926e-04
[210,] 0.999510093 9.798148e-04 4.899074e-04
[211,] 0.998943906 2.112187e-03 1.056094e-03
[212,] 0.998547563 2.904874e-03 1.452437e-03
[213,] 0.996972119 6.055762e-03 3.027881e-03
[214,] 0.995697213 8.605574e-03 4.302787e-03
[215,] 0.991384876 1.723025e-02 8.615124e-03
[216,] 0.990707164 1.858567e-02 9.292836e-03
[217,] 0.986755202 2.648960e-02 1.324480e-02
[218,] 0.973551892 5.289622e-02 2.644811e-02
[219,] 0.998003361 3.993278e-03 1.996639e-03
[220,] 0.994380018 1.123996e-02 5.619982e-03
[221,] 0.991073232 1.785354e-02 8.926768e-03
[222,] 0.979833375 4.033325e-02 2.016662e-02
[223,] 0.950938358 9.812328e-02 4.906164e-02
[224,] 0.983359564 3.328087e-02 1.664044e-02
> postscript(file="/var/fisher/rcomp/tmp/1vcvf1355077852.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/2yedt1355077852.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/39tcv1355077852.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/47u081355077852.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/5tvuo1355077852.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 241
Frequency = 1
1 2 3 4 5 6
-14.81039126 -3.28274488 -4.85329119 -25.62906724 -2.05412684 -0.96705652
7 8 9 10 11 12
9.96964770 -6.81536787 -3.85899892 7.68954283 -39.32999715 -15.16607298
13 14 15 16 17 18
-0.13153570 1.55439023 -1.21081096 2.59094278 4.25931370 -0.34666595
19 20 21 22 23 24
-21.90876488 -4.51886239 18.64955522 -8.09796387 27.19089233 -4.93465667
25 26 27 28 29 30
5.54314235 14.67181321 4.35485401 -35.36297557 21.09115789 8.54013996
31 32 33 34 35 36
-21.07677789 0.70892889 3.25687660 -13.23520969 -8.35929210 -4.65221215
37 38 39 40 41 42
-5.06926496 -30.82940630 7.17970054 1.29943326 -3.55867799 -1.94039714
43 44 45 46 47 48
-5.68342057 10.24959401 10.51949266 -5.62232648 17.90070387 22.44965205
49 50 51 52 53 54
9.43289039 32.61133355 -6.81711435 -0.65313832 4.75308874 -13.44323272
55 56 57 58 59 60
-4.80908383 -0.04434844 7.05527374 2.01473330 2.62446364 -0.63780120
61 62 63 64 65 66
-10.64845851 -11.45022224 6.12017563 -8.18713519 -4.23073130 39.26613131
67 68 69 70 71 72
4.87330707 -15.42067461 34.03243912 3.53316174 26.54658243 2.94134683
73 74 75 76 77 78
-3.46076381 -0.05704423 7.35246486 -7.07441768 -6.55026042 -22.66061701
79 80 81 82 83 84
7.13510151 0.38424341 -4.83275338 -2.25129954 -31.85807615 10.77964744
85 86 87 88 89 90
-31.82711692 6.39089137 0.01277661 -2.19793650 14.51178682 3.65710777
91 92 93 94 95 96
10.82302687 9.07263862 -11.37350322 10.46728077 -14.97325146 30.67256305
97 98 99 100 101 102
-18.92759704 15.30697666 -34.03503561 -7.24521421 10.61112881 0.77870706
103 104 105 106 107 108
-0.10387431 -16.26650709 -11.33621670 2.84564895 3.50491777 6.10891337
109 110 111 112 113 114
34.72095051 4.36559078 39.31253172 2.45697738 36.57612814 -4.15897706
115 116 117 118 119 120
-1.42987748 -13.17861725 0.68763782 0.64259142 9.81364736 0.58820526
121 122 123 124 125 126
9.06359411 -21.45344926 8.82159428 -14.05202291 -17.35022180 -9.66790360
127 128 129 130 131 132
3.34815497 1.07363064 -1.42595759 -21.61175693 4.76898665 -7.45834708
133 134 135 136 137 138
17.52726127 -12.23760010 -0.31758198 -15.25193192 -19.92141072 -6.64784694
139 140 141 142 143 144
-12.81370585 0.64525539 6.08556489 5.70684934 2.45811696 -4.70240054
145 146 147 148 149 150
-10.01302038 15.30227276 6.66023573 -27.41799633 -6.75046429 -5.71697520
151 152 153 154 155 156
28.08685829 5.69598785 2.03406432 13.22203964 8.88649583 18.84656657
157 158 159 160 161 162
25.04195522 10.71160067 1.13754737 7.45397873 -1.49700676 12.55633324
163 164 165 166 167 168
-14.74332440 -1.99871439 -10.90801810 4.10446577 -1.50769951 -47.28900791
169 170 171 172 173 174
13.63684852 10.28750780 -2.36257640 -23.95407368 60.80183956 4.20974463
175 176 177 178 179 180
-1.68746437 29.84659710 32.39292696 7.24086816 -5.94028119 6.67141031
181 182 183 184 185 186
16.13431335 6.33344102 -18.60496093 -19.82070931 16.01946395 28.64295676
187 188 189 190 191 192
60.62326468 -17.61620028 -21.91785606 3.66144519 -9.01293177 1.02438969
193 194 195 196 197 198
-14.67385396 -5.32970153 -2.78519468 3.96276515 -6.08143626 -40.03321448
199 200 201 202 203 204
31.72984528 -15.47080453 -1.91389584 -35.43998136 -8.19525273 -30.12628797
205 206 207 208 209 210
69.17735559 -16.80746881 -8.38221706 -0.33511430 -12.91486914 -3.86878834
211 212 213 214 215 216
-2.06973880 -5.61551025 11.97373499 -8.03815843 -6.42337482 -8.63532259
217 218 219 220 221 222
-4.51673371 4.60512706 1.38954254 5.13314407 -5.77963223 11.59462465
223 224 225 226 227 228
-3.09007877 9.48020087 -5.30046664 6.13748089 -21.69429581 0.09540140
229 230 231 232 233 234
-3.44513861 10.83315205 2.85723985 15.20208331 -8.97472734 1.11792549
235 236 237 238 239 240
-0.96617826 -6.49897069 -4.79495905 -1.66740634 -10.54649337 -3.67153055
241
-13.90538540
> postscript(file="/var/fisher/rcomp/tmp/6mmei1355077852.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 = 241
Frequency = 1
lag(myerror, k = 1) myerror
0 -14.81039126 NA
1 -3.28274488 -14.81039126
2 -4.85329119 -3.28274488
3 -25.62906724 -4.85329119
4 -2.05412684 -25.62906724
5 -0.96705652 -2.05412684
6 9.96964770 -0.96705652
7 -6.81536787 9.96964770
8 -3.85899892 -6.81536787
9 7.68954283 -3.85899892
10 -39.32999715 7.68954283
11 -15.16607298 -39.32999715
12 -0.13153570 -15.16607298
13 1.55439023 -0.13153570
14 -1.21081096 1.55439023
15 2.59094278 -1.21081096
16 4.25931370 2.59094278
17 -0.34666595 4.25931370
18 -21.90876488 -0.34666595
19 -4.51886239 -21.90876488
20 18.64955522 -4.51886239
21 -8.09796387 18.64955522
22 27.19089233 -8.09796387
23 -4.93465667 27.19089233
24 5.54314235 -4.93465667
25 14.67181321 5.54314235
26 4.35485401 14.67181321
27 -35.36297557 4.35485401
28 21.09115789 -35.36297557
29 8.54013996 21.09115789
30 -21.07677789 8.54013996
31 0.70892889 -21.07677789
32 3.25687660 0.70892889
33 -13.23520969 3.25687660
34 -8.35929210 -13.23520969
35 -4.65221215 -8.35929210
36 -5.06926496 -4.65221215
37 -30.82940630 -5.06926496
38 7.17970054 -30.82940630
39 1.29943326 7.17970054
40 -3.55867799 1.29943326
41 -1.94039714 -3.55867799
42 -5.68342057 -1.94039714
43 10.24959401 -5.68342057
44 10.51949266 10.24959401
45 -5.62232648 10.51949266
46 17.90070387 -5.62232648
47 22.44965205 17.90070387
48 9.43289039 22.44965205
49 32.61133355 9.43289039
50 -6.81711435 32.61133355
51 -0.65313832 -6.81711435
52 4.75308874 -0.65313832
53 -13.44323272 4.75308874
54 -4.80908383 -13.44323272
55 -0.04434844 -4.80908383
56 7.05527374 -0.04434844
57 2.01473330 7.05527374
58 2.62446364 2.01473330
59 -0.63780120 2.62446364
60 -10.64845851 -0.63780120
61 -11.45022224 -10.64845851
62 6.12017563 -11.45022224
63 -8.18713519 6.12017563
64 -4.23073130 -8.18713519
65 39.26613131 -4.23073130
66 4.87330707 39.26613131
67 -15.42067461 4.87330707
68 34.03243912 -15.42067461
69 3.53316174 34.03243912
70 26.54658243 3.53316174
71 2.94134683 26.54658243
72 -3.46076381 2.94134683
73 -0.05704423 -3.46076381
74 7.35246486 -0.05704423
75 -7.07441768 7.35246486
76 -6.55026042 -7.07441768
77 -22.66061701 -6.55026042
78 7.13510151 -22.66061701
79 0.38424341 7.13510151
80 -4.83275338 0.38424341
81 -2.25129954 -4.83275338
82 -31.85807615 -2.25129954
83 10.77964744 -31.85807615
84 -31.82711692 10.77964744
85 6.39089137 -31.82711692
86 0.01277661 6.39089137
87 -2.19793650 0.01277661
88 14.51178682 -2.19793650
89 3.65710777 14.51178682
90 10.82302687 3.65710777
91 9.07263862 10.82302687
92 -11.37350322 9.07263862
93 10.46728077 -11.37350322
94 -14.97325146 10.46728077
95 30.67256305 -14.97325146
96 -18.92759704 30.67256305
97 15.30697666 -18.92759704
98 -34.03503561 15.30697666
99 -7.24521421 -34.03503561
100 10.61112881 -7.24521421
101 0.77870706 10.61112881
102 -0.10387431 0.77870706
103 -16.26650709 -0.10387431
104 -11.33621670 -16.26650709
105 2.84564895 -11.33621670
106 3.50491777 2.84564895
107 6.10891337 3.50491777
108 34.72095051 6.10891337
109 4.36559078 34.72095051
110 39.31253172 4.36559078
111 2.45697738 39.31253172
112 36.57612814 2.45697738
113 -4.15897706 36.57612814
114 -1.42987748 -4.15897706
115 -13.17861725 -1.42987748
116 0.68763782 -13.17861725
117 0.64259142 0.68763782
118 9.81364736 0.64259142
119 0.58820526 9.81364736
120 9.06359411 0.58820526
121 -21.45344926 9.06359411
122 8.82159428 -21.45344926
123 -14.05202291 8.82159428
124 -17.35022180 -14.05202291
125 -9.66790360 -17.35022180
126 3.34815497 -9.66790360
127 1.07363064 3.34815497
128 -1.42595759 1.07363064
129 -21.61175693 -1.42595759
130 4.76898665 -21.61175693
131 -7.45834708 4.76898665
132 17.52726127 -7.45834708
133 -12.23760010 17.52726127
134 -0.31758198 -12.23760010
135 -15.25193192 -0.31758198
136 -19.92141072 -15.25193192
137 -6.64784694 -19.92141072
138 -12.81370585 -6.64784694
139 0.64525539 -12.81370585
140 6.08556489 0.64525539
141 5.70684934 6.08556489
142 2.45811696 5.70684934
143 -4.70240054 2.45811696
144 -10.01302038 -4.70240054
145 15.30227276 -10.01302038
146 6.66023573 15.30227276
147 -27.41799633 6.66023573
148 -6.75046429 -27.41799633
149 -5.71697520 -6.75046429
150 28.08685829 -5.71697520
151 5.69598785 28.08685829
152 2.03406432 5.69598785
153 13.22203964 2.03406432
154 8.88649583 13.22203964
155 18.84656657 8.88649583
156 25.04195522 18.84656657
157 10.71160067 25.04195522
158 1.13754737 10.71160067
159 7.45397873 1.13754737
160 -1.49700676 7.45397873
161 12.55633324 -1.49700676
162 -14.74332440 12.55633324
163 -1.99871439 -14.74332440
164 -10.90801810 -1.99871439
165 4.10446577 -10.90801810
166 -1.50769951 4.10446577
167 -47.28900791 -1.50769951
168 13.63684852 -47.28900791
169 10.28750780 13.63684852
170 -2.36257640 10.28750780
171 -23.95407368 -2.36257640
172 60.80183956 -23.95407368
173 4.20974463 60.80183956
174 -1.68746437 4.20974463
175 29.84659710 -1.68746437
176 32.39292696 29.84659710
177 7.24086816 32.39292696
178 -5.94028119 7.24086816
179 6.67141031 -5.94028119
180 16.13431335 6.67141031
181 6.33344102 16.13431335
182 -18.60496093 6.33344102
183 -19.82070931 -18.60496093
184 16.01946395 -19.82070931
185 28.64295676 16.01946395
186 60.62326468 28.64295676
187 -17.61620028 60.62326468
188 -21.91785606 -17.61620028
189 3.66144519 -21.91785606
190 -9.01293177 3.66144519
191 1.02438969 -9.01293177
192 -14.67385396 1.02438969
193 -5.32970153 -14.67385396
194 -2.78519468 -5.32970153
195 3.96276515 -2.78519468
196 -6.08143626 3.96276515
197 -40.03321448 -6.08143626
198 31.72984528 -40.03321448
199 -15.47080453 31.72984528
200 -1.91389584 -15.47080453
201 -35.43998136 -1.91389584
202 -8.19525273 -35.43998136
203 -30.12628797 -8.19525273
204 69.17735559 -30.12628797
205 -16.80746881 69.17735559
206 -8.38221706 -16.80746881
207 -0.33511430 -8.38221706
208 -12.91486914 -0.33511430
209 -3.86878834 -12.91486914
210 -2.06973880 -3.86878834
211 -5.61551025 -2.06973880
212 11.97373499 -5.61551025
213 -8.03815843 11.97373499
214 -6.42337482 -8.03815843
215 -8.63532259 -6.42337482
216 -4.51673371 -8.63532259
217 4.60512706 -4.51673371
218 1.38954254 4.60512706
219 5.13314407 1.38954254
220 -5.77963223 5.13314407
221 11.59462465 -5.77963223
222 -3.09007877 11.59462465
223 9.48020087 -3.09007877
224 -5.30046664 9.48020087
225 6.13748089 -5.30046664
226 -21.69429581 6.13748089
227 0.09540140 -21.69429581
228 -3.44513861 0.09540140
229 10.83315205 -3.44513861
230 2.85723985 10.83315205
231 15.20208331 2.85723985
232 -8.97472734 15.20208331
233 1.11792549 -8.97472734
234 -0.96617826 1.11792549
235 -6.49897069 -0.96617826
236 -4.79495905 -6.49897069
237 -1.66740634 -4.79495905
238 -10.54649337 -1.66740634
239 -3.67153055 -10.54649337
240 -13.90538540 -3.67153055
241 NA -13.90538540
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -3.28274488 -14.81039126
[2,] -4.85329119 -3.28274488
[3,] -25.62906724 -4.85329119
[4,] -2.05412684 -25.62906724
[5,] -0.96705652 -2.05412684
[6,] 9.96964770 -0.96705652
[7,] -6.81536787 9.96964770
[8,] -3.85899892 -6.81536787
[9,] 7.68954283 -3.85899892
[10,] -39.32999715 7.68954283
[11,] -15.16607298 -39.32999715
[12,] -0.13153570 -15.16607298
[13,] 1.55439023 -0.13153570
[14,] -1.21081096 1.55439023
[15,] 2.59094278 -1.21081096
[16,] 4.25931370 2.59094278
[17,] -0.34666595 4.25931370
[18,] -21.90876488 -0.34666595
[19,] -4.51886239 -21.90876488
[20,] 18.64955522 -4.51886239
[21,] -8.09796387 18.64955522
[22,] 27.19089233 -8.09796387
[23,] -4.93465667 27.19089233
[24,] 5.54314235 -4.93465667
[25,] 14.67181321 5.54314235
[26,] 4.35485401 14.67181321
[27,] -35.36297557 4.35485401
[28,] 21.09115789 -35.36297557
[29,] 8.54013996 21.09115789
[30,] -21.07677789 8.54013996
[31,] 0.70892889 -21.07677789
[32,] 3.25687660 0.70892889
[33,] -13.23520969 3.25687660
[34,] -8.35929210 -13.23520969
[35,] -4.65221215 -8.35929210
[36,] -5.06926496 -4.65221215
[37,] -30.82940630 -5.06926496
[38,] 7.17970054 -30.82940630
[39,] 1.29943326 7.17970054
[40,] -3.55867799 1.29943326
[41,] -1.94039714 -3.55867799
[42,] -5.68342057 -1.94039714
[43,] 10.24959401 -5.68342057
[44,] 10.51949266 10.24959401
[45,] -5.62232648 10.51949266
[46,] 17.90070387 -5.62232648
[47,] 22.44965205 17.90070387
[48,] 9.43289039 22.44965205
[49,] 32.61133355 9.43289039
[50,] -6.81711435 32.61133355
[51,] -0.65313832 -6.81711435
[52,] 4.75308874 -0.65313832
[53,] -13.44323272 4.75308874
[54,] -4.80908383 -13.44323272
[55,] -0.04434844 -4.80908383
[56,] 7.05527374 -0.04434844
[57,] 2.01473330 7.05527374
[58,] 2.62446364 2.01473330
[59,] -0.63780120 2.62446364
[60,] -10.64845851 -0.63780120
[61,] -11.45022224 -10.64845851
[62,] 6.12017563 -11.45022224
[63,] -8.18713519 6.12017563
[64,] -4.23073130 -8.18713519
[65,] 39.26613131 -4.23073130
[66,] 4.87330707 39.26613131
[67,] -15.42067461 4.87330707
[68,] 34.03243912 -15.42067461
[69,] 3.53316174 34.03243912
[70,] 26.54658243 3.53316174
[71,] 2.94134683 26.54658243
[72,] -3.46076381 2.94134683
[73,] -0.05704423 -3.46076381
[74,] 7.35246486 -0.05704423
[75,] -7.07441768 7.35246486
[76,] -6.55026042 -7.07441768
[77,] -22.66061701 -6.55026042
[78,] 7.13510151 -22.66061701
[79,] 0.38424341 7.13510151
[80,] -4.83275338 0.38424341
[81,] -2.25129954 -4.83275338
[82,] -31.85807615 -2.25129954
[83,] 10.77964744 -31.85807615
[84,] -31.82711692 10.77964744
[85,] 6.39089137 -31.82711692
[86,] 0.01277661 6.39089137
[87,] -2.19793650 0.01277661
[88,] 14.51178682 -2.19793650
[89,] 3.65710777 14.51178682
[90,] 10.82302687 3.65710777
[91,] 9.07263862 10.82302687
[92,] -11.37350322 9.07263862
[93,] 10.46728077 -11.37350322
[94,] -14.97325146 10.46728077
[95,] 30.67256305 -14.97325146
[96,] -18.92759704 30.67256305
[97,] 15.30697666 -18.92759704
[98,] -34.03503561 15.30697666
[99,] -7.24521421 -34.03503561
[100,] 10.61112881 -7.24521421
[101,] 0.77870706 10.61112881
[102,] -0.10387431 0.77870706
[103,] -16.26650709 -0.10387431
[104,] -11.33621670 -16.26650709
[105,] 2.84564895 -11.33621670
[106,] 3.50491777 2.84564895
[107,] 6.10891337 3.50491777
[108,] 34.72095051 6.10891337
[109,] 4.36559078 34.72095051
[110,] 39.31253172 4.36559078
[111,] 2.45697738 39.31253172
[112,] 36.57612814 2.45697738
[113,] -4.15897706 36.57612814
[114,] -1.42987748 -4.15897706
[115,] -13.17861725 -1.42987748
[116,] 0.68763782 -13.17861725
[117,] 0.64259142 0.68763782
[118,] 9.81364736 0.64259142
[119,] 0.58820526 9.81364736
[120,] 9.06359411 0.58820526
[121,] -21.45344926 9.06359411
[122,] 8.82159428 -21.45344926
[123,] -14.05202291 8.82159428
[124,] -17.35022180 -14.05202291
[125,] -9.66790360 -17.35022180
[126,] 3.34815497 -9.66790360
[127,] 1.07363064 3.34815497
[128,] -1.42595759 1.07363064
[129,] -21.61175693 -1.42595759
[130,] 4.76898665 -21.61175693
[131,] -7.45834708 4.76898665
[132,] 17.52726127 -7.45834708
[133,] -12.23760010 17.52726127
[134,] -0.31758198 -12.23760010
[135,] -15.25193192 -0.31758198
[136,] -19.92141072 -15.25193192
[137,] -6.64784694 -19.92141072
[138,] -12.81370585 -6.64784694
[139,] 0.64525539 -12.81370585
[140,] 6.08556489 0.64525539
[141,] 5.70684934 6.08556489
[142,] 2.45811696 5.70684934
[143,] -4.70240054 2.45811696
[144,] -10.01302038 -4.70240054
[145,] 15.30227276 -10.01302038
[146,] 6.66023573 15.30227276
[147,] -27.41799633 6.66023573
[148,] -6.75046429 -27.41799633
[149,] -5.71697520 -6.75046429
[150,] 28.08685829 -5.71697520
[151,] 5.69598785 28.08685829
[152,] 2.03406432 5.69598785
[153,] 13.22203964 2.03406432
[154,] 8.88649583 13.22203964
[155,] 18.84656657 8.88649583
[156,] 25.04195522 18.84656657
[157,] 10.71160067 25.04195522
[158,] 1.13754737 10.71160067
[159,] 7.45397873 1.13754737
[160,] -1.49700676 7.45397873
[161,] 12.55633324 -1.49700676
[162,] -14.74332440 12.55633324
[163,] -1.99871439 -14.74332440
[164,] -10.90801810 -1.99871439
[165,] 4.10446577 -10.90801810
[166,] -1.50769951 4.10446577
[167,] -47.28900791 -1.50769951
[168,] 13.63684852 -47.28900791
[169,] 10.28750780 13.63684852
[170,] -2.36257640 10.28750780
[171,] -23.95407368 -2.36257640
[172,] 60.80183956 -23.95407368
[173,] 4.20974463 60.80183956
[174,] -1.68746437 4.20974463
[175,] 29.84659710 -1.68746437
[176,] 32.39292696 29.84659710
[177,] 7.24086816 32.39292696
[178,] -5.94028119 7.24086816
[179,] 6.67141031 -5.94028119
[180,] 16.13431335 6.67141031
[181,] 6.33344102 16.13431335
[182,] -18.60496093 6.33344102
[183,] -19.82070931 -18.60496093
[184,] 16.01946395 -19.82070931
[185,] 28.64295676 16.01946395
[186,] 60.62326468 28.64295676
[187,] -17.61620028 60.62326468
[188,] -21.91785606 -17.61620028
[189,] 3.66144519 -21.91785606
[190,] -9.01293177 3.66144519
[191,] 1.02438969 -9.01293177
[192,] -14.67385396 1.02438969
[193,] -5.32970153 -14.67385396
[194,] -2.78519468 -5.32970153
[195,] 3.96276515 -2.78519468
[196,] -6.08143626 3.96276515
[197,] -40.03321448 -6.08143626
[198,] 31.72984528 -40.03321448
[199,] -15.47080453 31.72984528
[200,] -1.91389584 -15.47080453
[201,] -35.43998136 -1.91389584
[202,] -8.19525273 -35.43998136
[203,] -30.12628797 -8.19525273
[204,] 69.17735559 -30.12628797
[205,] -16.80746881 69.17735559
[206,] -8.38221706 -16.80746881
[207,] -0.33511430 -8.38221706
[208,] -12.91486914 -0.33511430
[209,] -3.86878834 -12.91486914
[210,] -2.06973880 -3.86878834
[211,] -5.61551025 -2.06973880
[212,] 11.97373499 -5.61551025
[213,] -8.03815843 11.97373499
[214,] -6.42337482 -8.03815843
[215,] -8.63532259 -6.42337482
[216,] -4.51673371 -8.63532259
[217,] 4.60512706 -4.51673371
[218,] 1.38954254 4.60512706
[219,] 5.13314407 1.38954254
[220,] -5.77963223 5.13314407
[221,] 11.59462465 -5.77963223
[222,] -3.09007877 11.59462465
[223,] 9.48020087 -3.09007877
[224,] -5.30046664 9.48020087
[225,] 6.13748089 -5.30046664
[226,] -21.69429581 6.13748089
[227,] 0.09540140 -21.69429581
[228,] -3.44513861 0.09540140
[229,] 10.83315205 -3.44513861
[230,] 2.85723985 10.83315205
[231,] 15.20208331 2.85723985
[232,] -8.97472734 15.20208331
[233,] 1.11792549 -8.97472734
[234,] -0.96617826 1.11792549
[235,] -6.49897069 -0.96617826
[236,] -4.79495905 -6.49897069
[237,] -1.66740634 -4.79495905
[238,] -10.54649337 -1.66740634
[239,] -3.67153055 -10.54649337
[240,] -13.90538540 -3.67153055
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -3.28274488 -14.81039126
2 -4.85329119 -3.28274488
3 -25.62906724 -4.85329119
4 -2.05412684 -25.62906724
5 -0.96705652 -2.05412684
6 9.96964770 -0.96705652
7 -6.81536787 9.96964770
8 -3.85899892 -6.81536787
9 7.68954283 -3.85899892
10 -39.32999715 7.68954283
11 -15.16607298 -39.32999715
12 -0.13153570 -15.16607298
13 1.55439023 -0.13153570
14 -1.21081096 1.55439023
15 2.59094278 -1.21081096
16 4.25931370 2.59094278
17 -0.34666595 4.25931370
18 -21.90876488 -0.34666595
19 -4.51886239 -21.90876488
20 18.64955522 -4.51886239
21 -8.09796387 18.64955522
22 27.19089233 -8.09796387
23 -4.93465667 27.19089233
24 5.54314235 -4.93465667
25 14.67181321 5.54314235
26 4.35485401 14.67181321
27 -35.36297557 4.35485401
28 21.09115789 -35.36297557
29 8.54013996 21.09115789
30 -21.07677789 8.54013996
31 0.70892889 -21.07677789
32 3.25687660 0.70892889
33 -13.23520969 3.25687660
34 -8.35929210 -13.23520969
35 -4.65221215 -8.35929210
36 -5.06926496 -4.65221215
37 -30.82940630 -5.06926496
38 7.17970054 -30.82940630
39 1.29943326 7.17970054
40 -3.55867799 1.29943326
41 -1.94039714 -3.55867799
42 -5.68342057 -1.94039714
43 10.24959401 -5.68342057
44 10.51949266 10.24959401
45 -5.62232648 10.51949266
46 17.90070387 -5.62232648
47 22.44965205 17.90070387
48 9.43289039 22.44965205
49 32.61133355 9.43289039
50 -6.81711435 32.61133355
51 -0.65313832 -6.81711435
52 4.75308874 -0.65313832
53 -13.44323272 4.75308874
54 -4.80908383 -13.44323272
55 -0.04434844 -4.80908383
56 7.05527374 -0.04434844
57 2.01473330 7.05527374
58 2.62446364 2.01473330
59 -0.63780120 2.62446364
60 -10.64845851 -0.63780120
61 -11.45022224 -10.64845851
62 6.12017563 -11.45022224
63 -8.18713519 6.12017563
64 -4.23073130 -8.18713519
65 39.26613131 -4.23073130
66 4.87330707 39.26613131
67 -15.42067461 4.87330707
68 34.03243912 -15.42067461
69 3.53316174 34.03243912
70 26.54658243 3.53316174
71 2.94134683 26.54658243
72 -3.46076381 2.94134683
73 -0.05704423 -3.46076381
74 7.35246486 -0.05704423
75 -7.07441768 7.35246486
76 -6.55026042 -7.07441768
77 -22.66061701 -6.55026042
78 7.13510151 -22.66061701
79 0.38424341 7.13510151
80 -4.83275338 0.38424341
81 -2.25129954 -4.83275338
82 -31.85807615 -2.25129954
83 10.77964744 -31.85807615
84 -31.82711692 10.77964744
85 6.39089137 -31.82711692
86 0.01277661 6.39089137
87 -2.19793650 0.01277661
88 14.51178682 -2.19793650
89 3.65710777 14.51178682
90 10.82302687 3.65710777
91 9.07263862 10.82302687
92 -11.37350322 9.07263862
93 10.46728077 -11.37350322
94 -14.97325146 10.46728077
95 30.67256305 -14.97325146
96 -18.92759704 30.67256305
97 15.30697666 -18.92759704
98 -34.03503561 15.30697666
99 -7.24521421 -34.03503561
100 10.61112881 -7.24521421
101 0.77870706 10.61112881
102 -0.10387431 0.77870706
103 -16.26650709 -0.10387431
104 -11.33621670 -16.26650709
105 2.84564895 -11.33621670
106 3.50491777 2.84564895
107 6.10891337 3.50491777
108 34.72095051 6.10891337
109 4.36559078 34.72095051
110 39.31253172 4.36559078
111 2.45697738 39.31253172
112 36.57612814 2.45697738
113 -4.15897706 36.57612814
114 -1.42987748 -4.15897706
115 -13.17861725 -1.42987748
116 0.68763782 -13.17861725
117 0.64259142 0.68763782
118 9.81364736 0.64259142
119 0.58820526 9.81364736
120 9.06359411 0.58820526
121 -21.45344926 9.06359411
122 8.82159428 -21.45344926
123 -14.05202291 8.82159428
124 -17.35022180 -14.05202291
125 -9.66790360 -17.35022180
126 3.34815497 -9.66790360
127 1.07363064 3.34815497
128 -1.42595759 1.07363064
129 -21.61175693 -1.42595759
130 4.76898665 -21.61175693
131 -7.45834708 4.76898665
132 17.52726127 -7.45834708
133 -12.23760010 17.52726127
134 -0.31758198 -12.23760010
135 -15.25193192 -0.31758198
136 -19.92141072 -15.25193192
137 -6.64784694 -19.92141072
138 -12.81370585 -6.64784694
139 0.64525539 -12.81370585
140 6.08556489 0.64525539
141 5.70684934 6.08556489
142 2.45811696 5.70684934
143 -4.70240054 2.45811696
144 -10.01302038 -4.70240054
145 15.30227276 -10.01302038
146 6.66023573 15.30227276
147 -27.41799633 6.66023573
148 -6.75046429 -27.41799633
149 -5.71697520 -6.75046429
150 28.08685829 -5.71697520
151 5.69598785 28.08685829
152 2.03406432 5.69598785
153 13.22203964 2.03406432
154 8.88649583 13.22203964
155 18.84656657 8.88649583
156 25.04195522 18.84656657
157 10.71160067 25.04195522
158 1.13754737 10.71160067
159 7.45397873 1.13754737
160 -1.49700676 7.45397873
161 12.55633324 -1.49700676
162 -14.74332440 12.55633324
163 -1.99871439 -14.74332440
164 -10.90801810 -1.99871439
165 4.10446577 -10.90801810
166 -1.50769951 4.10446577
167 -47.28900791 -1.50769951
168 13.63684852 -47.28900791
169 10.28750780 13.63684852
170 -2.36257640 10.28750780
171 -23.95407368 -2.36257640
172 60.80183956 -23.95407368
173 4.20974463 60.80183956
174 -1.68746437 4.20974463
175 29.84659710 -1.68746437
176 32.39292696 29.84659710
177 7.24086816 32.39292696
178 -5.94028119 7.24086816
179 6.67141031 -5.94028119
180 16.13431335 6.67141031
181 6.33344102 16.13431335
182 -18.60496093 6.33344102
183 -19.82070931 -18.60496093
184 16.01946395 -19.82070931
185 28.64295676 16.01946395
186 60.62326468 28.64295676
187 -17.61620028 60.62326468
188 -21.91785606 -17.61620028
189 3.66144519 -21.91785606
190 -9.01293177 3.66144519
191 1.02438969 -9.01293177
192 -14.67385396 1.02438969
193 -5.32970153 -14.67385396
194 -2.78519468 -5.32970153
195 3.96276515 -2.78519468
196 -6.08143626 3.96276515
197 -40.03321448 -6.08143626
198 31.72984528 -40.03321448
199 -15.47080453 31.72984528
200 -1.91389584 -15.47080453
201 -35.43998136 -1.91389584
202 -8.19525273 -35.43998136
203 -30.12628797 -8.19525273
204 69.17735559 -30.12628797
205 -16.80746881 69.17735559
206 -8.38221706 -16.80746881
207 -0.33511430 -8.38221706
208 -12.91486914 -0.33511430
209 -3.86878834 -12.91486914
210 -2.06973880 -3.86878834
211 -5.61551025 -2.06973880
212 11.97373499 -5.61551025
213 -8.03815843 11.97373499
214 -6.42337482 -8.03815843
215 -8.63532259 -6.42337482
216 -4.51673371 -8.63532259
217 4.60512706 -4.51673371
218 1.38954254 4.60512706
219 5.13314407 1.38954254
220 -5.77963223 5.13314407
221 11.59462465 -5.77963223
222 -3.09007877 11.59462465
223 9.48020087 -3.09007877
224 -5.30046664 9.48020087
225 6.13748089 -5.30046664
226 -21.69429581 6.13748089
227 0.09540140 -21.69429581
228 -3.44513861 0.09540140
229 10.83315205 -3.44513861
230 2.85723985 10.83315205
231 15.20208331 2.85723985
232 -8.97472734 15.20208331
233 1.11792549 -8.97472734
234 -0.96617826 1.11792549
235 -6.49897069 -0.96617826
236 -4.79495905 -6.49897069
237 -1.66740634 -4.79495905
238 -10.54649337 -1.66740634
239 -3.67153055 -10.54649337
240 -13.90538540 -3.67153055
> 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/7v6ko1355077852.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/8nnaf1355077852.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/9vksr1355077852.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/10ahwp1355077852.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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11fazp1355077852.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/12nnvz1355077852.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13ctdp1355077852.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$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/1488zf1355077852.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/15swvi1355077852.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/1699m61355077852.tab")
+ }
>
> try(system("convert tmp/1vcvf1355077852.ps tmp/1vcvf1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yedt1355077852.ps tmp/2yedt1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/39tcv1355077852.ps tmp/39tcv1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/47u081355077852.ps tmp/47u081355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/5tvuo1355077852.ps tmp/5tvuo1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mmei1355077852.ps tmp/6mmei1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/7v6ko1355077852.ps tmp/7v6ko1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nnaf1355077852.ps tmp/8nnaf1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/9vksr1355077852.ps tmp/9vksr1355077852.png",intern=TRUE))
character(0)
> try(system("convert tmp/10ahwp1355077852.ps tmp/10ahwp1355077852.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
10.557 1.624 12.186