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 'contributors()' for more information and
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Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(1
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+ ,0
+ ,-6.323531
+ ,0.218885
+ ,0.00435
+ ,26.436
+ ,0.7567
+ ,0
+ ,-6.085567
+ ,0.192375
+ ,0.0043
+ ,26.55
+ ,0.776158
+ ,0
+ ,-5.943501
+ ,0.19215
+ ,0.00478
+ ,26.547
+ ,0.7667
+ ,0
+ ,-6.012559
+ ,0.229298
+ ,0.0059
+ ,25.445
+ ,0.756482
+ ,0
+ ,-5.966779
+ ,0.197938
+ ,0.00401
+ ,26.005
+ ,0.761255
+ ,0
+ ,-6.016891
+ ,0.109256
+ ,0.00415
+ ,26.143
+ ,0.763242
+ ,1
+ ,-6.486822
+ ,0.197919
+ ,0.0057
+ ,24.151
+ ,0.745957
+ ,1
+ ,-6.311987
+ ,0.182459
+ ,0.00488
+ ,24.412
+ ,0.762508
+ ,1
+ ,-5.711205
+ ,0.240875
+ ,0.0054
+ ,23.683
+ ,0.778349
+ ,1
+ ,-6.261446
+ ,0.183218
+ ,0.00611
+ ,23.133
+ ,0.75932
+ ,1
+ ,-5.704053
+ ,0.216204
+ ,0.00639
+ ,22.866
+ ,0.768845
+ ,1
+ ,-6.27717
+ ,0.109397
+ ,0.00595
+ ,23.008
+ ,0.75718
+ ,0
+ ,-5.61907
+ ,0.191576
+ ,0.00955
+ ,23.079
+ ,0.669565
+ ,0
+ ,-5.198864
+ ,0.206768
+ ,0.01179
+ ,22.085
+ ,0.656516
+ ,0
+ ,-5.592584
+ ,0.133917
+ ,0.00737
+ ,24.199
+ ,0.654331
+ ,0
+ ,-6.431119
+ ,0.15331
+ ,0.01397
+ ,23.958
+ ,0.667654
+ ,0
+ ,-6.359018
+ ,0.116636
+ ,0.0068
+ ,25.023
+ ,0.663884
+ ,0
+ ,-6.710219
+ ,0.149694
+ ,0.00703
+ ,24.775
+ ,0.659132
+ ,0
+ ,-6.934474
+ ,0.15989
+ ,0.04441
+ ,19.368
+ ,0.683761
+ ,0
+ ,-6.538586
+ ,0.121952
+ ,0.02764
+ ,19.517
+ ,0.657899
+ ,0
+ ,-6.195325
+ ,0.129303
+ ,0.0181
+ ,19.147
+ ,0.683244
+ ,0
+ ,-6.787197
+ ,0.158453
+ ,0.10715
+ ,17.883
+ ,0.655683
+ ,0
+ ,-6.744577
+ ,0.207454
+ ,0.07223
+ ,19.02
+ ,0.643956
+ ,0
+ ,-5.724056
+ ,0.190667
+ ,0.04398
+ ,21.209
+ ,0.664357)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('status'
+ ,'spread1'
+ ,'spread2'
+ ,'NHR'
+ ,'HNR'
+ ,'DFA')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('status','spread1','spread2','NHR','HNR','DFA'),1:195))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
status spread1 spread2 NHR HNR DFA t
1 1 -4.813031 0.266482 0.02211 21.033 0.815285 1
2 1 -4.075192 0.335590 0.01929 19.085 0.819521 2
3 1 -4.443179 0.311173 0.01309 20.651 0.825288 3
4 1 -4.117501 0.334147 0.01353 20.644 0.819235 4
5 1 -3.747787 0.234513 0.01767 19.649 0.823484 5
6 1 -4.242867 0.299111 0.01222 21.378 0.825069 6
7 1 -5.634322 0.257682 0.00607 24.886 0.764112 7
8 1 -6.167603 0.183721 0.00344 26.892 0.763262 8
9 1 -5.498678 0.327769 0.01070 21.812 0.773587 9
10 1 -5.011879 0.325996 0.01022 21.862 0.798463 10
11 1 -5.249770 0.391002 0.01166 21.118 0.776156 11
12 1 -4.960234 0.363566 0.01141 21.414 0.792520 12
13 1 -6.547148 0.152813 0.00581 25.703 0.646846 13
14 1 -5.660217 0.254989 0.01041 24.889 0.665833 14
15 1 -6.105098 0.203653 0.00609 24.922 0.654027 15
16 1 -5.340115 0.210185 0.00839 25.175 0.658245 16
17 1 -5.440040 0.239764 0.01859 22.333 0.644692 17
18 1 -2.931070 0.434326 0.02919 20.376 0.605417 18
19 1 -3.949079 0.357870 0.03160 17.280 0.719467 19
20 1 -4.554466 0.340176 0.03365 17.153 0.686080 20
21 1 -4.095442 0.262564 0.03871 17.536 0.704087 21
22 1 -5.186960 0.237622 0.01849 19.493 0.698951 22
23 1 -4.330956 0.262384 0.01280 22.468 0.679834 23
24 1 -5.248776 0.210279 0.01840 20.422 0.686894 24
25 1 -5.557447 0.220890 0.01778 23.831 0.732479 25
26 1 -5.571843 0.236853 0.02887 22.066 0.737948 26
27 1 -6.183590 0.226278 0.01095 25.908 0.720916 27
28 1 -6.271690 0.196102 0.01328 25.119 0.726652 28
29 1 -7.120925 0.279789 0.00677 25.970 0.676258 29
30 1 -6.635729 0.209866 0.01170 25.678 0.723797 30
31 0 -7.348300 0.177551 0.00339 26.775 0.741367 31
32 0 -7.682587 0.173319 0.00167 30.940 0.742055 32
33 0 -7.067931 0.175181 0.00119 30.775 0.738703 33
34 0 -7.695734 0.178540 0.00072 32.684 0.742133 34
35 0 -7.964984 0.163519 0.00065 33.047 0.741899 35
36 0 -7.777685 0.170183 0.00135 31.732 0.742737 36
37 1 -6.149653 0.218037 0.00586 23.216 0.778834 37
38 1 -6.006414 0.196371 0.00340 24.951 0.783626 38
39 1 -6.452058 0.212294 0.00231 26.738 0.766209 39
40 1 -6.006647 0.266892 0.00265 26.310 0.758324 40
41 1 -6.647379 0.201095 0.00231 26.822 0.765623 41
42 1 -7.044105 0.063412 0.00257 26.453 0.759203 42
43 0 -7.310550 0.098648 0.00740 22.736 0.654172 43
44 0 -6.793547 0.158266 0.00675 23.145 0.634267 44
45 0 -7.057869 0.091608 0.00454 25.368 0.635285 45
46 0 -6.995820 0.102083 0.00476 25.032 0.638928 46
47 0 -7.156076 0.127642 0.00476 24.602 0.631653 47
48 0 -7.319510 0.200873 0.00432 26.805 0.635204 48
49 0 -6.439398 0.266392 0.00839 23.162 0.733659 49
50 0 -6.482096 0.264967 0.00462 24.971 0.754073 50
51 0 -6.650471 0.254498 0.00479 25.135 0.775933 51
52 0 -6.689151 0.291954 0.00474 25.030 0.760361 52
53 0 -7.072419 0.220434 0.00481 24.692 0.766204 53
54 0 -6.836811 0.269866 0.00484 25.429 0.785714 54
55 1 -4.649573 0.205558 0.01036 21.028 0.819032 55
56 1 -4.333543 0.221727 0.01180 20.767 0.811843 56
57 1 -4.438453 0.238298 0.00969 21.422 0.821364 57
58 1 -4.608260 0.290024 0.00681 22.817 0.817756 58
59 1 -4.476755 0.262633 0.00786 22.603 0.813432 59
60 1 -4.609161 0.221711 0.01143 21.660 0.817396 60
61 0 -7.040508 0.066994 0.00871 25.554 0.678874 61
62 0 -7.293801 0.086372 0.00301 26.138 0.686264 62
63 0 -6.966321 0.095882 0.00340 25.856 0.694399 63
64 0 -7.245620 0.018689 0.00351 25.964 0.683296 64
65 0 -7.496264 0.056844 0.00300 26.415 0.673636 65
66 0 -7.314237 0.006274 0.00420 24.547 0.681811 66
67 1 -5.409423 0.226850 0.02183 19.560 0.720908 67
68 1 -5.324574 0.205660 0.02659 19.979 0.729067 68
69 1 -5.869750 0.151814 0.04882 20.338 0.731444 69
70 1 -6.261141 0.120956 0.02431 21.718 0.727313 70
71 1 -5.720868 0.158830 0.02599 20.264 0.730387 71
72 1 -5.207985 0.224852 0.03361 18.570 0.733232 72
73 1 -5.791820 0.329066 0.00442 25.742 0.762959 73
74 1 -5.389129 0.306636 0.00623 24.178 0.789532 74
75 1 -5.313360 0.201861 0.00479 25.438 0.815908 75
76 1 -5.477592 0.315074 0.00472 25.197 0.807217 76
77 1 -5.775966 0.341169 0.00905 23.370 0.789977 77
78 1 -5.391029 0.250572 0.00420 25.820 0.816340 78
79 1 -5.115212 0.249494 0.01062 21.875 0.779612 79
80 1 -4.913885 0.265699 0.02220 19.200 0.790117 80
81 1 -4.441519 0.155097 0.01823 19.055 0.770466 81
82 1 -5.132032 0.210458 0.01825 19.659 0.778747 82
83 1 -5.022288 0.146948 0.01237 20.536 0.787896 83
84 1 -6.025367 0.078202 0.00882 22.244 0.772416 84
85 1 -5.288912 0.343073 0.05470 13.893 0.729586 85
86 1 -5.657899 0.315903 0.02782 16.176 0.727747 86
87 1 -6.366916 0.335753 0.03151 15.924 0.712199 87
88 1 -5.515071 0.299549 0.04824 13.922 0.740837 88
89 1 -5.783272 0.299793 0.04214 14.739 0.743937 89
90 1 -4.379411 0.375531 0.07223 11.866 0.745526 90
91 1 -4.508984 0.389232 0.08725 11.744 0.733165 91
92 1 -6.411497 0.207156 0.01658 19.664 0.714360 92
93 1 -5.952058 0.087840 0.01914 18.780 0.734504 93
94 1 -6.152551 0.173520 0.01211 20.969 0.697790 94
95 1 -6.251425 0.188056 0.00850 22.219 0.712170 95
96 1 -6.247076 0.180528 0.01018 21.693 0.705658 96
97 1 -6.417440 0.194627 0.00852 22.663 0.693429 97
98 1 -4.020042 0.265315 0.08151 15.338 0.714485 98
99 1 -5.159169 0.202146 0.10323 15.433 0.690892 99
100 1 -3.760348 0.242861 0.16744 12.435 0.674953 100
101 1 -3.700544 0.260481 0.31482 8.867 0.656846 101
102 1 -4.202730 0.310163 0.11843 15.060 0.643327 102
103 1 -3.269487 0.270641 0.25930 10.489 0.641418 103
104 1 -6.878393 0.089267 0.00495 26.759 0.722356 104
105 1 -7.111576 0.144780 0.00243 28.409 0.691483 105
106 1 -6.997403 0.210279 0.00578 27.421 0.719974 106
107 1 -6.981201 0.184550 0.00233 29.746 0.677930 107
108 1 -6.600023 0.249172 0.00659 26.833 0.700246 108
109 1 -6.739151 0.160686 0.00238 29.928 0.676066 109
110 1 -5.845099 0.278679 0.00947 21.934 0.740539 110
111 1 -5.258320 0.256454 0.00704 23.239 0.727863 111
112 1 -6.471427 0.184378 0.00830 22.407 0.712466 112
113 1 -4.876336 0.212054 0.01316 21.305 0.722085 113
114 1 -5.963040 0.250283 0.00620 23.671 0.722254 114
115 1 -6.729713 0.181701 0.01048 21.864 0.715121 115
116 1 -4.673241 0.261549 0.06051 23.693 0.662668 116
117 1 -6.051233 0.273280 0.01554 26.356 0.653823 117
118 1 -4.597834 0.372114 0.01802 25.690 0.676023 118
119 1 -4.913137 0.393056 0.00856 25.020 0.655239 119
120 1 -5.517173 0.389295 0.00681 24.581 0.582710 120
121 1 -6.186128 0.279933 0.02350 24.743 0.684130 121
122 1 -4.711007 0.281618 0.01161 27.166 0.656182 122
123 1 -5.418787 0.160267 0.01968 18.305 0.741480 123
124 1 -5.445140 0.142466 0.01813 18.784 0.732903 124
125 1 -5.944191 0.143359 0.02020 19.196 0.728421 125
126 1 -5.594275 0.127950 0.01874 18.857 0.735546 126
127 1 -5.540351 0.087165 0.01794 18.178 0.738245 127
128 1 -5.825257 0.115697 0.01796 18.330 0.736964 128
129 1 -6.890021 0.152941 0.01724 26.842 0.699787 129
130 1 -5.892061 0.195976 0.00487 26.369 0.718839 130
131 1 -6.135296 0.203630 0.01610 23.949 0.724045 131
132 1 -6.112667 0.217013 0.01015 26.017 0.735136 132
133 1 -5.436135 0.254909 0.00903 23.389 0.721308 133
134 1 -6.448134 0.178713 0.00504 25.619 0.723096 134
135 1 -5.301321 0.320385 0.03031 17.060 0.744064 135
136 1 -5.333619 0.322044 0.02529 17.707 0.706687 136
137 1 -4.378916 0.300067 0.02278 19.013 0.708144 137
138 1 -4.654894 0.304107 0.03690 16.747 0.708617 138
139 1 -5.634576 0.306014 0.02629 17.366 0.701404 139
140 1 -5.866357 0.233070 0.01827 18.801 0.696049 140
141 1 -4.796845 0.397749 0.02485 18.540 0.685057 141
142 1 -5.410336 0.288917 0.04238 15.648 0.665945 142
143 1 -5.585259 0.310746 0.01728 18.702 0.661735 143
144 1 -5.898673 0.213353 0.02010 18.687 0.632631 144
145 1 -6.132663 0.220617 0.01049 20.680 0.630409 145
146 1 -5.456811 0.345238 0.01493 20.366 0.574282 146
147 1 -3.297668 0.414758 0.07530 12.359 0.793509 147
148 1 -4.276605 0.355736 0.06057 14.367 0.768974 148
149 1 -3.377325 0.335357 0.08069 12.298 0.764036 149
150 1 -4.892495 0.262281 0.07889 14.989 0.775708 150
151 1 -4.484303 0.340256 0.10952 12.529 0.762726 151
152 1 -2.434031 0.450493 0.21713 8.441 0.768320 152
153 1 -2.839756 0.356224 0.16265 9.449 0.754449 153
154 1 -4.865194 0.246404 0.04179 21.520 0.670475 154
155 1 -4.239028 0.175691 0.04611 21.824 0.659333 155
156 1 -3.583722 0.207914 0.02631 22.431 0.652025 156
157 1 -5.435100 0.230532 0.03191 22.953 0.623731 157
158 1 -3.444478 0.303214 0.10748 19.075 0.646786 158
159 1 -5.070096 0.280091 0.03828 21.534 0.627337 159
160 1 -5.498456 0.234196 0.02663 19.651 0.675865 160
161 1 -5.185987 0.259229 0.02073 20.437 0.694571 161
162 1 -5.283009 0.226528 0.02810 19.388 0.684373 162
163 1 -5.529833 0.242750 0.02707 18.954 0.719576 163
164 1 -5.617124 0.184896 0.01435 21.219 0.673086 164
165 1 -2.929379 0.396746 0.03882 18.447 0.674562 165
166 0 -6.816086 0.172270 0.00620 24.078 0.628232 166
167 0 -7.018057 0.176316 0.00533 24.679 0.626710 167
168 0 -7.517934 0.160414 0.00910 21.083 0.628058 168
169 0 -5.736781 0.164529 0.01337 19.269 0.725216 169
170 0 -7.169701 0.073298 0.00965 21.020 0.646167 170
171 0 -7.304500 0.171088 0.01049 21.528 0.646818 171
172 0 -6.323531 0.218885 0.00435 26.436 0.756700 172
173 0 -6.085567 0.192375 0.00430 26.550 0.776158 173
174 0 -5.943501 0.192150 0.00478 26.547 0.766700 174
175 0 -6.012559 0.229298 0.00590 25.445 0.756482 175
176 0 -5.966779 0.197938 0.00401 26.005 0.761255 176
177 0 -6.016891 0.109256 0.00415 26.143 0.763242 177
178 1 -6.486822 0.197919 0.00570 24.151 0.745957 178
179 1 -6.311987 0.182459 0.00488 24.412 0.762508 179
180 1 -5.711205 0.240875 0.00540 23.683 0.778349 180
181 1 -6.261446 0.183218 0.00611 23.133 0.759320 181
182 1 -5.704053 0.216204 0.00639 22.866 0.768845 182
183 1 -6.277170 0.109397 0.00595 23.008 0.757180 183
184 0 -5.619070 0.191576 0.00955 23.079 0.669565 184
185 0 -5.198864 0.206768 0.01179 22.085 0.656516 185
186 0 -5.592584 0.133917 0.00737 24.199 0.654331 186
187 0 -6.431119 0.153310 0.01397 23.958 0.667654 187
188 0 -6.359018 0.116636 0.00680 25.023 0.663884 188
189 0 -6.710219 0.149694 0.00703 24.775 0.659132 189
190 0 -6.934474 0.159890 0.04441 19.368 0.683761 190
191 0 -6.538586 0.121952 0.02764 19.517 0.657899 191
192 0 -6.195325 0.129303 0.01810 19.147 0.683244 192
193 0 -6.787197 0.158453 0.10715 17.883 0.655683 193
194 0 -6.744577 0.207454 0.07223 19.020 0.643956 194
195 0 -5.724056 0.190667 0.04398 21.209 0.664357 195
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) spread1 spread2 NHR HNR DFA
1.941992 0.191864 0.570894 -1.939090 -0.014600 0.354748
t
-0.001162
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.73521 -0.30819 0.08137 0.26051 0.63598
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.9419916 0.5308708 3.658 0.00033 ***
spread1 0.1918637 0.0379164 5.060 9.93e-07 ***
spread2 0.5708936 0.3970460 1.438 0.15214
NHR -1.9390899 0.9098439 -2.131 0.03437 *
HNR -0.0145995 0.0093804 -1.556 0.12130
DFA 0.3547482 0.4989594 0.711 0.47798
t -0.0011619 0.0004827 -2.407 0.01704 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3456 on 188 degrees of freedom
Multiple R-squared: 0.3794, Adjusted R-squared: 0.3596
F-statistic: 19.15 on 6 and 188 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,] 4.860462e-49 9.720925e-49 1.0000000000
[2,] 8.988301e-64 1.797660e-63 1.0000000000
[3,] 2.271136e-77 4.542272e-77 1.0000000000
[4,] 1.725820e-107 3.451640e-107 1.0000000000
[5,] 7.989917e-110 1.597983e-109 1.0000000000
[6,] 4.027376e-124 8.054753e-124 1.0000000000
[7,] 0.000000e+00 0.000000e+00 1.0000000000
[8,] 1.064200e-166 2.128401e-166 1.0000000000
[9,] 3.984583e-168 7.969166e-168 1.0000000000
[10,] 1.188071e-183 2.376142e-183 1.0000000000
[11,] 5.920262e-211 1.184052e-210 1.0000000000
[12,] 9.663091e-245 1.932618e-244 1.0000000000
[13,] 1.077372e-226 2.154744e-226 1.0000000000
[14,] 2.864981e-245 5.729961e-245 1.0000000000
[15,] 1.754638e-256 3.509275e-256 1.0000000000
[16,] 1.120061e-282 2.240122e-282 1.0000000000
[17,] 0.000000e+00 0.000000e+00 1.0000000000
[18,] 2.161044e-312 4.322089e-312 1.0000000000
[19,] 7.004724e-318 1.400945e-317 1.0000000000
[20,] 0.000000e+00 0.000000e+00 1.0000000000
[21,] 0.000000e+00 0.000000e+00 1.0000000000
[22,] 7.711661e-05 1.542332e-04 0.9999228834
[23,] 1.307674e-03 2.615348e-03 0.9986923260
[24,] 2.494597e-03 4.989194e-03 0.9975054030
[25,] 1.952496e-03 3.904993e-03 0.9980475037
[26,] 1.289280e-03 2.578559e-03 0.9987107205
[27,] 9.127964e-04 1.825593e-03 0.9990872036
[28,] 1.225802e-03 2.451605e-03 0.9987741976
[29,] 1.445135e-03 2.890270e-03 0.9985548651
[30,] 2.100855e-03 4.201711e-03 0.9978991447
[31,] 2.030292e-03 4.060584e-03 0.9979697079
[32,] 2.037131e-03 4.074262e-03 0.9979628691
[33,] 1.642240e-03 3.284481e-03 0.9983577596
[34,] 4.841420e-02 9.682841e-02 0.9515857972
[35,] 1.383314e-01 2.766628e-01 0.8616685845
[36,] 1.867416e-01 3.734832e-01 0.8132583935
[37,] 2.235223e-01 4.470446e-01 0.7764776827
[38,] 2.443877e-01 4.887753e-01 0.7556123290
[39,] 2.507435e-01 5.014869e-01 0.7492565462
[40,] 3.310198e-01 6.620396e-01 0.6689801979
[41,] 3.936398e-01 7.872796e-01 0.6063601986
[42,] 4.396268e-01 8.792536e-01 0.5603731965
[43,] 4.899123e-01 9.798246e-01 0.5100877232
[44,] 5.222902e-01 9.554196e-01 0.4777097794
[45,] 5.886614e-01 8.226772e-01 0.4113385883
[46,] 5.869388e-01 8.261224e-01 0.4130612225
[47,] 5.625511e-01 8.748977e-01 0.4374488704
[48,] 5.384442e-01 9.231117e-01 0.4615558477
[49,] 5.278843e-01 9.442315e-01 0.4721157349
[50,] 5.041855e-01 9.916289e-01 0.4958144740
[51,] 4.751281e-01 9.502562e-01 0.5248718995
[52,] 5.111431e-01 9.777137e-01 0.4888568592
[53,] 5.425782e-01 9.148435e-01 0.4574217646
[54,] 6.079259e-01 7.841483e-01 0.3920741449
[55,] 6.528247e-01 6.943506e-01 0.3471752832
[56,] 7.060560e-01 5.878881e-01 0.2939440440
[57,] 7.773202e-01 4.453596e-01 0.2226797967
[58,] 8.279124e-01 3.441751e-01 0.1720875573
[59,] 8.449525e-01 3.100950e-01 0.1550475204
[60,] 8.383414e-01 3.233173e-01 0.1616586427
[61,] 8.538018e-01 2.923964e-01 0.1461982061
[62,] 8.499772e-01 3.000456e-01 0.1500228057
[63,] 8.363505e-01 3.272990e-01 0.1636494882
[64,] 8.677976e-01 2.644048e-01 0.1322024218
[65,] 8.704338e-01 2.591325e-01 0.1295662438
[66,] 8.595365e-01 2.809269e-01 0.1404634652
[67,] 8.579616e-01 2.840767e-01 0.1420383664
[68,] 8.630747e-01 2.738505e-01 0.1369252633
[69,] 8.546618e-01 2.906764e-01 0.1453382121
[70,] 8.476410e-01 3.047179e-01 0.1523589586
[71,] 8.400044e-01 3.199912e-01 0.1599956113
[72,] 8.326990e-01 3.346021e-01 0.1673010458
[73,] 8.246105e-01 3.507790e-01 0.1753894771
[74,] 8.175762e-01 3.648476e-01 0.1824238128
[75,] 8.205379e-01 3.589243e-01 0.1794621398
[76,] 8.053315e-01 3.893371e-01 0.1946685284
[77,] 8.052282e-01 3.895435e-01 0.1947717552
[78,] 8.106325e-01 3.787350e-01 0.1893674861
[79,] 7.947773e-01 4.104454e-01 0.2052226857
[80,] 7.792294e-01 4.415411e-01 0.2207705572
[81,] 8.250013e-01 3.499975e-01 0.1749987251
[82,] 8.545018e-01 2.909964e-01 0.1454982007
[83,] 8.637281e-01 2.725438e-01 0.1362718759
[84,] 8.555106e-01 2.889787e-01 0.1444893658
[85,] 8.550364e-01 2.899272e-01 0.1449636107
[86,] 8.547980e-01 2.904041e-01 0.1452020439
[87,] 8.514129e-01 2.971742e-01 0.1485871202
[88,] 8.494217e-01 3.011566e-01 0.1505782915
[89,] 8.615462e-01 2.769077e-01 0.1384538386
[90,] 8.437822e-01 3.124356e-01 0.1562178113
[91,] 8.320832e-01 3.358335e-01 0.1679167567
[92,] 8.054672e-01 3.890656e-01 0.1945327928
[93,] 7.938530e-01 4.122939e-01 0.2061469684
[94,] 7.713561e-01 4.572879e-01 0.2286439373
[95,] 7.813884e-01 4.372232e-01 0.2186116220
[96,] 7.955615e-01 4.088770e-01 0.2044385087
[97,] 7.922433e-01 4.155134e-01 0.2077567145
[98,] 7.865105e-01 4.269789e-01 0.2134894570
[99,] 7.630042e-01 4.739915e-01 0.2369957675
[100,] 7.434090e-01 5.131819e-01 0.2565909659
[101,] 7.193481e-01 5.613038e-01 0.2806519185
[102,] 7.026036e-01 5.947928e-01 0.2973964177
[103,] 6.729271e-01 6.541458e-01 0.3270728907
[104,] 6.648897e-01 6.702205e-01 0.3351102659
[105,] 6.311935e-01 7.376130e-01 0.3688064854
[106,] 6.032714e-01 7.934571e-01 0.3967285727
[107,] 5.737527e-01 8.524947e-01 0.4262473423
[108,] 5.317566e-01 9.364868e-01 0.4682433876
[109,] 5.286315e-01 9.427370e-01 0.4713685114
[110,] 5.164443e-01 9.671114e-01 0.4835557027
[111,] 4.822428e-01 9.644856e-01 0.5177572089
[112,] 4.436083e-01 8.872165e-01 0.5563917420
[113,] 4.254763e-01 8.509527e-01 0.5745236739
[114,] 3.910617e-01 7.821233e-01 0.6089383453
[115,] 3.556144e-01 7.112287e-01 0.6443856409
[116,] 3.175502e-01 6.351003e-01 0.6824498440
[117,] 2.826242e-01 5.652484e-01 0.7173757950
[118,] 2.500969e-01 5.001938e-01 0.7499030910
[119,] 2.192954e-01 4.385908e-01 0.7807045964
[120,] 2.048339e-01 4.096679e-01 0.7951660741
[121,] 1.746637e-01 3.493274e-01 0.8253363235
[122,] 1.480720e-01 2.961439e-01 0.8519280446
[123,] 1.241559e-01 2.483118e-01 0.8758441148
[124,] 1.045585e-01 2.091171e-01 0.8954414632
[125,] 8.872968e-02 1.774594e-01 0.9112703206
[126,] 7.583116e-02 1.516623e-01 0.9241688354
[127,] 6.322803e-02 1.264561e-01 0.9367719737
[128,] 6.079169e-02 1.215834e-01 0.9392083113
[129,] 5.435560e-02 1.087112e-01 0.9456443989
[130,] 4.315192e-02 8.630384e-02 0.9568480798
[131,] 3.354358e-02 6.708716e-02 0.9664564197
[132,] 2.886162e-02 5.772324e-02 0.9711383793
[133,] 2.203436e-02 4.406872e-02 0.9779656411
[134,] 1.658376e-02 3.316753e-02 0.9834162367
[135,] 1.273570e-02 2.547141e-02 0.9872642961
[136,] 1.026699e-02 2.053399e-02 0.9897330069
[137,] 7.777469e-03 1.555494e-02 0.9922225315
[138,] 1.134611e-02 2.269221e-02 0.9886538943
[139,] 1.060430e-02 2.120860e-02 0.9893957012
[140,] 1.393723e-02 2.787446e-02 0.9860627714
[141,] 1.042077e-02 2.084154e-02 0.9895792303
[142,] 8.142144e-03 1.628429e-02 0.9918578560
[143,] 8.939818e-03 1.787964e-02 0.9910601822
[144,] 2.540901e-02 5.081802e-02 0.9745909877
[145,] 1.928214e-02 3.856428e-02 0.9807178608
[146,] 1.559369e-02 3.118739e-02 0.9844063058
[147,] 1.347301e-02 2.694601e-02 0.9865269941
[148,] 2.020574e-02 4.041149e-02 0.9797942556
[149,] 1.652119e-02 3.304239e-02 0.9834788051
[150,] 2.542093e-02 5.084186e-02 0.9745790676
[151,] 2.314265e-02 4.628531e-02 0.9768573451
[152,] 1.965724e-02 3.931448e-02 0.9803427596
[153,] 2.007984e-02 4.015968e-02 0.9799201593
[154,] 1.575966e-02 3.151932e-02 0.9842403418
[155,] 5.325534e-02 1.065107e-01 0.9467446566
[156,] 1.301989e-01 2.603978e-01 0.8698011015
[157,] 1.665929e-01 3.331859e-01 0.8334070504
[158,] 2.313930e-01 4.627859e-01 0.7686070273
[159,] 2.150967e-01 4.301934e-01 0.7849032927
[160,] 2.714680e-01 5.429360e-01 0.7285320180
[161,] 2.597538e-01 5.195076e-01 0.7402461907
[162,] 2.789781e-01 5.579563e-01 0.7210218502
[163,] 2.674291e-01 5.348583e-01 0.7325708615
[164,] 3.054061e-01 6.108121e-01 0.6945939423
[165,] 3.467050e-01 6.934099e-01 0.6532950271
[166,] 5.256537e-01 9.486926e-01 0.4743463154
[167,] 8.519997e-01 2.960006e-01 0.1480002982
[168,] 9.997340e-01 5.319054e-04 0.0002659527
[169,] 9.996194e-01 7.612443e-04 0.0003806221
[170,] 9.988978e-01 2.204393e-03 0.0011021964
[171,] 9.979025e-01 4.195091e-03 0.0020975454
[172,] 9.948370e-01 1.032601e-02 0.0051630035
[173,] 9.856228e-01 2.875447e-02 0.0143772327
[174,] 1.000000e+00 0.000000e+00 0.0000000000
[175,] 1.000000e+00 0.000000e+00 0.0000000000
[176,] 1.000000e+00 0.000000e+00 0.0000000000
> postscript(file="/var/fisher/rcomp/tmp/13fcd1386338376.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/2fbgq1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3z5lo1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/4ancg1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/51n8z1386338376.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 = 195
Frequency = 1
1 2 3 4 5 6
-0.108792842 -0.324059529 -0.229560113 -0.301101383 -0.321999744 -0.248616292
7 8 9 10 11 12
0.104080927 0.274272314 0.001105254 -0.099145180 -0.089608497 -0.130303396
13 14 15 16 17 18
0.399083210 0.162043876 0.274162772 0.131480421 0.118022674 -0.467353965
19 20 21 22 23 24
-0.308210763 -0.166830694 -0.200415156 0.015593488 -0.122435287 0.063053152
25 26 27 28 29 30
0.149776378 0.138383451 0.290339507 0.316596135 0.450596934 0.387018239
31 32 33 34 35 36
-0.462986017 -0.338042875 -0.458024683 -0.312585488 -0.245941978 -0.302658636
37 38 39 40 41 42
0.230434052 0.235342631 0.343071483 0.224813457 0.390698017 0.543974018
43 44 45 46 47 48
-0.431499961 -0.551795612 -0.434057083 -0.456551401 -0.442930635 -0.422168956
49 50 51 52 53 54
-0.707493593 -0.685467633 -0.651054808 -0.659960749 -0.551305000 -0.619671229
55 56 57 58 59 60
-0.066815986 -0.133987416 -0.120063708 -0.099790357 -0.107776440 -0.066099516
61 62 63 64 65 66
-0.409406865 -0.375858276 -0.449203761 -0.344656699 -0.308165426 -0.340902829
67 68 69 70 71 72
0.116377857 0.125810448 0.309816138 0.377774109 0.234594731 0.088696537
73 74 75 76 77 78
0.179940148 0.087894831 0.140581064 0.108049471 0.139399875 0.135439602
79 80 81 82 83 84
0.052180765 -0.014861640 -0.044031584 0.063928685 0.078448502 0.334855202
85 86 87 88 89 90
0.025743810 0.095072444 0.229928533 0.081374446 0.132854736 -0.162732487
91 92 93 94 95 96
-0.112802989 0.342591777 0.308632835 0.330698678 0.348680418 0.351194063
97 98 99 100 101 102
0.392274476 -0.079769348 0.227886013 0.023815025 0.243559037 0.027101820
103 104 105 106 107 108
0.078873614 0.591612677 0.635976764 0.559804582 0.614715489 0.463666503
109 110 111 112 113 114
0.587637855 0.224070204 0.144175821 0.414995163 0.084240001 0.293062688
115 116 117 118 119 120
0.464922747 0.168260460 0.381927219 0.035021227 0.063970317 0.199098884
121 122 123 124 125 126
0.389793005 0.129204169 0.191464750 0.214875587 0.322896361 0.255411127
127 128 129 130 131 132
0.257089083 0.299337726 0.619590197 0.367060665 0.395119277 0.399018941
133 134 135 136 137 138
0.213110527 0.476123760 0.092979519 0.112362220 -0.043419181 0.002516151
139 140 141 142 143 144
0.181576928 0.276150941 -0.009053691 0.170496772 0.190167271 0.322636829
145 146 147 148 149 150
0.375796386 0.200077799 -0.330315731 -0.098179300 -0.247362458 0.117880478
151 152 153 154 155 156
0.024294628 -0.283851565 -0.237032784 0.187094572 0.125255304 -0.044647673
157 158 159 160 161 162
0.327331073 -0.013187348 0.221687072 0.263940385 0.184258343 0.225297984
163 164 165 166 167 168
0.243733783 0.319567069 -0.309439376 -0.399015422 -0.353785209 -0.293304366
169 170 171 172 173 174
-0.688900465 -0.314337224 -0.334325502 -0.527894957 -0.562590594 -0.584315350
175 176 177 178 179 180
-0.601403320 -0.588304060 -0.525318159 0.495444508 0.468236941 0.305527019
181 182 183 184 185 186
0.445273744 0.313926600 0.491382359 -0.641538112 -0.735210714 -0.593850341
187 188 189 190 191 192
-0.438322143 -0.427074226 -0.378891109 -0.355717092 -0.430021849 -0.531807712
193 194 195
-0.269729201 -0.351672106 -0.566785686
> postscript(file="/var/fisher/rcomp/tmp/6dkvi1386338376.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 = 195
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.108792842 NA
1 -0.324059529 -0.108792842
2 -0.229560113 -0.324059529
3 -0.301101383 -0.229560113
4 -0.321999744 -0.301101383
5 -0.248616292 -0.321999744
6 0.104080927 -0.248616292
7 0.274272314 0.104080927
8 0.001105254 0.274272314
9 -0.099145180 0.001105254
10 -0.089608497 -0.099145180
11 -0.130303396 -0.089608497
12 0.399083210 -0.130303396
13 0.162043876 0.399083210
14 0.274162772 0.162043876
15 0.131480421 0.274162772
16 0.118022674 0.131480421
17 -0.467353965 0.118022674
18 -0.308210763 -0.467353965
19 -0.166830694 -0.308210763
20 -0.200415156 -0.166830694
21 0.015593488 -0.200415156
22 -0.122435287 0.015593488
23 0.063053152 -0.122435287
24 0.149776378 0.063053152
25 0.138383451 0.149776378
26 0.290339507 0.138383451
27 0.316596135 0.290339507
28 0.450596934 0.316596135
29 0.387018239 0.450596934
30 -0.462986017 0.387018239
31 -0.338042875 -0.462986017
32 -0.458024683 -0.338042875
33 -0.312585488 -0.458024683
34 -0.245941978 -0.312585488
35 -0.302658636 -0.245941978
36 0.230434052 -0.302658636
37 0.235342631 0.230434052
38 0.343071483 0.235342631
39 0.224813457 0.343071483
40 0.390698017 0.224813457
41 0.543974018 0.390698017
42 -0.431499961 0.543974018
43 -0.551795612 -0.431499961
44 -0.434057083 -0.551795612
45 -0.456551401 -0.434057083
46 -0.442930635 -0.456551401
47 -0.422168956 -0.442930635
48 -0.707493593 -0.422168956
49 -0.685467633 -0.707493593
50 -0.651054808 -0.685467633
51 -0.659960749 -0.651054808
52 -0.551305000 -0.659960749
53 -0.619671229 -0.551305000
54 -0.066815986 -0.619671229
55 -0.133987416 -0.066815986
56 -0.120063708 -0.133987416
57 -0.099790357 -0.120063708
58 -0.107776440 -0.099790357
59 -0.066099516 -0.107776440
60 -0.409406865 -0.066099516
61 -0.375858276 -0.409406865
62 -0.449203761 -0.375858276
63 -0.344656699 -0.449203761
64 -0.308165426 -0.344656699
65 -0.340902829 -0.308165426
66 0.116377857 -0.340902829
67 0.125810448 0.116377857
68 0.309816138 0.125810448
69 0.377774109 0.309816138
70 0.234594731 0.377774109
71 0.088696537 0.234594731
72 0.179940148 0.088696537
73 0.087894831 0.179940148
74 0.140581064 0.087894831
75 0.108049471 0.140581064
76 0.139399875 0.108049471
77 0.135439602 0.139399875
78 0.052180765 0.135439602
79 -0.014861640 0.052180765
80 -0.044031584 -0.014861640
81 0.063928685 -0.044031584
82 0.078448502 0.063928685
83 0.334855202 0.078448502
84 0.025743810 0.334855202
85 0.095072444 0.025743810
86 0.229928533 0.095072444
87 0.081374446 0.229928533
88 0.132854736 0.081374446
89 -0.162732487 0.132854736
90 -0.112802989 -0.162732487
91 0.342591777 -0.112802989
92 0.308632835 0.342591777
93 0.330698678 0.308632835
94 0.348680418 0.330698678
95 0.351194063 0.348680418
96 0.392274476 0.351194063
97 -0.079769348 0.392274476
98 0.227886013 -0.079769348
99 0.023815025 0.227886013
100 0.243559037 0.023815025
101 0.027101820 0.243559037
102 0.078873614 0.027101820
103 0.591612677 0.078873614
104 0.635976764 0.591612677
105 0.559804582 0.635976764
106 0.614715489 0.559804582
107 0.463666503 0.614715489
108 0.587637855 0.463666503
109 0.224070204 0.587637855
110 0.144175821 0.224070204
111 0.414995163 0.144175821
112 0.084240001 0.414995163
113 0.293062688 0.084240001
114 0.464922747 0.293062688
115 0.168260460 0.464922747
116 0.381927219 0.168260460
117 0.035021227 0.381927219
118 0.063970317 0.035021227
119 0.199098884 0.063970317
120 0.389793005 0.199098884
121 0.129204169 0.389793005
122 0.191464750 0.129204169
123 0.214875587 0.191464750
124 0.322896361 0.214875587
125 0.255411127 0.322896361
126 0.257089083 0.255411127
127 0.299337726 0.257089083
128 0.619590197 0.299337726
129 0.367060665 0.619590197
130 0.395119277 0.367060665
131 0.399018941 0.395119277
132 0.213110527 0.399018941
133 0.476123760 0.213110527
134 0.092979519 0.476123760
135 0.112362220 0.092979519
136 -0.043419181 0.112362220
137 0.002516151 -0.043419181
138 0.181576928 0.002516151
139 0.276150941 0.181576928
140 -0.009053691 0.276150941
141 0.170496772 -0.009053691
142 0.190167271 0.170496772
143 0.322636829 0.190167271
144 0.375796386 0.322636829
145 0.200077799 0.375796386
146 -0.330315731 0.200077799
147 -0.098179300 -0.330315731
148 -0.247362458 -0.098179300
149 0.117880478 -0.247362458
150 0.024294628 0.117880478
151 -0.283851565 0.024294628
152 -0.237032784 -0.283851565
153 0.187094572 -0.237032784
154 0.125255304 0.187094572
155 -0.044647673 0.125255304
156 0.327331073 -0.044647673
157 -0.013187348 0.327331073
158 0.221687072 -0.013187348
159 0.263940385 0.221687072
160 0.184258343 0.263940385
161 0.225297984 0.184258343
162 0.243733783 0.225297984
163 0.319567069 0.243733783
164 -0.309439376 0.319567069
165 -0.399015422 -0.309439376
166 -0.353785209 -0.399015422
167 -0.293304366 -0.353785209
168 -0.688900465 -0.293304366
169 -0.314337224 -0.688900465
170 -0.334325502 -0.314337224
171 -0.527894957 -0.334325502
172 -0.562590594 -0.527894957
173 -0.584315350 -0.562590594
174 -0.601403320 -0.584315350
175 -0.588304060 -0.601403320
176 -0.525318159 -0.588304060
177 0.495444508 -0.525318159
178 0.468236941 0.495444508
179 0.305527019 0.468236941
180 0.445273744 0.305527019
181 0.313926600 0.445273744
182 0.491382359 0.313926600
183 -0.641538112 0.491382359
184 -0.735210714 -0.641538112
185 -0.593850341 -0.735210714
186 -0.438322143 -0.593850341
187 -0.427074226 -0.438322143
188 -0.378891109 -0.427074226
189 -0.355717092 -0.378891109
190 -0.430021849 -0.355717092
191 -0.531807712 -0.430021849
192 -0.269729201 -0.531807712
193 -0.351672106 -0.269729201
194 -0.566785686 -0.351672106
195 NA -0.566785686
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.324059529 -0.108792842
[2,] -0.229560113 -0.324059529
[3,] -0.301101383 -0.229560113
[4,] -0.321999744 -0.301101383
[5,] -0.248616292 -0.321999744
[6,] 0.104080927 -0.248616292
[7,] 0.274272314 0.104080927
[8,] 0.001105254 0.274272314
[9,] -0.099145180 0.001105254
[10,] -0.089608497 -0.099145180
[11,] -0.130303396 -0.089608497
[12,] 0.399083210 -0.130303396
[13,] 0.162043876 0.399083210
[14,] 0.274162772 0.162043876
[15,] 0.131480421 0.274162772
[16,] 0.118022674 0.131480421
[17,] -0.467353965 0.118022674
[18,] -0.308210763 -0.467353965
[19,] -0.166830694 -0.308210763
[20,] -0.200415156 -0.166830694
[21,] 0.015593488 -0.200415156
[22,] -0.122435287 0.015593488
[23,] 0.063053152 -0.122435287
[24,] 0.149776378 0.063053152
[25,] 0.138383451 0.149776378
[26,] 0.290339507 0.138383451
[27,] 0.316596135 0.290339507
[28,] 0.450596934 0.316596135
[29,] 0.387018239 0.450596934
[30,] -0.462986017 0.387018239
[31,] -0.338042875 -0.462986017
[32,] -0.458024683 -0.338042875
[33,] -0.312585488 -0.458024683
[34,] -0.245941978 -0.312585488
[35,] -0.302658636 -0.245941978
[36,] 0.230434052 -0.302658636
[37,] 0.235342631 0.230434052
[38,] 0.343071483 0.235342631
[39,] 0.224813457 0.343071483
[40,] 0.390698017 0.224813457
[41,] 0.543974018 0.390698017
[42,] -0.431499961 0.543974018
[43,] -0.551795612 -0.431499961
[44,] -0.434057083 -0.551795612
[45,] -0.456551401 -0.434057083
[46,] -0.442930635 -0.456551401
[47,] -0.422168956 -0.442930635
[48,] -0.707493593 -0.422168956
[49,] -0.685467633 -0.707493593
[50,] -0.651054808 -0.685467633
[51,] -0.659960749 -0.651054808
[52,] -0.551305000 -0.659960749
[53,] -0.619671229 -0.551305000
[54,] -0.066815986 -0.619671229
[55,] -0.133987416 -0.066815986
[56,] -0.120063708 -0.133987416
[57,] -0.099790357 -0.120063708
[58,] -0.107776440 -0.099790357
[59,] -0.066099516 -0.107776440
[60,] -0.409406865 -0.066099516
[61,] -0.375858276 -0.409406865
[62,] -0.449203761 -0.375858276
[63,] -0.344656699 -0.449203761
[64,] -0.308165426 -0.344656699
[65,] -0.340902829 -0.308165426
[66,] 0.116377857 -0.340902829
[67,] 0.125810448 0.116377857
[68,] 0.309816138 0.125810448
[69,] 0.377774109 0.309816138
[70,] 0.234594731 0.377774109
[71,] 0.088696537 0.234594731
[72,] 0.179940148 0.088696537
[73,] 0.087894831 0.179940148
[74,] 0.140581064 0.087894831
[75,] 0.108049471 0.140581064
[76,] 0.139399875 0.108049471
[77,] 0.135439602 0.139399875
[78,] 0.052180765 0.135439602
[79,] -0.014861640 0.052180765
[80,] -0.044031584 -0.014861640
[81,] 0.063928685 -0.044031584
[82,] 0.078448502 0.063928685
[83,] 0.334855202 0.078448502
[84,] 0.025743810 0.334855202
[85,] 0.095072444 0.025743810
[86,] 0.229928533 0.095072444
[87,] 0.081374446 0.229928533
[88,] 0.132854736 0.081374446
[89,] -0.162732487 0.132854736
[90,] -0.112802989 -0.162732487
[91,] 0.342591777 -0.112802989
[92,] 0.308632835 0.342591777
[93,] 0.330698678 0.308632835
[94,] 0.348680418 0.330698678
[95,] 0.351194063 0.348680418
[96,] 0.392274476 0.351194063
[97,] -0.079769348 0.392274476
[98,] 0.227886013 -0.079769348
[99,] 0.023815025 0.227886013
[100,] 0.243559037 0.023815025
[101,] 0.027101820 0.243559037
[102,] 0.078873614 0.027101820
[103,] 0.591612677 0.078873614
[104,] 0.635976764 0.591612677
[105,] 0.559804582 0.635976764
[106,] 0.614715489 0.559804582
[107,] 0.463666503 0.614715489
[108,] 0.587637855 0.463666503
[109,] 0.224070204 0.587637855
[110,] 0.144175821 0.224070204
[111,] 0.414995163 0.144175821
[112,] 0.084240001 0.414995163
[113,] 0.293062688 0.084240001
[114,] 0.464922747 0.293062688
[115,] 0.168260460 0.464922747
[116,] 0.381927219 0.168260460
[117,] 0.035021227 0.381927219
[118,] 0.063970317 0.035021227
[119,] 0.199098884 0.063970317
[120,] 0.389793005 0.199098884
[121,] 0.129204169 0.389793005
[122,] 0.191464750 0.129204169
[123,] 0.214875587 0.191464750
[124,] 0.322896361 0.214875587
[125,] 0.255411127 0.322896361
[126,] 0.257089083 0.255411127
[127,] 0.299337726 0.257089083
[128,] 0.619590197 0.299337726
[129,] 0.367060665 0.619590197
[130,] 0.395119277 0.367060665
[131,] 0.399018941 0.395119277
[132,] 0.213110527 0.399018941
[133,] 0.476123760 0.213110527
[134,] 0.092979519 0.476123760
[135,] 0.112362220 0.092979519
[136,] -0.043419181 0.112362220
[137,] 0.002516151 -0.043419181
[138,] 0.181576928 0.002516151
[139,] 0.276150941 0.181576928
[140,] -0.009053691 0.276150941
[141,] 0.170496772 -0.009053691
[142,] 0.190167271 0.170496772
[143,] 0.322636829 0.190167271
[144,] 0.375796386 0.322636829
[145,] 0.200077799 0.375796386
[146,] -0.330315731 0.200077799
[147,] -0.098179300 -0.330315731
[148,] -0.247362458 -0.098179300
[149,] 0.117880478 -0.247362458
[150,] 0.024294628 0.117880478
[151,] -0.283851565 0.024294628
[152,] -0.237032784 -0.283851565
[153,] 0.187094572 -0.237032784
[154,] 0.125255304 0.187094572
[155,] -0.044647673 0.125255304
[156,] 0.327331073 -0.044647673
[157,] -0.013187348 0.327331073
[158,] 0.221687072 -0.013187348
[159,] 0.263940385 0.221687072
[160,] 0.184258343 0.263940385
[161,] 0.225297984 0.184258343
[162,] 0.243733783 0.225297984
[163,] 0.319567069 0.243733783
[164,] -0.309439376 0.319567069
[165,] -0.399015422 -0.309439376
[166,] -0.353785209 -0.399015422
[167,] -0.293304366 -0.353785209
[168,] -0.688900465 -0.293304366
[169,] -0.314337224 -0.688900465
[170,] -0.334325502 -0.314337224
[171,] -0.527894957 -0.334325502
[172,] -0.562590594 -0.527894957
[173,] -0.584315350 -0.562590594
[174,] -0.601403320 -0.584315350
[175,] -0.588304060 -0.601403320
[176,] -0.525318159 -0.588304060
[177,] 0.495444508 -0.525318159
[178,] 0.468236941 0.495444508
[179,] 0.305527019 0.468236941
[180,] 0.445273744 0.305527019
[181,] 0.313926600 0.445273744
[182,] 0.491382359 0.313926600
[183,] -0.641538112 0.491382359
[184,] -0.735210714 -0.641538112
[185,] -0.593850341 -0.735210714
[186,] -0.438322143 -0.593850341
[187,] -0.427074226 -0.438322143
[188,] -0.378891109 -0.427074226
[189,] -0.355717092 -0.378891109
[190,] -0.430021849 -0.355717092
[191,] -0.531807712 -0.430021849
[192,] -0.269729201 -0.531807712
[193,] -0.351672106 -0.269729201
[194,] -0.566785686 -0.351672106
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.324059529 -0.108792842
2 -0.229560113 -0.324059529
3 -0.301101383 -0.229560113
4 -0.321999744 -0.301101383
5 -0.248616292 -0.321999744
6 0.104080927 -0.248616292
7 0.274272314 0.104080927
8 0.001105254 0.274272314
9 -0.099145180 0.001105254
10 -0.089608497 -0.099145180
11 -0.130303396 -0.089608497
12 0.399083210 -0.130303396
13 0.162043876 0.399083210
14 0.274162772 0.162043876
15 0.131480421 0.274162772
16 0.118022674 0.131480421
17 -0.467353965 0.118022674
18 -0.308210763 -0.467353965
19 -0.166830694 -0.308210763
20 -0.200415156 -0.166830694
21 0.015593488 -0.200415156
22 -0.122435287 0.015593488
23 0.063053152 -0.122435287
24 0.149776378 0.063053152
25 0.138383451 0.149776378
26 0.290339507 0.138383451
27 0.316596135 0.290339507
28 0.450596934 0.316596135
29 0.387018239 0.450596934
30 -0.462986017 0.387018239
31 -0.338042875 -0.462986017
32 -0.458024683 -0.338042875
33 -0.312585488 -0.458024683
34 -0.245941978 -0.312585488
35 -0.302658636 -0.245941978
36 0.230434052 -0.302658636
37 0.235342631 0.230434052
38 0.343071483 0.235342631
39 0.224813457 0.343071483
40 0.390698017 0.224813457
41 0.543974018 0.390698017
42 -0.431499961 0.543974018
43 -0.551795612 -0.431499961
44 -0.434057083 -0.551795612
45 -0.456551401 -0.434057083
46 -0.442930635 -0.456551401
47 -0.422168956 -0.442930635
48 -0.707493593 -0.422168956
49 -0.685467633 -0.707493593
50 -0.651054808 -0.685467633
51 -0.659960749 -0.651054808
52 -0.551305000 -0.659960749
53 -0.619671229 -0.551305000
54 -0.066815986 -0.619671229
55 -0.133987416 -0.066815986
56 -0.120063708 -0.133987416
57 -0.099790357 -0.120063708
58 -0.107776440 -0.099790357
59 -0.066099516 -0.107776440
60 -0.409406865 -0.066099516
61 -0.375858276 -0.409406865
62 -0.449203761 -0.375858276
63 -0.344656699 -0.449203761
64 -0.308165426 -0.344656699
65 -0.340902829 -0.308165426
66 0.116377857 -0.340902829
67 0.125810448 0.116377857
68 0.309816138 0.125810448
69 0.377774109 0.309816138
70 0.234594731 0.377774109
71 0.088696537 0.234594731
72 0.179940148 0.088696537
73 0.087894831 0.179940148
74 0.140581064 0.087894831
75 0.108049471 0.140581064
76 0.139399875 0.108049471
77 0.135439602 0.139399875
78 0.052180765 0.135439602
79 -0.014861640 0.052180765
80 -0.044031584 -0.014861640
81 0.063928685 -0.044031584
82 0.078448502 0.063928685
83 0.334855202 0.078448502
84 0.025743810 0.334855202
85 0.095072444 0.025743810
86 0.229928533 0.095072444
87 0.081374446 0.229928533
88 0.132854736 0.081374446
89 -0.162732487 0.132854736
90 -0.112802989 -0.162732487
91 0.342591777 -0.112802989
92 0.308632835 0.342591777
93 0.330698678 0.308632835
94 0.348680418 0.330698678
95 0.351194063 0.348680418
96 0.392274476 0.351194063
97 -0.079769348 0.392274476
98 0.227886013 -0.079769348
99 0.023815025 0.227886013
100 0.243559037 0.023815025
101 0.027101820 0.243559037
102 0.078873614 0.027101820
103 0.591612677 0.078873614
104 0.635976764 0.591612677
105 0.559804582 0.635976764
106 0.614715489 0.559804582
107 0.463666503 0.614715489
108 0.587637855 0.463666503
109 0.224070204 0.587637855
110 0.144175821 0.224070204
111 0.414995163 0.144175821
112 0.084240001 0.414995163
113 0.293062688 0.084240001
114 0.464922747 0.293062688
115 0.168260460 0.464922747
116 0.381927219 0.168260460
117 0.035021227 0.381927219
118 0.063970317 0.035021227
119 0.199098884 0.063970317
120 0.389793005 0.199098884
121 0.129204169 0.389793005
122 0.191464750 0.129204169
123 0.214875587 0.191464750
124 0.322896361 0.214875587
125 0.255411127 0.322896361
126 0.257089083 0.255411127
127 0.299337726 0.257089083
128 0.619590197 0.299337726
129 0.367060665 0.619590197
130 0.395119277 0.367060665
131 0.399018941 0.395119277
132 0.213110527 0.399018941
133 0.476123760 0.213110527
134 0.092979519 0.476123760
135 0.112362220 0.092979519
136 -0.043419181 0.112362220
137 0.002516151 -0.043419181
138 0.181576928 0.002516151
139 0.276150941 0.181576928
140 -0.009053691 0.276150941
141 0.170496772 -0.009053691
142 0.190167271 0.170496772
143 0.322636829 0.190167271
144 0.375796386 0.322636829
145 0.200077799 0.375796386
146 -0.330315731 0.200077799
147 -0.098179300 -0.330315731
148 -0.247362458 -0.098179300
149 0.117880478 -0.247362458
150 0.024294628 0.117880478
151 -0.283851565 0.024294628
152 -0.237032784 -0.283851565
153 0.187094572 -0.237032784
154 0.125255304 0.187094572
155 -0.044647673 0.125255304
156 0.327331073 -0.044647673
157 -0.013187348 0.327331073
158 0.221687072 -0.013187348
159 0.263940385 0.221687072
160 0.184258343 0.263940385
161 0.225297984 0.184258343
162 0.243733783 0.225297984
163 0.319567069 0.243733783
164 -0.309439376 0.319567069
165 -0.399015422 -0.309439376
166 -0.353785209 -0.399015422
167 -0.293304366 -0.353785209
168 -0.688900465 -0.293304366
169 -0.314337224 -0.688900465
170 -0.334325502 -0.314337224
171 -0.527894957 -0.334325502
172 -0.562590594 -0.527894957
173 -0.584315350 -0.562590594
174 -0.601403320 -0.584315350
175 -0.588304060 -0.601403320
176 -0.525318159 -0.588304060
177 0.495444508 -0.525318159
178 0.468236941 0.495444508
179 0.305527019 0.468236941
180 0.445273744 0.305527019
181 0.313926600 0.445273744
182 0.491382359 0.313926600
183 -0.641538112 0.491382359
184 -0.735210714 -0.641538112
185 -0.593850341 -0.735210714
186 -0.438322143 -0.593850341
187 -0.427074226 -0.438322143
188 -0.378891109 -0.427074226
189 -0.355717092 -0.378891109
190 -0.430021849 -0.355717092
191 -0.531807712 -0.430021849
192 -0.269729201 -0.531807712
193 -0.351672106 -0.269729201
194 -0.566785686 -0.351672106
> 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/7gset1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/845o91386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/fisher/rcomp/tmp/9bjdz1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/fisher/rcomp/tmp/108b7p1386338376.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/11edp51386338376.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/1250bt1386338376.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/13iuvi1386338376.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/fisher/rcomp/tmp/141s0x1386338376.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/15rjpu1386338376.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/fisher/rcomp/tmp/1652n81386338377.tab")
+ }
>
> try(system("convert tmp/13fcd1386338376.ps tmp/13fcd1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fbgq1386338376.ps tmp/2fbgq1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z5lo1386338376.ps tmp/3z5lo1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ancg1386338376.ps tmp/4ancg1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/51n8z1386338376.ps tmp/51n8z1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/6dkvi1386338376.ps tmp/6dkvi1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/7gset1386338376.ps tmp/7gset1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/845o91386338376.ps tmp/845o91386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/9bjdz1386338376.ps tmp/9bjdz1386338376.png",intern=TRUE))
character(0)
> try(system("convert tmp/108b7p1386338376.ps tmp/108b7p1386338376.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
14.185 2.701 16.924