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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You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
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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+ ,0.00003
+ ,0.02643
+ ,0.00881
+ ,1.872946
+ ,0.163118
+ ,0
+ ,0.00003
+ ,0.02436
+ ,0.00812
+ ,1.974857
+ ,0.184067
+ ,0
+ ,0.00003
+ ,0.02623
+ ,0.00874
+ ,2.004719
+ ,0.174429
+ ,1
+ ,0.00002
+ ,0.02184
+ ,0.00728
+ ,2.449763
+ ,0.132703
+ ,1
+ ,0.00002
+ ,0.02518
+ ,0.00839
+ ,2.251553
+ ,0.160306
+ ,1
+ ,0.00003
+ ,0.02175
+ ,0.00725
+ ,2.845109
+ ,0.19273
+ ,1
+ ,0.00003
+ ,0.03964
+ ,0.01321
+ ,2.264226
+ ,0.144105
+ ,1
+ ,0.00003
+ ,0.02849
+ ,0.0095
+ ,2.679185
+ ,0.19771
+ ,1
+ ,0.00002
+ ,0.03464
+ ,0.01155
+ ,2.209021
+ ,0.156368
+ ,0
+ ,0.00004
+ ,0.02592
+ ,0.00864
+ ,2.027228
+ ,0.215724
+ ,0
+ ,0.00005
+ ,0.02429
+ ,0.0081
+ ,2.120412
+ ,0.252404
+ ,0
+ ,0.00003
+ ,0.02001
+ ,0.00667
+ ,2.058658
+ ,0.214346
+ ,0
+ ,0.00004
+ ,0.0246
+ ,0.0082
+ ,2.161936
+ ,0.120605
+ ,0
+ ,0.00002
+ ,0.01892
+ ,0.00631
+ ,2.152083
+ ,0.138868
+ ,0
+ ,0.00003
+ ,0.01672
+ ,0.00557
+ ,1.91399
+ ,0.121777
+ ,0
+ ,0.00003
+ ,0.04363
+ ,0.01454
+ ,2.316346
+ ,0.112838
+ ,0
+ ,0.00003
+ ,0.07008
+ ,0.02336
+ ,2.657476
+ ,0.13305
+ ,0
+ ,0.00003
+ ,0.04812
+ ,0.01604
+ ,2.784312
+ ,0.168895
+ ,0
+ ,0.00008
+ ,0.03804
+ ,0.01268
+ ,2.679772
+ ,0.131728
+ ,0
+ ,0.00004
+ ,0.03794
+ ,0.01265
+ ,2.138608
+ ,0.123306
+ ,0
+ ,0.00003
+ ,0.03078
+ ,0.01026
+ ,2.555477
+ ,0.148569)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Jitter(Abs)'
+ ,'Shimmer:DDA'
+ ,'Shimmer:APQ3'
+ ,'D2'
+ ,'PPE')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('status','MDVP:Jitter(Abs)','Shimmer:DDA','Shimmer:APQ3','D2','PPE'),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 = 'No Linear Trend'
> par2 = 'Do not include Seasonal Dummies'
> par1 = '1'
> par3 <- 'No Linear Trend'
> par2 <- 'Do not include Seasonal Dummies'
> par1 <- '1'
> #'GNU S' R Code compiled by R2WASP v. 1.2.327 ()
> #Author: root
> #To cite this work: Wessa P., (2013), Multiple Regression (v1.0.29) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_multipleregression.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
status MDVP:Jitter(Abs) Shimmer:DDA Shimmer:APQ3 D2 PPE
1 1 7.0e-05 0.06545 0.02182 2.301442 0.284654
2 1 8.0e-05 0.09403 0.03134 2.486855 0.368674
3 1 9.0e-05 0.08270 0.02757 2.342259 0.332634
4 1 9.0e-05 0.08771 0.02924 2.405554 0.368975
5 1 1.1e-04 0.10470 0.03490 2.332180 0.410335
6 1 8.0e-05 0.06985 0.02328 2.187560 0.357775
7 1 3.0e-05 0.02337 0.00779 1.854785 0.211756
8 1 3.0e-05 0.02487 0.00829 2.064693 0.163755
9 1 6.0e-05 0.03218 0.01073 2.322511 0.231571
10 1 6.0e-05 0.04324 0.01441 2.432792 0.271362
11 1 6.0e-05 0.03237 0.01079 2.407313 0.249740
12 1 6.0e-05 0.04272 0.01424 2.642476 0.275931
13 1 2.0e-05 0.01968 0.00656 2.041277 0.138512
14 1 3.0e-05 0.02184 0.00728 2.519422 0.199889
15 1 2.0e-05 0.03191 0.01064 2.125618 0.170100
16 1 3.0e-05 0.02316 0.00772 2.205546 0.234589
17 1 4.0e-05 0.02908 0.00969 2.264501 0.218164
18 1 4.0e-05 0.04322 0.01441 3.007463 0.430788
19 1 5.0e-05 0.07413 0.02471 3.109010 0.377429
20 1 5.0e-05 0.05164 0.01721 2.856676 0.322111
21 1 5.0e-05 0.05000 0.01667 2.739710 0.365391
22 1 3.0e-05 0.06062 0.02021 2.557536 0.259765
23 1 3.0e-05 0.06685 0.02228 2.916777 0.285695
24 1 3.0e-05 0.06562 0.02187 2.547508 0.253556
25 1 5.0e-05 0.02214 0.00738 2.692176 0.215961
26 1 6.0e-05 0.05197 0.01732 2.846369 0.219514
27 1 3.0e-05 0.02666 0.00889 2.589702 0.147403
28 1 3.0e-05 0.02650 0.00883 2.314209 0.162999
29 1 2.0e-05 0.02307 0.00769 2.241742 0.108514
30 1 3.0e-05 0.02380 0.00793 1.957961 0.135242
31 0 1.0e-05 0.01689 0.00563 1.743867 0.085569
32 0 1.0e-05 0.01513 0.00504 2.103106 0.068501
33 0 1.0e-05 0.01919 0.00640 1.512275 0.096320
34 0 9.0e-06 0.01407 0.00469 1.544609 0.056141
35 0 9.0e-06 0.01403 0.00468 1.423287 0.044539
36 0 1.0e-05 0.01758 0.00586 2.447064 0.057610
37 1 2.0e-05 0.03463 0.01154 2.477082 0.165827
38 1 2.0e-05 0.02814 0.00938 2.536527 0.173218
39 1 2.0e-05 0.02177 0.00726 2.269398 0.141929
40 1 2.0e-05 0.02488 0.00829 2.382544 0.160691
41 1 2.0e-05 0.02321 0.00774 2.374073 0.130554
42 1 1.0e-05 0.02226 0.00742 2.361532 0.115730
43 0 1.0e-05 0.03104 0.01035 2.416838 0.095032
44 0 1.0e-05 0.03017 0.01006 2.256699 0.117399
45 0 9.0e-06 0.02330 0.00777 2.330716 0.091470
46 0 9.0e-06 0.02542 0.00847 2.365800 0.102706
47 0 1.0e-05 0.02719 0.00906 2.392122 0.097336
48 0 7.0e-06 0.01841 0.00614 2.028612 0.086398
49 0 4.0e-05 0.02566 0.00855 2.079922 0.133867
50 0 3.0e-05 0.02789 0.00930 2.054419 0.128872
51 0 3.0e-05 0.03724 0.01241 1.840198 0.103561
52 0 4.0e-05 0.03429 0.01143 2.431854 0.105993
53 0 3.0e-05 0.03969 0.01323 1.972297 0.119308
54 0 4.0e-05 0.04188 0.01396 2.223719 0.147491
55 1 7.0e-05 0.04450 0.01483 1.986899 0.316700
56 1 8.0e-05 0.05368 0.01789 2.014606 0.344834
57 1 7.0e-05 0.06097 0.02032 1.922940 0.335041
58 1 6.0e-05 0.03568 0.01189 2.021591 0.314464
59 1 7.0e-05 0.04183 0.01394 1.827012 0.326197
60 1 8.0e-05 0.05414 0.01805 1.831691 0.316395
61 0 1.0e-05 0.02925 0.00975 2.460791 0.101516
62 0 1.0e-05 0.03039 0.01013 2.321560 0.098555
63 0 1.0e-05 0.02602 0.00867 2.278687 0.103224
64 0 1.0e-05 0.02647 0.00882 2.498224 0.093534
65 0 9.0e-06 0.02308 0.00769 2.003032 0.073581
66 0 1.0e-05 0.02827 0.00942 2.118596 0.091546
67 1 6.0e-05 0.05490 0.01830 2.359973 0.226156
68 1 7.0e-05 0.04914 0.01638 2.291558 0.226247
69 1 8.0e-05 0.09455 0.03152 2.118496 0.185580
70 1 5.0e-05 0.10070 0.03357 2.137075 0.141958
71 1 6.0e-05 0.05605 0.01868 2.277927 0.180828
72 1 7.0e-05 0.08247 0.02749 2.642276 0.242981
73 1 3.0e-05 0.02921 0.00974 2.205024 0.188180
74 1 5.0e-05 0.04120 0.01373 1.928708 0.225461
75 1 4.0e-05 0.04295 0.01432 2.225815 0.244512
76 1 5.0e-05 0.03851 0.01284 1.862092 0.228624
77 1 4.0e-05 0.07238 0.02413 2.007923 0.193918
78 1 4.0e-05 0.03852 0.01284 1.777901 0.232744
79 1 6.0e-05 0.05408 0.01803 2.017753 0.260015
80 1 1.0e-04 0.05320 0.01773 2.398422 0.277948
81 1 7.0e-05 0.06799 0.02266 2.645959 0.327978
82 1 7.0e-05 0.05377 0.01792 2.232576 0.260633
83 1 6.0e-05 0.04114 0.01371 2.428306 0.264666
84 1 4.0e-05 0.03831 0.01277 2.053601 0.177275
85 1 4.0e-05 0.08037 0.02679 3.099301 0.242119
86 1 2.0e-05 0.06321 0.02107 3.098256 0.200423
87 1 2.0e-05 0.06219 0.02073 2.654271 0.144614
88 1 3.0e-05 0.11012 0.03671 3.136550 0.220968
89 1 3.0e-05 0.11363 0.03788 3.007096 0.194052
90 1 4.0e-05 0.06892 0.02297 3.671155 0.332086
91 1 4.0e-05 0.10949 0.03650 3.317586 0.301952
92 1 3.0e-05 0.13262 0.04421 2.344876 0.134120
93 1 3.0e-05 0.07150 0.02383 2.344336 0.186489
94 1 3.0e-05 0.10024 0.03341 2.080121 0.160809
95 1 2.0e-05 0.06185 0.02062 2.143851 0.160812
96 1 2.0e-05 0.05439 0.01813 2.344348 0.164916
97 1 2.0e-05 0.05417 0.01806 2.473239 0.151709
98 1 1.0e-04 0.06406 0.02135 2.671825 0.340623
99 1 1.1e-04 0.07625 0.02542 2.441612 0.260375
100 1 1.5e-04 0.10833 0.03611 2.634633 0.378483
101 1 2.6e-04 0.16074 0.05358 2.991063 0.370961
102 1 1.2e-04 0.09669 0.03223 2.638279 0.356881
103 1 2.2e-04 0.16654 0.05551 2.690917 0.444774
104 1 2.0e-05 0.01567 0.00522 2.004055 0.113942
105 1 1.0e-05 0.01406 0.00469 2.065477 0.093193
106 1 2.0e-05 0.01979 0.00660 1.994387 0.112878
107 1 1.0e-05 0.01567 0.00522 2.129924 0.106802
108 1 2.0e-05 0.01898 0.00633 2.499148 0.105306
109 1 1.0e-05 0.01364 0.00455 2.296873 0.115130
110 1 4.0e-05 0.05312 0.01771 2.608749 0.185668
111 1 3.0e-05 0.03576 0.01192 2.550961 0.232520
112 1 3.0e-05 0.02855 0.00952 2.502336 0.136390
113 1 4.0e-05 0.03831 0.01277 2.376749 0.268144
114 1 3.0e-05 0.02583 0.00861 2.489191 0.177807
115 1 2.0e-05 0.03320 0.01107 2.938114 0.115515
116 1 6.0e-05 0.02389 0.00796 2.702355 0.274407
117 1 3.0e-05 0.01818 0.00606 2.640798 0.170106
118 1 3.0e-05 0.02270 0.00757 2.975889 0.282780
119 1 3.0e-05 0.01851 0.00617 2.816781 0.251972
120 1 2.0e-05 0.02038 0.00679 2.925862 0.220657
121 1 5.0e-05 0.02548 0.00849 2.686240 0.152428
122 1 3.0e-05 0.01603 0.00534 2.655744 0.234809
123 1 5.0e-05 0.07761 0.02587 2.090438 0.229892
124 1 5.0e-05 0.04115 0.01372 2.174306 0.215558
125 1 4.0e-05 0.03867 0.01289 1.929715 0.181988
126 1 5.0e-05 0.03706 0.01235 1.765957 0.222716
127 1 4.0e-05 0.04451 0.01484 1.821297 0.214075
128 1 4.0e-05 0.04641 0.01547 1.996146 0.196535
129 1 4.0e-05 0.01614 0.00538 2.328513 0.112856
130 1 2.0e-05 0.01428 0.00476 2.108873 0.183572
131 1 4.0e-05 0.02110 0.00703 2.539724 0.169923
132 1 3.0e-05 0.02164 0.00721 2.527742 0.170633
133 1 3.0e-05 0.01898 0.00633 2.516320 0.232209
134 1 3.0e-05 0.01471 0.00490 2.034827 0.141422
135 1 6.0e-05 0.08050 0.02683 2.375138 0.243080
136 1 4.0e-05 0.06688 0.02229 2.631793 0.228319
137 1 4.0e-05 0.07154 0.02385 2.445502 0.259451
138 1 4.0e-05 0.08689 0.02896 2.672362 0.274387
139 1 4.0e-05 0.09211 0.03070 2.419253 0.209191
140 1 3.0e-05 0.04543 0.01514 2.445646 0.184985
141 1 3.0e-05 0.05139 0.01713 2.963799 0.277227
142 1 4.0e-05 0.12047 0.04016 2.665133 0.231723
143 1 2.0e-05 0.06165 0.02055 2.465528 0.209863
144 1 2.0e-05 0.03350 0.01117 2.470746 0.189032
145 1 1.0e-05 0.04426 0.01475 2.576563 0.159777
146 1 2.0e-05 0.04137 0.01379 2.840556 0.232861
147 1 9.0e-05 0.11411 0.03804 3.413649 0.457533
148 1 8.0e-05 0.08595 0.02865 3.142364 0.336085
149 1 9.0e-05 0.10422 0.03474 3.274865 0.418646
150 1 8.0e-05 0.10546 0.03515 2.910213 0.270173
151 1 1.0e-04 0.08096 0.02699 2.958815 0.301487
152 1 1.6e-04 0.16942 0.05647 3.079221 0.527367
153 1 1.4e-04 0.12851 0.04284 3.184027 0.454721
154 1 6.0e-05 0.04019 0.01340 2.013530 0.168581
155 1 6.0e-05 0.04451 0.01484 2.451130 0.247455
156 1 5.0e-05 0.04977 0.01659 2.439597 0.206256
157 1 6.0e-05 0.03615 0.01205 2.699645 0.220546
158 1 1.5e-04 0.07830 0.02610 2.964568 0.261305
159 1 8.0e-05 0.04499 0.01500 2.892300 0.249703
160 1 5.0e-05 0.04079 0.01360 2.103014 0.216638
161 1 5.0e-05 0.04736 0.01579 2.151121 0.244948
162 1 5.0e-05 0.04933 0.01644 2.442906 0.238281
163 1 6.0e-05 0.05592 0.01864 2.408689 0.220520
164 1 5.0e-05 0.02902 0.00967 1.871871 0.212386
165 1 9.0e-05 0.04736 0.01579 2.560422 0.367233
166 0 1.0e-05 0.04231 0.01410 2.235197 0.119652
167 0 1.0e-05 0.02089 0.00696 1.852402 0.091604
168 0 1.0e-05 0.03557 0.01186 1.881767 0.075587
169 0 4.0e-05 0.03836 0.01279 2.882450 0.202879
170 0 2.0e-05 0.03529 0.01176 2.266432 0.100881
171 0 2.0e-05 0.03253 0.01084 2.095237 0.096220
172 0 3.0e-05 0.01992 0.00664 2.193412 0.160376
173 0 3.0e-05 0.02261 0.00754 1.889002 0.174152
174 0 3.0e-05 0.02245 0.00748 1.852542 0.179677
175 0 3.0e-05 0.02643 0.00881 1.872946 0.163118
176 0 3.0e-05 0.02436 0.00812 1.974857 0.184067
177 0 3.0e-05 0.02623 0.00874 2.004719 0.174429
178 1 2.0e-05 0.02184 0.00728 2.449763 0.132703
179 1 2.0e-05 0.02518 0.00839 2.251553 0.160306
180 1 3.0e-05 0.02175 0.00725 2.845109 0.192730
181 1 3.0e-05 0.03964 0.01321 2.264226 0.144105
182 1 3.0e-05 0.02849 0.00950 2.679185 0.197710
183 1 2.0e-05 0.03464 0.01155 2.209021 0.156368
184 0 4.0e-05 0.02592 0.00864 2.027228 0.215724
185 0 5.0e-05 0.02429 0.00810 2.120412 0.252404
186 0 3.0e-05 0.02001 0.00667 2.058658 0.214346
187 0 4.0e-05 0.02460 0.00820 2.161936 0.120605
188 0 2.0e-05 0.01892 0.00631 2.152083 0.138868
189 0 3.0e-05 0.01672 0.00557 1.913990 0.121777
190 0 3.0e-05 0.04363 0.01454 2.316346 0.112838
191 0 3.0e-05 0.07008 0.02336 2.657476 0.133050
192 0 3.0e-05 0.04812 0.01604 2.784312 0.168895
193 0 8.0e-05 0.03804 0.01268 2.679772 0.131728
194 0 4.0e-05 0.03794 0.01265 2.138608 0.123306
195 0 3.0e-05 0.03078 0.01026 2.555477 0.148569
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Jitter(Abs)` `Shimmer:DDA` `Shimmer:APQ3`
-6.120e-03 -1.668e+03 -2.533e+03 7.601e+03
D2 PPE
1.055e-01 2.729e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.8580 -0.3740 0.1241 0.2974 0.5362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -6.120e-03 1.709e-01 -0.036 0.971
`MDVP:Jitter(Abs)` -1.668e+03 1.285e+03 -1.298 0.196
`Shimmer:DDA` -2.533e+03 3.214e+03 -0.788 0.432
`Shimmer:APQ3` 7.601e+03 9.641e+03 0.788 0.431
D2 1.055e-01 8.232e-02 1.282 0.201
PPE 2.729e+00 4.824e-01 5.657 5.64e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3659 on 189 degrees of freedom
Multiple R-squared: 0.3007, Adjusted R-squared: 0.2822
F-statistic: 16.25 on 5 and 189 DF, p-value: 2.524e-13
> 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,] 1.442836e-47 2.885671e-47 1.0000000
[2,] 3.130498e-63 6.260997e-63 1.0000000
[3,] 1.259821e-82 2.519641e-82 1.0000000
[4,] 1.280103e-92 2.560207e-92 1.0000000
[5,] 1.978852e-122 3.957704e-122 1.0000000
[6,] 2.186668e-122 4.373335e-122 1.0000000
[7,] 1.105953e-137 2.211907e-137 1.0000000
[8,] 0.000000e+00 0.000000e+00 1.0000000
[9,] 4.620499e-181 9.240998e-181 1.0000000
[10,] 2.788338e-185 5.576676e-185 1.0000000
[11,] 2.242172e-199 4.484345e-199 1.0000000
[12,] 5.100971e-225 1.020194e-224 1.0000000
[13,] 3.146844e-260 6.293687e-260 1.0000000
[14,] 1.069536e-248 2.139071e-248 1.0000000
[15,] 5.908246e-260 1.181649e-259 1.0000000
[16,] 1.161027e-278 2.322054e-278 1.0000000
[17,] 9.105652e-298 1.821130e-297 1.0000000
[18,] 0.000000e+00 0.000000e+00 1.0000000
[19,] 0.000000e+00 0.000000e+00 1.0000000
[20,] 0.000000e+00 0.000000e+00 1.0000000
[21,] 0.000000e+00 0.000000e+00 1.0000000
[22,] 0.000000e+00 0.000000e+00 1.0000000
[23,] 1.742296e-05 3.484591e-05 0.9999826
[24,] 1.667354e-03 3.334708e-03 0.9983326
[25,] 5.087676e-03 1.017535e-02 0.9949123
[26,] 7.197695e-03 1.439539e-02 0.9928023
[27,] 6.512722e-03 1.302544e-02 0.9934873
[28,] 1.830662e-02 3.661325e-02 0.9816934
[29,] 1.628215e-02 3.256431e-02 0.9837178
[30,] 1.338787e-02 2.677575e-02 0.9866121
[31,] 1.398442e-02 2.796884e-02 0.9860156
[32,] 1.167067e-02 2.334133e-02 0.9883293
[33,] 1.130902e-02 2.261803e-02 0.9886910
[34,] 1.158706e-02 2.317413e-02 0.9884129
[35,] 2.353237e-02 4.706474e-02 0.9764676
[36,] 3.534889e-02 7.069779e-02 0.9646511
[37,] 4.628686e-02 9.257371e-02 0.9537131
[38,] 7.402093e-02 1.480419e-01 0.9259791
[39,] 9.630092e-02 1.926018e-01 0.9036991
[40,] 9.929163e-02 1.985833e-01 0.9007084
[41,] 1.662189e-01 3.324379e-01 0.8337811
[42,] 1.989001e-01 3.978002e-01 0.8010999
[43,] 1.958733e-01 3.917467e-01 0.8041267
[44,] 2.211813e-01 4.423625e-01 0.7788187
[45,] 2.224050e-01 4.448101e-01 0.7775950
[46,] 2.555897e-01 5.111795e-01 0.7444103
[47,] 2.245942e-01 4.491883e-01 0.7754058
[48,] 1.965087e-01 3.930175e-01 0.8034913
[49,] 1.650878e-01 3.301756e-01 0.8349122
[50,] 1.425908e-01 2.851815e-01 0.8574092
[51,] 1.196117e-01 2.392235e-01 0.8803883
[52,] 9.852452e-02 1.970490e-01 0.9014755
[53,] 1.093223e-01 2.186446e-01 0.8906777
[54,] 1.141953e-01 2.283905e-01 0.8858047
[55,] 1.230344e-01 2.460687e-01 0.8769656
[56,] 1.322489e-01 2.644977e-01 0.8677511
[57,] 1.253206e-01 2.506413e-01 0.8746794
[58,] 1.246841e-01 2.493682e-01 0.8753159
[59,] 1.187074e-01 2.374147e-01 0.8812926
[60,] 1.059594e-01 2.119189e-01 0.8940406
[61,] 1.620655e-01 3.241311e-01 0.8379345
[62,] 2.518109e-01 5.036217e-01 0.7481891
[63,] 2.493568e-01 4.987137e-01 0.7506432
[64,] 2.196225e-01 4.392450e-01 0.7803775
[65,] 2.116815e-01 4.233631e-01 0.7883185
[66,] 2.014665e-01 4.029330e-01 0.7985335
[67,] 1.785178e-01 3.570356e-01 0.8214822
[68,] 1.621504e-01 3.243008e-01 0.8378496
[69,] 1.665783e-01 3.331566e-01 0.8334217
[70,] 1.561353e-01 3.122707e-01 0.8438647
[71,] 1.351405e-01 2.702810e-01 0.8648595
[72,] 1.169976e-01 2.339951e-01 0.8830024
[73,] 9.824900e-02 1.964980e-01 0.9017510
[74,] 8.406855e-02 1.681371e-01 0.9159315
[75,] 7.042743e-02 1.408549e-01 0.9295726
[76,] 7.147866e-02 1.429573e-01 0.9285213
[77,] 6.006498e-02 1.201300e-01 0.9399350
[78,] 5.344941e-02 1.068988e-01 0.9465506
[79,] 5.708518e-02 1.141704e-01 0.9429148
[80,] 4.716168e-02 9.432336e-02 0.9528383
[81,] 3.952485e-02 7.904971e-02 0.9604751
[82,] 3.455384e-02 6.910767e-02 0.9654462
[83,] 2.887733e-02 5.775466e-02 0.9711227
[84,] 2.894763e-02 5.789526e-02 0.9710524
[85,] 2.735299e-02 5.470597e-02 0.9726470
[86,] 2.768911e-02 5.537821e-02 0.9723109
[87,] 2.711532e-02 5.423063e-02 0.9728847
[88,] 2.637946e-02 5.275893e-02 0.9736205
[89,] 2.526997e-02 5.053995e-02 0.9747300
[90,] 2.033012e-02 4.066023e-02 0.9796699
[91,] 1.641277e-02 3.282555e-02 0.9835872
[92,] 1.471701e-02 2.943403e-02 0.9852830
[93,] 1.222590e-02 2.445180e-02 0.9877741
[94,] 9.777216e-03 1.955443e-02 0.9902228
[95,] 8.841434e-03 1.768287e-02 0.9911586
[96,] 1.278533e-02 2.557065e-02 0.9872147
[97,] 1.709098e-02 3.418196e-02 0.9829090
[98,] 2.140971e-02 4.281942e-02 0.9785903
[99,] 2.793241e-02 5.586483e-02 0.9720676
[100,] 3.165617e-02 6.331233e-02 0.9683438
[101,] 3.478687e-02 6.957374e-02 0.9652131
[102,] 3.091000e-02 6.182001e-02 0.9690900
[103,] 2.507044e-02 5.014089e-02 0.9749296
[104,] 2.600591e-02 5.201182e-02 0.9739941
[105,] 2.058722e-02 4.117443e-02 0.9794128
[106,] 1.883183e-02 3.766365e-02 0.9811682
[107,] 1.913566e-02 3.827132e-02 0.9808643
[108,] 1.481480e-02 2.962959e-02 0.9851852
[109,] 1.349871e-02 2.699741e-02 0.9865013
[110,] 1.045620e-02 2.091241e-02 0.9895438
[111,] 7.882631e-03 1.576526e-02 0.9921174
[112,] 6.039451e-03 1.207890e-02 0.9939605
[113,] 6.300438e-03 1.260088e-02 0.9936996
[114,] 4.837805e-03 9.675610e-03 0.9951622
[115,] 4.150008e-03 8.300016e-03 0.9958500
[116,] 3.700944e-03 7.401888e-03 0.9962991
[117,] 4.013358e-03 8.026715e-03 0.9959866
[118,] 3.981974e-03 7.963949e-03 0.9960180
[119,] 4.252618e-03 8.505237e-03 0.9957474
[120,] 4.707419e-03 9.414838e-03 0.9952926
[121,] 6.825088e-03 1.365018e-02 0.9931749
[122,] 7.045374e-03 1.409075e-02 0.9929546
[123,] 6.914681e-03 1.382936e-02 0.9930853
[124,] 6.768404e-03 1.353681e-02 0.9932316
[125,] 5.620295e-03 1.124059e-02 0.9943797
[126,] 8.917893e-03 1.783579e-02 0.9910821
[127,] 7.841851e-03 1.568370e-02 0.9921581
[128,] 6.464586e-03 1.292917e-02 0.9935354
[129,] 5.131380e-03 1.026276e-02 0.9948686
[130,] 3.807291e-03 7.614582e-03 0.9961927
[131,] 3.694383e-03 7.388766e-03 0.9963056
[132,] 3.981838e-03 7.963676e-03 0.9960182
[133,] 2.867462e-03 5.734924e-03 0.9971325
[134,] 2.488906e-03 4.977811e-03 0.9975111
[135,] 2.565609e-03 5.131219e-03 0.9974344
[136,] 2.679943e-03 5.359886e-03 0.9973201
[137,] 3.568568e-03 7.137136e-03 0.9964314
[138,] 3.086518e-03 6.173035e-03 0.9969135
[139,] 3.256711e-03 6.513423e-03 0.9967433
[140,] 2.325024e-03 4.650047e-03 0.9976750
[141,] 1.971059e-03 3.942119e-03 0.9980289
[142,] 1.648182e-03 3.296364e-03 0.9983518
[143,] 1.133177e-03 2.266354e-03 0.9988668
[144,] 1.299501e-03 2.599002e-03 0.9987005
[145,] 3.157957e-03 6.315915e-03 0.9968420
[146,] 6.514373e-03 1.302875e-02 0.9934856
[147,] 5.225668e-03 1.045134e-02 0.9947743
[148,] 4.897375e-03 9.794751e-03 0.9951026
[149,] 4.233005e-03 8.466011e-03 0.9957670
[150,] 3.032459e-03 6.064919e-03 0.9969675
[151,] 2.437743e-03 4.875486e-03 0.9975623
[152,] 3.734726e-03 7.469453e-03 0.9962653
[153,] 4.714466e-03 9.428931e-03 0.9952855
[154,] 3.931394e-03 7.862788e-03 0.9960686
[155,] 6.970650e-03 1.394130e-02 0.9930293
[156,] 2.499062e-02 4.998124e-02 0.9750094
[157,] 6.527686e-02 1.305537e-01 0.9347231
[158,] 6.985474e-02 1.397095e-01 0.9301453
[159,] 6.677472e-02 1.335494e-01 0.9332253
[160,] 5.651379e-02 1.130276e-01 0.9434862
[161,] 1.097937e-01 2.195875e-01 0.8902063
[162,] 1.102506e-01 2.205012e-01 0.8897494
[163,] 1.003172e-01 2.006343e-01 0.8996828
[164,] 1.128537e-01 2.257075e-01 0.8871463
[165,] 9.911191e-02 1.982238e-01 0.9008881
[166,] 8.364568e-02 1.672914e-01 0.9163543
[167,] 6.753900e-02 1.350780e-01 0.9324610
[168,] 5.725730e-02 1.145146e-01 0.9427427
[169,] 4.894470e-02 9.788940e-02 0.9510553
[170,] 4.311952e-02 8.623904e-02 0.9568805
[171,] 5.444023e-02 1.088805e-01 0.9455598
[172,] 4.631788e-02 9.263577e-02 0.9536821
[173,] 3.272386e-01 6.544771e-01 0.6727614
[174,] 5.275743e-01 9.448513e-01 0.4724257
[175,] 1.000000e+00 0.000000e+00 0.0000000
[176,] 1.000000e+00 0.000000e+00 0.0000000
[177,] 1.000000e+00 0.000000e+00 0.0000000
[178,] 1.000000e+00 0.000000e+00 0.0000000
> postscript(file="/var/wessaorg/rcomp/tmp/1rc6q1386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2g09w1386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/3rc9n1386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4g1m61386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/56atr1386258491.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
0.05188220 -0.14096489 -0.05685098 -0.16468951 -0.21786765 -0.07003217
7 8 9 10 11 12
0.27328514 0.38152403 0.19105233 0.11710950 0.15778345 0.05738363
13 14 15 16 17 18
0.43826261 0.23613572 0.31296882 0.17404467 0.25230673 -0.46260457
19 20 21 22 23 24
-0.29797838 -0.08612475 -0.24190572 0.02797953 -0.03249330 0.09466695
25 26 27 28 29 30
0.20728438 0.21148040 0.34469359 0.38194370 0.49762000 0.49635606
31 32 33 34 35 36
-0.40144849 -0.36674859 -0.43259568 -0.30066384 -0.28152078 -0.39963675
37 38 39 40 41 42
0.33713012 0.28793017 0.37869779 0.36499557 0.39811967 0.44893052
43 44 45 46 47 48
-0.52924874 -0.57303920 -0.50903298 -0.49356668 -0.48072598 -0.46470378
49 50 51 52 53 54
-0.49682313 -0.54873767 -0.41010334 -0.48666416 -0.49332434 -0.58095575
55 56 57 58 59 60
0.05662328 -0.01004231 0.00678038 0.04588944 0.04864142 0.03601143
61 62 63 64 65 66
-0.52553374 -0.50321326 -0.48435755 -0.48126205 -0.37487622 -0.43648940
67 68 69 70 71 72
0.21818561 0.24412388 0.34666306 0.41126132 0.37541589 0.14820662
73 74 75 76 77 78
0.27300263 0.27969467 0.12830768 0.22848996 0.27767313 0.23478575
79 80 81 82 83 84
0.13689478 0.16552110 -0.05303618 0.18001139 0.13668946 0.35713977
85 86 87 88 89 90
0.05312744 0.14047967 0.34003291 0.05308079 0.13879717 -0.22284148
91 92 93 94 95 96
-0.17008753 0.36468270 0.29678681 0.38332875 0.32449576 0.32043756
97 98 99 100 101 102
0.31762649 -0.03867565 0.16576604 -0.09759593 0.04795650 -0.08444323
103 104 105 106 107 108
-0.16547347 0.53616731 0.51959500 0.48778214 0.52568966 0.45549834
109 110 111 112 113 114
0.43547980 0.24443124 0.13823275 0.38321529 0.07507097 0.29799905
115 116 117 118 119 120
0.37566562 0.08803995 0.30605358 -0.06390957 0.06395195 0.14580872
121 122 123 124 125 126
0.40529103 0.15410277 0.21073480 0.23014761 0.35720966 0.30600543
127 128 129 130 131 132
0.25343495 0.30742785 0.51272410 0.31031309 0.35807387 0.34050801
133 134 135 136 137 138
0.12406727 0.47499126 0.18556837 0.17081758 0.05299967 0.03287493
139 140 141 142 143 144
0.23542127 0.30055642 -0.03354211 0.08604823 0.18211212 0.22425291
145 146 147 148 149 150
0.32263708 0.08783370 -0.52322361 -0.14333807 -0.37319621 0.07860946
151 152 153 154 155 156
-0.01955263 -0.53304871 -0.41364668 0.39236675 0.12923578 0.24944621
157 158 159 160 161 162
0.20509703 0.19927483 0.10970993 0.23486706 0.14992688 0.18721698
163 164 165 166 167 168
0.22801902 0.32621106 -0.16024946 -0.53106882 -0.40562389 -0.42151922
169 170 171 172 173 174
-0.82555515 -0.46367496 -0.43179289 -0.62087269 -0.65274493 -0.61323831
175 176 177 178 179 180
-0.59712205 -0.66422089 -0.61647858 0.41014805 0.37975070 0.22133688
181 182 183 184 185 186
0.43355693 0.19724383 0.34055496 -0.74007579 -0.85801348 -0.75396279
187 188 189 190 191 192
-0.49420339 -0.59943657 -0.45944717 -0.48820604 -0.61520521 -0.71768141
193 194 195
-0.51783030 -0.52974782 -0.63117707
> postscript(file="/var/wessaorg/rcomp/tmp/6lktt1386258491.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 0.05188220 NA
1 -0.14096489 0.05188220
2 -0.05685098 -0.14096489
3 -0.16468951 -0.05685098
4 -0.21786765 -0.16468951
5 -0.07003217 -0.21786765
6 0.27328514 -0.07003217
7 0.38152403 0.27328514
8 0.19105233 0.38152403
9 0.11710950 0.19105233
10 0.15778345 0.11710950
11 0.05738363 0.15778345
12 0.43826261 0.05738363
13 0.23613572 0.43826261
14 0.31296882 0.23613572
15 0.17404467 0.31296882
16 0.25230673 0.17404467
17 -0.46260457 0.25230673
18 -0.29797838 -0.46260457
19 -0.08612475 -0.29797838
20 -0.24190572 -0.08612475
21 0.02797953 -0.24190572
22 -0.03249330 0.02797953
23 0.09466695 -0.03249330
24 0.20728438 0.09466695
25 0.21148040 0.20728438
26 0.34469359 0.21148040
27 0.38194370 0.34469359
28 0.49762000 0.38194370
29 0.49635606 0.49762000
30 -0.40144849 0.49635606
31 -0.36674859 -0.40144849
32 -0.43259568 -0.36674859
33 -0.30066384 -0.43259568
34 -0.28152078 -0.30066384
35 -0.39963675 -0.28152078
36 0.33713012 -0.39963675
37 0.28793017 0.33713012
38 0.37869779 0.28793017
39 0.36499557 0.37869779
40 0.39811967 0.36499557
41 0.44893052 0.39811967
42 -0.52924874 0.44893052
43 -0.57303920 -0.52924874
44 -0.50903298 -0.57303920
45 -0.49356668 -0.50903298
46 -0.48072598 -0.49356668
47 -0.46470378 -0.48072598
48 -0.49682313 -0.46470378
49 -0.54873767 -0.49682313
50 -0.41010334 -0.54873767
51 -0.48666416 -0.41010334
52 -0.49332434 -0.48666416
53 -0.58095575 -0.49332434
54 0.05662328 -0.58095575
55 -0.01004231 0.05662328
56 0.00678038 -0.01004231
57 0.04588944 0.00678038
58 0.04864142 0.04588944
59 0.03601143 0.04864142
60 -0.52553374 0.03601143
61 -0.50321326 -0.52553374
62 -0.48435755 -0.50321326
63 -0.48126205 -0.48435755
64 -0.37487622 -0.48126205
65 -0.43648940 -0.37487622
66 0.21818561 -0.43648940
67 0.24412388 0.21818561
68 0.34666306 0.24412388
69 0.41126132 0.34666306
70 0.37541589 0.41126132
71 0.14820662 0.37541589
72 0.27300263 0.14820662
73 0.27969467 0.27300263
74 0.12830768 0.27969467
75 0.22848996 0.12830768
76 0.27767313 0.22848996
77 0.23478575 0.27767313
78 0.13689478 0.23478575
79 0.16552110 0.13689478
80 -0.05303618 0.16552110
81 0.18001139 -0.05303618
82 0.13668946 0.18001139
83 0.35713977 0.13668946
84 0.05312744 0.35713977
85 0.14047967 0.05312744
86 0.34003291 0.14047967
87 0.05308079 0.34003291
88 0.13879717 0.05308079
89 -0.22284148 0.13879717
90 -0.17008753 -0.22284148
91 0.36468270 -0.17008753
92 0.29678681 0.36468270
93 0.38332875 0.29678681
94 0.32449576 0.38332875
95 0.32043756 0.32449576
96 0.31762649 0.32043756
97 -0.03867565 0.31762649
98 0.16576604 -0.03867565
99 -0.09759593 0.16576604
100 0.04795650 -0.09759593
101 -0.08444323 0.04795650
102 -0.16547347 -0.08444323
103 0.53616731 -0.16547347
104 0.51959500 0.53616731
105 0.48778214 0.51959500
106 0.52568966 0.48778214
107 0.45549834 0.52568966
108 0.43547980 0.45549834
109 0.24443124 0.43547980
110 0.13823275 0.24443124
111 0.38321529 0.13823275
112 0.07507097 0.38321529
113 0.29799905 0.07507097
114 0.37566562 0.29799905
115 0.08803995 0.37566562
116 0.30605358 0.08803995
117 -0.06390957 0.30605358
118 0.06395195 -0.06390957
119 0.14580872 0.06395195
120 0.40529103 0.14580872
121 0.15410277 0.40529103
122 0.21073480 0.15410277
123 0.23014761 0.21073480
124 0.35720966 0.23014761
125 0.30600543 0.35720966
126 0.25343495 0.30600543
127 0.30742785 0.25343495
128 0.51272410 0.30742785
129 0.31031309 0.51272410
130 0.35807387 0.31031309
131 0.34050801 0.35807387
132 0.12406727 0.34050801
133 0.47499126 0.12406727
134 0.18556837 0.47499126
135 0.17081758 0.18556837
136 0.05299967 0.17081758
137 0.03287493 0.05299967
138 0.23542127 0.03287493
139 0.30055642 0.23542127
140 -0.03354211 0.30055642
141 0.08604823 -0.03354211
142 0.18211212 0.08604823
143 0.22425291 0.18211212
144 0.32263708 0.22425291
145 0.08783370 0.32263708
146 -0.52322361 0.08783370
147 -0.14333807 -0.52322361
148 -0.37319621 -0.14333807
149 0.07860946 -0.37319621
150 -0.01955263 0.07860946
151 -0.53304871 -0.01955263
152 -0.41364668 -0.53304871
153 0.39236675 -0.41364668
154 0.12923578 0.39236675
155 0.24944621 0.12923578
156 0.20509703 0.24944621
157 0.19927483 0.20509703
158 0.10970993 0.19927483
159 0.23486706 0.10970993
160 0.14992688 0.23486706
161 0.18721698 0.14992688
162 0.22801902 0.18721698
163 0.32621106 0.22801902
164 -0.16024946 0.32621106
165 -0.53106882 -0.16024946
166 -0.40562389 -0.53106882
167 -0.42151922 -0.40562389
168 -0.82555515 -0.42151922
169 -0.46367496 -0.82555515
170 -0.43179289 -0.46367496
171 -0.62087269 -0.43179289
172 -0.65274493 -0.62087269
173 -0.61323831 -0.65274493
174 -0.59712205 -0.61323831
175 -0.66422089 -0.59712205
176 -0.61647858 -0.66422089
177 0.41014805 -0.61647858
178 0.37975070 0.41014805
179 0.22133688 0.37975070
180 0.43355693 0.22133688
181 0.19724383 0.43355693
182 0.34055496 0.19724383
183 -0.74007579 0.34055496
184 -0.85801348 -0.74007579
185 -0.75396279 -0.85801348
186 -0.49420339 -0.75396279
187 -0.59943657 -0.49420339
188 -0.45944717 -0.59943657
189 -0.48820604 -0.45944717
190 -0.61520521 -0.48820604
191 -0.71768141 -0.61520521
192 -0.51783030 -0.71768141
193 -0.52974782 -0.51783030
194 -0.63117707 -0.52974782
195 NA -0.63117707
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.14096489 0.05188220
[2,] -0.05685098 -0.14096489
[3,] -0.16468951 -0.05685098
[4,] -0.21786765 -0.16468951
[5,] -0.07003217 -0.21786765
[6,] 0.27328514 -0.07003217
[7,] 0.38152403 0.27328514
[8,] 0.19105233 0.38152403
[9,] 0.11710950 0.19105233
[10,] 0.15778345 0.11710950
[11,] 0.05738363 0.15778345
[12,] 0.43826261 0.05738363
[13,] 0.23613572 0.43826261
[14,] 0.31296882 0.23613572
[15,] 0.17404467 0.31296882
[16,] 0.25230673 0.17404467
[17,] -0.46260457 0.25230673
[18,] -0.29797838 -0.46260457
[19,] -0.08612475 -0.29797838
[20,] -0.24190572 -0.08612475
[21,] 0.02797953 -0.24190572
[22,] -0.03249330 0.02797953
[23,] 0.09466695 -0.03249330
[24,] 0.20728438 0.09466695
[25,] 0.21148040 0.20728438
[26,] 0.34469359 0.21148040
[27,] 0.38194370 0.34469359
[28,] 0.49762000 0.38194370
[29,] 0.49635606 0.49762000
[30,] -0.40144849 0.49635606
[31,] -0.36674859 -0.40144849
[32,] -0.43259568 -0.36674859
[33,] -0.30066384 -0.43259568
[34,] -0.28152078 -0.30066384
[35,] -0.39963675 -0.28152078
[36,] 0.33713012 -0.39963675
[37,] 0.28793017 0.33713012
[38,] 0.37869779 0.28793017
[39,] 0.36499557 0.37869779
[40,] 0.39811967 0.36499557
[41,] 0.44893052 0.39811967
[42,] -0.52924874 0.44893052
[43,] -0.57303920 -0.52924874
[44,] -0.50903298 -0.57303920
[45,] -0.49356668 -0.50903298
[46,] -0.48072598 -0.49356668
[47,] -0.46470378 -0.48072598
[48,] -0.49682313 -0.46470378
[49,] -0.54873767 -0.49682313
[50,] -0.41010334 -0.54873767
[51,] -0.48666416 -0.41010334
[52,] -0.49332434 -0.48666416
[53,] -0.58095575 -0.49332434
[54,] 0.05662328 -0.58095575
[55,] -0.01004231 0.05662328
[56,] 0.00678038 -0.01004231
[57,] 0.04588944 0.00678038
[58,] 0.04864142 0.04588944
[59,] 0.03601143 0.04864142
[60,] -0.52553374 0.03601143
[61,] -0.50321326 -0.52553374
[62,] -0.48435755 -0.50321326
[63,] -0.48126205 -0.48435755
[64,] -0.37487622 -0.48126205
[65,] -0.43648940 -0.37487622
[66,] 0.21818561 -0.43648940
[67,] 0.24412388 0.21818561
[68,] 0.34666306 0.24412388
[69,] 0.41126132 0.34666306
[70,] 0.37541589 0.41126132
[71,] 0.14820662 0.37541589
[72,] 0.27300263 0.14820662
[73,] 0.27969467 0.27300263
[74,] 0.12830768 0.27969467
[75,] 0.22848996 0.12830768
[76,] 0.27767313 0.22848996
[77,] 0.23478575 0.27767313
[78,] 0.13689478 0.23478575
[79,] 0.16552110 0.13689478
[80,] -0.05303618 0.16552110
[81,] 0.18001139 -0.05303618
[82,] 0.13668946 0.18001139
[83,] 0.35713977 0.13668946
[84,] 0.05312744 0.35713977
[85,] 0.14047967 0.05312744
[86,] 0.34003291 0.14047967
[87,] 0.05308079 0.34003291
[88,] 0.13879717 0.05308079
[89,] -0.22284148 0.13879717
[90,] -0.17008753 -0.22284148
[91,] 0.36468270 -0.17008753
[92,] 0.29678681 0.36468270
[93,] 0.38332875 0.29678681
[94,] 0.32449576 0.38332875
[95,] 0.32043756 0.32449576
[96,] 0.31762649 0.32043756
[97,] -0.03867565 0.31762649
[98,] 0.16576604 -0.03867565
[99,] -0.09759593 0.16576604
[100,] 0.04795650 -0.09759593
[101,] -0.08444323 0.04795650
[102,] -0.16547347 -0.08444323
[103,] 0.53616731 -0.16547347
[104,] 0.51959500 0.53616731
[105,] 0.48778214 0.51959500
[106,] 0.52568966 0.48778214
[107,] 0.45549834 0.52568966
[108,] 0.43547980 0.45549834
[109,] 0.24443124 0.43547980
[110,] 0.13823275 0.24443124
[111,] 0.38321529 0.13823275
[112,] 0.07507097 0.38321529
[113,] 0.29799905 0.07507097
[114,] 0.37566562 0.29799905
[115,] 0.08803995 0.37566562
[116,] 0.30605358 0.08803995
[117,] -0.06390957 0.30605358
[118,] 0.06395195 -0.06390957
[119,] 0.14580872 0.06395195
[120,] 0.40529103 0.14580872
[121,] 0.15410277 0.40529103
[122,] 0.21073480 0.15410277
[123,] 0.23014761 0.21073480
[124,] 0.35720966 0.23014761
[125,] 0.30600543 0.35720966
[126,] 0.25343495 0.30600543
[127,] 0.30742785 0.25343495
[128,] 0.51272410 0.30742785
[129,] 0.31031309 0.51272410
[130,] 0.35807387 0.31031309
[131,] 0.34050801 0.35807387
[132,] 0.12406727 0.34050801
[133,] 0.47499126 0.12406727
[134,] 0.18556837 0.47499126
[135,] 0.17081758 0.18556837
[136,] 0.05299967 0.17081758
[137,] 0.03287493 0.05299967
[138,] 0.23542127 0.03287493
[139,] 0.30055642 0.23542127
[140,] -0.03354211 0.30055642
[141,] 0.08604823 -0.03354211
[142,] 0.18211212 0.08604823
[143,] 0.22425291 0.18211212
[144,] 0.32263708 0.22425291
[145,] 0.08783370 0.32263708
[146,] -0.52322361 0.08783370
[147,] -0.14333807 -0.52322361
[148,] -0.37319621 -0.14333807
[149,] 0.07860946 -0.37319621
[150,] -0.01955263 0.07860946
[151,] -0.53304871 -0.01955263
[152,] -0.41364668 -0.53304871
[153,] 0.39236675 -0.41364668
[154,] 0.12923578 0.39236675
[155,] 0.24944621 0.12923578
[156,] 0.20509703 0.24944621
[157,] 0.19927483 0.20509703
[158,] 0.10970993 0.19927483
[159,] 0.23486706 0.10970993
[160,] 0.14992688 0.23486706
[161,] 0.18721698 0.14992688
[162,] 0.22801902 0.18721698
[163,] 0.32621106 0.22801902
[164,] -0.16024946 0.32621106
[165,] -0.53106882 -0.16024946
[166,] -0.40562389 -0.53106882
[167,] -0.42151922 -0.40562389
[168,] -0.82555515 -0.42151922
[169,] -0.46367496 -0.82555515
[170,] -0.43179289 -0.46367496
[171,] -0.62087269 -0.43179289
[172,] -0.65274493 -0.62087269
[173,] -0.61323831 -0.65274493
[174,] -0.59712205 -0.61323831
[175,] -0.66422089 -0.59712205
[176,] -0.61647858 -0.66422089
[177,] 0.41014805 -0.61647858
[178,] 0.37975070 0.41014805
[179,] 0.22133688 0.37975070
[180,] 0.43355693 0.22133688
[181,] 0.19724383 0.43355693
[182,] 0.34055496 0.19724383
[183,] -0.74007579 0.34055496
[184,] -0.85801348 -0.74007579
[185,] -0.75396279 -0.85801348
[186,] -0.49420339 -0.75396279
[187,] -0.59943657 -0.49420339
[188,] -0.45944717 -0.59943657
[189,] -0.48820604 -0.45944717
[190,] -0.61520521 -0.48820604
[191,] -0.71768141 -0.61520521
[192,] -0.51783030 -0.71768141
[193,] -0.52974782 -0.51783030
[194,] -0.63117707 -0.52974782
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.14096489 0.05188220
2 -0.05685098 -0.14096489
3 -0.16468951 -0.05685098
4 -0.21786765 -0.16468951
5 -0.07003217 -0.21786765
6 0.27328514 -0.07003217
7 0.38152403 0.27328514
8 0.19105233 0.38152403
9 0.11710950 0.19105233
10 0.15778345 0.11710950
11 0.05738363 0.15778345
12 0.43826261 0.05738363
13 0.23613572 0.43826261
14 0.31296882 0.23613572
15 0.17404467 0.31296882
16 0.25230673 0.17404467
17 -0.46260457 0.25230673
18 -0.29797838 -0.46260457
19 -0.08612475 -0.29797838
20 -0.24190572 -0.08612475
21 0.02797953 -0.24190572
22 -0.03249330 0.02797953
23 0.09466695 -0.03249330
24 0.20728438 0.09466695
25 0.21148040 0.20728438
26 0.34469359 0.21148040
27 0.38194370 0.34469359
28 0.49762000 0.38194370
29 0.49635606 0.49762000
30 -0.40144849 0.49635606
31 -0.36674859 -0.40144849
32 -0.43259568 -0.36674859
33 -0.30066384 -0.43259568
34 -0.28152078 -0.30066384
35 -0.39963675 -0.28152078
36 0.33713012 -0.39963675
37 0.28793017 0.33713012
38 0.37869779 0.28793017
39 0.36499557 0.37869779
40 0.39811967 0.36499557
41 0.44893052 0.39811967
42 -0.52924874 0.44893052
43 -0.57303920 -0.52924874
44 -0.50903298 -0.57303920
45 -0.49356668 -0.50903298
46 -0.48072598 -0.49356668
47 -0.46470378 -0.48072598
48 -0.49682313 -0.46470378
49 -0.54873767 -0.49682313
50 -0.41010334 -0.54873767
51 -0.48666416 -0.41010334
52 -0.49332434 -0.48666416
53 -0.58095575 -0.49332434
54 0.05662328 -0.58095575
55 -0.01004231 0.05662328
56 0.00678038 -0.01004231
57 0.04588944 0.00678038
58 0.04864142 0.04588944
59 0.03601143 0.04864142
60 -0.52553374 0.03601143
61 -0.50321326 -0.52553374
62 -0.48435755 -0.50321326
63 -0.48126205 -0.48435755
64 -0.37487622 -0.48126205
65 -0.43648940 -0.37487622
66 0.21818561 -0.43648940
67 0.24412388 0.21818561
68 0.34666306 0.24412388
69 0.41126132 0.34666306
70 0.37541589 0.41126132
71 0.14820662 0.37541589
72 0.27300263 0.14820662
73 0.27969467 0.27300263
74 0.12830768 0.27969467
75 0.22848996 0.12830768
76 0.27767313 0.22848996
77 0.23478575 0.27767313
78 0.13689478 0.23478575
79 0.16552110 0.13689478
80 -0.05303618 0.16552110
81 0.18001139 -0.05303618
82 0.13668946 0.18001139
83 0.35713977 0.13668946
84 0.05312744 0.35713977
85 0.14047967 0.05312744
86 0.34003291 0.14047967
87 0.05308079 0.34003291
88 0.13879717 0.05308079
89 -0.22284148 0.13879717
90 -0.17008753 -0.22284148
91 0.36468270 -0.17008753
92 0.29678681 0.36468270
93 0.38332875 0.29678681
94 0.32449576 0.38332875
95 0.32043756 0.32449576
96 0.31762649 0.32043756
97 -0.03867565 0.31762649
98 0.16576604 -0.03867565
99 -0.09759593 0.16576604
100 0.04795650 -0.09759593
101 -0.08444323 0.04795650
102 -0.16547347 -0.08444323
103 0.53616731 -0.16547347
104 0.51959500 0.53616731
105 0.48778214 0.51959500
106 0.52568966 0.48778214
107 0.45549834 0.52568966
108 0.43547980 0.45549834
109 0.24443124 0.43547980
110 0.13823275 0.24443124
111 0.38321529 0.13823275
112 0.07507097 0.38321529
113 0.29799905 0.07507097
114 0.37566562 0.29799905
115 0.08803995 0.37566562
116 0.30605358 0.08803995
117 -0.06390957 0.30605358
118 0.06395195 -0.06390957
119 0.14580872 0.06395195
120 0.40529103 0.14580872
121 0.15410277 0.40529103
122 0.21073480 0.15410277
123 0.23014761 0.21073480
124 0.35720966 0.23014761
125 0.30600543 0.35720966
126 0.25343495 0.30600543
127 0.30742785 0.25343495
128 0.51272410 0.30742785
129 0.31031309 0.51272410
130 0.35807387 0.31031309
131 0.34050801 0.35807387
132 0.12406727 0.34050801
133 0.47499126 0.12406727
134 0.18556837 0.47499126
135 0.17081758 0.18556837
136 0.05299967 0.17081758
137 0.03287493 0.05299967
138 0.23542127 0.03287493
139 0.30055642 0.23542127
140 -0.03354211 0.30055642
141 0.08604823 -0.03354211
142 0.18211212 0.08604823
143 0.22425291 0.18211212
144 0.32263708 0.22425291
145 0.08783370 0.32263708
146 -0.52322361 0.08783370
147 -0.14333807 -0.52322361
148 -0.37319621 -0.14333807
149 0.07860946 -0.37319621
150 -0.01955263 0.07860946
151 -0.53304871 -0.01955263
152 -0.41364668 -0.53304871
153 0.39236675 -0.41364668
154 0.12923578 0.39236675
155 0.24944621 0.12923578
156 0.20509703 0.24944621
157 0.19927483 0.20509703
158 0.10970993 0.19927483
159 0.23486706 0.10970993
160 0.14992688 0.23486706
161 0.18721698 0.14992688
162 0.22801902 0.18721698
163 0.32621106 0.22801902
164 -0.16024946 0.32621106
165 -0.53106882 -0.16024946
166 -0.40562389 -0.53106882
167 -0.42151922 -0.40562389
168 -0.82555515 -0.42151922
169 -0.46367496 -0.82555515
170 -0.43179289 -0.46367496
171 -0.62087269 -0.43179289
172 -0.65274493 -0.62087269
173 -0.61323831 -0.65274493
174 -0.59712205 -0.61323831
175 -0.66422089 -0.59712205
176 -0.61647858 -0.66422089
177 0.41014805 -0.61647858
178 0.37975070 0.41014805
179 0.22133688 0.37975070
180 0.43355693 0.22133688
181 0.19724383 0.43355693
182 0.34055496 0.19724383
183 -0.74007579 0.34055496
184 -0.85801348 -0.74007579
185 -0.75396279 -0.85801348
186 -0.49420339 -0.75396279
187 -0.59943657 -0.49420339
188 -0.45944717 -0.59943657
189 -0.48820604 -0.45944717
190 -0.61520521 -0.48820604
191 -0.71768141 -0.61520521
192 -0.51783030 -0.71768141
193 -0.52974782 -0.51783030
194 -0.63117707 -0.52974782
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/7184s1386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8ird51386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/92kgt1386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/10plq51386258491.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11jg4n1386258492.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,signif(mysum$coefficients[i,1],6))
+ a<-table.element(a, signif(mysum$coefficients[i,2],6))
+ a<-table.element(a, signif(mysum$coefficients[i,3],4))
+ a<-table.element(a, signif(mysum$coefficients[i,4],6))
+ a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12f4r81386258492.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, signif(sqrt(mysum$r.squared),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, signif(mysum$adj.r.squared,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[1],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[2],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, signif(mysum$fstatistic[3],6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, signif(mysum$sigma,6))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, signif(sum(myerror*myerror),6))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/13kpkm1386258492.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,signif(x[i],6))
+ a<-table.element(a,signif(x[i]-mysum$resid[i],6))
+ a<-table.element(a,signif(mysum$resid[i],6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14flu41386258492.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,1],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
+ a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/15v3uv1386258492.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant1,6))
+ a<-table.element(a,signif(numsignificant1/numgqtests,6))
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant5,6))
+ a<-table.element(a,signif(numsignificant5/numgqtests,6))
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,signif(numsignificant10,6))
+ a<-table.element(a,signif(numsignificant10/numgqtests,6))
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/wessaorg/rcomp/tmp/16satt1386258492.tab")
+ }
>
> try(system("convert tmp/1rc6q1386258491.ps tmp/1rc6q1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g09w1386258491.ps tmp/2g09w1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rc9n1386258491.ps tmp/3rc9n1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/4g1m61386258491.ps tmp/4g1m61386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/56atr1386258491.ps tmp/56atr1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/6lktt1386258491.ps tmp/6lktt1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/7184s1386258491.ps tmp/7184s1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ird51386258491.ps tmp/8ird51386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/92kgt1386258491.ps tmp/92kgt1386258491.png",intern=TRUE))
character(0)
> try(system("convert tmp/10plq51386258491.ps tmp/10plq51386258491.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
15.214 2.935 18.173