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)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
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(0.04374
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+ ,0.03529
+ ,0.01897
+ ,0.181
+ ,0.01084
+ ,0.01121
+ ,0.01255
+ ,0.03253
+ ,0.01358
+ ,0.129
+ ,0.00664
+ ,0.00786
+ ,0.0114
+ ,0.01992
+ ,0.01484
+ ,0.133
+ ,0.00754
+ ,0.0095
+ ,0.01285
+ ,0.02261
+ ,0.01472
+ ,0.133
+ ,0.00748
+ ,0.00905
+ ,0.01148
+ ,0.02245
+ ,0.01657
+ ,0.145
+ ,0.00881
+ ,0.01062
+ ,0.01318
+ ,0.02643
+ ,0.01503
+ ,0.137
+ ,0.00812
+ ,0.00933
+ ,0.01133
+ ,0.02436
+ ,0.01725
+ ,0.155
+ ,0.00874
+ ,0.01021
+ ,0.01331
+ ,0.02623
+ ,0.01469
+ ,0.132
+ ,0.00728
+ ,0.00886
+ ,0.0123
+ ,0.02184
+ ,0.01574
+ ,0.142
+ ,0.00839
+ ,0.00956
+ ,0.01309
+ ,0.02518
+ ,0.0145
+ ,0.131
+ ,0.00725
+ ,0.00876
+ ,0.01263
+ ,0.02175
+ ,0.02551
+ ,0.237
+ ,0.01321
+ ,0.01574
+ ,0.02148
+ ,0.03964
+ ,0.01831
+ ,0.163
+ ,0.0095
+ ,0.01103
+ ,0.01559
+ ,0.02849
+ ,0.02145
+ ,0.198
+ ,0.01155
+ ,0.01341
+ ,0.01666
+ ,0.03464
+ ,0.01909
+ ,0.171
+ ,0.00864
+ ,0.01223
+ ,0.01949
+ ,0.02592
+ ,0.01795
+ ,0.163
+ ,0.0081
+ ,0.01144
+ ,0.01756
+ ,0.02429
+ ,0.01564
+ ,0.136
+ ,0.00667
+ ,0.0099
+ ,0.01691
+ ,0.02001
+ ,0.0166
+ ,0.154
+ ,0.0082
+ ,0.00972
+ ,0.01491
+ ,0.0246
+ ,0.013
+ ,0.117
+ ,0.00631
+ ,0.00789
+ ,0.01144
+ ,0.01892
+ ,0.01185
+ ,0.106
+ ,0.00557
+ ,0.00721
+ ,0.01095
+ ,0.01672
+ ,0.02574
+ ,0.255
+ ,0.01454
+ ,0.01582
+ ,0.01758
+ ,0.04363
+ ,0.04087
+ ,0.405
+ ,0.02336
+ ,0.02498
+ ,0.02745
+ ,0.07008
+ ,0.02751
+ ,0.263
+ ,0.01604
+ ,0.01657
+ ,0.01879
+ ,0.04812
+ ,0.02308
+ ,0.256
+ ,0.01268
+ ,0.01365
+ ,0.01667
+ ,0.03804
+ ,0.02296
+ ,0.241
+ ,0.01265
+ ,0.01321
+ ,0.01588
+ ,0.03794
+ ,0.01884
+ ,0.19
+ ,0.01026
+ ,0.01161
+ ,0.01373
+ ,0.03078)
+ ,dim=c(6
+ ,195)
+ ,dimnames=list(c('MDVP:Shimmer'
+ ,'MDVP:Shimmer(dB)'
+ ,'Shimmer:APQ3'
+ ,'Shimmer:APQ5'
+ ,'MDVP:APQ'
+ ,'Shimmer:DDA')
+ ,1:195))
> y <- array(NA,dim=c(6,195),dimnames=list(c('MDVP:Shimmer','MDVP:Shimmer(dB)','Shimmer:APQ3','Shimmer:APQ5','MDVP:APQ','Shimmer:DDA'),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
MDVP:Shimmer MDVP:Shimmer(dB) Shimmer:APQ3 Shimmer:APQ5 MDVP:APQ
1 0.04374 0.426 0.02182 0.03130 0.02971
2 0.06134 0.626 0.03134 0.04518 0.04368
3 0.05233 0.482 0.02757 0.03858 0.03590
4 0.05492 0.517 0.02924 0.04005 0.03772
5 0.06425 0.584 0.03490 0.04825 0.04465
6 0.04701 0.456 0.02328 0.03526 0.03243
7 0.01608 0.140 0.00779 0.00937 0.01351
8 0.01567 0.134 0.00829 0.00946 0.01256
9 0.02093 0.191 0.01073 0.01277 0.01717
10 0.02838 0.255 0.01441 0.01725 0.02444
11 0.02143 0.197 0.01079 0.01342 0.01892
12 0.02752 0.249 0.01424 0.01641 0.02214
13 0.01259 0.112 0.00656 0.00717 0.01140
14 0.01642 0.154 0.00728 0.00932 0.01797
15 0.01828 0.158 0.01064 0.00972 0.01246
16 0.01503 0.126 0.00772 0.00888 0.01359
17 0.02047 0.192 0.00969 0.01200 0.02074
18 0.03327 0.348 0.01441 0.01893 0.03430
19 0.05517 0.542 0.02471 0.03572 0.05767
20 0.03995 0.348 0.01721 0.02374 0.04310
21 0.03810 0.328 0.01667 0.02383 0.04055
22 0.04137 0.370 0.02021 0.02591 0.04525
23 0.04351 0.377 0.02228 0.02540 0.04246
24 0.04192 0.364 0.02187 0.02470 0.03772
25 0.01659 0.164 0.00738 0.00948 0.01497
26 0.03767 0.381 0.01732 0.02245 0.03780
27 0.01966 0.186 0.00889 0.01169 0.01872
28 0.01919 0.198 0.00883 0.01144 0.01826
29 0.01718 0.161 0.00769 0.01012 0.01661
30 0.01791 0.168 0.00793 0.01057 0.01799
31 0.01098 0.097 0.00563 0.00680 0.00802
32 0.01015 0.089 0.00504 0.00641 0.00762
33 0.01263 0.111 0.00640 0.00825 0.00951
34 0.00954 0.085 0.00469 0.00606 0.00719
35 0.00958 0.085 0.00468 0.00610 0.00726
36 0.01194 0.107 0.00586 0.00760 0.00957
37 0.02126 0.189 0.01154 0.01347 0.01612
38 0.01851 0.168 0.00938 0.01160 0.01491
39 0.01444 0.131 0.00726 0.00885 0.01190
40 0.01663 0.151 0.00829 0.01003 0.01366
41 0.01495 0.135 0.00774 0.00941 0.01233
42 0.01463 0.132 0.00742 0.00901 0.01234
43 0.01752 0.164 0.01035 0.01024 0.01133
44 0.01760 0.154 0.01006 0.01038 0.01251
45 0.01419 0.126 0.00777 0.00898 0.01033
46 0.01494 0.134 0.00847 0.00879 0.01014
47 0.01608 0.141 0.00906 0.00977 0.01149
48 0.01152 0.103 0.00614 0.00730 0.00860
49 0.01613 0.143 0.00855 0.00776 0.01433
50 0.01681 0.154 0.00930 0.00802 0.01400
51 0.02184 0.197 0.01241 0.01024 0.01685
52 0.02033 0.185 0.01143 0.00959 0.01614
53 0.02297 0.210 0.01323 0.01072 0.01677
54 0.02498 0.228 0.01396 0.01219 0.01947
55 0.02719 0.255 0.01483 0.01609 0.02067
56 0.03209 0.307 0.01789 0.01992 0.02454
57 0.03715 0.334 0.02032 0.02302 0.02802
58 0.02293 0.221 0.01189 0.01459 0.01948
59 0.02645 0.265 0.01394 0.01625 0.02137
60 0.03225 0.350 0.01805 0.01974 0.02519
61 0.01861 0.170 0.00975 0.01258 0.01382
62 0.01906 0.165 0.01013 0.01296 0.01340
63 0.01643 0.145 0.00867 0.01108 0.01200
64 0.01644 0.145 0.00882 0.01075 0.01179
65 0.01457 0.129 0.00769 0.00957 0.01016
66 0.01745 0.154 0.00942 0.01160 0.01234
67 0.03198 0.313 0.01830 0.01810 0.02428
68 0.03111 0.308 0.01638 0.01759 0.02603
69 0.05384 0.478 0.03152 0.02422 0.03392
70 0.05428 0.497 0.03357 0.02494 0.03635
71 0.03485 0.365 0.01868 0.01906 0.02949
72 0.04978 0.483 0.02749 0.02466 0.03736
73 0.01706 0.152 0.00974 0.00925 0.01345
74 0.02448 0.226 0.01373 0.01375 0.01956
75 0.02442 0.216 0.01432 0.01325 0.01831
76 0.02215 0.206 0.01284 0.01219 0.01715
77 0.03999 0.350 0.02413 0.02231 0.02704
78 0.02199 0.197 0.01284 0.01199 0.01636
79 0.03202 0.263 0.01803 0.01886 0.02455
80 0.03121 0.361 0.01773 0.01783 0.02139
81 0.04024 0.364 0.02266 0.02451 0.02876
82 0.03156 0.296 0.01792 0.01841 0.02190
83 0.02427 0.216 0.01371 0.01421 0.01751
84 0.02223 0.202 0.01277 0.01343 0.01552
85 0.04795 0.435 0.02679 0.03022 0.03510
86 0.03852 0.331 0.02107 0.02493 0.02877
87 0.03759 0.327 0.02073 0.02415 0.02784
88 0.06511 0.580 0.03671 0.04159 0.04683
89 0.06727 0.650 0.03788 0.04254 0.04802
90 0.04313 0.442 0.02297 0.02768 0.03455
91 0.06640 0.634 0.03650 0.04282 0.05114
92 0.07959 0.772 0.04421 0.04962 0.05690
93 0.04190 0.383 0.02383 0.02521 0.03051
94 0.05925 0.637 0.03341 0.03794 0.04398
95 0.03716 0.307 0.02062 0.02321 0.02764
96 0.03272 0.283 0.01813 0.01909 0.02571
97 0.03381 0.307 0.01806 0.02024 0.02809
98 0.03886 0.342 0.02135 0.02174 0.03088
99 0.04689 0.422 0.02542 0.02630 0.03908
100 0.06734 0.659 0.03611 0.03963 0.05783
101 0.09178 0.891 0.05358 0.04791 0.06196
102 0.06170 0.584 0.03223 0.03672 0.05174
103 0.09419 0.930 0.05551 0.05005 0.06023
104 0.01131 0.107 0.00522 0.00659 0.01009
105 0.01030 0.094 0.00469 0.00582 0.00871
106 0.01346 0.126 0.00660 0.00818 0.01059
107 0.01064 0.097 0.00522 0.00632 0.00928
108 0.01450 0.137 0.00633 0.00788 0.01267
109 0.01024 0.093 0.00455 0.00576 0.00993
110 0.03044 0.275 0.01771 0.01815 0.02084
111 0.02286 0.207 0.01192 0.01439 0.01852
112 0.01761 0.155 0.00952 0.01058 0.01307
113 0.02378 0.210 0.01277 0.01483 0.01767
114 0.01680 0.149 0.00861 0.01017 0.01301
115 0.02105 0.209 0.01107 0.01284 0.01604
116 0.01843 0.235 0.00796 0.00832 0.01271
117 0.01458 0.148 0.00606 0.00747 0.01312
118 0.01725 0.175 0.00757 0.00971 0.01652
119 0.01279 0.129 0.00617 0.00744 0.01151
120 0.01299 0.124 0.00679 0.00631 0.01075
121 0.02008 0.221 0.00849 0.01117 0.01734
122 0.01169 0.117 0.00534 0.00630 0.01104
123 0.04479 0.441 0.02587 0.02567 0.03220
124 0.02503 0.231 0.01372 0.01580 0.01931
125 0.02343 0.224 0.01289 0.01420 0.01720
126 0.02362 0.233 0.01235 0.01495 0.01944
127 0.02791 0.246 0.01484 0.01805 0.02259
128 0.02857 0.257 0.01547 0.01859 0.02301
129 0.01033 0.098 0.00538 0.00570 0.00811
130 0.01022 0.090 0.00476 0.00588 0.00903
131 0.01412 0.125 0.00703 0.00820 0.01194
132 0.01516 0.138 0.00721 0.00815 0.01310
133 0.01201 0.106 0.00633 0.00701 0.00915
134 0.01043 0.099 0.00490 0.00621 0.00903
135 0.04932 0.441 0.02683 0.03112 0.03651
136 0.04128 0.379 0.02229 0.02592 0.03316
137 0.04879 0.431 0.02385 0.02973 0.04370
138 0.05279 0.476 0.02896 0.03347 0.04134
139 0.05643 0.517 0.03070 0.03530 0.04451
140 0.03026 0.267 0.01514 0.01812 0.02770
141 0.03273 0.281 0.01713 0.01964 0.02824
142 0.06725 0.571 0.04016 0.04003 0.04464
143 0.03527 0.297 0.02055 0.02076 0.02530
144 0.01997 0.180 0.01117 0.01177 0.01506
145 0.02662 0.228 0.01475 0.01558 0.02006
146 0.02536 0.225 0.01379 0.01478 0.01909
147 0.08143 0.821 0.03804 0.05426 0.08808
148 0.06050 0.618 0.02865 0.04101 0.06359
149 0.07118 0.722 0.03474 0.04580 0.06824
150 0.07170 0.833 0.03515 0.04265 0.06460
151 0.05830 0.784 0.02699 0.03714 0.06259
152 0.11908 1.302 0.05647 0.07940 0.13778
153 0.08684 1.018 0.04284 0.05556 0.08318
154 0.02534 0.241 0.01340 0.01399 0.02056
155 0.02682 0.236 0.01484 0.01405 0.02018
156 0.03087 0.276 0.01659 0.01804 0.02402
157 0.02293 0.223 0.01205 0.01289 0.01771
158 0.04912 0.438 0.02610 0.02161 0.02916
159 0.02852 0.266 0.01500 0.01581 0.02157
160 0.03235 0.339 0.01360 0.01650 0.03105
161 0.04009 0.406 0.01579 0.01994 0.04114
162 0.03273 0.325 0.01644 0.01722 0.02931
163 0.03658 0.369 0.01864 0.01940 0.03091
164 0.01756 0.155 0.00967 0.01033 0.01363
165 0.02814 0.272 0.01579 0.01553 0.02073
166 0.02448 0.217 0.01410 0.01426 0.01621
167 0.01242 0.116 0.00696 0.00747 0.00882
168 0.02030 0.197 0.01186 0.01230 0.01367
169 0.02177 0.189 0.01279 0.01272 0.01439
170 0.02018 0.212 0.01176 0.01191 0.01344
171 0.01897 0.181 0.01084 0.01121 0.01255
172 0.01358 0.129 0.00664 0.00786 0.01140
173 0.01484 0.133 0.00754 0.00950 0.01285
174 0.01472 0.133 0.00748 0.00905 0.01148
175 0.01657 0.145 0.00881 0.01062 0.01318
176 0.01503 0.137 0.00812 0.00933 0.01133
177 0.01725 0.155 0.00874 0.01021 0.01331
178 0.01469 0.132 0.00728 0.00886 0.01230
179 0.01574 0.142 0.00839 0.00956 0.01309
180 0.01450 0.131 0.00725 0.00876 0.01263
181 0.02551 0.237 0.01321 0.01574 0.02148
182 0.01831 0.163 0.00950 0.01103 0.01559
183 0.02145 0.198 0.01155 0.01341 0.01666
184 0.01909 0.171 0.00864 0.01223 0.01949
185 0.01795 0.163 0.00810 0.01144 0.01756
186 0.01564 0.136 0.00667 0.00990 0.01691
187 0.01660 0.154 0.00820 0.00972 0.01491
188 0.01300 0.117 0.00631 0.00789 0.01144
189 0.01185 0.106 0.00557 0.00721 0.01095
190 0.02574 0.255 0.01454 0.01582 0.01758
191 0.04087 0.405 0.02336 0.02498 0.02745
192 0.02751 0.263 0.01604 0.01657 0.01879
193 0.02308 0.256 0.01268 0.01365 0.01667
194 0.02296 0.241 0.01265 0.01321 0.01588
195 0.01884 0.190 0.01026 0.01161 0.01373
Shimmer:DDA
1 0.06545
2 0.09403
3 0.08270
4 0.08771
5 0.10470
6 0.06985
7 0.02337
8 0.02487
9 0.03218
10 0.04324
11 0.03237
12 0.04272
13 0.01968
14 0.02184
15 0.03191
16 0.02316
17 0.02908
18 0.04322
19 0.07413
20 0.05164
21 0.05000
22 0.06062
23 0.06685
24 0.06562
25 0.02214
26 0.05197
27 0.02666
28 0.02650
29 0.02307
30 0.02380
31 0.01689
32 0.01513
33 0.01919
34 0.01407
35 0.01403
36 0.01758
37 0.03463
38 0.02814
39 0.02177
40 0.02488
41 0.02321
42 0.02226
43 0.03104
44 0.03017
45 0.02330
46 0.02542
47 0.02719
48 0.01841
49 0.02566
50 0.02789
51 0.03724
52 0.03429
53 0.03969
54 0.04188
55 0.04450
56 0.05368
57 0.06097
58 0.03568
59 0.04183
60 0.05414
61 0.02925
62 0.03039
63 0.02602
64 0.02647
65 0.02308
66 0.02827
67 0.05490
68 0.04914
69 0.09455
70 0.10070
71 0.05605
72 0.08247
73 0.02921
74 0.04120
75 0.04295
76 0.03851
77 0.07238
78 0.03852
79 0.05408
80 0.05320
81 0.06799
82 0.05377
83 0.04114
84 0.03831
85 0.08037
86 0.06321
87 0.06219
88 0.11012
89 0.11363
90 0.06892
91 0.10949
92 0.13262
93 0.07150
94 0.10024
95 0.06185
96 0.05439
97 0.05417
98 0.06406
99 0.07625
100 0.10833
101 0.16074
102 0.09669
103 0.16654
104 0.01567
105 0.01406
106 0.01979
107 0.01567
108 0.01898
109 0.01364
110 0.05312
111 0.03576
112 0.02855
113 0.03831
114 0.02583
115 0.03320
116 0.02389
117 0.01818
118 0.02270
119 0.01851
120 0.02038
121 0.02548
122 0.01603
123 0.07761
124 0.04115
125 0.03867
126 0.03706
127 0.04451
128 0.04641
129 0.01614
130 0.01428
131 0.02110
132 0.02164
133 0.01898
134 0.01471
135 0.08050
136 0.06688
137 0.07154
138 0.08689
139 0.09211
140 0.04543
141 0.05139
142 0.12047
143 0.06165
144 0.03350
145 0.04426
146 0.04137
147 0.11411
148 0.08595
149 0.10422
150 0.10546
151 0.08096
152 0.16942
153 0.12851
154 0.04019
155 0.04451
156 0.04977
157 0.03615
158 0.07830
159 0.04499
160 0.04079
161 0.04736
162 0.04933
163 0.05592
164 0.02902
165 0.04736
166 0.04231
167 0.02089
168 0.03557
169 0.03836
170 0.03529
171 0.03253
172 0.01992
173 0.02261
174 0.02245
175 0.02643
176 0.02436
177 0.02623
178 0.02184
179 0.02518
180 0.02175
181 0.03964
182 0.02849
183 0.03464
184 0.02592
185 0.02429
186 0.02001
187 0.02460
188 0.01892
189 0.01672
190 0.04363
191 0.07008
192 0.04812
193 0.03804
194 0.03794
195 0.03078
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Shimmer(dB)` `Shimmer:APQ3` `Shimmer:APQ5`
0.00092 0.01336 15.54880 0.19839
`MDVP:APQ` `Shimmer:DDA`
0.22673 -4.84219
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.0039018 -0.0004260 -0.0001264 0.0002145 0.0047699
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0009200 0.0001181 7.791 4.31e-13 ***
`MDVP:Shimmer(dB)` 0.0133599 0.0021208 6.300 2.05e-09 ***
`Shimmer:APQ3` 15.5487960 23.0285397 0.675 0.500
`Shimmer:APQ5` 0.1983926 0.0270536 7.333 6.40e-12 ***
`MDVP:APQ` 0.2267304 0.0159957 14.175 < 2e-16 ***
`Shimmer:DDA` -4.8421938 7.6767921 -0.631 0.529
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.0008758 on 189 degrees of freedom
Multiple R-squared: 0.9979, Adjusted R-squared: 0.9978
F-statistic: 1.795e+04 on 5 and 189 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,] 1.246027e-02 2.492053e-02 9.875397e-01
[2,] 5.959698e-02 1.191940e-01 9.404030e-01
[3,] 8.343530e-02 1.668706e-01 9.165647e-01
[4,] 8.218156e-02 1.643631e-01 9.178184e-01
[5,] 7.378464e-02 1.475693e-01 9.262154e-01
[6,] 5.324173e-02 1.064835e-01 9.467583e-01
[7,] 2.803917e-02 5.607834e-02 9.719608e-01
[8,] 1.514784e-02 3.029569e-02 9.848522e-01
[9,] 7.366968e-03 1.473394e-02 9.926330e-01
[10,] 3.723069e-03 7.446138e-03 9.962769e-01
[11,] 1.935561e-03 3.871121e-03 9.980644e-01
[12,] 4.116723e-02 8.233446e-02 9.588328e-01
[13,] 3.346116e-02 6.692231e-02 9.665388e-01
[14,] 9.428952e-02 1.885790e-01 9.057105e-01
[15,] 7.850943e-02 1.570189e-01 9.214906e-01
[16,] 6.772283e-02 1.354457e-01 9.322772e-01
[17,] 4.779035e-02 9.558070e-02 9.522096e-01
[18,] 3.544284e-02 7.088567e-02 9.645572e-01
[19,] 2.391234e-02 4.782468e-02 9.760877e-01
[20,] 1.860217e-02 3.720433e-02 9.813978e-01
[21,] 1.208512e-02 2.417024e-02 9.879149e-01
[22,] 7.759636e-03 1.551927e-02 9.922404e-01
[23,] 4.963352e-03 9.926704e-03 9.950366e-01
[24,] 3.105465e-03 6.210931e-03 9.968945e-01
[25,] 1.922740e-03 3.845480e-03 9.980773e-01
[26,] 1.178392e-03 2.356785e-03 9.988216e-01
[27,] 7.002584e-04 1.400517e-03 9.992997e-01
[28,] 4.080879e-04 8.161758e-04 9.995919e-01
[29,] 2.336815e-04 4.673630e-04 9.997663e-01
[30,] 1.281611e-04 2.563222e-04 9.998718e-01
[31,] 7.075319e-05 1.415064e-04 9.999292e-01
[32,] 3.803942e-05 7.607883e-05 9.999620e-01
[33,] 2.383347e-05 4.766694e-05 9.999762e-01
[34,] 1.337651e-05 2.675302e-05 9.999866e-01
[35,] 8.123598e-06 1.624720e-05 9.999919e-01
[36,] 4.282461e-06 8.564923e-06 9.999957e-01
[37,] 2.406618e-06 4.813236e-06 9.999976e-01
[38,] 1.218895e-06 2.437790e-06 9.999988e-01
[39,] 6.335694e-07 1.267139e-06 9.999994e-01
[40,] 3.571680e-07 7.143360e-07 9.999996e-01
[41,] 1.824997e-07 3.649993e-07 9.999998e-01
[42,] 9.695499e-08 1.939100e-07 9.999999e-01
[43,] 7.239550e-08 1.447910e-07 9.999999e-01
[44,] 4.155335e-08 8.310670e-08 1.000000e+00
[45,] 3.073459e-08 6.146919e-08 1.000000e+00
[46,] 1.893518e-08 3.787036e-08 1.000000e+00
[47,] 8.951429e-09 1.790286e-08 1.000000e+00
[48,] 1.295365e-08 2.590731e-08 1.000000e+00
[49,] 6.819886e-09 1.363977e-08 1.000000e+00
[50,] 6.872639e-09 1.374528e-08 1.000000e+00
[51,] 6.007571e-09 1.201514e-08 1.000000e+00
[52,] 2.479805e-07 4.959610e-07 9.999998e-01
[53,] 1.349572e-07 2.699145e-07 9.999999e-01
[54,] 6.949317e-08 1.389863e-07 9.999999e-01
[55,] 3.698403e-08 7.396806e-08 1.000000e+00
[56,] 1.874625e-08 3.749250e-08 1.000000e+00
[57,] 9.204924e-09 1.840985e-08 1.000000e+00
[58,] 4.597010e-09 9.194019e-09 1.000000e+00
[59,] 3.601590e-09 7.203180e-09 1.000000e+00
[60,] 1.728147e-09 3.456293e-09 1.000000e+00
[61,] 2.542404e-05 5.084809e-05 9.999746e-01
[62,] 4.363544e-05 8.727087e-05 9.999564e-01
[63,] 5.866330e-05 1.173266e-04 9.999413e-01
[64,] 5.136231e-05 1.027246e-04 9.999486e-01
[65,] 4.728850e-05 9.457700e-05 9.999527e-01
[66,] 3.997255e-05 7.994511e-05 9.999600e-01
[67,] 4.270928e-05 8.541857e-05 9.999573e-01
[68,] 6.061729e-05 1.212346e-04 9.999394e-01
[69,] 6.634243e-05 1.326849e-04 9.999337e-01
[70,] 6.451614e-05 1.290323e-04 9.999355e-01
[71,] 4.424285e-05 8.848569e-05 9.999558e-01
[72,] 1.010344e-04 2.020688e-04 9.998990e-01
[73,] 6.542486e-05 1.308497e-04 9.999346e-01
[74,] 4.180724e-05 8.361449e-05 9.999582e-01
[75,] 2.692382e-05 5.384764e-05 9.999731e-01
[76,] 1.982936e-05 3.965872e-05 9.999802e-01
[77,] 1.342137e-05 2.684273e-05 9.999866e-01
[78,] 9.441472e-06 1.888294e-05 9.999906e-01
[79,] 6.113717e-06 1.222743e-05 9.999939e-01
[80,] 4.801541e-06 9.603083e-06 9.999952e-01
[81,] 5.886121e-06 1.177224e-05 9.999941e-01
[82,] 7.711175e-06 1.542235e-05 9.999923e-01
[83,] 1.131016e-05 2.262033e-05 9.999887e-01
[84,] 1.641752e-05 3.283503e-05 9.999836e-01
[85,] 1.063293e-05 2.126586e-05 9.999894e-01
[86,] 1.139863e-04 2.279725e-04 9.998860e-01
[87,] 8.268883e-05 1.653777e-04 9.999173e-01
[88,] 5.549086e-05 1.109817e-04 9.999445e-01
[89,] 3.682746e-05 7.365491e-05 9.999632e-01
[90,] 2.592506e-05 5.185012e-05 9.999741e-01
[91,] 1.797832e-05 3.595665e-05 9.999820e-01
[92,] 1.958530e-05 3.917060e-05 9.999804e-01
[93,] 5.060744e-05 1.012149e-04 9.999494e-01
[94,] 4.733674e-05 9.467347e-05 9.999527e-01
[95,] 7.550758e-05 1.510152e-04 9.999245e-01
[96,] 5.073000e-05 1.014600e-04 9.999493e-01
[97,] 3.578052e-05 7.156104e-05 9.999642e-01
[98,] 2.397266e-05 4.794531e-05 9.999760e-01
[99,] 1.553674e-05 3.107347e-05 9.999845e-01
[100,] 1.697179e-05 3.394359e-05 9.999830e-01
[101,] 1.090231e-05 2.180461e-05 9.999891e-01
[102,] 8.403109e-06 1.680622e-05 9.999916e-01
[103,] 5.301379e-06 1.060276e-05 9.999947e-01
[104,] 3.283666e-06 6.567332e-06 9.999967e-01
[105,] 2.165559e-06 4.331117e-06 9.999978e-01
[106,] 1.407186e-06 2.814371e-06 9.999986e-01
[107,] 8.422598e-07 1.684520e-06 9.999992e-01
[108,] 5.091853e-06 1.018371e-05 9.999949e-01
[109,] 6.077474e-06 1.215495e-05 9.999939e-01
[110,] 4.091363e-06 8.182726e-06 9.999959e-01
[111,] 2.645501e-06 5.291003e-06 9.999974e-01
[112,] 2.065192e-06 4.130385e-06 9.999979e-01
[113,] 6.408514e-06 1.281703e-05 9.999936e-01
[114,] 3.950666e-06 7.901333e-06 9.999960e-01
[115,] 8.301054e-06 1.660211e-05 9.999917e-01
[116,] 5.841725e-06 1.168345e-05 9.999942e-01
[117,] 3.740750e-06 7.481500e-06 9.999963e-01
[118,] 2.550460e-06 5.100921e-06 9.999974e-01
[119,] 1.596784e-06 3.193568e-06 9.999984e-01
[120,] 1.057011e-06 2.114021e-06 9.999989e-01
[121,] 7.032543e-07 1.406509e-06 9.999993e-01
[122,] 4.131587e-07 8.263174e-07 9.999996e-01
[123,] 2.413010e-07 4.826020e-07 9.999998e-01
[124,] 1.639033e-07 3.278065e-07 9.999998e-01
[125,] 9.862809e-08 1.972562e-07 9.999999e-01
[126,] 5.525959e-08 1.105192e-07 9.999999e-01
[127,] 1.664162e-07 3.328323e-07 9.999998e-01
[128,] 1.099062e-07 2.198124e-07 9.999999e-01
[129,] 1.206772e-06 2.413544e-06 9.999988e-01
[130,] 1.198618e-06 2.397236e-06 9.999988e-01
[131,] 1.620709e-06 3.241419e-06 9.999984e-01
[132,] 1.069695e-06 2.139389e-06 9.999989e-01
[133,] 6.306100e-07 1.261220e-06 9.999994e-01
[134,] 4.135365e-07 8.270731e-07 9.999996e-01
[135,] 3.402546e-07 6.805093e-07 9.999997e-01
[136,] 3.075015e-07 6.150031e-07 9.999997e-01
[137,] 1.753512e-07 3.507024e-07 9.999998e-01
[138,] 9.916268e-08 1.983254e-07 9.999999e-01
[139,] 4.351119e-06 8.702237e-06 9.999956e-01
[140,] 5.416017e-05 1.083203e-04 9.999458e-01
[141,] 4.255462e-03 8.510925e-03 9.957445e-01
[142,] 6.676848e-03 1.335370e-02 9.933232e-01
[143,] 1.906047e-01 3.812095e-01 8.093953e-01
[144,] 7.930227e-01 4.139545e-01 2.069773e-01
[145,] 8.829013e-01 2.341975e-01 1.170987e-01
[146,] 8.805919e-01 2.388162e-01 1.194081e-01
[147,] 8.995872e-01 2.008256e-01 1.004128e-01
[148,] 8.730684e-01 2.538633e-01 1.269316e-01
[149,] 8.426419e-01 3.147162e-01 1.573581e-01
[150,] 9.999999e-01 2.488284e-07 1.244142e-07
[151,] 9.999999e-01 1.383965e-07 6.919826e-08
[152,] 1.000000e+00 8.338372e-08 4.169186e-08
[153,] 1.000000e+00 8.638555e-13 4.319278e-13
[154,] 1.000000e+00 2.730107e-12 1.365054e-12
[155,] 1.000000e+00 4.268836e-12 2.134418e-12
[156,] 1.000000e+00 1.387310e-11 6.936551e-12
[157,] 1.000000e+00 5.300839e-11 2.650419e-11
[158,] 1.000000e+00 6.302768e-11 3.151384e-11
[159,] 1.000000e+00 2.005701e-10 1.002851e-10
[160,] 1.000000e+00 3.879150e-10 1.939575e-10
[161,] 1.000000e+00 1.099407e-09 5.497033e-10
[162,] 1.000000e+00 9.450157e-10 4.725078e-10
[163,] 1.000000e+00 4.271171e-09 2.135586e-09
[164,] 1.000000e+00 1.263036e-08 6.315178e-09
[165,] 1.000000e+00 3.588638e-08 1.794319e-08
[166,] 9.999999e-01 1.091854e-07 5.459272e-08
[167,] 9.999998e-01 3.706645e-07 1.853322e-07
[168,] 9.999993e-01 1.342808e-06 6.714041e-07
[169,] 1.000000e+00 7.685727e-08 3.842864e-08
[170,] 9.999999e-01 1.222231e-07 6.111155e-08
[171,] 9.999997e-01 5.435805e-07 2.717903e-07
[172,] 9.999983e-01 3.419105e-06 1.709553e-06
[173,] 9.999898e-01 2.043068e-05 1.021534e-05
[174,] 9.999433e-01 1.133231e-04 5.666155e-05
[175,] 9.997778e-01 4.443546e-04 2.221773e-04
[176,] 9.990649e-01 1.870295e-03 9.351477e-04
[177,] 9.951917e-01 9.616620e-03 4.808310e-03
[178,] 9.824899e-01 3.502024e-02 1.751012e-02
> postscript(file="/var/wessaorg/rcomp/tmp/1g5m31386165795.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2tvt41386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/36xjv1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/40fqj1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/50k3c1386165795.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
1.829718e-03 1.201996e-03 9.460738e-04 6.570942e-04 1.566107e-04
6 7 8 9 10
1.900992e-03 4.045102e-04 -2.388994e-04 1.505696e-05 4.079918e-04
11 12 13 14 15
-1.037465e-04 4.416352e-04 -5.392251e-04 7.750368e-05 -4.290710e-04
16 17 18 19 20
-3.078229e-04 4.497879e-05 1.389820e-03 1.587920e-03 2.354967e-03
21 22 23 24 25
1.787624e-03 -6.004192e-04 1.606797e-04 3.770427e-04 6.601321e-04
26 27 28 29 30
9.792463e-04 5.555628e-04 2.373155e-04 4.745161e-04 5.119229e-04
31 32 33 34 35
-1.584137e-04 -6.194113e-05 -1.564764e-04 -1.422171e-04 -1.642237e-04
36 37 38 39 40
-7.726783e-05 -2.601811e-04 7.527521e-05 -1.536992e-04 1.799179e-04
41 42 43 44 45
-3.963955e-04 -2.236956e-04 -8.197466e-04 -6.050227e-04 -5.280536e-04
46 47 48 49 50
-4.228654e-04 -4.800017e-04 -4.990242e-04 -1.805373e-04 -4.877615e-04
51 52 53 54 55
-2.010918e-04 -3.074915e-04 -2.084977e-04 -8.900093e-05 -1.264292e-04
56 57 58 59 60
-6.864083e-04 1.248555e-04 -3.594854e-04 -2.807109e-04 -1.472944e-03
61 62 63 64 65
-1.769524e-04 -2.875711e-05 -1.603042e-04 -1.905535e-04 -8.801959e-05
66 67 68 69 70
-2.074563e-04 -9.240730e-04 -6.021979e-05 1.769609e-03 -8.335653e-04
71 72 73 74 75
-4.605273e-04 9.435112e-04 -7.801374e-04 -6.086481e-04 -8.523907e-04
76 77 78 79 80
-1.002611e-03 -8.773290e-04 -7.751551e-04 -2.004999e-04 -9.954447e-04
81 82 83 84 85
-4.130762e-05 -2.019753e-04 -2.910685e-04 -6.256311e-04 -1.203176e-04
86 87 88 89 90
1.708745e-04 7.472385e-06 -1.557917e-06 -4.310208e-04 -4.519362e-04
91 92 93 94 95
-4.395559e-04 3.704736e-04 -3.667889e-04 -1.782603e-03 1.405262e-04
96 97 98 99 100
-1.301341e-04 -1.054073e-04 2.805891e-04 2.206842e-04 -2.704208e-04
101 102 103 104 105
6.329181e-04 1.015871e-03 5.651152e-04 7.784810e-05 1.521063e-04
106 107 108 109 110
3.770154e-05 -2.213357e-04 7.946596e-04 -1.613139e-05 -6.316809e-04
111 112 113 114 115
-6.419609e-05 -2.230343e-04 5.227011e-05 1.207068e-04 -2.106554e-04
116 117 118 119 120
1.749666e-03 1.031433e-03 5.334686e-04 -2.461853e-04 -1.682384e-04
121 122 123 124 125
1.429750e-03 4.372377e-05 -8.598349e-04 -5.620912e-04 -3.758830e-04
126 127 128 129 130
-3.623744e-04 -2.174284e-04 -5.023188e-04 -3.683939e-04 1.795532e-05
131 132 133 134 135
5.829910e-05 4.875347e-04 -3.104924e-04 -5.244100e-05 6.788134e-04
136 137 138 139 140
-1.008334e-04 1.877341e-03 -5.742891e-05 1.743700e-04 4.697143e-04
141 142 143 144 145
2.460563e-04 -4.619274e-04 -4.792873e-04 -5.709653e-04 -1.445025e-05
146 147 148 149 150
7.717591e-05 -1.270915e-04 -5.166953e-04 5.439966e-04 6.606023e-04
151 152 153 154 155
-2.291467e-03 -3.901842e-03 -1.402544e-03 1.709641e-05 1.661607e-04
156 157 158 159 160
2.790819e-04 1.403989e-04 4.769870e-03 6.374846e-04 2.637020e-03
161 162 163 164 165
4.273080e-03 6.494813e-04 8.190893e-04 -4.068935e-04 -3.842205e-04
166 167 168 169 170
-2.082621e-04 -5.976817e-04 -9.634036e-04 -5.837551e-04 -9.552150e-04
171 172 173 174 175
-4.699573e-04 4.989324e-06 -4.109880e-04 2.708604e-05 -3.881175e-04
176 177 178 179 180
-4.405330e-04 3.301269e-04 1.824250e-05 -4.695841e-04 -1.827130e-04
181 182 183 184 185
-2.417852e-05 -2.701061e-04 -4.080167e-04 2.082218e-04 2.729944e-04
186 187 188 189 190
2.867985e-04 -6.849565e-05 -1.408046e-04 -4.455870e-05 -5.258240e-04
191 192 193 194 195
-5.192614e-04 -8.675848e-04 -7.094501e-04 -3.803981e-04 -5.226324e-04
> postscript(file="/var/wessaorg/rcomp/tmp/6tx8d1386165795.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 1.829718e-03 NA
1 1.201996e-03 1.829718e-03
2 9.460738e-04 1.201996e-03
3 6.570942e-04 9.460738e-04
4 1.566107e-04 6.570942e-04
5 1.900992e-03 1.566107e-04
6 4.045102e-04 1.900992e-03
7 -2.388994e-04 4.045102e-04
8 1.505696e-05 -2.388994e-04
9 4.079918e-04 1.505696e-05
10 -1.037465e-04 4.079918e-04
11 4.416352e-04 -1.037465e-04
12 -5.392251e-04 4.416352e-04
13 7.750368e-05 -5.392251e-04
14 -4.290710e-04 7.750368e-05
15 -3.078229e-04 -4.290710e-04
16 4.497879e-05 -3.078229e-04
17 1.389820e-03 4.497879e-05
18 1.587920e-03 1.389820e-03
19 2.354967e-03 1.587920e-03
20 1.787624e-03 2.354967e-03
21 -6.004192e-04 1.787624e-03
22 1.606797e-04 -6.004192e-04
23 3.770427e-04 1.606797e-04
24 6.601321e-04 3.770427e-04
25 9.792463e-04 6.601321e-04
26 5.555628e-04 9.792463e-04
27 2.373155e-04 5.555628e-04
28 4.745161e-04 2.373155e-04
29 5.119229e-04 4.745161e-04
30 -1.584137e-04 5.119229e-04
31 -6.194113e-05 -1.584137e-04
32 -1.564764e-04 -6.194113e-05
33 -1.422171e-04 -1.564764e-04
34 -1.642237e-04 -1.422171e-04
35 -7.726783e-05 -1.642237e-04
36 -2.601811e-04 -7.726783e-05
37 7.527521e-05 -2.601811e-04
38 -1.536992e-04 7.527521e-05
39 1.799179e-04 -1.536992e-04
40 -3.963955e-04 1.799179e-04
41 -2.236956e-04 -3.963955e-04
42 -8.197466e-04 -2.236956e-04
43 -6.050227e-04 -8.197466e-04
44 -5.280536e-04 -6.050227e-04
45 -4.228654e-04 -5.280536e-04
46 -4.800017e-04 -4.228654e-04
47 -4.990242e-04 -4.800017e-04
48 -1.805373e-04 -4.990242e-04
49 -4.877615e-04 -1.805373e-04
50 -2.010918e-04 -4.877615e-04
51 -3.074915e-04 -2.010918e-04
52 -2.084977e-04 -3.074915e-04
53 -8.900093e-05 -2.084977e-04
54 -1.264292e-04 -8.900093e-05
55 -6.864083e-04 -1.264292e-04
56 1.248555e-04 -6.864083e-04
57 -3.594854e-04 1.248555e-04
58 -2.807109e-04 -3.594854e-04
59 -1.472944e-03 -2.807109e-04
60 -1.769524e-04 -1.472944e-03
61 -2.875711e-05 -1.769524e-04
62 -1.603042e-04 -2.875711e-05
63 -1.905535e-04 -1.603042e-04
64 -8.801959e-05 -1.905535e-04
65 -2.074563e-04 -8.801959e-05
66 -9.240730e-04 -2.074563e-04
67 -6.021979e-05 -9.240730e-04
68 1.769609e-03 -6.021979e-05
69 -8.335653e-04 1.769609e-03
70 -4.605273e-04 -8.335653e-04
71 9.435112e-04 -4.605273e-04
72 -7.801374e-04 9.435112e-04
73 -6.086481e-04 -7.801374e-04
74 -8.523907e-04 -6.086481e-04
75 -1.002611e-03 -8.523907e-04
76 -8.773290e-04 -1.002611e-03
77 -7.751551e-04 -8.773290e-04
78 -2.004999e-04 -7.751551e-04
79 -9.954447e-04 -2.004999e-04
80 -4.130762e-05 -9.954447e-04
81 -2.019753e-04 -4.130762e-05
82 -2.910685e-04 -2.019753e-04
83 -6.256311e-04 -2.910685e-04
84 -1.203176e-04 -6.256311e-04
85 1.708745e-04 -1.203176e-04
86 7.472385e-06 1.708745e-04
87 -1.557917e-06 7.472385e-06
88 -4.310208e-04 -1.557917e-06
89 -4.519362e-04 -4.310208e-04
90 -4.395559e-04 -4.519362e-04
91 3.704736e-04 -4.395559e-04
92 -3.667889e-04 3.704736e-04
93 -1.782603e-03 -3.667889e-04
94 1.405262e-04 -1.782603e-03
95 -1.301341e-04 1.405262e-04
96 -1.054073e-04 -1.301341e-04
97 2.805891e-04 -1.054073e-04
98 2.206842e-04 2.805891e-04
99 -2.704208e-04 2.206842e-04
100 6.329181e-04 -2.704208e-04
101 1.015871e-03 6.329181e-04
102 5.651152e-04 1.015871e-03
103 7.784810e-05 5.651152e-04
104 1.521063e-04 7.784810e-05
105 3.770154e-05 1.521063e-04
106 -2.213357e-04 3.770154e-05
107 7.946596e-04 -2.213357e-04
108 -1.613139e-05 7.946596e-04
109 -6.316809e-04 -1.613139e-05
110 -6.419609e-05 -6.316809e-04
111 -2.230343e-04 -6.419609e-05
112 5.227011e-05 -2.230343e-04
113 1.207068e-04 5.227011e-05
114 -2.106554e-04 1.207068e-04
115 1.749666e-03 -2.106554e-04
116 1.031433e-03 1.749666e-03
117 5.334686e-04 1.031433e-03
118 -2.461853e-04 5.334686e-04
119 -1.682384e-04 -2.461853e-04
120 1.429750e-03 -1.682384e-04
121 4.372377e-05 1.429750e-03
122 -8.598349e-04 4.372377e-05
123 -5.620912e-04 -8.598349e-04
124 -3.758830e-04 -5.620912e-04
125 -3.623744e-04 -3.758830e-04
126 -2.174284e-04 -3.623744e-04
127 -5.023188e-04 -2.174284e-04
128 -3.683939e-04 -5.023188e-04
129 1.795532e-05 -3.683939e-04
130 5.829910e-05 1.795532e-05
131 4.875347e-04 5.829910e-05
132 -3.104924e-04 4.875347e-04
133 -5.244100e-05 -3.104924e-04
134 6.788134e-04 -5.244100e-05
135 -1.008334e-04 6.788134e-04
136 1.877341e-03 -1.008334e-04
137 -5.742891e-05 1.877341e-03
138 1.743700e-04 -5.742891e-05
139 4.697143e-04 1.743700e-04
140 2.460563e-04 4.697143e-04
141 -4.619274e-04 2.460563e-04
142 -4.792873e-04 -4.619274e-04
143 -5.709653e-04 -4.792873e-04
144 -1.445025e-05 -5.709653e-04
145 7.717591e-05 -1.445025e-05
146 -1.270915e-04 7.717591e-05
147 -5.166953e-04 -1.270915e-04
148 5.439966e-04 -5.166953e-04
149 6.606023e-04 5.439966e-04
150 -2.291467e-03 6.606023e-04
151 -3.901842e-03 -2.291467e-03
152 -1.402544e-03 -3.901842e-03
153 1.709641e-05 -1.402544e-03
154 1.661607e-04 1.709641e-05
155 2.790819e-04 1.661607e-04
156 1.403989e-04 2.790819e-04
157 4.769870e-03 1.403989e-04
158 6.374846e-04 4.769870e-03
159 2.637020e-03 6.374846e-04
160 4.273080e-03 2.637020e-03
161 6.494813e-04 4.273080e-03
162 8.190893e-04 6.494813e-04
163 -4.068935e-04 8.190893e-04
164 -3.842205e-04 -4.068935e-04
165 -2.082621e-04 -3.842205e-04
166 -5.976817e-04 -2.082621e-04
167 -9.634036e-04 -5.976817e-04
168 -5.837551e-04 -9.634036e-04
169 -9.552150e-04 -5.837551e-04
170 -4.699573e-04 -9.552150e-04
171 4.989324e-06 -4.699573e-04
172 -4.109880e-04 4.989324e-06
173 2.708604e-05 -4.109880e-04
174 -3.881175e-04 2.708604e-05
175 -4.405330e-04 -3.881175e-04
176 3.301269e-04 -4.405330e-04
177 1.824250e-05 3.301269e-04
178 -4.695841e-04 1.824250e-05
179 -1.827130e-04 -4.695841e-04
180 -2.417852e-05 -1.827130e-04
181 -2.701061e-04 -2.417852e-05
182 -4.080167e-04 -2.701061e-04
183 2.082218e-04 -4.080167e-04
184 2.729944e-04 2.082218e-04
185 2.867985e-04 2.729944e-04
186 -6.849565e-05 2.867985e-04
187 -1.408046e-04 -6.849565e-05
188 -4.455870e-05 -1.408046e-04
189 -5.258240e-04 -4.455870e-05
190 -5.192614e-04 -5.258240e-04
191 -8.675848e-04 -5.192614e-04
192 -7.094501e-04 -8.675848e-04
193 -3.803981e-04 -7.094501e-04
194 -5.226324e-04 -3.803981e-04
195 NA -5.226324e-04
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1.201996e-03 1.829718e-03
[2,] 9.460738e-04 1.201996e-03
[3,] 6.570942e-04 9.460738e-04
[4,] 1.566107e-04 6.570942e-04
[5,] 1.900992e-03 1.566107e-04
[6,] 4.045102e-04 1.900992e-03
[7,] -2.388994e-04 4.045102e-04
[8,] 1.505696e-05 -2.388994e-04
[9,] 4.079918e-04 1.505696e-05
[10,] -1.037465e-04 4.079918e-04
[11,] 4.416352e-04 -1.037465e-04
[12,] -5.392251e-04 4.416352e-04
[13,] 7.750368e-05 -5.392251e-04
[14,] -4.290710e-04 7.750368e-05
[15,] -3.078229e-04 -4.290710e-04
[16,] 4.497879e-05 -3.078229e-04
[17,] 1.389820e-03 4.497879e-05
[18,] 1.587920e-03 1.389820e-03
[19,] 2.354967e-03 1.587920e-03
[20,] 1.787624e-03 2.354967e-03
[21,] -6.004192e-04 1.787624e-03
[22,] 1.606797e-04 -6.004192e-04
[23,] 3.770427e-04 1.606797e-04
[24,] 6.601321e-04 3.770427e-04
[25,] 9.792463e-04 6.601321e-04
[26,] 5.555628e-04 9.792463e-04
[27,] 2.373155e-04 5.555628e-04
[28,] 4.745161e-04 2.373155e-04
[29,] 5.119229e-04 4.745161e-04
[30,] -1.584137e-04 5.119229e-04
[31,] -6.194113e-05 -1.584137e-04
[32,] -1.564764e-04 -6.194113e-05
[33,] -1.422171e-04 -1.564764e-04
[34,] -1.642237e-04 -1.422171e-04
[35,] -7.726783e-05 -1.642237e-04
[36,] -2.601811e-04 -7.726783e-05
[37,] 7.527521e-05 -2.601811e-04
[38,] -1.536992e-04 7.527521e-05
[39,] 1.799179e-04 -1.536992e-04
[40,] -3.963955e-04 1.799179e-04
[41,] -2.236956e-04 -3.963955e-04
[42,] -8.197466e-04 -2.236956e-04
[43,] -6.050227e-04 -8.197466e-04
[44,] -5.280536e-04 -6.050227e-04
[45,] -4.228654e-04 -5.280536e-04
[46,] -4.800017e-04 -4.228654e-04
[47,] -4.990242e-04 -4.800017e-04
[48,] -1.805373e-04 -4.990242e-04
[49,] -4.877615e-04 -1.805373e-04
[50,] -2.010918e-04 -4.877615e-04
[51,] -3.074915e-04 -2.010918e-04
[52,] -2.084977e-04 -3.074915e-04
[53,] -8.900093e-05 -2.084977e-04
[54,] -1.264292e-04 -8.900093e-05
[55,] -6.864083e-04 -1.264292e-04
[56,] 1.248555e-04 -6.864083e-04
[57,] -3.594854e-04 1.248555e-04
[58,] -2.807109e-04 -3.594854e-04
[59,] -1.472944e-03 -2.807109e-04
[60,] -1.769524e-04 -1.472944e-03
[61,] -2.875711e-05 -1.769524e-04
[62,] -1.603042e-04 -2.875711e-05
[63,] -1.905535e-04 -1.603042e-04
[64,] -8.801959e-05 -1.905535e-04
[65,] -2.074563e-04 -8.801959e-05
[66,] -9.240730e-04 -2.074563e-04
[67,] -6.021979e-05 -9.240730e-04
[68,] 1.769609e-03 -6.021979e-05
[69,] -8.335653e-04 1.769609e-03
[70,] -4.605273e-04 -8.335653e-04
[71,] 9.435112e-04 -4.605273e-04
[72,] -7.801374e-04 9.435112e-04
[73,] -6.086481e-04 -7.801374e-04
[74,] -8.523907e-04 -6.086481e-04
[75,] -1.002611e-03 -8.523907e-04
[76,] -8.773290e-04 -1.002611e-03
[77,] -7.751551e-04 -8.773290e-04
[78,] -2.004999e-04 -7.751551e-04
[79,] -9.954447e-04 -2.004999e-04
[80,] -4.130762e-05 -9.954447e-04
[81,] -2.019753e-04 -4.130762e-05
[82,] -2.910685e-04 -2.019753e-04
[83,] -6.256311e-04 -2.910685e-04
[84,] -1.203176e-04 -6.256311e-04
[85,] 1.708745e-04 -1.203176e-04
[86,] 7.472385e-06 1.708745e-04
[87,] -1.557917e-06 7.472385e-06
[88,] -4.310208e-04 -1.557917e-06
[89,] -4.519362e-04 -4.310208e-04
[90,] -4.395559e-04 -4.519362e-04
[91,] 3.704736e-04 -4.395559e-04
[92,] -3.667889e-04 3.704736e-04
[93,] -1.782603e-03 -3.667889e-04
[94,] 1.405262e-04 -1.782603e-03
[95,] -1.301341e-04 1.405262e-04
[96,] -1.054073e-04 -1.301341e-04
[97,] 2.805891e-04 -1.054073e-04
[98,] 2.206842e-04 2.805891e-04
[99,] -2.704208e-04 2.206842e-04
[100,] 6.329181e-04 -2.704208e-04
[101,] 1.015871e-03 6.329181e-04
[102,] 5.651152e-04 1.015871e-03
[103,] 7.784810e-05 5.651152e-04
[104,] 1.521063e-04 7.784810e-05
[105,] 3.770154e-05 1.521063e-04
[106,] -2.213357e-04 3.770154e-05
[107,] 7.946596e-04 -2.213357e-04
[108,] -1.613139e-05 7.946596e-04
[109,] -6.316809e-04 -1.613139e-05
[110,] -6.419609e-05 -6.316809e-04
[111,] -2.230343e-04 -6.419609e-05
[112,] 5.227011e-05 -2.230343e-04
[113,] 1.207068e-04 5.227011e-05
[114,] -2.106554e-04 1.207068e-04
[115,] 1.749666e-03 -2.106554e-04
[116,] 1.031433e-03 1.749666e-03
[117,] 5.334686e-04 1.031433e-03
[118,] -2.461853e-04 5.334686e-04
[119,] -1.682384e-04 -2.461853e-04
[120,] 1.429750e-03 -1.682384e-04
[121,] 4.372377e-05 1.429750e-03
[122,] -8.598349e-04 4.372377e-05
[123,] -5.620912e-04 -8.598349e-04
[124,] -3.758830e-04 -5.620912e-04
[125,] -3.623744e-04 -3.758830e-04
[126,] -2.174284e-04 -3.623744e-04
[127,] -5.023188e-04 -2.174284e-04
[128,] -3.683939e-04 -5.023188e-04
[129,] 1.795532e-05 -3.683939e-04
[130,] 5.829910e-05 1.795532e-05
[131,] 4.875347e-04 5.829910e-05
[132,] -3.104924e-04 4.875347e-04
[133,] -5.244100e-05 -3.104924e-04
[134,] 6.788134e-04 -5.244100e-05
[135,] -1.008334e-04 6.788134e-04
[136,] 1.877341e-03 -1.008334e-04
[137,] -5.742891e-05 1.877341e-03
[138,] 1.743700e-04 -5.742891e-05
[139,] 4.697143e-04 1.743700e-04
[140,] 2.460563e-04 4.697143e-04
[141,] -4.619274e-04 2.460563e-04
[142,] -4.792873e-04 -4.619274e-04
[143,] -5.709653e-04 -4.792873e-04
[144,] -1.445025e-05 -5.709653e-04
[145,] 7.717591e-05 -1.445025e-05
[146,] -1.270915e-04 7.717591e-05
[147,] -5.166953e-04 -1.270915e-04
[148,] 5.439966e-04 -5.166953e-04
[149,] 6.606023e-04 5.439966e-04
[150,] -2.291467e-03 6.606023e-04
[151,] -3.901842e-03 -2.291467e-03
[152,] -1.402544e-03 -3.901842e-03
[153,] 1.709641e-05 -1.402544e-03
[154,] 1.661607e-04 1.709641e-05
[155,] 2.790819e-04 1.661607e-04
[156,] 1.403989e-04 2.790819e-04
[157,] 4.769870e-03 1.403989e-04
[158,] 6.374846e-04 4.769870e-03
[159,] 2.637020e-03 6.374846e-04
[160,] 4.273080e-03 2.637020e-03
[161,] 6.494813e-04 4.273080e-03
[162,] 8.190893e-04 6.494813e-04
[163,] -4.068935e-04 8.190893e-04
[164,] -3.842205e-04 -4.068935e-04
[165,] -2.082621e-04 -3.842205e-04
[166,] -5.976817e-04 -2.082621e-04
[167,] -9.634036e-04 -5.976817e-04
[168,] -5.837551e-04 -9.634036e-04
[169,] -9.552150e-04 -5.837551e-04
[170,] -4.699573e-04 -9.552150e-04
[171,] 4.989324e-06 -4.699573e-04
[172,] -4.109880e-04 4.989324e-06
[173,] 2.708604e-05 -4.109880e-04
[174,] -3.881175e-04 2.708604e-05
[175,] -4.405330e-04 -3.881175e-04
[176,] 3.301269e-04 -4.405330e-04
[177,] 1.824250e-05 3.301269e-04
[178,] -4.695841e-04 1.824250e-05
[179,] -1.827130e-04 -4.695841e-04
[180,] -2.417852e-05 -1.827130e-04
[181,] -2.701061e-04 -2.417852e-05
[182,] -4.080167e-04 -2.701061e-04
[183,] 2.082218e-04 -4.080167e-04
[184,] 2.729944e-04 2.082218e-04
[185,] 2.867985e-04 2.729944e-04
[186,] -6.849565e-05 2.867985e-04
[187,] -1.408046e-04 -6.849565e-05
[188,] -4.455870e-05 -1.408046e-04
[189,] -5.258240e-04 -4.455870e-05
[190,] -5.192614e-04 -5.258240e-04
[191,] -8.675848e-04 -5.192614e-04
[192,] -7.094501e-04 -8.675848e-04
[193,] -3.803981e-04 -7.094501e-04
[194,] -5.226324e-04 -3.803981e-04
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1.201996e-03 1.829718e-03
2 9.460738e-04 1.201996e-03
3 6.570942e-04 9.460738e-04
4 1.566107e-04 6.570942e-04
5 1.900992e-03 1.566107e-04
6 4.045102e-04 1.900992e-03
7 -2.388994e-04 4.045102e-04
8 1.505696e-05 -2.388994e-04
9 4.079918e-04 1.505696e-05
10 -1.037465e-04 4.079918e-04
11 4.416352e-04 -1.037465e-04
12 -5.392251e-04 4.416352e-04
13 7.750368e-05 -5.392251e-04
14 -4.290710e-04 7.750368e-05
15 -3.078229e-04 -4.290710e-04
16 4.497879e-05 -3.078229e-04
17 1.389820e-03 4.497879e-05
18 1.587920e-03 1.389820e-03
19 2.354967e-03 1.587920e-03
20 1.787624e-03 2.354967e-03
21 -6.004192e-04 1.787624e-03
22 1.606797e-04 -6.004192e-04
23 3.770427e-04 1.606797e-04
24 6.601321e-04 3.770427e-04
25 9.792463e-04 6.601321e-04
26 5.555628e-04 9.792463e-04
27 2.373155e-04 5.555628e-04
28 4.745161e-04 2.373155e-04
29 5.119229e-04 4.745161e-04
30 -1.584137e-04 5.119229e-04
31 -6.194113e-05 -1.584137e-04
32 -1.564764e-04 -6.194113e-05
33 -1.422171e-04 -1.564764e-04
34 -1.642237e-04 -1.422171e-04
35 -7.726783e-05 -1.642237e-04
36 -2.601811e-04 -7.726783e-05
37 7.527521e-05 -2.601811e-04
38 -1.536992e-04 7.527521e-05
39 1.799179e-04 -1.536992e-04
40 -3.963955e-04 1.799179e-04
41 -2.236956e-04 -3.963955e-04
42 -8.197466e-04 -2.236956e-04
43 -6.050227e-04 -8.197466e-04
44 -5.280536e-04 -6.050227e-04
45 -4.228654e-04 -5.280536e-04
46 -4.800017e-04 -4.228654e-04
47 -4.990242e-04 -4.800017e-04
48 -1.805373e-04 -4.990242e-04
49 -4.877615e-04 -1.805373e-04
50 -2.010918e-04 -4.877615e-04
51 -3.074915e-04 -2.010918e-04
52 -2.084977e-04 -3.074915e-04
53 -8.900093e-05 -2.084977e-04
54 -1.264292e-04 -8.900093e-05
55 -6.864083e-04 -1.264292e-04
56 1.248555e-04 -6.864083e-04
57 -3.594854e-04 1.248555e-04
58 -2.807109e-04 -3.594854e-04
59 -1.472944e-03 -2.807109e-04
60 -1.769524e-04 -1.472944e-03
61 -2.875711e-05 -1.769524e-04
62 -1.603042e-04 -2.875711e-05
63 -1.905535e-04 -1.603042e-04
64 -8.801959e-05 -1.905535e-04
65 -2.074563e-04 -8.801959e-05
66 -9.240730e-04 -2.074563e-04
67 -6.021979e-05 -9.240730e-04
68 1.769609e-03 -6.021979e-05
69 -8.335653e-04 1.769609e-03
70 -4.605273e-04 -8.335653e-04
71 9.435112e-04 -4.605273e-04
72 -7.801374e-04 9.435112e-04
73 -6.086481e-04 -7.801374e-04
74 -8.523907e-04 -6.086481e-04
75 -1.002611e-03 -8.523907e-04
76 -8.773290e-04 -1.002611e-03
77 -7.751551e-04 -8.773290e-04
78 -2.004999e-04 -7.751551e-04
79 -9.954447e-04 -2.004999e-04
80 -4.130762e-05 -9.954447e-04
81 -2.019753e-04 -4.130762e-05
82 -2.910685e-04 -2.019753e-04
83 -6.256311e-04 -2.910685e-04
84 -1.203176e-04 -6.256311e-04
85 1.708745e-04 -1.203176e-04
86 7.472385e-06 1.708745e-04
87 -1.557917e-06 7.472385e-06
88 -4.310208e-04 -1.557917e-06
89 -4.519362e-04 -4.310208e-04
90 -4.395559e-04 -4.519362e-04
91 3.704736e-04 -4.395559e-04
92 -3.667889e-04 3.704736e-04
93 -1.782603e-03 -3.667889e-04
94 1.405262e-04 -1.782603e-03
95 -1.301341e-04 1.405262e-04
96 -1.054073e-04 -1.301341e-04
97 2.805891e-04 -1.054073e-04
98 2.206842e-04 2.805891e-04
99 -2.704208e-04 2.206842e-04
100 6.329181e-04 -2.704208e-04
101 1.015871e-03 6.329181e-04
102 5.651152e-04 1.015871e-03
103 7.784810e-05 5.651152e-04
104 1.521063e-04 7.784810e-05
105 3.770154e-05 1.521063e-04
106 -2.213357e-04 3.770154e-05
107 7.946596e-04 -2.213357e-04
108 -1.613139e-05 7.946596e-04
109 -6.316809e-04 -1.613139e-05
110 -6.419609e-05 -6.316809e-04
111 -2.230343e-04 -6.419609e-05
112 5.227011e-05 -2.230343e-04
113 1.207068e-04 5.227011e-05
114 -2.106554e-04 1.207068e-04
115 1.749666e-03 -2.106554e-04
116 1.031433e-03 1.749666e-03
117 5.334686e-04 1.031433e-03
118 -2.461853e-04 5.334686e-04
119 -1.682384e-04 -2.461853e-04
120 1.429750e-03 -1.682384e-04
121 4.372377e-05 1.429750e-03
122 -8.598349e-04 4.372377e-05
123 -5.620912e-04 -8.598349e-04
124 -3.758830e-04 -5.620912e-04
125 -3.623744e-04 -3.758830e-04
126 -2.174284e-04 -3.623744e-04
127 -5.023188e-04 -2.174284e-04
128 -3.683939e-04 -5.023188e-04
129 1.795532e-05 -3.683939e-04
130 5.829910e-05 1.795532e-05
131 4.875347e-04 5.829910e-05
132 -3.104924e-04 4.875347e-04
133 -5.244100e-05 -3.104924e-04
134 6.788134e-04 -5.244100e-05
135 -1.008334e-04 6.788134e-04
136 1.877341e-03 -1.008334e-04
137 -5.742891e-05 1.877341e-03
138 1.743700e-04 -5.742891e-05
139 4.697143e-04 1.743700e-04
140 2.460563e-04 4.697143e-04
141 -4.619274e-04 2.460563e-04
142 -4.792873e-04 -4.619274e-04
143 -5.709653e-04 -4.792873e-04
144 -1.445025e-05 -5.709653e-04
145 7.717591e-05 -1.445025e-05
146 -1.270915e-04 7.717591e-05
147 -5.166953e-04 -1.270915e-04
148 5.439966e-04 -5.166953e-04
149 6.606023e-04 5.439966e-04
150 -2.291467e-03 6.606023e-04
151 -3.901842e-03 -2.291467e-03
152 -1.402544e-03 -3.901842e-03
153 1.709641e-05 -1.402544e-03
154 1.661607e-04 1.709641e-05
155 2.790819e-04 1.661607e-04
156 1.403989e-04 2.790819e-04
157 4.769870e-03 1.403989e-04
158 6.374846e-04 4.769870e-03
159 2.637020e-03 6.374846e-04
160 4.273080e-03 2.637020e-03
161 6.494813e-04 4.273080e-03
162 8.190893e-04 6.494813e-04
163 -4.068935e-04 8.190893e-04
164 -3.842205e-04 -4.068935e-04
165 -2.082621e-04 -3.842205e-04
166 -5.976817e-04 -2.082621e-04
167 -9.634036e-04 -5.976817e-04
168 -5.837551e-04 -9.634036e-04
169 -9.552150e-04 -5.837551e-04
170 -4.699573e-04 -9.552150e-04
171 4.989324e-06 -4.699573e-04
172 -4.109880e-04 4.989324e-06
173 2.708604e-05 -4.109880e-04
174 -3.881175e-04 2.708604e-05
175 -4.405330e-04 -3.881175e-04
176 3.301269e-04 -4.405330e-04
177 1.824250e-05 3.301269e-04
178 -4.695841e-04 1.824250e-05
179 -1.827130e-04 -4.695841e-04
180 -2.417852e-05 -1.827130e-04
181 -2.701061e-04 -2.417852e-05
182 -4.080167e-04 -2.701061e-04
183 2.082218e-04 -4.080167e-04
184 2.729944e-04 2.082218e-04
185 2.867985e-04 2.729944e-04
186 -6.849565e-05 2.867985e-04
187 -1.408046e-04 -6.849565e-05
188 -4.455870e-05 -1.408046e-04
189 -5.258240e-04 -4.455870e-05
190 -5.192614e-04 -5.258240e-04
191 -8.675848e-04 -5.192614e-04
192 -7.094501e-04 -8.675848e-04
193 -3.803981e-04 -7.094501e-04
194 -5.226324e-04 -3.803981e-04
> 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/7f5bn1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8nimg1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/9au781386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/101gpc1386165795.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11a6ra1386165795.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/12rsqa1386165795.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/13cx5v1386165795.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/1465xo1386165795.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/15510n1386165796.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/1647401386165796.tab")
+ }
>
> try(system("convert tmp/1g5m31386165795.ps tmp/1g5m31386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tvt41386165795.ps tmp/2tvt41386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/36xjv1386165795.ps tmp/36xjv1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/40fqj1386165795.ps tmp/40fqj1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/50k3c1386165795.ps tmp/50k3c1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/6tx8d1386165795.ps tmp/6tx8d1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/7f5bn1386165795.ps tmp/7f5bn1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nimg1386165795.ps tmp/8nimg1386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/9au781386165795.ps tmp/9au781386165795.png",intern=TRUE))
character(0)
> try(system("convert tmp/101gpc1386165795.ps tmp/101gpc1386165795.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
12.438 2.326 14.783