R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
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Type '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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+ ,186.695
+ ,0.08096
+ ,0.06259
+ ,0.01872
+ ,2.958815
+ ,0.507826
+ ,1
+ ,192.818
+ ,0.16942
+ ,0.13778
+ ,0.03107
+ ,3.079221
+ ,0.625866
+ ,1
+ ,198.116
+ ,0.12851
+ ,0.08318
+ ,0.02714
+ ,3.184027
+ ,0.584164
+ ,1
+ ,121.345
+ ,0.04019
+ ,0.02056
+ ,0.00684
+ ,2.01353
+ ,0.566867
+ ,1
+ ,119.1
+ ,0.04451
+ ,0.02018
+ ,0.00692
+ ,2.45113
+ ,0.65168
+ ,1
+ ,117.87
+ ,0.04977
+ ,0.02402
+ ,0.00647
+ ,2.439597
+ ,0.6283
+ ,1
+ ,122.336
+ ,0.03615
+ ,0.01771
+ ,0.00727
+ ,2.699645
+ ,0.611679
+ ,1
+ ,117.963
+ ,0.0783
+ ,0.02916
+ ,0.01813
+ ,2.964568
+ ,0.630547
+ ,1
+ ,126.144
+ ,0.04499
+ ,0.02157
+ ,0.00975
+ ,2.8923
+ ,0.635015
+ ,1
+ ,127.93
+ ,0.04079
+ ,0.03105
+ ,0.00605
+ ,2.103014
+ ,0.654945
+ ,1
+ ,114.238
+ ,0.04736
+ ,0.04114
+ ,0.00581
+ ,2.151121
+ ,0.653139
+ ,1
+ ,115.322
+ ,0.04933
+ ,0.02931
+ ,0.00619
+ ,2.442906
+ ,0.577802
+ ,1
+ ,114.554
+ ,0.05592
+ ,0.03091
+ ,0.00651
+ ,2.408689
+ ,0.685151
+ ,1
+ ,112.15
+ ,0.02902
+ ,0.01363
+ ,0.00519
+ ,1.871871
+ ,0.557045
+ ,1
+ ,102.273
+ ,0.04736
+ ,0.02073
+ ,0.00907
+ ,2.560422
+ ,0.671378
+ ,0
+ ,236.2
+ ,0.04231
+ ,0.01621
+ ,0.00277
+ ,2.235197
+ ,0.469928
+ ,0
+ ,237.323
+ ,0.02089
+ ,0.00882
+ ,0.00303
+ ,1.852402
+ ,0.384868
+ ,0
+ ,260.105
+ ,0.03557
+ ,0.01367
+ ,0.00339
+ ,1.881767
+ ,0.440988
+ ,0
+ ,197.569
+ ,0.03836
+ ,0.01439
+ ,0.00803
+ ,2.88245
+ ,0.372222
+ ,0
+ ,240.301
+ ,0.03529
+ ,0.01344
+ ,0.00517
+ ,2.266432
+ ,0.371837
+ ,0
+ ,244.99
+ ,0.03253
+ ,0.01255
+ ,0.00451
+ ,2.095237
+ ,0.522812
+ ,0
+ ,112.547
+ ,0.01992
+ ,0.0114
+ ,0.00355
+ ,2.193412
+ ,0.413295
+ ,0
+ ,110.739
+ ,0.02261
+ ,0.01285
+ ,0.00356
+ ,1.889002
+ ,0.36909
+ ,0
+ ,113.715
+ ,0.02245
+ ,0.01148
+ ,0.00349
+ ,1.852542
+ ,0.380253
+ ,0
+ ,117.004
+ ,0.02643
+ ,0.01318
+ ,0.00353
+ ,1.872946
+ ,0.387482
+ ,0
+ ,115.38
+ ,0.02436
+ ,0.01133
+ ,0.00332
+ ,1.974857
+ ,0.405991
+ ,0
+ ,116.388
+ ,0.02623
+ ,0.01331
+ ,0.00346
+ ,2.004719
+ ,0.361232
+ ,1
+ ,151.737
+ ,0.02184
+ ,0.0123
+ ,0.00314
+ ,2.449763
+ ,0.39661
+ ,1
+ ,148.79
+ ,0.02518
+ ,0.01309
+ ,0.00309
+ ,2.251553
+ ,0.402591
+ ,1
+ ,148.143
+ ,0.02175
+ ,0.01263
+ ,0.00392
+ ,2.845109
+ ,0.398499
+ ,1
+ ,150.44
+ ,0.03964
+ ,0.02148
+ ,0.00396
+ ,2.264226
+ ,0.352396
+ ,1
+ ,148.462
+ ,0.02849
+ ,0.01559
+ ,0.00397
+ ,2.679185
+ ,0.408598
+ ,1
+ ,149.818
+ ,0.03464
+ ,0.01666
+ ,0.00336
+ ,2.209021
+ ,0.329577
+ ,0
+ ,117.226
+ ,0.02592
+ ,0.01949
+ ,0.00417
+ ,2.027228
+ ,0.603515
+ ,0
+ ,116.848
+ ,0.02429
+ ,0.01756
+ ,0.00531
+ ,2.120412
+ ,0.663842
+ ,0
+ ,116.286
+ ,0.02001
+ ,0.01691
+ ,0.00314
+ ,2.058658
+ ,0.598515
+ ,0
+ ,116.556
+ ,0.0246
+ ,0.01491
+ ,0.00496
+ ,2.161936
+ ,0.566424
+ ,0
+ ,116.342
+ ,0.01892
+ ,0.01144
+ ,0.00267
+ ,2.152083
+ ,0.528485
+ ,0
+ ,114.563
+ ,0.01672
+ ,0.01095
+ ,0.00327
+ ,1.91399
+ ,0.555303
+ ,0
+ ,201.774
+ ,0.04363
+ ,0.01758
+ ,0.00694
+ ,2.316346
+ ,0.508479
+ ,0
+ ,174.188
+ ,0.07008
+ ,0.02745
+ ,0.00459
+ ,2.657476
+ ,0.448439
+ ,0
+ ,209.516
+ ,0.04812
+ ,0.01879
+ ,0.00564
+ ,2.784312
+ ,0.431674
+ ,0
+ ,174.688
+ ,0.03804
+ ,0.01667
+ ,0.0136
+ ,2.679772
+ ,0.407567
+ ,0
+ ,198.764
+ ,0.03794
+ ,0.01588
+ ,0.0074
+ ,2.138608
+ ,0.451221
+ ,0
+ ,214.289
+ ,0.03078
+ ,0.01373
+ ,0.00567
+ ,2.555477
+ ,0.462803)
+ ,dim=c(7
+ ,195)
+ ,dimnames=list(c('status'
+ ,'MDVP:Fo(Hz)'
+ ,'Shimmer:DDA'
+ ,'MDVP:APQ'
+ ,'MDVP:Jitter(%)'
+ ,'D2'
+ ,'RPDE')
+ ,1:195))
> y <- array(NA,dim=c(7,195),dimnames=list(c('status','MDVP:Fo(Hz)','Shimmer:DDA','MDVP:APQ','MDVP:Jitter(%)','D2','RPDE'),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:Fo(Hz) Shimmer:DDA MDVP:APQ MDVP:Jitter(%) D2 RPDE
1 1 119.992 0.06545 0.02971 0.00784 2.301442 0.414783
2 1 122.400 0.09403 0.04368 0.00968 2.486855 0.458359
3 1 116.682 0.08270 0.03590 0.01050 2.342259 0.429895
4 1 116.676 0.08771 0.03772 0.00997 2.405554 0.434969
5 1 116.014 0.10470 0.04465 0.01284 2.332180 0.417356
6 1 120.552 0.06985 0.03243 0.00968 2.187560 0.415564
7 1 120.267 0.02337 0.01351 0.00333 1.854785 0.596040
8 1 107.332 0.02487 0.01256 0.00290 2.064693 0.637420
9 1 95.730 0.03218 0.01717 0.00551 2.322511 0.615551
10 1 95.056 0.04324 0.02444 0.00532 2.432792 0.547037
11 1 88.333 0.03237 0.01892 0.00505 2.407313 0.611137
12 1 91.904 0.04272 0.02214 0.00540 2.642476 0.583390
13 1 136.926 0.01968 0.01140 0.00293 2.041277 0.460600
14 1 139.173 0.02184 0.01797 0.00390 2.519422 0.430166
15 1 152.845 0.03191 0.01246 0.00294 2.125618 0.474791
16 1 142.167 0.02316 0.01359 0.00369 2.205546 0.565924
17 1 144.188 0.02908 0.02074 0.00544 2.264501 0.567380
18 1 168.778 0.04322 0.03430 0.00718 3.007463 0.631099
19 1 153.046 0.07413 0.05767 0.00742 3.109010 0.665318
20 1 156.405 0.05164 0.04310 0.00768 2.856676 0.649554
21 1 153.848 0.05000 0.04055 0.00840 2.739710 0.660125
22 1 153.880 0.06062 0.04525 0.00480 2.557536 0.629017
23 1 167.930 0.06685 0.04246 0.00442 2.916777 0.619060
24 1 173.917 0.06562 0.03772 0.00476 2.547508 0.537264
25 1 163.656 0.02214 0.01497 0.00742 2.692176 0.397937
26 1 104.400 0.05197 0.03780 0.00633 2.846369 0.522746
27 1 171.041 0.02666 0.01872 0.00455 2.589702 0.418622
28 1 146.845 0.02650 0.01826 0.00496 2.314209 0.358773
29 1 155.358 0.02307 0.01661 0.00310 2.241742 0.470478
30 1 162.568 0.02380 0.01799 0.00502 1.957961 0.427785
31 0 197.076 0.01689 0.00802 0.00289 1.743867 0.422229
32 0 199.228 0.01513 0.00762 0.00241 2.103106 0.432439
33 0 198.383 0.01919 0.00951 0.00212 1.512275 0.465946
34 0 202.266 0.01407 0.00719 0.00180 1.544609 0.368535
35 0 203.184 0.01403 0.00726 0.00178 1.423287 0.340068
36 0 201.464 0.01758 0.00957 0.00198 2.447064 0.344252
37 1 177.876 0.03463 0.01612 0.00411 2.477082 0.360148
38 1 176.170 0.02814 0.01491 0.00369 2.536527 0.341435
39 1 180.198 0.02177 0.01190 0.00284 2.269398 0.403884
40 1 187.733 0.02488 0.01366 0.00316 2.382544 0.396793
41 1 186.163 0.02321 0.01233 0.00298 2.374073 0.326480
42 1 184.055 0.02226 0.01234 0.00258 2.361532 0.306443
43 0 237.226 0.03104 0.01133 0.00298 2.416838 0.305062
44 0 241.404 0.03017 0.01251 0.00281 2.256699 0.457702
45 0 243.439 0.02330 0.01033 0.00210 2.330716 0.438296
46 0 242.852 0.02542 0.01014 0.00225 2.365800 0.431285
47 0 245.510 0.02719 0.01149 0.00235 2.392122 0.467489
48 0 252.455 0.01841 0.00860 0.00185 2.028612 0.610367
49 0 122.188 0.02566 0.01433 0.00524 2.079922 0.579597
50 0 122.964 0.02789 0.01400 0.00428 2.054419 0.538688
51 0 124.445 0.03724 0.01685 0.00431 1.840198 0.553134
52 0 126.344 0.03429 0.01614 0.00448 2.431854 0.507504
53 0 128.001 0.03969 0.01677 0.00436 1.972297 0.459766
54 0 129.336 0.04188 0.01947 0.00490 2.223719 0.420383
55 1 108.807 0.04450 0.02067 0.00761 1.986899 0.536009
56 1 109.860 0.05368 0.02454 0.00874 2.014606 0.558586
57 1 110.417 0.06097 0.02802 0.00784 1.922940 0.541781
58 1 117.274 0.03568 0.01948 0.00752 2.021591 0.530529
59 1 116.879 0.04183 0.02137 0.00788 1.827012 0.540049
60 1 114.847 0.05414 0.02519 0.00867 1.831691 0.547975
61 0 209.144 0.02925 0.01382 0.00282 2.460791 0.341788
62 0 223.365 0.03039 0.01340 0.00264 2.321560 0.447979
63 0 222.236 0.02602 0.01200 0.00266 2.278687 0.364867
64 0 228.832 0.02647 0.01179 0.00296 2.498224 0.256570
65 0 229.401 0.02308 0.01016 0.00205 2.003032 0.276850
66 0 228.969 0.02827 0.01234 0.00238 2.118596 0.305429
67 1 140.341 0.05490 0.02428 0.00817 2.359973 0.460139
68 1 136.969 0.04914 0.02603 0.00923 2.291558 0.498133
69 1 143.533 0.09455 0.03392 0.01101 2.118496 0.513237
70 1 148.090 0.10070 0.03635 0.00762 2.137075 0.487407
71 1 142.729 0.05605 0.02949 0.00831 2.277927 0.489345
72 1 136.358 0.08247 0.03736 0.00971 2.642276 0.543299
73 1 120.080 0.02921 0.01345 0.00405 2.205024 0.495954
74 1 112.014 0.04120 0.01956 0.00533 1.928708 0.509127
75 1 110.793 0.04295 0.01831 0.00494 2.225815 0.437031
76 1 110.707 0.03851 0.01715 0.00516 1.862092 0.463514
77 1 112.876 0.07238 0.02704 0.00500 2.007923 0.489538
78 1 110.568 0.03852 0.01636 0.00462 1.777901 0.429484
79 1 95.385 0.05408 0.02455 0.00608 2.017753 0.644954
80 1 100.770 0.05320 0.02139 0.01038 2.398422 0.594387
81 1 96.106 0.06799 0.02876 0.00694 2.645959 0.544805
82 1 95.605 0.05377 0.02190 0.00702 2.232576 0.576084
83 1 100.960 0.04114 0.01751 0.00606 2.428306 0.554610
84 1 98.804 0.03831 0.01552 0.00432 2.053601 0.576644
85 1 176.858 0.08037 0.03510 0.00747 3.099301 0.556494
86 1 180.978 0.06321 0.02877 0.00406 3.098256 0.583574
87 1 178.222 0.06219 0.02784 0.00321 2.654271 0.598714
88 1 176.281 0.11012 0.04683 0.00520 3.136550 0.602874
89 1 173.898 0.11363 0.04802 0.00448 3.007096 0.599371
90 1 179.711 0.06892 0.03455 0.00709 3.671155 0.590951
91 1 166.605 0.10949 0.05114 0.00742 3.317586 0.653410
92 1 151.955 0.13262 0.05690 0.00419 2.344876 0.501037
93 1 148.272 0.07150 0.03051 0.00459 2.344336 0.454444
94 1 152.125 0.10024 0.04398 0.00382 2.080121 0.447456
95 1 157.821 0.06185 0.02764 0.00358 2.143851 0.502380
96 1 157.447 0.05439 0.02571 0.00369 2.344348 0.447285
97 1 159.116 0.05417 0.02809 0.00342 2.473239 0.366329
98 1 125.036 0.06406 0.03088 0.01280 2.671825 0.629574
99 1 125.791 0.07625 0.03908 0.01378 2.441612 0.571010
100 1 126.512 0.10833 0.05783 0.01936 2.634633 0.638545
101 1 125.641 0.16074 0.06196 0.03316 2.991063 0.671299
102 1 128.451 0.09669 0.05174 0.01551 2.638279 0.639808
103 1 139.224 0.16654 0.06023 0.03011 2.690917 0.596362
104 1 150.258 0.01567 0.01009 0.00248 2.004055 0.296888
105 1 154.003 0.01406 0.00871 0.00183 2.065477 0.263654
106 1 149.689 0.01979 0.01059 0.00257 1.994387 0.365488
107 1 155.078 0.01567 0.00928 0.00168 2.129924 0.334171
108 1 151.884 0.01898 0.01267 0.00258 2.499148 0.393563
109 1 151.989 0.01364 0.00993 0.00174 2.296873 0.311369
110 1 193.030 0.05312 0.02084 0.00766 2.608749 0.497554
111 1 200.714 0.03576 0.01852 0.00621 2.550961 0.436084
112 1 208.519 0.02855 0.01307 0.00609 2.502336 0.338097
113 1 204.664 0.03831 0.01767 0.00841 2.376749 0.498877
114 1 210.141 0.02583 0.01301 0.00534 2.489191 0.441097
115 1 206.327 0.03320 0.01604 0.00495 2.938114 0.331508
116 1 151.872 0.02389 0.01271 0.00856 2.702355 0.407701
117 1 158.219 0.01818 0.01312 0.00476 2.640798 0.450798
118 1 170.756 0.02270 0.01652 0.00555 2.975889 0.486738
119 1 178.285 0.01851 0.01151 0.00462 2.816781 0.470422
120 1 217.116 0.02038 0.01075 0.00404 2.925862 0.462516
121 1 128.940 0.02548 0.01734 0.00581 2.686240 0.487756
122 1 176.824 0.01603 0.01104 0.00460 2.655744 0.400088
123 1 138.190 0.07761 0.03220 0.00704 2.090438 0.538016
124 1 182.018 0.04115 0.01931 0.00842 2.174306 0.589956
125 1 156.239 0.03867 0.01720 0.00694 1.929715 0.618663
126 1 145.174 0.03706 0.01944 0.00733 1.765957 0.637518
127 1 138.145 0.04451 0.02259 0.00544 1.821297 0.623209
128 1 166.888 0.04641 0.02301 0.00638 1.996146 0.585169
129 1 119.031 0.01614 0.00811 0.00440 2.328513 0.457541
130 1 120.078 0.01428 0.00903 0.00270 2.108873 0.491345
131 1 120.289 0.02110 0.01194 0.00492 2.539724 0.467160
132 1 120.256 0.02164 0.01310 0.00407 2.527742 0.468621
133 1 119.056 0.01898 0.00915 0.00346 2.516320 0.470972
134 1 118.747 0.01471 0.00903 0.00331 2.034827 0.482296
135 1 106.516 0.08050 0.03651 0.00589 2.375138 0.637814
136 1 110.453 0.06688 0.03316 0.00494 2.631793 0.653427
137 1 113.400 0.07154 0.04370 0.00451 2.445502 0.647900
138 1 113.166 0.08689 0.04134 0.00502 2.672362 0.625362
139 1 112.239 0.09211 0.04451 0.00472 2.419253 0.640945
140 1 116.150 0.04543 0.02770 0.00381 2.445646 0.624811
141 1 170.368 0.05139 0.02824 0.00571 2.963799 0.677131
142 1 208.083 0.12047 0.04464 0.00757 2.665133 0.606344
143 1 198.458 0.06165 0.02530 0.00376 2.465528 0.606273
144 1 202.805 0.03350 0.01506 0.00370 2.470746 0.536102
145 1 202.544 0.04426 0.02006 0.00254 2.576563 0.497480
146 1 223.361 0.04137 0.01909 0.00352 2.840556 0.566849
147 1 169.774 0.11411 0.08808 0.01568 3.413649 0.561610
148 1 183.520 0.08595 0.06359 0.01466 3.142364 0.478024
149 1 188.620 0.10422 0.06824 0.01719 3.274865 0.552870
150 1 202.632 0.10546 0.06460 0.01627 2.910213 0.427627
151 1 186.695 0.08096 0.06259 0.01872 2.958815 0.507826
152 1 192.818 0.16942 0.13778 0.03107 3.079221 0.625866
153 1 198.116 0.12851 0.08318 0.02714 3.184027 0.584164
154 1 121.345 0.04019 0.02056 0.00684 2.013530 0.566867
155 1 119.100 0.04451 0.02018 0.00692 2.451130 0.651680
156 1 117.870 0.04977 0.02402 0.00647 2.439597 0.628300
157 1 122.336 0.03615 0.01771 0.00727 2.699645 0.611679
158 1 117.963 0.07830 0.02916 0.01813 2.964568 0.630547
159 1 126.144 0.04499 0.02157 0.00975 2.892300 0.635015
160 1 127.930 0.04079 0.03105 0.00605 2.103014 0.654945
161 1 114.238 0.04736 0.04114 0.00581 2.151121 0.653139
162 1 115.322 0.04933 0.02931 0.00619 2.442906 0.577802
163 1 114.554 0.05592 0.03091 0.00651 2.408689 0.685151
164 1 112.150 0.02902 0.01363 0.00519 1.871871 0.557045
165 1 102.273 0.04736 0.02073 0.00907 2.560422 0.671378
166 0 236.200 0.04231 0.01621 0.00277 2.235197 0.469928
167 0 237.323 0.02089 0.00882 0.00303 1.852402 0.384868
168 0 260.105 0.03557 0.01367 0.00339 1.881767 0.440988
169 0 197.569 0.03836 0.01439 0.00803 2.882450 0.372222
170 0 240.301 0.03529 0.01344 0.00517 2.266432 0.371837
171 0 244.990 0.03253 0.01255 0.00451 2.095237 0.522812
172 0 112.547 0.01992 0.01140 0.00355 2.193412 0.413295
173 0 110.739 0.02261 0.01285 0.00356 1.889002 0.369090
174 0 113.715 0.02245 0.01148 0.00349 1.852542 0.380253
175 0 117.004 0.02643 0.01318 0.00353 1.872946 0.387482
176 0 115.380 0.02436 0.01133 0.00332 1.974857 0.405991
177 0 116.388 0.02623 0.01331 0.00346 2.004719 0.361232
178 1 151.737 0.02184 0.01230 0.00314 2.449763 0.396610
179 1 148.790 0.02518 0.01309 0.00309 2.251553 0.402591
180 1 148.143 0.02175 0.01263 0.00392 2.845109 0.398499
181 1 150.440 0.03964 0.02148 0.00396 2.264226 0.352396
182 1 148.462 0.02849 0.01559 0.00397 2.679185 0.408598
183 1 149.818 0.03464 0.01666 0.00336 2.209021 0.329577
184 0 117.226 0.02592 0.01949 0.00417 2.027228 0.603515
185 0 116.848 0.02429 0.01756 0.00531 2.120412 0.663842
186 0 116.286 0.02001 0.01691 0.00314 2.058658 0.598515
187 0 116.556 0.02460 0.01491 0.00496 2.161936 0.566424
188 0 116.342 0.01892 0.01144 0.00267 2.152083 0.528485
189 0 114.563 0.01672 0.01095 0.00327 1.913990 0.555303
190 0 201.774 0.04363 0.01758 0.00694 2.316346 0.508479
191 0 174.188 0.07008 0.02745 0.00459 2.657476 0.448439
192 0 209.516 0.04812 0.01879 0.00564 2.784312 0.431674
193 0 174.688 0.03804 0.01667 0.01360 2.679772 0.407567
194 0 198.764 0.03794 0.01588 0.00740 2.138608 0.451221
195 0 214.289 0.03078 0.01373 0.00567 2.555477 0.462803
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `MDVP:Fo(Hz)` `Shimmer:DDA` `MDVP:APQ`
0.463870 -0.004597 1.553194 3.152706
`MDVP:Jitter(%)` D2 RPDE
-8.427733 0.389008 -0.048370
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.87095 -0.17401 0.06666 0.26230 0.53213
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.4638699 0.2398993 1.934 0.0547 .
`MDVP:Fo(Hz)` -0.0045967 0.0007064 -6.507 6.76e-10 ***
`Shimmer:DDA` 1.5531936 1.9586049 0.793 0.4288
`MDVP:APQ` 3.1527057 3.7567873 0.839 0.4024
`MDVP:Jitter(%)` -8.4277327 8.3827349 -1.005 0.3160
D2 0.3890080 0.0829281 4.691 5.22e-06 ***
RPDE -0.0483704 0.3024676 -0.160 0.8731
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3565 on 188 degrees of freedom
Multiple R-squared: 0.3395, Adjusted R-squared: 0.3184
F-statistic: 16.11 on 6 and 188 DF, p-value: 6.398e-15
> 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,] 5.554537e-48 1.110907e-47 1.00000000
[2,] 9.694563e-67 1.938913e-66 1.00000000
[3,] 7.877504e-78 1.575501e-77 1.00000000
[4,] 3.394295e-105 6.788589e-105 1.00000000
[5,] 2.617049e-107 5.234098e-107 1.00000000
[6,] 1.046881e-122 2.093762e-122 1.00000000
[7,] 0.000000e+00 0.000000e+00 1.00000000
[8,] 1.899656e-164 3.799312e-164 1.00000000
[9,] 1.433005e-169 2.866010e-169 1.00000000
[10,] 9.316034e-184 1.863207e-183 1.00000000
[11,] 1.177416e-208 2.354832e-208 1.00000000
[12,] 1.404943e-242 2.809886e-242 1.00000000
[13,] 6.889065e-233 1.377813e-232 1.00000000
[14,] 2.161122e-244 4.322245e-244 1.00000000
[15,] 6.908181e-263 1.381636e-262 1.00000000
[16,] 9.220697e-282 1.844139e-281 1.00000000
[17,] 4.940656e-324 9.881313e-324 1.00000000
[18,] 1.741507e-311 3.483014e-311 1.00000000
[19,] 8.596742e-322 1.719348e-321 1.00000000
[20,] 0.000000e+00 0.000000e+00 1.00000000
[21,] 0.000000e+00 0.000000e+00 1.00000000
[22,] 7.528210e-06 1.505642e-05 0.99999247
[23,] 2.832349e-04 5.664698e-04 0.99971677
[24,] 4.267357e-04 8.534714e-04 0.99957326
[25,] 4.250234e-04 8.500469e-04 0.99957498
[26,] 2.921199e-04 5.842398e-04 0.99970788
[27,] 8.649546e-04 1.729909e-03 0.99913505
[28,] 1.280587e-03 2.561175e-03 0.99871941
[29,] 1.224197e-03 2.448393e-03 0.99877580
[30,] 1.784397e-03 3.568794e-03 0.99821560
[31,] 2.205398e-03 4.410797e-03 0.99779460
[32,] 2.231452e-03 4.462904e-03 0.99776855
[33,] 1.997844e-03 3.995688e-03 0.99800216
[34,] 2.638849e-03 5.277698e-03 0.99736115
[35,] 1.833691e-03 3.667381e-03 0.99816631
[36,] 1.295033e-03 2.590067e-03 0.99870497
[37,] 8.984947e-04 1.796989e-03 0.99910151
[38,] 5.966946e-04 1.193389e-03 0.99940331
[39,] 4.870787e-04 9.741573e-04 0.99951292
[40,] 6.169760e-03 1.233952e-02 0.99383024
[41,] 2.897474e-02 5.794947e-02 0.97102526
[42,] 5.469901e-02 1.093980e-01 0.94530099
[43,] 1.780463e-01 3.560926e-01 0.82195368
[44,] 2.746902e-01 5.493803e-01 0.72530984
[45,] 4.628258e-01 9.256516e-01 0.53717418
[46,] 4.453615e-01 8.907231e-01 0.55463846
[47,] 4.236425e-01 8.472850e-01 0.57635748
[48,] 4.047240e-01 8.094480e-01 0.59527600
[49,] 3.737123e-01 7.474246e-01 0.62628772
[50,] 3.539821e-01 7.079643e-01 0.64601785
[51,] 3.336263e-01 6.672526e-01 0.66637368
[52,] 3.670171e-01 7.340342e-01 0.63298289
[53,] 3.522640e-01 7.045281e-01 0.64773597
[54,] 3.458276e-01 6.916553e-01 0.65417237
[55,] 3.600192e-01 7.200384e-01 0.63998078
[56,] 3.360671e-01 6.721343e-01 0.66393286
[57,] 3.189534e-01 6.379067e-01 0.68104663
[58,] 2.912610e-01 5.825219e-01 0.70873903
[59,] 2.588342e-01 5.176683e-01 0.74116584
[60,] 2.610576e-01 5.221152e-01 0.73894242
[61,] 2.772426e-01 5.544853e-01 0.72275736
[62,] 2.477457e-01 4.954913e-01 0.75225433
[63,] 2.139967e-01 4.279934e-01 0.78600328
[64,] 1.974887e-01 3.949774e-01 0.80251128
[65,] 1.810854e-01 3.621707e-01 0.81891465
[66,] 1.580688e-01 3.161376e-01 0.84193120
[67,] 1.456422e-01 2.912843e-01 0.85435784
[68,] 1.329028e-01 2.658056e-01 0.86709719
[69,] 1.244798e-01 2.489597e-01 0.87552016
[70,] 1.066191e-01 2.132383e-01 0.89338085
[71,] 8.914279e-02 1.782856e-01 0.91085721
[72,] 7.384061e-02 1.476812e-01 0.92615939
[73,] 6.069602e-02 1.213920e-01 0.93930398
[74,] 4.914904e-02 9.829807e-02 0.95085096
[75,] 4.216085e-02 8.432170e-02 0.95783915
[76,] 3.576070e-02 7.152140e-02 0.96423930
[77,] 3.195435e-02 6.390869e-02 0.96804565
[78,] 3.123391e-02 6.246783e-02 0.96876609
[79,] 2.520452e-02 5.040903e-02 0.97479548
[80,] 2.016692e-02 4.033385e-02 0.97983308
[81,] 1.678110e-02 3.356219e-02 0.98321890
[82,] 1.440993e-02 2.881986e-02 0.98559007
[83,] 1.101093e-02 2.202186e-02 0.98898907
[84,] 8.906418e-03 1.781284e-02 0.99109358
[85,] 7.206841e-03 1.441368e-02 0.99279316
[86,] 6.839775e-03 1.367955e-02 0.99316023
[87,] 5.881704e-03 1.176341e-02 0.99411830
[88,] 4.674782e-03 9.349563e-03 0.99532522
[89,] 3.424984e-03 6.849968e-03 0.99657502
[90,] 2.576178e-03 5.152355e-03 0.99742382
[91,] 2.018834e-03 4.037668e-03 0.99798117
[92,] 1.452563e-03 2.905127e-03 0.99854744
[93,] 1.051917e-03 2.103835e-03 0.99894808
[94,] 8.165301e-04 1.633060e-03 0.99918347
[95,] 9.567903e-04 1.913581e-03 0.99904321
[96,] 1.139023e-03 2.278046e-03 0.99886098
[97,] 1.435741e-03 2.871483e-03 0.99856426
[98,] 1.694359e-03 3.388718e-03 0.99830564
[99,] 1.407821e-03 2.815642e-03 0.99859218
[100,] 1.461603e-03 2.923206e-03 0.99853840
[101,] 1.766405e-03 3.532809e-03 0.99823360
[102,] 2.245905e-03 4.491810e-03 0.99775410
[103,] 3.615880e-03 7.231761e-03 0.99638412
[104,] 6.047639e-03 1.209528e-02 0.99395236
[105,] 8.870522e-03 1.774104e-02 0.99112948
[106,] 8.363645e-03 1.672729e-02 0.99163635
[107,] 7.340176e-03 1.468035e-02 0.99265982
[108,] 6.246599e-03 1.249320e-02 0.99375340
[109,] 4.736710e-03 9.473419e-03 0.99526329
[110,] 4.029955e-03 8.059911e-03 0.99597004
[111,] 4.232321e-03 8.464643e-03 0.99576768
[112,] 3.310794e-03 6.621588e-03 0.99668921
[113,] 3.551192e-03 7.102383e-03 0.99644881
[114,] 3.238910e-03 6.477819e-03 0.99676109
[115,] 4.790365e-03 9.580730e-03 0.99520963
[116,] 6.774940e-03 1.354988e-02 0.99322506
[117,] 1.034238e-02 2.068475e-02 0.98965762
[118,] 1.339966e-02 2.679932e-02 0.98660034
[119,] 2.093203e-02 4.186407e-02 0.97906797
[120,] 2.066209e-02 4.132418e-02 0.97933791
[121,] 2.329883e-02 4.659766e-02 0.97670117
[122,] 2.051446e-02 4.102893e-02 0.97948554
[123,] 1.789884e-02 3.579767e-02 0.98210116
[124,] 1.601135e-02 3.202270e-02 0.98398865
[125,] 2.334194e-02 4.668387e-02 0.97665806
[126,] 1.788991e-02 3.577983e-02 0.98211009
[127,] 1.392711e-02 2.785422e-02 0.98607289
[128,] 1.059269e-02 2.118539e-02 0.98940731
[129,] 8.730909e-03 1.746182e-02 0.99126909
[130,] 6.789162e-03 1.357832e-02 0.99321084
[131,] 4.949527e-03 9.899055e-03 0.99505047
[132,] 3.649647e-03 7.299295e-03 0.99635035
[133,] 3.215417e-03 6.430834e-03 0.99678458
[134,] 3.058331e-03 6.116661e-03 0.99694167
[135,] 4.501453e-03 9.002907e-03 0.99549855
[136,] 5.099110e-03 1.019822e-02 0.99490089
[137,] 5.724282e-03 1.144856e-02 0.99427572
[138,] 6.301765e-03 1.260353e-02 0.99369824
[139,] 4.457100e-03 8.914199e-03 0.99554290
[140,] 3.262896e-03 6.525792e-03 0.99673710
[141,] 2.474875e-03 4.949749e-03 0.99752513
[142,] 2.045820e-03 4.091640e-03 0.99795418
[143,] 2.050751e-03 4.101502e-03 0.99794925
[144,] 2.007140e-03 4.014280e-03 0.99799286
[145,] 3.146750e-03 6.293500e-03 0.99685325
[146,] 2.991998e-03 5.983995e-03 0.99700800
[147,] 2.534844e-03 5.069688e-03 0.99746516
[148,] 2.345706e-03 4.691412e-03 0.99765429
[149,] 2.189880e-03 4.379759e-03 0.99781012
[150,] 2.104804e-03 4.209607e-03 0.99789520
[151,] 1.814670e-03 3.629341e-03 0.99818533
[152,] 1.203923e-03 2.407845e-03 0.99879608
[153,] 8.702349e-04 1.740470e-03 0.99912977
[154,] 1.013433e-03 2.026865e-03 0.99898657
[155,] 1.605952e-02 3.211904e-02 0.98394048
[156,] 5.668190e-01 8.663621e-01 0.43318104
[157,] 5.177784e-01 9.644431e-01 0.48222156
[158,] 5.018697e-01 9.962607e-01 0.49813035
[159,] 4.380839e-01 8.761678e-01 0.56191612
[160,] 5.066728e-01 9.866544e-01 0.49332719
[161,] 5.697568e-01 8.604865e-01 0.43024324
[162,] 5.044962e-01 9.910077e-01 0.49550383
[163,] 5.878896e-01 8.242208e-01 0.41211038
[164,] 6.550668e-01 6.898664e-01 0.34493320
[165,] 6.468202e-01 7.063596e-01 0.35317981
[166,] 6.460040e-01 7.079919e-01 0.35399597
[167,] 6.273784e-01 7.452431e-01 0.37262156
[168,] 9.355089e-01 1.289822e-01 0.06449111
[169,] 9.079337e-01 1.841326e-01 0.09206631
[170,] 9.206079e-01 1.587842e-01 0.07939212
[171,] 8.895071e-01 2.209857e-01 0.11049285
[172,] 8.226799e-01 3.546402e-01 0.17732010
[173,] 9.280763e-01 1.438474e-01 0.07192370
[174,] 1.000000e+00 0.000000e+00 0.00000000
[175,] 1.000000e+00 0.000000e+00 0.00000000
[176,] 1.000000e+00 0.000000e+00 0.00000000
> postscript(file="/var/wessaorg/rcomp/tmp/13tbw1386616249.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/2podr1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3lw2j1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/4wtfl1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/5em1n1386616249.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
0.083232827 -0.048644181 0.028980470 -0.013410062 -0.012811379 0.130243389
7 8 9 10 11 12
0.345440687 0.203369231 0.044795649 -0.046216537 -0.032097502 -0.131782619
13 14 15 16 17 18
0.351931569 0.158892778 0.370730603 0.311311093 0.280749292 0.057797841
19 20 21 22 23 24
-0.172030353 0.023864896 0.074777737 0.082634797 0.002906490 0.189838783
25 26 27 28 29 30
0.241330274 -0.212492090 0.273110076 0.271316182 0.338895299 0.491062284
31 32 33 34 35 36
-0.243084824 -0.372496286 -0.159630401 -0.146501603 -0.096790669 -0.513863113
37 38 39 40 41 42
0.337620161 0.316103785 0.453775262 0.436371485 0.434318803 0.426611196
43 44 45 46 47 48
-0.357639943 -0.272557892 -0.281375813 -0.299490762 -0.301923495 -0.103145464
49 50 51 52 53 54
-0.724149537 -0.723154254 -0.655568820 -0.870952660 -0.698258444 -0.799194795
55 56 57 58 59 60
0.219143566 0.197361770 0.204888925 0.260995796 0.322856596 0.287574050
61 62 63 64 65 66
-0.508464925 -0.385760084 -0.366922197 -0.424750843 -0.225785714 -0.283497258
67 68 69 70 71 72
0.192482746 0.217796953 0.235618863 0.202306070 0.219757068 -0.002701834
73 74 75 76 77 78
0.200680609 0.244631509 0.117890588 0.272674839 0.142039113 0.301064952
79 80 81 82 83 84
0.110707216 0.032499898 -0.162829922 0.041577587 0.014380337 0.147304024
85 86 87 88 89 90
-0.002174370 0.036351335 0.194481536 -0.119393113 -0.095429278 -0.193533508
91 92 93 94 95 96
-0.225750999 -0.003377759 0.159151076 0.185710864 0.298878618 0.235098141
97 98 99 100 101 102
0.179277326 0.012998057 0.066664070 -0.063754650 -0.182949023 -0.051366473
103 104 105 106 107 108
-0.036636331 0.426341663 0.419428513 0.423588258 0.397148674 0.233464715
109 110 111 112 113 114
0.318511377 0.349024756 0.425910073 0.503332708 0.532134443 0.518977289
115 116 117 118 119 120
0.297223372 0.197689820 0.228446687 0.146379313 0.256558186 0.386840868
121 122 123 124 125 126
0.062176275 0.314250121 0.221447634 0.501697514 0.477766347 0.490244304
127 128 129 130 131 132
0.398283441 0.464195971 0.186047790 0.263598684 0.094736681 0.087657317
133 134 135 136 137 138
0.098141994 0.290320284 -0.057840757 -0.115119421 -0.073462886 -0.175982036
139 140 141 142 143 144
-0.101658076 0.023102997 0.078345885 0.221146994 0.374770647 0.464828959
145 146 147 148 149 150
0.378345334 0.390501170 -0.307017323 -0.029990549 -0.076185692 0.125814320
151 152 153 154 155 156
0.102567316 -0.180781208 0.003342556 0.268461810 0.087177180 0.060810083
157 158 159 160 161 162
0.027164546 -0.105121808 -0.034145631 0.227519838 0.101742335 0.027013648
163 164 165 166 167 168
0.029403573 0.306118083 -0.019775344 -0.318381253 -0.109664103 -0.048707970
169 170 171 172 173 174
-0.696266649 -0.276562526 -0.179579171 -0.816749174 -0.717445559 -0.685064773
175 176 177 178 179 180
-0.688738033 -0.727674209 -0.744789236 0.253591440 0.309339813 0.089042495
181 182 183 184 185 186
0.267988287 0.136163955 0.303405923 -0.750992584 -0.767837378 -0.759149078
187 188 189 190 191 192
-0.785121461 -0.783644867 -0.687886650 -0.477556370 -0.831972111 -0.649470884
193 194 195
-0.680639711 -0.406946539 -0.493868504
> postscript(file="/var/wessaorg/rcomp/tmp/6fvme1386616249.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 0.083232827 NA
1 -0.048644181 0.083232827
2 0.028980470 -0.048644181
3 -0.013410062 0.028980470
4 -0.012811379 -0.013410062
5 0.130243389 -0.012811379
6 0.345440687 0.130243389
7 0.203369231 0.345440687
8 0.044795649 0.203369231
9 -0.046216537 0.044795649
10 -0.032097502 -0.046216537
11 -0.131782619 -0.032097502
12 0.351931569 -0.131782619
13 0.158892778 0.351931569
14 0.370730603 0.158892778
15 0.311311093 0.370730603
16 0.280749292 0.311311093
17 0.057797841 0.280749292
18 -0.172030353 0.057797841
19 0.023864896 -0.172030353
20 0.074777737 0.023864896
21 0.082634797 0.074777737
22 0.002906490 0.082634797
23 0.189838783 0.002906490
24 0.241330274 0.189838783
25 -0.212492090 0.241330274
26 0.273110076 -0.212492090
27 0.271316182 0.273110076
28 0.338895299 0.271316182
29 0.491062284 0.338895299
30 -0.243084824 0.491062284
31 -0.372496286 -0.243084824
32 -0.159630401 -0.372496286
33 -0.146501603 -0.159630401
34 -0.096790669 -0.146501603
35 -0.513863113 -0.096790669
36 0.337620161 -0.513863113
37 0.316103785 0.337620161
38 0.453775262 0.316103785
39 0.436371485 0.453775262
40 0.434318803 0.436371485
41 0.426611196 0.434318803
42 -0.357639943 0.426611196
43 -0.272557892 -0.357639943
44 -0.281375813 -0.272557892
45 -0.299490762 -0.281375813
46 -0.301923495 -0.299490762
47 -0.103145464 -0.301923495
48 -0.724149537 -0.103145464
49 -0.723154254 -0.724149537
50 -0.655568820 -0.723154254
51 -0.870952660 -0.655568820
52 -0.698258444 -0.870952660
53 -0.799194795 -0.698258444
54 0.219143566 -0.799194795
55 0.197361770 0.219143566
56 0.204888925 0.197361770
57 0.260995796 0.204888925
58 0.322856596 0.260995796
59 0.287574050 0.322856596
60 -0.508464925 0.287574050
61 -0.385760084 -0.508464925
62 -0.366922197 -0.385760084
63 -0.424750843 -0.366922197
64 -0.225785714 -0.424750843
65 -0.283497258 -0.225785714
66 0.192482746 -0.283497258
67 0.217796953 0.192482746
68 0.235618863 0.217796953
69 0.202306070 0.235618863
70 0.219757068 0.202306070
71 -0.002701834 0.219757068
72 0.200680609 -0.002701834
73 0.244631509 0.200680609
74 0.117890588 0.244631509
75 0.272674839 0.117890588
76 0.142039113 0.272674839
77 0.301064952 0.142039113
78 0.110707216 0.301064952
79 0.032499898 0.110707216
80 -0.162829922 0.032499898
81 0.041577587 -0.162829922
82 0.014380337 0.041577587
83 0.147304024 0.014380337
84 -0.002174370 0.147304024
85 0.036351335 -0.002174370
86 0.194481536 0.036351335
87 -0.119393113 0.194481536
88 -0.095429278 -0.119393113
89 -0.193533508 -0.095429278
90 -0.225750999 -0.193533508
91 -0.003377759 -0.225750999
92 0.159151076 -0.003377759
93 0.185710864 0.159151076
94 0.298878618 0.185710864
95 0.235098141 0.298878618
96 0.179277326 0.235098141
97 0.012998057 0.179277326
98 0.066664070 0.012998057
99 -0.063754650 0.066664070
100 -0.182949023 -0.063754650
101 -0.051366473 -0.182949023
102 -0.036636331 -0.051366473
103 0.426341663 -0.036636331
104 0.419428513 0.426341663
105 0.423588258 0.419428513
106 0.397148674 0.423588258
107 0.233464715 0.397148674
108 0.318511377 0.233464715
109 0.349024756 0.318511377
110 0.425910073 0.349024756
111 0.503332708 0.425910073
112 0.532134443 0.503332708
113 0.518977289 0.532134443
114 0.297223372 0.518977289
115 0.197689820 0.297223372
116 0.228446687 0.197689820
117 0.146379313 0.228446687
118 0.256558186 0.146379313
119 0.386840868 0.256558186
120 0.062176275 0.386840868
121 0.314250121 0.062176275
122 0.221447634 0.314250121
123 0.501697514 0.221447634
124 0.477766347 0.501697514
125 0.490244304 0.477766347
126 0.398283441 0.490244304
127 0.464195971 0.398283441
128 0.186047790 0.464195971
129 0.263598684 0.186047790
130 0.094736681 0.263598684
131 0.087657317 0.094736681
132 0.098141994 0.087657317
133 0.290320284 0.098141994
134 -0.057840757 0.290320284
135 -0.115119421 -0.057840757
136 -0.073462886 -0.115119421
137 -0.175982036 -0.073462886
138 -0.101658076 -0.175982036
139 0.023102997 -0.101658076
140 0.078345885 0.023102997
141 0.221146994 0.078345885
142 0.374770647 0.221146994
143 0.464828959 0.374770647
144 0.378345334 0.464828959
145 0.390501170 0.378345334
146 -0.307017323 0.390501170
147 -0.029990549 -0.307017323
148 -0.076185692 -0.029990549
149 0.125814320 -0.076185692
150 0.102567316 0.125814320
151 -0.180781208 0.102567316
152 0.003342556 -0.180781208
153 0.268461810 0.003342556
154 0.087177180 0.268461810
155 0.060810083 0.087177180
156 0.027164546 0.060810083
157 -0.105121808 0.027164546
158 -0.034145631 -0.105121808
159 0.227519838 -0.034145631
160 0.101742335 0.227519838
161 0.027013648 0.101742335
162 0.029403573 0.027013648
163 0.306118083 0.029403573
164 -0.019775344 0.306118083
165 -0.318381253 -0.019775344
166 -0.109664103 -0.318381253
167 -0.048707970 -0.109664103
168 -0.696266649 -0.048707970
169 -0.276562526 -0.696266649
170 -0.179579171 -0.276562526
171 -0.816749174 -0.179579171
172 -0.717445559 -0.816749174
173 -0.685064773 -0.717445559
174 -0.688738033 -0.685064773
175 -0.727674209 -0.688738033
176 -0.744789236 -0.727674209
177 0.253591440 -0.744789236
178 0.309339813 0.253591440
179 0.089042495 0.309339813
180 0.267988287 0.089042495
181 0.136163955 0.267988287
182 0.303405923 0.136163955
183 -0.750992584 0.303405923
184 -0.767837378 -0.750992584
185 -0.759149078 -0.767837378
186 -0.785121461 -0.759149078
187 -0.783644867 -0.785121461
188 -0.687886650 -0.783644867
189 -0.477556370 -0.687886650
190 -0.831972111 -0.477556370
191 -0.649470884 -0.831972111
192 -0.680639711 -0.649470884
193 -0.406946539 -0.680639711
194 -0.493868504 -0.406946539
195 NA -0.493868504
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.048644181 0.083232827
[2,] 0.028980470 -0.048644181
[3,] -0.013410062 0.028980470
[4,] -0.012811379 -0.013410062
[5,] 0.130243389 -0.012811379
[6,] 0.345440687 0.130243389
[7,] 0.203369231 0.345440687
[8,] 0.044795649 0.203369231
[9,] -0.046216537 0.044795649
[10,] -0.032097502 -0.046216537
[11,] -0.131782619 -0.032097502
[12,] 0.351931569 -0.131782619
[13,] 0.158892778 0.351931569
[14,] 0.370730603 0.158892778
[15,] 0.311311093 0.370730603
[16,] 0.280749292 0.311311093
[17,] 0.057797841 0.280749292
[18,] -0.172030353 0.057797841
[19,] 0.023864896 -0.172030353
[20,] 0.074777737 0.023864896
[21,] 0.082634797 0.074777737
[22,] 0.002906490 0.082634797
[23,] 0.189838783 0.002906490
[24,] 0.241330274 0.189838783
[25,] -0.212492090 0.241330274
[26,] 0.273110076 -0.212492090
[27,] 0.271316182 0.273110076
[28,] 0.338895299 0.271316182
[29,] 0.491062284 0.338895299
[30,] -0.243084824 0.491062284
[31,] -0.372496286 -0.243084824
[32,] -0.159630401 -0.372496286
[33,] -0.146501603 -0.159630401
[34,] -0.096790669 -0.146501603
[35,] -0.513863113 -0.096790669
[36,] 0.337620161 -0.513863113
[37,] 0.316103785 0.337620161
[38,] 0.453775262 0.316103785
[39,] 0.436371485 0.453775262
[40,] 0.434318803 0.436371485
[41,] 0.426611196 0.434318803
[42,] -0.357639943 0.426611196
[43,] -0.272557892 -0.357639943
[44,] -0.281375813 -0.272557892
[45,] -0.299490762 -0.281375813
[46,] -0.301923495 -0.299490762
[47,] -0.103145464 -0.301923495
[48,] -0.724149537 -0.103145464
[49,] -0.723154254 -0.724149537
[50,] -0.655568820 -0.723154254
[51,] -0.870952660 -0.655568820
[52,] -0.698258444 -0.870952660
[53,] -0.799194795 -0.698258444
[54,] 0.219143566 -0.799194795
[55,] 0.197361770 0.219143566
[56,] 0.204888925 0.197361770
[57,] 0.260995796 0.204888925
[58,] 0.322856596 0.260995796
[59,] 0.287574050 0.322856596
[60,] -0.508464925 0.287574050
[61,] -0.385760084 -0.508464925
[62,] -0.366922197 -0.385760084
[63,] -0.424750843 -0.366922197
[64,] -0.225785714 -0.424750843
[65,] -0.283497258 -0.225785714
[66,] 0.192482746 -0.283497258
[67,] 0.217796953 0.192482746
[68,] 0.235618863 0.217796953
[69,] 0.202306070 0.235618863
[70,] 0.219757068 0.202306070
[71,] -0.002701834 0.219757068
[72,] 0.200680609 -0.002701834
[73,] 0.244631509 0.200680609
[74,] 0.117890588 0.244631509
[75,] 0.272674839 0.117890588
[76,] 0.142039113 0.272674839
[77,] 0.301064952 0.142039113
[78,] 0.110707216 0.301064952
[79,] 0.032499898 0.110707216
[80,] -0.162829922 0.032499898
[81,] 0.041577587 -0.162829922
[82,] 0.014380337 0.041577587
[83,] 0.147304024 0.014380337
[84,] -0.002174370 0.147304024
[85,] 0.036351335 -0.002174370
[86,] 0.194481536 0.036351335
[87,] -0.119393113 0.194481536
[88,] -0.095429278 -0.119393113
[89,] -0.193533508 -0.095429278
[90,] -0.225750999 -0.193533508
[91,] -0.003377759 -0.225750999
[92,] 0.159151076 -0.003377759
[93,] 0.185710864 0.159151076
[94,] 0.298878618 0.185710864
[95,] 0.235098141 0.298878618
[96,] 0.179277326 0.235098141
[97,] 0.012998057 0.179277326
[98,] 0.066664070 0.012998057
[99,] -0.063754650 0.066664070
[100,] -0.182949023 -0.063754650
[101,] -0.051366473 -0.182949023
[102,] -0.036636331 -0.051366473
[103,] 0.426341663 -0.036636331
[104,] 0.419428513 0.426341663
[105,] 0.423588258 0.419428513
[106,] 0.397148674 0.423588258
[107,] 0.233464715 0.397148674
[108,] 0.318511377 0.233464715
[109,] 0.349024756 0.318511377
[110,] 0.425910073 0.349024756
[111,] 0.503332708 0.425910073
[112,] 0.532134443 0.503332708
[113,] 0.518977289 0.532134443
[114,] 0.297223372 0.518977289
[115,] 0.197689820 0.297223372
[116,] 0.228446687 0.197689820
[117,] 0.146379313 0.228446687
[118,] 0.256558186 0.146379313
[119,] 0.386840868 0.256558186
[120,] 0.062176275 0.386840868
[121,] 0.314250121 0.062176275
[122,] 0.221447634 0.314250121
[123,] 0.501697514 0.221447634
[124,] 0.477766347 0.501697514
[125,] 0.490244304 0.477766347
[126,] 0.398283441 0.490244304
[127,] 0.464195971 0.398283441
[128,] 0.186047790 0.464195971
[129,] 0.263598684 0.186047790
[130,] 0.094736681 0.263598684
[131,] 0.087657317 0.094736681
[132,] 0.098141994 0.087657317
[133,] 0.290320284 0.098141994
[134,] -0.057840757 0.290320284
[135,] -0.115119421 -0.057840757
[136,] -0.073462886 -0.115119421
[137,] -0.175982036 -0.073462886
[138,] -0.101658076 -0.175982036
[139,] 0.023102997 -0.101658076
[140,] 0.078345885 0.023102997
[141,] 0.221146994 0.078345885
[142,] 0.374770647 0.221146994
[143,] 0.464828959 0.374770647
[144,] 0.378345334 0.464828959
[145,] 0.390501170 0.378345334
[146,] -0.307017323 0.390501170
[147,] -0.029990549 -0.307017323
[148,] -0.076185692 -0.029990549
[149,] 0.125814320 -0.076185692
[150,] 0.102567316 0.125814320
[151,] -0.180781208 0.102567316
[152,] 0.003342556 -0.180781208
[153,] 0.268461810 0.003342556
[154,] 0.087177180 0.268461810
[155,] 0.060810083 0.087177180
[156,] 0.027164546 0.060810083
[157,] -0.105121808 0.027164546
[158,] -0.034145631 -0.105121808
[159,] 0.227519838 -0.034145631
[160,] 0.101742335 0.227519838
[161,] 0.027013648 0.101742335
[162,] 0.029403573 0.027013648
[163,] 0.306118083 0.029403573
[164,] -0.019775344 0.306118083
[165,] -0.318381253 -0.019775344
[166,] -0.109664103 -0.318381253
[167,] -0.048707970 -0.109664103
[168,] -0.696266649 -0.048707970
[169,] -0.276562526 -0.696266649
[170,] -0.179579171 -0.276562526
[171,] -0.816749174 -0.179579171
[172,] -0.717445559 -0.816749174
[173,] -0.685064773 -0.717445559
[174,] -0.688738033 -0.685064773
[175,] -0.727674209 -0.688738033
[176,] -0.744789236 -0.727674209
[177,] 0.253591440 -0.744789236
[178,] 0.309339813 0.253591440
[179,] 0.089042495 0.309339813
[180,] 0.267988287 0.089042495
[181,] 0.136163955 0.267988287
[182,] 0.303405923 0.136163955
[183,] -0.750992584 0.303405923
[184,] -0.767837378 -0.750992584
[185,] -0.759149078 -0.767837378
[186,] -0.785121461 -0.759149078
[187,] -0.783644867 -0.785121461
[188,] -0.687886650 -0.783644867
[189,] -0.477556370 -0.687886650
[190,] -0.831972111 -0.477556370
[191,] -0.649470884 -0.831972111
[192,] -0.680639711 -0.649470884
[193,] -0.406946539 -0.680639711
[194,] -0.493868504 -0.406946539
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.048644181 0.083232827
2 0.028980470 -0.048644181
3 -0.013410062 0.028980470
4 -0.012811379 -0.013410062
5 0.130243389 -0.012811379
6 0.345440687 0.130243389
7 0.203369231 0.345440687
8 0.044795649 0.203369231
9 -0.046216537 0.044795649
10 -0.032097502 -0.046216537
11 -0.131782619 -0.032097502
12 0.351931569 -0.131782619
13 0.158892778 0.351931569
14 0.370730603 0.158892778
15 0.311311093 0.370730603
16 0.280749292 0.311311093
17 0.057797841 0.280749292
18 -0.172030353 0.057797841
19 0.023864896 -0.172030353
20 0.074777737 0.023864896
21 0.082634797 0.074777737
22 0.002906490 0.082634797
23 0.189838783 0.002906490
24 0.241330274 0.189838783
25 -0.212492090 0.241330274
26 0.273110076 -0.212492090
27 0.271316182 0.273110076
28 0.338895299 0.271316182
29 0.491062284 0.338895299
30 -0.243084824 0.491062284
31 -0.372496286 -0.243084824
32 -0.159630401 -0.372496286
33 -0.146501603 -0.159630401
34 -0.096790669 -0.146501603
35 -0.513863113 -0.096790669
36 0.337620161 -0.513863113
37 0.316103785 0.337620161
38 0.453775262 0.316103785
39 0.436371485 0.453775262
40 0.434318803 0.436371485
41 0.426611196 0.434318803
42 -0.357639943 0.426611196
43 -0.272557892 -0.357639943
44 -0.281375813 -0.272557892
45 -0.299490762 -0.281375813
46 -0.301923495 -0.299490762
47 -0.103145464 -0.301923495
48 -0.724149537 -0.103145464
49 -0.723154254 -0.724149537
50 -0.655568820 -0.723154254
51 -0.870952660 -0.655568820
52 -0.698258444 -0.870952660
53 -0.799194795 -0.698258444
54 0.219143566 -0.799194795
55 0.197361770 0.219143566
56 0.204888925 0.197361770
57 0.260995796 0.204888925
58 0.322856596 0.260995796
59 0.287574050 0.322856596
60 -0.508464925 0.287574050
61 -0.385760084 -0.508464925
62 -0.366922197 -0.385760084
63 -0.424750843 -0.366922197
64 -0.225785714 -0.424750843
65 -0.283497258 -0.225785714
66 0.192482746 -0.283497258
67 0.217796953 0.192482746
68 0.235618863 0.217796953
69 0.202306070 0.235618863
70 0.219757068 0.202306070
71 -0.002701834 0.219757068
72 0.200680609 -0.002701834
73 0.244631509 0.200680609
74 0.117890588 0.244631509
75 0.272674839 0.117890588
76 0.142039113 0.272674839
77 0.301064952 0.142039113
78 0.110707216 0.301064952
79 0.032499898 0.110707216
80 -0.162829922 0.032499898
81 0.041577587 -0.162829922
82 0.014380337 0.041577587
83 0.147304024 0.014380337
84 -0.002174370 0.147304024
85 0.036351335 -0.002174370
86 0.194481536 0.036351335
87 -0.119393113 0.194481536
88 -0.095429278 -0.119393113
89 -0.193533508 -0.095429278
90 -0.225750999 -0.193533508
91 -0.003377759 -0.225750999
92 0.159151076 -0.003377759
93 0.185710864 0.159151076
94 0.298878618 0.185710864
95 0.235098141 0.298878618
96 0.179277326 0.235098141
97 0.012998057 0.179277326
98 0.066664070 0.012998057
99 -0.063754650 0.066664070
100 -0.182949023 -0.063754650
101 -0.051366473 -0.182949023
102 -0.036636331 -0.051366473
103 0.426341663 -0.036636331
104 0.419428513 0.426341663
105 0.423588258 0.419428513
106 0.397148674 0.423588258
107 0.233464715 0.397148674
108 0.318511377 0.233464715
109 0.349024756 0.318511377
110 0.425910073 0.349024756
111 0.503332708 0.425910073
112 0.532134443 0.503332708
113 0.518977289 0.532134443
114 0.297223372 0.518977289
115 0.197689820 0.297223372
116 0.228446687 0.197689820
117 0.146379313 0.228446687
118 0.256558186 0.146379313
119 0.386840868 0.256558186
120 0.062176275 0.386840868
121 0.314250121 0.062176275
122 0.221447634 0.314250121
123 0.501697514 0.221447634
124 0.477766347 0.501697514
125 0.490244304 0.477766347
126 0.398283441 0.490244304
127 0.464195971 0.398283441
128 0.186047790 0.464195971
129 0.263598684 0.186047790
130 0.094736681 0.263598684
131 0.087657317 0.094736681
132 0.098141994 0.087657317
133 0.290320284 0.098141994
134 -0.057840757 0.290320284
135 -0.115119421 -0.057840757
136 -0.073462886 -0.115119421
137 -0.175982036 -0.073462886
138 -0.101658076 -0.175982036
139 0.023102997 -0.101658076
140 0.078345885 0.023102997
141 0.221146994 0.078345885
142 0.374770647 0.221146994
143 0.464828959 0.374770647
144 0.378345334 0.464828959
145 0.390501170 0.378345334
146 -0.307017323 0.390501170
147 -0.029990549 -0.307017323
148 -0.076185692 -0.029990549
149 0.125814320 -0.076185692
150 0.102567316 0.125814320
151 -0.180781208 0.102567316
152 0.003342556 -0.180781208
153 0.268461810 0.003342556
154 0.087177180 0.268461810
155 0.060810083 0.087177180
156 0.027164546 0.060810083
157 -0.105121808 0.027164546
158 -0.034145631 -0.105121808
159 0.227519838 -0.034145631
160 0.101742335 0.227519838
161 0.027013648 0.101742335
162 0.029403573 0.027013648
163 0.306118083 0.029403573
164 -0.019775344 0.306118083
165 -0.318381253 -0.019775344
166 -0.109664103 -0.318381253
167 -0.048707970 -0.109664103
168 -0.696266649 -0.048707970
169 -0.276562526 -0.696266649
170 -0.179579171 -0.276562526
171 -0.816749174 -0.179579171
172 -0.717445559 -0.816749174
173 -0.685064773 -0.717445559
174 -0.688738033 -0.685064773
175 -0.727674209 -0.688738033
176 -0.744789236 -0.727674209
177 0.253591440 -0.744789236
178 0.309339813 0.253591440
179 0.089042495 0.309339813
180 0.267988287 0.089042495
181 0.136163955 0.267988287
182 0.303405923 0.136163955
183 -0.750992584 0.303405923
184 -0.767837378 -0.750992584
185 -0.759149078 -0.767837378
186 -0.785121461 -0.759149078
187 -0.783644867 -0.785121461
188 -0.687886650 -0.783644867
189 -0.477556370 -0.687886650
190 -0.831972111 -0.477556370
191 -0.649470884 -0.831972111
192 -0.680639711 -0.649470884
193 -0.406946539 -0.680639711
194 -0.493868504 -0.406946539
> 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/7jkvq1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/8o0pr1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/97tvz1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/wessaorg/rcomp/tmp/1036de1386616249.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/wessaorg/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wgcb1386616249.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/12mvy11386616249.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/13ie1f1386616249.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/14a6ug1386616249.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/15srrq1386616249.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/162u6k1386616249.tab")
+ }
>
> try(system("convert tmp/13tbw1386616249.ps tmp/13tbw1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/2podr1386616249.ps tmp/2podr1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lw2j1386616249.ps tmp/3lw2j1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wtfl1386616249.ps tmp/4wtfl1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/5em1n1386616249.ps tmp/5em1n1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/6fvme1386616249.ps tmp/6fvme1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/7jkvq1386616249.ps tmp/7jkvq1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/8o0pr1386616249.ps tmp/8o0pr1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/97tvz1386616249.ps tmp/97tvz1386616249.png",intern=TRUE))
character(0)
> try(system("convert tmp/1036de1386616249.ps tmp/1036de1386616249.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
16.063 3.187 19.351