R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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(22.037
+ ,17.759
+ ,14.116
+ ,104.708
+ ,158.620
+ ,21.732
+ ,17.530
+ ,13.504
+ ,101.817
+ ,154.583
+ ,21.172
+ ,17.139
+ ,13.168
+ ,97.898
+ ,149.377
+ ,21.388
+ ,16.916
+ ,13.064
+ ,95.559
+ ,146.927
+ ,22.053
+ ,16.543
+ ,12.828
+ ,92.822
+ ,144.246
+ ,22.687
+ ,16.317
+ ,12.541
+ ,90.848
+ ,142.393
+ ,24.793
+ ,18.161
+ ,13.547
+ ,101.141
+ ,157.642
+ ,26.113
+ ,19.144
+ ,13.710
+ ,105.841
+ ,164.808
+ ,23.708
+ ,16.947
+ ,12.535
+ ,93.647
+ ,146.837
+ ,23.554
+ ,16.491
+ ,12.386
+ ,90.923
+ ,143.354
+ ,23.222
+ ,16.428
+ ,12.253
+ ,89.130
+ ,141.033
+ ,23.363
+ ,16.639
+ ,12.484
+ ,90.212
+ ,142.698
+ ,24.023
+ ,16.821
+ ,12.966
+ ,93.196
+ ,147.006
+ ,23.355
+ ,16.765
+ ,12.770
+ ,91.861
+ ,144.751
+ ,23.276
+ ,16.533
+ ,12.660
+ ,90.593
+ ,143.062
+ ,23.085
+ ,16.554
+ ,12.514
+ ,89.895
+ ,142.048
+ ,23.173
+ ,16.494
+ ,12.430
+ ,88.819
+ ,140.916
+ ,23.487
+ ,16.612
+ ,12.372
+ ,87.924
+ ,140.395
+ ,25.576
+ ,17.933
+ ,13.085
+ ,96.906
+ ,153.500
+ ,26.311
+ ,19.070
+ ,13.454
+ ,101.217
+ ,160.052
+ ,27.109
+ ,18.179
+ ,13.361
+ ,98.709
+ ,157.358
+ ,27.060
+ ,17.830
+ ,13.713
+ ,98.139
+ ,156.742
+ ,26.490
+ ,17.349
+ ,13.601
+ ,95.529
+ ,152.969
+ ,27.157
+ ,17.919
+ ,14.090
+ ,98.577
+ ,157.743
+ ,26.973
+ ,18.269
+ ,14.452
+ ,100.772
+ ,160.466
+ ,27.589
+ ,18.385
+ ,14.108
+ ,100.180
+ ,160.262
+ ,27.246
+ ,18.260
+ ,14.036
+ ,99.200
+ ,158.742
+ ,26.845
+ ,17.905
+ ,13.332
+ ,96.251
+ ,154.333
+ ,26.582
+ ,17.730
+ ,13.421
+ ,94.514
+ ,152.247
+ ,26.544
+ ,17.827
+ ,13.279
+ ,93.780
+ ,151.430
+ ,29.105
+ ,19.978
+ ,14.583
+ ,105.192
+ ,168.858
+ ,28.703
+ ,20.315
+ ,14.991
+ ,107.682
+ ,171.691
+ ,27.921
+ ,18.931
+ ,14.313
+ ,99.687
+ ,160.852
+ ,28.566
+ ,18.732
+ ,14.769
+ ,99.436
+ ,161.503
+ ,29.860
+ ,19.155
+ ,15.365
+ ,102.049
+ ,166.429
+ ,30.194
+ ,19.270
+ ,15.448
+ ,102.673
+ ,167.585
+ ,31.330
+ ,19.754
+ ,16.485
+ ,105.813
+ ,173.382
+ ,31.018
+ ,19.845
+ ,16.493
+ ,105.056
+ ,172.412
+ ,30.954
+ ,19.937
+ ,16.748
+ ,103.916
+ ,171.555
+ ,31.398
+ ,20.097
+ ,16.921
+ ,103.513
+ ,171.929
+ ,31.267
+ ,19.981
+ ,16.906
+ ,101.893
+ ,170.047
+ ,32.069
+ ,20.502
+ ,17.050
+ ,102.503
+ ,172.124
+ ,34.665
+ ,22.712
+ ,18.873
+ ,113.149
+ ,189.399
+ ,35.834
+ ,23.101
+ ,19.684
+ ,116.696
+ ,195.315
+ ,34.034
+ ,21.381
+ ,18.260
+ ,108.500
+ ,182.175
+ ,34.435
+ ,21.255
+ ,18.338
+ ,107.800
+ ,181.828
+ ,34.000
+ ,21.053
+ ,18.358
+ ,105.941
+ ,179.352
+ ,35.216
+ ,21.561
+ ,19.394
+ ,108.742
+ ,184.913
+ ,35.734
+ ,21.923
+ ,20.568
+ ,111.680
+ ,189.905
+ ,35.347
+ ,22.001
+ ,20.956
+ ,111.270
+ ,189.574
+ ,35.357
+ ,22.369
+ ,21.523
+ ,110.698
+ ,189.947
+ ,34.802
+ ,22.320
+ ,22.712
+ ,108.517
+ ,188.351
+ ,34.493
+ ,22.149
+ ,22.382
+ ,107.127
+ ,186.151
+ ,35.047
+ ,22.581
+ ,23.168
+ ,107.088
+ ,187.884
+ ,37.386
+ ,24.896
+ ,24.777
+ ,116.321
+ ,203.380
+ ,38.691
+ ,26.610
+ ,33.608
+ ,125.045
+ ,223.954
+ ,37.249
+ ,25.417
+ ,33.137
+ ,116.779
+ ,212.582
+ ,37.668
+ ,26.484
+ ,34.897
+ ,122.887
+ ,221.936
+ ,36.764
+ ,26.329
+ ,35.344
+ ,120.162
+ ,218.599
+ ,37.926
+ ,26.989
+ ,36.152
+ ,123.198
+ ,224.265
+ ,38.145
+ ,27.180
+ ,37.291
+ ,123.610
+ ,226.226
+ ,37.664
+ ,27.284
+ ,37.625
+ ,122.293
+ ,224.866
+ ,37.449
+ ,27.436
+ ,38.034
+ ,121.289
+ ,224.208
+ ,37.389
+ ,27.082
+ ,38.244
+ ,119.393
+ ,222.108
+ ,37.121
+ ,26.818
+ ,38.461
+ ,117.494
+ ,219.894
+ ,37.447
+ ,27.003
+ ,39.078
+ ,116.693
+ ,220.221
+ ,39.751
+ ,29.344
+ ,40.701
+ ,125.062
+ ,234.858
+ ,40.154
+ ,29.777
+ ,41.686
+ ,127.281
+ ,238.898
+ ,38.814
+ ,28.070
+ ,41.294
+ ,120.195
+ ,228.373
+ ,38.673
+ ,27.993
+ ,41.927
+ ,119.804
+ ,228.397
+ ,37.948
+ ,27.672
+ ,42.339
+ ,117.113
+ ,225.072
+ ,39.161
+ ,27.802
+ ,43.170
+ ,119.240
+ ,229.373
+ ,37.408
+ ,27.328
+ ,43.703
+ ,115.823
+ ,224.262
+ ,37.356
+ ,27.666
+ ,44.177
+ ,116.281
+ ,225.480
+ ,36.606
+ ,27.456
+ ,44.703
+ ,113.816
+ ,222.581
+ ,37.040
+ ,27.796
+ ,45.319
+ ,114.632
+ ,224.787
+ ,36.349
+ ,27.642
+ ,45.790
+ ,112.987
+ ,222.768
+ ,36.158
+ ,27.651
+ ,45.838
+ ,111.633
+ ,221.280
+ ,37.342
+ ,29.604
+ ,46.806
+ ,116.721
+ ,230.473
+ ,36.800
+ ,29.196
+ ,47.014
+ ,114.850
+ ,227.860
+ ,37.135
+ ,28.328
+ ,47.381
+ ,112.797
+ ,225.641
+ ,34.265
+ ,27.986
+ ,47.049
+ ,105.368
+ ,214.668
+ ,33.226
+ ,27.738
+ ,46.910
+ ,102.524
+ ,210.398
+ ,32.357
+ ,27.867
+ ,46.853
+ ,101.327
+ ,208.404
+ ,36.870
+ ,27.580
+ ,46.608
+ ,98.873
+ ,209.931
+ ,35.880
+ ,27.381
+ ,46.139
+ ,95.993
+ ,205.393
+ ,34.808
+ ,27.292
+ ,45.954
+ ,93.244
+ ,201.298
+ ,34.025
+ ,26.944
+ ,45.367
+ ,90.403
+ ,196.739
+ ,33.901
+ ,26.329
+ ,44.538
+ ,88.539
+ ,193.307
+ ,37.459
+ ,29.023
+ ,45.897
+ ,98.106
+ ,210.485
+ ,37.152
+ ,28.705
+ ,45.744
+ ,96.963
+ ,208.564
+ ,34.929
+ ,27.213
+ ,44.819
+ ,90.781
+ ,197.742
+ ,34.116
+ ,27.063
+ ,44.836
+ ,89.253
+ ,195.268
+ ,33.710
+ ,27.010
+ ,44.779
+ ,87.794
+ ,193.293
+ ,34.264
+ ,27.709
+ ,45.383
+ ,89.810
+ ,197.166
+ ,34.826
+ ,27.802
+ ,45.613
+ ,90.864
+ ,199.105
+ ,34.096
+ ,27.687
+ ,45.331
+ ,89.025
+ ,196.139
+ ,33.955
+ ,27.719
+ ,45.212
+ ,87.621
+ ,194.507
+ ,34.111
+ ,27.961
+ ,45.329
+ ,87.718
+ ,195.119
+ ,32.344
+ ,27.203
+ ,44.603
+ ,83.433
+ ,187.583
+ ,32.871
+ ,27.747
+ ,44.405
+ ,84.535
+ ,189.558
+ ,36.244
+ ,30.713
+ ,45.701
+ ,92.223
+ ,204.881
+ ,35.988
+ ,30.395
+ ,45.647
+ ,91.052
+ ,203.082
+ ,35.439
+ ,28.895
+ ,45.186
+ ,88.456
+ ,197.976
+ ,35.692
+ ,28.460
+ ,45.113
+ ,88.706
+ ,197.971
+ ,35.804
+ ,28.286
+ ,45.301
+ ,89.137
+ ,198.528
+ ,37.747
+ ,28.984
+ ,46.342
+ ,94.066
+ ,207.139
+ ,40.673
+ ,29.624
+ ,47.309
+ ,99.258
+ ,216.864
+ ,41.601
+ ,29.734
+ ,47.659
+ ,100.673
+ ,219.667
+ ,42.273
+ ,30.603
+ ,48.106
+ ,102.269
+ ,223.251
+ ,41.952
+ ,30.427
+ ,48.087
+ ,100.833
+ ,221.299
+ ,41.463
+ ,30.269
+ ,48.188
+ ,99.314
+ ,219.234
+ ,42.759
+ ,30.798
+ ,48.917
+ ,101.764
+ ,224.238
+ ,45.434
+ ,32.676
+ ,50.312
+ ,108.242
+ ,236.664
+ ,45.776
+ ,32.680
+ ,50.531
+ ,108.148
+ ,237.135
+ ,44.630
+ ,30.737
+ ,50.005
+ ,104.761
+ ,230.133
+ ,44.793
+ ,30.300
+ ,50.306
+ ,103.772
+ ,229.171
+ ,44.757
+ ,30.321
+ ,50.598
+ ,103.737
+ ,229.413
+ ,49.099
+ ,31.282
+ ,51.856
+ ,111.043
+ ,243.280
+ ,47.974
+ ,30.868
+ ,52.132
+ ,109.906
+ ,240.880
+ ,47.919
+ ,30.749
+ ,52.167
+ ,109.335
+ ,240.170
+ ,47.519
+ ,30.236
+ ,52.149
+ ,107.247
+ ,237.151
+ ,47.136
+ ,29.990
+ ,51.976
+ ,105.690
+ ,234.792
+ ,45.910
+ ,29.427
+ ,51.797
+ ,102.755
+ ,229.889
+ ,46.436
+ ,29.376
+ ,51.907
+ ,102.280
+ ,229.999
+ ,50.334
+ ,30.828
+ ,53.589
+ ,110.590
+ ,245.341
+ ,50.294
+ ,30.532
+ ,53.814
+ ,109.122
+ ,243.762
+ ,47.224
+ ,29.166
+ ,52.436
+ ,102.795
+ ,231.621
+ ,47.030
+ ,29.124
+ ,52.448
+ ,101.416
+ ,230.018
+ ,45.790
+ ,28.904
+ ,52.322
+ ,99.138
+ ,226.154
+ ,38.252
+ ,27.992
+ ,47.040
+ ,102.612
+ ,215.896)
+ ,dim=c(5
+ ,131)
+ ,dimnames=list(c('Allochtonen'
+ ,'PmAH'
+ ,'50+'
+ ,'Kort-geschoolden'
+ ,'Totaal_NWW')
+ ,1:131))
> y <- array(NA,dim=c(5,131),dimnames=list(c('Allochtonen','PmAH','50+','Kort-geschoolden','Totaal_NWW'),1:131))
> 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'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
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
Allochtonen PmAH 50+ Kort-geschoolden Totaal_NWW
1 22.037 17.759 14.116 104.708 158.620
2 21.732 17.530 13.504 101.817 154.583
3 21.172 17.139 13.168 97.898 149.377
4 21.388 16.916 13.064 95.559 146.927
5 22.053 16.543 12.828 92.822 144.246
6 22.687 16.317 12.541 90.848 142.393
7 24.793 18.161 13.547 101.141 157.642
8 26.113 19.144 13.710 105.841 164.808
9 23.708 16.947 12.535 93.647 146.837
10 23.554 16.491 12.386 90.923 143.354
11 23.222 16.428 12.253 89.130 141.033
12 23.363 16.639 12.484 90.212 142.698
13 24.023 16.821 12.966 93.196 147.006
14 23.355 16.765 12.770 91.861 144.751
15 23.276 16.533 12.660 90.593 143.062
16 23.085 16.554 12.514 89.895 142.048
17 23.173 16.494 12.430 88.819 140.916
18 23.487 16.612 12.372 87.924 140.395
19 25.576 17.933 13.085 96.906 153.500
20 26.311 19.070 13.454 101.217 160.052
21 27.109 18.179 13.361 98.709 157.358
22 27.060 17.830 13.713 98.139 156.742
23 26.490 17.349 13.601 95.529 152.969
24 27.157 17.919 14.090 98.577 157.743
25 26.973 18.269 14.452 100.772 160.466
26 27.589 18.385 14.108 100.180 160.262
27 27.246 18.260 14.036 99.200 158.742
28 26.845 17.905 13.332 96.251 154.333
29 26.582 17.730 13.421 94.514 152.247
30 26.544 17.827 13.279 93.780 151.430
31 29.105 19.978 14.583 105.192 168.858
32 28.703 20.315 14.991 107.682 171.691
33 27.921 18.931 14.313 99.687 160.852
34 28.566 18.732 14.769 99.436 161.503
35 29.860 19.155 15.365 102.049 166.429
36 30.194 19.270 15.448 102.673 167.585
37 31.330 19.754 16.485 105.813 173.382
38 31.018 19.845 16.493 105.056 172.412
39 30.954 19.937 16.748 103.916 171.555
40 31.398 20.097 16.921 103.513 171.929
41 31.267 19.981 16.906 101.893 170.047
42 32.069 20.502 17.050 102.503 172.124
43 34.665 22.712 18.873 113.149 189.399
44 35.834 23.101 19.684 116.696 195.315
45 34.034 21.381 18.260 108.500 182.175
46 34.435 21.255 18.338 107.800 181.828
47 34.000 21.053 18.358 105.941 179.352
48 35.216 21.561 19.394 108.742 184.913
49 35.734 21.923 20.568 111.680 189.905
50 35.347 22.001 20.956 111.270 189.574
51 35.357 22.369 21.523 110.698 189.947
52 34.802 22.320 22.712 108.517 188.351
53 34.493 22.149 22.382 107.127 186.151
54 35.047 22.581 23.168 107.088 187.884
55 37.386 24.896 24.777 116.321 203.380
56 38.691 26.610 33.608 125.045 223.954
57 37.249 25.417 33.137 116.779 212.582
58 37.668 26.484 34.897 122.887 221.936
59 36.764 26.329 35.344 120.162 218.599
60 37.926 26.989 36.152 123.198 224.265
61 38.145 27.180 37.291 123.610 226.226
62 37.664 27.284 37.625 122.293 224.866
63 37.449 27.436 38.034 121.289 224.208
64 37.389 27.082 38.244 119.393 222.108
65 37.121 26.818 38.461 117.494 219.894
66 37.447 27.003 39.078 116.693 220.221
67 39.751 29.344 40.701 125.062 234.858
68 40.154 29.777 41.686 127.281 238.898
69 38.814 28.070 41.294 120.195 228.373
70 38.673 27.993 41.927 119.804 228.397
71 37.948 27.672 42.339 117.113 225.072
72 39.161 27.802 43.170 119.240 229.373
73 37.408 27.328 43.703 115.823 224.262
74 37.356 27.666 44.177 116.281 225.480
75 36.606 27.456 44.703 113.816 222.581
76 37.040 27.796 45.319 114.632 224.787
77 36.349 27.642 45.790 112.987 222.768
78 36.158 27.651 45.838 111.633 221.280
79 37.342 29.604 46.806 116.721 230.473
80 36.800 29.196 47.014 114.850 227.860
81 37.135 28.328 47.381 112.797 225.641
82 34.265 27.986 47.049 105.368 214.668
83 33.226 27.738 46.910 102.524 210.398
84 32.357 27.867 46.853 101.327 208.404
85 36.870 27.580 46.608 98.873 209.931
86 35.880 27.381 46.139 95.993 205.393
87 34.808 27.292 45.954 93.244 201.298
88 34.025 26.944 45.367 90.403 196.739
89 33.901 26.329 44.538 88.539 193.307
90 37.459 29.023 45.897 98.106 210.485
91 37.152 28.705 45.744 96.963 208.564
92 34.929 27.213 44.819 90.781 197.742
93 34.116 27.063 44.836 89.253 195.268
94 33.710 27.010 44.779 87.794 193.293
95 34.264 27.709 45.383 89.810 197.166
96 34.826 27.802 45.613 90.864 199.105
97 34.096 27.687 45.331 89.025 196.139
98 33.955 27.719 45.212 87.621 194.507
99 34.111 27.961 45.329 87.718 195.119
100 32.344 27.203 44.603 83.433 187.583
101 32.871 27.747 44.405 84.535 189.558
102 36.244 30.713 45.701 92.223 204.881
103 35.988 30.395 45.647 91.052 203.082
104 35.439 28.895 45.186 88.456 197.976
105 35.692 28.460 45.113 88.706 197.971
106 35.804 28.286 45.301 89.137 198.528
107 37.747 28.984 46.342 94.066 207.139
108 40.673 29.624 47.309 99.258 216.864
109 41.601 29.734 47.659 100.673 219.667
110 42.273 30.603 48.106 102.269 223.251
111 41.952 30.427 48.087 100.833 221.299
112 41.463 30.269 48.188 99.314 219.234
113 42.759 30.798 48.917 101.764 224.238
114 45.434 32.676 50.312 108.242 236.664
115 45.776 32.680 50.531 108.148 237.135
116 44.630 30.737 50.005 104.761 230.133
117 44.793 30.300 50.306 103.772 229.171
118 44.757 30.321 50.598 103.737 229.413
119 49.099 31.282 51.856 111.043 243.280
120 47.974 30.868 52.132 109.906 240.880
121 47.919 30.749 52.167 109.335 240.170
122 47.519 30.236 52.149 107.247 237.151
123 47.136 29.990 51.976 105.690 234.792
124 45.910 29.427 51.797 102.755 229.889
125 46.436 29.376 51.907 102.280 229.999
126 50.334 30.828 53.589 110.590 245.341
127 50.294 30.532 53.814 109.122 243.762
128 47.224 29.166 52.436 102.795 231.621
129 47.030 29.124 52.448 101.416 230.018
130 45.790 28.904 52.322 99.138 226.154
131 38.252 27.992 47.040 102.612 215.896
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PmAH `50+` `Kort-geschoolden`
5.96e-14 -1.00e+00 -1.00e+00 -1.00e+00
Totaal_NWW
1.00e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-1.394e-13 -7.001e-15 -1.134e-15 6.188e-15 2.650e-13
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.960e-14 3.239e-14 1.840e+00 0.0681 .
PmAH -1.000e+00 3.310e-15 -3.021e+14 <2e-16 ***
`50+` -1.000e+00 1.180e-15 -8.475e+14 <2e-16 ***
`Kort-geschoolden` -1.000e+00 1.004e-15 -9.958e+14 <2e-16 ***
Totaal_NWW 1.000e+00 8.528e-16 1.173e+15 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2.839e-14 on 126 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 2.077e+30 on 4 and 126 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,] 2.797688e-02 5.595376e-02 9.720231e-01
[2,] 5.319685e-04 1.063937e-03 9.994680e-01
[3,] 1.380684e-04 2.761368e-04 9.998619e-01
[4,] 3.306894e-03 6.613788e-03 9.966931e-01
[5,] 9.203765e-01 1.592470e-01 7.962349e-02
[6,] 1.890941e-02 3.781882e-02 9.810906e-01
[7,] 7.217025e-01 5.565950e-01 2.782975e-01
[8,] 2.070596e-02 4.141192e-02 9.792940e-01
[9,] 1.209049e-08 2.418098e-08 1.000000e+00
[10,] 4.601289e-08 9.202577e-08 1.000000e+00
[11,] 1.020700e-03 2.041399e-03 9.989793e-01
[12,] 1.792387e-01 3.584774e-01 8.207613e-01
[13,] 9.976992e-01 4.601510e-03 2.300755e-03
[14,] 1.038875e-11 2.077749e-11 1.000000e+00
[15,] 3.534835e-04 7.069669e-04 9.996465e-01
[16,] 7.017229e-06 1.403446e-05 9.999930e-01
[17,] 3.122194e-16 6.244388e-16 1.000000e+00
[18,] 9.999658e-01 6.832189e-05 3.416094e-05
[19,] 5.400203e-20 1.080041e-19 1.000000e+00
[20,] 1.528457e-12 3.056914e-12 1.000000e+00
[21,] 5.422750e-01 9.154501e-01 4.577250e-01
[22,] 2.001291e-07 4.002583e-07 9.999998e-01
[23,] 3.073148e-16 6.146297e-16 1.000000e+00
[24,] 1.000000e+00 4.046694e-11 2.023347e-11
[25,] 1.179166e-02 2.358332e-02 9.882083e-01
[26,] 4.613562e-11 9.227124e-11 1.000000e+00
[27,] 6.073287e-08 1.214657e-07 9.999999e-01
[28,] 1.579219e-25 3.158439e-25 1.000000e+00
[29,] 3.661028e-12 7.322055e-12 1.000000e+00
[30,] 1.917517e-06 3.835033e-06 9.999981e-01
[31,] 9.770325e-05 1.954065e-04 9.999023e-01
[32,] 6.394465e-07 1.278893e-06 9.999994e-01
[33,] 1.851160e-45 3.702320e-45 1.000000e+00
[34,] 7.225802e-05 1.445160e-04 9.999277e-01
[35,] 2.699446e-22 5.398893e-22 1.000000e+00
[36,] 1.915420e-01 3.830840e-01 8.084580e-01
[37,] 5.663969e-12 1.132794e-11 1.000000e+00
[38,] 3.563083e-14 7.126166e-14 1.000000e+00
[39,] 8.573336e-08 1.714667e-07 9.999999e-01
[40,] 9.999998e-01 3.187299e-07 1.593650e-07
[41,] 9.993990e-01 1.202009e-03 6.010045e-04
[42,] 3.947416e-03 7.894832e-03 9.960526e-01
[43,] 4.034060e-22 8.068119e-22 1.000000e+00
[44,] 7.899682e-01 4.200636e-01 2.100318e-01
[45,] 1.228798e-02 2.457596e-02 9.877120e-01
[46,] 1.372586e-14 2.745172e-14 1.000000e+00
[47,] 1.309246e-03 2.618491e-03 9.986908e-01
[48,] 4.808050e-42 9.616101e-42 1.000000e+00
[49,] 4.641674e-01 9.283349e-01 5.358326e-01
[50,] 9.972685e-01 5.462969e-03 2.731484e-03
[51,] 3.570315e-15 7.140631e-15 1.000000e+00
[52,] 4.765483e-27 9.530967e-27 1.000000e+00
[53,] 2.803403e-09 5.606807e-09 1.000000e+00
[54,] 1.000000e+00 7.738879e-08 3.869439e-08
[55,] 4.909296e-01 9.818592e-01 5.090704e-01
[56,] 9.999994e-01 1.266473e-06 6.332364e-07
[57,] 9.998541e-01 2.918233e-04 1.459116e-04
[58,] 7.410196e-01 5.179608e-01 2.589804e-01
[59,] 8.205304e-01 3.589391e-01 1.794696e-01
[60,] 1.000000e+00 4.522788e-27 2.261394e-27
[61,] 7.835783e-02 1.567157e-01 9.216422e-01
[62,] 3.274873e-20 6.549745e-20 1.000000e+00
[63,] 9.897718e-01 2.045646e-02 1.022823e-02
[64,] 1.178519e-24 2.357038e-24 1.000000e+00
[65,] 5.791654e-09 1.158331e-08 1.000000e+00
[66,] 1.407353e-04 2.814707e-04 9.998593e-01
[67,] 3.090216e-03 6.180431e-03 9.969098e-01
[68,] 9.763307e-01 4.733863e-02 2.366932e-02
[69,] 4.141153e-02 8.282306e-02 9.585885e-01
[70,] 9.956956e-01 8.608725e-03 4.304363e-03
[71,] 9.055758e-01 1.888485e-01 9.442423e-02
[72,] 1.243028e-06 2.486057e-06 9.999988e-01
[73,] 4.947008e-02 9.894015e-02 9.505299e-01
[74,] 3.004358e-36 6.008716e-36 1.000000e+00
[75,] 6.738362e-07 1.347672e-06 9.999993e-01
[76,] 9.999465e-01 1.070739e-04 5.353695e-05
[77,] 8.713925e-21 1.742785e-20 1.000000e+00
[78,] 6.727691e-02 1.345538e-01 9.327231e-01
[79,] 4.479922e-21 8.959844e-21 1.000000e+00
[80,] 1.953737e-21 3.907475e-21 1.000000e+00
[81,] 9.978972e-01 4.205551e-03 2.102775e-03
[82,] 9.999688e-01 6.249385e-05 3.124693e-05
[83,] 1.000000e+00 1.994695e-15 9.973477e-16
[84,] 1.000000e+00 3.696530e-21 1.848265e-21
[85,] 1.081782e-06 2.163564e-06 9.999989e-01
[86,] 9.995448e-01 9.103757e-04 4.551878e-04
[87,] 9.912394e-01 1.752129e-02 8.760643e-03
[88,] 7.350405e-06 1.470081e-05 9.999926e-01
[89,] 4.371211e-05 8.742422e-05 9.999563e-01
[90,] 1.000000e+00 1.204089e-13 6.020446e-14
[91,] 3.688521e-04 7.377041e-04 9.996311e-01
[92,] 3.103594e-04 6.207188e-04 9.996896e-01
[93,] 6.414504e-08 1.282901e-07 9.999999e-01
[94,] 9.993004e-01 1.399230e-03 6.996152e-04
[95,] 6.044055e-01 7.911889e-01 3.955945e-01
[96,] 9.963589e-01 7.282170e-03 3.641085e-03
[97,] 9.480925e-01 1.038150e-01 5.190752e-02
[98,] 9.999961e-01 7.866325e-06 3.933163e-06
[99,] 1.349898e-13 2.699796e-13 1.000000e+00
[100,] 9.999999e-01 1.639789e-07 8.198943e-08
[101,] 9.978719e-01 4.256100e-03 2.128050e-03
[102,] 1.000000e+00 1.546875e-10 7.734377e-11
[103,] 9.999999e-01 1.139844e-07 5.699219e-08
[104,] 9.993923e-01 1.215391e-03 6.076953e-04
[105,] 8.857046e-01 2.285907e-01 1.142954e-01
[106,] 9.994121e-01 1.175764e-03 5.878820e-04
[107,] 9.999571e-01 8.578914e-05 4.289457e-05
[108,] 9.999991e-01 1.856434e-06 9.282170e-07
[109,] 7.307882e-01 5.384236e-01 2.692118e-01
[110,] 9.973425e-01 5.314978e-03 2.657489e-03
[111,] 9.999143e-01 1.714482e-04 8.572410e-05
[112,] 5.804071e-01 8.391857e-01 4.195929e-01
[113,] 9.988049e-01 2.390266e-03 1.195133e-03
[114,] 9.996371e-01 7.257686e-04 3.628843e-04
[115,] 9.743490e-01 5.130208e-02 2.565104e-02
[116,] 9.680269e-01 6.394629e-02 3.197315e-02
> postscript(file="/var/www/html/rcomp/tmp/19m301292852241.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2kvk31292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3kvk31292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4u4j61292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5u4j61292852241.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 = 131
Frequency = 1
1 2 3 4 5
-1.394365e-13 2.649884e-13 -1.637086e-14 -2.832445e-14 -2.190865e-14
6 7 8 9 10
-4.704885e-15 6.324648e-15 -2.559424e-15 1.253299e-14 -1.895176e-14
11 12 13 14 15
9.099527e-15 -7.441075e-15 -9.275290e-15 -1.810557e-15 -6.427027e-15
16 17 18 19 20
-1.349873e-14 2.670755e-15 -1.292947e-14 7.188219e-15 5.601789e-15
21 22 23 24 25
-4.067388e-15 1.776653e-15 2.181727e-15 7.192680e-15 -6.906573e-15
26 27 28 29 30
6.051553e-15 6.955656e-15 7.526338e-15 -1.426130e-14 -3.588986e-15
31 32 33 34 35
-8.670591e-15 -1.134406e-15 -7.194622e-15 2.069076e-14 2.130901e-15
36 37 38 39 40
-5.230991e-15 -3.543368e-16 -7.961120e-15 -6.946641e-15 3.574784e-15
41 42 43 44 45
5.028296e-15 6.454373e-15 -6.781044e-16 5.108585e-15 -1.107957e-14
46 47 48 49 50
-4.774742e-15 8.508686e-17 -6.760340e-15 1.035837e-14 -1.182651e-14
51 52 53 54 55
-1.145143e-14 -3.059896e-15 -8.546966e-15 3.752233e-15 5.364178e-15
56 57 58 59 60
-7.055916e-15 7.899239e-15 -7.263221e-15 1.932187e-14 9.928126e-15
61 62 63 64 65
-4.353403e-15 -6.524081e-15 -4.297055e-15 -2.565693e-15 -4.106817e-15
66 67 68 69 70
-5.802130e-16 -1.247733e-14 1.220619e-14 -1.229273e-15 8.057267e-15
71 72 73 74 75
-3.957739e-15 3.193491e-15 -5.014593e-15 1.792787e-14 1.808541e-14
76 77 78 79 80
-3.881406e-15 -1.214604e-14 -4.974332e-15 -1.209906e-14 -2.287192e-14
81 82 83 84 85
3.035125e-15 -1.450308e-14 -4.155713e-15 -1.026039e-15 -1.410729e-14
86 87 88 89 90
-8.743140e-16 -8.896432e-16 -2.126124e-15 1.554084e-14 -2.080249e-14
91 92 93 94 95
6.156088e-16 1.865056e-14 -4.525016e-15 -2.420016e-15 6.954379e-15
96 97 98 99 100
9.646359e-15 -5.102833e-15 -1.096347e-14 -1.129098e-15 5.877278e-15
101 102 103 104 105
7.563757e-15 -4.849849e-15 4.073859e-15 -2.260713e-16 -4.618962e-16
106 107 108 109 110
1.272034e-14 -4.144073e-15 -1.008755e-14 4.457543e-15 5.488986e-15
111 112 113 114 115
-5.545956e-15 -1.134642e-14 -3.413219e-15 1.345861e-14 1.256016e-14
116 117 118 119 120
-1.196096e-14 9.908733e-15 -1.453121e-14 9.335574e-15 3.549831e-15
121 122 123 124 125
1.016748e-14 -9.608760e-15 1.235739e-15 -1.783531e-14 9.648103e-15
126 127 128 129 130
4.881875e-15 7.606892e-15 -4.366874e-15 1.607008e-15 1.619756e-14
131
1.049443e-14
> postscript(file="/var/www/html/rcomp/tmp/6u4j61292852241.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 = 131
Frequency = 1
lag(myerror, k = 1) myerror
0 -1.394365e-13 NA
1 2.649884e-13 -1.394365e-13
2 -1.637086e-14 2.649884e-13
3 -2.832445e-14 -1.637086e-14
4 -2.190865e-14 -2.832445e-14
5 -4.704885e-15 -2.190865e-14
6 6.324648e-15 -4.704885e-15
7 -2.559424e-15 6.324648e-15
8 1.253299e-14 -2.559424e-15
9 -1.895176e-14 1.253299e-14
10 9.099527e-15 -1.895176e-14
11 -7.441075e-15 9.099527e-15
12 -9.275290e-15 -7.441075e-15
13 -1.810557e-15 -9.275290e-15
14 -6.427027e-15 -1.810557e-15
15 -1.349873e-14 -6.427027e-15
16 2.670755e-15 -1.349873e-14
17 -1.292947e-14 2.670755e-15
18 7.188219e-15 -1.292947e-14
19 5.601789e-15 7.188219e-15
20 -4.067388e-15 5.601789e-15
21 1.776653e-15 -4.067388e-15
22 2.181727e-15 1.776653e-15
23 7.192680e-15 2.181727e-15
24 -6.906573e-15 7.192680e-15
25 6.051553e-15 -6.906573e-15
26 6.955656e-15 6.051553e-15
27 7.526338e-15 6.955656e-15
28 -1.426130e-14 7.526338e-15
29 -3.588986e-15 -1.426130e-14
30 -8.670591e-15 -3.588986e-15
31 -1.134406e-15 -8.670591e-15
32 -7.194622e-15 -1.134406e-15
33 2.069076e-14 -7.194622e-15
34 2.130901e-15 2.069076e-14
35 -5.230991e-15 2.130901e-15
36 -3.543368e-16 -5.230991e-15
37 -7.961120e-15 -3.543368e-16
38 -6.946641e-15 -7.961120e-15
39 3.574784e-15 -6.946641e-15
40 5.028296e-15 3.574784e-15
41 6.454373e-15 5.028296e-15
42 -6.781044e-16 6.454373e-15
43 5.108585e-15 -6.781044e-16
44 -1.107957e-14 5.108585e-15
45 -4.774742e-15 -1.107957e-14
46 8.508686e-17 -4.774742e-15
47 -6.760340e-15 8.508686e-17
48 1.035837e-14 -6.760340e-15
49 -1.182651e-14 1.035837e-14
50 -1.145143e-14 -1.182651e-14
51 -3.059896e-15 -1.145143e-14
52 -8.546966e-15 -3.059896e-15
53 3.752233e-15 -8.546966e-15
54 5.364178e-15 3.752233e-15
55 -7.055916e-15 5.364178e-15
56 7.899239e-15 -7.055916e-15
57 -7.263221e-15 7.899239e-15
58 1.932187e-14 -7.263221e-15
59 9.928126e-15 1.932187e-14
60 -4.353403e-15 9.928126e-15
61 -6.524081e-15 -4.353403e-15
62 -4.297055e-15 -6.524081e-15
63 -2.565693e-15 -4.297055e-15
64 -4.106817e-15 -2.565693e-15
65 -5.802130e-16 -4.106817e-15
66 -1.247733e-14 -5.802130e-16
67 1.220619e-14 -1.247733e-14
68 -1.229273e-15 1.220619e-14
69 8.057267e-15 -1.229273e-15
70 -3.957739e-15 8.057267e-15
71 3.193491e-15 -3.957739e-15
72 -5.014593e-15 3.193491e-15
73 1.792787e-14 -5.014593e-15
74 1.808541e-14 1.792787e-14
75 -3.881406e-15 1.808541e-14
76 -1.214604e-14 -3.881406e-15
77 -4.974332e-15 -1.214604e-14
78 -1.209906e-14 -4.974332e-15
79 -2.287192e-14 -1.209906e-14
80 3.035125e-15 -2.287192e-14
81 -1.450308e-14 3.035125e-15
82 -4.155713e-15 -1.450308e-14
83 -1.026039e-15 -4.155713e-15
84 -1.410729e-14 -1.026039e-15
85 -8.743140e-16 -1.410729e-14
86 -8.896432e-16 -8.743140e-16
87 -2.126124e-15 -8.896432e-16
88 1.554084e-14 -2.126124e-15
89 -2.080249e-14 1.554084e-14
90 6.156088e-16 -2.080249e-14
91 1.865056e-14 6.156088e-16
92 -4.525016e-15 1.865056e-14
93 -2.420016e-15 -4.525016e-15
94 6.954379e-15 -2.420016e-15
95 9.646359e-15 6.954379e-15
96 -5.102833e-15 9.646359e-15
97 -1.096347e-14 -5.102833e-15
98 -1.129098e-15 -1.096347e-14
99 5.877278e-15 -1.129098e-15
100 7.563757e-15 5.877278e-15
101 -4.849849e-15 7.563757e-15
102 4.073859e-15 -4.849849e-15
103 -2.260713e-16 4.073859e-15
104 -4.618962e-16 -2.260713e-16
105 1.272034e-14 -4.618962e-16
106 -4.144073e-15 1.272034e-14
107 -1.008755e-14 -4.144073e-15
108 4.457543e-15 -1.008755e-14
109 5.488986e-15 4.457543e-15
110 -5.545956e-15 5.488986e-15
111 -1.134642e-14 -5.545956e-15
112 -3.413219e-15 -1.134642e-14
113 1.345861e-14 -3.413219e-15
114 1.256016e-14 1.345861e-14
115 -1.196096e-14 1.256016e-14
116 9.908733e-15 -1.196096e-14
117 -1.453121e-14 9.908733e-15
118 9.335574e-15 -1.453121e-14
119 3.549831e-15 9.335574e-15
120 1.016748e-14 3.549831e-15
121 -9.608760e-15 1.016748e-14
122 1.235739e-15 -9.608760e-15
123 -1.783531e-14 1.235739e-15
124 9.648103e-15 -1.783531e-14
125 4.881875e-15 9.648103e-15
126 7.606892e-15 4.881875e-15
127 -4.366874e-15 7.606892e-15
128 1.607008e-15 -4.366874e-15
129 1.619756e-14 1.607008e-15
130 1.049443e-14 1.619756e-14
131 NA 1.049443e-14
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 2.649884e-13 -1.394365e-13
[2,] -1.637086e-14 2.649884e-13
[3,] -2.832445e-14 -1.637086e-14
[4,] -2.190865e-14 -2.832445e-14
[5,] -4.704885e-15 -2.190865e-14
[6,] 6.324648e-15 -4.704885e-15
[7,] -2.559424e-15 6.324648e-15
[8,] 1.253299e-14 -2.559424e-15
[9,] -1.895176e-14 1.253299e-14
[10,] 9.099527e-15 -1.895176e-14
[11,] -7.441075e-15 9.099527e-15
[12,] -9.275290e-15 -7.441075e-15
[13,] -1.810557e-15 -9.275290e-15
[14,] -6.427027e-15 -1.810557e-15
[15,] -1.349873e-14 -6.427027e-15
[16,] 2.670755e-15 -1.349873e-14
[17,] -1.292947e-14 2.670755e-15
[18,] 7.188219e-15 -1.292947e-14
[19,] 5.601789e-15 7.188219e-15
[20,] -4.067388e-15 5.601789e-15
[21,] 1.776653e-15 -4.067388e-15
[22,] 2.181727e-15 1.776653e-15
[23,] 7.192680e-15 2.181727e-15
[24,] -6.906573e-15 7.192680e-15
[25,] 6.051553e-15 -6.906573e-15
[26,] 6.955656e-15 6.051553e-15
[27,] 7.526338e-15 6.955656e-15
[28,] -1.426130e-14 7.526338e-15
[29,] -3.588986e-15 -1.426130e-14
[30,] -8.670591e-15 -3.588986e-15
[31,] -1.134406e-15 -8.670591e-15
[32,] -7.194622e-15 -1.134406e-15
[33,] 2.069076e-14 -7.194622e-15
[34,] 2.130901e-15 2.069076e-14
[35,] -5.230991e-15 2.130901e-15
[36,] -3.543368e-16 -5.230991e-15
[37,] -7.961120e-15 -3.543368e-16
[38,] -6.946641e-15 -7.961120e-15
[39,] 3.574784e-15 -6.946641e-15
[40,] 5.028296e-15 3.574784e-15
[41,] 6.454373e-15 5.028296e-15
[42,] -6.781044e-16 6.454373e-15
[43,] 5.108585e-15 -6.781044e-16
[44,] -1.107957e-14 5.108585e-15
[45,] -4.774742e-15 -1.107957e-14
[46,] 8.508686e-17 -4.774742e-15
[47,] -6.760340e-15 8.508686e-17
[48,] 1.035837e-14 -6.760340e-15
[49,] -1.182651e-14 1.035837e-14
[50,] -1.145143e-14 -1.182651e-14
[51,] -3.059896e-15 -1.145143e-14
[52,] -8.546966e-15 -3.059896e-15
[53,] 3.752233e-15 -8.546966e-15
[54,] 5.364178e-15 3.752233e-15
[55,] -7.055916e-15 5.364178e-15
[56,] 7.899239e-15 -7.055916e-15
[57,] -7.263221e-15 7.899239e-15
[58,] 1.932187e-14 -7.263221e-15
[59,] 9.928126e-15 1.932187e-14
[60,] -4.353403e-15 9.928126e-15
[61,] -6.524081e-15 -4.353403e-15
[62,] -4.297055e-15 -6.524081e-15
[63,] -2.565693e-15 -4.297055e-15
[64,] -4.106817e-15 -2.565693e-15
[65,] -5.802130e-16 -4.106817e-15
[66,] -1.247733e-14 -5.802130e-16
[67,] 1.220619e-14 -1.247733e-14
[68,] -1.229273e-15 1.220619e-14
[69,] 8.057267e-15 -1.229273e-15
[70,] -3.957739e-15 8.057267e-15
[71,] 3.193491e-15 -3.957739e-15
[72,] -5.014593e-15 3.193491e-15
[73,] 1.792787e-14 -5.014593e-15
[74,] 1.808541e-14 1.792787e-14
[75,] -3.881406e-15 1.808541e-14
[76,] -1.214604e-14 -3.881406e-15
[77,] -4.974332e-15 -1.214604e-14
[78,] -1.209906e-14 -4.974332e-15
[79,] -2.287192e-14 -1.209906e-14
[80,] 3.035125e-15 -2.287192e-14
[81,] -1.450308e-14 3.035125e-15
[82,] -4.155713e-15 -1.450308e-14
[83,] -1.026039e-15 -4.155713e-15
[84,] -1.410729e-14 -1.026039e-15
[85,] -8.743140e-16 -1.410729e-14
[86,] -8.896432e-16 -8.743140e-16
[87,] -2.126124e-15 -8.896432e-16
[88,] 1.554084e-14 -2.126124e-15
[89,] -2.080249e-14 1.554084e-14
[90,] 6.156088e-16 -2.080249e-14
[91,] 1.865056e-14 6.156088e-16
[92,] -4.525016e-15 1.865056e-14
[93,] -2.420016e-15 -4.525016e-15
[94,] 6.954379e-15 -2.420016e-15
[95,] 9.646359e-15 6.954379e-15
[96,] -5.102833e-15 9.646359e-15
[97,] -1.096347e-14 -5.102833e-15
[98,] -1.129098e-15 -1.096347e-14
[99,] 5.877278e-15 -1.129098e-15
[100,] 7.563757e-15 5.877278e-15
[101,] -4.849849e-15 7.563757e-15
[102,] 4.073859e-15 -4.849849e-15
[103,] -2.260713e-16 4.073859e-15
[104,] -4.618962e-16 -2.260713e-16
[105,] 1.272034e-14 -4.618962e-16
[106,] -4.144073e-15 1.272034e-14
[107,] -1.008755e-14 -4.144073e-15
[108,] 4.457543e-15 -1.008755e-14
[109,] 5.488986e-15 4.457543e-15
[110,] -5.545956e-15 5.488986e-15
[111,] -1.134642e-14 -5.545956e-15
[112,] -3.413219e-15 -1.134642e-14
[113,] 1.345861e-14 -3.413219e-15
[114,] 1.256016e-14 1.345861e-14
[115,] -1.196096e-14 1.256016e-14
[116,] 9.908733e-15 -1.196096e-14
[117,] -1.453121e-14 9.908733e-15
[118,] 9.335574e-15 -1.453121e-14
[119,] 3.549831e-15 9.335574e-15
[120,] 1.016748e-14 3.549831e-15
[121,] -9.608760e-15 1.016748e-14
[122,] 1.235739e-15 -9.608760e-15
[123,] -1.783531e-14 1.235739e-15
[124,] 9.648103e-15 -1.783531e-14
[125,] 4.881875e-15 9.648103e-15
[126,] 7.606892e-15 4.881875e-15
[127,] -4.366874e-15 7.606892e-15
[128,] 1.607008e-15 -4.366874e-15
[129,] 1.619756e-14 1.607008e-15
[130,] 1.049443e-14 1.619756e-14
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 2.649884e-13 -1.394365e-13
2 -1.637086e-14 2.649884e-13
3 -2.832445e-14 -1.637086e-14
4 -2.190865e-14 -2.832445e-14
5 -4.704885e-15 -2.190865e-14
6 6.324648e-15 -4.704885e-15
7 -2.559424e-15 6.324648e-15
8 1.253299e-14 -2.559424e-15
9 -1.895176e-14 1.253299e-14
10 9.099527e-15 -1.895176e-14
11 -7.441075e-15 9.099527e-15
12 -9.275290e-15 -7.441075e-15
13 -1.810557e-15 -9.275290e-15
14 -6.427027e-15 -1.810557e-15
15 -1.349873e-14 -6.427027e-15
16 2.670755e-15 -1.349873e-14
17 -1.292947e-14 2.670755e-15
18 7.188219e-15 -1.292947e-14
19 5.601789e-15 7.188219e-15
20 -4.067388e-15 5.601789e-15
21 1.776653e-15 -4.067388e-15
22 2.181727e-15 1.776653e-15
23 7.192680e-15 2.181727e-15
24 -6.906573e-15 7.192680e-15
25 6.051553e-15 -6.906573e-15
26 6.955656e-15 6.051553e-15
27 7.526338e-15 6.955656e-15
28 -1.426130e-14 7.526338e-15
29 -3.588986e-15 -1.426130e-14
30 -8.670591e-15 -3.588986e-15
31 -1.134406e-15 -8.670591e-15
32 -7.194622e-15 -1.134406e-15
33 2.069076e-14 -7.194622e-15
34 2.130901e-15 2.069076e-14
35 -5.230991e-15 2.130901e-15
36 -3.543368e-16 -5.230991e-15
37 -7.961120e-15 -3.543368e-16
38 -6.946641e-15 -7.961120e-15
39 3.574784e-15 -6.946641e-15
40 5.028296e-15 3.574784e-15
41 6.454373e-15 5.028296e-15
42 -6.781044e-16 6.454373e-15
43 5.108585e-15 -6.781044e-16
44 -1.107957e-14 5.108585e-15
45 -4.774742e-15 -1.107957e-14
46 8.508686e-17 -4.774742e-15
47 -6.760340e-15 8.508686e-17
48 1.035837e-14 -6.760340e-15
49 -1.182651e-14 1.035837e-14
50 -1.145143e-14 -1.182651e-14
51 -3.059896e-15 -1.145143e-14
52 -8.546966e-15 -3.059896e-15
53 3.752233e-15 -8.546966e-15
54 5.364178e-15 3.752233e-15
55 -7.055916e-15 5.364178e-15
56 7.899239e-15 -7.055916e-15
57 -7.263221e-15 7.899239e-15
58 1.932187e-14 -7.263221e-15
59 9.928126e-15 1.932187e-14
60 -4.353403e-15 9.928126e-15
61 -6.524081e-15 -4.353403e-15
62 -4.297055e-15 -6.524081e-15
63 -2.565693e-15 -4.297055e-15
64 -4.106817e-15 -2.565693e-15
65 -5.802130e-16 -4.106817e-15
66 -1.247733e-14 -5.802130e-16
67 1.220619e-14 -1.247733e-14
68 -1.229273e-15 1.220619e-14
69 8.057267e-15 -1.229273e-15
70 -3.957739e-15 8.057267e-15
71 3.193491e-15 -3.957739e-15
72 -5.014593e-15 3.193491e-15
73 1.792787e-14 -5.014593e-15
74 1.808541e-14 1.792787e-14
75 -3.881406e-15 1.808541e-14
76 -1.214604e-14 -3.881406e-15
77 -4.974332e-15 -1.214604e-14
78 -1.209906e-14 -4.974332e-15
79 -2.287192e-14 -1.209906e-14
80 3.035125e-15 -2.287192e-14
81 -1.450308e-14 3.035125e-15
82 -4.155713e-15 -1.450308e-14
83 -1.026039e-15 -4.155713e-15
84 -1.410729e-14 -1.026039e-15
85 -8.743140e-16 -1.410729e-14
86 -8.896432e-16 -8.743140e-16
87 -2.126124e-15 -8.896432e-16
88 1.554084e-14 -2.126124e-15
89 -2.080249e-14 1.554084e-14
90 6.156088e-16 -2.080249e-14
91 1.865056e-14 6.156088e-16
92 -4.525016e-15 1.865056e-14
93 -2.420016e-15 -4.525016e-15
94 6.954379e-15 -2.420016e-15
95 9.646359e-15 6.954379e-15
96 -5.102833e-15 9.646359e-15
97 -1.096347e-14 -5.102833e-15
98 -1.129098e-15 -1.096347e-14
99 5.877278e-15 -1.129098e-15
100 7.563757e-15 5.877278e-15
101 -4.849849e-15 7.563757e-15
102 4.073859e-15 -4.849849e-15
103 -2.260713e-16 4.073859e-15
104 -4.618962e-16 -2.260713e-16
105 1.272034e-14 -4.618962e-16
106 -4.144073e-15 1.272034e-14
107 -1.008755e-14 -4.144073e-15
108 4.457543e-15 -1.008755e-14
109 5.488986e-15 4.457543e-15
110 -5.545956e-15 5.488986e-15
111 -1.134642e-14 -5.545956e-15
112 -3.413219e-15 -1.134642e-14
113 1.345861e-14 -3.413219e-15
114 1.256016e-14 1.345861e-14
115 -1.196096e-14 1.256016e-14
116 9.908733e-15 -1.196096e-14
117 -1.453121e-14 9.908733e-15
118 9.335574e-15 -1.453121e-14
119 3.549831e-15 9.335574e-15
120 1.016748e-14 3.549831e-15
121 -9.608760e-15 1.016748e-14
122 1.235739e-15 -9.608760e-15
123 -1.783531e-14 1.235739e-15
124 9.648103e-15 -1.783531e-14
125 4.881875e-15 9.648103e-15
126 7.606892e-15 4.881875e-15
127 -4.366874e-15 7.606892e-15
128 1.607008e-15 -4.366874e-15
129 1.619756e-14 1.607008e-15
130 1.049443e-14 1.619756e-14
> 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/www/html/rcomp/tmp/7nv091292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8nv091292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9gn0u1292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10gn0u1292852241.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/11j5y01292852241.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/12nox61292852241.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13bpuh1292852241.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/14mgb21292852241.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15pyr81292852241.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/16bzqw1292852241.tab")
+ }
>
> try(system("convert tmp/19m301292852241.ps tmp/19m301292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/2kvk31292852241.ps tmp/2kvk31292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kvk31292852241.ps tmp/3kvk31292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u4j61292852241.ps tmp/4u4j61292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/5u4j61292852241.ps tmp/5u4j61292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/6u4j61292852241.ps tmp/6u4j61292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/7nv091292852241.ps tmp/7nv091292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/8nv091292852241.ps tmp/8nv091292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/9gn0u1292852241.ps tmp/9gn0u1292852241.png",intern=TRUE))
character(0)
> try(system("convert tmp/10gn0u1292852241.ps tmp/10gn0u1292852241.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.470 1.739 11.208