R version 3.1.0 (2014-04-10) -- "Spring Dance"
Copyright (C) 2014 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(426483000
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+ ,8085780
+ ,818812
+ ,2201360
+ ,5234681
+ ,10501150
+ ,2.68840173
+ ,2.288625225
+ ,3.002692573
+ ,1.835910698
+ ,1.218576951
+ ,2.75776767
+ ,1.883603496
+ ,0.410007021
+ ,1.070603697
+ ,364431300
+ ,1.26
+ ,2.42
+ ,7777563
+ ,813389
+ ,2215184
+ ,5456357
+ ,9476948
+ ,2.702713317
+ ,2.299230359
+ ,2.999452007
+ ,1.879256466
+ ,1.227618962
+ ,2.837470317
+ ,1.882887244
+ ,0.411940992
+ ,1.086029757
+ ,378402900
+ ,1.27
+ ,2.39
+ ,8192525
+ ,791213
+ ,2140796
+ ,5055154
+ ,9854999
+ ,2.711022051
+ ,2.321891253
+ ,2.973988498
+ ,1.860613281
+ ,1.219326172
+ ,2.795636668
+ ,1.879571383
+ ,0.407448988
+ ,1.072047077
+ ,364713400
+ ,1.26
+ ,2.39
+ ,8222640
+ ,753162
+ ,2064345
+ ,4986559
+ ,9020688
+ ,2.687873468
+ ,2.337535129
+ ,3.009436817
+ ,1.857021191
+ ,1.225537073
+ ,2.76981684
+ ,1.878656159
+ ,0.41077387
+ ,1.078852318
+ ,399466200
+ ,1.25
+ ,2.39
+ ,8852425
+ ,744738
+ ,2246763
+ ,5314687
+ ,9639666
+ ,2.719101948
+ ,2.33880085
+ ,3.034231024
+ ,1.876345796
+ ,1.230269355
+ ,2.795837284
+ ,1.841411286
+ ,0.406615344
+ ,1.072090819
+ ,360783600
+ ,1.25
+ ,2.41
+ ,8047626
+ ,740853
+ ,2196948
+ ,5029952
+ ,10016963
+ ,2.714630813
+ ,2.290887791
+ ,3.007439311
+ ,1.880816873
+ ,1.218710873
+ ,2.791096748
+ ,1.855609066
+ ,0.40071109
+ ,1.083545324
+ ,356600800
+ ,1.25
+ ,2.37
+ ,8079925
+ ,828505
+ ,1987852
+ ,4569712
+ ,9221363
+ ,2.709502388
+ ,2.193859495
+ ,2.958101457
+ ,1.89514652
+ ,1.211570297
+ ,2.786898772
+ ,1.862204445
+ ,0.401803464
+ ,1.095450614
+ ,351141200
+ ,1.26
+ ,2.38
+ ,8099820
+ ,764325
+ ,2013311
+ ,4661941
+ ,9163961
+ ,2.735607888
+ ,2.017806522
+ ,3.081504548
+ ,1.891959507
+ ,1.185688863
+ ,2.776968326
+ ,1.860246189
+ ,0.40525702
+ ,1.086912011
+ ,325866500
+ ,1.26
+ ,2.41
+ ,7444464
+ ,779152
+ ,2024477
+ ,4649692
+ ,9600997
+ ,2.820171462
+ ,2.130092524
+ ,3.092087101
+ ,1.906442444
+ ,1.188298584
+ ,2.802245654
+ ,1.875842876
+ ,0.406466124
+ ,1.069476232)
+ ,dim=c(17
+ ,120)
+ ,dimnames=list(c('QBEPIL'
+ ,'PBEPIL'
+ ,'PBELUX'
+ ,'BUDBEER'
+ ,'BUDCHIL'
+ ,'BUDAMB'
+ ,'BUDWATER'
+ ,'BUDSISSS'
+ ,'Gpspontanegisting'
+ ,'GPHogegisting'
+ ,'GPAngelsa'
+ ,'GPZuur'
+ ,'GPlaagalc'
+ ,'GPChill'
+ ,'GPAmbient'
+ ,'GPWaters'
+ ,'Gpsiss
')
+ ,1:120))
> y <- array(NA,dim=c(17,120),dimnames=list(c('QBEPIL','PBEPIL','PBELUX','BUDBEER','BUDCHIL','BUDAMB','BUDWATER','BUDSISSS','Gpspontanegisting','GPHogegisting','GPAngelsa','GPZuur','GPlaagalc','GPChill','GPAmbient','GPWaters','Gpsiss
'),1:120))
> 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'
> 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
QBEPIL PBEPIL PBELUX BUDBEER BUDCHIL BUDAMB BUDWATER BUDSISSS
1 426483000 1.23 2.45 8890176 484574 2254011 6304844 10064618
2 392467400 1.22 2.46 8194413 478106 2013875 5471891 11338363
3 373475300 1.21 2.45 7722000 506039 2308944 5581708 9435079
4 376229000 1.22 2.43 7769178 508171 2278649 5421028 8143581
5 360973900 1.21 2.44 7449343 468388 2109718 5136152 7775342
6 387759400 1.22 2.41 7929370 466709 2070365 4948893 7656876
7 363641500 1.21 2.41 7473017 499053 2041975 4866528 8203164
8 357819500 1.20 2.41 7472424 499697 2130112 5110882 8447687
9 360434200 1.18 2.41 7292436 456662 2012391 4775552 8482877
10 345951300 1.19 2.38 7215340 467478 1995215 4690143 8131924
11 336657100 1.20 2.41 7216230 453126 1959695 4521167 8184292
12 337127700 1.19 2.39 7378041 449584 2079820 4618744 8006102
13 372484800 1.19 2.37 7877412 423896 2201750 4921010 8052832
14 335083000 1.20 2.40 7158125 460454 1980527 4739711 7854934
15 330515900 1.21 2.35 7137912 454105 2023721 4767867 7609626
16 339073600 1.20 2.35 7290803 453042 2136317 4856393 7640934
17 334975800 1.20 2.33 7425266 433082 2205673 4684931 8422297
18 325365500 1.20 2.35 7450430 460163 2163485 4583205 7980377
19 373425000 1.21 2.36 9214042 421051 2844091 5216686 9541323
20 345543300 1.21 2.41 8158864 435182 2458147 4583585 8839590
21 296672600 1.21 2.37 6515759 495363 1972304 4307098 7677033
22 299371600 1.20 2.34 6308487 472805 2153601 4748004 8354688
23 300932000 1.21 2.37 6366367 452921 2066530 4710073 8150927
24 316971300 1.21 2.34 6770097 450870 2152437 4867230 7846633
25 317006100 1.21 2.34 6700697 472551 2189294 4794611 8461058
26 336893400 1.20 2.33 7140792 462772 2253024 4883881 8425126
27 329263800 1.19 2.33 6891715 507189 2151817 4711492 8351766
28 333734400 1.20 2.34 7057521 513235 2141496 4810043 7956264
29 320830600 1.20 2.37 6806593 602342 2240864 5020983 8502847
30 335913000 1.20 2.38 7068776 638260 2198530 5071676 8671279
31 322307800 1.22 2.41 6868085 618068 2213237 5096684 8230049
32 343715900 1.22 2.39 7245015 607338 2252202 5263979 8404517
33 340015600 1.21 2.38 7160726 1002379 2419597 5523848 8872254
34 365757600 1.25 2.45 7927365 755302 2334515 5259355 9651748
35 376561300 1.25 2.41 8275238 724580 2155819 5044615 9070647
36 348192100 1.27 2.46 7510220 706447 2532345 5875038 8649186
37 360480000 1.28 2.40 7751398 991278 2221561 5321561 9030492
38 398134000 1.27 2.31 8701633 852996 2302538 5261199 9069668
39 373407800 1.28 2.42 8164755 673183 2350319 5621057 9116009
40 401817300 1.29 2.46 8534307 686730 2287028 5303894 10336764
41 388741700 1.26 2.45 8333017 768403 2262802 5325086 8941018
42 391988000 1.27 2.48 8568251 720603 2641195 6602036 10163717
43 401446600 1.25 2.45 8613013 688646 2886395 7354948 10028886
44 419775800 1.27 2.45 9139357 717093 2430852 6231237 10190148
45 389653100 1.27 2.43 8385716 806356 2412703 6066821 11198930
46 396474200 1.27 2.44 8451237 649995 2365468 6209715 10355548
47 420184700 1.29 2.46 9033401 540044 2057798 5353594 9396952
48 405051200 1.26 2.48 8565930 591115 2390239 6427650 9238064
49 399740200 1.27 2.52 8562307 493197 2456918 6941697 9286880
50 431447900 1.27 2.51 9255216 574142 2048758 5514399 10943146
51 492574400 1.28 2.50 10502760 545220 2513095 7322716 11297607
52 513063100 1.28 2.50 10855161 484423 2887292 9651951 9982802
53 444485500 1.28 2.53 9473338 561620 2295291 6686974 11849225
54 396731900 1.27 2.54 8521439 554667 2160295 5573380 9895998
55 393125000 1.24 2.54 8169912 695658 2430452 5428766 10512292
56 423595200 1.25 2.53 8705590 694559 2381670 5352882 10001971
57 416921900 1.25 2.48 8600302 613095 2215665 5114736 9450060
58 377906400 1.24 2.47 7884570 602933 2350453 5800681 9047810
59 355881000 1.24 2.44 7509946 614260 2263940 5430653 9034858
60 369946600 1.23 2.44 7796000 580581 2223827 5325139 9626461
61 365069300 1.24 2.43 7651158 617713 2071658 4874369 8887882
62 352563300 1.23 2.41 7430052 605519 2118606 4747271 8699165
63 347027600 1.24 2.42 7581024 609843 1980701 4500918 8756626
64 385909400 1.24 2.43 8431470 592140 2141976 4660010 9120578
65 366115500 1.24 2.42 7903994 582844 2262595 4916788 9410935
66 335636500 1.25 2.46 7462642 614646 2044949 4649568 8540660
67 334444000 1.26 2.47 7424743 607572 2055490 4677774 8577630
68 333868400 1.26 2.46 7480504 620835 2111968 4862450 8963865
69 340429400 1.27 2.43 7863944 581938 2153156 4836102 8831677
70 328931900 1.26 2.46 7703698 609333 2149987 4707458 8680975
71 346925200 1.28 2.46 8508132 619133 2805043 5364205 10889743
72 357185000 1.29 2.47 8933008 572585 2449477 4351596 9842291
73 363991400 1.28 2.48 8491850 599516 2168905 4208876 8005657
74 309173000 1.27 2.43 6940275 655034 2218929 5062032 8714475
75 307814900 1.30 2.42 6917191 668502 2144176 4893322 8555468
76 318811500 1.30 2.45 7096722 666124 2170967 4848894 8571300
77 324608200 1.28 2.43 7105114 732417 2240876 4922093 8764326
78 348699200 1.29 2.44 7647797 702229 2330906 5351141 9089938
79 337818700 1.27 2.42 7440408 684271 2188360 5017799 8778446
80 328230600 1.26 2.43 7255613 633638 2067367 4923300 8809264
81 328834500 1.27 2.43 7231703 693374 2189597 4915221 9521789
82 332574900 1.27 2.40 7278022 707616 2356724 5348984 9438993
83 335226200 1.27 2.39 7382680 722553 2250295 5135063 9045288
84 353195400 1.28 2.42 7622740 712532 2243913 5339400 9272049
85 372262200 1.29 2.41 8295038 687023 2172504 5122639 9978418
86 380936500 1.28 2.37 8136158 646716 2301051 5710269 9776284
87 375061700 1.30 2.38 8240817 657284 2245784 5187058 9601480
88 361528600 1.30 2.37 7993962 701042 2159896 5277273 11193789
89 369655600 1.30 2.38 7997958 744939 2374240 5431043 9607554
90 412395900 1.29 2.37 8914911 823561 2533022 6064885 9870457
91 413616300 1.30 2.40 9082346 810516 2419167 5849883 10260040
92 393339200 1.29 2.66 8690947 755964 2379061 5763961 9578120
93 403557600 1.28 2.50 8678669 707347 2264684 5612253 9693065
94 455120200 1.30 2.60 9768461 727181 2378165 5996108 12413462
95 403219500 1.30 2.64 8751448 1110335 2536093 6163859 13143933
96 397089300 1.31 2.67 8737854 939274 2559486 6806073 11118547
97 448901600 1.32 2.72 9684075 842499 2340159 5770678 11289800
98 542612700 1.33 2.73 11529582 785788 2235562 5305632 11573959
99 457822400 1.32 2.48 9854882 812169 2300728 5714880 10511958
100 412639000 1.30 2.41 9030507 730023 2090042 5307840 12515693
101 489210000 1.31 2.47 10656814 823033 1976051 4951640 12966759
102 412869700 1.30 2.54 9111428 976731 2104956 5576975 10668160
103 440872100 1.30 2.56 9642906 738606 2489023 6787849 13948692
104 419946500 1.30 2.52 9217060 685173 2598916 7685812 16087616
105 407476700 1.29 2.52 8816389 642519 2302455 6451885 12159456
106 416175800 1.29 2.51 9074790 677849 2427969 5521297 10633146
107 389131900 1.30 2.51 8601172 826348 2132820 5268035 10770809
108 447030200 1.30 2.51 9735782 757562 2560376 6159480 10548925
109 428311100 1.29 2.46 9222117 825217 2454605 6391178 10123204
110 384596200 1.27 2.45 8197462 831800 2169005 5446149 11471988
111 391147100 1.26 2.45 8161117 890944 2072759 5055640 10599314
112 379847800 1.25 2.43 8085780 818812 2201360 5234681 10501150
113 364431300 1.26 2.42 7777563 813389 2215184 5456357 9476948
114 378402900 1.27 2.39 8192525 791213 2140796 5055154 9854999
115 364713400 1.26 2.39 8222640 753162 2064345 4986559 9020688
116 399466200 1.25 2.39 8852425 744738 2246763 5314687 9639666
117 360783600 1.25 2.41 8047626 740853 2196948 5029952 10016963
118 356600800 1.25 2.37 8079925 828505 1987852 4569712 9221363
119 351141200 1.26 2.38 8099820 764325 2013311 4661941 9163961
120 325866500 1.26 2.41 7444464 779152 2024477 4649692 9600997
Gpspontanegisting GPHogegisting GPAngelsa GPZuur GPlaagalc GPChill
1 2.504817 2.255626 2.962674 1.827410 1.208306 1.936779
2 2.588857 2.254290 2.938077 1.831346 1.185837 1.906554
3 2.556707 2.287742 2.939493 1.907241 1.170278 1.894045
4 2.594016 2.239527 2.956607 1.888364 1.200371 1.905172
5 2.575035 2.233534 2.932734 1.881639 1.155476 1.954426
6 2.615119 2.251360 2.952193 1.792642 1.159318 1.980370
7 2.594231 2.246389 2.935101 1.777924 1.156773 1.952754
8 2.575671 2.199554 2.902322 1.824054 1.152792 2.013871
9 2.574788 2.235533 2.903412 1.787896 1.156076 2.008427
10 2.570519 2.296328 2.928809 1.781207 1.156706 2.024103
11 2.545130 2.343531 2.893995 1.833009 1.149696 1.971698
12 2.512918 2.235156 2.838043 1.729242 1.140621 2.018155
13 2.498667 2.285846 2.907516 1.744426 1.148619 2.013227
14 2.585555 2.251970 2.923323 1.718288 1.145658 1.983055
15 2.625265 2.279027 2.928429 1.783162 1.122519 2.022637
16 2.633980 2.295531 2.963569 1.799558 1.125494 2.003846
17 2.610122 2.377658 2.978760 1.811653 1.135484 1.997123
18 2.637667 2.407761 2.995808 1.840064 1.150970 1.989838
19 2.670848 2.438597 3.021960 1.848192 1.166471 2.012842
20 2.628940 2.404122 2.995749 1.828288 1.181252 2.012008
21 2.579245 2.382701 2.968704 1.834119 1.163747 1.989896
22 2.557072 2.323591 2.869143 1.782762 1.132997 2.039054
23 2.574247 2.343267 2.904964 1.815650 1.146313 2.146552
24 2.549499 2.198970 2.886225 1.812557 1.153624 2.125246
25 2.577397 2.249485 2.901268 1.747184 1.159082 2.159488
26 2.498397 2.194953 2.930933 1.823167 1.169422 2.186124
27 2.563524 2.222291 2.919416 1.820785 1.152116 2.337412
28 2.624862 2.265730 2.894431 1.756938 1.166866 2.311677
29 2.709465 2.244914 2.933674 1.806361 1.156562 2.520299
30 2.744713 2.294221 2.945369 1.822648 1.178925 2.638087
31 2.755161 2.304894 2.981639 1.828013 1.173544 2.629312
32 2.656235 2.297782 2.921159 1.812349 1.187552 2.654890
33 2.611063 2.273074 2.925545 1.861115 1.186151 3.752995
34 2.675910 2.287185 2.965121 1.895892 1.145795 2.947602
35 2.696625 2.282507 2.935907 1.821686 1.139174 2.783817
36 2.698153 2.286902 2.942463 1.854124 1.152087 2.756392
37 2.715738 2.274888 2.897592 1.825785 1.156027 3.279356
38 2.765955 2.249845 2.907356 1.867838 1.123517 3.006372
39 2.764383 2.285657 2.938902 1.953847 1.158492 2.663468
40 2.720862 2.291512 2.955250 1.960182 1.177596 2.650500
41 2.694286 2.281690 2.950777 1.929867 1.178394 2.601235
42 2.698284 2.268141 2.958700 1.807260 1.173076 2.669183
43 2.691642 2.268784 2.947820 1.907539 1.172441 2.669255
44 2.662469 2.273535 2.958429 2.025662 1.187008 2.661866
45 2.739787 2.291996 2.915143 2.001330 1.181594 2.544646
46 2.761340 2.281173 2.929916 2.022590 1.180677 2.524337
47 2.794039 2.283323 2.932974 1.979554 1.174044 2.504138
48 2.780640 2.250932 2.949276 2.010072 1.174056 2.464021
49 2.803383 2.214853 2.961243 2.001885 1.191268 2.469471
50 2.777276 2.235229 2.962110 1.965538 1.212969 2.484135
51 2.755297 2.244127 2.962679 1.984683 1.203682 2.480214
52 2.730574 2.241384 2.964296 2.025362 1.202189 2.493923
53 2.722272 2.277891 3.010427 1.954345 1.196644 2.486597
54 2.731978 2.276741 2.957666 1.896173 1.191585 2.548860
55 2.735119 2.273255 2.960842 1.877050 1.180899 2.668823
56 2.722927 2.280802 2.947316 1.891072 1.177675 2.673613
57 2.709467 2.258515 2.986858 1.929852 1.183238 2.695064
58 2.658229 2.238688 2.965530 1.812321 1.177930 2.647119
59 2.677261 2.265618 2.963806 1.805078 1.167292 2.602528
60 2.668783 2.258537 2.970767 1.873293 1.170424 2.608127
61 2.684284 2.267802 2.945490 1.892642 1.168552 2.607034
62 2.674774 2.274064 2.880279 1.903231 1.152289 2.607221
63 2.708287 2.328437 2.933970 1.847039 1.163890 2.716975
64 2.687686 2.328077 2.945146 1.872792 1.172497 2.701632
65 2.637088 2.332852 2.950612 1.828068 1.176204 2.695714
66 2.664911 2.301822 3.006646 1.830311 1.187869 2.673619
67 2.703793 2.284728 2.987296 1.892167 1.155841 2.659863
68 2.692752 2.272586 2.995862 1.905202 1.169219 2.644923
69 2.710269 2.319870 3.016847 1.921287 1.177711 2.593917
70 2.744609 2.375733 3.065345 1.838643 1.177653 2.485946
71 2.778108 2.386711 3.075626 1.868692 1.190557 2.410138
72 2.787452 2.429903 3.085168 1.953690 1.209077 2.448618
73 2.749925 2.385904 3.009987 1.962835 1.217625 2.582711
74 2.686136 2.365395 2.976340 1.951968 1.169011 2.747865
75 2.689715 2.348605 2.960359 1.932535 1.173842 2.852021
76 2.693923 2.309905 2.979497 1.961825 1.179643 2.880488
77 2.718535 2.244988 2.926041 1.979972 1.188395 2.919031
78 2.691122 2.260825 2.958450 1.937882 1.196103 2.835467
79 2.702145 2.273762 2.955948 1.964856 1.179695 2.822412
80 2.681099 2.225945 2.947880 1.985382 1.141435 2.790287
81 2.700621 2.265040 2.959192 2.010111 1.170487 2.790061
82 2.717976 2.292874 2.912328 1.999618 1.187415 2.844154
83 2.701417 2.276312 2.823567 1.974707 1.178149 2.885756
84 2.661857 2.272200 2.869076 1.981144 1.191702 2.864557
85 2.679763 2.288586 2.885145 1.935806 1.184893 2.823581
86 2.654425 2.289200 2.925044 1.894852 1.198022 2.754786
87 2.689197 2.319057 2.964361 1.873605 1.185171 2.718411
88 2.662270 2.318440 3.013074 1.810098 1.169359 2.743356
89 2.693766 2.294204 2.966661 1.871590 1.171629 2.914864
90 2.694993 2.301021 2.995957 1.910747 1.179453 2.907180
91 2.653484 2.240610 3.001361 1.862265 1.185852 2.893968
92 2.656735 2.211580 2.986246 1.886935 1.205150 2.926765
93 2.631039 2.210328 2.925391 1.807662 1.191240 2.907748
94 2.650414 2.194222 2.982453 1.825420 1.235219 2.877294
95 2.595485 2.205946 2.959797 1.778533 1.219948 2.508281
96 2.568429 2.221525 2.986794 1.809919 1.208440 2.680641
97 2.649223 2.187732 2.963375 1.805011 1.226141 2.707410
98 2.692906 2.186877 2.938725 1.834128 1.256186 2.739564
99 2.710380 2.244485 2.950913 1.821729 1.231559 2.605515
100 2.718515 2.279784 2.959052 1.882905 1.219802 2.580888
101 2.729006 2.265457 2.959048 1.859727 1.258145 2.552855
102 2.724738 2.289462 2.944277 1.896960 1.232654 2.413232
103 2.730724 2.291888 2.981037 1.892069 1.205648 2.578435
104 2.716969 2.296685 2.959621 1.821483 1.201145 2.642836
105 2.702114 2.318185 2.976083 1.903058 1.203997 2.635683
106 2.718918 2.294194 2.940904 1.904193 1.212756 2.668008
107 2.704543 2.234100 2.973255 1.857930 1.206462 2.841810
108 2.717311 2.186379 2.973363 1.864551 1.213635 2.732194
109 2.696537 2.204682 2.973023 1.762011 1.209635 2.763599
110 2.653233 2.264960 2.944000 1.707752 1.200428 2.752181
111 2.656848 2.285277 2.970712 1.725128 1.221405 2.668272
112 2.688402 2.288625 3.002693 1.835911 1.218577 2.757768
113 2.702713 2.299230 2.999452 1.879256 1.227619 2.837470
114 2.711022 2.321891 2.973988 1.860613 1.219326 2.795637
115 2.687873 2.337535 3.009437 1.857021 1.225537 2.769817
116 2.719102 2.338801 3.034231 1.876346 1.230269 2.795837
117 2.714631 2.290888 3.007439 1.880817 1.218711 2.791097
118 2.709502 2.193859 2.958101 1.895147 1.211570 2.786899
119 2.735608 2.017807 3.081505 1.891960 1.185689 2.776968
120 2.820171 2.130093 3.092087 1.906442 1.188299 2.802246
GPAmbient GPWaters Gpsiss\r
1 1.892837 0.3986731 0.9726207
2 1.885170 0.3995056 0.9844742
3 1.924781 0.4044869 0.9837565
4 1.882149 0.4029204 0.9662647
5 1.886685 0.4016707 0.9735052
6 1.925799 0.4070727 0.9723885
7 1.916127 0.4059942 0.9564963
8 1.877603 0.4015238 0.9672903
9 1.916247 0.4030917 0.9728278
10 1.937465 0.4031561 0.9783994
11 1.921863 0.3991506 0.9717012
12 1.914079 0.4099229 0.9615553
13 1.932872 0.4101513 0.9731397
14 1.918440 0.3998166 0.9751464
15 1.942432 0.3980013 0.9735413
16 1.942733 0.3975232 0.9706740
17 1.952413 0.3932981 0.9510937
18 1.935578 0.3922377 0.9578929
19 1.866731 0.3955714 0.9386917
20 1.869075 0.3953363 0.9437179
21 1.960129 0.4025385 0.9858554
22 1.939334 0.3965310 0.9515081
23 1.954225 0.3991643 0.9522978
24 1.969225 0.4022793 0.9684022
25 1.949595 0.3965838 0.9697683
26 1.950339 0.3970200 0.9789014
27 1.952322 0.3953227 0.9625468
28 1.942532 0.3954445 0.9694322
29 1.906300 0.3945235 0.9768748
30 1.958036 0.3983113 0.9873436
31 1.990794 0.4003191 0.9843350
32 1.968342 0.4101977 0.9749716
33 1.963643 0.4074575 0.9877233
34 1.935723 0.4032614 0.9834557
35 1.935064 0.4107673 0.9899489
36 1.907060 0.4021767 0.9985700
37 1.961858 0.4150180 0.9862628
38 1.927311 0.4080279 0.9937886
39 1.945845 0.4102984 0.9911725
40 1.959757 0.4185205 0.9957127
41 1.968207 0.4249026 1.0033660
42 1.945666 0.4180358 1.0134893
43 1.959442 0.4167337 1.0066081
44 1.957944 0.4230970 1.0129651
45 1.941834 0.4169236 1.0186267
46 1.917999 0.4131758 1.0151165
47 1.919697 0.4120369 0.9964063
48 1.922802 0.4134982 1.0121066
49 1.896221 0.4095313 1.0125440
50 1.903100 0.4113986 1.0136737
51 1.909858 0.4153504 1.0087372
52 1.915356 0.4100889 1.0150826
53 1.915205 0.4082473 1.0211143
54 1.901720 0.4146280 1.0152173
55 1.964234 0.4133286 1.0032163
56 1.943163 0.4163900 1.0192423
57 1.939779 0.4144072 1.0338856
58 1.938011 0.4175810 1.0361631
59 1.939640 0.4148303 1.0350353
60 1.947521 0.4123735 1.0271311
61 1.944771 0.4092420 1.0323013
62 1.958178 0.4076164 1.0368261
63 1.911132 0.4018576 1.0313512
64 1.907531 0.4001497 1.0253436
65 1.908275 0.3999142 1.0292487
66 1.911806 0.4033707 1.0424900
67 1.904032 0.3996238 1.0318106
68 1.914369 0.4025927 1.0293246
69 1.910906 0.4004252 1.0189608
70 1.892128 0.4007275 1.0223302
71 1.848373 0.4101947 0.9997759
72 1.837562 0.4047870 1.0116507
73 1.881404 0.4045373 1.0333977
74 1.883127 0.4053867 1.0153571
75 1.897918 0.4045749 1.0312431
76 1.900153 0.4043666 1.0377902
77 1.890945 0.4035968 1.0298783
78 1.894071 0.4036896 1.0299831
79 1.878418 0.4044273 1.0373879
80 1.859589 0.4030926 1.0477187
81 1.861562 0.4059046 1.0415178
82 1.863975 0.4154740 1.0517865
83 1.884345 0.4160767 1.0551777
84 1.907276 0.4166577 1.0516100
85 1.914874 0.4141642 1.0414799
86 1.815561 0.4136573 1.0468619
87 1.837459 0.4030488 1.0497595
88 1.881033 0.4082140 1.0369219
89 1.896798 0.4114884 1.0539311
90 1.849252 0.4088535 1.0403434
91 1.878185 0.4177080 1.0490315
92 1.903390 0.4258757 1.0512508
93 1.859972 0.4194219 1.0574021
94 1.878527 0.4235290 1.0502398
95 1.887459 0.4247242 1.0479938
96 1.873311 0.4250451 1.0654631
97 1.847874 0.4152436 1.0651864
98 1.860067 0.4129597 1.0608092
99 1.857240 0.4076503 1.0544369
100 1.866741 0.4106230 1.0862270
101 1.863427 0.4114922 1.0843381
102 1.870199 0.4159268 1.0748324
103 1.863886 0.4163844 1.0625204
104 1.864638 0.4141133 1.0868784
105 1.868264 0.4098520 1.0845753
106 1.883177 0.4090131 1.0673976
107 1.894816 0.4108221 1.0431508
108 1.898971 0.4171230 1.0675124
109 1.888831 0.4134018 1.1008632
110 1.875125 0.4126517 1.1137371
111 1.875859 0.4109875 1.1029705
112 1.883603 0.4100070 1.0706037
113 1.882887 0.4119410 1.0860298
114 1.879571 0.4074490 1.0720471
115 1.878656 0.4107739 1.0788523
116 1.841411 0.4066153 1.0720908
117 1.855609 0.4007111 1.0835453
118 1.862204 0.4018035 1.0954506
119 1.860246 0.4052570 1.0869120
120 1.875843 0.4064661 1.0694762
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) PBEPIL PBELUX BUDBEER
2.384e+08 -1.182e+08 2.065e+07 4.569e+01
BUDCHIL BUDAMB BUDWATER BUDSISSS
-2.141e+00 -3.395e+01 1.074e+01 -2.758e-01
Gpspontanegisting GPHogegisting GPAngelsa GPZuur
-1.441e+07 -3.553e+07 -9.357e+07 -1.220e+07
GPlaagalc GPChill GPAmbient GPWaters
7.912e+07 4.348e+06 8.303e+07 1.175e+08
`Gpsiss\\r`
-5.813e+06
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-21131991 -4818838 -249768 3924737 20545328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.384e+08 1.231e+08 1.936 0.0556 .
PBEPIL -1.182e+08 4.911e+07 -2.407 0.0179 *
PBELUX 2.065e+07 1.550e+07 1.332 0.1857
BUDBEER 4.569e+01 1.568e+00 29.145 < 2e-16 ***
BUDCHIL -2.141e+00 1.111e+01 -0.193 0.8475
BUDAMB -3.395e+01 7.142e+00 -4.754 6.49e-06 ***
BUDWATER 1.074e+01 1.957e+00 5.487 2.95e-07 ***
BUDSISSS -2.758e-01 9.592e-01 -0.288 0.7743
Gpspontanegisting -1.441e+07 1.849e+07 -0.779 0.4377
GPHogegisting -3.553e+07 1.590e+07 -2.235 0.0276 *
GPAngelsa -9.357e+07 2.153e+07 -4.345 3.27e-05 ***
GPZuur -1.220e+07 1.480e+07 -0.824 0.4117
GPlaagalc 7.912e+07 5.175e+07 1.529 0.1294
GPChill 4.348e+06 4.674e+06 0.930 0.3544
GPAmbient 8.303e+07 3.276e+07 2.535 0.0128 *
GPWaters 1.175e+08 1.727e+08 0.680 0.4978
`Gpsiss\\r` -5.813e+06 4.230e+07 -0.137 0.8910
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 8067000 on 103 degrees of freedom
Multiple R-squared: 0.9719, Adjusted R-squared: 0.9675
F-statistic: 222.4 on 16 and 103 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,] 0.45838149 0.91676298 0.5416185
[2,] 0.32574453 0.65148907 0.6742555
[3,] 0.51051028 0.97897945 0.4894897
[4,] 0.63949852 0.72100297 0.3605015
[5,] 0.57450064 0.85099873 0.4254994
[6,] 0.74960984 0.50078032 0.2503902
[7,] 0.70219493 0.59561014 0.2978051
[8,] 0.61201144 0.77597712 0.3879886
[9,] 0.53359090 0.93281820 0.4664091
[10,] 0.46107478 0.92214956 0.5389252
[11,] 0.40567489 0.81134979 0.5943251
[12,] 0.37479515 0.74959031 0.6252048
[13,] 0.30313542 0.60627083 0.6968646
[14,] 0.25277575 0.50555150 0.7472242
[15,] 0.24990923 0.49981847 0.7500908
[16,] 0.21052511 0.42105023 0.7894749
[17,] 0.18845947 0.37691895 0.8115405
[18,] 0.16296308 0.32592616 0.8370369
[19,] 0.18718685 0.37437371 0.8128131
[20,] 0.16276477 0.32552953 0.8372352
[21,] 0.18653701 0.37307402 0.8134630
[22,] 0.17865135 0.35730270 0.8213486
[23,] 0.20247298 0.40494595 0.7975270
[24,] 0.17845097 0.35690194 0.8215490
[25,] 0.22274389 0.44548777 0.7772561
[26,] 0.18159045 0.36318090 0.8184095
[27,] 0.14322100 0.28644200 0.8567790
[28,] 0.10910282 0.21820564 0.8908972
[29,] 0.09347397 0.18694795 0.9065260
[30,] 0.11893851 0.23787703 0.8810615
[31,] 0.09744389 0.19488777 0.9025561
[32,] 0.07618249 0.15236499 0.9238175
[33,] 0.07820524 0.15641047 0.9217948
[34,] 0.05782433 0.11564865 0.9421757
[35,] 0.04196225 0.08392451 0.9580377
[36,] 0.04355694 0.08711389 0.9564431
[37,] 0.14100694 0.28201388 0.8589931
[38,] 0.41593109 0.83186218 0.5840689
[39,] 0.38688421 0.77376843 0.6131158
[40,] 0.36353146 0.72706292 0.6364685
[41,] 0.37167297 0.74334594 0.6283270
[42,] 0.47366455 0.94732909 0.5263355
[43,] 0.53329682 0.93340636 0.4667032
[44,] 0.47472710 0.94945421 0.5252729
[45,] 0.41513459 0.83026917 0.5848654
[46,] 0.40766484 0.81532969 0.5923352
[47,] 0.45776184 0.91552368 0.5422382
[48,] 0.47864960 0.95729920 0.5213504
[49,] 0.51794474 0.96411052 0.4820553
[50,] 0.49393873 0.98787746 0.5060613
[51,] 0.44935500 0.89871000 0.5506450
[52,] 0.39502326 0.79004652 0.6049767
[53,] 0.60451212 0.79097575 0.3954879
[54,] 0.70412139 0.59175722 0.2958786
[55,] 0.69879042 0.60241917 0.3012096
[56,] 0.69999606 0.60000788 0.3000039
[57,] 0.64966799 0.70066402 0.3503320
[58,] 0.58786176 0.82427649 0.4121382
[59,] 0.52430757 0.95138486 0.4756924
[60,] 0.46153045 0.92306090 0.5384695
[61,] 0.47926668 0.95853336 0.5207333
[62,] 0.53714875 0.92570249 0.4628512
[63,] 0.48537556 0.97075111 0.5146244
[64,] 0.47962979 0.95925958 0.5203702
[65,] 0.48150378 0.96300757 0.5184962
[66,] 0.43957993 0.87915985 0.5604201
[67,] 0.58025296 0.83949408 0.4197470
[68,] 0.57876797 0.84246406 0.4212320
[69,] 0.51856133 0.96287734 0.4814387
[70,] 0.47460932 0.94921864 0.5253907
[71,] 0.57615412 0.84769176 0.4238459
[72,] 0.66464137 0.67071726 0.3353586
[73,] 0.67118115 0.65763770 0.3288189
[74,] 0.61997846 0.76004308 0.3800215
[75,] 0.53143959 0.93712083 0.4685604
[76,] 0.46876268 0.93752536 0.5312373
[77,] 0.38273686 0.76547372 0.6172631
[78,] 0.34033805 0.68067610 0.6596619
[79,] 0.24930708 0.49861416 0.7506929
[80,] 0.23082657 0.46165314 0.7691734
[81,] 0.16551348 0.33102695 0.8344865
> postscript(file="/var/wessaorg/rcomp/tmp/19yyj1399803344.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/2gb7b1399803344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/38d7e1399803344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/45x881399803344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/wessaorg/rcomp/tmp/50fvk1399803344.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$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 = 120
Frequency = 1
1 2 3 4 5 6
2690077.48 1631577.07 10814537.66 14741192.33 10195440.11 15128244.07
7 8 9 10 11 12
9722503.38 2583024.71 7908589.25 1548557.07 -5436441.66 -21131990.53
13 14 15 16 17 18
-1176666.76 -6693539.07 -4366381.62 2963171.37 464101.33 -7336591.82
19 20 21 22 23 24
-14223538.82 -7221805.14 -4526284.85 730351.19 -801824.72 -10726951.35
25 26 27 28 29 30
-741160.85 -1130397.54 1382208.26 -3158581.87 4056049.18 1685402.28
31 32 33 34 35 36
540331.52 -3407887.38 -4765412.84 3911897.44 -7400861.01 7898152.71
37 38 39 40 41 42
-5740063.00 1037811.79 -707339.56 10433736.18 -820590.35 -6705176.27
43 44 45 46 47 48
-1749972.13 -7925865.76 -1125342.29 2917249.39 1030838.54 3506566.93
49 50 51 52 53 54
-3379184.08 -3606043.78 -1725669.65 -9687386.61 2033501.39 -1875966.17
55 56 57 58 59 60
13432090.23 20545328.41 19562227.70 4895746.52 3861876.07 3963253.71
61 62 63 64 65 66
5844478.69 55635.71 -3505849.00 1042063.58 6081768.33 -5447605.80
67 68 69 70 71 72
-1368574.17 -6060898.91 -9532781.30 -6192509.26 -2871505.05 -9068947.27
73 74 75 76 77 78
-5707710.64 921593.29 -523576.80 3164570.45 1626138.24 2541870.34
79 80 81 82 83 84
1125662.63 -2160413.44 5482170.19 2636625.64 -11307188.12 -5466370.37
85 86 87 88 89 90
-13575541.11 10402371.69 11220461.60 6037797.53 12538734.05 18303538.22
91 92 93 94 95 96
4287667.37 -12207121.98 -3126811.97 -684332.53 -3508286.41 -11009348.31
97 98 99 100 101 102
686652.22 7809535.33 7741146.44 -136611.87 -1043743.96 -10694572.59
103 104 105 106 107 108
-1586703.26 -10135054.95 -393284.34 4653720.10 -7970630.88 366488.61
109 110 111 112 113 114
-362923.56 1638668.15 11742104.75 9414502.37 6932910.08 5459472.56
115 116 117 118 119 120
-10117745.00 3604780.55 -958883.61 -15967155.42 -14212053.07 -4979111.54
> postscript(file="/var/wessaorg/rcomp/tmp/6e21y1399803344.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 = 120
Frequency = 1
lag(myerror, k = 1) myerror
0 2690077.48 NA
1 1631577.07 2690077.48
2 10814537.66 1631577.07
3 14741192.33 10814537.66
4 10195440.11 14741192.33
5 15128244.07 10195440.11
6 9722503.38 15128244.07
7 2583024.71 9722503.38
8 7908589.25 2583024.71
9 1548557.07 7908589.25
10 -5436441.66 1548557.07
11 -21131990.53 -5436441.66
12 -1176666.76 -21131990.53
13 -6693539.07 -1176666.76
14 -4366381.62 -6693539.07
15 2963171.37 -4366381.62
16 464101.33 2963171.37
17 -7336591.82 464101.33
18 -14223538.82 -7336591.82
19 -7221805.14 -14223538.82
20 -4526284.85 -7221805.14
21 730351.19 -4526284.85
22 -801824.72 730351.19
23 -10726951.35 -801824.72
24 -741160.85 -10726951.35
25 -1130397.54 -741160.85
26 1382208.26 -1130397.54
27 -3158581.87 1382208.26
28 4056049.18 -3158581.87
29 1685402.28 4056049.18
30 540331.52 1685402.28
31 -3407887.38 540331.52
32 -4765412.84 -3407887.38
33 3911897.44 -4765412.84
34 -7400861.01 3911897.44
35 7898152.71 -7400861.01
36 -5740063.00 7898152.71
37 1037811.79 -5740063.00
38 -707339.56 1037811.79
39 10433736.18 -707339.56
40 -820590.35 10433736.18
41 -6705176.27 -820590.35
42 -1749972.13 -6705176.27
43 -7925865.76 -1749972.13
44 -1125342.29 -7925865.76
45 2917249.39 -1125342.29
46 1030838.54 2917249.39
47 3506566.93 1030838.54
48 -3379184.08 3506566.93
49 -3606043.78 -3379184.08
50 -1725669.65 -3606043.78
51 -9687386.61 -1725669.65
52 2033501.39 -9687386.61
53 -1875966.17 2033501.39
54 13432090.23 -1875966.17
55 20545328.41 13432090.23
56 19562227.70 20545328.41
57 4895746.52 19562227.70
58 3861876.07 4895746.52
59 3963253.71 3861876.07
60 5844478.69 3963253.71
61 55635.71 5844478.69
62 -3505849.00 55635.71
63 1042063.58 -3505849.00
64 6081768.33 1042063.58
65 -5447605.80 6081768.33
66 -1368574.17 -5447605.80
67 -6060898.91 -1368574.17
68 -9532781.30 -6060898.91
69 -6192509.26 -9532781.30
70 -2871505.05 -6192509.26
71 -9068947.27 -2871505.05
72 -5707710.64 -9068947.27
73 921593.29 -5707710.64
74 -523576.80 921593.29
75 3164570.45 -523576.80
76 1626138.24 3164570.45
77 2541870.34 1626138.24
78 1125662.63 2541870.34
79 -2160413.44 1125662.63
80 5482170.19 -2160413.44
81 2636625.64 5482170.19
82 -11307188.12 2636625.64
83 -5466370.37 -11307188.12
84 -13575541.11 -5466370.37
85 10402371.69 -13575541.11
86 11220461.60 10402371.69
87 6037797.53 11220461.60
88 12538734.05 6037797.53
89 18303538.22 12538734.05
90 4287667.37 18303538.22
91 -12207121.98 4287667.37
92 -3126811.97 -12207121.98
93 -684332.53 -3126811.97
94 -3508286.41 -684332.53
95 -11009348.31 -3508286.41
96 686652.22 -11009348.31
97 7809535.33 686652.22
98 7741146.44 7809535.33
99 -136611.87 7741146.44
100 -1043743.96 -136611.87
101 -10694572.59 -1043743.96
102 -1586703.26 -10694572.59
103 -10135054.95 -1586703.26
104 -393284.34 -10135054.95
105 4653720.10 -393284.34
106 -7970630.88 4653720.10
107 366488.61 -7970630.88
108 -362923.56 366488.61
109 1638668.15 -362923.56
110 11742104.75 1638668.15
111 9414502.37 11742104.75
112 6932910.08 9414502.37
113 5459472.56 6932910.08
114 -10117745.00 5459472.56
115 3604780.55 -10117745.00
116 -958883.61 3604780.55
117 -15967155.42 -958883.61
118 -14212053.07 -15967155.42
119 -4979111.54 -14212053.07
120 NA -4979111.54
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1631577.07 2690077.48
[2,] 10814537.66 1631577.07
[3,] 14741192.33 10814537.66
[4,] 10195440.11 14741192.33
[5,] 15128244.07 10195440.11
[6,] 9722503.38 15128244.07
[7,] 2583024.71 9722503.38
[8,] 7908589.25 2583024.71
[9,] 1548557.07 7908589.25
[10,] -5436441.66 1548557.07
[11,] -21131990.53 -5436441.66
[12,] -1176666.76 -21131990.53
[13,] -6693539.07 -1176666.76
[14,] -4366381.62 -6693539.07
[15,] 2963171.37 -4366381.62
[16,] 464101.33 2963171.37
[17,] -7336591.82 464101.33
[18,] -14223538.82 -7336591.82
[19,] -7221805.14 -14223538.82
[20,] -4526284.85 -7221805.14
[21,] 730351.19 -4526284.85
[22,] -801824.72 730351.19
[23,] -10726951.35 -801824.72
[24,] -741160.85 -10726951.35
[25,] -1130397.54 -741160.85
[26,] 1382208.26 -1130397.54
[27,] -3158581.87 1382208.26
[28,] 4056049.18 -3158581.87
[29,] 1685402.28 4056049.18
[30,] 540331.52 1685402.28
[31,] -3407887.38 540331.52
[32,] -4765412.84 -3407887.38
[33,] 3911897.44 -4765412.84
[34,] -7400861.01 3911897.44
[35,] 7898152.71 -7400861.01
[36,] -5740063.00 7898152.71
[37,] 1037811.79 -5740063.00
[38,] -707339.56 1037811.79
[39,] 10433736.18 -707339.56
[40,] -820590.35 10433736.18
[41,] -6705176.27 -820590.35
[42,] -1749972.13 -6705176.27
[43,] -7925865.76 -1749972.13
[44,] -1125342.29 -7925865.76
[45,] 2917249.39 -1125342.29
[46,] 1030838.54 2917249.39
[47,] 3506566.93 1030838.54
[48,] -3379184.08 3506566.93
[49,] -3606043.78 -3379184.08
[50,] -1725669.65 -3606043.78
[51,] -9687386.61 -1725669.65
[52,] 2033501.39 -9687386.61
[53,] -1875966.17 2033501.39
[54,] 13432090.23 -1875966.17
[55,] 20545328.41 13432090.23
[56,] 19562227.70 20545328.41
[57,] 4895746.52 19562227.70
[58,] 3861876.07 4895746.52
[59,] 3963253.71 3861876.07
[60,] 5844478.69 3963253.71
[61,] 55635.71 5844478.69
[62,] -3505849.00 55635.71
[63,] 1042063.58 -3505849.00
[64,] 6081768.33 1042063.58
[65,] -5447605.80 6081768.33
[66,] -1368574.17 -5447605.80
[67,] -6060898.91 -1368574.17
[68,] -9532781.30 -6060898.91
[69,] -6192509.26 -9532781.30
[70,] -2871505.05 -6192509.26
[71,] -9068947.27 -2871505.05
[72,] -5707710.64 -9068947.27
[73,] 921593.29 -5707710.64
[74,] -523576.80 921593.29
[75,] 3164570.45 -523576.80
[76,] 1626138.24 3164570.45
[77,] 2541870.34 1626138.24
[78,] 1125662.63 2541870.34
[79,] -2160413.44 1125662.63
[80,] 5482170.19 -2160413.44
[81,] 2636625.64 5482170.19
[82,] -11307188.12 2636625.64
[83,] -5466370.37 -11307188.12
[84,] -13575541.11 -5466370.37
[85,] 10402371.69 -13575541.11
[86,] 11220461.60 10402371.69
[87,] 6037797.53 11220461.60
[88,] 12538734.05 6037797.53
[89,] 18303538.22 12538734.05
[90,] 4287667.37 18303538.22
[91,] -12207121.98 4287667.37
[92,] -3126811.97 -12207121.98
[93,] -684332.53 -3126811.97
[94,] -3508286.41 -684332.53
[95,] -11009348.31 -3508286.41
[96,] 686652.22 -11009348.31
[97,] 7809535.33 686652.22
[98,] 7741146.44 7809535.33
[99,] -136611.87 7741146.44
[100,] -1043743.96 -136611.87
[101,] -10694572.59 -1043743.96
[102,] -1586703.26 -10694572.59
[103,] -10135054.95 -1586703.26
[104,] -393284.34 -10135054.95
[105,] 4653720.10 -393284.34
[106,] -7970630.88 4653720.10
[107,] 366488.61 -7970630.88
[108,] -362923.56 366488.61
[109,] 1638668.15 -362923.56
[110,] 11742104.75 1638668.15
[111,] 9414502.37 11742104.75
[112,] 6932910.08 9414502.37
[113,] 5459472.56 6932910.08
[114,] -10117745.00 5459472.56
[115,] 3604780.55 -10117745.00
[116,] -958883.61 3604780.55
[117,] -15967155.42 -958883.61
[118,] -14212053.07 -15967155.42
[119,] -4979111.54 -14212053.07
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1631577.07 2690077.48
2 10814537.66 1631577.07
3 14741192.33 10814537.66
4 10195440.11 14741192.33
5 15128244.07 10195440.11
6 9722503.38 15128244.07
7 2583024.71 9722503.38
8 7908589.25 2583024.71
9 1548557.07 7908589.25
10 -5436441.66 1548557.07
11 -21131990.53 -5436441.66
12 -1176666.76 -21131990.53
13 -6693539.07 -1176666.76
14 -4366381.62 -6693539.07
15 2963171.37 -4366381.62
16 464101.33 2963171.37
17 -7336591.82 464101.33
18 -14223538.82 -7336591.82
19 -7221805.14 -14223538.82
20 -4526284.85 -7221805.14
21 730351.19 -4526284.85
22 -801824.72 730351.19
23 -10726951.35 -801824.72
24 -741160.85 -10726951.35
25 -1130397.54 -741160.85
26 1382208.26 -1130397.54
27 -3158581.87 1382208.26
28 4056049.18 -3158581.87
29 1685402.28 4056049.18
30 540331.52 1685402.28
31 -3407887.38 540331.52
32 -4765412.84 -3407887.38
33 3911897.44 -4765412.84
34 -7400861.01 3911897.44
35 7898152.71 -7400861.01
36 -5740063.00 7898152.71
37 1037811.79 -5740063.00
38 -707339.56 1037811.79
39 10433736.18 -707339.56
40 -820590.35 10433736.18
41 -6705176.27 -820590.35
42 -1749972.13 -6705176.27
43 -7925865.76 -1749972.13
44 -1125342.29 -7925865.76
45 2917249.39 -1125342.29
46 1030838.54 2917249.39
47 3506566.93 1030838.54
48 -3379184.08 3506566.93
49 -3606043.78 -3379184.08
50 -1725669.65 -3606043.78
51 -9687386.61 -1725669.65
52 2033501.39 -9687386.61
53 -1875966.17 2033501.39
54 13432090.23 -1875966.17
55 20545328.41 13432090.23
56 19562227.70 20545328.41
57 4895746.52 19562227.70
58 3861876.07 4895746.52
59 3963253.71 3861876.07
60 5844478.69 3963253.71
61 55635.71 5844478.69
62 -3505849.00 55635.71
63 1042063.58 -3505849.00
64 6081768.33 1042063.58
65 -5447605.80 6081768.33
66 -1368574.17 -5447605.80
67 -6060898.91 -1368574.17
68 -9532781.30 -6060898.91
69 -6192509.26 -9532781.30
70 -2871505.05 -6192509.26
71 -9068947.27 -2871505.05
72 -5707710.64 -9068947.27
73 921593.29 -5707710.64
74 -523576.80 921593.29
75 3164570.45 -523576.80
76 1626138.24 3164570.45
77 2541870.34 1626138.24
78 1125662.63 2541870.34
79 -2160413.44 1125662.63
80 5482170.19 -2160413.44
81 2636625.64 5482170.19
82 -11307188.12 2636625.64
83 -5466370.37 -11307188.12
84 -13575541.11 -5466370.37
85 10402371.69 -13575541.11
86 11220461.60 10402371.69
87 6037797.53 11220461.60
88 12538734.05 6037797.53
89 18303538.22 12538734.05
90 4287667.37 18303538.22
91 -12207121.98 4287667.37
92 -3126811.97 -12207121.98
93 -684332.53 -3126811.97
94 -3508286.41 -684332.53
95 -11009348.31 -3508286.41
96 686652.22 -11009348.31
97 7809535.33 686652.22
98 7741146.44 7809535.33
99 -136611.87 7741146.44
100 -1043743.96 -136611.87
101 -10694572.59 -1043743.96
102 -1586703.26 -10694572.59
103 -10135054.95 -1586703.26
104 -393284.34 -10135054.95
105 4653720.10 -393284.34
106 -7970630.88 4653720.10
107 366488.61 -7970630.88
108 -362923.56 366488.61
109 1638668.15 -362923.56
110 11742104.75 1638668.15
111 9414502.37 11742104.75
112 6932910.08 9414502.37
113 5459472.56 6932910.08
114 -10117745.00 5459472.56
115 3604780.55 -10117745.00
116 -958883.61 3604780.55
117 -15967155.42 -958883.61
118 -14212053.07 -15967155.42
119 -4979111.54 -14212053.07
> 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/7pcrc1399803344.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/84crx1399803344.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/9wwj71399803344.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/10fvi91399803344.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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/11wq2j1399803344.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/12841w1399803344.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$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$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/131onv1399803345.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/wessaorg/rcomp/tmp/14kls31399803345.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/wessaorg/rcomp/tmp/154a031399803345.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/wessaorg/rcomp/tmp/16nuhh1399803345.tab")
+ }
>
> try(system("convert tmp/19yyj1399803344.ps tmp/19yyj1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/2gb7b1399803344.ps tmp/2gb7b1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/38d7e1399803344.ps tmp/38d7e1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/45x881399803344.ps tmp/45x881399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/50fvk1399803344.ps tmp/50fvk1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/6e21y1399803344.ps tmp/6e21y1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/7pcrc1399803344.ps tmp/7pcrc1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/84crx1399803344.ps tmp/84crx1399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/9wwj71399803344.ps tmp/9wwj71399803344.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fvi91399803344.ps tmp/10fvi91399803344.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
8.212 0.965 9.240