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Author's title

Author*Unverified author*
R Software Modulerwasp_cross.wasp
Title produced by softwareCross Correlation Function
Date of computationTue, 02 Jul 2019 15:26:26 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2019/Jul/02/t1562074134tg0yand05gb238n.htm/, Retrieved Wed, 12 Aug 2020 14:25:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318837, Retrieved Wed, 12 Aug 2020 14:25:44 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact13
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Cross Correlation Function] [] [2019-07-02 13:26:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
2577.78658237715
2546.34364538838
2557.73768495090
2561.08175825684
2568.18289329269
2568.25930242876
2568.95021809814
2541.44108718573
2535.00259442559
2545.06843468073
2548.01638895014
2537.84653371923
2541.94869783773
2527.43115803584
2523.55432573509
2538.73321182357
2573.48391147532
2585.32937188732
2586.26280493140
2596.55914804718
2617.71055771101
2617.71055771101
2601.10995662936
2567.72453580218
2537.68653825796
2528.12351906368
2529.49041049269
2493.64758509354
2508.67109220572
2524.69962530673
2524.69962530673
2513.57025160463
2514.36967089911
2511.65065290403
2544.88700114201
2561.46045016320
2564.99030594557
2556.26641398036
2568.51826947604
2572.86319454757
2554.81815061038
2559.59346067513
2563.91222936423
2569.85681643879
2592.10523428099
2595.22993589212
2564.43290248606
2540.50890257440
2531.87146272903
2510.25979823619
2503.99161095405
2526.44567844494
2529.76639387689
2532.35013174984
2501.99100604721
2500.07192275445
2504.49776620910
2498.55420114350
2476.12782173141
2481.77486127679
2496.46149353557
2506.57057638590
2514.38406046258
2531.53342217601
2550.27871990734
2562.38349663899
2567.84238991710
2554.42291744411
2523.01789410307
2535.00096781432
2548.51759311387
2556.86609555526
2544.01774736352
2514.38120557221
2491.37681497532
2514.86689457295
2528.23203302419
2538.03939928860
2539.71091541279
2531.57295712017
2540.01862679123
2550.42239194180
2548.25796390820
2533.65992275641
2549.94311381251
2562.79937302695
2558.17441592449
2527.04361870027
2521.14432012857
2500.74269447134
2533.30812397050
2537.44337271801
2545.82382305240
2551.12655248290
2548.12064537819
2553.66968811230
2554.17317628015
2522.49269932728
2502.83889778979
2527.61920991939
2539.69465188591
2546.28898410643
2539.65347764904
2511.85227187077
2526.68453915957
2537.54306357325
2542.13051761330
2525.56198490798
2532.59273225524
2572.32444328135
2575.03850612620
2598.99290722972
2621.61978865377
2602.99220426055
2571.73382356086
2556.30821288508
2543.16303610410
2546.34245874786
2563.99057007853
2546.02824995490
2561.42709284226
2580.63744549989
2563.42267609208
2514.69395969077
2567.56724282339
2583.64118909928
2567.76425157725
2537.51075794016
2540.39730257668
2554.55114176767
2574.39209124413
2556.49607674528
2525.45995851193
2534.67753940590
2542.57351746237
2534.28011368980
2535.95500186946
2537.31762714545
2509.49949701239
2526.44542504601
2534.73681802475
2510.19545944648
2492.09287452334
2535.56554288887
2542.96551893334
2544.94333449906
2549.51276075763
2553.21925489134
2555.35955123985
2542.16463854710
2578.90698235938
2578.90698235938
2527.22320002137
2582.54330708549
2582.54983770111
2565.43993764934
2539.29959569400
2575.33628087563
2586.76025311183
2601.60024170394
2642.39128096842
2642.92045977184
2624.09991919448
2578.62625960304
2612.16543595814
2622.98174894263
2624.16172635703
2563.53036628331
2518.51929544163
2534.68494974389
2560.42908802054
2560.82248672056
2527.59089214898
2500.69116897858
2508.03666855617
2526.42589984326
2535.88224823210
2549.27548927729
2549.27548927729
2502.15775745771
2529.52141304366
2531.35413546376
2509.46883234492
2536.16207011847
2549.65088546285
2549.77229240298
2554.42084103396
2570.00863906624
2570.97382391475
2539.73715163026
2510.91744991658
2544.86071738495
2573.68741821592
2574.65493445252
2567.33493843373
2574.81905217271
2575.01853715077
2568.46385991094
2527.76198245675
2517.66723453377
2555.46122390319
2555.46122390319
2556.69186959675
2557.96156132018
2552.57783620293
2558.40244613112
2558.40244613112
2553.99114032876
2569.95562071404
2573.16734281628
2573.63697523057
2566.07736819032
2556.70842953437
2584.11886902724
2585.36633552658
2578.16001342392
2554.37967932891
2548.03327281453
2534.93223733666
2515.93621461584
2532.94133028139
2560.01427049263
2561.09294493681
2561.48996057583
2576.97605930647
2577.03118535610
2566.94597959516
2590.09569795453
2591.84376620167
2544.51810653303
2559.57992328131
2569.70160216137
2588.49005435608
2588.90351094569
2598.63150753851
2608.12484292743
2608.31147871042
2580.05093789837
2566.46577545917
2557.90131459024
2540.47088741932
2558.86625589763
2588.73746886810
2589.24952149350
2542.93853211479
2537.21896971022
2537.71840785767
2545.55601515170
2565.98471001044
2566.50383980401
2541.80999773275
2524.11647121122
2523.49046421753
2523.89190808989
2535.87186252605
2565.77432191963
2567.71478808570
2559.93659440664
2578.04685099836
2579.04599989376
2572.13302478033
2546.75218287560
2560.55511377296
2578.04211893162
2586.75613080524
2588.45106377406
2571.54633052637
2527.52569996814
2498.50035944607
2515.80317366693
2533.18356525637
2533.18356525637
2560.43804458360
2565.97463499332
2565.97463499332
2545.95887976041
2552.92480226223
2580.70247970444
2581.81481808960
2574.57646761344
2503.51177174800
2515.41860841320
2562.00276992499
2563.12183128106
2552.17073641602
2552.75335556010
2511.54589478165
2555.50042632456
2607.23344940405
2612.85730296111
2614.36628648875
2575.78430957612
2577.76547375328
2579.11098698719
2566.60395452239
2522.39906200617
2516.79414517669
2505.06836490706
2538.56861184930
2538.77213932758
Dataseries Y:
2354.64034339618
2336.91626762722
2338.23720047502
2344.57747409075
2350.86161028111
2333.61293573658
2341.83333298252
2350.75922836212
2351.28264030431
2337.49540755567
2335.47962673568
2335.70588813324
2335.91531598516
2342.38103676176
2344.80527311759
2338.88254789830
2340.33401728029
2333.23661066547
2319.14456180670
2318.04935592525
2323.67285693365
2317.78777089064
2320.41702490471
2319.53200206164
2312.78668750393
2315.44031730709
2317.54207743895
2317.36053130446
2318.98372131495
2323.00601412832
2323.79694458242
2318.78014556205
2313.70693233100
2322.49418005495
2331.28969683071
2336.76749891697
2329.41770840194
2328.43443987233
2328.14514039399
2325.86691220367
2326.24805004148
2325.47894818582
2324.31609020038
2323.07944334964
2325.32912255970
2313.70564338539
2305.85874093555
2305.85931539790
2316.05848064653
2326.60754810069
2331.49504529092
2322.28626213106
2321.44037361368
2330.50264823798
2333.09390579692
2325.65404425790
2323.98004801473
2317.27605786821
2313.44830138823
2317.48670931923
2320.54820710938
2326.42704103246
2329.33389607150
2330.68974956685
2331.59852335614
2336.06172679779
2333.43223449996
2339.33247288404
2346.87363960820
2335.47007703556
2332.77839469246
2333.30166645132
2322.10350767588
2322.48716624186
2334.42716249483
2342.31481332755
2338.73898667463
2320.53652934546
2328.94901310419
2341.25023640711
2326.39858524257
2319.53215334455
2317.60275764472
2320.43227082803
2322.39848785598
2332.04712043874
2324.20107909311
2320.00546228082
2318.73765396463
2314.40460746850
2312.88836789163
2322.20575267688
2324.16015660702
2324.23395492007
2317.66658447122
2302.78827952410
2308.22489545232
2316.62183934618
2320.98503980062
2319.87386712679
2321.10937800690
2321.46879472075
2323.91332260606
2324.63236256084
2337.95376011008
2349.27744262349
2348.17926816581
2344.80754153926
2344.11272512256
2343.01020949487
2347.99795893732
2349.83069023353
2347.13120135730
2347.97402836074
2341.29022779235
2344.68888247136
2343.18841486533
2341.44944018624
2339.38042334193
2341.54223612104
2343.72703032045
2337.83088608559
2342.55933314001
2338.78906327431
2329.77852413878
2333.48554334222
2342.19242287308
2340.81322466615
2331.45379666216
2318.04858520767
2318.48726131538
2318.95293660827
2329.98323674125
2336.94249777459
2329.78196309060
2321.90512481448
2319.33524556053
2325.09590163700
2328.50782048060
2324.72601211962
2312.99629155339
2315.92743576986
2326.80769644103
2327.87996318713
2330.38836638500
2339.66038694021
2345.76843881872
2345.60802285012
2336.65141393863
2334.76915964171
2342.84153682762
2342.84153682762
2336.97398373579
2338.33853423558
2338.70937197142
2314.99013082022
2329.02776764195
2332.75507086698
2348.46637650083
2350.36986501382
2353.39516535085
2362.79143475553
2363.31433562451
2354.92272708282
2359.29118237857
2359.88130452832
2332.40862039651
2325.76069632854
2328.22585888283
2328.36893074677
2328.22452942881
2327.73626562777
2318.29936352381
2320.42781041785
2333.77281704300
2334.00673846743
2339.47842083353
2346.47520972196
2347.06660117994
2336.20170534678
2336.20170534678
2333.29792737083
2320.32565062688
2329.62396060687
2343.09471105028
2344.85319341403
2344.85319341403
2336.47241058755
2319.01486140062
2348.33332942281
2364.30152180166
2365.17472129012
2356.60280743493
2340.97523145583
2350.55273990389
2351.27388276146
2340.35289919914
2329.45442842683
2321.79632242601
2313.77732365967
2305.49139137674
2315.66467756377
2318.52450216423
2319.44281782908
2310.20489908617
2320.90821986093
2329.25722169390
2329.35367483974
2338.63773496906
2339.41356300651
2339.67307214754
2342.39492762354
2344.97987278451
2323.95464773702
2319.12825163113
2319.26579632345
2321.58953112643
2322.86870996144
2316.80720111049
2325.48829470540
2330.09439182303
2331.67188601909
2333.35968794884
2338.65652527036
2338.88323230941
2339.46686564324
2342.14812254835
2343.50678930528
2334.65657803523
2339.26213125875
2339.26213125875
2334.38921342048
2330.27605487686
2322.93856995459
2324.08752196181
2321.78011639480
2324.98502143281
2326.52851003211
2309.41048366340
2327.36836435279
2346.73187462146
2351.34354445280
2353.37246997568
2350.19145364440
2347.84714373572
2344.43875015961
2338.93462828052
2334.22036201211
2326.53160883217
2330.82647533135
2340.14137697337
2342.32292511960
2336.76891462703
2343.09749211890
2344.20944169651
2334.51541761740
2336.30976629488
2337.21855063864
2315.25601692030
2306.95013741532
2310.53682254414
2310.89036961198
2326.20068793955
2341.18615900653
2345.94455202375
2346.93087604994
2347.61606605532
2333.07770874715
2336.43232452979
2355.35509214517
2359.22302674335
2359.22302674335
2336.34729318935
2320.15583704113
2309.70322753549
2321.82287164973
2324.08441397267
2315.80782346581
2340.47904776836
2347.96921954077
2349.48319907250
2340.54458052365
2331.66496858789
2319.04454259455
2301.58385302851
2289.74994694628
2303.13752924910
2305.43145991546
2308.01452905377
2306.29932782011
2304.68119241336
2287.67260417141
2287.45301892136
2313.21874364469
2315.14916594011
2322.26412326656
2323.53009363486
2327.03744101948
2329.99339040391
2331.70901628270




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318837&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=318837&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318837&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center







Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-210.0109715038246957
-200.0195138917976026
-19-0.0113120714761499
-18-0.0234020803799428
-170.00358168872997273
-160.0391500850538094
-150.0713733515843115
-140.065001714969422
-13-0.0158951510661438
-12-0.0601696862224505
-11-0.0623728106620963
-10-0.0493312280898401
-9-0.00783765782714867
-80.0229235813483514
-70.0291220491635631
-60.0508107485247371
-50.0570711879718172
-40.0541584866275704
-30.0451062231622238
-20.0360319755985632
-10.0814047963138499
00.148599685748581
10.22195479789855
20.248419027584322
30.21886290708788
40.161161968357004
50.144432826771989
60.187628177026935
70.211373006500112
80.206414639362077
90.169495305304944
100.138341994508775
110.126213973053166
120.136836775794764
130.112357564763033
140.0542961260206896
150.0191553273374102
160.013453219848239
17-0.00815264794379489
18-0.000828260153060675
190.00901971111845288
20-0.0174815304439747
21-0.0443723199823588

\begin{tabular}{lllllllll}
\hline
Cross Correlation Function \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) of X series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of X series & 0 \tabularnewline
Degree of seasonal differencing (D) of X series & 0 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Box-Cox transformation parameter (lambda) of Y series & 1 \tabularnewline
Degree of non-seasonal differencing (d) of Y series & 0 \tabularnewline
Degree of seasonal differencing (D) of Y series & 0 \tabularnewline
k & rho(Y[t],X[t+k]) \tabularnewline
-21 & 0.0109715038246957 \tabularnewline
-20 & 0.0195138917976026 \tabularnewline
-19 & -0.0113120714761499 \tabularnewline
-18 & -0.0234020803799428 \tabularnewline
-17 & 0.00358168872997273 \tabularnewline
-16 & 0.0391500850538094 \tabularnewline
-15 & 0.0713733515843115 \tabularnewline
-14 & 0.065001714969422 \tabularnewline
-13 & -0.0158951510661438 \tabularnewline
-12 & -0.0601696862224505 \tabularnewline
-11 & -0.0623728106620963 \tabularnewline
-10 & -0.0493312280898401 \tabularnewline
-9 & -0.00783765782714867 \tabularnewline
-8 & 0.0229235813483514 \tabularnewline
-7 & 0.0291220491635631 \tabularnewline
-6 & 0.0508107485247371 \tabularnewline
-5 & 0.0570711879718172 \tabularnewline
-4 & 0.0541584866275704 \tabularnewline
-3 & 0.0451062231622238 \tabularnewline
-2 & 0.0360319755985632 \tabularnewline
-1 & 0.0814047963138499 \tabularnewline
0 & 0.148599685748581 \tabularnewline
1 & 0.22195479789855 \tabularnewline
2 & 0.248419027584322 \tabularnewline
3 & 0.21886290708788 \tabularnewline
4 & 0.161161968357004 \tabularnewline
5 & 0.144432826771989 \tabularnewline
6 & 0.187628177026935 \tabularnewline
7 & 0.211373006500112 \tabularnewline
8 & 0.206414639362077 \tabularnewline
9 & 0.169495305304944 \tabularnewline
10 & 0.138341994508775 \tabularnewline
11 & 0.126213973053166 \tabularnewline
12 & 0.136836775794764 \tabularnewline
13 & 0.112357564763033 \tabularnewline
14 & 0.0542961260206896 \tabularnewline
15 & 0.0191553273374102 \tabularnewline
16 & 0.013453219848239 \tabularnewline
17 & -0.00815264794379489 \tabularnewline
18 & -0.000828260153060675 \tabularnewline
19 & 0.00901971111845288 \tabularnewline
20 & -0.0174815304439747 \tabularnewline
21 & -0.0443723199823588 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=318837&T=1

[TABLE]
[ROW][C]Cross Correlation Function[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of X series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of X series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of X series[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]1[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda) of Y series[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d) of Y series[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D) of Y series[/C][C]0[/C][/ROW]
[ROW][C]k[/C][C]rho(Y[t],X[t+k])[/C][/ROW]
[ROW][C]-21[/C][C]0.0109715038246957[/C][/ROW]
[ROW][C]-20[/C][C]0.0195138917976026[/C][/ROW]
[ROW][C]-19[/C][C]-0.0113120714761499[/C][/ROW]
[ROW][C]-18[/C][C]-0.0234020803799428[/C][/ROW]
[ROW][C]-17[/C][C]0.00358168872997273[/C][/ROW]
[ROW][C]-16[/C][C]0.0391500850538094[/C][/ROW]
[ROW][C]-15[/C][C]0.0713733515843115[/C][/ROW]
[ROW][C]-14[/C][C]0.065001714969422[/C][/ROW]
[ROW][C]-13[/C][C]-0.0158951510661438[/C][/ROW]
[ROW][C]-12[/C][C]-0.0601696862224505[/C][/ROW]
[ROW][C]-11[/C][C]-0.0623728106620963[/C][/ROW]
[ROW][C]-10[/C][C]-0.0493312280898401[/C][/ROW]
[ROW][C]-9[/C][C]-0.00783765782714867[/C][/ROW]
[ROW][C]-8[/C][C]0.0229235813483514[/C][/ROW]
[ROW][C]-7[/C][C]0.0291220491635631[/C][/ROW]
[ROW][C]-6[/C][C]0.0508107485247371[/C][/ROW]
[ROW][C]-5[/C][C]0.0570711879718172[/C][/ROW]
[ROW][C]-4[/C][C]0.0541584866275704[/C][/ROW]
[ROW][C]-3[/C][C]0.0451062231622238[/C][/ROW]
[ROW][C]-2[/C][C]0.0360319755985632[/C][/ROW]
[ROW][C]-1[/C][C]0.0814047963138499[/C][/ROW]
[ROW][C]0[/C][C]0.148599685748581[/C][/ROW]
[ROW][C]1[/C][C]0.22195479789855[/C][/ROW]
[ROW][C]2[/C][C]0.248419027584322[/C][/ROW]
[ROW][C]3[/C][C]0.21886290708788[/C][/ROW]
[ROW][C]4[/C][C]0.161161968357004[/C][/ROW]
[ROW][C]5[/C][C]0.144432826771989[/C][/ROW]
[ROW][C]6[/C][C]0.187628177026935[/C][/ROW]
[ROW][C]7[/C][C]0.211373006500112[/C][/ROW]
[ROW][C]8[/C][C]0.206414639362077[/C][/ROW]
[ROW][C]9[/C][C]0.169495305304944[/C][/ROW]
[ROW][C]10[/C][C]0.138341994508775[/C][/ROW]
[ROW][C]11[/C][C]0.126213973053166[/C][/ROW]
[ROW][C]12[/C][C]0.136836775794764[/C][/ROW]
[ROW][C]13[/C][C]0.112357564763033[/C][/ROW]
[ROW][C]14[/C][C]0.0542961260206896[/C][/ROW]
[ROW][C]15[/C][C]0.0191553273374102[/C][/ROW]
[ROW][C]16[/C][C]0.013453219848239[/C][/ROW]
[ROW][C]17[/C][C]-0.00815264794379489[/C][/ROW]
[ROW][C]18[/C][C]-0.000828260153060675[/C][/ROW]
[ROW][C]19[/C][C]0.00901971111845288[/C][/ROW]
[ROW][C]20[/C][C]-0.0174815304439747[/C][/ROW]
[ROW][C]21[/C][C]-0.0443723199823588[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=318837&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318837&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Cross Correlation Function
ParameterValue
Box-Cox transformation parameter (lambda) of X series1
Degree of non-seasonal differencing (d) of X series0
Degree of seasonal differencing (D) of X series0
Seasonal Period (s)1
Box-Cox transformation parameter (lambda) of Y series1
Degree of non-seasonal differencing (d) of Y series0
Degree of seasonal differencing (D) of Y series0
krho(Y[t],X[t+k])
-210.0109715038246957
-200.0195138917976026
-19-0.0113120714761499
-18-0.0234020803799428
-170.00358168872997273
-160.0391500850538094
-150.0713733515843115
-140.065001714969422
-13-0.0158951510661438
-12-0.0601696862224505
-11-0.0623728106620963
-10-0.0493312280898401
-9-0.00783765782714867
-80.0229235813483514
-70.0291220491635631
-60.0508107485247371
-50.0570711879718172
-40.0541584866275704
-30.0451062231622238
-20.0360319755985632
-10.0814047963138499
00.148599685748581
10.22195479789855
20.248419027584322
30.21886290708788
40.161161968357004
50.144432826771989
60.187628177026935
70.211373006500112
80.206414639362077
90.169495305304944
100.138341994508775
110.126213973053166
120.136836775794764
130.112357564763033
140.0542961260206896
150.0191553273374102
160.013453219848239
17-0.00815264794379489
18-0.000828260153060675
190.00901971111845288
20-0.0174815304439747
21-0.0443723199823588



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
Parameters (R input):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 1 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = na.fail ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
if (par8=='na.fail') par8 <- na.fail else par8 <- na.pass
ccf <- function (x, y, lag.max = NULL, type = c('correlation', 'covariance'), plot = TRUE, na.action = na.fail, ...) {
type <- match.arg(type)
if (is.matrix(x) || is.matrix(y))
stop('univariate time series only')
X <- na.action(ts.intersect(as.ts(x), as.ts(y)))
colnames(X) <- c(deparse(substitute(x))[1L], deparse(substitute(y))[1L])
acf.out <- acf(X, lag.max = lag.max, plot = FALSE, type = type, na.action=na.action)
lag <- c(rev(acf.out$lag[-1, 2, 1]), acf.out$lag[, 1, 2])
y <- c(rev(acf.out$acf[-1, 2, 1]), acf.out$acf[, 1, 2])
acf.out$acf <- array(y, dim = c(length(y), 1L, 1L))
acf.out$lag <- array(lag, dim = c(length(y), 1L, 1L))
acf.out$snames <- paste(acf.out$snames, collapse = ' & ')
if (plot) {
plot(acf.out, ...)
return(invisible(acf.out))
}
else return(acf.out)
}
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par5 == 0) {
y <- log(y)
} else {
y <- (y ^ par5 - 1) / par5
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par6 > 0) y <- diff(y,lag=1,difference=par6)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
if (par7 > 0) y <- diff(y,lag=par4,difference=par7)
print(x)
print(y)
bitmap(file='test1.png')
(r <- ccf(x,y,na.action=par8,main='Cross Correlation Function',ylab='CCF',xlab='Lag (k)'))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Cross Correlation Function',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'Value',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of X series',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of X series',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of X series',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal Period (s)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Box-Cox transformation parameter (lambda) of Y series',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of non-seasonal differencing (d) of Y series',header=TRUE)
a<-table.element(a,par6)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Degree of seasonal differencing (D) of Y series',header=TRUE)
a<-table.element(a,par7)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'k',header=TRUE)
a<-table.element(a,'rho(Y[t],X[t+k])',header=TRUE)
a<-table.row.end(a)
mylength <- length(r$acf)
myhalf <- floor((mylength-1)/2)
for (i in 1:mylength) {
a<-table.row.start(a)
a<-table.element(a,i-myhalf-1,header=TRUE)
a<-table.element(a,r$acf[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')