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Author

*Unverified author*

R Software Module

rwasp_spectrum.wasp

Title produced by software

Spectral Analysis

Date of computation

Tue, 02 Dec 2008 03:18:13 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228213114ckijs4fzzleexre.htm/, Retrieved Sat, 18 May 2024 04:02:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27584, Retrieved Sat, 18 May 2024 04:02:42 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

211

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F     [Univariate Data Series] [Airline data] [2007-10-18 09:58:47] [42daae401fd3def69a25014f2252b4c2]
F RMPD    [Spectral Analysis] [] [2008-12-02 10:18:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

2008-12-04 09:37:25 [72e979bcc364082694890d2eccc1a66f] [reply] 
De regelmatige pieken in de raw periodogram komen overeen met een grote intensiteit van de golfbeweging. Een sterk stijgend cumulatief perdiogram wil zeggen dat er een lange termijn trend aanwezig is en de seizoenaliteit zie je aan de trap. Dit gaan we verbeteren door te differentiëren.
2008-12-05 16:39:39 [Bert Moons] [reply] 
Het “raw periodogram” begint hoog, bij een kleine frequentie (en dus een grote periode en een LT-trend). En daalt geleidelijk. De pieken op het “raw periodogram” komen overeen met de seizonalitiet (12m, 6m, 4m,…). 
De “cummulatieve periodogram” geeft ook aan dat er een LT-trend optreed, er is een stijl stijgen verloop bij zeer lage frequenties (lange periode). De trappen in het cummulatief periodogram komen overeen met de seizonaliteit. 
2008-12-06 16:05:40 [Bénédicte Soens] [reply] 
Hier wordt de tijdreeks ontbonden in alle mogelijke golfbewegingen. In de tabel zijn de exacte waarden te vinden: het is duidelijk dat er bij maanden 6,12, 24,... een veel grote bedrag is dan bij de andere, dit wijst op seizoenaliteit. Deze maanden worden ook door de grote pieken voorgesteld in de eerste grafiek. Daar is er opnieuw een lange termijn trend op te merken door de lage frequentie. Deze LT trend is ook duidelijk in cum.periodogram. want deze begint sterk stijgend, waarna er trappen komen (seizoenaliteit aanwezig!). 
2008-12-07 12:17:43 [Lana Van Wesemael] [reply] 
Hier zou de student bij kunnen zetten dat hij eerst het raw periodogram en het cumulatief periodogram bekijkt zonder differentiatie. Dit is slechts een detail, maar zo wordt het duidelijker voor de lezer waar de student mee bezig is. 

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Dataseries X:

Download CSV

Histogram

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27584&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27584&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27584&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 Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	0
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0069 (144)	3792.028873
0.0139 (72)	1238.009494
0.0208 (48)	1826.626686
0.0278 (36)	201.90456
0.0347 (28.8)	331.907846
0.0417 (24)	695.956872
0.0486 (20.5714)	189.838038
0.0556 (18)	632.668292
0.0625 (16)	287.593613
0.0694 (14.4)	1791.509224
0.0764 (13.0909)	9958.986159
0.0833 (12)	68001.700911
0.0903 (11.0769)	6339.30519
0.0972 (10.2857)	1529.925619
0.1042 (9.6)	854.106417
0.1111 (9)	454.283437
0.1181 (8.4706)	125.530203
0.125 (8)	30.347246
0.1319 (7.5789)	70.029431
0.1389 (7.2)	204.158401
0.1458 (6.8571)	203.994506
0.1528 (6.5455)	348.864801
0.1597 (6.2609)	1597.138661
0.1667 (6)	18608.352793
0.1736 (5.76)	2154.647278
0.1806 (5.5385)	608.650096
0.1875 (5.3333)	615.202265
0.1944 (5.1429)	120.078527
0.2014 (4.9655)	130.147126
0.2083 (4.8)	121.85189
0.2153 (4.6452)	40.141534
0.2222 (4.5)	217.826943
0.2292 (4.3636)	68.288003
0.2361 (4.2353)	214.055484
0.2431 (4.1143)	331.595519
0.25 (4)	3179.724197
0.2569 (3.8919)	203.863339
0.2639 (3.7895)	64.50686
0.2708 (3.6923)	3.43721
0.2778 (3.6)	31.782121
0.2847 (3.5122)	6.688219
0.2917 (3.4286)	16.986738
0.2986 (3.3488)	42.173804
0.3056 (3.2727)	29.803217
0.3125 (3.2)	33.804933
0.3194 (3.1304)	60.212446
0.3264 (3.0638)	379.911387
0.3333 (3)	2005.72132
0.3403 (2.9388)	75.434588
0.3472 (2.88)	140.072127
0.3542 (2.8235)	9.294066
0.3611 (2.7692)	15.430795
0.3681 (2.717)	10.698128
0.375 (2.6667)	4.21834
0.3819 (2.6182)	35.411804
0.3889 (2.5714)	16.11576
0.3958 (2.5263)	11.881808
0.4028 (2.4828)	183.988521
0.4097 (2.4407)	159.788028
0.4167 (2.4)	1276.34537
0.4236 (2.3607)	113.356981
0.4306 (2.3226)	208.3604
0.4375 (2.2857)	105.674799
0.4444 (2.25)	48.09594
0.4514 (2.2154)	10.461349
0.4583 (2.1818)	50.391079
0.4653 (2.1493)	26.292831
0.4722 (2.1176)	29.647284
0.4792 (2.087)	24.120557
0.4861 (2.0571)	8.778387
0.4931 (2.0282)	8.689016
0.5 (2)	25.907638

\begin{tabular}{lllllllll}
\hline
Raw Periodogram \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) & 1 \tabularnewline
Degree of non-seasonal differencing (d) & 0 \tabularnewline
Degree of seasonal differencing (D) & 0 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0069 (144) & 3792.028873 \tabularnewline
0.0139 (72) & 1238.009494 \tabularnewline
0.0208 (48) & 1826.626686 \tabularnewline
0.0278 (36) & 201.90456 \tabularnewline
0.0347 (28.8) & 331.907846 \tabularnewline
0.0417 (24) & 695.956872 \tabularnewline
0.0486 (20.5714) & 189.838038 \tabularnewline
0.0556 (18) & 632.668292 \tabularnewline
0.0625 (16) & 287.593613 \tabularnewline
0.0694 (14.4) & 1791.509224 \tabularnewline
0.0764 (13.0909) & 9958.986159 \tabularnewline
0.0833 (12) & 68001.700911 \tabularnewline
0.0903 (11.0769) & 6339.30519 \tabularnewline
0.0972 (10.2857) & 1529.925619 \tabularnewline
0.1042 (9.6) & 854.106417 \tabularnewline
0.1111 (9) & 454.283437 \tabularnewline
0.1181 (8.4706) & 125.530203 \tabularnewline
0.125 (8) & 30.347246 \tabularnewline
0.1319 (7.5789) & 70.029431 \tabularnewline
0.1389 (7.2) & 204.158401 \tabularnewline
0.1458 (6.8571) & 203.994506 \tabularnewline
0.1528 (6.5455) & 348.864801 \tabularnewline
0.1597 (6.2609) & 1597.138661 \tabularnewline
0.1667 (6) & 18608.352793 \tabularnewline
0.1736 (5.76) & 2154.647278 \tabularnewline
0.1806 (5.5385) & 608.650096 \tabularnewline
0.1875 (5.3333) & 615.202265 \tabularnewline
0.1944 (5.1429) & 120.078527 \tabularnewline
0.2014 (4.9655) & 130.147126 \tabularnewline
0.2083 (4.8) & 121.85189 \tabularnewline
0.2153 (4.6452) & 40.141534 \tabularnewline
0.2222 (4.5) & 217.826943 \tabularnewline
0.2292 (4.3636) & 68.288003 \tabularnewline
0.2361 (4.2353) & 214.055484 \tabularnewline
0.2431 (4.1143) & 331.595519 \tabularnewline
0.25 (4) & 3179.724197 \tabularnewline
0.2569 (3.8919) & 203.863339 \tabularnewline
0.2639 (3.7895) & 64.50686 \tabularnewline
0.2708 (3.6923) & 3.43721 \tabularnewline
0.2778 (3.6) & 31.782121 \tabularnewline
0.2847 (3.5122) & 6.688219 \tabularnewline
0.2917 (3.4286) & 16.986738 \tabularnewline
0.2986 (3.3488) & 42.173804 \tabularnewline
0.3056 (3.2727) & 29.803217 \tabularnewline
0.3125 (3.2) & 33.804933 \tabularnewline
0.3194 (3.1304) & 60.212446 \tabularnewline
0.3264 (3.0638) & 379.911387 \tabularnewline
0.3333 (3) & 2005.72132 \tabularnewline
0.3403 (2.9388) & 75.434588 \tabularnewline
0.3472 (2.88) & 140.072127 \tabularnewline
0.3542 (2.8235) & 9.294066 \tabularnewline
0.3611 (2.7692) & 15.430795 \tabularnewline
0.3681 (2.717) & 10.698128 \tabularnewline
0.375 (2.6667) & 4.21834 \tabularnewline
0.3819 (2.6182) & 35.411804 \tabularnewline
0.3889 (2.5714) & 16.11576 \tabularnewline
0.3958 (2.5263) & 11.881808 \tabularnewline
0.4028 (2.4828) & 183.988521 \tabularnewline
0.4097 (2.4407) & 159.788028 \tabularnewline
0.4167 (2.4) & 1276.34537 \tabularnewline
0.4236 (2.3607) & 113.356981 \tabularnewline
0.4306 (2.3226) & 208.3604 \tabularnewline
0.4375 (2.2857) & 105.674799 \tabularnewline
0.4444 (2.25) & 48.09594 \tabularnewline
0.4514 (2.2154) & 10.461349 \tabularnewline
0.4583 (2.1818) & 50.391079 \tabularnewline
0.4653 (2.1493) & 26.292831 \tabularnewline
0.4722 (2.1176) & 29.647284 \tabularnewline
0.4792 (2.087) & 24.120557 \tabularnewline
0.4861 (2.0571) & 8.778387 \tabularnewline
0.4931 (2.0282) & 8.689016 \tabularnewline
0.5 (2) & 25.907638 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27584&T=1

[TABLE]
[ROW][C]Raw Periodogram[/C][/ROW]
[ROW][C]Parameter[/C][C]Value[/C][/ROW]
[ROW][C]Box-Cox transformation parameter (lambda)[/C][C]1[/C][/ROW]
[ROW][C]Degree of non-seasonal differencing (d)[/C][C]0[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D)[/C][C]0[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Frequency (Period)[/C][C]Spectrum[/C][/ROW]
[ROW][C]0.0069 (144)[/C][C]3792.028873[/C][/ROW]
[ROW][C]0.0139 (72)[/C][C]1238.009494[/C][/ROW]
[ROW][C]0.0208 (48)[/C][C]1826.626686[/C][/ROW]
[ROW][C]0.0278 (36)[/C][C]201.90456[/C][/ROW]
[ROW][C]0.0347 (28.8)[/C][C]331.907846[/C][/ROW]
[ROW][C]0.0417 (24)[/C][C]695.956872[/C][/ROW]
[ROW][C]0.0486 (20.5714)[/C][C]189.838038[/C][/ROW]
[ROW][C]0.0556 (18)[/C][C]632.668292[/C][/ROW]
[ROW][C]0.0625 (16)[/C][C]287.593613[/C][/ROW]
[ROW][C]0.0694 (14.4)[/C][C]1791.509224[/C][/ROW]
[ROW][C]0.0764 (13.0909)[/C][C]9958.986159[/C][/ROW]
[ROW][C]0.0833 (12)[/C][C]68001.700911[/C][/ROW]
[ROW][C]0.0903 (11.0769)[/C][C]6339.30519[/C][/ROW]
[ROW][C]0.0972 (10.2857)[/C][C]1529.925619[/C][/ROW]
[ROW][C]0.1042 (9.6)[/C][C]854.106417[/C][/ROW]
[ROW][C]0.1111 (9)[/C][C]454.283437[/C][/ROW]
[ROW][C]0.1181 (8.4706)[/C][C]125.530203[/C][/ROW]
[ROW][C]0.125 (8)[/C][C]30.347246[/C][/ROW]
[ROW][C]0.1319 (7.5789)[/C][C]70.029431[/C][/ROW]
[ROW][C]0.1389 (7.2)[/C][C]204.158401[/C][/ROW]
[ROW][C]0.1458 (6.8571)[/C][C]203.994506[/C][/ROW]
[ROW][C]0.1528 (6.5455)[/C][C]348.864801[/C][/ROW]
[ROW][C]0.1597 (6.2609)[/C][C]1597.138661[/C][/ROW]
[ROW][C]0.1667 (6)[/C][C]18608.352793[/C][/ROW]
[ROW][C]0.1736 (5.76)[/C][C]2154.647278[/C][/ROW]
[ROW][C]0.1806 (5.5385)[/C][C]608.650096[/C][/ROW]
[ROW][C]0.1875 (5.3333)[/C][C]615.202265[/C][/ROW]
[ROW][C]0.1944 (5.1429)[/C][C]120.078527[/C][/ROW]
[ROW][C]0.2014 (4.9655)[/C][C]130.147126[/C][/ROW]
[ROW][C]0.2083 (4.8)[/C][C]121.85189[/C][/ROW]
[ROW][C]0.2153 (4.6452)[/C][C]40.141534[/C][/ROW]
[ROW][C]0.2222 (4.5)[/C][C]217.826943[/C][/ROW]
[ROW][C]0.2292 (4.3636)[/C][C]68.288003[/C][/ROW]
[ROW][C]0.2361 (4.2353)[/C][C]214.055484[/C][/ROW]
[ROW][C]0.2431 (4.1143)[/C][C]331.595519[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]3179.724197[/C][/ROW]
[ROW][C]0.2569 (3.8919)[/C][C]203.863339[/C][/ROW]
[ROW][C]0.2639 (3.7895)[/C][C]64.50686[/C][/ROW]
[ROW][C]0.2708 (3.6923)[/C][C]3.43721[/C][/ROW]
[ROW][C]0.2778 (3.6)[/C][C]31.782121[/C][/ROW]
[ROW][C]0.2847 (3.5122)[/C][C]6.688219[/C][/ROW]
[ROW][C]0.2917 (3.4286)[/C][C]16.986738[/C][/ROW]
[ROW][C]0.2986 (3.3488)[/C][C]42.173804[/C][/ROW]
[ROW][C]0.3056 (3.2727)[/C][C]29.803217[/C][/ROW]
[ROW][C]0.3125 (3.2)[/C][C]33.804933[/C][/ROW]
[ROW][C]0.3194 (3.1304)[/C][C]60.212446[/C][/ROW]
[ROW][C]0.3264 (3.0638)[/C][C]379.911387[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]2005.72132[/C][/ROW]
[ROW][C]0.3403 (2.9388)[/C][C]75.434588[/C][/ROW]
[ROW][C]0.3472 (2.88)[/C][C]140.072127[/C][/ROW]
[ROW][C]0.3542 (2.8235)[/C][C]9.294066[/C][/ROW]
[ROW][C]0.3611 (2.7692)[/C][C]15.430795[/C][/ROW]
[ROW][C]0.3681 (2.717)[/C][C]10.698128[/C][/ROW]
[ROW][C]0.375 (2.6667)[/C][C]4.21834[/C][/ROW]
[ROW][C]0.3819 (2.6182)[/C][C]35.411804[/C][/ROW]
[ROW][C]0.3889 (2.5714)[/C][C]16.11576[/C][/ROW]
[ROW][C]0.3958 (2.5263)[/C][C]11.881808[/C][/ROW]
[ROW][C]0.4028 (2.4828)[/C][C]183.988521[/C][/ROW]
[ROW][C]0.4097 (2.4407)[/C][C]159.788028[/C][/ROW]
[ROW][C]0.4167 (2.4)[/C][C]1276.34537[/C][/ROW]
[ROW][C]0.4236 (2.3607)[/C][C]113.356981[/C][/ROW]
[ROW][C]0.4306 (2.3226)[/C][C]208.3604[/C][/ROW]
[ROW][C]0.4375 (2.2857)[/C][C]105.674799[/C][/ROW]
[ROW][C]0.4444 (2.25)[/C][C]48.09594[/C][/ROW]
[ROW][C]0.4514 (2.2154)[/C][C]10.461349[/C][/ROW]
[ROW][C]0.4583 (2.1818)[/C][C]50.391079[/C][/ROW]
[ROW][C]0.4653 (2.1493)[/C][C]26.292831[/C][/ROW]
[ROW][C]0.4722 (2.1176)[/C][C]29.647284[/C][/ROW]
[ROW][C]0.4792 (2.087)[/C][C]24.120557[/C][/ROW]
[ROW][C]0.4861 (2.0571)[/C][C]8.778387[/C][/ROW]
[ROW][C]0.4931 (2.0282)[/C][C]8.689016[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]25.907638[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27584&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27584&T=1

As an alternative you can also use a QR Code:

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

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	0
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0069 (144)	3792.028873
0.0139 (72)	1238.009494
0.0208 (48)	1826.626686
0.0278 (36)	201.90456
0.0347 (28.8)	331.907846
0.0417 (24)	695.956872
0.0486 (20.5714)	189.838038
0.0556 (18)	632.668292
0.0625 (16)	287.593613
0.0694 (14.4)	1791.509224
0.0764 (13.0909)	9958.986159
0.0833 (12)	68001.700911
0.0903 (11.0769)	6339.30519
0.0972 (10.2857)	1529.925619
0.1042 (9.6)	854.106417
0.1111 (9)	454.283437
0.1181 (8.4706)	125.530203
0.125 (8)	30.347246
0.1319 (7.5789)	70.029431
0.1389 (7.2)	204.158401
0.1458 (6.8571)	203.994506
0.1528 (6.5455)	348.864801
0.1597 (6.2609)	1597.138661
0.1667 (6)	18608.352793
0.1736 (5.76)	2154.647278
0.1806 (5.5385)	608.650096
0.1875 (5.3333)	615.202265
0.1944 (5.1429)	120.078527
0.2014 (4.9655)	130.147126
0.2083 (4.8)	121.85189
0.2153 (4.6452)	40.141534
0.2222 (4.5)	217.826943
0.2292 (4.3636)	68.288003
0.2361 (4.2353)	214.055484
0.2431 (4.1143)	331.595519
0.25 (4)	3179.724197
0.2569 (3.8919)	203.863339
0.2639 (3.7895)	64.50686
0.2708 (3.6923)	3.43721
0.2778 (3.6)	31.782121
0.2847 (3.5122)	6.688219
0.2917 (3.4286)	16.986738
0.2986 (3.3488)	42.173804
0.3056 (3.2727)	29.803217
0.3125 (3.2)	33.804933
0.3194 (3.1304)	60.212446
0.3264 (3.0638)	379.911387
0.3333 (3)	2005.72132
0.3403 (2.9388)	75.434588
0.3472 (2.88)	140.072127
0.3542 (2.8235)	9.294066
0.3611 (2.7692)	15.430795
0.3681 (2.717)	10.698128
0.375 (2.6667)	4.21834
0.3819 (2.6182)	35.411804
0.3889 (2.5714)	16.11576
0.3958 (2.5263)	11.881808
0.4028 (2.4828)	183.988521
0.4097 (2.4407)	159.788028
0.4167 (2.4)	1276.34537
0.4236 (2.3607)	113.356981
0.4306 (2.3226)	208.3604
0.4375 (2.2857)	105.674799
0.4444 (2.25)	48.09594
0.4514 (2.2154)	10.461349
0.4583 (2.1818)	50.391079
0.4653 (2.1493)	26.292831
0.4722 (2.1176)	29.647284
0.4792 (2.087)	24.120557
0.4861 (2.0571)	8.778387
0.4931 (2.0282)	8.689016
0.5 (2)	25.907638

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ;

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)
if (par1 == 0) {
x <- log(x)
} else {
x <- (x ^ par1 - 1) / par1
}
if (par2 > 0) x <- diff(x,lag=1,difference=par2)
if (par3 > 0) x <- diff(x,lag=par4,difference=par3)
bitmap(file='test1.png')
r <- spectrum(x,main='Raw Periodogram')
dev.off()
bitmap(file='test2.png')
cpgram(x,main='Cumulative Periodogram')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Raw Periodogram',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)',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)',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)',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,'Frequency (Period)',header=TRUE)
a<-table.element(a,'Spectrum',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(r$freq)) {
a<-table.row.start(a)
mylab <- round(r$freq[i],4)
mylab <- paste(mylab,' (',sep='')
mylab <- paste(mylab,round(1/r$freq[i],4),sep='')
mylab <- paste(mylab,')',sep='')
a<-table.element(a,mylab,header=TRUE)
a<-table.element(a,round(r$spec[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code