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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 07:48:13 -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/t12282294539sgw3zmopy0i0fr.htm/, Retrieved Fri, 17 May 2024 04:10:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27886, Retrieved Fri, 17 May 2024 04:10:09 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

189

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] [q6 Spectrum in th...] [2008-12-02 14:48:13] [d41d8cd98f00b204e9800998ecf8427e] [Current]

Feedback Forum

2008-12-06 18:24:13 [a2386b643d711541400692649981f2dc] [reply] 
Bij het raw periodogram merken we dat de pieken grotendeels zijn verdwenen door de differentiatie en dat ze meer schommelen rond hetzelfde niveau, ze zijn dus stationair geworden. Je kon ook zeggen dat frequentie het omgekeerde is van een periode. Dit wil zeggen dat bij een lage (hoge) frequentie een lange (korte) periode hoort. 
2008-12-08 16:09:19 [Jonas Scheltjens] [reply] 
Bij de Spectrale analyse nam de student niet eens de moeite om te kijken en bespreken wat men vindt indien men de differentiatie van de seizoenaliteit en de niet-seizoenaliteit nog niet uitvoert. De student ging meteen over naar de differentiatie van beiden. 
Wat men zou moeten doen is dus eerst alles berekenen zonder te differentiëren en vervolgens stap voor stap differentiëren aangezien men zo duidelijk kan zien at er daadwerkelijk gebeurd. 
Hier volgt alleszins een korte bespreking van wat er had moeten bekeken worden en een korte bespreking hiervan.  
(d=0, D=0) Deze methode wordt gebruikt om de trend te onderzoeken. Hoe kleiner de frequentie, hoe langer de periode. Men moet kijken naar de hoge spectrum waarden, dezen geven aan hoe sterk de golfbeweging is. Er is ook seizoenaliteit aanwezig. Dit merken we aan de hoge spectrumwaarde bij een periode van 12 maanden. Ook uit het raw periodogram blijkt dat er inderdaad seizoenaliteit aanwezig is door de dalende trend in de pieken. 
Bij het Cumulative Periodogram ziet men een trapsgewijs verloop aan toont dat er sprake is van seizoenaliteit. Dit kunnen we ook niet toeschrijven aan de toevalsfactor aagezien de grafiek niet ligt tussen de blauwe stippellijnen dewelke het betrouwbaarheidsinterval voorstellen. We kunnen ook nog vermelden dat de grafiek gekenmerkt wordt door een zeer steil begin welk duidt op de aanwezigheid van een lange termijn trend. Nu moeten we gaan differentiëren om de lange termijn trend en seizoenaliteit te verwijderen. 
Wanneer we dan differentiëren zien we dat de trapfunctie verdwenen is. Ook is er geen steil begin meer wat er op wijst dat de lange termijn trend verdwenen is. Het is ook zo dat er nog wel een voorspelbaarheid aanwezig is waardoor we verder onderzoek moeten doen. We kunnen de vertoning ook niet toeschrijven aan het toeval. Want indien dit toch het geval zou zijn, zou op de grafiek een diagonaal te zien zijn die perfect binnen het betrouwbaarheidsinterval zou liggen. Als algemene conclusie mocht nog gezegd worden dat er om te differentiëren de parameters d en D beide de waarde 1 moeten hebben.  

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Dataseries X:

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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	1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27886&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]1 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=27886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27886&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	1 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)	1
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0074 (135)	12.240463
0.0148 (67.5)	39.882056
0.0222 (45)	69.90144
0.0296 (33.75)	0.898912
0.037 (27)	147.88508
0.0444 (22.5)	39.66141
0.0519 (19.2857)	96.869228
0.0593 (16.875)	42.153012
0.0667 (15)	9.848032
0.0741 (13.5)	9.274109
0.0815 (12.2727)	366.63833
0.0889 (11.25)	86.565121
0.0963 (10.3846)	6.034478
0.1037 (9.6429)	37.480501
0.1111 (9)	74.068165
0.1185 (8.4375)	11.920285
0.1259 (7.9412)	383.663877
0.1333 (7.5)	76.93499
0.1407 (7.1053)	211.965602
0.1481 (6.75)	5.233055
0.1556 (6.4286)	62.10061
0.163 (6.1364)	164.435381
0.1704 (5.8696)	234.006969
0.1778 (5.625)	36.612894
0.1852 (5.4)	134.170406
0.1926 (5.1923)	22.584301
0.2 (5)	72.335961
0.2074 (4.8214)	161.639123
0.2148 (4.6552)	202.018874
0.2222 (4.5)	443.045866
0.2296 (4.3548)	7.057626
0.237 (4.2188)	110.821554
0.2444 (4.0909)	107.737548
0.2519 (3.9706)	119.740065
0.2593 (3.8571)	53.113647
0.2667 (3.75)	16.031001
0.2741 (3.6486)	110.381385
0.2815 (3.5526)	6.652357
0.2889 (3.4615)	105.099533
0.2963 (3.375)	99.405649
0.3037 (3.2927)	10.822999
0.3111 (3.2143)	72.495068
0.3185 (3.1395)	33.691369
0.3259 (3.0682)	84.321331
0.3333 (3)	85.814236
0.3407 (2.9348)	174.463684
0.3481 (2.8723)	567.110079
0.3556 (2.8125)	40.048261
0.363 (2.7551)	43.640918
0.3704 (2.7)	16.822971
0.3778 (2.6471)	198.336774
0.3852 (2.5962)	461.199919
0.3926 (2.5472)	49.401504
0.4 (2.5)	249.761936
0.4074 (2.4545)	18.930166
0.4148 (2.4107)	59.633363
0.4222 (2.3684)	63.237612
0.4296 (2.3276)	506.964807
0.437 (2.2881)	628.874832
0.4444 (2.25)	312.573293
0.4519 (2.2131)	64.503761
0.4593 (2.1774)	664.343873
0.4667 (2.1429)	617.152591
0.4741 (2.1094)	222.223882
0.4815 (2.0769)	255.172709
0.4889 (2.0455)	2.61327
0.4963 (2.0149)	42.731955

\begin{tabular}{lllllllll}
\hline
Raw Periodogram \tabularnewline
Parameter & Value \tabularnewline
Box-Cox transformation parameter (lambda) & 1 \tabularnewline
Degree of non-seasonal differencing (d) & 1 \tabularnewline
Degree of seasonal differencing (D) & 1 \tabularnewline
Seasonal Period (s) & 12 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0074 (135) & 12.240463 \tabularnewline
0.0148 (67.5) & 39.882056 \tabularnewline
0.0222 (45) & 69.90144 \tabularnewline
0.0296 (33.75) & 0.898912 \tabularnewline
0.037 (27) & 147.88508 \tabularnewline
0.0444 (22.5) & 39.66141 \tabularnewline
0.0519 (19.2857) & 96.869228 \tabularnewline
0.0593 (16.875) & 42.153012 \tabularnewline
0.0667 (15) & 9.848032 \tabularnewline
0.0741 (13.5) & 9.274109 \tabularnewline
0.0815 (12.2727) & 366.63833 \tabularnewline
0.0889 (11.25) & 86.565121 \tabularnewline
0.0963 (10.3846) & 6.034478 \tabularnewline
0.1037 (9.6429) & 37.480501 \tabularnewline
0.1111 (9) & 74.068165 \tabularnewline
0.1185 (8.4375) & 11.920285 \tabularnewline
0.1259 (7.9412) & 383.663877 \tabularnewline
0.1333 (7.5) & 76.93499 \tabularnewline
0.1407 (7.1053) & 211.965602 \tabularnewline
0.1481 (6.75) & 5.233055 \tabularnewline
0.1556 (6.4286) & 62.10061 \tabularnewline
0.163 (6.1364) & 164.435381 \tabularnewline
0.1704 (5.8696) & 234.006969 \tabularnewline
0.1778 (5.625) & 36.612894 \tabularnewline
0.1852 (5.4) & 134.170406 \tabularnewline
0.1926 (5.1923) & 22.584301 \tabularnewline
0.2 (5) & 72.335961 \tabularnewline
0.2074 (4.8214) & 161.639123 \tabularnewline
0.2148 (4.6552) & 202.018874 \tabularnewline
0.2222 (4.5) & 443.045866 \tabularnewline
0.2296 (4.3548) & 7.057626 \tabularnewline
0.237 (4.2188) & 110.821554 \tabularnewline
0.2444 (4.0909) & 107.737548 \tabularnewline
0.2519 (3.9706) & 119.740065 \tabularnewline
0.2593 (3.8571) & 53.113647 \tabularnewline
0.2667 (3.75) & 16.031001 \tabularnewline
0.2741 (3.6486) & 110.381385 \tabularnewline
0.2815 (3.5526) & 6.652357 \tabularnewline
0.2889 (3.4615) & 105.099533 \tabularnewline
0.2963 (3.375) & 99.405649 \tabularnewline
0.3037 (3.2927) & 10.822999 \tabularnewline
0.3111 (3.2143) & 72.495068 \tabularnewline
0.3185 (3.1395) & 33.691369 \tabularnewline
0.3259 (3.0682) & 84.321331 \tabularnewline
0.3333 (3) & 85.814236 \tabularnewline
0.3407 (2.9348) & 174.463684 \tabularnewline
0.3481 (2.8723) & 567.110079 \tabularnewline
0.3556 (2.8125) & 40.048261 \tabularnewline
0.363 (2.7551) & 43.640918 \tabularnewline
0.3704 (2.7) & 16.822971 \tabularnewline
0.3778 (2.6471) & 198.336774 \tabularnewline
0.3852 (2.5962) & 461.199919 \tabularnewline
0.3926 (2.5472) & 49.401504 \tabularnewline
0.4 (2.5) & 249.761936 \tabularnewline
0.4074 (2.4545) & 18.930166 \tabularnewline
0.4148 (2.4107) & 59.633363 \tabularnewline
0.4222 (2.3684) & 63.237612 \tabularnewline
0.4296 (2.3276) & 506.964807 \tabularnewline
0.437 (2.2881) & 628.874832 \tabularnewline
0.4444 (2.25) & 312.573293 \tabularnewline
0.4519 (2.2131) & 64.503761 \tabularnewline
0.4593 (2.1774) & 664.343873 \tabularnewline
0.4667 (2.1429) & 617.152591 \tabularnewline
0.4741 (2.1094) & 222.223882 \tabularnewline
0.4815 (2.0769) & 255.172709 \tabularnewline
0.4889 (2.0455) & 2.61327 \tabularnewline
0.4963 (2.0149) & 42.731955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27886&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]1[/C][/ROW]
[ROW][C]Degree of seasonal differencing (D)[/C][C]1[/C][/ROW]
[ROW][C]Seasonal Period (s)[/C][C]12[/C][/ROW]
[ROW][C]Frequency (Period)[/C][C]Spectrum[/C][/ROW]
[ROW][C]0.0074 (135)[/C][C]12.240463[/C][/ROW]
[ROW][C]0.0148 (67.5)[/C][C]39.882056[/C][/ROW]
[ROW][C]0.0222 (45)[/C][C]69.90144[/C][/ROW]
[ROW][C]0.0296 (33.75)[/C][C]0.898912[/C][/ROW]
[ROW][C]0.037 (27)[/C][C]147.88508[/C][/ROW]
[ROW][C]0.0444 (22.5)[/C][C]39.66141[/C][/ROW]
[ROW][C]0.0519 (19.2857)[/C][C]96.869228[/C][/ROW]
[ROW][C]0.0593 (16.875)[/C][C]42.153012[/C][/ROW]
[ROW][C]0.0667 (15)[/C][C]9.848032[/C][/ROW]
[ROW][C]0.0741 (13.5)[/C][C]9.274109[/C][/ROW]
[ROW][C]0.0815 (12.2727)[/C][C]366.63833[/C][/ROW]
[ROW][C]0.0889 (11.25)[/C][C]86.565121[/C][/ROW]
[ROW][C]0.0963 (10.3846)[/C][C]6.034478[/C][/ROW]
[ROW][C]0.1037 (9.6429)[/C][C]37.480501[/C][/ROW]
[ROW][C]0.1111 (9)[/C][C]74.068165[/C][/ROW]
[ROW][C]0.1185 (8.4375)[/C][C]11.920285[/C][/ROW]
[ROW][C]0.1259 (7.9412)[/C][C]383.663877[/C][/ROW]
[ROW][C]0.1333 (7.5)[/C][C]76.93499[/C][/ROW]
[ROW][C]0.1407 (7.1053)[/C][C]211.965602[/C][/ROW]
[ROW][C]0.1481 (6.75)[/C][C]5.233055[/C][/ROW]
[ROW][C]0.1556 (6.4286)[/C][C]62.10061[/C][/ROW]
[ROW][C]0.163 (6.1364)[/C][C]164.435381[/C][/ROW]
[ROW][C]0.1704 (5.8696)[/C][C]234.006969[/C][/ROW]
[ROW][C]0.1778 (5.625)[/C][C]36.612894[/C][/ROW]
[ROW][C]0.1852 (5.4)[/C][C]134.170406[/C][/ROW]
[ROW][C]0.1926 (5.1923)[/C][C]22.584301[/C][/ROW]
[ROW][C]0.2 (5)[/C][C]72.335961[/C][/ROW]
[ROW][C]0.2074 (4.8214)[/C][C]161.639123[/C][/ROW]
[ROW][C]0.2148 (4.6552)[/C][C]202.018874[/C][/ROW]
[ROW][C]0.2222 (4.5)[/C][C]443.045866[/C][/ROW]
[ROW][C]0.2296 (4.3548)[/C][C]7.057626[/C][/ROW]
[ROW][C]0.237 (4.2188)[/C][C]110.821554[/C][/ROW]
[ROW][C]0.2444 (4.0909)[/C][C]107.737548[/C][/ROW]
[ROW][C]0.2519 (3.9706)[/C][C]119.740065[/C][/ROW]
[ROW][C]0.2593 (3.8571)[/C][C]53.113647[/C][/ROW]
[ROW][C]0.2667 (3.75)[/C][C]16.031001[/C][/ROW]
[ROW][C]0.2741 (3.6486)[/C][C]110.381385[/C][/ROW]
[ROW][C]0.2815 (3.5526)[/C][C]6.652357[/C][/ROW]
[ROW][C]0.2889 (3.4615)[/C][C]105.099533[/C][/ROW]
[ROW][C]0.2963 (3.375)[/C][C]99.405649[/C][/ROW]
[ROW][C]0.3037 (3.2927)[/C][C]10.822999[/C][/ROW]
[ROW][C]0.3111 (3.2143)[/C][C]72.495068[/C][/ROW]
[ROW][C]0.3185 (3.1395)[/C][C]33.691369[/C][/ROW]
[ROW][C]0.3259 (3.0682)[/C][C]84.321331[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]85.814236[/C][/ROW]
[ROW][C]0.3407 (2.9348)[/C][C]174.463684[/C][/ROW]
[ROW][C]0.3481 (2.8723)[/C][C]567.110079[/C][/ROW]
[ROW][C]0.3556 (2.8125)[/C][C]40.048261[/C][/ROW]
[ROW][C]0.363 (2.7551)[/C][C]43.640918[/C][/ROW]
[ROW][C]0.3704 (2.7)[/C][C]16.822971[/C][/ROW]
[ROW][C]0.3778 (2.6471)[/C][C]198.336774[/C][/ROW]
[ROW][C]0.3852 (2.5962)[/C][C]461.199919[/C][/ROW]
[ROW][C]0.3926 (2.5472)[/C][C]49.401504[/C][/ROW]
[ROW][C]0.4 (2.5)[/C][C]249.761936[/C][/ROW]
[ROW][C]0.4074 (2.4545)[/C][C]18.930166[/C][/ROW]
[ROW][C]0.4148 (2.4107)[/C][C]59.633363[/C][/ROW]
[ROW][C]0.4222 (2.3684)[/C][C]63.237612[/C][/ROW]
[ROW][C]0.4296 (2.3276)[/C][C]506.964807[/C][/ROW]
[ROW][C]0.437 (2.2881)[/C][C]628.874832[/C][/ROW]
[ROW][C]0.4444 (2.25)[/C][C]312.573293[/C][/ROW]
[ROW][C]0.4519 (2.2131)[/C][C]64.503761[/C][/ROW]
[ROW][C]0.4593 (2.1774)[/C][C]664.343873[/C][/ROW]
[ROW][C]0.4667 (2.1429)[/C][C]617.152591[/C][/ROW]
[ROW][C]0.4741 (2.1094)[/C][C]222.223882[/C][/ROW]
[ROW][C]0.4815 (2.0769)[/C][C]255.172709[/C][/ROW]
[ROW][C]0.4889 (2.0455)[/C][C]2.61327[/C][/ROW]
[ROW][C]0.4963 (2.0149)[/C][C]42.731955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27886&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)	1
Degree of seasonal differencing (D)	1
Seasonal Period (s)	12
Frequency (Period)	Spectrum
0.0074 (135)	12.240463
0.0148 (67.5)	39.882056
0.0222 (45)	69.90144
0.0296 (33.75)	0.898912
0.037 (27)	147.88508
0.0444 (22.5)	39.66141
0.0519 (19.2857)	96.869228
0.0593 (16.875)	42.153012
0.0667 (15)	9.848032
0.0741 (13.5)	9.274109
0.0815 (12.2727)	366.63833
0.0889 (11.25)	86.565121
0.0963 (10.3846)	6.034478
0.1037 (9.6429)	37.480501
0.1111 (9)	74.068165
0.1185 (8.4375)	11.920285
0.1259 (7.9412)	383.663877
0.1333 (7.5)	76.93499
0.1407 (7.1053)	211.965602
0.1481 (6.75)	5.233055
0.1556 (6.4286)	62.10061
0.163 (6.1364)	164.435381
0.1704 (5.8696)	234.006969
0.1778 (5.625)	36.612894
0.1852 (5.4)	134.170406
0.1926 (5.1923)	22.584301
0.2 (5)	72.335961
0.2074 (4.8214)	161.639123
0.2148 (4.6552)	202.018874
0.2222 (4.5)	443.045866
0.2296 (4.3548)	7.057626
0.237 (4.2188)	110.821554
0.2444 (4.0909)	107.737548
0.2519 (3.9706)	119.740065
0.2593 (3.8571)	53.113647
0.2667 (3.75)	16.031001
0.2741 (3.6486)	110.381385
0.2815 (3.5526)	6.652357
0.2889 (3.4615)	105.099533
0.2963 (3.375)	99.405649
0.3037 (3.2927)	10.822999
0.3111 (3.2143)	72.495068
0.3185 (3.1395)	33.691369
0.3259 (3.0682)	84.321331
0.3333 (3)	85.814236
0.3407 (2.9348)	174.463684
0.3481 (2.8723)	567.110079
0.3556 (2.8125)	40.048261
0.363 (2.7551)	43.640918
0.3704 (2.7)	16.822971
0.3778 (2.6471)	198.336774
0.3852 (2.5962)	461.199919
0.3926 (2.5472)	49.401504
0.4 (2.5)	249.761936
0.4074 (2.4545)	18.930166
0.4148 (2.4107)	59.633363
0.4222 (2.3684)	63.237612
0.4296 (2.3276)	506.964807
0.437 (2.2881)	628.874832
0.4444 (2.25)	312.573293
0.4519 (2.2131)	64.503761
0.4593 (2.1774)	664.343873
0.4667 (2.1429)	617.152591
0.4741 (2.1094)	222.223882
0.4815 (2.0769)	255.172709
0.4889 (2.0455)	2.61327
0.4963 (2.0149)	42.731955

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 1 ; 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