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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 14:42:53 -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/t1228254298c5eww3041n92g21.htm/, Retrieved Sun, 19 May 2024 12:19:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28490, Retrieved Sun, 19 May 2024 12:19:05 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

142

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] [W7Q6] [2008-12-02 21:42:53] [823d674fbf3a4e0ec71bbbd5140f82c6] [Current]

Feedback Forum

2008-12-08 15:54:39 [Kevin Vermeiren] [reply] 
Het antwoord van de student is correct. Hier had nog vermeld mogen worden waarom deze analyse gebruikt wordt. Deze methode wordt gebruikt om de trend te onderzoeken. Het klopt dat hoe kleiner de frequentie hoe langer de periode. Dit is duidelijk af te leiden uit de formule frequentie=1/periode. Het klopt dat er gekeken moet worden naar de hoge spectrum waarden. Hier had nog vermeld mogen worden dat de spectrum waarden een indicatie geven van hoe sterk de golfbeweging voorkomt. Het klopt dat er seizoenaliteit aanwezig is. 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. Het cumulative periodogram is niet volledig besproken. Het klopt dat het trapsgewijs verloop aan toont dat er sprake is van seizoenaliteit. Het voorkomen er van is inderdaad niet toe te schrijven aan toeval want de grafiek ligt niet tussen de blauwe stippellijnen welke het betrouwbaarheidsinterval voorstellen. Verder diende nog vermeld te worden dat de grafiek gekenmerkt wordt door een zeer steil begin welk duidt op de aanwezigheid van een lange termijn trend. Het klopt dat we nu gaan differentiëren om deze lange termijn trend en seizoenaliteit te verwijderen.
2008-12-08 21:44:06 [] [reply] 
De spectraalanalyse wordt gebruikt om de trend te onderzoeken. De hoge spectrumwarden geven seizoenaliteit weer. Het dalend patroon geeft de lange termijn-trend weer. 
De trapbeweging bij de cumulatieve periodogram wijst op seizoenaliteit. En de snelle stijging op een lange termijn trend 
2008-12-09 11:33:33 [Yannick Van Schil] [reply] 
correct geantwoord, op het cumulative perlodogram zie je eerst zeer stijle stijging dit verwijst naar een lange termijntrend en het trapgewijs verloop op seizonaliteit

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Dataseries X:

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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	1 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28490&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28490&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28490&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	'George Udny Yule' @ 72.249.76.132

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=28490&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=28490&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28490&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