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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 07:50:00 -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/t1228229774xj844f3xn2gxc7v.htm/, Retrieved Sun, 19 May 2024 11:11:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=27903, Retrieved Sun, 19 May 2024 11:11:23 +0000

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No (this computation is public)

User-defined keywords

Estimated Impact

173

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

F       [Spectral Analysis] [] [2008-12-02 14:50:00] [cdc575afe547a0c8f1ab59a46ec2fd93] [Current]
F         [Spectral Analysis] [Q6] [2008-12-02 18:19:05] [74be16979710d4c4e7c6647856088456] 

Feedback Forum

2008-12-06 13:35:26 [Maarten Van Gucht] [reply] 
De student heeft een goede conclusie weergegeven maar deze kon nog een beetje vervolledigd worden. de raw periododram kunnen we rechts een vertical blauw lijntje zien. Met een puntje in het midden. Deze kunnen we verschuiven over de grafiek en de toppen die buiten deze betrouwbaarheidsinterval liggen dan is er een significant verschil. In de cumulatieve periodogram kunnen we in het begin een sterke stijging waarnemen, dit wijst op een lange termijn trend en bovenaan kunnen we trappen waarnemen in de grafiek, dit wijst op seizoenaliteit.  
door deze methode te gebruiken wordt de tijdreeks ontbonden in regelmatige golfbewegingen (=periodieke functies). De tabel geeft de frequentie weer van de golven. Spectrum is de intensiteit waarmee de golfbeweging zich voordoet. Wanneer we relatief een grotere waarde hebben tegenover anderen dan spreken we over een lange termijn trend. De eerste waarde in deze tabel is zeer belangrijk. Dit verwijst naar de periode (144). Dit is het grootst, maw we hebben een periode van 144 maanden om van de ene top naar de andere top te gaan.  
Seizoenaliteit vinden we bij de perioden 4,6 en 12. Deze zijn niet willekeurig gekozen want het zijn veelvouden van 12 (1jaar). dit heeft de student ook goed vermeld in zijn antwoord. Dus bijvoorbeeld om van de ene top naar de andere te gaan hebben we een periode van 4 maanden. (grootste waarde). Je kan ook op een periode van 6 maanden van de ene top naar de andere gaan (hoogste waarde in zijn categorie)  
er is dus een patroon en seizoenaliteit, we moeten dus kleine en grote d gelijk aan 1 nemen. als we dit doen, gaat de lijn min of meer volledig binnen de betrouwbaarheidsintervallen liggen. 
2008-12-10 09:40:11 [Peter Van Doninck] [reply] 
Het raw periodogram heeft een dalend verloop. De student merkt ook terecht op dat in het cumulatieve periodogram de lange termijntrend aanwezig is. Er dient echter nog toegevoegd te worden dat de seizoenaliteit (de verkregen trapvorm) belangrijker is als de lange termijntrend! Wanneer de student de opslitsing zou gemaakt hebben tussen de lange termijntrend en de seizoenaliteit, was dit duidelijker geweest. Het valt op dat het cumulatieve periodogram in het begin boven het 95% betrouwbaarheidsinterval ligt en dus significant verschilt van nul.

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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	'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=27903&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=27903&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27903&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	'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)	1
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) & 1 \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=27903&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]1[/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=27903&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=27903&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)	1
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 = 1 ;

Parameters (R input):

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

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