Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*Unverified author*

R Software Module

rwasp_spectrum.wasp

Title produced by software

Spectral Analysis

Date of computation

Tue, 02 Dec 2008 13:58:26 -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/t1228251532tu90249p1iryyo8.htm/, Retrieved Fri, 17 May 2024 03:05:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28450, Retrieved Fri, 17 May 2024 03:05:07 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

155

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 20:58:26] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F           [Spectral Analysis] [] [2008-12-02 21:06:35] [74be16979710d4c4e7c6647856088456] 
-             [Spectral Analysis] [] [2008-12-02 21:09:51] [74be16979710d4c4e7c6647856088456] 
F   P         [Spectral Analysis] [] [2008-12-02 21:17:14] [74be16979710d4c4e7c6647856088456] 

Feedback Forum

2008-12-04 14:43:10 [Angelique Van de Vijver] [reply] 
Goede berekening en juiste conclusies. 
Goede uitleg van de student over de verschillende waarden in tabel. Het getal tussen haakjes komt overeen met de periode van de golfbeweging nodig om van de ene top naar de andere te gaan.   
In het eerste geval (144) is het spectrum gelijk aan 3792.028873. Dit wijst dus inderdaad op de langetermijntrend. Dit spectrum is zeer hoog t.o.v. de andere getallen die veel lager zijn. Ook bij periode 4 en 6 kom je een hoog spectrum uit. 
Bij periode 12 is het spectrum 68000, dit wijst inderdaad op de belangrijkheid van de seizoenaliteit. Goed van student om deze belangrijke waarden aan te duiden in de tabel. 
Een korte golfbeweging( met een laag getal tussen haakjes) is minder belangrijk. 
 
Goede interpretatie van de grafieken door de student. 
Op het raw periodogram zien we inderdaad een langzaam dalend verloop=> langetermijntrend. De steil stijgende lijn aan linkerkant bij het cumulatieve periodogram wijst op de langetermijntrend. Deze lijn is ook ‘getrapt’ en dit wijst op de seizoenaliteit. 
2008-12-10 08:53:53 [Peter Van Doninck] [reply] 
Duidelijk aangetoond dat de seizoenaliteit groter is dan de lange termijntrend. Er kan ook aan toegevoegd worden dat de lange termijntrend dalend is. Het klopt dat het cumulatieve periodogram een lange termijntrend toont en de trapjes een seizoenaaltrend.

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	0 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 & 0 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28450&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]0 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=28450&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28450&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	0 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=28450&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=28450&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28450&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 = 500 ; par2 = 0.5 ;

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