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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 10:14:04 -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/t1228238065339tpo7hlbhbog2.htm/, Retrieved Sat, 18 May 2024 06:12:21 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28094, Retrieved Sat, 18 May 2024 06:12:21 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

168

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 17:14:04] [ee5aee65e0c44ac54c8097a6e28e37f4] [Current]

Feedback Forum

2008-12-04 11:49:53 [Loïque Verhasselt] [reply] 
Q6:Via spectrale analyse:Alle regelmatige golfbewegingen worden gezuiverd. We gaan dus de oorspronkelijke tijdreeks reconstrueren op basis van golfbewegingen met een bepaald aantal frequenties en perioden.We starten en laten alle parameters van de calculator ongewijzigd.In de output zien we bij het spectum,bij het eerste een frequentie van 0,0069 van 144 maanden met een intensiteit van voorkomen van 3792. Vanaf dan zien we een dalende trend in de intensiteit. Wat wil zeggen dat kortere golfbewegingen hier allemaal minder belangrijk zijn dan de 1ste. De meest dominante is de lange periode = lange termijn trend(op het eerste zicht).We onderzoeken nu de belangrijkheid van de seizoenaliteit. Dit vinden we in een perioden van 12 maanden met een intensiteit van voorkomen van 68000. Het is dus duidelijk dat de seizoenaliteit veel belangrijker is dan de LT-trend.We vinden dezelfde conclusie in het raw periodogram.We zien een LT-trend en seizoenaliteit dus we voeren een differentiatie door van d=1 en D=1.We zien nu duidelijk dat door beide differentiaties de trend verdwenen is en er ook geen sprake meer is van seizoenaliteit. Zoals de vorige methoden ook concluderen.
2008-12-08 13:38:28 [Li Tang Hu] [reply] 
ook hier heeft de student de seasonal period niet veranderd naar 12, waardoor ze een foutieve uitkomst is bekomen. Ook hier zou d=1 en D=1 moeten zijn zoals de 2 andere methoden.

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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	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28094&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28094&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28094&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Raw Periodogram
Parameter	Value
Box-Cox transformation parameter (lambda)	1
Degree of non-seasonal differencing (d)	0
Degree of seasonal differencing (D)	1
Seasonal Period (s)	1
Frequency (Period)	Spectrum
0.0069 (144)	22.864629
0.0139 (72)	11.109593
0.0208 (48)	62.477726
0.0278 (36)	1.321039
0.0347 (28.8)	4.783007
0.0417 (24)	95.354005
0.0486 (20.5714)	81.256015
0.0556 (18)	184.15768
0.0625 (16)	143.916498
0.0694 (14.4)	527.436724
0.0764 (13.0909)	2615.40237
0.0833 (12)	17782.490476
0.0903 (11.0769)	1731.745774
0.0972 (10.2857)	426.472749
0.1042 (9.6)	263.090594
0.1111 (9)	154.003726
0.1181 (8.4706)	26.638245
0.125 (8)	7.049552
0.1319 (7.5789)	67.155888
0.1389 (7.2)	125.857184
0.1458 (6.8571)	224.571167
0.1528 (6.5455)	360.399525
0.1597 (6.2609)	1615.025511
0.1667 (6)	18730.737873
0.1736 (5.76)	2207.333782
0.1806 (5.5385)	641.144313
0.1875 (5.3333)	688.944264
0.1944 (5.1429)	123.270898
0.2014 (4.9655)	154.435568
0.2083 (4.8)	150.267973
0.2153 (4.6452)	87.532576
0.2222 (4.5)	410.644779
0.2292 (4.3636)	147.443662
0.2361 (4.2353)	434.392266
0.2431 (4.1143)	675.04323
0.25 (4)	6295.181879
0.2569 (3.8919)	404.170414
0.2639 (3.7895)	122.565922
0.2708 (3.6923)	8.777755
0.2778 (3.6)	60.252051
0.2847 (3.5122)	10.986062
0.2917 (3.4286)	31.839996
0.2986 (3.3488)	93.082075
0.3056 (3.2727)	68.818148
0.3125 (3.2)	104.02149
0.3194 (3.1304)	174.260602
0.3264 (3.0638)	1128.690233
0.3333 (3)	5931.628068
0.3403 (2.9388)	256.639235
0.3472 (2.88)	428.878527
0.3542 (2.8235)	25.884357
0.3611 (2.7692)	53.110396
0.3681 (2.717)	34.155691
0.375 (2.6667)	14.479564
0.3819 (2.6182)	122.626129
0.3889 (2.5714)	55.6181
0.3958 (2.5263)	42.471135
0.4028 (2.4828)	677.379184
0.4097 (2.4407)	601.658575
0.4167 (2.4)	4775.923246
0.4236 (2.3607)	422.040122
0.4306 (2.3226)	793.712856
0.4375 (2.2857)	416.948083
0.4444 (2.25)	190.900303
0.4514 (2.2154)	41.977093
0.4583 (2.1818)	201.033783
0.4653 (2.1493)	106.534236
0.4722 (2.1176)	117.820975
0.4792 (2.087)	93.034658
0.4861 (2.0571)	38.351208
0.4931 (2.0282)	31.532934
0.5 (2)	97.653023

\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) & 1 \tabularnewline
Seasonal Period (s) & 1 \tabularnewline
Frequency (Period) & Spectrum \tabularnewline
0.0069 (144) & 22.864629 \tabularnewline
0.0139 (72) & 11.109593 \tabularnewline
0.0208 (48) & 62.477726 \tabularnewline
0.0278 (36) & 1.321039 \tabularnewline
0.0347 (28.8) & 4.783007 \tabularnewline
0.0417 (24) & 95.354005 \tabularnewline
0.0486 (20.5714) & 81.256015 \tabularnewline
0.0556 (18) & 184.15768 \tabularnewline
0.0625 (16) & 143.916498 \tabularnewline
0.0694 (14.4) & 527.436724 \tabularnewline
0.0764 (13.0909) & 2615.40237 \tabularnewline
0.0833 (12) & 17782.490476 \tabularnewline
0.0903 (11.0769) & 1731.745774 \tabularnewline
0.0972 (10.2857) & 426.472749 \tabularnewline
0.1042 (9.6) & 263.090594 \tabularnewline
0.1111 (9) & 154.003726 \tabularnewline
0.1181 (8.4706) & 26.638245 \tabularnewline
0.125 (8) & 7.049552 \tabularnewline
0.1319 (7.5789) & 67.155888 \tabularnewline
0.1389 (7.2) & 125.857184 \tabularnewline
0.1458 (6.8571) & 224.571167 \tabularnewline
0.1528 (6.5455) & 360.399525 \tabularnewline
0.1597 (6.2609) & 1615.025511 \tabularnewline
0.1667 (6) & 18730.737873 \tabularnewline
0.1736 (5.76) & 2207.333782 \tabularnewline
0.1806 (5.5385) & 641.144313 \tabularnewline
0.1875 (5.3333) & 688.944264 \tabularnewline
0.1944 (5.1429) & 123.270898 \tabularnewline
0.2014 (4.9655) & 154.435568 \tabularnewline
0.2083 (4.8) & 150.267973 \tabularnewline
0.2153 (4.6452) & 87.532576 \tabularnewline
0.2222 (4.5) & 410.644779 \tabularnewline
0.2292 (4.3636) & 147.443662 \tabularnewline
0.2361 (4.2353) & 434.392266 \tabularnewline
0.2431 (4.1143) & 675.04323 \tabularnewline
0.25 (4) & 6295.181879 \tabularnewline
0.2569 (3.8919) & 404.170414 \tabularnewline
0.2639 (3.7895) & 122.565922 \tabularnewline
0.2708 (3.6923) & 8.777755 \tabularnewline
0.2778 (3.6) & 60.252051 \tabularnewline
0.2847 (3.5122) & 10.986062 \tabularnewline
0.2917 (3.4286) & 31.839996 \tabularnewline
0.2986 (3.3488) & 93.082075 \tabularnewline
0.3056 (3.2727) & 68.818148 \tabularnewline
0.3125 (3.2) & 104.02149 \tabularnewline
0.3194 (3.1304) & 174.260602 \tabularnewline
0.3264 (3.0638) & 1128.690233 \tabularnewline
0.3333 (3) & 5931.628068 \tabularnewline
0.3403 (2.9388) & 256.639235 \tabularnewline
0.3472 (2.88) & 428.878527 \tabularnewline
0.3542 (2.8235) & 25.884357 \tabularnewline
0.3611 (2.7692) & 53.110396 \tabularnewline
0.3681 (2.717) & 34.155691 \tabularnewline
0.375 (2.6667) & 14.479564 \tabularnewline
0.3819 (2.6182) & 122.626129 \tabularnewline
0.3889 (2.5714) & 55.6181 \tabularnewline
0.3958 (2.5263) & 42.471135 \tabularnewline
0.4028 (2.4828) & 677.379184 \tabularnewline
0.4097 (2.4407) & 601.658575 \tabularnewline
0.4167 (2.4) & 4775.923246 \tabularnewline
0.4236 (2.3607) & 422.040122 \tabularnewline
0.4306 (2.3226) & 793.712856 \tabularnewline
0.4375 (2.2857) & 416.948083 \tabularnewline
0.4444 (2.25) & 190.900303 \tabularnewline
0.4514 (2.2154) & 41.977093 \tabularnewline
0.4583 (2.1818) & 201.033783 \tabularnewline
0.4653 (2.1493) & 106.534236 \tabularnewline
0.4722 (2.1176) & 117.820975 \tabularnewline
0.4792 (2.087) & 93.034658 \tabularnewline
0.4861 (2.0571) & 38.351208 \tabularnewline
0.4931 (2.0282) & 31.532934 \tabularnewline
0.5 (2) & 97.653023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=28094&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]1[/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]22.864629[/C][/ROW]
[ROW][C]0.0139 (72)[/C][C]11.109593[/C][/ROW]
[ROW][C]0.0208 (48)[/C][C]62.477726[/C][/ROW]
[ROW][C]0.0278 (36)[/C][C]1.321039[/C][/ROW]
[ROW][C]0.0347 (28.8)[/C][C]4.783007[/C][/ROW]
[ROW][C]0.0417 (24)[/C][C]95.354005[/C][/ROW]
[ROW][C]0.0486 (20.5714)[/C][C]81.256015[/C][/ROW]
[ROW][C]0.0556 (18)[/C][C]184.15768[/C][/ROW]
[ROW][C]0.0625 (16)[/C][C]143.916498[/C][/ROW]
[ROW][C]0.0694 (14.4)[/C][C]527.436724[/C][/ROW]
[ROW][C]0.0764 (13.0909)[/C][C]2615.40237[/C][/ROW]
[ROW][C]0.0833 (12)[/C][C]17782.490476[/C][/ROW]
[ROW][C]0.0903 (11.0769)[/C][C]1731.745774[/C][/ROW]
[ROW][C]0.0972 (10.2857)[/C][C]426.472749[/C][/ROW]
[ROW][C]0.1042 (9.6)[/C][C]263.090594[/C][/ROW]
[ROW][C]0.1111 (9)[/C][C]154.003726[/C][/ROW]
[ROW][C]0.1181 (8.4706)[/C][C]26.638245[/C][/ROW]
[ROW][C]0.125 (8)[/C][C]7.049552[/C][/ROW]
[ROW][C]0.1319 (7.5789)[/C][C]67.155888[/C][/ROW]
[ROW][C]0.1389 (7.2)[/C][C]125.857184[/C][/ROW]
[ROW][C]0.1458 (6.8571)[/C][C]224.571167[/C][/ROW]
[ROW][C]0.1528 (6.5455)[/C][C]360.399525[/C][/ROW]
[ROW][C]0.1597 (6.2609)[/C][C]1615.025511[/C][/ROW]
[ROW][C]0.1667 (6)[/C][C]18730.737873[/C][/ROW]
[ROW][C]0.1736 (5.76)[/C][C]2207.333782[/C][/ROW]
[ROW][C]0.1806 (5.5385)[/C][C]641.144313[/C][/ROW]
[ROW][C]0.1875 (5.3333)[/C][C]688.944264[/C][/ROW]
[ROW][C]0.1944 (5.1429)[/C][C]123.270898[/C][/ROW]
[ROW][C]0.2014 (4.9655)[/C][C]154.435568[/C][/ROW]
[ROW][C]0.2083 (4.8)[/C][C]150.267973[/C][/ROW]
[ROW][C]0.2153 (4.6452)[/C][C]87.532576[/C][/ROW]
[ROW][C]0.2222 (4.5)[/C][C]410.644779[/C][/ROW]
[ROW][C]0.2292 (4.3636)[/C][C]147.443662[/C][/ROW]
[ROW][C]0.2361 (4.2353)[/C][C]434.392266[/C][/ROW]
[ROW][C]0.2431 (4.1143)[/C][C]675.04323[/C][/ROW]
[ROW][C]0.25 (4)[/C][C]6295.181879[/C][/ROW]
[ROW][C]0.2569 (3.8919)[/C][C]404.170414[/C][/ROW]
[ROW][C]0.2639 (3.7895)[/C][C]122.565922[/C][/ROW]
[ROW][C]0.2708 (3.6923)[/C][C]8.777755[/C][/ROW]
[ROW][C]0.2778 (3.6)[/C][C]60.252051[/C][/ROW]
[ROW][C]0.2847 (3.5122)[/C][C]10.986062[/C][/ROW]
[ROW][C]0.2917 (3.4286)[/C][C]31.839996[/C][/ROW]
[ROW][C]0.2986 (3.3488)[/C][C]93.082075[/C][/ROW]
[ROW][C]0.3056 (3.2727)[/C][C]68.818148[/C][/ROW]
[ROW][C]0.3125 (3.2)[/C][C]104.02149[/C][/ROW]
[ROW][C]0.3194 (3.1304)[/C][C]174.260602[/C][/ROW]
[ROW][C]0.3264 (3.0638)[/C][C]1128.690233[/C][/ROW]
[ROW][C]0.3333 (3)[/C][C]5931.628068[/C][/ROW]
[ROW][C]0.3403 (2.9388)[/C][C]256.639235[/C][/ROW]
[ROW][C]0.3472 (2.88)[/C][C]428.878527[/C][/ROW]
[ROW][C]0.3542 (2.8235)[/C][C]25.884357[/C][/ROW]
[ROW][C]0.3611 (2.7692)[/C][C]53.110396[/C][/ROW]
[ROW][C]0.3681 (2.717)[/C][C]34.155691[/C][/ROW]
[ROW][C]0.375 (2.6667)[/C][C]14.479564[/C][/ROW]
[ROW][C]0.3819 (2.6182)[/C][C]122.626129[/C][/ROW]
[ROW][C]0.3889 (2.5714)[/C][C]55.6181[/C][/ROW]
[ROW][C]0.3958 (2.5263)[/C][C]42.471135[/C][/ROW]
[ROW][C]0.4028 (2.4828)[/C][C]677.379184[/C][/ROW]
[ROW][C]0.4097 (2.4407)[/C][C]601.658575[/C][/ROW]
[ROW][C]0.4167 (2.4)[/C][C]4775.923246[/C][/ROW]
[ROW][C]0.4236 (2.3607)[/C][C]422.040122[/C][/ROW]
[ROW][C]0.4306 (2.3226)[/C][C]793.712856[/C][/ROW]
[ROW][C]0.4375 (2.2857)[/C][C]416.948083[/C][/ROW]
[ROW][C]0.4444 (2.25)[/C][C]190.900303[/C][/ROW]
[ROW][C]0.4514 (2.2154)[/C][C]41.977093[/C][/ROW]
[ROW][C]0.4583 (2.1818)[/C][C]201.033783[/C][/ROW]
[ROW][C]0.4653 (2.1493)[/C][C]106.534236[/C][/ROW]
[ROW][C]0.4722 (2.1176)[/C][C]117.820975[/C][/ROW]
[ROW][C]0.4792 (2.087)[/C][C]93.034658[/C][/ROW]
[ROW][C]0.4861 (2.0571)[/C][C]38.351208[/C][/ROW]
[ROW][C]0.4931 (2.0282)[/C][C]31.532934[/C][/ROW]
[ROW][C]0.5 (2)[/C][C]97.653023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=28094&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28094&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)	1
Seasonal Period (s)	1
Frequency (Period)	Spectrum
0.0069 (144)	22.864629
0.0139 (72)	11.109593
0.0208 (48)	62.477726
0.0278 (36)	1.321039
0.0347 (28.8)	4.783007
0.0417 (24)	95.354005
0.0486 (20.5714)	81.256015
0.0556 (18)	184.15768
0.0625 (16)	143.916498
0.0694 (14.4)	527.436724
0.0764 (13.0909)	2615.40237
0.0833 (12)	17782.490476
0.0903 (11.0769)	1731.745774
0.0972 (10.2857)	426.472749
0.1042 (9.6)	263.090594
0.1111 (9)	154.003726
0.1181 (8.4706)	26.638245
0.125 (8)	7.049552
0.1319 (7.5789)	67.155888
0.1389 (7.2)	125.857184
0.1458 (6.8571)	224.571167
0.1528 (6.5455)	360.399525
0.1597 (6.2609)	1615.025511
0.1667 (6)	18730.737873
0.1736 (5.76)	2207.333782
0.1806 (5.5385)	641.144313
0.1875 (5.3333)	688.944264
0.1944 (5.1429)	123.270898
0.2014 (4.9655)	154.435568
0.2083 (4.8)	150.267973
0.2153 (4.6452)	87.532576
0.2222 (4.5)	410.644779
0.2292 (4.3636)	147.443662
0.2361 (4.2353)	434.392266
0.2431 (4.1143)	675.04323
0.25 (4)	6295.181879
0.2569 (3.8919)	404.170414
0.2639 (3.7895)	122.565922
0.2708 (3.6923)	8.777755
0.2778 (3.6)	60.252051
0.2847 (3.5122)	10.986062
0.2917 (3.4286)	31.839996
0.2986 (3.3488)	93.082075
0.3056 (3.2727)	68.818148
0.3125 (3.2)	104.02149
0.3194 (3.1304)	174.260602
0.3264 (3.0638)	1128.690233
0.3333 (3)	5931.628068
0.3403 (2.9388)	256.639235
0.3472 (2.88)	428.878527
0.3542 (2.8235)	25.884357
0.3611 (2.7692)	53.110396
0.3681 (2.717)	34.155691
0.375 (2.6667)	14.479564
0.3819 (2.6182)	122.626129
0.3889 (2.5714)	55.6181
0.3958 (2.5263)	42.471135
0.4028 (2.4828)	677.379184
0.4097 (2.4407)	601.658575
0.4167 (2.4)	4775.923246
0.4236 (2.3607)	422.040122
0.4306 (2.3226)	793.712856
0.4375 (2.2857)	416.948083
0.4444 (2.25)	190.900303
0.4514 (2.2154)	41.977093
0.4583 (2.1818)	201.033783
0.4653 (2.1493)	106.534236
0.4722 (2.1176)	117.820975
0.4792 (2.087)	93.034658
0.4861 (2.0571)	38.351208
0.4931 (2.0282)	31.532934
0.5 (2)	97.653023

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

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

Parameters (R input):

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