Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_spectrum.wasp
Title produced by softwareSpectral Analysis
Date of computationTue, 02 Dec 2008 09:47:55 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/02/t1228236565pzpb9y1btbn0ic3.htm/, Retrieved Sun, 19 May 2024 08:46:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=28068, Retrieved Sun, 19 May 2024 08:46:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Law of Averages] [Random Walk Simul...] [2008-11-25 18:40:39] [b98453cac15ba1066b407e146608df68]
F RMPD    [Spectral Analysis] [] [2008-12-02 16:47:55] [e02910eed3830f1815f587e12f46cbdb] [Current]
Feedback Forum
2008-12-06 10:41:41 [Angelique Van de Vijver] [reply
goede berekening en goede conclusies van de student.
Goed om antwoord te staven met verschillende methoden(spectraalanalyse, VRM, ACF). Goed van de student om de evolutie te laten zien van voor en na de differentiatie en een goede interpretatie van de grafieken.
We zien op het raw periodogram inderdaad een langzaam dalend patroon, wat dus wijst op een langetermijntrend.
Op het cumulatief periodogram zien we een steil stijgende lijn aan de linkerkant wat wijst op een langetermijntrend. We zien ook dat de functie getrapt is, dit wijst op een seizoenaliteit. Deze seizoenaliteit en langetermijntrend moeten we wegwerken door te differentiëren.
Als we kijken naar de tabel kunnen we verschillende dingen opmerken:
Het getal tussen haakjes komt overeen met de periode van de golfbeweging nodig om van de ene top naar de andere te gaan.
Bij periode 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.
Een korte golfbeweging(laag getal tussen haakjes) is minder belangrijk.
We kunnen dus vaststellen dat we zowel niet-seizoenaal als seizoenaal moeten differentiëren om een stationaire tijdreeks te verkrijgen.
2008-12-09 21:15:33 [Anouk Greeve] [reply
Via spectrale analyse: de student voert dit correct uit. De seizoenaliteit is veel belangrijker dan de lange termijn trend.De student heeft alle regelmatige golfbewegingen gezuiverd. We gaan dus de oorspronkelijke tijdreeks reconstrueren op basis van golfbewegingen met een bepaald aantal frequenties en perioden.

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Dataseries X:
112
118
132
129
121
135
148
148
136
119
104
118
115
126
141
135
125
149
170
170
158
133
114
140
145
150
178
163
172
178
199
199
184
162
146
166
171
180
193
181
183
218
230
242
209
191
172
194
196
196
236
235
229
243
264
272
237
211
180
201
204
188
235
227
234
264
302
293
259
229
203
229
242
233
267
269
270
315
364
347
312
274
237
278
284
277
317
313
318
374
413
405
355
306
271
306
315
301
356
348
355
422
465
467
404
347
305
336
340
318
362
348
363
435
491
505
404
359
310
337
360
342
406
396
420
472
548
559
463
407
362
405
417
391
419
461
472
535
622
606
508
461
390
432




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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=28068&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=28068&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28068&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132







Raw Periodogram
ParameterValue
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=28068&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=28068&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=28068&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
ParameterValue
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



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')