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*The author of this computation has been verified*
R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)
Title produced by software: Decomposition by Loess
Date of computation: Tue, 30 Nov 2010 13:30:23 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a.htm/, Retrieved Tue, 30 Nov 2010 14:28:51 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
235.1 280.7 264.6 240.7 201.4 240.8 241.1 223.8 206.1 174.7 203.3 220.5 299.5 347.4 338.3 327.7 351.6 396.6 438.8 395.6 363.5 378.8 357 369 464.8 479.1 431.3 366.5 326.3 355.1 331.6 261.3 249 205.5 235.6 240.9 264.9 253.8 232.3 193.8 177 213.2 207.2 180.6 188.6 175.4 199 179.6 225.8 234 200.2 183.6 178.2 203.2 208.5 191.8 172.8 148 159.4 154.5 213.2 196.4 182.8 176.4 153.6 173.2 171 151.2 161.9 157.2 201.7 236.4 356.1 398.3 403.7 384.6 365.8 368.1 367.9 347 343.3 292.9 311.5 300.9 366.9 356.9 329.7 316.2 269 289.3 266.2 253.6 233.8 228.4 253.6 260.1 306.6 309.2 309.5 271 279.9 317.9 298.4 246.7 227.3 209.1 259.9 266 320.6 308.5 282.2 262.7 263.5 313.1 284.3 252.6 250.3 246.5 312.7 333.2 446.4 511.6 515.5 506.4 483.2 522.3 509.8 460.7 405.8 375 378.5 406.8 467.8 469.8 429.8 355.8 332.7 378 360.5 334.7 319.5 323.1 363.6 352.1 411.9 388.6 416.4 360.7 338 417.2 388. etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal37210373
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1235.1214.82909453061945.7272473679813209.643658101400-20.270905469381
2280.7293.79906118565654.383674531868213.21726428247613.0990611856560
3264.6281.53353477728030.8755947591674216.79087046355216.9335347772804
4240.7269.75280229795-9.14411997212568220.79131767417629.05280229795
5201.4208.788200694984-30.7799655797835224.7917648847997.38820069498445
6240.8211.24207795285141.0692991453643229.288622901785-29.5579220471488
7241.1230.99274984362117.4217692376092233.78548091877-10.1072501563793
8223.8223.647314722719-14.5248380372195238.477523314501-0.152685277280966
9206.1201.463179816058-32.4327455262893243.169565710231-4.63682018394161
10174.7147.954837313879-48.7799560450433250.225118731164-26.7451626861207
11203.3176.933596335576-27.6142680876735257.280671752097-26.3664036644236
12220.5196.77363111899-26.2016910964047270.428059977415-23.7263688810099
13299.5269.69730442928645.7272473679813283.575448202732-29.8026955707136
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16327.7334.024311334212-9.14411997212568330.5198086379136.32431133421221
17351.6387.818221419585-30.7799655797835346.16174416019936.2182214195845
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33249245.185398869613-32.4327455262893285.247346656676-3.81460113038673
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36240.9262.947178934096-26.2016910964047245.05451216230922.0471789340962
37264.9250.35430202898045.7272473679813233.718450603038-14.5456979710197
38253.8227.35680015873454.383674531868225.859525309398-26.4431998412663
39232.3215.72380522507430.8755947591674218.000600015758-16.5761947749256
40193.8183.23812197173-9.14411997212568213.505998000396-10.5618780282699
41177175.768569594751-30.7799655797835209.011395985033-1.23143040524928
42213.2178.85448915752541.0692991453643206.476211697111-34.3455108424754
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45188.6208.99350730797-32.4327455262893200.63923821831920.3935073079701
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81343.3371.679536738457-32.4327455262893347.35320878783328.3795367384565
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193451.8463.87660691901145.7272473679813393.99614571300712.0766069190112
194446.1445.94145747044754.383674531868391.874867997685-0.158542529552847
195422.5424.3708149584730.8755947591674389.7535902823621.87081495847036
196383.1388.661329376843-9.14411997212568386.6827905952835.56132937684248
197352.8352.767974671579-30.7799655797835383.611990908204-0.032025328420616
198445.3469.69518597935741.0692991453643379.83551487527824.3951859793573
199367.5341.51919192003817.4217692376092376.059038842353-25.980808079962
200355.1352.344263411052-14.5248380372195372.380574626167-2.75573658894791
201326.2316.130635116307-32.4327455262893368.702110409982-10.0693648836929
202319.8322.818100072876-48.7799560450433365.5618559721673.01810007287645
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204340.9348.184903862619-26.2016910964047359.8167872337857.2849038626195
205394.1385.260779698845.7272473679813357.211972933219-8.83922030120021
206417.2425.63332454445254.383674531868354.383000923688.43332454445175
207369.9357.37037632669130.8755947591674351.554028914142-12.5296236733091
208349.2359.784946522622-9.14411997212568347.75917344950310.5849465226223
209321.4329.615647594918-30.7799655797835343.9643179848658.21564759491844
210405.7431.4833624702641.0692991453643338.84733838437625.7833624702599
211342.9334.64787197850417.4217692376092333.730358783887-8.25212802149582
212316.5320.461945683398-14.5248380372195327.0628923538213.96194568339843
213284.2280.437319602534-32.4327455262893320.395425923756-3.76268039746634
214270.9276.501932724054-48.7799560450433314.0780233209895.60193272405428
215288.8297.453647369451-27.6142680876735307.7606207182228.65364736945116
216278.8280.41018538248-26.2016910964047303.3915057139251.61018538247993
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218310.9271.11738547545154.383674531868296.298939992681-39.7826145245485
219299273.54891596509930.8755947591674293.575489275734-25.4510840349011
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222359.2387.56416370822741.0692991453643289.76653714640928.3641637082268
223305303.31692859682217.4217692376092289.261302165569-1.68307140317813
224282.1289.952903374137-14.5248380372195288.7719346630827.85290337413744
225250.3244.750178365694-32.4327455262893288.282567160595-5.549821634306
226246.5254.927960423689-48.7799560450433286.8519956213548.42796042368946
227257.9257.992844005561-27.6142680876735285.4214240821120.0928440055610622
228266.5274.395815619937-26.2016910964047284.8058754764677.89581561993748
229315.9301.88242576119645.7272473679813284.190326870822-14.0175742388035
230318.4296.53998079945254.383674531868285.876344668680-21.8600192005484
231295.4272.36204277429430.8755947591674287.562362466539-23.0379572257061
232266.4251.232088456701-9.14411997212568290.712031515424-15.1679115432988
233245.8228.518265015473-30.7799655797835293.86170056431-17.2817349845266
234362.8388.1301124410941.0692991453643296.40058841354625.3301124410897
235324.9333.43875449960917.4217692376092298.9394762627828.5387544996089
236294.2303.337448334323-14.5248380372195299.5873897028979.13744833432287
237289.5311.197442383278-32.4327455262893300.23530314301121.6974423832779
238295.2340.4415933589-48.7799560450433298.73836268614345.2415933589
239290.3310.972845858398-27.6142680876735297.24142222927520.6728458583982
240272275.136447655772-26.2016910964047295.0652434406333.13644765577180
241307.4276.18368798002845.7272473679813292.889064651991-31.216312019972
242328.7312.3166136528354.383674531868290.699711815302-16.3833863471701
243292.9266.41404626221930.8755947591674288.510358978614-26.4859537377809
244249.1220.603401882334-9.14411997212568286.740718089791-28.4965981176657
245230.4206.608888378814-30.7799655797835284.971077200969-23.7911116211856
246361.5398.19683352513741.0692991453643283.73386732949936.6968335251370
247321.7343.48157330436217.4217692376092282.49665745802821.7815733043625
248277.2287.885830460587-14.5248380372195281.03900757663210.6858304605872
249260.7274.251387831053-32.4327455262893279.58135769523613.5513878310528
250251273.27837801177-48.7799560450433277.50157803327322.2783780117703
251257.6267.392469716364-27.6142680876735275.4217983713109.79246971636394
252241.8235.995338258405-26.2016910964047273.806352838-5.80466174159545
253287.5257.08184532732845.7272473679813272.190907304691-30.4181546726721
254292.3257.46414689200454.383674531868272.752178576128-34.8358531079963
255274.7245.21095539326730.8755947591674273.313449847566-29.4890446067333
256254.2241.22896003174-9.14411997212568276.315159940385-12.9710399682598
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258339353.10796047023441.0692991453643283.82274038440114.1079604702344
259318.2330.64962002679317.4217692376092288.32861073559812.4496200267932
260287294.091662809662-14.5248380372195294.4331752275577.09166280966207
261295.8323.495005806772-32.4327455262893300.53773971951727.695005806772
262284308.820462641852-48.7799560450433307.95949340319124.8204626418520
263271254.233021000808-27.6142680876735315.381247086865-16.7669789991916
264262.7227.033715436327-26.2016910964047324.567975660078-35.6662845636734
265340.6301.71804839872745.7272473679813333.754704233291-38.8819516012725
266379.4359.22449451721754.383674531868345.191830950915-20.1755054827828
267373.3359.09544757229430.8755947591674356.628957668538-14.2045524277058
268355.2348.549197385736-9.14411997212568370.99492258639-6.65080261426397
269338.4322.219078075543-30.7799655797835385.360887504241-16.1809219244574
270466.9491.15663152615641.0692991453643401.57406932847924.2566315261563
271451466.79097960967317.4217692376092417.78725115271815.7909796096728
272422426.427743978008-14.5248380372195432.0970940592114.42774397800804
273429.2444.425808560584-32.4327455262893446.40693696570515.2258085605842
274425.9444.072916637118-48.7799560450433456.50703940792518.1729166371184
275460.7482.407126237529-27.6142680876735466.60714185014521.7071262375289
276463.6479.828155289703-26.2016910964047473.57353580670216.2281552897027
277541.4556.53282286875945.7272473679813480.5399297632615.1328228687591
278544.2548.42147978803454.383674531868485.5948456800984.22147978803383
279517.5513.47464364389630.8755947591674490.649761596937-4.02535635610428
280469.4454.131087100473-9.14411997212568493.813032871653-15.2689128995270
281439.4412.603661433415-30.7799655797835496.976304146368-26.796338566585
282549558.45679179427841.0692991453643498.4739090603589.4567917942776
283533548.60671678804317.4217692376092499.97151397434815.606716788043
284506.1526.108673685953-14.5248380372195500.61616435126620.0086736859531
285484499.171930798104-32.4327455262893501.26081472818515.1719307981043
286457461.982220859915-48.7799560450433500.7977351851294.98222085991466
287481.5490.279612445601-27.6142680876735500.3346556420728.77961244560123
288469.5466.379203956554-26.2016910964047498.82248713985-3.12079604344569
289544.7546.3624339943945.7272473679813497.3103186376291.66243399439003
290541.2532.42099228699754.383674531868495.595333181135-8.77900771300312
291521.5518.24405751619130.8755947591674493.880347724642-3.25594248380901
292469.7456.638818458819-9.14411997212568491.905301513306-13.0611815411805
293434.4409.649710277813-30.7799655797835489.930255301971-24.7502897221873
294542.6557.8870815167441.0692991453643486.24361933789615.2870815167402
295517.3534.6212473885717.4217692376092482.5569833738217.3212473885704
296485.7508.482353032142-14.5248380372195477.44248500507722.7823530321422
297465.8491.704758889955-32.4327455262893472.32798663633425.9047588899549
298447476.283353657493-48.7799560450433466.49660238755129.2833536574926
299426.6420.149049948907-27.6142680876735460.665218138767-6.45095005109346
300411.6394.754195906014-26.2016910964047454.647495190391-16.8458040939859
301467.5440.64298039000445.7272473679813448.629772242014-26.8570196099957
302484.5471.16993125320754.383674531868443.446394214925-13.3300687467927
303451.2433.26138905299830.8755947591674438.263016187835-17.9386109470024
304417.4408.588920891702-9.14411997212568435.355199080424-8.81107910829832
305379.9358.132583606770-30.7799655797835432.447381973013-21.7674163932295
306484.7495.79391952252041.0692991453643432.53678133211611.0939195225195
307455459.95205007117117.4217692376092432.6261806912204.95205007117124
308420.8421.629786515983-14.5248380372195434.4950515212360.829786515983187
309416.5429.068823175036-32.4327455262893436.36392235125312.5688231750361
310376.3362.737391673691-48.7799560450433438.642564371353-13.5626083263094
311405.6397.893061696221-27.6142680876735440.921206391452-7.70693830377888
312405.8393.20593646185-26.2016910964047444.595754634554-12.5940635381497
313500.8507.60244975436245.7272473679813448.2703028776566.80244975436221
314514518.7520696954254.383674531868454.8642557727124.75206969542012
315475.5458.66619657306530.8755947591674461.458208667767-16.8338034269348
316430.1396.985507890066-9.14411997212568472.358612082059-33.1144921099336
317414.4376.320950083432-30.7799655797835483.259015496351-38.0790499165678
318538533.60938039926241.0692991453643501.321320455374-4.39061960073838
319526515.19460534799417.4217692376092519.383625414397-10.8053946520062
320488.5445.929816598916-14.5248380372195545.595021438303-42.5701834010836
321520.2501.02632806408-32.4327455262893571.806417462209-19.1736719359199
322504.4455.492843514318-48.7799560450433602.087112530725-48.9071564856815
323568.5532.246460488433-27.6142680876735632.36780759924-36.2535395115668
324610.6586.17369994429-26.2016910964047661.227991152115-24.4263000557100
325818900.1845779270345.7272473679813690.08817470498982.1845779270294
326830.9893.21468141787254.383674531868714.2016440502662.3146814178722
327835.9902.60929184530230.8755947591674738.3151133955366.7092918453022
328782818.767234243614-9.14411997212568754.37688572851236.7672342436139
329762.3784.94130751829-30.7799655797835770.43865806149322.6413075182903
330856.9896.47021830389741.0692991453643776.26048255073939.5702183038966
331820.9842.29592372240617.4217692376092782.08230703998521.3959237224058
332769.6774.491826208536-14.5248380372195779.2330118286844.8918262085358
333752.2760.449028908907-32.4327455262893776.3837166173828.2490289089069
334724.4728.719956043672-48.7799560450433768.8600000013714.31995604367228
335723.1712.477984702314-27.6142680876735761.33628338536-10.6220152976859
336719.5711.792069242996-26.2016910964047753.409621853408-7.70793075700374
337817.4843.58979231056145.7272473679813745.48296032145826.1897923105611
338803.3812.05615568618454.383674531868740.1601697819488.75615568618446
339752.5739.28702599839530.8755947591674734.837379242437-13.2129740016048
340689655.172295290788-9.14411997212568731.971824681338-33.8277047092120
341630.4562.473695459546-30.7799655797835729.106270120238-67.9263045404542
342765.5761.99356103171541.0692991453643727.93713982292-3.50643896828478
343757.7771.21022123678817.4217692376092726.76800952560313.5102212367875
344732.2751.82654245377-14.5248380372195727.0982955834519.6265424537695
345702.6710.204163884993-32.4327455262893727.4285816412977.60416388499254
346683.3688.558515066544-48.7799560450433726.8214409784995.25851506654396
347709.5720.399967771972-27.6142680876735726.21430031570210.8999677719718
348702.2707.766749750501-26.2016910964047722.8349413459045.56674975050089
349784.8804.41717025591345.7272473679813719.45558237610619.6171702559126
350810.9853.30055489880854.383674531868714.11577056932442.4005548988079
351755.6771.5484464782930.8755947591674708.77595876254215.9484464782904
352656.8620.197589074522-9.14411997212568702.546530897603-36.6024109254778
353615.1564.662862547119-30.7799655797835696.317103032664-50.437137452881
354745.3761.07575859312141.0692991453643688.45494226151415.7757585931212
355694.1690.18544927202617.4217692376092680.592781490364-3.91455072797362
356675.7693.6588622882-14.5248380372195672.2659757490217.9588622882002
357643.7655.893575518615-32.4327455262893663.93917000767412.1935755186151
358622.1637.039310150027-48.7799560450433655.94064589501614.9393101500272
359634.6648.872146305316-27.6142680876735647.94212178235814.2721463053156
360588561.989746209275-26.2016910964047640.21194488713-26.0102537907250
361689.7701.19098464011745.7272473679813632.48176799190211.4909846401172
362673.9667.74424540596354.383674531868625.672080062169-6.15575459403681
363647.9646.06201310839630.8755947591674618.862392132436-1.83798689160358
364568.8531.505368588922-9.14411997212568615.238751383203-37.2946314110778
365545.7510.564854945813-30.7799655797835611.615110633971-35.1351450541872
366632.6616.09556261465241.0692991453643608.035138239984-16.5044373853482
367643.8665.72306491639417.4217692376092604.45516584599721.9230649163937
368593.1599.405807160758-14.5248380372195601.3190308764626.3058071607577
369579.7593.649849619363-32.4327455262893598.18289590692713.9498496193627
370546545.275613718931-48.7799560450433595.504342326112-0.724386281068746
371562.9560.588479342376-27.6142680876735592.825788745297-2.31152065762376
372572.5580.789109242788-26.2016910964047590.4125818536178.28910924278773
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/1qtun1291123814.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/1qtun1291123814.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/2qtun1291123814.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/2qtun1291123814.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/3qtun1291123814.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/3qtun1291123814.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/4jkbq1291123814.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291123730m1y8la6hqk9bw9a/4jkbq1291123814.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
 





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