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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: Mon, 13 Dec 2010 10:43:18 +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/Dec/13/t1292236891cstza9dovw1ebio.htm/, Retrieved Mon, 13 Dec 2010 11:41:31 +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/Dec/13/t1292236891cstza9dovw1ebio.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 «
10 10 10 10 10 9,94 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 10,06 9,94 9,94 9,94 9,94 9,94 9,94 10,06 10,06 9,94 10,06 10,06 10,06 10,18 10,28 10,28 10,18 10,28 10,28 10,28 10,18 10,28 10,28 10,18 10,18 10,18 10,28 10,28 10,18 10,18 10,18 10,18 10,18 10,18 10,28 10,28 10,28 10,18 10,18 10,18 10,28 10,18 10,18 10,28 10,18 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,18 10,18 10,28 10,28 10,28 10,28 10,28 10,28 10,28 10,18 10,28 10,28 10,28 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,34 10,34 10,34 10,42 10,42 10,42 10,42 10,34 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,42 10,34 10,34 10,34 10,3 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
Seasonal35010351
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
11010.00341265998540.004849490752522559.991737849262110.00341265998536677
21010.0058420521596-0.003473069137457179.997631016977850.00584205215960587
31010.0089196361151-0.012443820808659510.00352418469360.00891963611506341
41010.0087644293109-0.018211463108091710.00944703379720.00876442931089016
5109.99757474427594-0.012944627176748910.0153698829008-0.00242525572405583
69.949.854721622039850.004163783315706510.0211145946444-0.0852783779601474
710.0610.08393742547530.009203268136640510.02685930638810.0239374254752835
810.0610.08431688702880.0032752444460937410.03240786852510.0243168870288013
910.0610.08193773734490.00010583199293967610.03795643066210.0219377373449241
1010.0610.06399068092590.012216833281184810.04379248579300.00399068092585075
1110.0610.06500916704560.0053622920306099510.04962854092380.00500916704559806
1210.0610.05831099188750.0078962372014989910.0537927709110-0.00168900811252470
1310.0610.05719350834920.0048494907525225510.0579570008983-0.00280649165078195
1410.0610.0671023582280-0.0034730691374571710.05637071090940.0071023582280425
1510.0610.0776593998881-0.012443820808659510.05478442092060.0176593998880872
1610.0610.0910903736056-0.018211463108091710.04712108950250.0310903736055668
1710.0610.0934868690923-0.012944627176748910.03945775808450.0334868690922701
1810.0610.08732927859450.004163783315706510.02850693808980.0273292785944719
1910.0610.09324061376820.009203268136640510.01755611809520.0332406137681929
2010.0610.10843544652530.0032752444460937410.00828930902860.0484354465252572
219.949.880871668044930.0001058319929396769.99902249996213-0.0591283319550726
229.949.874208109780940.01221683328118489.99357505693787-0.0657918902190602
239.949.886510094055770.005362292030609959.98812761391362-0.0534899059442289
249.949.88452587808230.007896237201498999.9875778847162-0.0554741219177046
259.949.888122353728680.004849490752522559.9870281555188-0.0518776462713184
269.949.88738375838135-0.003473069137457179.9960893107561-0.0526162416186509
2710.0610.1272933548152-0.012443820808659510.00515046599340.0672933548152379
2810.0610.1116181879192-0.018211463108091710.02659327518890.0516181879192086
299.949.8449085427924-0.012944627176748910.0480360843843-0.0950914572075963
3010.0610.04184917427460.004163783315706510.0739870424097-0.0181508257254315
3110.0610.01085873142830.009203268136640510.0999380004351-0.0491412685717485
3210.069.99212650844790.0032752444460937410.1245982471060-0.067873491552108
3310.1810.21063567423010.00010583199293967610.14925849377690.0306356742301368
3410.2810.37682864958680.012216833281184810.1709545171320.096828649586815
3510.2810.36198716748230.0053622920306099510.19265054048710.0819871674823158
3610.1810.14262748568170.0078962372014989910.2094762771168-0.037372514318319
3710.2810.32884849550090.0048494907525225510.22630201374660.048848495500911
3810.2810.3305503060417-0.0034730691374571710.23292276309580.0505503060417034
3910.2810.3329003083637-0.012443820808659510.23954351244490.0529003083637161
4010.1810.1390996689978-0.018211463108091710.2391117941103-0.0409003310022058
4110.2810.3342645514011-0.012944627176748910.23868007577570.0542645514010953
4210.2810.32095684876200.004163783315706510.23487936792230.0409568487620238
4310.1810.11971807179450.009203268136640510.2310786600689-0.0602819282055282
4410.1810.13178740143840.0032752444460937410.2249373541155-0.0482125985616371
4510.1810.14109811984490.00010583199293967610.2187960481622-0.0389018801551408
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5110.1810.1575572096556-0.012443820808659510.2148866111531-0.0224427903444351
5210.1810.1635639676839-0.018211463108091710.2146474954242-0.0164360323161041
5310.1810.1585362474815-0.012944627176748910.2144083796953-0.0214637525185477
5410.2810.34127690415840.004163783315706510.21455931252590.0612769041584329
5510.2810.33608648650690.009203268136640510.21471024535640.056086486506933
5610.2810.34016816793560.0032752444460937410.21655658761830.0601681679355703
5710.1810.14149123812680.00010583199293967610.2184029298802-0.0385087618731852
5810.1810.12976985177720.012216833281184810.2180133149416-0.050230148222802
5910.1810.13701400796640.0053622920306099510.2176237000030-0.0429859920335982
6010.2810.33617616234300.0078962372014989910.21592760045550.0561761623429717
6110.1810.14091900833940.0048494907525225510.2142315009081-0.0390809916605956
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6310.2810.3540017630669-0.012443820808659510.21844205774170.0740017630669154
6410.1810.1535302840346-0.018211463108091710.2246811790735-0.0264697159653871
6510.1810.1420243267715-0.012944627176748910.2309203004052-0.0379756732284644
6610.1810.12112279250790.004163783315706510.2347134241764-0.0588772074921042
6710.2810.31229018391580.009203268136640510.23850654794760.032290183915773
6810.2810.31833045330040.0032752444460937410.23839430225360.0383304533003539
6910.2810.32161211144750.00010583199293967610.23828205655950.0416121114475416
7010.2810.31197850260450.012216833281184810.23580466411430.0319785026045292
7110.2810.32131043630030.0053622920306099510.23332727166910.0413104363003374
7210.2810.32361821608140.0078962372014989910.22848554671710.0436182160814269
7310.1810.13150668748240.0048494907525225510.2236438217651-0.0484933125176177
7410.1810.1472309804988-0.0034730691374571710.2162420886387-0.0327690195012327
7510.1810.1636034652964-0.012443820808659510.2088403555123-0.0163965347036257
7610.1810.1729124098535-0.018211463108091710.2052990532546-0.00708759014649551
7710.1810.1711868761799-0.012944627176748910.2017577509969-0.00881312382013988
7810.1810.15093585223230.004163783315706510.2049003644520-0.0290641477677269
7910.1810.14275375395620.009203268136640510.2080429779072-0.0372462460437948
8010.1810.14057471560280.0032752444460937410.2161500399511-0.0394252843971987
8110.1810.1356370660120.00010583199293967610.2242571019951-0.0443629339879958
8210.2810.3145874844980.012216833281184810.23319568222080.0345874844979956
8310.2810.31250344552280.0053622920306099510.24213426244660.0325034455228081
8410.2810.30259250392150.0078962372014989910.24951125887700.0225925039214872
8510.2810.29826225394000.0048494907525225510.25688825530740.0182622539400299
8610.2810.301547835175-0.0034730691374571710.26192523396250.0215478351749976
8710.2810.3054816081912-0.012443820808659510.26696221261750.0254816081911855
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8910.2810.3053195194772-0.012944627176748910.26762510769950.0253195194772147
9010.1810.09056734047990.004163783315706510.2652688762044-0.089432659520119
9110.2810.28788408715410.009203268136640510.26291264470930.00788408715406774
9210.2810.29705657242490.0032752444460937410.2596681831290.0170565724249077
9310.2810.30347044645840.00010583199293967610.25642372154870.0234704464583562
9410.2810.29387912846220.012216833281184810.25390403825660.0138791284621824
9510.1810.10325335300480.0053622920306099510.2513843549646-0.0767466469951685
9610.2810.30063861411640.0078962372014989910.25146514868210.0206386141163648
9710.2810.30360456684780.0048494907525225510.25154594239970.0236045668477605
9810.2810.3105193142413-0.0034730691374571710.25295375489610.0305193142413351
9910.1810.1180822534161-0.012443820808659510.2543615673925-0.0619177465838714
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10110.2810.3157806950242-0.012944627176748910.25716393215260.035780695024183
10210.2810.29803426948720.004163783315706510.25780194719710.0180342694871918
10310.2810.29235676962170.009203268136640510.25843996224160.0123567696217179
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10510.2810.2958670135290.00010583199293967610.26402715447810.0158670135290055
10610.2810.27445182515730.012216833281184810.2733313415615-0.00554817484268177
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11510.4210.43843206042070.009203268136640510.39236467144260.0184320604207286
11610.4210.43862357538710.0032752444460937410.39810118016680.0186235753871333
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12510.4210.4599289247941-0.012944627176748910.39301570238260.0399289247941468
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18010.4210.43388556722310.0078962372014989910.39821819557540.013885567223058
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18310.3410.2943923338675-0.012443820808659510.3980514869412-0.0456076661324918
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18510.4210.4522791301830-0.012944627176748910.40066549699380.0322791301829533
18610.4210.43193335956320.004163783315706510.40390285712110.0119333595632050
18710.4210.42365651461500.009203268136640510.40714021724840.00365651461497407
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18910.4210.42485585836650.00010583199293967610.41503830964050.00485585836651836
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27010.6310.64123379172800.004163783315706510.61460242495630.0112337917279817
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27910.610.5592996952506-0.012443820808659510.6531441255581-0.0407003047493966
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28110.710.7442636655709-0.012944627176748910.66868096160590.0442636655708579
28210.710.72064472061830.004163783315706510.6751914960660.0206447206182840
28310.710.70909470133720.009203268136640510.68170203052610.00909470133723111
28410.710.71392402657660.0032752444460937410.68280072897730.0139240265766318
28510.710.71599474057860.00010583199293967610.68389942742840.0159947405786411
28610.710.70869642557630.012216833281184810.67908674114250.0086964255763231
28710.710.72036365311280.0053622920306099510.67427405485660.0203636531128257
28810.710.72297454545150.0078962372014989910.6691292173470.0229745454514987
28910.710.73116612941000.0048494907525225510.66398437983740.0311661294100336
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29510.710.73095481846510.009203268136640510.65984191339820.0309548184651121
29610.610.53081631092260.0032752444460937410.6659084446313-0.0691836890774233
29710.710.72791919214260.00010583199293967610.67197497586440.0279191921426474
29810.710.70899662287730.012216833281184810.67878654384150.00899662287733705
29910.710.70903959615080.0053622920306099510.68559811181850.00903959615084737
30010.710.70210021218170.0078962372014989910.69000355061680.00210021218165402
30110.710.70074151983230.0048494907525225510.69440898941510.000741519832326176
30210.710.7064476928251-0.0034730691374571710.69702537631230.00644769282513913
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30410.710.7154850368779-0.018211463108091710.70272642623020.0154850368779123
30510.710.7071335379259-0.012944627176748910.70581108925090.0071335379258759
30610.710.68416205116610.004163783315706510.7116741655181-0.0158379488338554
30710.710.67325949007790.009203268136640510.7175372417854-0.0267405099220674
30810.710.67312098255250.0032752444460937410.7236037730014-0.0268790174474738
30910.710.67022386378970.00010583199293967610.7296703042173-0.0297761362102715
31010.710.65370397146210.012216833281184810.7340791952567-0.0462960285378742
31110.810.85614962167330.0053622920306099510.73848808629600.0561496216733453
31210.810.84843852450770.0078962372014989910.74366523829080.048438524507656
31310.810.84630811896180.0048494907525225510.74884239028560.0463081189618304
31410.810.8504142616116-0.0034730691374571710.75305880752580.0504142616116425
31510.710.6551685960427-0.012443820808659510.7572752247660-0.0448314039573265
31610.710.6607650565365-0.018211463108091710.7574464065716-0.0392349434634642
31710.710.6553270387996-0.012944627176748910.7576175883771-0.0446729612003764
31810.8510.94090446918990.004163783315706510.75493174749440.0909044691899261
31910.7510.73855082525170.009203268136640510.7522459066116-0.0114491747482504
32010.7510.74539007671240.0032752444460937410.7513346788415-0.00460992328762089
32110.7510.74947071693560.00010583199293967610.7504234510714-0.0005292830643846
32210.7510.73565947536050.012216833281184810.7521236913583-0.0143405246395059
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32410.7510.73423797456310.0078962372014989910.7578657882354-0.0157620254369153
32510.7510.73324286442180.0048494907525225510.7619076448256-0.0167571355781586
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32710.7510.7352820824643-0.012443820808659510.7771617383444-0.0147179175357000
32810.7510.7316377518743-0.018211463108091710.7865737112338-0.0183622481257046
32910.7510.7169589430535-0.012944627176748910.7959856841232-0.0330410569464821
33010.8510.89065357331250.004163783315706510.80518264337180.0406535733124862
33110.8510.87641712924300.009203268136640510.81437960262040.026417129242974
33210.8510.87473227697060.0032752444460937410.82199247858330.024732276970596
33310.8510.87028881346080.00010583199293967610.82960535454620.0202888134608248
33410.8510.85529799306810.012216833281184810.83248517365070.00529799306809409
33510.8510.85927271521420.0053622920306099510.83536499275520.00927271521418227
33610.8510.85741736705100.0078962372014989910.83468639574750.0074173670509623
33710.8510.86114271050760.0048494907525225510.83400779873990.0111427105076043
33810.8510.8713271836200-0.0034730691374571710.83214588551740.0213271836200377
33910.8510.8821598485137-0.012443820808659510.83028397229500.0321598485136896
34010.7510.6925009860512-0.018211463108091710.8257104770569-0.0574990139488243
34110.7510.6918076453579-0.012944627176748910.8211369818189-0.0581923546421148
34210.8510.88339480737240.004163783315706510.81244140931190.0333948073723569
34310.8510.88705089505830.009203268136640510.8037458368050.0370508950583499
34410.8510.90097603526850.0032752444460937410.79574872028540.0509760352685422
34510.8510.91214256424130.00010583199293967610.78775160376570.0621425642413431
34610.7510.70805152766220.012216833281184810.7797316390566-0.0419484723377632
34710.7510.72292603362200.0053622920306099510.7717116743474-0.0270739663780475
34810.7510.72889566406610.0078962372014989910.7632080987324-0.0211043359339040
34910.7510.74044598613010.0048494907525225510.7547045231174-0.00955401386989685
35010.7510.7576290509911-0.0034730691374571710.74584401814640.00762905099110078
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/1lt071292236987.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/1lt071292236987.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/2lt071292236987.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/2lt071292236987.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/3ekzs1292236987.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/3ekzs1292236987.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/4ekzs1292236987.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/13/t1292236891cstza9dovw1ebio/4ekzs1292236987.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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