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Loess CPI

*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: Sat, 04 Dec 2010 10:38:36 +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/04/t1291459011gl29wxf2759nquz.htm/, Retrieved Sat, 04 Dec 2010 11:36:56 +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/04/t1291459011gl29wxf2759nquz.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 «
115.65 116.00 115.92 116.10 116.44 116.65 117.45 117.58 117.43 117.24 117.25 117.29 117.83 118.22 118.11 118.23 118.15 118.23 119.03 119.38 118.97 118.78 118.97 118.94 119.86 120.09 120.13 120.15 119.90 120.00 120.84 121.17 120.81 121.00 121.12 121.29 122.09 121.88 121.31 121.33 121.45 121.67 122.78 122.84 122.34 122.37 122.72 122.68 122.78 123.08 122.92 123.51 124.18 124.05 124.36 123.87 123.84 123.85 123.83 123.84 124.27 124.56 124.57 124.87 125.08 124.86 124.89 124.58 124.83 124.97 125.19 125.42 125.74 126.07 126.35 126.69 126.85 127.12 127.43 127.49 128.05 127.85 128.35 128.29 128.38 128.80 129.18 130.14 130.77 131.19 131.32 131.41 131.61 131.69 131.94 131.70 132.54 132.74 133.02 132.76 133.05 132.74 133.16 133.10 133.37 133.15 133.18 133.29 133.76 134.51 134.82 134.71 134.52 134.86 135.11 135.28 135.61 135.22 135.47 135.42 135.85 136.27 136.30 136.85 137.05 137.03 137.45 137.49 etc...
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal20310204
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1115.65115.724850489428-0.128964828118213115.7041143386900.0748504894284707
2116115.9742355838770.129129806475789115.896634609648-0.0257644161234225
3115.92115.776561626381-0.0257165069867153116.089154880606-0.143438373618821
4116.1115.8659695703820.0564896506866032116.277540778931-0.234030429617789
5116.44116.3130245993220.101048723420725116.465926677257-0.126975400677566
6116.65116.6450043046410.0049414021150451116.650054293244-0.00499569535917033
7117.45117.7511018191710.314716271597234116.8341819092310.301101819171365
8117.58117.982552822480.158828000540973117.0186191769790.402552822480047
9117.43117.6392977099410.0176458453322728117.2030564447270.209297709941183
10117.24117.255661210734-0.151013064247288117.3753518535130.0156612107338958
11117.25117.102024565316-0.14967182761646117.547647262300-0.147975434683772
12117.29117.223415389621-0.327433526672032117.684018137051-0.066584610379266
13117.83117.968575816316-0.128964828118213117.8203890118020.138575816315807
14118.22118.3580650367230.129129806475789117.9528051568020.138065036722594
15118.11118.160495205186-0.0257165069867153118.0852213018010.050495205185868
16118.23118.1803810577940.0564896506866032118.223129291519-0.0496189422055409
17118.15117.8379139953420.101048723420725118.361037281237-0.312086004657758
18118.23117.9485544939270.0049414021150451118.506504103958-0.281445506073339
19119.03119.0933128017230.314716271597234118.6519709266800.0633128017231996
20119.38119.7886857451020.158828000540973118.8124862543570.40868574510155
21118.97118.9493525726320.0176458453322728118.973001582035-0.0206474273676349
22118.78118.574952556512-0.151013064247288119.136060507735-0.205047443487516
23118.97118.790552394182-0.14967182761646119.299119433434-0.179447605817799
24118.94118.757293474885-0.327433526672032119.450140051787-0.182706525115279
25119.86120.247804157978-0.128964828118213119.6011606701400.38780415797784
26120.09120.2978355393340.129129806475789119.7530346541910.207835539333686
27120.13120.380807868746-0.0257165069867153119.9049086382410.250807868746023
28120.15120.1713694465820.0564896506866032120.0721409027310.0213694465819145
29119.9119.4595781093570.101048723420725120.239373167222-0.440421890643009
30120119.5814178523900.0049414021150451120.413640745495-0.418582147609655
31120.84120.7773754046360.314716271597234120.587908323767-0.06262459536417
32121.17121.4339672207500.158828000540973120.7472047787090.263967220749620
33120.81120.6958529210160.0176458453322728120.906501233652-0.114147078984132
34121121.108663274689-0.151013064247288121.0423497895580.108663274689391
35121.12121.211473482153-0.14967182761646121.1781983454640.0914734821525371
36121.29121.602917965049-0.327433526672032121.3045155616230.312917965049124
37122.09122.878132050336-0.128964828118213121.4308327777820.788132050336301
38121.88122.0779697363130.129129806475789121.5529004572110.197969736313127
39121.31120.970748370346-0.0257165069867153121.674968136640-0.339251629653532
40121.33120.8121624970290.0564896506866032121.791347852285-0.517837502971361
41121.45120.891223708650.101048723420725121.907727567929-0.558776291350014
42121.67121.3149952127450.0049414021150451122.020063385140-0.355004787254728
43122.78123.1128845260530.314716271597234122.132399202350.332884526052680
44122.84123.2594599212260.158828000540973122.2617120782330.419459921226235
45122.34122.2713292005520.0176458453322728122.391024954115-0.0686707994477587
46122.37122.336521938149-0.151013064247288122.554491126098-0.0334780618505448
47122.72122.871714529536-0.14967182761646122.7179572980800.151714529536250
48122.68122.806984354132-0.327433526672032122.8804491725400.126984354132318
49122.78122.646023781119-0.128964828118213123.042941046999-0.133976218881045
50123.08122.8541393839900.129129806475789123.176730809534-0.225860616009768
51122.92122.555195934918-0.0257165069867153123.310520572069-0.364804065081984
52123.51123.5361786184350.0564896506866032123.4273317308780.0261786184352388
53124.18124.7148083868920.101048723420725123.5441428896880.534808386891626
54124.05124.4399301855340.0049414021150451123.6551284123510.389930185533743
55124.36124.6391697933880.314716271597234123.7661139350150.279169793388007
56123.87123.7053695638610.158828000540973123.875802435598-0.164630436138594
57123.84123.6768632184870.0176458453322728123.985490936180-0.163136781512733
58123.85123.768727744064-0.151013064247288124.082285320184-0.0812722559362982
59123.83123.630592123430-0.14967182761646124.179079704187-0.199407876570234
60123.84123.748996779928-0.327433526672032124.258436746744-0.0910032200717836
61124.27124.331171038817-0.128964828118213124.3377937893010.0611710388172355
62124.56124.5759927232580.129129806475789124.4148774702660.015992723258222
63124.57124.673755355756-0.0257165069867153124.4919611512310.103755355755709
64124.87125.1038427165150.0564896506866032124.5796676327980.233842716515326
65125.08125.3915771622140.101048723420725124.6673741143650.311577162214121
66124.86124.9406473379040.0049414021150451124.7744112599810.0806473379036419
67124.89124.5838353228050.314716271597234124.881448405597-0.306164677194701
68124.58123.9926773625470.158828000540973125.008494636912-0.587322637452587
69124.83124.5068132864420.0176458453322728125.135540868226-0.323186713558016
70124.97124.798129040487-0.151013064247288125.29288402376-0.171870959512802
71125.19125.079444648322-0.14967182761646125.450227179294-0.110555351677988
72125.42125.518474725071-0.327433526672032125.6489588016010.0984747250708722
73125.74125.761274404210-0.128964828118213125.8476904239080.021274404210331
74126.07125.9284236303070.129129806475789126.082446563217-0.141576369692956
75126.35126.408513804460-0.0257165069867153126.3172027025260.0585138044602616
76126.69126.7597064160750.0564896506866032126.5638039332380.0697064160749363
77126.85126.7885461126290.101048723420725126.810405163950-0.0614538873712291
78127.12127.1823572011300.0049414021150451127.0527013967550.0623572011302684
79127.43127.2502860988440.314716271597234127.294997629559-0.179713901156106
80127.49127.2855849474620.158828000540973127.535587051997-0.204415052538039
81128.05128.3061776802330.0176458453322728127.7761764744350.256177680232497
82127.85127.801282901211-0.151013064247288128.049730163037-0.0487170987894103
83128.35128.526387975978-0.14967182761646128.3232838516380.176387975978315
84128.29128.268740396363-0.327433526672032128.638693130309-0.021259603636679
85128.38127.934862419139-0.128964828118213128.954102408979-0.445137580861029
86128.8128.1897994470460.129129806475789129.281070746479-0.610200552954296
87129.18128.777677423009-0.0257165069867153129.608039083978-0.402322576991082
88130.14130.2931051029070.0564896506866032129.9304052464070.153105102906864
89130.77131.1861798677440.101048723420725130.2527714088350.416179867744063
90131.19131.7997106646520.0049414021150451130.5753479332330.609710664652312
91131.32131.4273592707730.314716271597234130.8979244576300.107359270772662
92131.41131.4619410771810.158828000540973131.1992309222780.051941077180544
93131.61131.7018167677410.0176458453322728131.5005373869270.0918167677408803
94131.69131.794970483668-0.151013064247288131.7360425805790.104970483668325
95131.94132.058124053385-0.14967182761646131.9715477742310.118124053385401
96131.7131.585171932622-0.327433526672032132.142261594050-0.114828067378369
97132.54132.895989414248-0.128964828118213132.3129754138700.355989414248484
98132.74132.8979115299360.129129806475789132.4529586635880.157911529935774
99133.02133.472774593680-0.0257165069867153132.5929419133070.452774593679521
100132.76132.7523896375220.0564896506866032132.711120711791-0.00761036247803304
101133.05133.1696517663040.101048723420725132.8292995102760.11965176630369
102132.74132.5357861484820.0049414021150451132.939272449403-0.204213851517608
103133.16132.9560383398730.314716271597234133.049245388530-0.203961660126794
104133.1132.8626409629380.158828000540973133.178531036521-0.237359037061509
105133.37133.4145374701560.0176458453322728133.3078166845110.0445374701562287
106133.15132.987175277039-0.151013064247288133.463837787208-0.162824722960522
107133.18132.889812937712-0.14967182761646133.619858889904-0.290187062287629
108133.29133.116969846098-0.327433526672032133.790463680574-0.173030153902289
109133.76133.687896356874-0.128964828118213133.961068471245-0.0721036431263542
110134.51134.7510228977800.129129806475789134.1398472957450.241022897779686
111134.82135.347090386742-0.0257165069867153134.3186261202440.527090386742259
112134.71134.8653373387450.0564896506866032134.4981730105680.155337338745369
113134.52134.2612313756880.101048723420725134.677719900892-0.258768624312296
114134.86134.8694922289870.0049414021150451134.8455663688980.00949222898663038
115135.11134.8918708914980.314716271597234135.013412836905-0.218129108502296
116135.28135.2321744835350.158828000540973135.168997515924-0.0478255164645986
117135.61135.8777719597260.0176458453322728135.3245821949420.267771959725565
118135.22135.095193030830-0.151013064247288135.495820033417-0.124806969169697
119135.47135.422613955725-0.14967182761646135.667057871892-0.0473860442752994
120135.42135.311915200491-0.327433526672032135.855518326181-0.108084799508674
121135.85135.784986047649-0.128964828118213136.043978780470-0.0650139523514497
122136.27136.1762685973720.129129806475789136.234601596152-0.0937314026280092
123136.3136.200492095152-0.0257165069867153136.425224411835-0.0995079048480534
124136.85137.0164784848690.0564896506866032136.6270318644440.166478484869430
125137.05137.1701119595260.101048723420725136.8288393170530.120111959526156
126137.03137.0157618060500.0049414021150451137.039296791835-0.0142381939496943
127137.45137.3355294617870.314716271597234137.249754266616-0.114470538213453
128137.49137.3452834169340.158828000540973137.475888582525-0.144716583066298
129137.55137.3803312562330.0176458453322728137.702022898434-0.169668743766721
130138.04138.280607185203-0.151013064247288137.9504058790440.24060718520343
131138.03138.010882967963-0.14967182761646138.198788859653-0.0191170320368030
132137.75137.36137005521-0.327433526672032138.466063471462-0.38862994479004
133138.27137.935626744847-0.128964828118213138.733338083271-0.334373255152656
134138.99138.8504830968370.129129806475789139.000387096687-0.139516903162672
135139.74140.238280396884-0.0257165069867153139.2674361101030.498280396883814
136139.7139.8256024014600.0564896506866032139.5179079478540.125602401459702
137139.97140.0705714909750.101048723420725139.7683797856040.100571490974858
138140.21140.4140249090860.0049414021150451140.0010336887990.204024909085575
139140.78141.0115961364080.314716271597234140.2336875919940.231596136408427
140140.8141.0022965923360.158828000540973140.4388754071230.202296592336268
141140.64140.6182909324160.0176458453322728140.644063222251-0.0217090675834868
142140.42140.157588134407-0.151013064247288140.833424929841-0.262411865593265
143140.85140.826885190187-0.14967182761646141.022786637430-0.0231148098133929
144140.96141.028177443281-0.327433526672032141.2192560833920.0681774432805184
145141.04140.793239298765-0.128964828118213141.415725529353-0.24676070123499
146141.71141.66850346920.129129806475789141.622366724324-0.0414965308000603
147141.6141.396708587691-0.0257165069867153141.829007919295-0.203291412308658
148142.11142.1161414789810.0564896506866032142.0473688703320.00614147898104989
149142.59142.813221455210.101048723420725142.2657298213690.223221455209966
150142.56142.6220764052220.0049414021150451142.4929821926630.0620764052218306
151143142.9650491644460.314716271597234142.720234563957-0.0349508355542127
152143.18143.2508862385880.158828000540973142.9502857608710.0708862385878604
153143.15143.1020171968820.0176458453322728143.180336957785-0.0479828031176339
154143.1142.962148042805-0.151013064247288143.388865021442-0.137851957194812
155143.45143.452278742518-0.14967182761646143.5973930850990.00227874251766025
156143.59143.735074981741-0.327433526672032143.7723585449310.145074981741402
157143.92144.021640823356-0.128964828118213143.9473240047620.101640823355751
158144.66145.0826606783480.129129806475789144.1082095151760.422660678347995
159144.34144.436621481397-0.0257165069867153144.269095025590.0966214813967383
160144.82145.1188354645390.0564896506866032144.4646748847750.298835464538513
161144.49144.2186965326200.101048723420725144.660254743960-0.271303467380420
162144.41143.8781537045700.0049414021150451144.936904893315-0.531846295430114
163144.99144.4517286857320.314716271597234145.213555042670-0.538271314267661
164144.95144.1415647875880.158828000540973145.599607211871-0.808435212411666
165145143.9966947735970.0176458453322728145.985659381071-1.0033052264032
166145.66144.979024227511-0.151013064247288146.491988836736-0.680975772489035
167146.68146.511353535215-0.14967182761646146.998318292402-0.168646464785212
168147.38147.481824701188-0.327433526672032147.6056088254840.101824701187979
169147.94147.796065469552-0.128964828118213148.212899358566-0.143934530448206
170149.12149.2709274058820.129129806475789148.8399427876420.15092740588247
171149.95150.458730290270-0.0257165069867153149.4669862167170.508730290269625
172150.19150.2966915880560.0564896506866032150.0268187612570.106691588056293
173151.16151.6322999707820.101048723420725150.5866513057970.472299970782188
174151.74152.4431810816020.0049414021150451151.0318775162830.703181081601855
175152.56153.3281800016340.314716271597234151.4771037267690.768180001633624
176152.09152.2295141765720.158828000540973151.7916578228870.139514176571879
177152.46152.7961422356630.0176458453322728152.1062119190050.33614223566255
178152.66153.202405451633-0.151013064247288152.2686076126140.542405451632789
179152.38152.478668521393-0.14967182761646152.4310033062240.0986685213926535
180152.59153.057781665179-0.327433526672032152.4496518614930.467781665178961
181152.88153.420664411356-0.128964828118213152.4683004167620.540664411355834
182153.29154.0484539552230.129129806475789152.4024162383020.758453955222564
183152.35152.389184447146-0.0257165069867153152.3365320598410.0391844471458000
184152.49152.672181884750.0564896506866032152.2513284645630.182181884750037
185152.2152.1328264072930.101048723420725152.166124869286-0.0671735927065242
186151.57151.0177770991550.0049414021150451152.11728149873-0.552222900845067
187151.55150.7168456002290.314716271597234152.068438128174-0.833154399771473
188151.79151.3126171633380.158828000540973152.108554836121-0.47738283666186
189151.52150.8736826106000.0176458453322728152.148671544067-0.646317389399769
190151.76151.360900164632-0.151013064247288152.310112899615-0.399099835367707
191151.92151.518117572454-0.14967182761646152.471554255162-0.401882427546013
192152.2152.002532520792-0.327433526672032152.724901005880-0.197467479208370
193152.75152.65071707152-0.128964828118213152.978247756598-0.099282928480136
194153.49153.5781891521740.129129806475789153.272681041350.0881891521741238
195153.78154.018602180885-0.0257165069867153153.5671143261020.238602180884897
196154.1154.2738005759230.0564896506866032153.8697097733900.173800575923082
197154.62154.9666460559010.101048723420725154.1723052206790.346646055900521
198154.65154.8195012334640.0049414021150451154.4755573644210.169501233464388
199154.81154.5264742202400.314716271597234154.778809508162-0.283525779759628
200154.92154.6026787669380.158828000540973155.078493232521-0.317321233062358
201155.4155.4041771977870.0176458453322728155.3781769568800.00417719778738501
202155.63155.736599292873-0.151013064247288155.6744137713750.106599292872630
203155.76155.699021241747-0.14967182761646155.970650585869-0.060978758252503
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/12dgl1291459108.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/12dgl1291459108.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/22dgl1291459108.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/22dgl1291459108.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/3c4xo1291459108.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/3c4xo1291459108.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/4c4xo1291459108.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/04/t1291459011gl29wxf2759nquz/4c4xo1291459108.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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Software written by Ed van Stee & Patrick Wessa


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