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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationSat, 13 Dec 2014 11:02:44 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/13/t1418468571fkp7kruvd69iplk.htm/, Retrieved Thu, 31 Oct 2024 23:11:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266978, Retrieved Thu, 31 Oct 2024 23:11:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Decomposition by Loess] [] [2014-12-13 11:02:44] [c7f962214140f976f2c4b1bb2571d9df] [Current]
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Dataseries X:
325.87
302.25
294.00
285.43
286.19
276.70
267.77
267.03
257.87
257.19
275.60
305.68
358.06
320.07
295.90
291.27
272.87
269.27
271.32
267.45
260.33
277.94
277.07
312.65
319.71
318.39
304.90
303.73
273.29
274.33
270.45
278.23
274.03
279.00
287.50
336.87
334.10
296.07
286.84
277.63
261.32
264.07
261.94
252.84
257.83
271.16
273.63
304.87
323.90
336.11
335.65
282.23
273.03
270.07
246.03
242.35
250.33
267.45
268.80
302.68
313.10
306.39
305.61
277.27
264.94
268.63
293.90
248.65
256.00
258.52
266.90
281.23
306.00
325.46
291.13
282.53
256.52
258.63
252.74
245.16
255.03
268.35
293.73
278.39




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266978&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266978&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266978&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'Gertrude Mary Cox' @ cox.wessa.net







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal841085
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 841 & 0 & 85 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266978&T=1

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Parameters[/C][/ROW]
[ROW][C]Component[/C][C]Window[/C][C]Degree[/C][C]Jump[/C][/ROW]
[ROW][C]Seasonal[/C][C]841[/C][C]0[/C][C]85[/C][/ROW]
[ROW][C]Trend[/C][C]19[/C][C]1[/C][C]2[/C][/ROW]
[ROW][C]Low-pass[/C][C]13[/C][C]1[/C][C]2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266978&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266978&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal841085
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1325.87328.6784976178942.5683785048262280.4931238772842.80849761788994
2302.25291.47477000281131.7377320129386281.28749798425-10.775229997189
3294287.11245909629418.8056688124888282.081872091217-6.88754090370571
4285.43285.587069843262.45652927232124282.8164008844190.15706984326016
5286.19302.434534835911-13.6054645135311283.5509296776216.2445348359108
6276.7283.554944518035-14.4752930057362284.3203484877016.85494451803544
7267.77267.379642988462-16.9294102862428285.089767297781-0.390357011538185
8267.03273.976182413399-25.8572754983544285.9410930849566.94618241339862
9257.87253.42557845884-24.47799733097286.79241887213-4.44442154116047
10257.19241.99500312801-14.7190100833203287.104006955311-15.1949968719903
11275.6269.397296962414-5.61289200090442287.415595038491-6.20270303758633
12305.68304.02422064787320.1090332158501287.226746136277-1.65577935212747
13358.06386.5137242611142.5683785048262287.03789723406428.4537242611099
14320.07321.05611730265531.7377320129386287.3461506844070.986117302654748
15295.9285.33992705276218.8056688124888287.654404134749-10.5600729472382
16291.27292.106465730382.45652927232124287.9770049972990.836465730379985
17272.87271.045858653683-13.6054645135311288.299605859848-1.82414134631705
18269.27265.005428704777-14.4752930057362288.009864300959-4.26457129522282
19271.32271.849287544173-16.9294102862428287.720122742070.52928754417303
20267.45273.346851948064-25.8572754983544287.410423550295.89685194806412
21260.33258.037272972459-24.47799733097287.100724358511-2.29272702754071
22277.94283.208022111701-14.7190100833203287.3909879716195.26802211170133
23277.07272.071640416177-5.61289200090442287.681251584727-4.99835958382289
24312.65317.1858470594320.1090332158501288.0051197247194.53584705943047
25319.71308.52263363046242.5683785048262288.328987864712-11.1873663695378
26318.39316.16763929681631.7377320129386288.874628690245-2.22236070318365
27304.9301.57406167173318.8056688124888289.420269515779-3.3259383282674
28303.73314.6561227048962.45652927232124290.34734802278310.9261227048957
29273.29268.911037983744-13.6054645135311291.274426529788-4.37896201625637
30274.33270.894776257115-14.4752930057362292.240516748622-3.4352237428854
31270.45264.622803318787-16.9294102862428293.206606967456-5.82719668121274
32278.23289.383374631683-25.8572754983544292.93390086667111.1533746316831
33274.03279.876802565083-24.47799733097292.6611947658875.8468025650829
34279281.469731194873-14.7190100833203291.2492788884482.46973119487262
35287.5290.775528989896-5.61289200090442289.8373630110083.27552898989609
36336.87365.43848505404620.1090332158501288.19248173010428.5684850540456
37334.1339.08402104597442.5683785048262286.54760044924.98402104597358
38296.07275.523436418131.7377320129386284.878831568961-20.5465635818997
39286.84271.66426849878918.8056688124888283.210062688722-15.1757315012107
40277.63271.0459740901742.45652927232124281.757496637505-6.58402590982581
41261.32255.940533927244-13.6054645135311280.304930586287-5.37946607275609
42264.07262.779626080657-14.4752930057362279.835666925079-1.2903739193427
43261.94261.443007022372-16.9294102862428279.36640326387-0.496992977627656
44252.84250.747167076649-25.8572754983544280.790108421706-2.0928329233513
45257.83257.924183751429-24.47799733097282.2138135795410.0941837514291137
46271.16273.05877171309-14.7190100833203283.980238370231.8987717130903
47273.63267.126228839985-5.61289200090442285.746663160919-6.50377116001482
48304.87303.37936660234520.1090332158501286.251600181805-1.49063339765496
49323.9318.47508429248342.5683785048262286.756537202691-5.42491570751673
50336.11354.27237162631531.7377320129386286.20989636074718.1623716263146
51335.65366.83107566870818.8056688124888285.66325551880331.1810756687079
52282.23277.1941683040442.45652927232124284.809302423635-5.03583169595612
53273.03275.710115185065-13.6054645135311283.9553493284672.68011518506461
54270.07271.909791470878-14.4752930057362282.7055015348581.83979147087831
55246.03227.533756544994-16.9294102862428281.455653741249-18.4962434550064
56242.35230.673347163262-25.8572754983544279.883928335093-11.6766528367382
57250.33246.825794402034-24.47799733097278.312202928936-3.50420559796601
58267.45272.117873738918-14.7190100833203277.5011363444034.66787373891776
59268.8266.522822241035-5.61289200090442276.690069759869-2.27717775896457
60302.68307.81256729513520.1090332158501277.4383994890155.13256729513506
61313.1305.44489227701342.5683785048262278.186729218161-7.65510772298677
62306.39301.8029029913431.7377320129386279.239364995721-4.58709700865984
63305.61312.12233041422918.8056688124888280.2920007732826.51233041422933
64277.27271.6310283465932.45652927232124280.452442381085-5.63897165340671
65264.94262.872580524642-13.6054645135311280.612883988889-2.06741947535795
66268.63271.643122088403-14.4752930057362280.0921709173333.01312208840329
67293.9325.157952440466-16.9294102862428279.57145784577731.2579524404661
68248.65244.026911035943-25.8572754983544279.130364462412-4.6230889640575
69256257.788726251923-24.47799733097278.6892710790471.78872625192292
70258.52253.743762493931-14.7190100833203278.015247589389-4.7762375060687
71266.9262.071667901173-5.61289200090442277.341224099731-4.82833209882648
72281.23266.09534120300220.1090332158501276.255625581148-15.1346587969982
73306294.26159443260842.5683785048262275.170027062565-11.7384055673915
74325.46344.6723869351131.7377320129386274.50988105195119.21238693511
75291.13289.60459614617418.8056688124888273.849735041338-1.52540385382633
76282.53288.0831539637052.45652927232124274.5203167639735.55315396370537
77256.52251.454566026922-13.6054645135311275.190898486609-5.06543397307809
78258.63256.127246443482-14.4752930057362275.608046562254-2.50275355651775
79252.74246.384215648344-16.9294102862428276.025194637899-6.35578435165581
80245.16239.796320479547-25.8572754983544276.380955018808-5.36367952045327
81255.03257.801281931253-24.47799733097276.7367153997172.77128193125327
82268.35274.292535158965-14.7190100833203277.1264749243565.94253515896463
83293.73315.55665755191-5.61289200090442277.51623444899521.8266575519098
84278.39258.73006102655920.1090332158501277.940905757591-19.6599389734413

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 325.87 & 328.67849761789 & 42.5683785048262 & 280.493123877284 & 2.80849761788994 \tabularnewline
2 & 302.25 & 291.474770002811 & 31.7377320129386 & 281.28749798425 & -10.775229997189 \tabularnewline
3 & 294 & 287.112459096294 & 18.8056688124888 & 282.081872091217 & -6.88754090370571 \tabularnewline
4 & 285.43 & 285.58706984326 & 2.45652927232124 & 282.816400884419 & 0.15706984326016 \tabularnewline
5 & 286.19 & 302.434534835911 & -13.6054645135311 & 283.55092967762 & 16.2445348359108 \tabularnewline
6 & 276.7 & 283.554944518035 & -14.4752930057362 & 284.320348487701 & 6.85494451803544 \tabularnewline
7 & 267.77 & 267.379642988462 & -16.9294102862428 & 285.089767297781 & -0.390357011538185 \tabularnewline
8 & 267.03 & 273.976182413399 & -25.8572754983544 & 285.941093084956 & 6.94618241339862 \tabularnewline
9 & 257.87 & 253.42557845884 & -24.47799733097 & 286.79241887213 & -4.44442154116047 \tabularnewline
10 & 257.19 & 241.99500312801 & -14.7190100833203 & 287.104006955311 & -15.1949968719903 \tabularnewline
11 & 275.6 & 269.397296962414 & -5.61289200090442 & 287.415595038491 & -6.20270303758633 \tabularnewline
12 & 305.68 & 304.024220647873 & 20.1090332158501 & 287.226746136277 & -1.65577935212747 \tabularnewline
13 & 358.06 & 386.51372426111 & 42.5683785048262 & 287.037897234064 & 28.4537242611099 \tabularnewline
14 & 320.07 & 321.056117302655 & 31.7377320129386 & 287.346150684407 & 0.986117302654748 \tabularnewline
15 & 295.9 & 285.339927052762 & 18.8056688124888 & 287.654404134749 & -10.5600729472382 \tabularnewline
16 & 291.27 & 292.10646573038 & 2.45652927232124 & 287.977004997299 & 0.836465730379985 \tabularnewline
17 & 272.87 & 271.045858653683 & -13.6054645135311 & 288.299605859848 & -1.82414134631705 \tabularnewline
18 & 269.27 & 265.005428704777 & -14.4752930057362 & 288.009864300959 & -4.26457129522282 \tabularnewline
19 & 271.32 & 271.849287544173 & -16.9294102862428 & 287.72012274207 & 0.52928754417303 \tabularnewline
20 & 267.45 & 273.346851948064 & -25.8572754983544 & 287.41042355029 & 5.89685194806412 \tabularnewline
21 & 260.33 & 258.037272972459 & -24.47799733097 & 287.100724358511 & -2.29272702754071 \tabularnewline
22 & 277.94 & 283.208022111701 & -14.7190100833203 & 287.390987971619 & 5.26802211170133 \tabularnewline
23 & 277.07 & 272.071640416177 & -5.61289200090442 & 287.681251584727 & -4.99835958382289 \tabularnewline
24 & 312.65 & 317.18584705943 & 20.1090332158501 & 288.005119724719 & 4.53584705943047 \tabularnewline
25 & 319.71 & 308.522633630462 & 42.5683785048262 & 288.328987864712 & -11.1873663695378 \tabularnewline
26 & 318.39 & 316.167639296816 & 31.7377320129386 & 288.874628690245 & -2.22236070318365 \tabularnewline
27 & 304.9 & 301.574061671733 & 18.8056688124888 & 289.420269515779 & -3.3259383282674 \tabularnewline
28 & 303.73 & 314.656122704896 & 2.45652927232124 & 290.347348022783 & 10.9261227048957 \tabularnewline
29 & 273.29 & 268.911037983744 & -13.6054645135311 & 291.274426529788 & -4.37896201625637 \tabularnewline
30 & 274.33 & 270.894776257115 & -14.4752930057362 & 292.240516748622 & -3.4352237428854 \tabularnewline
31 & 270.45 & 264.622803318787 & -16.9294102862428 & 293.206606967456 & -5.82719668121274 \tabularnewline
32 & 278.23 & 289.383374631683 & -25.8572754983544 & 292.933900866671 & 11.1533746316831 \tabularnewline
33 & 274.03 & 279.876802565083 & -24.47799733097 & 292.661194765887 & 5.8468025650829 \tabularnewline
34 & 279 & 281.469731194873 & -14.7190100833203 & 291.249278888448 & 2.46973119487262 \tabularnewline
35 & 287.5 & 290.775528989896 & -5.61289200090442 & 289.837363011008 & 3.27552898989609 \tabularnewline
36 & 336.87 & 365.438485054046 & 20.1090332158501 & 288.192481730104 & 28.5684850540456 \tabularnewline
37 & 334.1 & 339.084021045974 & 42.5683785048262 & 286.5476004492 & 4.98402104597358 \tabularnewline
38 & 296.07 & 275.5234364181 & 31.7377320129386 & 284.878831568961 & -20.5465635818997 \tabularnewline
39 & 286.84 & 271.664268498789 & 18.8056688124888 & 283.210062688722 & -15.1757315012107 \tabularnewline
40 & 277.63 & 271.045974090174 & 2.45652927232124 & 281.757496637505 & -6.58402590982581 \tabularnewline
41 & 261.32 & 255.940533927244 & -13.6054645135311 & 280.304930586287 & -5.37946607275609 \tabularnewline
42 & 264.07 & 262.779626080657 & -14.4752930057362 & 279.835666925079 & -1.2903739193427 \tabularnewline
43 & 261.94 & 261.443007022372 & -16.9294102862428 & 279.36640326387 & -0.496992977627656 \tabularnewline
44 & 252.84 & 250.747167076649 & -25.8572754983544 & 280.790108421706 & -2.0928329233513 \tabularnewline
45 & 257.83 & 257.924183751429 & -24.47799733097 & 282.213813579541 & 0.0941837514291137 \tabularnewline
46 & 271.16 & 273.05877171309 & -14.7190100833203 & 283.98023837023 & 1.8987717130903 \tabularnewline
47 & 273.63 & 267.126228839985 & -5.61289200090442 & 285.746663160919 & -6.50377116001482 \tabularnewline
48 & 304.87 & 303.379366602345 & 20.1090332158501 & 286.251600181805 & -1.49063339765496 \tabularnewline
49 & 323.9 & 318.475084292483 & 42.5683785048262 & 286.756537202691 & -5.42491570751673 \tabularnewline
50 & 336.11 & 354.272371626315 & 31.7377320129386 & 286.209896360747 & 18.1623716263146 \tabularnewline
51 & 335.65 & 366.831075668708 & 18.8056688124888 & 285.663255518803 & 31.1810756687079 \tabularnewline
52 & 282.23 & 277.194168304044 & 2.45652927232124 & 284.809302423635 & -5.03583169595612 \tabularnewline
53 & 273.03 & 275.710115185065 & -13.6054645135311 & 283.955349328467 & 2.68011518506461 \tabularnewline
54 & 270.07 & 271.909791470878 & -14.4752930057362 & 282.705501534858 & 1.83979147087831 \tabularnewline
55 & 246.03 & 227.533756544994 & -16.9294102862428 & 281.455653741249 & -18.4962434550064 \tabularnewline
56 & 242.35 & 230.673347163262 & -25.8572754983544 & 279.883928335093 & -11.6766528367382 \tabularnewline
57 & 250.33 & 246.825794402034 & -24.47799733097 & 278.312202928936 & -3.50420559796601 \tabularnewline
58 & 267.45 & 272.117873738918 & -14.7190100833203 & 277.501136344403 & 4.66787373891776 \tabularnewline
59 & 268.8 & 266.522822241035 & -5.61289200090442 & 276.690069759869 & -2.27717775896457 \tabularnewline
60 & 302.68 & 307.812567295135 & 20.1090332158501 & 277.438399489015 & 5.13256729513506 \tabularnewline
61 & 313.1 & 305.444892277013 & 42.5683785048262 & 278.186729218161 & -7.65510772298677 \tabularnewline
62 & 306.39 & 301.80290299134 & 31.7377320129386 & 279.239364995721 & -4.58709700865984 \tabularnewline
63 & 305.61 & 312.122330414229 & 18.8056688124888 & 280.292000773282 & 6.51233041422933 \tabularnewline
64 & 277.27 & 271.631028346593 & 2.45652927232124 & 280.452442381085 & -5.63897165340671 \tabularnewline
65 & 264.94 & 262.872580524642 & -13.6054645135311 & 280.612883988889 & -2.06741947535795 \tabularnewline
66 & 268.63 & 271.643122088403 & -14.4752930057362 & 280.092170917333 & 3.01312208840329 \tabularnewline
67 & 293.9 & 325.157952440466 & -16.9294102862428 & 279.571457845777 & 31.2579524404661 \tabularnewline
68 & 248.65 & 244.026911035943 & -25.8572754983544 & 279.130364462412 & -4.6230889640575 \tabularnewline
69 & 256 & 257.788726251923 & -24.47799733097 & 278.689271079047 & 1.78872625192292 \tabularnewline
70 & 258.52 & 253.743762493931 & -14.7190100833203 & 278.015247589389 & -4.7762375060687 \tabularnewline
71 & 266.9 & 262.071667901173 & -5.61289200090442 & 277.341224099731 & -4.82833209882648 \tabularnewline
72 & 281.23 & 266.095341203002 & 20.1090332158501 & 276.255625581148 & -15.1346587969982 \tabularnewline
73 & 306 & 294.261594432608 & 42.5683785048262 & 275.170027062565 & -11.7384055673915 \tabularnewline
74 & 325.46 & 344.67238693511 & 31.7377320129386 & 274.509881051951 & 19.21238693511 \tabularnewline
75 & 291.13 & 289.604596146174 & 18.8056688124888 & 273.849735041338 & -1.52540385382633 \tabularnewline
76 & 282.53 & 288.083153963705 & 2.45652927232124 & 274.520316763973 & 5.55315396370537 \tabularnewline
77 & 256.52 & 251.454566026922 & -13.6054645135311 & 275.190898486609 & -5.06543397307809 \tabularnewline
78 & 258.63 & 256.127246443482 & -14.4752930057362 & 275.608046562254 & -2.50275355651775 \tabularnewline
79 & 252.74 & 246.384215648344 & -16.9294102862428 & 276.025194637899 & -6.35578435165581 \tabularnewline
80 & 245.16 & 239.796320479547 & -25.8572754983544 & 276.380955018808 & -5.36367952045327 \tabularnewline
81 & 255.03 & 257.801281931253 & -24.47799733097 & 276.736715399717 & 2.77128193125327 \tabularnewline
82 & 268.35 & 274.292535158965 & -14.7190100833203 & 277.126474924356 & 5.94253515896463 \tabularnewline
83 & 293.73 & 315.55665755191 & -5.61289200090442 & 277.516234448995 & 21.8266575519098 \tabularnewline
84 & 278.39 & 258.730061026559 & 20.1090332158501 & 277.940905757591 & -19.6599389734413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266978&T=2

[TABLE]
[ROW][C]Seasonal Decomposition by Loess - Time Series Components[/C][/ROW]
[ROW][C]t[/C][C]Observed[/C][C]Fitted[/C][C]Seasonal[/C][C]Trend[/C][C]Remainder[/C][/ROW]
[ROW][C]1[/C][C]325.87[/C][C]328.67849761789[/C][C]42.5683785048262[/C][C]280.493123877284[/C][C]2.80849761788994[/C][/ROW]
[ROW][C]2[/C][C]302.25[/C][C]291.474770002811[/C][C]31.7377320129386[/C][C]281.28749798425[/C][C]-10.775229997189[/C][/ROW]
[ROW][C]3[/C][C]294[/C][C]287.112459096294[/C][C]18.8056688124888[/C][C]282.081872091217[/C][C]-6.88754090370571[/C][/ROW]
[ROW][C]4[/C][C]285.43[/C][C]285.58706984326[/C][C]2.45652927232124[/C][C]282.816400884419[/C][C]0.15706984326016[/C][/ROW]
[ROW][C]5[/C][C]286.19[/C][C]302.434534835911[/C][C]-13.6054645135311[/C][C]283.55092967762[/C][C]16.2445348359108[/C][/ROW]
[ROW][C]6[/C][C]276.7[/C][C]283.554944518035[/C][C]-14.4752930057362[/C][C]284.320348487701[/C][C]6.85494451803544[/C][/ROW]
[ROW][C]7[/C][C]267.77[/C][C]267.379642988462[/C][C]-16.9294102862428[/C][C]285.089767297781[/C][C]-0.390357011538185[/C][/ROW]
[ROW][C]8[/C][C]267.03[/C][C]273.976182413399[/C][C]-25.8572754983544[/C][C]285.941093084956[/C][C]6.94618241339862[/C][/ROW]
[ROW][C]9[/C][C]257.87[/C][C]253.42557845884[/C][C]-24.47799733097[/C][C]286.79241887213[/C][C]-4.44442154116047[/C][/ROW]
[ROW][C]10[/C][C]257.19[/C][C]241.99500312801[/C][C]-14.7190100833203[/C][C]287.104006955311[/C][C]-15.1949968719903[/C][/ROW]
[ROW][C]11[/C][C]275.6[/C][C]269.397296962414[/C][C]-5.61289200090442[/C][C]287.415595038491[/C][C]-6.20270303758633[/C][/ROW]
[ROW][C]12[/C][C]305.68[/C][C]304.024220647873[/C][C]20.1090332158501[/C][C]287.226746136277[/C][C]-1.65577935212747[/C][/ROW]
[ROW][C]13[/C][C]358.06[/C][C]386.51372426111[/C][C]42.5683785048262[/C][C]287.037897234064[/C][C]28.4537242611099[/C][/ROW]
[ROW][C]14[/C][C]320.07[/C][C]321.056117302655[/C][C]31.7377320129386[/C][C]287.346150684407[/C][C]0.986117302654748[/C][/ROW]
[ROW][C]15[/C][C]295.9[/C][C]285.339927052762[/C][C]18.8056688124888[/C][C]287.654404134749[/C][C]-10.5600729472382[/C][/ROW]
[ROW][C]16[/C][C]291.27[/C][C]292.10646573038[/C][C]2.45652927232124[/C][C]287.977004997299[/C][C]0.836465730379985[/C][/ROW]
[ROW][C]17[/C][C]272.87[/C][C]271.045858653683[/C][C]-13.6054645135311[/C][C]288.299605859848[/C][C]-1.82414134631705[/C][/ROW]
[ROW][C]18[/C][C]269.27[/C][C]265.005428704777[/C][C]-14.4752930057362[/C][C]288.009864300959[/C][C]-4.26457129522282[/C][/ROW]
[ROW][C]19[/C][C]271.32[/C][C]271.849287544173[/C][C]-16.9294102862428[/C][C]287.72012274207[/C][C]0.52928754417303[/C][/ROW]
[ROW][C]20[/C][C]267.45[/C][C]273.346851948064[/C][C]-25.8572754983544[/C][C]287.41042355029[/C][C]5.89685194806412[/C][/ROW]
[ROW][C]21[/C][C]260.33[/C][C]258.037272972459[/C][C]-24.47799733097[/C][C]287.100724358511[/C][C]-2.29272702754071[/C][/ROW]
[ROW][C]22[/C][C]277.94[/C][C]283.208022111701[/C][C]-14.7190100833203[/C][C]287.390987971619[/C][C]5.26802211170133[/C][/ROW]
[ROW][C]23[/C][C]277.07[/C][C]272.071640416177[/C][C]-5.61289200090442[/C][C]287.681251584727[/C][C]-4.99835958382289[/C][/ROW]
[ROW][C]24[/C][C]312.65[/C][C]317.18584705943[/C][C]20.1090332158501[/C][C]288.005119724719[/C][C]4.53584705943047[/C][/ROW]
[ROW][C]25[/C][C]319.71[/C][C]308.522633630462[/C][C]42.5683785048262[/C][C]288.328987864712[/C][C]-11.1873663695378[/C][/ROW]
[ROW][C]26[/C][C]318.39[/C][C]316.167639296816[/C][C]31.7377320129386[/C][C]288.874628690245[/C][C]-2.22236070318365[/C][/ROW]
[ROW][C]27[/C][C]304.9[/C][C]301.574061671733[/C][C]18.8056688124888[/C][C]289.420269515779[/C][C]-3.3259383282674[/C][/ROW]
[ROW][C]28[/C][C]303.73[/C][C]314.656122704896[/C][C]2.45652927232124[/C][C]290.347348022783[/C][C]10.9261227048957[/C][/ROW]
[ROW][C]29[/C][C]273.29[/C][C]268.911037983744[/C][C]-13.6054645135311[/C][C]291.274426529788[/C][C]-4.37896201625637[/C][/ROW]
[ROW][C]30[/C][C]274.33[/C][C]270.894776257115[/C][C]-14.4752930057362[/C][C]292.240516748622[/C][C]-3.4352237428854[/C][/ROW]
[ROW][C]31[/C][C]270.45[/C][C]264.622803318787[/C][C]-16.9294102862428[/C][C]293.206606967456[/C][C]-5.82719668121274[/C][/ROW]
[ROW][C]32[/C][C]278.23[/C][C]289.383374631683[/C][C]-25.8572754983544[/C][C]292.933900866671[/C][C]11.1533746316831[/C][/ROW]
[ROW][C]33[/C][C]274.03[/C][C]279.876802565083[/C][C]-24.47799733097[/C][C]292.661194765887[/C][C]5.8468025650829[/C][/ROW]
[ROW][C]34[/C][C]279[/C][C]281.469731194873[/C][C]-14.7190100833203[/C][C]291.249278888448[/C][C]2.46973119487262[/C][/ROW]
[ROW][C]35[/C][C]287.5[/C][C]290.775528989896[/C][C]-5.61289200090442[/C][C]289.837363011008[/C][C]3.27552898989609[/C][/ROW]
[ROW][C]36[/C][C]336.87[/C][C]365.438485054046[/C][C]20.1090332158501[/C][C]288.192481730104[/C][C]28.5684850540456[/C][/ROW]
[ROW][C]37[/C][C]334.1[/C][C]339.084021045974[/C][C]42.5683785048262[/C][C]286.5476004492[/C][C]4.98402104597358[/C][/ROW]
[ROW][C]38[/C][C]296.07[/C][C]275.5234364181[/C][C]31.7377320129386[/C][C]284.878831568961[/C][C]-20.5465635818997[/C][/ROW]
[ROW][C]39[/C][C]286.84[/C][C]271.664268498789[/C][C]18.8056688124888[/C][C]283.210062688722[/C][C]-15.1757315012107[/C][/ROW]
[ROW][C]40[/C][C]277.63[/C][C]271.045974090174[/C][C]2.45652927232124[/C][C]281.757496637505[/C][C]-6.58402590982581[/C][/ROW]
[ROW][C]41[/C][C]261.32[/C][C]255.940533927244[/C][C]-13.6054645135311[/C][C]280.304930586287[/C][C]-5.37946607275609[/C][/ROW]
[ROW][C]42[/C][C]264.07[/C][C]262.779626080657[/C][C]-14.4752930057362[/C][C]279.835666925079[/C][C]-1.2903739193427[/C][/ROW]
[ROW][C]43[/C][C]261.94[/C][C]261.443007022372[/C][C]-16.9294102862428[/C][C]279.36640326387[/C][C]-0.496992977627656[/C][/ROW]
[ROW][C]44[/C][C]252.84[/C][C]250.747167076649[/C][C]-25.8572754983544[/C][C]280.790108421706[/C][C]-2.0928329233513[/C][/ROW]
[ROW][C]45[/C][C]257.83[/C][C]257.924183751429[/C][C]-24.47799733097[/C][C]282.213813579541[/C][C]0.0941837514291137[/C][/ROW]
[ROW][C]46[/C][C]271.16[/C][C]273.05877171309[/C][C]-14.7190100833203[/C][C]283.98023837023[/C][C]1.8987717130903[/C][/ROW]
[ROW][C]47[/C][C]273.63[/C][C]267.126228839985[/C][C]-5.61289200090442[/C][C]285.746663160919[/C][C]-6.50377116001482[/C][/ROW]
[ROW][C]48[/C][C]304.87[/C][C]303.379366602345[/C][C]20.1090332158501[/C][C]286.251600181805[/C][C]-1.49063339765496[/C][/ROW]
[ROW][C]49[/C][C]323.9[/C][C]318.475084292483[/C][C]42.5683785048262[/C][C]286.756537202691[/C][C]-5.42491570751673[/C][/ROW]
[ROW][C]50[/C][C]336.11[/C][C]354.272371626315[/C][C]31.7377320129386[/C][C]286.209896360747[/C][C]18.1623716263146[/C][/ROW]
[ROW][C]51[/C][C]335.65[/C][C]366.831075668708[/C][C]18.8056688124888[/C][C]285.663255518803[/C][C]31.1810756687079[/C][/ROW]
[ROW][C]52[/C][C]282.23[/C][C]277.194168304044[/C][C]2.45652927232124[/C][C]284.809302423635[/C][C]-5.03583169595612[/C][/ROW]
[ROW][C]53[/C][C]273.03[/C][C]275.710115185065[/C][C]-13.6054645135311[/C][C]283.955349328467[/C][C]2.68011518506461[/C][/ROW]
[ROW][C]54[/C][C]270.07[/C][C]271.909791470878[/C][C]-14.4752930057362[/C][C]282.705501534858[/C][C]1.83979147087831[/C][/ROW]
[ROW][C]55[/C][C]246.03[/C][C]227.533756544994[/C][C]-16.9294102862428[/C][C]281.455653741249[/C][C]-18.4962434550064[/C][/ROW]
[ROW][C]56[/C][C]242.35[/C][C]230.673347163262[/C][C]-25.8572754983544[/C][C]279.883928335093[/C][C]-11.6766528367382[/C][/ROW]
[ROW][C]57[/C][C]250.33[/C][C]246.825794402034[/C][C]-24.47799733097[/C][C]278.312202928936[/C][C]-3.50420559796601[/C][/ROW]
[ROW][C]58[/C][C]267.45[/C][C]272.117873738918[/C][C]-14.7190100833203[/C][C]277.501136344403[/C][C]4.66787373891776[/C][/ROW]
[ROW][C]59[/C][C]268.8[/C][C]266.522822241035[/C][C]-5.61289200090442[/C][C]276.690069759869[/C][C]-2.27717775896457[/C][/ROW]
[ROW][C]60[/C][C]302.68[/C][C]307.812567295135[/C][C]20.1090332158501[/C][C]277.438399489015[/C][C]5.13256729513506[/C][/ROW]
[ROW][C]61[/C][C]313.1[/C][C]305.444892277013[/C][C]42.5683785048262[/C][C]278.186729218161[/C][C]-7.65510772298677[/C][/ROW]
[ROW][C]62[/C][C]306.39[/C][C]301.80290299134[/C][C]31.7377320129386[/C][C]279.239364995721[/C][C]-4.58709700865984[/C][/ROW]
[ROW][C]63[/C][C]305.61[/C][C]312.122330414229[/C][C]18.8056688124888[/C][C]280.292000773282[/C][C]6.51233041422933[/C][/ROW]
[ROW][C]64[/C][C]277.27[/C][C]271.631028346593[/C][C]2.45652927232124[/C][C]280.452442381085[/C][C]-5.63897165340671[/C][/ROW]
[ROW][C]65[/C][C]264.94[/C][C]262.872580524642[/C][C]-13.6054645135311[/C][C]280.612883988889[/C][C]-2.06741947535795[/C][/ROW]
[ROW][C]66[/C][C]268.63[/C][C]271.643122088403[/C][C]-14.4752930057362[/C][C]280.092170917333[/C][C]3.01312208840329[/C][/ROW]
[ROW][C]67[/C][C]293.9[/C][C]325.157952440466[/C][C]-16.9294102862428[/C][C]279.571457845777[/C][C]31.2579524404661[/C][/ROW]
[ROW][C]68[/C][C]248.65[/C][C]244.026911035943[/C][C]-25.8572754983544[/C][C]279.130364462412[/C][C]-4.6230889640575[/C][/ROW]
[ROW][C]69[/C][C]256[/C][C]257.788726251923[/C][C]-24.47799733097[/C][C]278.689271079047[/C][C]1.78872625192292[/C][/ROW]
[ROW][C]70[/C][C]258.52[/C][C]253.743762493931[/C][C]-14.7190100833203[/C][C]278.015247589389[/C][C]-4.7762375060687[/C][/ROW]
[ROW][C]71[/C][C]266.9[/C][C]262.071667901173[/C][C]-5.61289200090442[/C][C]277.341224099731[/C][C]-4.82833209882648[/C][/ROW]
[ROW][C]72[/C][C]281.23[/C][C]266.095341203002[/C][C]20.1090332158501[/C][C]276.255625581148[/C][C]-15.1346587969982[/C][/ROW]
[ROW][C]73[/C][C]306[/C][C]294.261594432608[/C][C]42.5683785048262[/C][C]275.170027062565[/C][C]-11.7384055673915[/C][/ROW]
[ROW][C]74[/C][C]325.46[/C][C]344.67238693511[/C][C]31.7377320129386[/C][C]274.509881051951[/C][C]19.21238693511[/C][/ROW]
[ROW][C]75[/C][C]291.13[/C][C]289.604596146174[/C][C]18.8056688124888[/C][C]273.849735041338[/C][C]-1.52540385382633[/C][/ROW]
[ROW][C]76[/C][C]282.53[/C][C]288.083153963705[/C][C]2.45652927232124[/C][C]274.520316763973[/C][C]5.55315396370537[/C][/ROW]
[ROW][C]77[/C][C]256.52[/C][C]251.454566026922[/C][C]-13.6054645135311[/C][C]275.190898486609[/C][C]-5.06543397307809[/C][/ROW]
[ROW][C]78[/C][C]258.63[/C][C]256.127246443482[/C][C]-14.4752930057362[/C][C]275.608046562254[/C][C]-2.50275355651775[/C][/ROW]
[ROW][C]79[/C][C]252.74[/C][C]246.384215648344[/C][C]-16.9294102862428[/C][C]276.025194637899[/C][C]-6.35578435165581[/C][/ROW]
[ROW][C]80[/C][C]245.16[/C][C]239.796320479547[/C][C]-25.8572754983544[/C][C]276.380955018808[/C][C]-5.36367952045327[/C][/ROW]
[ROW][C]81[/C][C]255.03[/C][C]257.801281931253[/C][C]-24.47799733097[/C][C]276.736715399717[/C][C]2.77128193125327[/C][/ROW]
[ROW][C]82[/C][C]268.35[/C][C]274.292535158965[/C][C]-14.7190100833203[/C][C]277.126474924356[/C][C]5.94253515896463[/C][/ROW]
[ROW][C]83[/C][C]293.73[/C][C]315.55665755191[/C][C]-5.61289200090442[/C][C]277.516234448995[/C][C]21.8266575519098[/C][/ROW]
[ROW][C]84[/C][C]278.39[/C][C]258.730061026559[/C][C]20.1090332158501[/C][C]277.940905757591[/C][C]-19.6599389734413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266978&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266978&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1325.87328.6784976178942.5683785048262280.4931238772842.80849761788994
2302.25291.47477000281131.7377320129386281.28749798425-10.775229997189
3294287.11245909629418.8056688124888282.081872091217-6.88754090370571
4285.43285.587069843262.45652927232124282.8164008844190.15706984326016
5286.19302.434534835911-13.6054645135311283.5509296776216.2445348359108
6276.7283.554944518035-14.4752930057362284.3203484877016.85494451803544
7267.77267.379642988462-16.9294102862428285.089767297781-0.390357011538185
8267.03273.976182413399-25.8572754983544285.9410930849566.94618241339862
9257.87253.42557845884-24.47799733097286.79241887213-4.44442154116047
10257.19241.99500312801-14.7190100833203287.104006955311-15.1949968719903
11275.6269.397296962414-5.61289200090442287.415595038491-6.20270303758633
12305.68304.02422064787320.1090332158501287.226746136277-1.65577935212747
13358.06386.5137242611142.5683785048262287.03789723406428.4537242611099
14320.07321.05611730265531.7377320129386287.3461506844070.986117302654748
15295.9285.33992705276218.8056688124888287.654404134749-10.5600729472382
16291.27292.106465730382.45652927232124287.9770049972990.836465730379985
17272.87271.045858653683-13.6054645135311288.299605859848-1.82414134631705
18269.27265.005428704777-14.4752930057362288.009864300959-4.26457129522282
19271.32271.849287544173-16.9294102862428287.720122742070.52928754417303
20267.45273.346851948064-25.8572754983544287.410423550295.89685194806412
21260.33258.037272972459-24.47799733097287.100724358511-2.29272702754071
22277.94283.208022111701-14.7190100833203287.3909879716195.26802211170133
23277.07272.071640416177-5.61289200090442287.681251584727-4.99835958382289
24312.65317.1858470594320.1090332158501288.0051197247194.53584705943047
25319.71308.52263363046242.5683785048262288.328987864712-11.1873663695378
26318.39316.16763929681631.7377320129386288.874628690245-2.22236070318365
27304.9301.57406167173318.8056688124888289.420269515779-3.3259383282674
28303.73314.6561227048962.45652927232124290.34734802278310.9261227048957
29273.29268.911037983744-13.6054645135311291.274426529788-4.37896201625637
30274.33270.894776257115-14.4752930057362292.240516748622-3.4352237428854
31270.45264.622803318787-16.9294102862428293.206606967456-5.82719668121274
32278.23289.383374631683-25.8572754983544292.93390086667111.1533746316831
33274.03279.876802565083-24.47799733097292.6611947658875.8468025650829
34279281.469731194873-14.7190100833203291.2492788884482.46973119487262
35287.5290.775528989896-5.61289200090442289.8373630110083.27552898989609
36336.87365.43848505404620.1090332158501288.19248173010428.5684850540456
37334.1339.08402104597442.5683785048262286.54760044924.98402104597358
38296.07275.523436418131.7377320129386284.878831568961-20.5465635818997
39286.84271.66426849878918.8056688124888283.210062688722-15.1757315012107
40277.63271.0459740901742.45652927232124281.757496637505-6.58402590982581
41261.32255.940533927244-13.6054645135311280.304930586287-5.37946607275609
42264.07262.779626080657-14.4752930057362279.835666925079-1.2903739193427
43261.94261.443007022372-16.9294102862428279.36640326387-0.496992977627656
44252.84250.747167076649-25.8572754983544280.790108421706-2.0928329233513
45257.83257.924183751429-24.47799733097282.2138135795410.0941837514291137
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47273.63267.126228839985-5.61289200090442285.746663160919-6.50377116001482
48304.87303.37936660234520.1090332158501286.251600181805-1.49063339765496
49323.9318.47508429248342.5683785048262286.756537202691-5.42491570751673
50336.11354.27237162631531.7377320129386286.20989636074718.1623716263146
51335.65366.83107566870818.8056688124888285.66325551880331.1810756687079
52282.23277.1941683040442.45652927232124284.809302423635-5.03583169595612
53273.03275.710115185065-13.6054645135311283.9553493284672.68011518506461
54270.07271.909791470878-14.4752930057362282.7055015348581.83979147087831
55246.03227.533756544994-16.9294102862428281.455653741249-18.4962434550064
56242.35230.673347163262-25.8572754983544279.883928335093-11.6766528367382
57250.33246.825794402034-24.47799733097278.312202928936-3.50420559796601
58267.45272.117873738918-14.7190100833203277.5011363444034.66787373891776
59268.8266.522822241035-5.61289200090442276.690069759869-2.27717775896457
60302.68307.81256729513520.1090332158501277.4383994890155.13256729513506
61313.1305.44489227701342.5683785048262278.186729218161-7.65510772298677
62306.39301.8029029913431.7377320129386279.239364995721-4.58709700865984
63305.61312.12233041422918.8056688124888280.2920007732826.51233041422933
64277.27271.6310283465932.45652927232124280.452442381085-5.63897165340671
65264.94262.872580524642-13.6054645135311280.612883988889-2.06741947535795
66268.63271.643122088403-14.4752930057362280.0921709173333.01312208840329
67293.9325.157952440466-16.9294102862428279.57145784577731.2579524404661
68248.65244.026911035943-25.8572754983544279.130364462412-4.6230889640575
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70258.52253.743762493931-14.7190100833203278.015247589389-4.7762375060687
71266.9262.071667901173-5.61289200090442277.341224099731-4.82833209882648
72281.23266.09534120300220.1090332158501276.255625581148-15.1346587969982
73306294.26159443260842.5683785048262275.170027062565-11.7384055673915
74325.46344.6723869351131.7377320129386274.50988105195119.21238693511
75291.13289.60459614617418.8056688124888273.849735041338-1.52540385382633
76282.53288.0831539637052.45652927232124274.5203167639735.55315396370537
77256.52251.454566026922-13.6054645135311275.190898486609-5.06543397307809
78258.63256.127246443482-14.4752930057362275.608046562254-2.50275355651775
79252.74246.384215648344-16.9294102862428276.025194637899-6.35578435165581
80245.16239.796320479547-25.8572754983544276.380955018808-5.36367952045327
81255.03257.801281931253-24.47799733097276.7367153997172.77128193125327
82268.35274.292535158965-14.7190100833203277.1264749243565.94253515896463
83293.73315.55665755191-5.61289200090442277.51623444899521.8266575519098
84278.39258.73006102655920.1090332158501277.940905757591-19.6599389734413



Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par4 = ; par5 = 1 ; par6 = ; 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')