Free Statistics

of Irreproducible Research!

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 computationFri, 25 Nov 2011 09:33:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/25/t1322231618rdds7d6sa4xu56k.htm/, Retrieved Sat, 18 May 2024 01:42:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147321, Retrieved Sat, 18 May 2024 01:42:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [HPC Retail Sales] [2008-03-02 15:42:48] [74be16979710d4c4e7c6647856088456]
- RMPD  [Classical Decomposition] [Klassieke composi...] [2011-11-25 13:14:44] [aa6b3f8e5b050429abaad141c7204e84]
- RMP       [Decomposition by Loess] [WS8] [2011-11-25 14:33:18] [7a9891c1925ad1e8ddfe52b8c5887b5b] [Current]
Feedback Forum

Post a new message
Dataseries X:
68897
38683
44720
39525
45315
50380
40600
36279
42438
38064
31879
11379
70249
39253
47060
41697
38708
49267
39018
32228
40870
39383
34571
12066
70938
34077
45409
40809
37013
44953
37848
32745
39401
34931
33008
8620
68906
39556
50669
36432
40891
48428
36222
33425
39401
37967
34801
12657
69116
41519
51321
38529
41547
52073
38401
40898
40439
41888
37898
8771
68184
50530
47221
41756
45633
48138
39486
39341
41117
41629
29722
7054
56676
34870
35117
30169
30936
35699
33228
27733
33666
35429
27438
8170




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=147321&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=147321&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147321&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=147321&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=147321&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147321&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
16889769202.382110531327906.352530516540685.2653589522305.382110531253
23868336420.9494800285222.57929261969440722.4712273518-2262.05051997153
34472042211.93894330986468.3839609387340759.6770957515-2508.06105669019
43952539317.2386830724-1037.7072053968240770.4685223244-207.761316927586
54531549288.8220412673559.91800983533740781.25994889733973.82204126733
65038052370.92006791687595.9572754839640793.12265659921990.92006791679
74060041910.1653500111-1515.1507143122440804.98536430121310.16535001108
83627936323.0326211115-4607.0955860003940842.062964888844.0326211115462
94243843576.3309117904420.52852273305540879.14056547651138.33091179041
103806435944.2211361075-639.2526521308540823.0315160234-2119.77886389252
113187929251.9663516258-6260.8888181960540766.9224665702-2627.03364837416
121137911271.7322365359-29113.623119564240599.8908830283-107.267763464071
137024972158.788169997127906.352530516540432.85929948641909.78816999706
143925337957.3555146607222.57929261969440326.0651927196-1295.64448533926
154706047432.34495310856468.3839609387340219.2710859527372.344953108543
164169744221.1721617854-1037.7072053968240210.53504361142524.17216178537
173870836654.2829888945559.91800983533740201.7990012702-2053.7170111055
184926750726.38249319357595.9572754839640211.66023132251459.38249319352
193901839329.6292529374-1515.1507143122440221.5214613749311.629252937375
203222828948.6470767625-4607.0955860003940114.4485092379-3279.3529232375
214087041312.095920166420.52852273305540007.3755571009442.095920166037
223938339554.46485947-639.2526521308539850.7877926608171.464859470012
233457135708.6887899753-6260.8888181960539694.20002822081137.68878997528
241206613704.6461362138-29113.623119564239540.97698335041638.64613621379
257093874581.893531003427906.352530516539387.75393848013643.89353100336
263407728709.1015057187222.57929261969439222.3192016617-5367.89849428135
274540945292.73157421816468.3839609387339056.8844648432-116.26842578192
284080943819.2626948164-1037.7072053968238836.44451058043010.26269481644
293701334850.0774338471559.91800983533738616.0045563176-2162.9225661529
304495343825.86893602297595.9572754839638484.1737884931-1127.13106397705
313784838858.8076936436-1515.1507143122438352.34302066861010.80769364362
323274531647.8818824365-4607.0955860003938449.2137035639-1097.11811756351
333940139835.3870908078420.52852273305538546.0843864592434.387090807773
343493131776.7752316462-639.2526521308538724.4774204846-3154.22476835378
353300833374.018363686-6260.8888181960538902.8704545101366.018363685966
3686207293.47980228087-29113.623119564239060.1433172834-1326.52019771913
376890670688.231289426827906.352530516539217.41618005661782.23128942682
383955639577.7503872274222.57929261969439311.670320152921.7503872274174
395066955463.69157881216468.3839609387339405.92446024914794.69157881214
403643234400.8259422447-1037.7072053968239500.8812631521-2031.17405775526
414089141626.2439241096559.91800983533739595.838066055735.243924109644
424842849568.07420887987595.9572754839639691.96851563621140.0742088798
433622234171.0517490948-1515.1507143122439788.0989652175-2050.94825090522
443342531548.3304835358-4607.0955860003939908.7651024646-1876.66951646422
453940138352.0402375552420.52852273305540029.4312397118-1048.95976244481
463796736363.0909679384-639.2526521308540210.1616841925-1603.9090320616
473480135471.9966895229-6260.8888181960540390.8921286732670.996689522894
481265713757.4105668598-29113.623119564240670.21255270451100.41056685978
496911669376.114492747727906.352530516540949.5329767358260.114492747707
504151941559.0160175359222.57929261969441256.404689844440.0160175358833
515132154610.33963610826468.3839609387341563.27640295313289.33963610818
523852936336.7775570752-1037.7072053968241758.9296483216-2192.22244292482
534154740579.4990964745559.91800983533741954.5828936902-967.500903525521
545207354543.75645399377595.9572754839642006.28627052232470.75645399374
553840136259.1610669578-1515.1507143122442057.9896473544-2141.83893304219
564089844252.0844991845-4607.0955860003942151.01108681593354.08449918448
574043938213.4389509895420.52852273305542244.0325262774-2225.56104901045
584188842049.7834358143-639.2526521308542365.4692163165161.783435814323
593789839569.9829118404-6260.8888181960542486.90590635571671.98291184038
6087714122.75969487651-29113.623119564242532.8634246877-4648.24030512349
616818465882.826526463727906.352530516542578.8209430198-2301.17347353631
625053058277.4089882475222.57929261969442560.01171913287747.40898824755
634722145432.41354381556468.3839609387342541.2024952457-1788.58645618447
644175642164.9428866886-1037.7072053968242384.7643187082408.94288668863
654563348477.755847994559.91800983533742228.32614217062844.75584799403
664813847005.89263712827595.9572754839641674.1500873879-1132.10736287183
673948639367.1766817071-1515.1507143122441119.9740326051-118.823318292882
683934143152.2032311148-4607.0955860003940136.89235488563811.20323111479
694111742659.6608001009420.52852273305539153.81067716611542.66080010087
704162945886.6431095785-639.2526521308538010.60954255234257.64310957852
712972228837.4804102575-6260.8888181960536867.4084079386-884.519589742544
7270547426.16551848926-29113.623119564235795.457601075372.165518489259
735667650722.140675272127906.352530516534723.5067942114-5953.85932472789
743487035612.9749235609222.57929261969433904.4457838195742.974923560854
753511730680.23126563376468.3839609387333085.3847734275-4436.76873436627
763016928313.5741847836-1037.7072053968233062.1330206132-1855.42581521641
773093628273.2007223657559.91800983533733038.8812677989-2662.79927763425
783569930704.53300627647595.9572754839633097.5097182396-4994.46699372361
793322834815.0125456319-1515.1507143122433156.13816868041587.01254563186
802773326780.6302243289-4607.0955860003933292.4653616715-952.369775671064
813366633482.6789226044420.52852273305533428.7925546625-183.321077395587
823542937844.2943114199-639.2526521308533652.9583407112415.29431141986
832743827259.7646914366-6260.8888181960533877.1241267594-178.235308563388
84817011289.5594324142-29113.623119564234164.063687153119.55943241419

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 68897 & 69202.3821105313 & 27906.3525305165 & 40685.2653589522 & 305.382110531253 \tabularnewline
2 & 38683 & 36420.9494800285 & 222.579292619694 & 40722.4712273518 & -2262.05051997153 \tabularnewline
3 & 44720 & 42211.9389433098 & 6468.38396093873 & 40759.6770957515 & -2508.06105669019 \tabularnewline
4 & 39525 & 39317.2386830724 & -1037.70720539682 & 40770.4685223244 & -207.761316927586 \tabularnewline
5 & 45315 & 49288.8220412673 & 559.918009835337 & 40781.2599488973 & 3973.82204126733 \tabularnewline
6 & 50380 & 52370.9200679168 & 7595.95727548396 & 40793.1226565992 & 1990.92006791679 \tabularnewline
7 & 40600 & 41910.1653500111 & -1515.15071431224 & 40804.9853643012 & 1310.16535001108 \tabularnewline
8 & 36279 & 36323.0326211115 & -4607.09558600039 & 40842.0629648888 & 44.0326211115462 \tabularnewline
9 & 42438 & 43576.3309117904 & 420.528522733055 & 40879.1405654765 & 1138.33091179041 \tabularnewline
10 & 38064 & 35944.2211361075 & -639.25265213085 & 40823.0315160234 & -2119.77886389252 \tabularnewline
11 & 31879 & 29251.9663516258 & -6260.88881819605 & 40766.9224665702 & -2627.03364837416 \tabularnewline
12 & 11379 & 11271.7322365359 & -29113.6231195642 & 40599.8908830283 & -107.267763464071 \tabularnewline
13 & 70249 & 72158.7881699971 & 27906.3525305165 & 40432.8592994864 & 1909.78816999706 \tabularnewline
14 & 39253 & 37957.3555146607 & 222.579292619694 & 40326.0651927196 & -1295.64448533926 \tabularnewline
15 & 47060 & 47432.3449531085 & 6468.38396093873 & 40219.2710859527 & 372.344953108543 \tabularnewline
16 & 41697 & 44221.1721617854 & -1037.70720539682 & 40210.5350436114 & 2524.17216178537 \tabularnewline
17 & 38708 & 36654.2829888945 & 559.918009835337 & 40201.7990012702 & -2053.7170111055 \tabularnewline
18 & 49267 & 50726.3824931935 & 7595.95727548396 & 40211.6602313225 & 1459.38249319352 \tabularnewline
19 & 39018 & 39329.6292529374 & -1515.15071431224 & 40221.5214613749 & 311.629252937375 \tabularnewline
20 & 32228 & 28948.6470767625 & -4607.09558600039 & 40114.4485092379 & -3279.3529232375 \tabularnewline
21 & 40870 & 41312.095920166 & 420.528522733055 & 40007.3755571009 & 442.095920166037 \tabularnewline
22 & 39383 & 39554.46485947 & -639.25265213085 & 39850.7877926608 & 171.464859470012 \tabularnewline
23 & 34571 & 35708.6887899753 & -6260.88881819605 & 39694.2000282208 & 1137.68878997528 \tabularnewline
24 & 12066 & 13704.6461362138 & -29113.6231195642 & 39540.9769833504 & 1638.64613621379 \tabularnewline
25 & 70938 & 74581.8935310034 & 27906.3525305165 & 39387.7539384801 & 3643.89353100336 \tabularnewline
26 & 34077 & 28709.1015057187 & 222.579292619694 & 39222.3192016617 & -5367.89849428135 \tabularnewline
27 & 45409 & 45292.7315742181 & 6468.38396093873 & 39056.8844648432 & -116.26842578192 \tabularnewline
28 & 40809 & 43819.2626948164 & -1037.70720539682 & 38836.4445105804 & 3010.26269481644 \tabularnewline
29 & 37013 & 34850.0774338471 & 559.918009835337 & 38616.0045563176 & -2162.9225661529 \tabularnewline
30 & 44953 & 43825.8689360229 & 7595.95727548396 & 38484.1737884931 & -1127.13106397705 \tabularnewline
31 & 37848 & 38858.8076936436 & -1515.15071431224 & 38352.3430206686 & 1010.80769364362 \tabularnewline
32 & 32745 & 31647.8818824365 & -4607.09558600039 & 38449.2137035639 & -1097.11811756351 \tabularnewline
33 & 39401 & 39835.3870908078 & 420.528522733055 & 38546.0843864592 & 434.387090807773 \tabularnewline
34 & 34931 & 31776.7752316462 & -639.25265213085 & 38724.4774204846 & -3154.22476835378 \tabularnewline
35 & 33008 & 33374.018363686 & -6260.88881819605 & 38902.8704545101 & 366.018363685966 \tabularnewline
36 & 8620 & 7293.47980228087 & -29113.6231195642 & 39060.1433172834 & -1326.52019771913 \tabularnewline
37 & 68906 & 70688.2312894268 & 27906.3525305165 & 39217.4161800566 & 1782.23128942682 \tabularnewline
38 & 39556 & 39577.7503872274 & 222.579292619694 & 39311.6703201529 & 21.7503872274174 \tabularnewline
39 & 50669 & 55463.6915788121 & 6468.38396093873 & 39405.9244602491 & 4794.69157881214 \tabularnewline
40 & 36432 & 34400.8259422447 & -1037.70720539682 & 39500.8812631521 & -2031.17405775526 \tabularnewline
41 & 40891 & 41626.2439241096 & 559.918009835337 & 39595.838066055 & 735.243924109644 \tabularnewline
42 & 48428 & 49568.0742088798 & 7595.95727548396 & 39691.9685156362 & 1140.0742088798 \tabularnewline
43 & 36222 & 34171.0517490948 & -1515.15071431224 & 39788.0989652175 & -2050.94825090522 \tabularnewline
44 & 33425 & 31548.3304835358 & -4607.09558600039 & 39908.7651024646 & -1876.66951646422 \tabularnewline
45 & 39401 & 38352.0402375552 & 420.528522733055 & 40029.4312397118 & -1048.95976244481 \tabularnewline
46 & 37967 & 36363.0909679384 & -639.25265213085 & 40210.1616841925 & -1603.9090320616 \tabularnewline
47 & 34801 & 35471.9966895229 & -6260.88881819605 & 40390.8921286732 & 670.996689522894 \tabularnewline
48 & 12657 & 13757.4105668598 & -29113.6231195642 & 40670.2125527045 & 1100.41056685978 \tabularnewline
49 & 69116 & 69376.1144927477 & 27906.3525305165 & 40949.5329767358 & 260.114492747707 \tabularnewline
50 & 41519 & 41559.0160175359 & 222.579292619694 & 41256.4046898444 & 40.0160175358833 \tabularnewline
51 & 51321 & 54610.3396361082 & 6468.38396093873 & 41563.2764029531 & 3289.33963610818 \tabularnewline
52 & 38529 & 36336.7775570752 & -1037.70720539682 & 41758.9296483216 & -2192.22244292482 \tabularnewline
53 & 41547 & 40579.4990964745 & 559.918009835337 & 41954.5828936902 & -967.500903525521 \tabularnewline
54 & 52073 & 54543.7564539937 & 7595.95727548396 & 42006.2862705223 & 2470.75645399374 \tabularnewline
55 & 38401 & 36259.1610669578 & -1515.15071431224 & 42057.9896473544 & -2141.83893304219 \tabularnewline
56 & 40898 & 44252.0844991845 & -4607.09558600039 & 42151.0110868159 & 3354.08449918448 \tabularnewline
57 & 40439 & 38213.4389509895 & 420.528522733055 & 42244.0325262774 & -2225.56104901045 \tabularnewline
58 & 41888 & 42049.7834358143 & -639.25265213085 & 42365.4692163165 & 161.783435814323 \tabularnewline
59 & 37898 & 39569.9829118404 & -6260.88881819605 & 42486.9059063557 & 1671.98291184038 \tabularnewline
60 & 8771 & 4122.75969487651 & -29113.6231195642 & 42532.8634246877 & -4648.24030512349 \tabularnewline
61 & 68184 & 65882.8265264637 & 27906.3525305165 & 42578.8209430198 & -2301.17347353631 \tabularnewline
62 & 50530 & 58277.4089882475 & 222.579292619694 & 42560.0117191328 & 7747.40898824755 \tabularnewline
63 & 47221 & 45432.4135438155 & 6468.38396093873 & 42541.2024952457 & -1788.58645618447 \tabularnewline
64 & 41756 & 42164.9428866886 & -1037.70720539682 & 42384.7643187082 & 408.94288668863 \tabularnewline
65 & 45633 & 48477.755847994 & 559.918009835337 & 42228.3261421706 & 2844.75584799403 \tabularnewline
66 & 48138 & 47005.8926371282 & 7595.95727548396 & 41674.1500873879 & -1132.10736287183 \tabularnewline
67 & 39486 & 39367.1766817071 & -1515.15071431224 & 41119.9740326051 & -118.823318292882 \tabularnewline
68 & 39341 & 43152.2032311148 & -4607.09558600039 & 40136.8923548856 & 3811.20323111479 \tabularnewline
69 & 41117 & 42659.6608001009 & 420.528522733055 & 39153.8106771661 & 1542.66080010087 \tabularnewline
70 & 41629 & 45886.6431095785 & -639.25265213085 & 38010.6095425523 & 4257.64310957852 \tabularnewline
71 & 29722 & 28837.4804102575 & -6260.88881819605 & 36867.4084079386 & -884.519589742544 \tabularnewline
72 & 7054 & 7426.16551848926 & -29113.6231195642 & 35795.457601075 & 372.165518489259 \tabularnewline
73 & 56676 & 50722.1406752721 & 27906.3525305165 & 34723.5067942114 & -5953.85932472789 \tabularnewline
74 & 34870 & 35612.9749235609 & 222.579292619694 & 33904.4457838195 & 742.974923560854 \tabularnewline
75 & 35117 & 30680.2312656337 & 6468.38396093873 & 33085.3847734275 & -4436.76873436627 \tabularnewline
76 & 30169 & 28313.5741847836 & -1037.70720539682 & 33062.1330206132 & -1855.42581521641 \tabularnewline
77 & 30936 & 28273.2007223657 & 559.918009835337 & 33038.8812677989 & -2662.79927763425 \tabularnewline
78 & 35699 & 30704.5330062764 & 7595.95727548396 & 33097.5097182396 & -4994.46699372361 \tabularnewline
79 & 33228 & 34815.0125456319 & -1515.15071431224 & 33156.1381686804 & 1587.01254563186 \tabularnewline
80 & 27733 & 26780.6302243289 & -4607.09558600039 & 33292.4653616715 & -952.369775671064 \tabularnewline
81 & 33666 & 33482.6789226044 & 420.528522733055 & 33428.7925546625 & -183.321077395587 \tabularnewline
82 & 35429 & 37844.2943114199 & -639.25265213085 & 33652.958340711 & 2415.29431141986 \tabularnewline
83 & 27438 & 27259.7646914366 & -6260.88881819605 & 33877.1241267594 & -178.235308563388 \tabularnewline
84 & 8170 & 11289.5594324142 & -29113.6231195642 & 34164.06368715 & 3119.55943241419 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147321&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]68897[/C][C]69202.3821105313[/C][C]27906.3525305165[/C][C]40685.2653589522[/C][C]305.382110531253[/C][/ROW]
[ROW][C]2[/C][C]38683[/C][C]36420.9494800285[/C][C]222.579292619694[/C][C]40722.4712273518[/C][C]-2262.05051997153[/C][/ROW]
[ROW][C]3[/C][C]44720[/C][C]42211.9389433098[/C][C]6468.38396093873[/C][C]40759.6770957515[/C][C]-2508.06105669019[/C][/ROW]
[ROW][C]4[/C][C]39525[/C][C]39317.2386830724[/C][C]-1037.70720539682[/C][C]40770.4685223244[/C][C]-207.761316927586[/C][/ROW]
[ROW][C]5[/C][C]45315[/C][C]49288.8220412673[/C][C]559.918009835337[/C][C]40781.2599488973[/C][C]3973.82204126733[/C][/ROW]
[ROW][C]6[/C][C]50380[/C][C]52370.9200679168[/C][C]7595.95727548396[/C][C]40793.1226565992[/C][C]1990.92006791679[/C][/ROW]
[ROW][C]7[/C][C]40600[/C][C]41910.1653500111[/C][C]-1515.15071431224[/C][C]40804.9853643012[/C][C]1310.16535001108[/C][/ROW]
[ROW][C]8[/C][C]36279[/C][C]36323.0326211115[/C][C]-4607.09558600039[/C][C]40842.0629648888[/C][C]44.0326211115462[/C][/ROW]
[ROW][C]9[/C][C]42438[/C][C]43576.3309117904[/C][C]420.528522733055[/C][C]40879.1405654765[/C][C]1138.33091179041[/C][/ROW]
[ROW][C]10[/C][C]38064[/C][C]35944.2211361075[/C][C]-639.25265213085[/C][C]40823.0315160234[/C][C]-2119.77886389252[/C][/ROW]
[ROW][C]11[/C][C]31879[/C][C]29251.9663516258[/C][C]-6260.88881819605[/C][C]40766.9224665702[/C][C]-2627.03364837416[/C][/ROW]
[ROW][C]12[/C][C]11379[/C][C]11271.7322365359[/C][C]-29113.6231195642[/C][C]40599.8908830283[/C][C]-107.267763464071[/C][/ROW]
[ROW][C]13[/C][C]70249[/C][C]72158.7881699971[/C][C]27906.3525305165[/C][C]40432.8592994864[/C][C]1909.78816999706[/C][/ROW]
[ROW][C]14[/C][C]39253[/C][C]37957.3555146607[/C][C]222.579292619694[/C][C]40326.0651927196[/C][C]-1295.64448533926[/C][/ROW]
[ROW][C]15[/C][C]47060[/C][C]47432.3449531085[/C][C]6468.38396093873[/C][C]40219.2710859527[/C][C]372.344953108543[/C][/ROW]
[ROW][C]16[/C][C]41697[/C][C]44221.1721617854[/C][C]-1037.70720539682[/C][C]40210.5350436114[/C][C]2524.17216178537[/C][/ROW]
[ROW][C]17[/C][C]38708[/C][C]36654.2829888945[/C][C]559.918009835337[/C][C]40201.7990012702[/C][C]-2053.7170111055[/C][/ROW]
[ROW][C]18[/C][C]49267[/C][C]50726.3824931935[/C][C]7595.95727548396[/C][C]40211.6602313225[/C][C]1459.38249319352[/C][/ROW]
[ROW][C]19[/C][C]39018[/C][C]39329.6292529374[/C][C]-1515.15071431224[/C][C]40221.5214613749[/C][C]311.629252937375[/C][/ROW]
[ROW][C]20[/C][C]32228[/C][C]28948.6470767625[/C][C]-4607.09558600039[/C][C]40114.4485092379[/C][C]-3279.3529232375[/C][/ROW]
[ROW][C]21[/C][C]40870[/C][C]41312.095920166[/C][C]420.528522733055[/C][C]40007.3755571009[/C][C]442.095920166037[/C][/ROW]
[ROW][C]22[/C][C]39383[/C][C]39554.46485947[/C][C]-639.25265213085[/C][C]39850.7877926608[/C][C]171.464859470012[/C][/ROW]
[ROW][C]23[/C][C]34571[/C][C]35708.6887899753[/C][C]-6260.88881819605[/C][C]39694.2000282208[/C][C]1137.68878997528[/C][/ROW]
[ROW][C]24[/C][C]12066[/C][C]13704.6461362138[/C][C]-29113.6231195642[/C][C]39540.9769833504[/C][C]1638.64613621379[/C][/ROW]
[ROW][C]25[/C][C]70938[/C][C]74581.8935310034[/C][C]27906.3525305165[/C][C]39387.7539384801[/C][C]3643.89353100336[/C][/ROW]
[ROW][C]26[/C][C]34077[/C][C]28709.1015057187[/C][C]222.579292619694[/C][C]39222.3192016617[/C][C]-5367.89849428135[/C][/ROW]
[ROW][C]27[/C][C]45409[/C][C]45292.7315742181[/C][C]6468.38396093873[/C][C]39056.8844648432[/C][C]-116.26842578192[/C][/ROW]
[ROW][C]28[/C][C]40809[/C][C]43819.2626948164[/C][C]-1037.70720539682[/C][C]38836.4445105804[/C][C]3010.26269481644[/C][/ROW]
[ROW][C]29[/C][C]37013[/C][C]34850.0774338471[/C][C]559.918009835337[/C][C]38616.0045563176[/C][C]-2162.9225661529[/C][/ROW]
[ROW][C]30[/C][C]44953[/C][C]43825.8689360229[/C][C]7595.95727548396[/C][C]38484.1737884931[/C][C]-1127.13106397705[/C][/ROW]
[ROW][C]31[/C][C]37848[/C][C]38858.8076936436[/C][C]-1515.15071431224[/C][C]38352.3430206686[/C][C]1010.80769364362[/C][/ROW]
[ROW][C]32[/C][C]32745[/C][C]31647.8818824365[/C][C]-4607.09558600039[/C][C]38449.2137035639[/C][C]-1097.11811756351[/C][/ROW]
[ROW][C]33[/C][C]39401[/C][C]39835.3870908078[/C][C]420.528522733055[/C][C]38546.0843864592[/C][C]434.387090807773[/C][/ROW]
[ROW][C]34[/C][C]34931[/C][C]31776.7752316462[/C][C]-639.25265213085[/C][C]38724.4774204846[/C][C]-3154.22476835378[/C][/ROW]
[ROW][C]35[/C][C]33008[/C][C]33374.018363686[/C][C]-6260.88881819605[/C][C]38902.8704545101[/C][C]366.018363685966[/C][/ROW]
[ROW][C]36[/C][C]8620[/C][C]7293.47980228087[/C][C]-29113.6231195642[/C][C]39060.1433172834[/C][C]-1326.52019771913[/C][/ROW]
[ROW][C]37[/C][C]68906[/C][C]70688.2312894268[/C][C]27906.3525305165[/C][C]39217.4161800566[/C][C]1782.23128942682[/C][/ROW]
[ROW][C]38[/C][C]39556[/C][C]39577.7503872274[/C][C]222.579292619694[/C][C]39311.6703201529[/C][C]21.7503872274174[/C][/ROW]
[ROW][C]39[/C][C]50669[/C][C]55463.6915788121[/C][C]6468.38396093873[/C][C]39405.9244602491[/C][C]4794.69157881214[/C][/ROW]
[ROW][C]40[/C][C]36432[/C][C]34400.8259422447[/C][C]-1037.70720539682[/C][C]39500.8812631521[/C][C]-2031.17405775526[/C][/ROW]
[ROW][C]41[/C][C]40891[/C][C]41626.2439241096[/C][C]559.918009835337[/C][C]39595.838066055[/C][C]735.243924109644[/C][/ROW]
[ROW][C]42[/C][C]48428[/C][C]49568.0742088798[/C][C]7595.95727548396[/C][C]39691.9685156362[/C][C]1140.0742088798[/C][/ROW]
[ROW][C]43[/C][C]36222[/C][C]34171.0517490948[/C][C]-1515.15071431224[/C][C]39788.0989652175[/C][C]-2050.94825090522[/C][/ROW]
[ROW][C]44[/C][C]33425[/C][C]31548.3304835358[/C][C]-4607.09558600039[/C][C]39908.7651024646[/C][C]-1876.66951646422[/C][/ROW]
[ROW][C]45[/C][C]39401[/C][C]38352.0402375552[/C][C]420.528522733055[/C][C]40029.4312397118[/C][C]-1048.95976244481[/C][/ROW]
[ROW][C]46[/C][C]37967[/C][C]36363.0909679384[/C][C]-639.25265213085[/C][C]40210.1616841925[/C][C]-1603.9090320616[/C][/ROW]
[ROW][C]47[/C][C]34801[/C][C]35471.9966895229[/C][C]-6260.88881819605[/C][C]40390.8921286732[/C][C]670.996689522894[/C][/ROW]
[ROW][C]48[/C][C]12657[/C][C]13757.4105668598[/C][C]-29113.6231195642[/C][C]40670.2125527045[/C][C]1100.41056685978[/C][/ROW]
[ROW][C]49[/C][C]69116[/C][C]69376.1144927477[/C][C]27906.3525305165[/C][C]40949.5329767358[/C][C]260.114492747707[/C][/ROW]
[ROW][C]50[/C][C]41519[/C][C]41559.0160175359[/C][C]222.579292619694[/C][C]41256.4046898444[/C][C]40.0160175358833[/C][/ROW]
[ROW][C]51[/C][C]51321[/C][C]54610.3396361082[/C][C]6468.38396093873[/C][C]41563.2764029531[/C][C]3289.33963610818[/C][/ROW]
[ROW][C]52[/C][C]38529[/C][C]36336.7775570752[/C][C]-1037.70720539682[/C][C]41758.9296483216[/C][C]-2192.22244292482[/C][/ROW]
[ROW][C]53[/C][C]41547[/C][C]40579.4990964745[/C][C]559.918009835337[/C][C]41954.5828936902[/C][C]-967.500903525521[/C][/ROW]
[ROW][C]54[/C][C]52073[/C][C]54543.7564539937[/C][C]7595.95727548396[/C][C]42006.2862705223[/C][C]2470.75645399374[/C][/ROW]
[ROW][C]55[/C][C]38401[/C][C]36259.1610669578[/C][C]-1515.15071431224[/C][C]42057.9896473544[/C][C]-2141.83893304219[/C][/ROW]
[ROW][C]56[/C][C]40898[/C][C]44252.0844991845[/C][C]-4607.09558600039[/C][C]42151.0110868159[/C][C]3354.08449918448[/C][/ROW]
[ROW][C]57[/C][C]40439[/C][C]38213.4389509895[/C][C]420.528522733055[/C][C]42244.0325262774[/C][C]-2225.56104901045[/C][/ROW]
[ROW][C]58[/C][C]41888[/C][C]42049.7834358143[/C][C]-639.25265213085[/C][C]42365.4692163165[/C][C]161.783435814323[/C][/ROW]
[ROW][C]59[/C][C]37898[/C][C]39569.9829118404[/C][C]-6260.88881819605[/C][C]42486.9059063557[/C][C]1671.98291184038[/C][/ROW]
[ROW][C]60[/C][C]8771[/C][C]4122.75969487651[/C][C]-29113.6231195642[/C][C]42532.8634246877[/C][C]-4648.24030512349[/C][/ROW]
[ROW][C]61[/C][C]68184[/C][C]65882.8265264637[/C][C]27906.3525305165[/C][C]42578.8209430198[/C][C]-2301.17347353631[/C][/ROW]
[ROW][C]62[/C][C]50530[/C][C]58277.4089882475[/C][C]222.579292619694[/C][C]42560.0117191328[/C][C]7747.40898824755[/C][/ROW]
[ROW][C]63[/C][C]47221[/C][C]45432.4135438155[/C][C]6468.38396093873[/C][C]42541.2024952457[/C][C]-1788.58645618447[/C][/ROW]
[ROW][C]64[/C][C]41756[/C][C]42164.9428866886[/C][C]-1037.70720539682[/C][C]42384.7643187082[/C][C]408.94288668863[/C][/ROW]
[ROW][C]65[/C][C]45633[/C][C]48477.755847994[/C][C]559.918009835337[/C][C]42228.3261421706[/C][C]2844.75584799403[/C][/ROW]
[ROW][C]66[/C][C]48138[/C][C]47005.8926371282[/C][C]7595.95727548396[/C][C]41674.1500873879[/C][C]-1132.10736287183[/C][/ROW]
[ROW][C]67[/C][C]39486[/C][C]39367.1766817071[/C][C]-1515.15071431224[/C][C]41119.9740326051[/C][C]-118.823318292882[/C][/ROW]
[ROW][C]68[/C][C]39341[/C][C]43152.2032311148[/C][C]-4607.09558600039[/C][C]40136.8923548856[/C][C]3811.20323111479[/C][/ROW]
[ROW][C]69[/C][C]41117[/C][C]42659.6608001009[/C][C]420.528522733055[/C][C]39153.8106771661[/C][C]1542.66080010087[/C][/ROW]
[ROW][C]70[/C][C]41629[/C][C]45886.6431095785[/C][C]-639.25265213085[/C][C]38010.6095425523[/C][C]4257.64310957852[/C][/ROW]
[ROW][C]71[/C][C]29722[/C][C]28837.4804102575[/C][C]-6260.88881819605[/C][C]36867.4084079386[/C][C]-884.519589742544[/C][/ROW]
[ROW][C]72[/C][C]7054[/C][C]7426.16551848926[/C][C]-29113.6231195642[/C][C]35795.457601075[/C][C]372.165518489259[/C][/ROW]
[ROW][C]73[/C][C]56676[/C][C]50722.1406752721[/C][C]27906.3525305165[/C][C]34723.5067942114[/C][C]-5953.85932472789[/C][/ROW]
[ROW][C]74[/C][C]34870[/C][C]35612.9749235609[/C][C]222.579292619694[/C][C]33904.4457838195[/C][C]742.974923560854[/C][/ROW]
[ROW][C]75[/C][C]35117[/C][C]30680.2312656337[/C][C]6468.38396093873[/C][C]33085.3847734275[/C][C]-4436.76873436627[/C][/ROW]
[ROW][C]76[/C][C]30169[/C][C]28313.5741847836[/C][C]-1037.70720539682[/C][C]33062.1330206132[/C][C]-1855.42581521641[/C][/ROW]
[ROW][C]77[/C][C]30936[/C][C]28273.2007223657[/C][C]559.918009835337[/C][C]33038.8812677989[/C][C]-2662.79927763425[/C][/ROW]
[ROW][C]78[/C][C]35699[/C][C]30704.5330062764[/C][C]7595.95727548396[/C][C]33097.5097182396[/C][C]-4994.46699372361[/C][/ROW]
[ROW][C]79[/C][C]33228[/C][C]34815.0125456319[/C][C]-1515.15071431224[/C][C]33156.1381686804[/C][C]1587.01254563186[/C][/ROW]
[ROW][C]80[/C][C]27733[/C][C]26780.6302243289[/C][C]-4607.09558600039[/C][C]33292.4653616715[/C][C]-952.369775671064[/C][/ROW]
[ROW][C]81[/C][C]33666[/C][C]33482.6789226044[/C][C]420.528522733055[/C][C]33428.7925546625[/C][C]-183.321077395587[/C][/ROW]
[ROW][C]82[/C][C]35429[/C][C]37844.2943114199[/C][C]-639.25265213085[/C][C]33652.958340711[/C][C]2415.29431141986[/C][/ROW]
[ROW][C]83[/C][C]27438[/C][C]27259.7646914366[/C][C]-6260.88881819605[/C][C]33877.1241267594[/C][C]-178.235308563388[/C][/ROW]
[ROW][C]84[/C][C]8170[/C][C]11289.5594324142[/C][C]-29113.6231195642[/C][C]34164.06368715[/C][C]3119.55943241419[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147321&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147321&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
16889769202.382110531327906.352530516540685.2653589522305.382110531253
23868336420.9494800285222.57929261969440722.4712273518-2262.05051997153
34472042211.93894330986468.3839609387340759.6770957515-2508.06105669019
43952539317.2386830724-1037.7072053968240770.4685223244-207.761316927586
54531549288.8220412673559.91800983533740781.25994889733973.82204126733
65038052370.92006791687595.9572754839640793.12265659921990.92006791679
74060041910.1653500111-1515.1507143122440804.98536430121310.16535001108
83627936323.0326211115-4607.0955860003940842.062964888844.0326211115462
94243843576.3309117904420.52852273305540879.14056547651138.33091179041
103806435944.2211361075-639.2526521308540823.0315160234-2119.77886389252
113187929251.9663516258-6260.8888181960540766.9224665702-2627.03364837416
121137911271.7322365359-29113.623119564240599.8908830283-107.267763464071
137024972158.788169997127906.352530516540432.85929948641909.78816999706
143925337957.3555146607222.57929261969440326.0651927196-1295.64448533926
154706047432.34495310856468.3839609387340219.2710859527372.344953108543
164169744221.1721617854-1037.7072053968240210.53504361142524.17216178537
173870836654.2829888945559.91800983533740201.7990012702-2053.7170111055
184926750726.38249319357595.9572754839640211.66023132251459.38249319352
193901839329.6292529374-1515.1507143122440221.5214613749311.629252937375
203222828948.6470767625-4607.0955860003940114.4485092379-3279.3529232375
214087041312.095920166420.52852273305540007.3755571009442.095920166037
223938339554.46485947-639.2526521308539850.7877926608171.464859470012
233457135708.6887899753-6260.8888181960539694.20002822081137.68878997528
241206613704.6461362138-29113.623119564239540.97698335041638.64613621379
257093874581.893531003427906.352530516539387.75393848013643.89353100336
263407728709.1015057187222.57929261969439222.3192016617-5367.89849428135
274540945292.73157421816468.3839609387339056.8844648432-116.26842578192
284080943819.2626948164-1037.7072053968238836.44451058043010.26269481644
293701334850.0774338471559.91800983533738616.0045563176-2162.9225661529
304495343825.86893602297595.9572754839638484.1737884931-1127.13106397705
313784838858.8076936436-1515.1507143122438352.34302066861010.80769364362
323274531647.8818824365-4607.0955860003938449.2137035639-1097.11811756351
333940139835.3870908078420.52852273305538546.0843864592434.387090807773
343493131776.7752316462-639.2526521308538724.4774204846-3154.22476835378
353300833374.018363686-6260.8888181960538902.8704545101366.018363685966
3686207293.47980228087-29113.623119564239060.1433172834-1326.52019771913
376890670688.231289426827906.352530516539217.41618005661782.23128942682
383955639577.7503872274222.57929261969439311.670320152921.7503872274174
395066955463.69157881216468.3839609387339405.92446024914794.69157881214
403643234400.8259422447-1037.7072053968239500.8812631521-2031.17405775526
414089141626.2439241096559.91800983533739595.838066055735.243924109644
424842849568.07420887987595.9572754839639691.96851563621140.0742088798
433622234171.0517490948-1515.1507143122439788.0989652175-2050.94825090522
443342531548.3304835358-4607.0955860003939908.7651024646-1876.66951646422
453940138352.0402375552420.52852273305540029.4312397118-1048.95976244481
463796736363.0909679384-639.2526521308540210.1616841925-1603.9090320616
473480135471.9966895229-6260.8888181960540390.8921286732670.996689522894
481265713757.4105668598-29113.623119564240670.21255270451100.41056685978
496911669376.114492747727906.352530516540949.5329767358260.114492747707
504151941559.0160175359222.57929261969441256.404689844440.0160175358833
515132154610.33963610826468.3839609387341563.27640295313289.33963610818
523852936336.7775570752-1037.7072053968241758.9296483216-2192.22244292482
534154740579.4990964745559.91800983533741954.5828936902-967.500903525521
545207354543.75645399377595.9572754839642006.28627052232470.75645399374
553840136259.1610669578-1515.1507143122442057.9896473544-2141.83893304219
564089844252.0844991845-4607.0955860003942151.01108681593354.08449918448
574043938213.4389509895420.52852273305542244.0325262774-2225.56104901045
584188842049.7834358143-639.2526521308542365.4692163165161.783435814323
593789839569.9829118404-6260.8888181960542486.90590635571671.98291184038
6087714122.75969487651-29113.623119564242532.8634246877-4648.24030512349
616818465882.826526463727906.352530516542578.8209430198-2301.17347353631
625053058277.4089882475222.57929261969442560.01171913287747.40898824755
634722145432.41354381556468.3839609387342541.2024952457-1788.58645618447
644175642164.9428866886-1037.7072053968242384.7643187082408.94288668863
654563348477.755847994559.91800983533742228.32614217062844.75584799403
664813847005.89263712827595.9572754839641674.1500873879-1132.10736287183
673948639367.1766817071-1515.1507143122441119.9740326051-118.823318292882
683934143152.2032311148-4607.0955860003940136.89235488563811.20323111479
694111742659.6608001009420.52852273305539153.81067716611542.66080010087
704162945886.6431095785-639.2526521308538010.60954255234257.64310957852
712972228837.4804102575-6260.8888181960536867.4084079386-884.519589742544
7270547426.16551848926-29113.623119564235795.457601075372.165518489259
735667650722.140675272127906.352530516534723.5067942114-5953.85932472789
743487035612.9749235609222.57929261969433904.4457838195742.974923560854
753511730680.23126563376468.3839609387333085.3847734275-4436.76873436627
763016928313.5741847836-1037.7072053968233062.1330206132-1855.42581521641
773093628273.2007223657559.91800983533733038.8812677989-2662.79927763425
783569930704.53300627647595.9572754839633097.5097182396-4994.46699372361
793322834815.0125456319-1515.1507143122433156.13816868041587.01254563186
802773326780.6302243289-4607.0955860003933292.4653616715-952.369775671064
813366633482.6789226044420.52852273305533428.7925546625-183.321077395587
823542937844.2943114199-639.2526521308533652.9583407112415.29431141986
832743827259.7646914366-6260.8888181960533877.1241267594-178.235308563388
84817011289.5594324142-29113.623119564234164.063687153119.55943241419



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')