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Description of Statistical Computation

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 computationMon, 12 Dec 2011 07:02:29 -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/Dec/12/t1323691386k85m292jbyjc7o6.htm/, Retrieved Fri, 03 May 2024 09:27:58 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153929, Retrieved Fri, 03 May 2024 09:27:58 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [paper] [2011-11-24 11:53:20] [74be16979710d4c4e7c6647856088456]
- RMPD        [Decomposition by Loess] [paper] [2011-12-12 12:02:29] [7a9891c1925ad1e8ddfe52b8c5887b5b] [Current]
- RMP           [Exponential Smoothing] [Paper] [2011-12-12 12:08:53] [aa6b3f8e5b050429abaad141c7204e84]
- RMP           [Spectral Analysis] [Paper] [2011-12-12 13:01:06] [aa6b3f8e5b050429abaad141c7204e84]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
374.92
375.63
376.51
377.75
378.54
378.21
376.65
374.28
373.12
373.1
374.67
375.97
377.03
377.87
378.88
380.42
380.62
379.66
377.48
376.07
374.1
374.47
376.15
377.51
378.43
379.7
380.91
382.2
382.45
382.14
380.6
378.6
376.72
376.98
378.29
380.07
381.36
382.19
382.65
384.65
384.94
384.01
382.15
380.33
378.81
379.06
380.17
381.85
382.88
383.77
384.42
386.36
386.53
386.01
384.45
381.96
380.81
381.09
382.37
383.84
385.42
385.72
385.96
387.18
388.5
387.88
386.38
384.15
383.07
382.98
384.11
385.54
386.92
387.41
388.77
389.46
390.18
389.43
387.74
385.91
384.77
384.38
385.99
387.26

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153929&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153929&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153929&T=0

As an alternative you can also use a QR Code:  
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 time1 seconds
R Server'Gwilym Jenkins' @ jenkins.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=153929&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=153929&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153929&T=1

As an alternative you can also use a QR Code:  
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
1374.92374.952700428140.147601391400337374.7396981804590.0327004281404584
2375.63375.5912222177150.739240754534131374.92953702775-0.0387777822845123
3376.51376.5011729650091.39945115994953375.119375875042-0.00882703499109994
4377.75377.516556252532.67567766771024375.307766079759-0.233443747469551
5378.54378.5147973540753.06904636144787375.496156284477-0.0252026459248214
6378.21378.4198816267452.31631123531322375.6838071379420.209881626745187
7376.65376.932108740250.496433268343457375.8714579914060.282108740250237
8374.28374.151445021607-1.65214619273637376.060701171129-0.128554978392856
9373.12373.183638645591-3.19358299644316376.2499443508520.0636386455910269
10373.1373.020280220681-3.25407166172173376.433791441041-0.0797197793187365
11374.67374.746922221811-2.02456075304026376.6176385312290.0769222218114578
12375.97375.895921910206-0.719400199428804376.763478289223-0.0740780897937725
13377.03377.0030805613830.147601391400337376.909318047216-0.0269194386166305
14377.87377.9779004809350.739240754534131377.0228587645310.107900480934688
15378.88379.2241493582041.39945115994953377.1363994818460.34414935820439
16380.42380.9223773627712.67567766771024377.2419449695180.502377362771256
17380.62380.8234631813613.06904636144787377.3474904571910.203463181361258
18379.66379.5461056579392.31631123531322377.457583106748-0.113894342061201
19377.48376.8958909753510.496433268343457377.567675756305-0.584109024648626
20376.07376.091339362846-1.65214619273637377.700806829890.0213393628460494
21374.1373.559645092968-3.19358299644316377.833937903476-0.540354907032338
22374.47374.190885667999-3.25407166172173378.003185993723-0.279114332001143
23376.15376.15212666907-2.02456075304026378.172434083970.00212666906986669
24377.51377.352733492965-0.719400199428804378.386666706464-0.157266507035445
25378.43378.1114992796420.147601391400337378.600899328958-0.318500720358429
26379.7379.8309448745640.739240754534131378.8298143709020.130944874563966
27380.91381.3618194272051.39945115994953379.0587294128460.451819427204896
28382.2382.4529739201782.67567766771024379.2713484121120.252973920177624
29382.45382.3469862271733.06904636144787379.483967411379-0.1030137728265
30382.14382.2808847260672.31631123531322379.6828040386190.140884726067327
31380.6380.8219260657960.496433268343457379.881640665860.22192606579631
32378.6378.778686156492-1.65214619273637380.0734600362440.178686156492176
33376.72376.368303589815-3.19358299644316380.265279406628-0.351696410185014
34376.98376.762557633656-3.25407166172173380.451514028065-0.217442366343562
35378.29377.966812103538-2.02456075304026380.637748649502-0.323187896462173
36380.07380.045668561101-0.719400199428804380.813731638328-0.0243314388987983
37381.36381.5826839814470.147601391400337380.9897146271530.222683981447005
38382.19382.4869428153210.739240754534131381.1538164301440.296942815321472
39382.65382.5826306069141.39945115994953381.317918233136-0.0673693930855848
40384.65385.1508758989372.67567766771024381.4734464333520.500875898937238
41384.94385.1819790049833.06904636144787381.6289746335690.241979004983136
42384.01383.9375137459132.31631123531322381.766175018774-0.0724862540867548
43382.15381.9001913276780.496433268343457381.903375403978-0.249808672321649
44380.33380.278990386706-1.65214619273637382.03315580603-0.0510096132936155
45378.81378.650646788361-3.19358299644316382.162936208082-0.159353211638631
46379.06379.065529914141-3.25407166172173382.3085417475810.00552991414122062
47380.17379.910413465961-2.02456075304026382.454147287079-0.259586534038931
48381.85381.800857156207-0.719400199428804382.618543043222-0.0491428437927084
49382.88382.8294598092360.147601391400337382.782938799364-0.0505401907641385
50383.77383.8479665173590.739240754534131382.9527927281070.0779665173587887
51384.42384.31790218321.39945115994953383.12264665685-0.102097816799755
52386.36386.7527211638182.67567766771024383.2916011684720.392721163818237
53386.53386.5303979584593.06904636144787383.4605556800930.000397958459302572
54386.01386.069688816972.31631123531322383.6339999477170.0596888169698104
55384.45384.5961225163150.496433268343457383.8074442153410.146122516315359
56381.96381.608556427976-1.65214619273637383.96358976476-0.351443572023811
57380.81380.693847682264-3.19358299644316384.119735314179-0.116152317736066
58381.09381.178282251064-3.25407166172173384.2557894106580.0882822510636743
59382.37382.372717245903-2.02456075304026384.3918435071370.00271724590345457
60383.84383.860062553604-0.719400199428804384.5393376458240.0200625536043617
61385.42386.0055668240880.147601391400337384.6868317845120.585566824087664
62385.72385.8475022774870.739240754534131384.8532569679790.127502277486485
63385.96385.5008666886041.39945115994953385.019682151447-0.459133311396329
64387.18386.5045749312272.67567766771024385.179747401063-0.675425068773279
65388.5388.5911409878733.06904636144787385.3398126506790.0911409878728477
66387.88387.9512988069382.31631123531322385.4923899577490.0712988069381595
67386.38386.6185994668390.496433268343457385.6449672648180.238599466838537
68384.15384.139474459111-1.65214619273637385.812671733626-0.010525540889148
69383.07383.35320679401-3.19358299644316385.9803762024330.283206794010084
70382.98383.064140051741-3.25407166172173386.149931609980.0841400517414286
71384.11383.925073735513-2.02456075304026386.319487017528-0.184926264487331
72385.54385.340795958388-0.719400199428804386.458604241041-0.199204041612461
73386.92387.0946771440450.147601391400337386.5977214645550.174677144044779
74387.41387.3510283140750.739240754534131386.729730931391-0.058971685925485
75388.77389.2788084418231.39945115994953386.8617403982280.508808441822623
76389.46389.2471784241752.67567766771024386.997143908115-0.21282157582516
77390.18390.158406220553.06904636144787387.132547418002-0.0215937794498018
78389.43389.2789162775962.31631123531322387.264772487091-0.151083722403712
79387.74387.5865691754770.496433268343457387.396997556179-0.153430824522559
80385.91385.94387300039-1.65214619273637387.5282731923470.0338730003897467
81384.77385.074034167929-3.19358299644316387.6595488285140.304034167928933
82384.38384.223002190121-3.25407166172173387.7910694716-0.15699780987859
83385.99386.081970638354-2.02456075304026387.9225901146860.0919706383538141
84387.26387.185170583227-0.719400199428804388.054229616201-0.0748294167726158

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 374.92 & 374.95270042814 & 0.147601391400337 & 374.739698180459 & 0.0327004281404584 \tabularnewline
2 & 375.63 & 375.591222217715 & 0.739240754534131 & 374.92953702775 & -0.0387777822845123 \tabularnewline
3 & 376.51 & 376.501172965009 & 1.39945115994953 & 375.119375875042 & -0.00882703499109994 \tabularnewline
4 & 377.75 & 377.51655625253 & 2.67567766771024 & 375.307766079759 & -0.233443747469551 \tabularnewline
5 & 378.54 & 378.514797354075 & 3.06904636144787 & 375.496156284477 & -0.0252026459248214 \tabularnewline
6 & 378.21 & 378.419881626745 & 2.31631123531322 & 375.683807137942 & 0.209881626745187 \tabularnewline
7 & 376.65 & 376.93210874025 & 0.496433268343457 & 375.871457991406 & 0.282108740250237 \tabularnewline
8 & 374.28 & 374.151445021607 & -1.65214619273637 & 376.060701171129 & -0.128554978392856 \tabularnewline
9 & 373.12 & 373.183638645591 & -3.19358299644316 & 376.249944350852 & 0.0636386455910269 \tabularnewline
10 & 373.1 & 373.020280220681 & -3.25407166172173 & 376.433791441041 & -0.0797197793187365 \tabularnewline
11 & 374.67 & 374.746922221811 & -2.02456075304026 & 376.617638531229 & 0.0769222218114578 \tabularnewline
12 & 375.97 & 375.895921910206 & -0.719400199428804 & 376.763478289223 & -0.0740780897937725 \tabularnewline
13 & 377.03 & 377.003080561383 & 0.147601391400337 & 376.909318047216 & -0.0269194386166305 \tabularnewline
14 & 377.87 & 377.977900480935 & 0.739240754534131 & 377.022858764531 & 0.107900480934688 \tabularnewline
15 & 378.88 & 379.224149358204 & 1.39945115994953 & 377.136399481846 & 0.34414935820439 \tabularnewline
16 & 380.42 & 380.922377362771 & 2.67567766771024 & 377.241944969518 & 0.502377362771256 \tabularnewline
17 & 380.62 & 380.823463181361 & 3.06904636144787 & 377.347490457191 & 0.203463181361258 \tabularnewline
18 & 379.66 & 379.546105657939 & 2.31631123531322 & 377.457583106748 & -0.113894342061201 \tabularnewline
19 & 377.48 & 376.895890975351 & 0.496433268343457 & 377.567675756305 & -0.584109024648626 \tabularnewline
20 & 376.07 & 376.091339362846 & -1.65214619273637 & 377.70080682989 & 0.0213393628460494 \tabularnewline
21 & 374.1 & 373.559645092968 & -3.19358299644316 & 377.833937903476 & -0.540354907032338 \tabularnewline
22 & 374.47 & 374.190885667999 & -3.25407166172173 & 378.003185993723 & -0.279114332001143 \tabularnewline
23 & 376.15 & 376.15212666907 & -2.02456075304026 & 378.17243408397 & 0.00212666906986669 \tabularnewline
24 & 377.51 & 377.352733492965 & -0.719400199428804 & 378.386666706464 & -0.157266507035445 \tabularnewline
25 & 378.43 & 378.111499279642 & 0.147601391400337 & 378.600899328958 & -0.318500720358429 \tabularnewline
26 & 379.7 & 379.830944874564 & 0.739240754534131 & 378.829814370902 & 0.130944874563966 \tabularnewline
27 & 380.91 & 381.361819427205 & 1.39945115994953 & 379.058729412846 & 0.451819427204896 \tabularnewline
28 & 382.2 & 382.452973920178 & 2.67567766771024 & 379.271348412112 & 0.252973920177624 \tabularnewline
29 & 382.45 & 382.346986227173 & 3.06904636144787 & 379.483967411379 & -0.1030137728265 \tabularnewline
30 & 382.14 & 382.280884726067 & 2.31631123531322 & 379.682804038619 & 0.140884726067327 \tabularnewline
31 & 380.6 & 380.821926065796 & 0.496433268343457 & 379.88164066586 & 0.22192606579631 \tabularnewline
32 & 378.6 & 378.778686156492 & -1.65214619273637 & 380.073460036244 & 0.178686156492176 \tabularnewline
33 & 376.72 & 376.368303589815 & -3.19358299644316 & 380.265279406628 & -0.351696410185014 \tabularnewline
34 & 376.98 & 376.762557633656 & -3.25407166172173 & 380.451514028065 & -0.217442366343562 \tabularnewline
35 & 378.29 & 377.966812103538 & -2.02456075304026 & 380.637748649502 & -0.323187896462173 \tabularnewline
36 & 380.07 & 380.045668561101 & -0.719400199428804 & 380.813731638328 & -0.0243314388987983 \tabularnewline
37 & 381.36 & 381.582683981447 & 0.147601391400337 & 380.989714627153 & 0.222683981447005 \tabularnewline
38 & 382.19 & 382.486942815321 & 0.739240754534131 & 381.153816430144 & 0.296942815321472 \tabularnewline
39 & 382.65 & 382.582630606914 & 1.39945115994953 & 381.317918233136 & -0.0673693930855848 \tabularnewline
40 & 384.65 & 385.150875898937 & 2.67567766771024 & 381.473446433352 & 0.500875898937238 \tabularnewline
41 & 384.94 & 385.181979004983 & 3.06904636144787 & 381.628974633569 & 0.241979004983136 \tabularnewline
42 & 384.01 & 383.937513745913 & 2.31631123531322 & 381.766175018774 & -0.0724862540867548 \tabularnewline
43 & 382.15 & 381.900191327678 & 0.496433268343457 & 381.903375403978 & -0.249808672321649 \tabularnewline
44 & 380.33 & 380.278990386706 & -1.65214619273637 & 382.03315580603 & -0.0510096132936155 \tabularnewline
45 & 378.81 & 378.650646788361 & -3.19358299644316 & 382.162936208082 & -0.159353211638631 \tabularnewline
46 & 379.06 & 379.065529914141 & -3.25407166172173 & 382.308541747581 & 0.00552991414122062 \tabularnewline
47 & 380.17 & 379.910413465961 & -2.02456075304026 & 382.454147287079 & -0.259586534038931 \tabularnewline
48 & 381.85 & 381.800857156207 & -0.719400199428804 & 382.618543043222 & -0.0491428437927084 \tabularnewline
49 & 382.88 & 382.829459809236 & 0.147601391400337 & 382.782938799364 & -0.0505401907641385 \tabularnewline
50 & 383.77 & 383.847966517359 & 0.739240754534131 & 382.952792728107 & 0.0779665173587887 \tabularnewline
51 & 384.42 & 384.3179021832 & 1.39945115994953 & 383.12264665685 & -0.102097816799755 \tabularnewline
52 & 386.36 & 386.752721163818 & 2.67567766771024 & 383.291601168472 & 0.392721163818237 \tabularnewline
53 & 386.53 & 386.530397958459 & 3.06904636144787 & 383.460555680093 & 0.000397958459302572 \tabularnewline
54 & 386.01 & 386.06968881697 & 2.31631123531322 & 383.633999947717 & 0.0596888169698104 \tabularnewline
55 & 384.45 & 384.596122516315 & 0.496433268343457 & 383.807444215341 & 0.146122516315359 \tabularnewline
56 & 381.96 & 381.608556427976 & -1.65214619273637 & 383.96358976476 & -0.351443572023811 \tabularnewline
57 & 380.81 & 380.693847682264 & -3.19358299644316 & 384.119735314179 & -0.116152317736066 \tabularnewline
58 & 381.09 & 381.178282251064 & -3.25407166172173 & 384.255789410658 & 0.0882822510636743 \tabularnewline
59 & 382.37 & 382.372717245903 & -2.02456075304026 & 384.391843507137 & 0.00271724590345457 \tabularnewline
60 & 383.84 & 383.860062553604 & -0.719400199428804 & 384.539337645824 & 0.0200625536043617 \tabularnewline
61 & 385.42 & 386.005566824088 & 0.147601391400337 & 384.686831784512 & 0.585566824087664 \tabularnewline
62 & 385.72 & 385.847502277487 & 0.739240754534131 & 384.853256967979 & 0.127502277486485 \tabularnewline
63 & 385.96 & 385.500866688604 & 1.39945115994953 & 385.019682151447 & -0.459133311396329 \tabularnewline
64 & 387.18 & 386.504574931227 & 2.67567766771024 & 385.179747401063 & -0.675425068773279 \tabularnewline
65 & 388.5 & 388.591140987873 & 3.06904636144787 & 385.339812650679 & 0.0911409878728477 \tabularnewline
66 & 387.88 & 387.951298806938 & 2.31631123531322 & 385.492389957749 & 0.0712988069381595 \tabularnewline
67 & 386.38 & 386.618599466839 & 0.496433268343457 & 385.644967264818 & 0.238599466838537 \tabularnewline
68 & 384.15 & 384.139474459111 & -1.65214619273637 & 385.812671733626 & -0.010525540889148 \tabularnewline
69 & 383.07 & 383.35320679401 & -3.19358299644316 & 385.980376202433 & 0.283206794010084 \tabularnewline
70 & 382.98 & 383.064140051741 & -3.25407166172173 & 386.14993160998 & 0.0841400517414286 \tabularnewline
71 & 384.11 & 383.925073735513 & -2.02456075304026 & 386.319487017528 & -0.184926264487331 \tabularnewline
72 & 385.54 & 385.340795958388 & -0.719400199428804 & 386.458604241041 & -0.199204041612461 \tabularnewline
73 & 386.92 & 387.094677144045 & 0.147601391400337 & 386.597721464555 & 0.174677144044779 \tabularnewline
74 & 387.41 & 387.351028314075 & 0.739240754534131 & 386.729730931391 & -0.058971685925485 \tabularnewline
75 & 388.77 & 389.278808441823 & 1.39945115994953 & 386.861740398228 & 0.508808441822623 \tabularnewline
76 & 389.46 & 389.247178424175 & 2.67567766771024 & 386.997143908115 & -0.21282157582516 \tabularnewline
77 & 390.18 & 390.15840622055 & 3.06904636144787 & 387.132547418002 & -0.0215937794498018 \tabularnewline
78 & 389.43 & 389.278916277596 & 2.31631123531322 & 387.264772487091 & -0.151083722403712 \tabularnewline
79 & 387.74 & 387.586569175477 & 0.496433268343457 & 387.396997556179 & -0.153430824522559 \tabularnewline
80 & 385.91 & 385.94387300039 & -1.65214619273637 & 387.528273192347 & 0.0338730003897467 \tabularnewline
81 & 384.77 & 385.074034167929 & -3.19358299644316 & 387.659548828514 & 0.304034167928933 \tabularnewline
82 & 384.38 & 384.223002190121 & -3.25407166172173 & 387.7910694716 & -0.15699780987859 \tabularnewline
83 & 385.99 & 386.081970638354 & -2.02456075304026 & 387.922590114686 & 0.0919706383538141 \tabularnewline
84 & 387.26 & 387.185170583227 & -0.719400199428804 & 388.054229616201 & -0.0748294167726158 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153929&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]374.92[/C][C]374.95270042814[/C][C]0.147601391400337[/C][C]374.739698180459[/C][C]0.0327004281404584[/C][/ROW]
[ROW][C]2[/C][C]375.63[/C][C]375.591222217715[/C][C]0.739240754534131[/C][C]374.92953702775[/C][C]-0.0387777822845123[/C][/ROW]
[ROW][C]3[/C][C]376.51[/C][C]376.501172965009[/C][C]1.39945115994953[/C][C]375.119375875042[/C][C]-0.00882703499109994[/C][/ROW]
[ROW][C]4[/C][C]377.75[/C][C]377.51655625253[/C][C]2.67567766771024[/C][C]375.307766079759[/C][C]-0.233443747469551[/C][/ROW]
[ROW][C]5[/C][C]378.54[/C][C]378.514797354075[/C][C]3.06904636144787[/C][C]375.496156284477[/C][C]-0.0252026459248214[/C][/ROW]
[ROW][C]6[/C][C]378.21[/C][C]378.419881626745[/C][C]2.31631123531322[/C][C]375.683807137942[/C][C]0.209881626745187[/C][/ROW]
[ROW][C]7[/C][C]376.65[/C][C]376.93210874025[/C][C]0.496433268343457[/C][C]375.871457991406[/C][C]0.282108740250237[/C][/ROW]
[ROW][C]8[/C][C]374.28[/C][C]374.151445021607[/C][C]-1.65214619273637[/C][C]376.060701171129[/C][C]-0.128554978392856[/C][/ROW]
[ROW][C]9[/C][C]373.12[/C][C]373.183638645591[/C][C]-3.19358299644316[/C][C]376.249944350852[/C][C]0.0636386455910269[/C][/ROW]
[ROW][C]10[/C][C]373.1[/C][C]373.020280220681[/C][C]-3.25407166172173[/C][C]376.433791441041[/C][C]-0.0797197793187365[/C][/ROW]
[ROW][C]11[/C][C]374.67[/C][C]374.746922221811[/C][C]-2.02456075304026[/C][C]376.617638531229[/C][C]0.0769222218114578[/C][/ROW]
[ROW][C]12[/C][C]375.97[/C][C]375.895921910206[/C][C]-0.719400199428804[/C][C]376.763478289223[/C][C]-0.0740780897937725[/C][/ROW]
[ROW][C]13[/C][C]377.03[/C][C]377.003080561383[/C][C]0.147601391400337[/C][C]376.909318047216[/C][C]-0.0269194386166305[/C][/ROW]
[ROW][C]14[/C][C]377.87[/C][C]377.977900480935[/C][C]0.739240754534131[/C][C]377.022858764531[/C][C]0.107900480934688[/C][/ROW]
[ROW][C]15[/C][C]378.88[/C][C]379.224149358204[/C][C]1.39945115994953[/C][C]377.136399481846[/C][C]0.34414935820439[/C][/ROW]
[ROW][C]16[/C][C]380.42[/C][C]380.922377362771[/C][C]2.67567766771024[/C][C]377.241944969518[/C][C]0.502377362771256[/C][/ROW]
[ROW][C]17[/C][C]380.62[/C][C]380.823463181361[/C][C]3.06904636144787[/C][C]377.347490457191[/C][C]0.203463181361258[/C][/ROW]
[ROW][C]18[/C][C]379.66[/C][C]379.546105657939[/C][C]2.31631123531322[/C][C]377.457583106748[/C][C]-0.113894342061201[/C][/ROW]
[ROW][C]19[/C][C]377.48[/C][C]376.895890975351[/C][C]0.496433268343457[/C][C]377.567675756305[/C][C]-0.584109024648626[/C][/ROW]
[ROW][C]20[/C][C]376.07[/C][C]376.091339362846[/C][C]-1.65214619273637[/C][C]377.70080682989[/C][C]0.0213393628460494[/C][/ROW]
[ROW][C]21[/C][C]374.1[/C][C]373.559645092968[/C][C]-3.19358299644316[/C][C]377.833937903476[/C][C]-0.540354907032338[/C][/ROW]
[ROW][C]22[/C][C]374.47[/C][C]374.190885667999[/C][C]-3.25407166172173[/C][C]378.003185993723[/C][C]-0.279114332001143[/C][/ROW]
[ROW][C]23[/C][C]376.15[/C][C]376.15212666907[/C][C]-2.02456075304026[/C][C]378.17243408397[/C][C]0.00212666906986669[/C][/ROW]
[ROW][C]24[/C][C]377.51[/C][C]377.352733492965[/C][C]-0.719400199428804[/C][C]378.386666706464[/C][C]-0.157266507035445[/C][/ROW]
[ROW][C]25[/C][C]378.43[/C][C]378.111499279642[/C][C]0.147601391400337[/C][C]378.600899328958[/C][C]-0.318500720358429[/C][/ROW]
[ROW][C]26[/C][C]379.7[/C][C]379.830944874564[/C][C]0.739240754534131[/C][C]378.829814370902[/C][C]0.130944874563966[/C][/ROW]
[ROW][C]27[/C][C]380.91[/C][C]381.361819427205[/C][C]1.39945115994953[/C][C]379.058729412846[/C][C]0.451819427204896[/C][/ROW]
[ROW][C]28[/C][C]382.2[/C][C]382.452973920178[/C][C]2.67567766771024[/C][C]379.271348412112[/C][C]0.252973920177624[/C][/ROW]
[ROW][C]29[/C][C]382.45[/C][C]382.346986227173[/C][C]3.06904636144787[/C][C]379.483967411379[/C][C]-0.1030137728265[/C][/ROW]
[ROW][C]30[/C][C]382.14[/C][C]382.280884726067[/C][C]2.31631123531322[/C][C]379.682804038619[/C][C]0.140884726067327[/C][/ROW]
[ROW][C]31[/C][C]380.6[/C][C]380.821926065796[/C][C]0.496433268343457[/C][C]379.88164066586[/C][C]0.22192606579631[/C][/ROW]
[ROW][C]32[/C][C]378.6[/C][C]378.778686156492[/C][C]-1.65214619273637[/C][C]380.073460036244[/C][C]0.178686156492176[/C][/ROW]
[ROW][C]33[/C][C]376.72[/C][C]376.368303589815[/C][C]-3.19358299644316[/C][C]380.265279406628[/C][C]-0.351696410185014[/C][/ROW]
[ROW][C]34[/C][C]376.98[/C][C]376.762557633656[/C][C]-3.25407166172173[/C][C]380.451514028065[/C][C]-0.217442366343562[/C][/ROW]
[ROW][C]35[/C][C]378.29[/C][C]377.966812103538[/C][C]-2.02456075304026[/C][C]380.637748649502[/C][C]-0.323187896462173[/C][/ROW]
[ROW][C]36[/C][C]380.07[/C][C]380.045668561101[/C][C]-0.719400199428804[/C][C]380.813731638328[/C][C]-0.0243314388987983[/C][/ROW]
[ROW][C]37[/C][C]381.36[/C][C]381.582683981447[/C][C]0.147601391400337[/C][C]380.989714627153[/C][C]0.222683981447005[/C][/ROW]
[ROW][C]38[/C][C]382.19[/C][C]382.486942815321[/C][C]0.739240754534131[/C][C]381.153816430144[/C][C]0.296942815321472[/C][/ROW]
[ROW][C]39[/C][C]382.65[/C][C]382.582630606914[/C][C]1.39945115994953[/C][C]381.317918233136[/C][C]-0.0673693930855848[/C][/ROW]
[ROW][C]40[/C][C]384.65[/C][C]385.150875898937[/C][C]2.67567766771024[/C][C]381.473446433352[/C][C]0.500875898937238[/C][/ROW]
[ROW][C]41[/C][C]384.94[/C][C]385.181979004983[/C][C]3.06904636144787[/C][C]381.628974633569[/C][C]0.241979004983136[/C][/ROW]
[ROW][C]42[/C][C]384.01[/C][C]383.937513745913[/C][C]2.31631123531322[/C][C]381.766175018774[/C][C]-0.0724862540867548[/C][/ROW]
[ROW][C]43[/C][C]382.15[/C][C]381.900191327678[/C][C]0.496433268343457[/C][C]381.903375403978[/C][C]-0.249808672321649[/C][/ROW]
[ROW][C]44[/C][C]380.33[/C][C]380.278990386706[/C][C]-1.65214619273637[/C][C]382.03315580603[/C][C]-0.0510096132936155[/C][/ROW]
[ROW][C]45[/C][C]378.81[/C][C]378.650646788361[/C][C]-3.19358299644316[/C][C]382.162936208082[/C][C]-0.159353211638631[/C][/ROW]
[ROW][C]46[/C][C]379.06[/C][C]379.065529914141[/C][C]-3.25407166172173[/C][C]382.308541747581[/C][C]0.00552991414122062[/C][/ROW]
[ROW][C]47[/C][C]380.17[/C][C]379.910413465961[/C][C]-2.02456075304026[/C][C]382.454147287079[/C][C]-0.259586534038931[/C][/ROW]
[ROW][C]48[/C][C]381.85[/C][C]381.800857156207[/C][C]-0.719400199428804[/C][C]382.618543043222[/C][C]-0.0491428437927084[/C][/ROW]
[ROW][C]49[/C][C]382.88[/C][C]382.829459809236[/C][C]0.147601391400337[/C][C]382.782938799364[/C][C]-0.0505401907641385[/C][/ROW]
[ROW][C]50[/C][C]383.77[/C][C]383.847966517359[/C][C]0.739240754534131[/C][C]382.952792728107[/C][C]0.0779665173587887[/C][/ROW]
[ROW][C]51[/C][C]384.42[/C][C]384.3179021832[/C][C]1.39945115994953[/C][C]383.12264665685[/C][C]-0.102097816799755[/C][/ROW]
[ROW][C]52[/C][C]386.36[/C][C]386.752721163818[/C][C]2.67567766771024[/C][C]383.291601168472[/C][C]0.392721163818237[/C][/ROW]
[ROW][C]53[/C][C]386.53[/C][C]386.530397958459[/C][C]3.06904636144787[/C][C]383.460555680093[/C][C]0.000397958459302572[/C][/ROW]
[ROW][C]54[/C][C]386.01[/C][C]386.06968881697[/C][C]2.31631123531322[/C][C]383.633999947717[/C][C]0.0596888169698104[/C][/ROW]
[ROW][C]55[/C][C]384.45[/C][C]384.596122516315[/C][C]0.496433268343457[/C][C]383.807444215341[/C][C]0.146122516315359[/C][/ROW]
[ROW][C]56[/C][C]381.96[/C][C]381.608556427976[/C][C]-1.65214619273637[/C][C]383.96358976476[/C][C]-0.351443572023811[/C][/ROW]
[ROW][C]57[/C][C]380.81[/C][C]380.693847682264[/C][C]-3.19358299644316[/C][C]384.119735314179[/C][C]-0.116152317736066[/C][/ROW]
[ROW][C]58[/C][C]381.09[/C][C]381.178282251064[/C][C]-3.25407166172173[/C][C]384.255789410658[/C][C]0.0882822510636743[/C][/ROW]
[ROW][C]59[/C][C]382.37[/C][C]382.372717245903[/C][C]-2.02456075304026[/C][C]384.391843507137[/C][C]0.00271724590345457[/C][/ROW]
[ROW][C]60[/C][C]383.84[/C][C]383.860062553604[/C][C]-0.719400199428804[/C][C]384.539337645824[/C][C]0.0200625536043617[/C][/ROW]
[ROW][C]61[/C][C]385.42[/C][C]386.005566824088[/C][C]0.147601391400337[/C][C]384.686831784512[/C][C]0.585566824087664[/C][/ROW]
[ROW][C]62[/C][C]385.72[/C][C]385.847502277487[/C][C]0.739240754534131[/C][C]384.853256967979[/C][C]0.127502277486485[/C][/ROW]
[ROW][C]63[/C][C]385.96[/C][C]385.500866688604[/C][C]1.39945115994953[/C][C]385.019682151447[/C][C]-0.459133311396329[/C][/ROW]
[ROW][C]64[/C][C]387.18[/C][C]386.504574931227[/C][C]2.67567766771024[/C][C]385.179747401063[/C][C]-0.675425068773279[/C][/ROW]
[ROW][C]65[/C][C]388.5[/C][C]388.591140987873[/C][C]3.06904636144787[/C][C]385.339812650679[/C][C]0.0911409878728477[/C][/ROW]
[ROW][C]66[/C][C]387.88[/C][C]387.951298806938[/C][C]2.31631123531322[/C][C]385.492389957749[/C][C]0.0712988069381595[/C][/ROW]
[ROW][C]67[/C][C]386.38[/C][C]386.618599466839[/C][C]0.496433268343457[/C][C]385.644967264818[/C][C]0.238599466838537[/C][/ROW]
[ROW][C]68[/C][C]384.15[/C][C]384.139474459111[/C][C]-1.65214619273637[/C][C]385.812671733626[/C][C]-0.010525540889148[/C][/ROW]
[ROW][C]69[/C][C]383.07[/C][C]383.35320679401[/C][C]-3.19358299644316[/C][C]385.980376202433[/C][C]0.283206794010084[/C][/ROW]
[ROW][C]70[/C][C]382.98[/C][C]383.064140051741[/C][C]-3.25407166172173[/C][C]386.14993160998[/C][C]0.0841400517414286[/C][/ROW]
[ROW][C]71[/C][C]384.11[/C][C]383.925073735513[/C][C]-2.02456075304026[/C][C]386.319487017528[/C][C]-0.184926264487331[/C][/ROW]
[ROW][C]72[/C][C]385.54[/C][C]385.340795958388[/C][C]-0.719400199428804[/C][C]386.458604241041[/C][C]-0.199204041612461[/C][/ROW]
[ROW][C]73[/C][C]386.92[/C][C]387.094677144045[/C][C]0.147601391400337[/C][C]386.597721464555[/C][C]0.174677144044779[/C][/ROW]
[ROW][C]74[/C][C]387.41[/C][C]387.351028314075[/C][C]0.739240754534131[/C][C]386.729730931391[/C][C]-0.058971685925485[/C][/ROW]
[ROW][C]75[/C][C]388.77[/C][C]389.278808441823[/C][C]1.39945115994953[/C][C]386.861740398228[/C][C]0.508808441822623[/C][/ROW]
[ROW][C]76[/C][C]389.46[/C][C]389.247178424175[/C][C]2.67567766771024[/C][C]386.997143908115[/C][C]-0.21282157582516[/C][/ROW]
[ROW][C]77[/C][C]390.18[/C][C]390.15840622055[/C][C]3.06904636144787[/C][C]387.132547418002[/C][C]-0.0215937794498018[/C][/ROW]
[ROW][C]78[/C][C]389.43[/C][C]389.278916277596[/C][C]2.31631123531322[/C][C]387.264772487091[/C][C]-0.151083722403712[/C][/ROW]
[ROW][C]79[/C][C]387.74[/C][C]387.586569175477[/C][C]0.496433268343457[/C][C]387.396997556179[/C][C]-0.153430824522559[/C][/ROW]
[ROW][C]80[/C][C]385.91[/C][C]385.94387300039[/C][C]-1.65214619273637[/C][C]387.528273192347[/C][C]0.0338730003897467[/C][/ROW]
[ROW][C]81[/C][C]384.77[/C][C]385.074034167929[/C][C]-3.19358299644316[/C][C]387.659548828514[/C][C]0.304034167928933[/C][/ROW]
[ROW][C]82[/C][C]384.38[/C][C]384.223002190121[/C][C]-3.25407166172173[/C][C]387.7910694716[/C][C]-0.15699780987859[/C][/ROW]
[ROW][C]83[/C][C]385.99[/C][C]386.081970638354[/C][C]-2.02456075304026[/C][C]387.922590114686[/C][C]0.0919706383538141[/C][/ROW]
[ROW][C]84[/C][C]387.26[/C][C]387.185170583227[/C][C]-0.719400199428804[/C][C]388.054229616201[/C][C]-0.0748294167726158[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153929&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153929&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1374.92374.952700428140.147601391400337374.7396981804590.0327004281404584
2375.63375.5912222177150.739240754534131374.92953702775-0.0387777822845123
3376.51376.5011729650091.39945115994953375.119375875042-0.00882703499109994
4377.75377.516556252532.67567766771024375.307766079759-0.233443747469551
5378.54378.5147973540753.06904636144787375.496156284477-0.0252026459248214
6378.21378.4198816267452.31631123531322375.6838071379420.209881626745187
7376.65376.932108740250.496433268343457375.8714579914060.282108740250237
8374.28374.151445021607-1.65214619273637376.060701171129-0.128554978392856
9373.12373.183638645591-3.19358299644316376.2499443508520.0636386455910269
10373.1373.020280220681-3.25407166172173376.433791441041-0.0797197793187365
11374.67374.746922221811-2.02456075304026376.6176385312290.0769222218114578
12375.97375.895921910206-0.719400199428804376.763478289223-0.0740780897937725
13377.03377.0030805613830.147601391400337376.909318047216-0.0269194386166305
14377.87377.9779004809350.739240754534131377.0228587645310.107900480934688
15378.88379.2241493582041.39945115994953377.1363994818460.34414935820439
16380.42380.9223773627712.67567766771024377.2419449695180.502377362771256
17380.62380.8234631813613.06904636144787377.3474904571910.203463181361258
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20376.07376.091339362846-1.65214619273637377.700806829890.0213393628460494
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25378.43378.1114992796420.147601391400337378.600899328958-0.318500720358429
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27380.91381.3618194272051.39945115994953379.0587294128460.451819427204896
28382.2382.4529739201782.67567766771024379.2713484121120.252973920177624
29382.45382.3469862271733.06904636144787379.483967411379-0.1030137728265
30382.14382.2808847260672.31631123531322379.6828040386190.140884726067327
31380.6380.8219260657960.496433268343457379.881640665860.22192606579631
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34376.98376.762557633656-3.25407166172173380.451514028065-0.217442366343562
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36380.07380.045668561101-0.719400199428804380.813731638328-0.0243314388987983
37381.36381.5826839814470.147601391400337380.9897146271530.222683981447005
38382.19382.4869428153210.739240754534131381.1538164301440.296942815321472
39382.65382.5826306069141.39945115994953381.317918233136-0.0673693930855848
40384.65385.1508758989372.67567766771024381.4734464333520.500875898937238
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49382.88382.8294598092360.147601391400337382.782938799364-0.0505401907641385
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58381.09381.178282251064-3.25407166172173384.2557894106580.0882822510636743
59382.37382.372717245903-2.02456075304026384.3918435071370.00271724590345457
60383.84383.860062553604-0.719400199428804384.5393376458240.0200625536043617
61385.42386.0055668240880.147601391400337384.6868317845120.585566824087664
62385.72385.8475022774870.739240754534131384.8532569679790.127502277486485
63385.96385.5008666886041.39945115994953385.019682151447-0.459133311396329
64387.18386.5045749312272.67567766771024385.179747401063-0.675425068773279
65388.5388.5911409878733.06904636144787385.3398126506790.0911409878728477
66387.88387.9512988069382.31631123531322385.4923899577490.0712988069381595
67386.38386.6185994668390.496433268343457385.6449672648180.238599466838537
68384.15384.139474459111-1.65214619273637385.812671733626-0.010525540889148
69383.07383.35320679401-3.19358299644316385.9803762024330.283206794010084
70382.98383.064140051741-3.25407166172173386.149931609980.0841400517414286
71384.11383.925073735513-2.02456075304026386.319487017528-0.184926264487331
72385.54385.340795958388-0.719400199428804386.458604241041-0.199204041612461
73386.92387.0946771440450.147601391400337386.5977214645550.174677144044779
74387.41387.3510283140750.739240754534131386.729730931391-0.058971685925485
75388.77389.2788084418231.39945115994953386.8617403982280.508808441822623
76389.46389.2471784241752.67567766771024386.997143908115-0.21282157582516
77390.18390.158406220553.06904636144787387.132547418002-0.0215937794498018
78389.43389.2789162775962.31631123531322387.264772487091-0.151083722403712
79387.74387.5865691754770.496433268343457387.396997556179-0.153430824522559
80385.91385.94387300039-1.65214619273637387.5282731923470.0338730003897467
81384.77385.074034167929-3.19358299644316387.6595488285140.304034167928933
82384.38384.223002190121-3.25407166172173387.7910694716-0.15699780987859
83385.99386.081970638354-2.02456075304026387.9225901146860.0919706383538141
84387.26387.185170583227-0.719400199428804388.054229616201-0.0748294167726158


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 12 ;
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')