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Author*The author of this computation has been verified*
R Software Modulerwasp_decomposeloess.wasp
Title produced by softwareDecomposition by Loess
Date of computationSun, 11 Dec 2016 18:01:48 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/11/t1481475790dk55iy68hxgpgyd.htm/, Retrieved Thu, 02 May 2024 09:52:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298834, Retrieved Thu, 02 May 2024 09:52:51 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact51
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Decomposition by Loess] [Decomposition by ...] [2016-12-11 17:01:48] [2322cf848a5cbdeb3105c2829b69db5d] [Current]
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Dataseries X:
5692.4
5634.45
5555.38
5352.26
5233.07
4880.16
4861.88
4661.93
4330.68
3681.56
3540.08
3328.03
3254.92
3217.27
3301.29
4272.3
4424.8
4449.8
4678
4722.2
4708.9
4121.4
4230.6
4263
4241.9
4309.8
4457.9
4543.9
4937
4917.9
5041.1
5017.2
4833.9
4815.4
4785.9




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298834&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298834&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298834&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center







Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal351036
Trend1912
Low-pass1312

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Parameters \tabularnewline
Component & Window & Degree & Jump \tabularnewline
Seasonal & 351 & 0 & 36 \tabularnewline
Trend & 19 & 1 & 2 \tabularnewline
Low-pass & 13 & 1 & 2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298834&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]351[/C][C]0[/C][C]36[/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=298834&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298834&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
Seasonal351036
Trend1912
Low-pass1312







Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
15692.45806.06053402944-298.842811003815877.58227697437113.66053402944
25634.455856.55658791993-256.5077109083355668.8511229884222.10658791993
35555.385804.56260801227-153.9225770147095460.11996900244249.18260801227
45352.265284.68470214833161.4453202626485258.38997758902-67.5752978516693
55233.075075.16050161568334.319512208725056.6599861756-157.909498384324
64880.164662.2382142348237.2218151692334860.85997059597-217.921785765205
74861.884691.86593979098366.834105192684665.05995501634-170.014060209019
84661.934525.99983420986324.4733906370434473.3867751531-135.930165790145
94330.684213.60036306737166.0460416427614281.71359528987-117.079636932627
103681.563461.88315751822-239.0688073366044140.30564981838-219.676842481776
113540.083327.6657874796-246.4034918264933998.8977043469-212.414212520403
123328.033098.82618411218-395.5941796588883952.82799554671-229.203815887817
133254.922901.9245242573-298.842811003813906.75828674651-352.995475742705
143217.272772.64817494721-256.5077109083353918.39953596113-444.621825052792
153301.292826.46179183897-153.9225770147093930.04078517574-474.828208161031
164272.34399.43952192082161.4453202626483983.71515781653127.139521920818
174424.84477.89095733395334.319512208724037.3895304573353.0909573339513
184449.84544.64868306996237.2218151692334117.729501760894.848683069963
1946784791.09642174304366.834105192684198.06947306428113.096421743041
204722.24842.83864171583324.4733906370434277.08796764712120.638641715832
214708.94895.64749612727166.0460416427614356.10646222997186.747496127267
224121.44073.2527991373-239.0688073366044408.61600819931-48.1472008627043
234230.64246.47793765785-246.4034918264934461.1255541686415.8779376578486
2442634428.99788494665-395.5941796588884492.59629471224165.997884946645
254241.94258.57577574797-298.842811003814524.0670352558416.6757757479691
264309.84323.32585414641-256.5077109083354552.7818567619313.5258541464054
274457.94488.22589874669-153.9225770147094581.4966782680230.3258987466888
284543.94307.20705762488161.4453202626484619.14762211247-236.692942375115
2949374882.88192183437334.319512208724656.79856595691-54.1180781656331
304917.94903.51581527141237.2218151692334695.06236955935-14.3841847285885
315041.14982.03972164552366.834105192684733.3261731618-59.0602783544764
325017.24937.39973030467324.4733906370434772.52687905829-79.8002696953317
334833.94690.02637340246166.0460416427614811.72758495478-143.873626597542
344815.45016.89812303623-239.0688073366044852.97068430037201.498123036232
354785.94923.98970818053-246.4034918264934894.21378364596138.089708180531

\begin{tabular}{lllllllll}
\hline
Seasonal Decomposition by Loess - Time Series Components \tabularnewline
t & Observed & Fitted & Seasonal & Trend & Remainder \tabularnewline
1 & 5692.4 & 5806.06053402944 & -298.84281100381 & 5877.58227697437 & 113.66053402944 \tabularnewline
2 & 5634.45 & 5856.55658791993 & -256.507710908335 & 5668.8511229884 & 222.10658791993 \tabularnewline
3 & 5555.38 & 5804.56260801227 & -153.922577014709 & 5460.11996900244 & 249.18260801227 \tabularnewline
4 & 5352.26 & 5284.68470214833 & 161.445320262648 & 5258.38997758902 & -67.5752978516693 \tabularnewline
5 & 5233.07 & 5075.16050161568 & 334.31951220872 & 5056.6599861756 & -157.909498384324 \tabularnewline
6 & 4880.16 & 4662.2382142348 & 237.221815169233 & 4860.85997059597 & -217.921785765205 \tabularnewline
7 & 4861.88 & 4691.86593979098 & 366.83410519268 & 4665.05995501634 & -170.014060209019 \tabularnewline
8 & 4661.93 & 4525.99983420986 & 324.473390637043 & 4473.3867751531 & -135.930165790145 \tabularnewline
9 & 4330.68 & 4213.60036306737 & 166.046041642761 & 4281.71359528987 & -117.079636932627 \tabularnewline
10 & 3681.56 & 3461.88315751822 & -239.068807336604 & 4140.30564981838 & -219.676842481776 \tabularnewline
11 & 3540.08 & 3327.6657874796 & -246.403491826493 & 3998.8977043469 & -212.414212520403 \tabularnewline
12 & 3328.03 & 3098.82618411218 & -395.594179658888 & 3952.82799554671 & -229.203815887817 \tabularnewline
13 & 3254.92 & 2901.9245242573 & -298.84281100381 & 3906.75828674651 & -352.995475742705 \tabularnewline
14 & 3217.27 & 2772.64817494721 & -256.507710908335 & 3918.39953596113 & -444.621825052792 \tabularnewline
15 & 3301.29 & 2826.46179183897 & -153.922577014709 & 3930.04078517574 & -474.828208161031 \tabularnewline
16 & 4272.3 & 4399.43952192082 & 161.445320262648 & 3983.71515781653 & 127.139521920818 \tabularnewline
17 & 4424.8 & 4477.89095733395 & 334.31951220872 & 4037.38953045733 & 53.0909573339513 \tabularnewline
18 & 4449.8 & 4544.64868306996 & 237.221815169233 & 4117.7295017608 & 94.848683069963 \tabularnewline
19 & 4678 & 4791.09642174304 & 366.83410519268 & 4198.06947306428 & 113.096421743041 \tabularnewline
20 & 4722.2 & 4842.83864171583 & 324.473390637043 & 4277.08796764712 & 120.638641715832 \tabularnewline
21 & 4708.9 & 4895.64749612727 & 166.046041642761 & 4356.10646222997 & 186.747496127267 \tabularnewline
22 & 4121.4 & 4073.2527991373 & -239.068807336604 & 4408.61600819931 & -48.1472008627043 \tabularnewline
23 & 4230.6 & 4246.47793765785 & -246.403491826493 & 4461.12555416864 & 15.8779376578486 \tabularnewline
24 & 4263 & 4428.99788494665 & -395.594179658888 & 4492.59629471224 & 165.997884946645 \tabularnewline
25 & 4241.9 & 4258.57577574797 & -298.84281100381 & 4524.06703525584 & 16.6757757479691 \tabularnewline
26 & 4309.8 & 4323.32585414641 & -256.507710908335 & 4552.78185676193 & 13.5258541464054 \tabularnewline
27 & 4457.9 & 4488.22589874669 & -153.922577014709 & 4581.49667826802 & 30.3258987466888 \tabularnewline
28 & 4543.9 & 4307.20705762488 & 161.445320262648 & 4619.14762211247 & -236.692942375115 \tabularnewline
29 & 4937 & 4882.88192183437 & 334.31951220872 & 4656.79856595691 & -54.1180781656331 \tabularnewline
30 & 4917.9 & 4903.51581527141 & 237.221815169233 & 4695.06236955935 & -14.3841847285885 \tabularnewline
31 & 5041.1 & 4982.03972164552 & 366.83410519268 & 4733.3261731618 & -59.0602783544764 \tabularnewline
32 & 5017.2 & 4937.39973030467 & 324.473390637043 & 4772.52687905829 & -79.8002696953317 \tabularnewline
33 & 4833.9 & 4690.02637340246 & 166.046041642761 & 4811.72758495478 & -143.873626597542 \tabularnewline
34 & 4815.4 & 5016.89812303623 & -239.068807336604 & 4852.97068430037 & 201.498123036232 \tabularnewline
35 & 4785.9 & 4923.98970818053 & -246.403491826493 & 4894.21378364596 & 138.089708180531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298834&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]5692.4[/C][C]5806.06053402944[/C][C]-298.84281100381[/C][C]5877.58227697437[/C][C]113.66053402944[/C][/ROW]
[ROW][C]2[/C][C]5634.45[/C][C]5856.55658791993[/C][C]-256.507710908335[/C][C]5668.8511229884[/C][C]222.10658791993[/C][/ROW]
[ROW][C]3[/C][C]5555.38[/C][C]5804.56260801227[/C][C]-153.922577014709[/C][C]5460.11996900244[/C][C]249.18260801227[/C][/ROW]
[ROW][C]4[/C][C]5352.26[/C][C]5284.68470214833[/C][C]161.445320262648[/C][C]5258.38997758902[/C][C]-67.5752978516693[/C][/ROW]
[ROW][C]5[/C][C]5233.07[/C][C]5075.16050161568[/C][C]334.31951220872[/C][C]5056.6599861756[/C][C]-157.909498384324[/C][/ROW]
[ROW][C]6[/C][C]4880.16[/C][C]4662.2382142348[/C][C]237.221815169233[/C][C]4860.85997059597[/C][C]-217.921785765205[/C][/ROW]
[ROW][C]7[/C][C]4861.88[/C][C]4691.86593979098[/C][C]366.83410519268[/C][C]4665.05995501634[/C][C]-170.014060209019[/C][/ROW]
[ROW][C]8[/C][C]4661.93[/C][C]4525.99983420986[/C][C]324.473390637043[/C][C]4473.3867751531[/C][C]-135.930165790145[/C][/ROW]
[ROW][C]9[/C][C]4330.68[/C][C]4213.60036306737[/C][C]166.046041642761[/C][C]4281.71359528987[/C][C]-117.079636932627[/C][/ROW]
[ROW][C]10[/C][C]3681.56[/C][C]3461.88315751822[/C][C]-239.068807336604[/C][C]4140.30564981838[/C][C]-219.676842481776[/C][/ROW]
[ROW][C]11[/C][C]3540.08[/C][C]3327.6657874796[/C][C]-246.403491826493[/C][C]3998.8977043469[/C][C]-212.414212520403[/C][/ROW]
[ROW][C]12[/C][C]3328.03[/C][C]3098.82618411218[/C][C]-395.594179658888[/C][C]3952.82799554671[/C][C]-229.203815887817[/C][/ROW]
[ROW][C]13[/C][C]3254.92[/C][C]2901.9245242573[/C][C]-298.84281100381[/C][C]3906.75828674651[/C][C]-352.995475742705[/C][/ROW]
[ROW][C]14[/C][C]3217.27[/C][C]2772.64817494721[/C][C]-256.507710908335[/C][C]3918.39953596113[/C][C]-444.621825052792[/C][/ROW]
[ROW][C]15[/C][C]3301.29[/C][C]2826.46179183897[/C][C]-153.922577014709[/C][C]3930.04078517574[/C][C]-474.828208161031[/C][/ROW]
[ROW][C]16[/C][C]4272.3[/C][C]4399.43952192082[/C][C]161.445320262648[/C][C]3983.71515781653[/C][C]127.139521920818[/C][/ROW]
[ROW][C]17[/C][C]4424.8[/C][C]4477.89095733395[/C][C]334.31951220872[/C][C]4037.38953045733[/C][C]53.0909573339513[/C][/ROW]
[ROW][C]18[/C][C]4449.8[/C][C]4544.64868306996[/C][C]237.221815169233[/C][C]4117.7295017608[/C][C]94.848683069963[/C][/ROW]
[ROW][C]19[/C][C]4678[/C][C]4791.09642174304[/C][C]366.83410519268[/C][C]4198.06947306428[/C][C]113.096421743041[/C][/ROW]
[ROW][C]20[/C][C]4722.2[/C][C]4842.83864171583[/C][C]324.473390637043[/C][C]4277.08796764712[/C][C]120.638641715832[/C][/ROW]
[ROW][C]21[/C][C]4708.9[/C][C]4895.64749612727[/C][C]166.046041642761[/C][C]4356.10646222997[/C][C]186.747496127267[/C][/ROW]
[ROW][C]22[/C][C]4121.4[/C][C]4073.2527991373[/C][C]-239.068807336604[/C][C]4408.61600819931[/C][C]-48.1472008627043[/C][/ROW]
[ROW][C]23[/C][C]4230.6[/C][C]4246.47793765785[/C][C]-246.403491826493[/C][C]4461.12555416864[/C][C]15.8779376578486[/C][/ROW]
[ROW][C]24[/C][C]4263[/C][C]4428.99788494665[/C][C]-395.594179658888[/C][C]4492.59629471224[/C][C]165.997884946645[/C][/ROW]
[ROW][C]25[/C][C]4241.9[/C][C]4258.57577574797[/C][C]-298.84281100381[/C][C]4524.06703525584[/C][C]16.6757757479691[/C][/ROW]
[ROW][C]26[/C][C]4309.8[/C][C]4323.32585414641[/C][C]-256.507710908335[/C][C]4552.78185676193[/C][C]13.5258541464054[/C][/ROW]
[ROW][C]27[/C][C]4457.9[/C][C]4488.22589874669[/C][C]-153.922577014709[/C][C]4581.49667826802[/C][C]30.3258987466888[/C][/ROW]
[ROW][C]28[/C][C]4543.9[/C][C]4307.20705762488[/C][C]161.445320262648[/C][C]4619.14762211247[/C][C]-236.692942375115[/C][/ROW]
[ROW][C]29[/C][C]4937[/C][C]4882.88192183437[/C][C]334.31951220872[/C][C]4656.79856595691[/C][C]-54.1180781656331[/C][/ROW]
[ROW][C]30[/C][C]4917.9[/C][C]4903.51581527141[/C][C]237.221815169233[/C][C]4695.06236955935[/C][C]-14.3841847285885[/C][/ROW]
[ROW][C]31[/C][C]5041.1[/C][C]4982.03972164552[/C][C]366.83410519268[/C][C]4733.3261731618[/C][C]-59.0602783544764[/C][/ROW]
[ROW][C]32[/C][C]5017.2[/C][C]4937.39973030467[/C][C]324.473390637043[/C][C]4772.52687905829[/C][C]-79.8002696953317[/C][/ROW]
[ROW][C]33[/C][C]4833.9[/C][C]4690.02637340246[/C][C]166.046041642761[/C][C]4811.72758495478[/C][C]-143.873626597542[/C][/ROW]
[ROW][C]34[/C][C]4815.4[/C][C]5016.89812303623[/C][C]-239.068807336604[/C][C]4852.97068430037[/C][C]201.498123036232[/C][/ROW]
[ROW][C]35[/C][C]4785.9[/C][C]4923.98970818053[/C][C]-246.403491826493[/C][C]4894.21378364596[/C][C]138.089708180531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298834&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298834&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
15692.45806.06053402944-298.842811003815877.58227697437113.66053402944
25634.455856.55658791993-256.5077109083355668.8511229884222.10658791993
35555.385804.56260801227-153.9225770147095460.11996900244249.18260801227
45352.265284.68470214833161.4453202626485258.38997758902-67.5752978516693
55233.075075.16050161568334.319512208725056.6599861756-157.909498384324
64880.164662.2382142348237.2218151692334860.85997059597-217.921785765205
74861.884691.86593979098366.834105192684665.05995501634-170.014060209019
84661.934525.99983420986324.4733906370434473.3867751531-135.930165790145
94330.684213.60036306737166.0460416427614281.71359528987-117.079636932627
103681.563461.88315751822-239.0688073366044140.30564981838-219.676842481776
113540.083327.6657874796-246.4034918264933998.8977043469-212.414212520403
123328.033098.82618411218-395.5941796588883952.82799554671-229.203815887817
133254.922901.9245242573-298.842811003813906.75828674651-352.995475742705
143217.272772.64817494721-256.5077109083353918.39953596113-444.621825052792
153301.292826.46179183897-153.9225770147093930.04078517574-474.828208161031
164272.34399.43952192082161.4453202626483983.71515781653127.139521920818
174424.84477.89095733395334.319512208724037.3895304573353.0909573339513
184449.84544.64868306996237.2218151692334117.729501760894.848683069963
1946784791.09642174304366.834105192684198.06947306428113.096421743041
204722.24842.83864171583324.4733906370434277.08796764712120.638641715832
214708.94895.64749612727166.0460416427614356.10646222997186.747496127267
224121.44073.2527991373-239.0688073366044408.61600819931-48.1472008627043
234230.64246.47793765785-246.4034918264934461.1255541686415.8779376578486
2442634428.99788494665-395.5941796588884492.59629471224165.997884946645
254241.94258.57577574797-298.842811003814524.0670352558416.6757757479691
264309.84323.32585414641-256.5077109083354552.7818567619313.5258541464054
274457.94488.22589874669-153.9225770147094581.4966782680230.3258987466888
284543.94307.20705762488161.4453202626484619.14762211247-236.692942375115
2949374882.88192183437334.319512208724656.79856595691-54.1180781656331
304917.94903.51581527141237.2218151692334695.06236955935-14.3841847285885
315041.14982.03972164552366.834105192684733.3261731618-59.0602783544764
325017.24937.39973030467324.4733906370434772.52687905829-79.8002696953317
334833.94690.02637340246166.0460416427614811.72758495478-143.873626597542
344815.45016.89812303623-239.0688073366044852.97068430037201.498123036232
354785.94923.98970818053-246.4034918264934894.21378364596138.089708180531



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')