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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 21 May 2008 08:05:50 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/May/21/t12113788698egg78p87mh5omh.htm/, Retrieved Thu, 31 Oct 2024 23:09:08 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=12979, Retrieved Thu, 31 Oct 2024 23:09:08 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact265
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Variability] [Clélia Comes- opg...] [2008-05-09 16:38:39] [712952ff6dd47326e4d4669a276f9efc]
- RMPD    [Classical Decomposition] [Clélia Comes - ve...] [2008-05-21 14:05:50] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
1.48
1.57
1.58
1.58
1.58
1.58
1.59
1.6
1.6
1.61
1.61
1.61
1.62
1.63
1.63
1.64
1.64
1.64
1.64
1.64
1.65
1.65
1.65
1.65
1.65
1.66
1.66
1.67
1.68
1.68
1.68
1.68
1.69
1.7
1.7
1.71
1.72
1.73
1.74
1.74
1.75
1.75
1.75
1.76
1.79
1.83




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12979&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12979&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12979&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11.48NANA0.998930426407109NA
21.57NANA1.00277601969426NA
31.58NANA1.00046606725575NA
41.58NANA1.00429045486190NA
51.58NANA1.00224297177349NA
61.58NANA1.00020382025412NA
71.591.585833864111431.588333333333330.9984263572579811.00262709479401
81.61.591729514806571.596666666666670.9969078380834441.00519591118749
91.61.603593279102351.601251.001463406152910.997759232874584
101.611.605744830980431.605833333333330.9999448869623851.00264996588342
111.611.607900152819911.610833333333330.9981790912487781.00130595620407
121.611.609642526527361.615833333333330.9961686600478751.00022208252252
131.621.618683511790521.620416666666670.9989304264071091.00081330797521
141.631.628675385320091.624166666666671.002776019694261.00081330797521
151.631.628675385320091.627916666666671.000466067255751.00081330797521
161.641.638667258849661.631666666666671.004290454861901.00081330797521
171.641.638667258849661.6351.002242971773491.00081330797521
181.641.638667258849661.638333333333331.000203820254121.00081330797521
191.641.638667258849661.641250.9984263572579811.00081330797521
201.641.638667258849661.643750.9969078380834441.00081330797521
211.651.648659132379231.646251.001463406152911.00081330797521
221.651.648659132379231.648750.9999448869623851.00081330797521
231.651.648659132379231.651666666666670.9981790912487781.00081330797521
241.651.648659132379231.6550.9961686600478751.00081330797521
251.651.656559623791791.658333333333330.9989304264071090.9960402126808
261.661.666279486058621.661666666666671.002776019694260.996231432895163
271.661.665776001980821.6651.000466067255750.996532545808106
281.671.675909696550791.668751.004290454861900.99647373807613
291.681.676668971529411.672916666666671.002242971773491.00198669416990
301.681.677841908476281.67751.000203820254121.00128623055177
311.681.680268357068741.682916666666670.9984263572579810.999840289161184
321.681.683528111563421.688750.9969078380834440.997904334629649
331.691.697480473429191.6951.001463406152910.995593190292154
341.71.701156238944761.701250.9999448869623850.999320321721023
351.71.703974890352601.707083333333330.9981790912487780.99766728349396
361.711.706353900607001.712916666666670.9961686600478751.00213677795192
371.72NA1.71875NANA
381.73NA1.725NANA
391.74NA1.7325NANA
401.74NA1.74208333333333NANA
411.75NANANANA
421.75NANANANA
431.75NANANANA
441.76NANANANA
451.79NANANANA
461.83NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 1.48 & NA & NA & 0.998930426407109 & NA \tabularnewline
2 & 1.57 & NA & NA & 1.00277601969426 & NA \tabularnewline
3 & 1.58 & NA & NA & 1.00046606725575 & NA \tabularnewline
4 & 1.58 & NA & NA & 1.00429045486190 & NA \tabularnewline
5 & 1.58 & NA & NA & 1.00224297177349 & NA \tabularnewline
6 & 1.58 & NA & NA & 1.00020382025412 & NA \tabularnewline
7 & 1.59 & 1.58583386411143 & 1.58833333333333 & 0.998426357257981 & 1.00262709479401 \tabularnewline
8 & 1.6 & 1.59172951480657 & 1.59666666666667 & 0.996907838083444 & 1.00519591118749 \tabularnewline
9 & 1.6 & 1.60359327910235 & 1.60125 & 1.00146340615291 & 0.997759232874584 \tabularnewline
10 & 1.61 & 1.60574483098043 & 1.60583333333333 & 0.999944886962385 & 1.00264996588342 \tabularnewline
11 & 1.61 & 1.60790015281991 & 1.61083333333333 & 0.998179091248778 & 1.00130595620407 \tabularnewline
12 & 1.61 & 1.60964252652736 & 1.61583333333333 & 0.996168660047875 & 1.00022208252252 \tabularnewline
13 & 1.62 & 1.61868351179052 & 1.62041666666667 & 0.998930426407109 & 1.00081330797521 \tabularnewline
14 & 1.63 & 1.62867538532009 & 1.62416666666667 & 1.00277601969426 & 1.00081330797521 \tabularnewline
15 & 1.63 & 1.62867538532009 & 1.62791666666667 & 1.00046606725575 & 1.00081330797521 \tabularnewline
16 & 1.64 & 1.63866725884966 & 1.63166666666667 & 1.00429045486190 & 1.00081330797521 \tabularnewline
17 & 1.64 & 1.63866725884966 & 1.635 & 1.00224297177349 & 1.00081330797521 \tabularnewline
18 & 1.64 & 1.63866725884966 & 1.63833333333333 & 1.00020382025412 & 1.00081330797521 \tabularnewline
19 & 1.64 & 1.63866725884966 & 1.64125 & 0.998426357257981 & 1.00081330797521 \tabularnewline
20 & 1.64 & 1.63866725884966 & 1.64375 & 0.996907838083444 & 1.00081330797521 \tabularnewline
21 & 1.65 & 1.64865913237923 & 1.64625 & 1.00146340615291 & 1.00081330797521 \tabularnewline
22 & 1.65 & 1.64865913237923 & 1.64875 & 0.999944886962385 & 1.00081330797521 \tabularnewline
23 & 1.65 & 1.64865913237923 & 1.65166666666667 & 0.998179091248778 & 1.00081330797521 \tabularnewline
24 & 1.65 & 1.64865913237923 & 1.655 & 0.996168660047875 & 1.00081330797521 \tabularnewline
25 & 1.65 & 1.65655962379179 & 1.65833333333333 & 0.998930426407109 & 0.9960402126808 \tabularnewline
26 & 1.66 & 1.66627948605862 & 1.66166666666667 & 1.00277601969426 & 0.996231432895163 \tabularnewline
27 & 1.66 & 1.66577600198082 & 1.665 & 1.00046606725575 & 0.996532545808106 \tabularnewline
28 & 1.67 & 1.67590969655079 & 1.66875 & 1.00429045486190 & 0.99647373807613 \tabularnewline
29 & 1.68 & 1.67666897152941 & 1.67291666666667 & 1.00224297177349 & 1.00198669416990 \tabularnewline
30 & 1.68 & 1.67784190847628 & 1.6775 & 1.00020382025412 & 1.00128623055177 \tabularnewline
31 & 1.68 & 1.68026835706874 & 1.68291666666667 & 0.998426357257981 & 0.999840289161184 \tabularnewline
32 & 1.68 & 1.68352811156342 & 1.68875 & 0.996907838083444 & 0.997904334629649 \tabularnewline
33 & 1.69 & 1.69748047342919 & 1.695 & 1.00146340615291 & 0.995593190292154 \tabularnewline
34 & 1.7 & 1.70115623894476 & 1.70125 & 0.999944886962385 & 0.999320321721023 \tabularnewline
35 & 1.7 & 1.70397489035260 & 1.70708333333333 & 0.998179091248778 & 0.99766728349396 \tabularnewline
36 & 1.71 & 1.70635390060700 & 1.71291666666667 & 0.996168660047875 & 1.00213677795192 \tabularnewline
37 & 1.72 & NA & 1.71875 & NA & NA \tabularnewline
38 & 1.73 & NA & 1.725 & NA & NA \tabularnewline
39 & 1.74 & NA & 1.7325 & NA & NA \tabularnewline
40 & 1.74 & NA & 1.74208333333333 & NA & NA \tabularnewline
41 & 1.75 & NA & NA & NA & NA \tabularnewline
42 & 1.75 & NA & NA & NA & NA \tabularnewline
43 & 1.75 & NA & NA & NA & NA \tabularnewline
44 & 1.76 & NA & NA & NA & NA \tabularnewline
45 & 1.79 & NA & NA & NA & NA \tabularnewline
46 & 1.83 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=12979&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]1.48[/C][C]NA[/C][C]NA[/C][C]0.998930426407109[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]1.57[/C][C]NA[/C][C]NA[/C][C]1.00277601969426[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.58[/C][C]NA[/C][C]NA[/C][C]1.00046606725575[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.58[/C][C]NA[/C][C]NA[/C][C]1.00429045486190[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.58[/C][C]NA[/C][C]NA[/C][C]1.00224297177349[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.58[/C][C]NA[/C][C]NA[/C][C]1.00020382025412[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]1.59[/C][C]1.58583386411143[/C][C]1.58833333333333[/C][C]0.998426357257981[/C][C]1.00262709479401[/C][/ROW]
[ROW][C]8[/C][C]1.6[/C][C]1.59172951480657[/C][C]1.59666666666667[/C][C]0.996907838083444[/C][C]1.00519591118749[/C][/ROW]
[ROW][C]9[/C][C]1.6[/C][C]1.60359327910235[/C][C]1.60125[/C][C]1.00146340615291[/C][C]0.997759232874584[/C][/ROW]
[ROW][C]10[/C][C]1.61[/C][C]1.60574483098043[/C][C]1.60583333333333[/C][C]0.999944886962385[/C][C]1.00264996588342[/C][/ROW]
[ROW][C]11[/C][C]1.61[/C][C]1.60790015281991[/C][C]1.61083333333333[/C][C]0.998179091248778[/C][C]1.00130595620407[/C][/ROW]
[ROW][C]12[/C][C]1.61[/C][C]1.60964252652736[/C][C]1.61583333333333[/C][C]0.996168660047875[/C][C]1.00022208252252[/C][/ROW]
[ROW][C]13[/C][C]1.62[/C][C]1.61868351179052[/C][C]1.62041666666667[/C][C]0.998930426407109[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]14[/C][C]1.63[/C][C]1.62867538532009[/C][C]1.62416666666667[/C][C]1.00277601969426[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]15[/C][C]1.63[/C][C]1.62867538532009[/C][C]1.62791666666667[/C][C]1.00046606725575[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]16[/C][C]1.64[/C][C]1.63866725884966[/C][C]1.63166666666667[/C][C]1.00429045486190[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]17[/C][C]1.64[/C][C]1.63866725884966[/C][C]1.635[/C][C]1.00224297177349[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]18[/C][C]1.64[/C][C]1.63866725884966[/C][C]1.63833333333333[/C][C]1.00020382025412[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]19[/C][C]1.64[/C][C]1.63866725884966[/C][C]1.64125[/C][C]0.998426357257981[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]20[/C][C]1.64[/C][C]1.63866725884966[/C][C]1.64375[/C][C]0.996907838083444[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]21[/C][C]1.65[/C][C]1.64865913237923[/C][C]1.64625[/C][C]1.00146340615291[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]22[/C][C]1.65[/C][C]1.64865913237923[/C][C]1.64875[/C][C]0.999944886962385[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]23[/C][C]1.65[/C][C]1.64865913237923[/C][C]1.65166666666667[/C][C]0.998179091248778[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]24[/C][C]1.65[/C][C]1.64865913237923[/C][C]1.655[/C][C]0.996168660047875[/C][C]1.00081330797521[/C][/ROW]
[ROW][C]25[/C][C]1.65[/C][C]1.65655962379179[/C][C]1.65833333333333[/C][C]0.998930426407109[/C][C]0.9960402126808[/C][/ROW]
[ROW][C]26[/C][C]1.66[/C][C]1.66627948605862[/C][C]1.66166666666667[/C][C]1.00277601969426[/C][C]0.996231432895163[/C][/ROW]
[ROW][C]27[/C][C]1.66[/C][C]1.66577600198082[/C][C]1.665[/C][C]1.00046606725575[/C][C]0.996532545808106[/C][/ROW]
[ROW][C]28[/C][C]1.67[/C][C]1.67590969655079[/C][C]1.66875[/C][C]1.00429045486190[/C][C]0.99647373807613[/C][/ROW]
[ROW][C]29[/C][C]1.68[/C][C]1.67666897152941[/C][C]1.67291666666667[/C][C]1.00224297177349[/C][C]1.00198669416990[/C][/ROW]
[ROW][C]30[/C][C]1.68[/C][C]1.67784190847628[/C][C]1.6775[/C][C]1.00020382025412[/C][C]1.00128623055177[/C][/ROW]
[ROW][C]31[/C][C]1.68[/C][C]1.68026835706874[/C][C]1.68291666666667[/C][C]0.998426357257981[/C][C]0.999840289161184[/C][/ROW]
[ROW][C]32[/C][C]1.68[/C][C]1.68352811156342[/C][C]1.68875[/C][C]0.996907838083444[/C][C]0.997904334629649[/C][/ROW]
[ROW][C]33[/C][C]1.69[/C][C]1.69748047342919[/C][C]1.695[/C][C]1.00146340615291[/C][C]0.995593190292154[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]1.70115623894476[/C][C]1.70125[/C][C]0.999944886962385[/C][C]0.999320321721023[/C][/ROW]
[ROW][C]35[/C][C]1.7[/C][C]1.70397489035260[/C][C]1.70708333333333[/C][C]0.998179091248778[/C][C]0.99766728349396[/C][/ROW]
[ROW][C]36[/C][C]1.71[/C][C]1.70635390060700[/C][C]1.71291666666667[/C][C]0.996168660047875[/C][C]1.00213677795192[/C][/ROW]
[ROW][C]37[/C][C]1.72[/C][C]NA[/C][C]1.71875[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]38[/C][C]1.73[/C][C]NA[/C][C]1.725[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]39[/C][C]1.74[/C][C]NA[/C][C]1.7325[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]40[/C][C]1.74[/C][C]NA[/C][C]1.74208333333333[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]41[/C][C]1.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]42[/C][C]1.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]43[/C][C]1.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]44[/C][C]1.76[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]45[/C][C]1.79[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]46[/C][C]1.83[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=12979&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=12979&T=1

As an alternative you can also use a QR Code:  

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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11.48NANA0.998930426407109NA
21.57NANA1.00277601969426NA
31.58NANA1.00046606725575NA
41.58NANA1.00429045486190NA
51.58NANA1.00224297177349NA
61.58NANA1.00020382025412NA
71.591.585833864111431.588333333333330.9984263572579811.00262709479401
81.61.591729514806571.596666666666670.9969078380834441.00519591118749
91.61.603593279102351.601251.001463406152910.997759232874584
101.611.605744830980431.605833333333330.9999448869623851.00264996588342
111.611.607900152819911.610833333333330.9981790912487781.00130595620407
121.611.609642526527361.615833333333330.9961686600478751.00022208252252
131.621.618683511790521.620416666666670.9989304264071091.00081330797521
141.631.628675385320091.624166666666671.002776019694261.00081330797521
151.631.628675385320091.627916666666671.000466067255751.00081330797521
161.641.638667258849661.631666666666671.004290454861901.00081330797521
171.641.638667258849661.6351.002242971773491.00081330797521
181.641.638667258849661.638333333333331.000203820254121.00081330797521
191.641.638667258849661.641250.9984263572579811.00081330797521
201.641.638667258849661.643750.9969078380834441.00081330797521
211.651.648659132379231.646251.001463406152911.00081330797521
221.651.648659132379231.648750.9999448869623851.00081330797521
231.651.648659132379231.651666666666670.9981790912487781.00081330797521
241.651.648659132379231.6550.9961686600478751.00081330797521
251.651.656559623791791.658333333333330.9989304264071090.9960402126808
261.661.666279486058621.661666666666671.002776019694260.996231432895163
271.661.665776001980821.6651.000466067255750.996532545808106
281.671.675909696550791.668751.004290454861900.99647373807613
291.681.676668971529411.672916666666671.002242971773491.00198669416990
301.681.677841908476281.67751.000203820254121.00128623055177
311.681.680268357068741.682916666666670.9984263572579810.999840289161184
321.681.683528111563421.688750.9969078380834440.997904334629649
331.691.697480473429191.6951.001463406152910.995593190292154
341.71.701156238944761.701250.9999448869623850.999320321721023
351.71.703974890352601.707083333333330.9981790912487780.99766728349396
361.711.706353900607001.712916666666670.9961686600478751.00213677795192
371.72NA1.71875NANA
381.73NA1.725NANA
391.74NA1.7325NANA
401.74NA1.74208333333333NANA
411.75NANANANA
421.75NANANANA
431.75NANANANA
441.76NANANANA
451.79NANANANA
461.83NANANANA



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')