SHWWS9klasmeth1

*The author of this computation has been verified*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Tue, 01 Dec 2009 12:48:34 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e.htm/, Retrieved Tue, 01 Dec 2009 20:49:42 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

161 149 139 135 130 127 122 117 112 113 149 157 157 147 137 132 125 123 117 114 111 112 144 150 149 134 123 116 117 111 105 102 95 93 124 130 124 115 106 105 105 101 95 93 84 87 116 120 117 109 105 107 109 109 108 107 99 103 131 137

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 2 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 161 NA NA 1.16522731642156 NA 2 149 NA NA 1.0781156702622 NA 3 139 NA NA 1.00812823195083 NA 4 135 NA NA 0.988278973176862 NA 5 130 NA NA 0.983837152317663 NA 6 127 NA NA 0.96081126607071 NA 7 122 122.069847725332 134.083333333333 0.910402841953998 0.999427805255487 8 117 119.188839900641 133.833333333333 0.890576636866561 0.981635529782268 9 112 112.767787176819 133.666666666667 0.84364928062458 0.993191431737372 10 113 114.091551811255 133.458333333333 0.854885183724673 0.99043266750319 11 149 150.551583797784 133.125 1.13090391585190 0.989694005478758 12 157 157.33311371084 132.75 1.18518353077846 0.997882748882398 13 157 154.246966011304 132.375 1.16522731642156 1.01784822132900 14 147 142.356189960871 132.041666666667 1.0781156702622 1.03262106158085 15 137 132.946910588516 131.875 1.00812823195083 1.03048652573830 16 132 130.246933006601 131.791666666667 0.988278973176862 1.01345956448211 17 125 129.415578744453 131.541666666667 0.983837152317663 0.96588062436307 18 123 125.906309658016 131.041666666667 0.96081126607071 0.976916886326745 19 117 118.731703971501 130.416666666667 0.910402841953998 0.985414982573515 20 114 115.366781834089 129.541666666667 0.890576636866561 0.988152726353634 21 111 108.338628453540 128.416666666667 0.84364928062458 1.02456530587889 22 112 108.712899196988 127.166666666667 0.854885183724673 1.03023652967856 23 144 142.682377383315 126.166666666667 1.13090391585190 1.00923465560954 24 150 148.543002524233 125.333333333333 1.18518353077846 1.00980859044861 25 149 144.876596341747 124.333333333333 1.16522731642156 1.02846148903530 26 134 132.967599332338 123.333333333333 1.0781156702622 1.00776430252818 27 123 123.159665669994 122.166666666667 1.00812823195083 0.998703587987796 28 116 119.293507720557 120.708333333333 0.988278973176862 0.972391559410995 29 117 117.158607555162 119.083333333333 0.983837152317663 0.99864621508849 30 111 112.815256157803 117.416666666667 0.96081126607071 0.983909479802417 31 105 105.189461697435 115.541666666667 0.910402841953998 0.99819885286627 32 102 101.265985083702 113.708333333333 0.890576636866561 1.00724838568145 33 95 94.6644796967498 112.208333333333 0.84364928062458 1.00354431043539 34 93 94.9278756094272 111.041666666667 0.854885183724673 0.979691153972946 35 124 124.493672736697 110.083333333333 1.13090391585190 0.996034555605562 36 130 129.382535443315 109.166666666667 1.18518353077846 1.00477239493390 37 124 126.232959279002 108.333333333333 1.16522731642156 0.982310806212926 38 115 115.942356039448 107.541666666667 1.0781156702622 0.991872202086985 39 106 107.575683417754 106.708333333333 1.00812823195083 0.985352791935007 40 105 104.757571156747 106 0.988278973176862 1.00231418923306 41 105 103.712833140154 105.416666666667 0.983837152317663 1.01241087357152 42 101 100.564912515401 104.666666666667 0.96081126607071 1.00432643427729 43 95 94.6439621114678 103.958333333333 0.910402841953998 1.00376186584531 44 93 92.1004671959501 103.416666666667 0.890576636866561 1.00976686472324 45 84 87.0013320644099 103.125 0.84364928062458 0.965502458488936 46 87 88.1956547875954 103.166666666667 0.854885183724673 0.986443155385886 47 116 116.954313297684 103.416666666667 1.13090391585190 0.991840289846728 48 120 123.160321906728 103.916666666667 1.18518353077846 0.974339772275675 49 117 122.106112533342 104.791666666667 1.16522731642156 0.958182989963355 50 109 114.190418075271 105.916666666667 1.0781156702622 0.954545940344574 51 105 107.995736847733 107.125 1.00812823195083 0.972260600879487 52 107 107.145912008591 108.416666666667 0.988278973176862 0.998638193414418 53 109 107.935134252184 109.708333333333 0.983837152317663 1.00986579351751 54 109 106.690084336602 111.041666666667 0.96081126607071 1.02165070613414 55 108 NA NA 0.910402841953998 NA 56 107 NA NA 0.890576636866561 NA 57 99 NA NA 0.84364928062458 NA 58 103 NA NA 0.854885183724673 NA 59 131 NA NA 1.13090391585190 NA 60 137 NA NA 1.18518353077846 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/1ogfp1259696912.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/1ogfp1259696912.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/248jz1259696912.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/248jz1259696912.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/3qz0z1259696912.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/3qz0z1259696912.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/4wq5g1259696912.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/01/t1259696976mqbenrdsjlojo0e/4wq5g1259696912.ps (open in new window)

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')

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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