WS 9

*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: Thu, 03 Dec 2009 08:08:45 -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/03/t1259853016cmb9irexju2zs0i.htm/, Retrieved Thu, 03 Dec 2009 16:10:22 +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/03/t1259853016cmb9irexju2zs0i.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:

WS 9 link 8

Dataseries X:

» Textbox « » Textfile « » CSV «

162 161 149 139 135 130 127 122 117 112 113 149 157 157 147 137 132 125 123 117 114 111 112 144 150 149 134 123 116 117 111 105 102 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 1 162 NA NA 1.18952770150201 NA 2 161 NA NA 1.16949833965368 NA 3 149 NA NA 1.08206739453927 NA 4 139 NA NA 1.01182342432999 NA 5 135 NA NA 0.991901410099487 NA 6 130 NA NA 0.987443308193821 NA 7 127 129.662611035097 134.458333333333 0.964333022882657 0.979465082386961 8 122 123.686848427896 134.083333333333 0.922462511581571 0.986361941877119 9 117 120.827184057670 133.833333333333 0.902818311763416 0.968325140674935 10 112 114.403180167371 133.666666666667 0.855884140903026 0.978993764300474 11 113 114.214050154276 133.458333333333 0.855803060787585 0.989370395738208 12 149 141.969475382265 133.125 1.06643737376349 1.04952138196471 13 157 157.909802374392 132.75 1.18952770150201 0.99423846803231 14 157 154.812342711656 132.375 1.16949833965368 1.01413102631241 15 147 142.877982220623 132.041666666667 1.08206739453927 1.02884991595845 16 137 133.434214083517 131.875 1.01182342432999 1.02672317546871 17 132 130.724340006028 131.791666666667 0.991901410099487 1.00975839689772 18 125 129.889938498662 131.541666666667 0.987443308193821 0.962353215690278 19 123 126.367806540248 131.041666666667 0.964333022882657 0.973349173080919 20 117 120.304485885430 130.416666666667 0.922462511581571 0.972532313644756 21 114 116.952588803019 129.541666666667 0.902818311763416 0.974753967969087 22 111 109.909788427630 128.416666666667 0.855884140903026 1.00991914904001 23 112 108.829622563488 127.166666666667 0.855803060787585 1.02913156695607 24 144 134.548848656494 126.166666666667 1.06643737376349 1.07024327177734 25 150 149.087471921585 125.333333333333 1.18952770150201 1.00612075626914 26 149 145.407626896941 124.333333333333 1.16949833965368 1.02470553422624 27 134 133.454978659843 123.333333333333 1.08206739453927 1.00408393411494 28 123 123.611095005647 122.166666666667 1.01182342432999 0.995056309422558 29 116 119.730766044092 120.708333333333 0.991901410099487 0.96884037271825 30 117 117.588040617414 119.083333333333 0.987443308193821 0.994999146049831 31 111 113.228769103472 117.416666666667 0.964333022882657 0.980316229513762 32 105 106.582856025654 115.541666666667 0.922462511581571 0.985149056005095 33 102 102.657965533432 113.708333333333 0.902818311763416 0.99359070160788 34 95 96.0373329771604 112.208333333333 0.855884140903026 0.989198648640033 35 93 95.0297982082881 111.041666666667 0.855803060787585 0.97864040283618 36 124 117.396980895131 110.083333333333 1.06643737376349 1.05624522074181 37 130 129.856774080636 109.166666666667 1.18952770150201 1.00110295300633 38 124 126.695653462482 108.333333333333 1.16949833965368 0.97872339430113 39 115 116.367331054410 107.541666666667 1.08206739453927 0.988249871832404 40 106 107.969991237879 106.708333333333 1.01182342432999 0.981754270651568 41 105 105.141549470546 106 0.991901410099487 0.998653724704854 42 105 104.092982072099 105.416666666667 0.987443308193821 1.00871353582005 43 101 100.933523061718 104.666666666667 0.964333022882657 1.00065862100386 44 95 95.8976652665008 103.958333333333 0.922462511581571 0.99063934180247 45 93 93.3664604081999 103.416666666667 0.902818311763416 0.996075031584171 46 84 88.2630520306246 103.125 0.855884140903026 0.951700604810884 47 87 88.290349104586 103.166666666667 0.855803060787585 0.985385162504484 48 116 110.287398403375 103.416666666667 1.06643737376349 1.05179741003348 49 120 123.611753647750 103.916666666667 1.18952770150201 0.970781470684069 50 117 122.553680176209 104.791666666667 1.16949833965368 0.954683693152067 51 109 114.608971538284 105.916666666667 1.08206739453927 0.95105992608606 52 105 108.391584331350 107.125 1.01182342432999 0.968709892449013 53 107 107.538644544953 108.416666666667 0.991901410099487 0.994991153671018 54 109 108.330759603097 109.708333333333 0.987443308193821 1.00617775043169 55 109 107.081146082595 111.041666666667 0.964333022882657 1.01791962439331 56 108 NA NA 0.922462511581571 NA 57 107 NA NA 0.902818311763416 NA 58 99 NA NA 0.855884140903026 NA 59 103 NA NA 0.855803060787585 NA 60 131 NA NA 1.06643737376349 NA 61 137 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/1jqaj1259852923.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/1jqaj1259852923.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/2ddjf1259852923.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/2ddjf1259852923.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/31uin1259852923.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/31uin1259852923.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/4tkf91259852923.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t1259853016cmb9irexju2zs0i/4tkf91259852923.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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