classical decomposition multiplicative

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 19 May 2008 10:19:08 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1.htm/, Retrieved Mon, 19 May 2008 18:20:17 +0200

User-defined keywords:

Dataseries X:

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104,7 104,7 104,7 104,7 106 107 107 107 107 107 107 107 107 107 107 107 107,6 109,9 109,9 109,9 109,9 109,9 109,9 109,9 109,9 109,9 109,9 109,9 110,6 114,3 114,3 114,3 114,3 114,3 114,3 114,3 114,3 114,3 114,3 114,3 114,3 119,01 119,01 119,01 119,01 119,01 119,01 119,01 119,01 119,01 119,01 119,01 121,27 123,54 123,54 123,54 123,54 123,54 123,54 123,54 123,54 123,54 123,54 123,54 123,54 125,24 125,24 125,24 125,24 125,24 125,24 125,24 125,24 125,24 125,24 125,24 125,24 128,35 128,35 128,35 128,35 128,35 128,35 128,35

Text written by user:

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 1 104.7 NA NA 0.995535352336965 NA 2 104.7 NA NA 0.992900042396295 NA 3 104.7 NA NA 0.990280103498155 NA 4 104.7 NA NA 0.98767539439766 NA 5 106 NA NA 0.991170558649357 NA 6 107 NA NA 1.01392585017503 NA 7 107 107.443811201634 106.245833333333 1.01127552799687 0.995869364678424 8 107 107.357166314666 106.4375 1.00864043513485 0.996673102253661 9 107 107.271120511037 106.629166666667 1.00602043384975 0.99747256754898 10 107 107.185667936694 106.820833333333 1.00341538810339 0.998267791391628 11 107 107.075622492774 106.983333333333 1.00086264987793 0.99929374687708 12 107 106.98845682349 107.170833333333 0.99829826358375 1.00010789179368 13 107 106.932941032894 107.4125 0.995535352336965 1.00062711234217 14 107 106.889826647471 107.654166666667 0.992900042396295 1.0010307187876 15 107 106.847097000353 107.895833333333 0.990280103498155 1.00143104495994 16 107 106.804747961677 108.1375 0.98767539439766 1.00182812133402 17 107.6 107.422239170952 108.379166666667 0.991170558649357 1.00165478610779 18 109.9 110.133470784220 108.620833333333 1.01392585017503 0.99788011053717 19 109.9 110.089982166559 108.8625 1.01127552799687 0.998274301050644 20 109.9 110.046874141692 109.104166666667 1.00864043513485 0.998665349262872 21 109.9 110.004142689662 109.345833333333 1.00602043384975 0.9990532839299 22 109.9 109.961783843780 109.5875 1.00341538810339 0.999438133489469 23 109.9 109.928081044926 109.833333333333 1.00086264987793 0.999744550758467 24 109.9 109.954234581553 110.141666666667 0.99829826358375 0.999506753134522 25 109.9 110.014952561171 110.508333333333 0.995535352336965 0.998955118749819 26 109.9 110.087792200689 110.875 0.992900042396295 0.998294159625376 27 109.9 110.160409179974 111.241666666667 0.990280103498155 0.997636091024785 28 109.9 110.232804643066 111.608333333333 0.98767539439766 0.996980892900773 29 110.6 110.986323304762 111.975 0.991170558649357 0.99651918098322 30 114.3 113.90611988508 112.341666666667 1.01392585017503 1.00345793637179 31 114.3 113.979179301314 112.708333333333 1.01127552799687 1.00281473073111 32 114.3 114.052017202873 113.075 1.00864043513485 1.00217429558204 33 114.3 114.124634716638 113.441666666667 1.00602043384975 1.00153661200141 34 114.3 114.197032961066 113.808333333333 1.00341538810339 1.00090166124516 35 114.3 114.244301222525 114.145833333333 1.00086264987793 1.00048754097035 36 114.3 114.301407561851 114.49625 0.99829826358375 0.999987685524781 37 114.3 114.375812210803 114.88875 0.995535352336965 0.999337165705423 38 114.3 114.462758012498 115.28125 0.992900042396295 0.998578070148544 39 114.3 114.549413122020 115.67375 0.990280103498155 0.997822659102111 40 114.3 114.635779245008 116.06625 0.98767539439766 0.997070903628702 41 114.3 115.430484297106 116.45875 0.991170558649357 0.99020636269535 42 119.01 118.478503000265 116.85125 1.01392585017503 1.00448602055458 43 119.01 118.565735185583 117.24375 1.01127552799687 1.00374699160530 44 119.01 118.652678387632 117.63625 1.00864043513485 1.00301149217383 45 119.01 118.739334281743 118.02875 1.00602043384975 1.00227949499544 46 119.01 118.825704528438 118.42125 1.00341538810339 1.00155097310210 47 119.01 119.010492566464 118.907916666667 1.00086264987793 0.999995861150948 48 119.01 119.183917985995 119.387083333333 0.99829826358375 0.998540759618127 49 119.01 119.229876666240 119.764583333333 0.995535352336965 0.998155859316576 50 119.01 119.289079635246 120.142083333333 0.992900042396295 0.997660476247287 51 119.01 119.348145456888 120.519583333333 0.990280103498155 0.997166730529466 52 119.01 119.407074462777 120.897083333333 0.98767539439766 0.9966746152641 53 121.27 120.203796512468 121.274583333333 0.991170558649357 1.00886996516305 54 123.54 123.346192019314 121.652083333333 1.01392585017503 1.00157125224146 55 123.54 123.405531316655 122.029583333333 1.01127552799687 1.00108964875326 56 123.54 123.464733796921 122.407083333333 1.00864043513485 1.00060961701989 57 123.54 123.523799795060 122.784583333333 1.00602043384975 1.00013115047438 58 123.54 123.582729647539 123.162083333333 1.00341538810339 0.999654242565604 59 123.54 123.551906840285 123.445416666667 1.00086264987793 0.999903628842404 60 123.54 123.400480276807 123.610833333333 0.99829826358375 1.00113062544716 61 123.54 123.199988690080 123.7525 0.995535352336965 1.00275983231439 62 123.54 123.014523335987 123.894166666667 0.992900042396295 1.00427166361957 63 123.54 122.830217870813 124.035833333333 0.990280103498155 1.00577856281207 64 123.54 122.647061287815 124.1775 0.98767539439766 1.00728055530078 65 123.54 123.221497875823 124.319166666667 0.991170558649357 1.00258479347896 66 125.24 126.194056250993 124.460833333333 1.01392585017503 0.992439768723376 67 125.24 126.00745897723 124.6025 1.01127552799687 0.993909416288056 68 125.24 125.822010547201 124.744166666667 1.00864043513485 0.995374334389748 69 125.24 125.637700231687 124.885833333333 1.00602043384975 0.996834547027257 70 125.24 125.454517436096 125.0275 1.00341538810339 0.99829007802604 71 125.24 125.277143833012 125.169166666667 1.00086264987793 0.999703506706204 72 125.24 125.156237347885 125.369583333333 0.99829826358375 1.00066926470378 73 125.24 125.067861894902 125.62875 0.995535352336965 1.00137635762289 74 125.24 124.994117795515 125.887916666667 0.992900042396295 1.00196715020532 75 125.24 124.920946739324 126.147083333333 0.990280103498155 1.00255404132777 76 125.24 124.848342823079 126.40625 0.98767539439766 1.00313706348090 77 125.24 125.547031799054 126.665416666667 0.991170558649357 0.997554447965404 78 128.35 128.692116064362 126.924583333333 1.01392585017503 0.997341592672308 79 128.35 NA NA 1.01127552799687 NA 80 128.35 NA NA 1.00864043513485 NA 81 128.35 NA NA 1.00602043384975 NA 82 128.35 NA NA 1.00341538810339 NA 83 128.35 NA NA 1.00086264987793 NA 84 128.35 NA NA 0.99829826358375 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/1vymd1211213945.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/1vymd1211213945.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/24x2b1211213945.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/24x2b1211213945.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/34g841211213945.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/34g841211213945.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/4uz7l1211213945.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/19/t12112140122gjg7yng7dut1n1/4uz7l1211213945.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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