*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 20 May 2011 01:41:30 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i.htm/, Retrieved Fri, 20 May 2011 03:45:13 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

90 51 47 59 54 79 59 80 46 62 55 77 72 72 71 50 66 78 59 52 71 98 70 84 90 98 98 78 59 0 58 55 62 80 91 86 61 49 61 56 73 85 82 32 39 30 51 48 57 59 32 56 54 74 62 78 72 48 59 61 80 69 58 63 27 23 34 45 51 51 73 37 35 66 54 30 66 61 37 55 64 53 63 70 72 52 53 50 60 73 66 78

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 90 NA NA 1.22344330851761 NA 2 51 NA NA 0.670235382929591 NA 3 47 NA NA 0.87552574869771 NA 4 59 60.3502254401592 62.7142857142857 0.962304278089098 0.977626836845904 5 54 65.4497609946263 61.2857142857143 1.06794481809414 0.825060308538539 6 79 63.167963542202 60.5714285714286 1.04286732263069 1.25063395382726 7 59 72.6030204167245 62.7142857142857 1.15767914104116 0.812638367678833 8 80 76.0282627435941 62.1428571428571 1.22344330851761 1.05224027372295 9 46 43.8525436259647 65.4285714285714 0.670235382929591 1.04896993871899 10 62 56.4088732375239 64.4285714285714 0.87552574869771 1.09911785932921 11 55 63.7870264333345 66.2857142857143 0.962304278089098 0.862244300688353 12 77 69.4164131761188 65 1.06794481809414 1.10924774814626 13 72 68.3823001553555 65.5714285714286 1.04286732263069 1.05290403856591 14 72 76.5722060431513 66.1428571428571 1.15767914104116 0.940288960193014 15 71 84.9419211342224 69.4285714285714 1.22344330851761 0.835865248300756 16 50 44.8100227444355 66.8571428571428 0.670235382929591 1.11582179471687 17 66 56.0336479166535 64 0.87552574869771 1.17786370250552 18 78 61.4500017579753 63.8571428571428 0.962304278089098 1.26932461787728 19 59 72.3151205395172 67.7142857142857 1.06794481809414 0.815873631404085 20 52 73.5966367685089 70.5714285714286 1.04286732263069 0.706554025879755 21 71 84.6759600304395 73.1428571428571 1.15767914104116 0.838490640962049 22 98 91.583470523318 74.8571428571429 1.22344330851761 1.0700620913361 23 70 53.9060743699086 80.4285714285714 0.670235382929591 1.29855495541474 24 84 76.1707401367008 87 0.87552574869771 1.10278566086201 25 90 84.6827764718406 88 0.962304278089098 1.06278990545294 26 98 88.0291657200452 82.4285714285714 1.06794481809414 1.11326739494118 27 98 75.5333903676802 72.4285714285714 1.04286732263069 1.29743944397249 28 78 79.5490952629715 68.7142857142857 1.15767914104116 0.980526550831905 29 59 77.9508165141218 63.7142857142857 1.22344330851761 0.756887517519607 30 0 39.2566438573046 58.5714285714286 0.670235382929591 0 31 58 49.0294419270718 56 0.87552574869771 1.18296267957264 32 55 55.6761760894407 57.8571428571429 0.962304278089098 0.987855198813323 33 62 65.9074516309524 61.7142857142857 1.06794481809414 0.94071305240518 34 80 73.4476557224188 70.4285714285714 1.04286732263069 1.08921107437853 35 91 80.0452434662748 69.1428571428571 1.15767914104116 1.13685705807542 36 86 85.6410315962325 70 1.22344330851761 1.00419154693815 37 61 46.3419893339889 69.1428571428571 0.670235382929591 1.31630085105691 38 49 59.6608260184011 68.1428571428571 0.87552574869771 0.821309446585386 39 61 64.7493307114236 67.2857142857143 0.962304278089098 0.942094680049533 40 56 71.247175721423 66.7142857142857 1.06794481809414 0.785996068376947 41 73 65.2536981874634 62.5714285714286 1.04286732263069 1.11871054097628 42 85 70.7838103379455 61.1428571428571 1.15767914104116 1.2008395647844 43 82 69.3867133544986 56.7142857142857 1.22344330851761 1.18178244847915 44 32 37.5331814440571 56 0.670235382929591 0.852578938657138 45 39 45.9025642531514 52.4285714285714 0.87552574869771 0.849625737353497 46 30 46.6030214674578 48.4285714285714 0.962304278089098 0.643735085308762 47 51 48.2100803596781 45.1428571428571 1.06794481809414 1.05787004749852 48 48 47.0780105644713 45.1428571428571 1.04286732263069 1.01958429051003 49 57 55.0724505666725 47.5714285714286 1.15767914104116 1.03500024810034 50 59 62.395608734398 51 1.22344330851761 0.945579363623934 51 32 36.3842065018921 54.2857142857143 0.670235382929591 0.879502484088416 52 56 49.2795921409854 56.2857142857143 0.87552574869771 1.13637304139588 53 54 57.0508964867108 59.2857142857143 0.962304278089098 0.94652325073592 54 74 65.2971974491843 61.1428571428571 1.06794481809414 1.13327987862861 55 62 66.147584464004 63.4285714285714 1.04286732263069 0.937298020817964 56 78 73.9260822922001 63.8571428571428 1.15767914104116 1.05510798870279 57 72 79.3490374381419 64.8571428571429 1.22344330851761 0.907383407846995 58 48 44.0440394496589 65.7142857142857 0.670235382929591 1.08981829550086 59 59 58.410074948833 66.7142857142857 0.87552574869771 1.01009971399084 60 61 61.4500017579753 63.8571428571428 0.962304278089098 0.992676944750179 61 80 66.8228329036045 62.5714285714286 1.06794481809414 1.19719557707774 62 69 62.1250962195713 59.5714285714286 1.04286732263069 1.11066226370307 63 58 63.0108218195262 54.4285714285714 1.15767914104116 0.920476805811578 64 63 61.8712758878904 50.5714285714286 1.22344330851761 1.0182431038622 65 27 30.5435838792199 45.5714285714286 0.670235382929591 0.883982708341218 66 23 37.6476071940015 43 0.87552574869771 0.610928601158605 67 34 40.4167796797421 42 0.962304278089098 0.841234761141587 68 45 46.3793178143739 43.4285714285714 1.06794481809414 0.97026006678463 69 51 46.7800484722911 44.8571428571429 1.04286732263069 1.09020836158835 70 51 53.9147714256314 46.5714285714286 1.15767914104116 0.945937424038755 71 73 62.5703863499005 51.1428571428571 1.22344330851761 1.16668609958353 72 37 35.13948364788 52.4285714285714 0.670235382929591 1.05294660475844 73 35 43.2759870070583 49.4285714285714 0.87552574869771 0.808762605328714 74 66 49.6274063414521 51.5714285714286 0.962304278089098 1.32991032305616 75 54 53.2446773592648 49.8571428571429 1.06794481809414 1.01418588069637 76 30 51.9943850854446 49.8571428571429 1.04286732263069 0.576985379300087 77 66 61.0262290063128 52.7142857142857 1.15767914104116 1.08150218479291 78 61 64.1433848894231 52.4285714285714 1.22344330851761 0.95099440269886 79 37 35.0437357360329 52.2857142857143 0.670235382929591 1.05582350805013 80 55 49.9049676757695 57 0.87552574869771 1.10209469240282 81 64 55.4012320099866 57.5714285714286 0.962304278089098 1.15520896698585 82 53 63.161307812996 59.1428571428571 1.06794481809414 0.839121320238001 83 63 63.9128687726525 61.2857142857143 1.04286732263069 0.985716980160918 84 70 70.618427603511 61 1.15767914104116 0.991242687999466 85 72 72.1831552025388 59 1.22344330851761 0.997462632354808 86 52 40.2141229757755 60 0.670235382929591 1.29307805696333 87 53 53.7822959914308 61.4285714285714 0.87552574869771 0.985454395781924 88 50 58.563088923708 60.8571428571429 0.962304278089098 0.853780101407161 89 60 65.9074516309524 61.7142857142857 1.06794481809414 0.91036747006953 90 73 NA NA 1.04286732263069 NA 91 66 NA NA 1.15767914104116 NA 92 78 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/1sy3u1305855687.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/1sy3u1305855687.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/2s39p1305855687.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/2s39p1305855687.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/3yb3u1305855687.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/3yb3u1305855687.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/4b4uz1305855687.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/20/t13058559135q550lr7yk2v88i/4b4uz1305855687.ps (open in new window)

Parameters (Session):

par1 = 60 ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

Parameters (R input):

par1 = multiplicative ; par2 = 7 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;

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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