Multiplicatief decompositiemodel - Blue Jeans (D) - Alexia Versluys

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 23 May 2008 06:44:41 -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/23/t1211546775oq2yqbsgfgz5pe7.htm/, Retrieved Fri, 23 May 2008 14:46:15 +0200

User-defined keywords:

Multiplicatief decompositiemodel - Blue Jeans (D) - Alexia Versluys

Dataseries X:

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44,13 44,13 44,17 44,14 44,15 44,14 44,14 44,14 44,19 44,29 44,29 44,29 44,29 44,27 44,26 44,33 44,32 44,34 44,34 44,34 44,37 44,47 44,51 44,51 44,51 44,52 44,7 44,84 44,9 44,95 44,94 44,94 44,91 45,28 45,36 45,34 45,34 45,34 45,44 45,62 45,75 45,77 45,77 45,77 46,09 46,25 46,35 46,34 46,34 46,28 46,59 46,42 46,29 46,29 46,29 46,3 46,52 46,66 46,67 46,72 46,72 46,72 46,76 46,89 47,04 47,02 47,02 47,18 47,22 47,8 47,88 47,91

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 44.13 NA NA 0.99997581792736 NA 2 44.13 NA NA 0.998604347384568 NA 3 44.17 NA NA 1.00074767725670 NA 4 44.14 NA NA 1.00091607839687 NA 5 44.15 NA NA 1.00011371293273 NA 6 44.14 NA NA 0.999510282162221 NA 7 44.14 44.1167443144233 44.19 0.998342256492946 1.00052713965951 8 44.14 44.0823701114498 44.2025 0.99728228293535 1.00130732282326 9 44.19 44.1712707924242 44.2120833333333 0.999076891703985 1.00042401332902 10 44.29 44.3188854764606 44.22375 1.00215123042394 0.999348235494868 11 44.29 44.3363017205637 44.23875 1.00220511928035 0.99895567021229 12 44.29 44.3017090552363 44.2541666666667 1.00107430310298 0.999735697437278 13 44.29 44.2697627728259 44.2708333333333 0.99997581792736 1.00045713430356 14 44.27 44.2256900347941 44.2875 0.998604347384568 1.00100190557052 15 44.26 44.3364579280628 44.3033333333333 1.00074767725670 0.99827550662286 16 44.33 44.3589324010852 44.3183333333333 1.00091607839687 0.999347766063808 17 44.32 44.3400414628727 44.335 1.00011371293273 0.999548005319538 18 44.34 44.3316127148351 44.3533333333333 0.999510282162221 1.00018919422623 19 44.34 44.2981098243528 44.3716666666667 0.998342256492946 1.00094564250740 20 44.34 44.2706071423539 44.39125 0.99728228293535 1.00156747020485 21 44.37 44.378995529491 44.42 0.999076891703985 0.999797302093396 22 44.47 44.5552261416355 44.4595833333333 1.00215123042394 0.998087179686517 23 44.51 44.6031388335721 44.505 1.00220511928035 0.997911832305802 24 44.51 44.6024484604601 44.5545833333333 1.00107430310298 0.997927278352396 25 44.51 44.6039213586499 44.605 0.99997581792736 0.997894325077503 26 44.52 44.5926771324579 44.655 0.998604347384568 0.99837020028553 27 44.7 44.7359230425678 44.7025 1.00074767725670 0.999196997845924 28 44.84 44.7997525239459 44.75875 1.00091607839687 1.00089838612463 29 44.9 44.8330141805391 44.8279166666667 1.00011371293273 1.00149411813337 30 44.95 44.8759293559959 44.8979166666667 0.999510282162221 1.00165056512627 31 44.94 44.8925394429063 44.9670833333333 0.998342256492946 1.00105720366196 32 44.94 44.9134386805626 45.0358333333333 0.99728228293535 1.00059138913024 33 44.91 45.0592003799261 45.1008333333333 0.999076891703985 0.996688792107536 34 45.28 45.2613251960717 45.1641666666667 1.00215123042394 1.00041259958358 35 45.36 45.3318254723822 45.2320833333333 1.00220511928035 1.00062151760544 36 45.34 45.3503343877366 45.3016666666667 1.00107430310298 0.99977212102455 37 45.34 45.3693195159552 45.3704166666667 0.99997581792736 0.999353758966016 38 45.34 45.37616546001 45.4395833333333 0.998604347384568 0.99920298554002 39 45.44 45.557370094316 45.5233333333333 1.00074767725670 0.997423685913542 40 45.62 45.6547016742432 45.6129166666667 1.00091607839687 0.999239910174186 41 45.75 45.6997793984142 45.6945833333333 1.00011371293273 1.00109892437659 42 45.77 45.7550819416811 45.7775 0.999510282162221 1.00032604156054 43 45.77 45.7848078346469 45.8608333333333 0.998342256492946 0.999676577551655 44 45.77 45.8168102151882 45.9416666666667 0.99728228293535 0.998978317893184 45 46.09 45.9862604790198 46.02875 0.999076891703985 1.00225588077612 46 46.25 46.2091932348477 46.11 1.00215123042394 1.00088308759136 47 46.35 46.2676345025102 46.1658333333333 1.00220511928035 1.0017801968563 48 46.34 46.2596435463885 46.21 1.00107430310298 1.00173707463895 49 46.34 46.2522148318669 46.2533333333333 0.99997581792736 1.00189796679904 50 46.28 46.2324686878923 46.2970833333333 0.998604347384568 1.00102809375006 51 46.59 46.3717285166836 46.3370833333333 1.00074767725670 1.0047069947638 52 46.42 46.4145637970928 46.3720833333333 1.00091607839687 1.00011712278351 53 46.29 46.4077765643611 46.4025 1.00011371293273 0.997462137316624 54 46.29 46.4089282512622 46.4316666666667 0.999510282162221 0.997437384233087 55 46.29 46.3863090441839 46.4633333333333 0.998342256492946 0.997923761425119 56 46.3 46.3711329507865 46.4975 0.99728228293535 0.998466007917858 57 46.52 46.4799709763368 46.5229166666667 0.999076891703985 1.00086121016908 58 46.66 46.6497222132216 46.5495833333333 1.00215123042394 1.00022031828467 59 46.67 46.7031761439308 46.6004166666667 1.00220511928035 0.999289638378586 60 46.72 46.7122125542496 46.6620833333333 1.00107430310298 1.00016671113023 61 46.72 46.7217868097019 46.7229166666667 0.99997581792736 0.999961756391955 62 46.72 46.7246974141240 46.79 0.998604347384568 0.999899466141379 63 46.76 46.8908663742605 46.8558333333333 1.00074767725670 0.9972091286773 64 46.89 46.975493849361 46.9325 1.00091607839687 0.998180032984109 65 47.04 47.0357646332735 47.0304166666667 1.00011371293273 1.00009004566545 66 47.02 47.1073360609231 47.1304166666667 0.999510282162221 0.998146019957272 67 47.02 NA NA 0.998342256492946 NA 68 47.18 NA NA 0.99728228293535 NA 69 47.22 NA NA 0.999076891703985 NA 70 47.8 NA NA 1.00215123042394 NA 71 47.88 NA NA 1.00220511928035 NA 72 47.91 NA NA 1.00107430310298 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/1yoa81211546676.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/1yoa81211546676.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/28cp01211546676.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/28cp01211546676.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/33xqq1211546676.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/33xqq1211546676.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/4hnbw1211546676.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/23/t1211546775oq2yqbsgfgz5pe7/4hnbw1211546676.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')

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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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