Klassieke decompositie - prijs van stof voor jurken

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 May 2008 08:44:38 -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/24/t1211640573fsxh0uewowe0c3l.htm/, Retrieved Sat, 24 May 2008 16:49:33 +0200

User-defined keywords:

Dataseries X:

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20.66 20.66 20.67 20.71 20.73 20.73 20.74 20.74 20.75 20.75 20.77 20.78 20.78 20.8 20.84 20.85 20.86 20.86 20.86 20.86 20.9 20.92 20.95 20.95 20.95 20.96 21.1 21.18 21.19 21.19 21.19 21.19 21.19 21.21 21.22 21.22 21.22 21.23 21.41 21.42 21.43 21.44 21.44 21.44 21.48 21.53 21.54 21.54 21.54 21.54 21.54 21.54 21.54 21.54 21.54 21.54 21.57 21.6 21.61 21.6 21.6 21.71 21.75 21.84 21.85 21.92 21.92 21.93 22 22 21.99 22.01 22.01

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 20.66 NA NA 0.998498838540187 NA 2 20.66 NA NA 0.998189033040509 NA 3 20.67 NA NA 1.00164531676306 NA 4 20.71 NA NA 1.00200768733897 NA 5 20.73 NA NA 1.00153015665378 NA 6 20.73 NA NA 1.00083016105406 NA 7 20.74 20.7296784877690 20.7291666666667 1.00002469086725 1.00049790990425 8 20.74 20.7229430370479 20.74 0.999177581342715 1.00082309558645 9 20.75 20.7441733191847 20.7529166666667 0.99957869307614 1.00028088276769 10 20.75 20.7669091943619 20.7658333333333 1.00005180919115 0.999185762589724 11 20.77 20.7727402109570 20.7770833333333 0.999790965733419 0.999868086206768 12 20.78 20.7603740573753 20.7879166666667 0.998675066398762 1.00094535592521 13 20.78 20.7671116769050 20.7983333333333 0.998498838540187 1.00062061221105 14 20.8 20.7706501291846 20.8083333333333 0.998189033040509 1.00141304536126 15 20.84 20.8538381427917 20.8195833333333 1.00164531676306 0.999336422259686 16 20.85 20.8747426496921 20.8329166666667 1.00200768733897 0.998814708755585 17 20.86 20.8793999408397 20.8475 1.00153015665378 0.999070857357268 18 20.86 20.8794022224233 20.8620833333333 1.00083016105406 0.999070748184426 19 20.86 20.8767654527174 20.87625 1.00002469086725 0.999196932457984 20 20.86 20.8728196742493 20.89 0.999177581342715 0.999385819719167 21 20.9 20.8986915254894 20.9075 0.99957869307614 1.00006261035573 22 20.92 20.9331678076399 20.9320833333333 1.00005180919115 0.999370959629193 23 20.95 20.9552020622034 20.9595833333333 0.999790965733419 0.999751753183388 24 20.95 20.959276841433 20.9870833333333 0.998675066398762 0.999557387332435 25 20.95 20.9830370507393 21.0145833333333 0.998498838540187 0.998425535318867 26 20.96 21.0039768156578 21.0420833333333 0.998189033040509 0.997906262416695 27 21.1 21.1025800631212 21.0679166666667 1.00164531676306 0.99987773707701 28 21.18 21.1344296419941 21.0920833333333 1.00200768733897 1.00215621423326 29 21.19 21.1477265619765 21.1154166666667 1.00153015665378 1.00199895898501 30 21.19 21.1554645418474 21.1379166666667 1.00083016105406 1.00163246040210 31 21.19 21.1609391357055 21.1604166666667 1.00002469086725 1.00137332582964 32 21.19 21.1654954407843 21.1829166666667 0.999177581342715 1.00115775977389 33 21.19 21.1981486422901 21.2070833333333 0.99957869307614 0.999615596511392 34 21.21 21.2310999091281 21.23 1.00005180919115 0.999006179179676 35 21.22 21.2455580218351 21.25 0.999790965733419 0.998797018096259 36 21.22 21.2422347769127 21.2704166666667 0.998675066398762 0.998953275060455 37 21.22 21.2592883960687 21.29125 0.998498838540187 0.998151942090592 38 21.23 21.2734878545787 21.3120833333333 0.998189033040509 0.997955772232742 39 21.41 21.3696854809246 21.3345833333333 1.00164531676306 1.00188652842417 40 21.42 21.4028842015604 21.36 1.00200768733897 1.00079969588577 41 21.43 21.4193916169688 21.3866666666667 1.00153015665378 1.00049527004412 42 21.44 21.4311098487043 21.4133333333333 1.00083016105406 1.00041482458717 43 21.44 21.4405293721938 21.44 1.00002469086725 0.999975309742378 44 21.44 21.4485957554981 21.46625 0.999177581342715 0.999599239241765 45 21.48 21.4755317296187 21.4845833333333 0.99957869307614 1.00020806331771 46 21.53 21.4961136385637 21.495 1.00005180919115 1.00157639478494 47 21.54 21.5000881385281 21.5045833333333 0.999790965733419 1.00185635803978 48 21.54 21.4848295951254 21.5133333333333 0.998675066398762 1.00256787723777 49 21.54 21.4893591701157 21.5216666666667 0.998498838540187 1.00235655374753 50 21.54 21.4910098813622 21.53 0.998189033040509 1.00227956335734 51 21.54 21.5733533619998 21.5379166666667 1.00164531676306 0.998453955607173 52 21.54 21.587838120515 21.5445833333333 1.00200768733897 0.997784024493423 53 21.54 21.5833921801209 21.5504166666667 1.00153015665378 0.997989556981647 54 21.54 21.5737281466546 21.5558333333333 1.00083016105406 0.99843661019434 55 21.54 21.5613656890069 21.5608333333333 1.00002469086725 0.999009075338035 56 21.54 21.5526767535546 21.5704166666667 0.999177581342715 0.999411824633221 57 21.57 21.5771555634148 21.58625 0.99957869307614 0.999668373183212 58 21.6 21.6086194670977 21.6075 1.00005180919115 0.999601109774233 59 21.61 21.6283946457972 21.6329166666667 0.999790965733419 0.999149514048616 60 21.6 21.6329663966412 21.6616666666667 0.998675066398762 0.998476103737382 61 21.6 21.6607681373984 21.6933333333333 0.998498838540187 0.997194552981086 62 21.71 21.6860726549022 21.7254166666667 0.998189033040509 1.00110335077626 63 21.75 21.7953847405489 21.7595833333333 1.00164531676306 0.99791769032347 64 21.84 21.8379225391467 21.7941666666667 1.00200768733897 1.00009513088297 65 21.85 21.8600648858965 21.8266666666667 1.00153015665378 0.99953957657724 66 21.92 21.8777303080747 21.8595833333333 1.00083016105406 1.00193208762198 67 21.92 21.8942905756748 21.89375 1.00002469086725 1.00117425244889 68 21.93 NA NA 0.999177581342715 NA 69 22 NA NA 0.99957869307614 NA 70 22 NA NA 1.00005180919115 NA 71 21.99 NA NA 0.999790965733419 NA 72 22.01 NA NA 0.998675066398762 NA 73 22.01 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/1dd371211640273.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/1dd371211640273.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/20ty01211640273.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/20ty01211640273.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/34gjk1211640273.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/34gjk1211640273.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/4zqa31211640273.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211640573fsxh0uewowe0c3l/4zqa31211640273.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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