Classical Decomposition_Gem consumptieprijs roze zalm_Dominique Van Santfoort

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 16 Jul 2008 03:51:34 -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/Jul/16/t12162019414iun6h4ctgy997c.htm/, Retrieved Wed, 16 Jul 2008 09:52:26 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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11.73 11.74 11.65 11.38 11.53 11.75 11.82 11.83 11.63 11.55 11.4 11.4 11.63 11.46 11.35 11.7 11.52 11.64 11.9 11.73 11.7 11.54 11.97 11.64 11.98 11.79 11.66 11.96 11.83 12.36 12.53 12.55 12.53 12.24 12.34 12.05 12.22 12.23 11.92 12.13 12.1 12.15 12.23 12.08 12.02 11.93 12.16 11.87 11.93 11.79 11.43 11.63 11.93 11.89 11.83 11.59 12.04 11.81 11.9 11.72 11.91 11.94 11.91 11.84 12.01 11.89 11.8 11.7 11.5 11.76 11.61 11.27 11.64 11.39 11.54 11.62 11.59 11.44 11.31 11.56 11.4 11.51 11.5 11.24 11.8 11.87 11.86 12.11 11.92 12.61 13.34 13.31 13.47 13.3 13.18 13.24

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 11.73 NA NA 0.0633564814814815 NA 2 11.74 NA NA -0.0495601851851849 NA 3 11.65 NA NA -0.177754629629629 NA 4 11.38 NA NA 0.00245370370370433 NA 5 11.53 NA NA 0.0187037037037035 NA 6 11.75 NA NA 0.0841203703703704 NA 7 11.82 11.7357175925926 11.6133333333333 0.122384259259259 0.0842824074074056 8 11.83 11.6508564814815 11.5975 0.0533564814814811 0.179143518518517 9 11.63 11.6169675925926 11.5733333333333 0.0436342592592592 0.0130324074074064 10 11.55 11.5447453703704 11.5741666666667 -0.0294212962962966 0.00525462962963097 11 11.4 11.6670370370370 11.5870833333333 0.0799537037037034 -0.267037037037035 12 11.4 11.3708564814815 11.5820833333333 -0.211226851851851 0.0291435185185183 13 11.63 11.6441898148148 11.5808333333333 0.0633564814814815 -0.0141898148148130 14 11.46 11.5304398148148 11.58 -0.0495601851851849 -0.0704398148148115 15 11.35 11.4009953703704 11.57875 -0.177754629629629 -0.0509953703703694 16 11.7 11.5837037037037 11.58125 0.00245370370370433 0.116296296296298 17 11.52 11.6232870370370 11.6045833333333 0.0187037037037035 -0.103287037037035 18 11.64 11.7224537037037 11.6383333333333 0.0841203703703704 -0.0824537037037025 19 11.9 11.7853009259259 11.6629166666667 0.122384259259259 0.114699074074075 20 11.73 11.7446064814815 11.69125 0.0533564814814811 -0.0146064814814810 21 11.7 11.7615509259259 11.7179166666667 0.0436342592592592 -0.0615509259259248 22 11.54 11.7122453703704 11.7416666666667 -0.0294212962962966 -0.172245370370371 23 11.97 11.8453703703704 11.7654166666667 0.0799537037037034 0.124629629629629 24 11.64 11.5971064814815 11.8083333333333 -0.211226851851851 0.0428935185185217 25 11.98 11.9279398148148 11.8645833333333 0.0633564814814815 0.0520601851851872 26 11.79 11.8754398148148 11.925 -0.0495601851851849 -0.0854398148148157 27 11.66 11.8159953703704 11.99375 -0.177754629629629 -0.155995370370372 28 11.96 12.0599537037037 12.0575 0.00245370370370433 -0.0999537037037044 29 11.83 12.1207870370370 12.1020833333333 0.0187037037037035 -0.290787037037036 30 12.36 12.2187037037037 12.1345833333333 0.0841203703703704 0.141296296296296 31 12.53 12.2840509259259 12.1616666666667 0.122384259259259 0.245949074074073 32 12.55 12.2433564814815 12.19 0.0533564814814811 0.306643518518518 33 12.53 12.2628009259259 12.2191666666667 0.0436342592592592 0.267199074074076 34 12.24 12.2076620370370 12.2370833333333 -0.0294212962962966 0.0323379629629645 35 12.34 12.3353703703704 12.2554166666667 0.0799537037037034 0.00462962962963331 36 12.05 12.0466898148148 12.2579166666667 -0.211226851851851 0.00331018518518533 37 12.22 12.3000231481481 12.2366666666667 0.0633564814814815 -0.0800231481481468 38 12.23 12.1550231481481 12.2045833333333 -0.0495601851851849 0.0749768518518525 39 11.92 11.9859953703704 12.16375 -0.177754629629629 -0.06599537037037 40 12.13 12.1320370370370 12.1295833333333 0.00245370370370433 -0.00203703703703439 41 12.1 12.1278703703704 12.1091666666667 0.0187037037037035 -0.0278703703703691 42 12.15 12.1782870370370 12.0941666666667 0.0841203703703704 -0.0282870370370389 43 12.23 12.1969675925926 12.0745833333333 0.122384259259259 0.0330324074074078 44 12.08 12.0975231481481 12.0441666666667 0.0533564814814811 -0.0175231481481468 45 12.02 12.0490509259259 12.0054166666667 0.0436342592592592 -0.0290509259259242 46 11.93 11.9347453703704 11.9641666666667 -0.0294212962962966 -0.00474537037036882 47 12.16 12.0162037037037 11.93625 0.0799537037037034 0.143796296296298 48 11.87 11.7071064814815 11.9183333333333 -0.211226851851851 0.162893518518517 49 11.93 11.9541898148148 11.8908333333333 0.0633564814814815 -0.0241898148148145 50 11.79 11.8041898148148 11.85375 -0.0495601851851849 -0.0141898148148165 51 11.43 11.6564120370370 11.8341666666667 -0.177754629629629 -0.226412037037036 52 11.63 11.8324537037037 11.83 0.00245370370370433 -0.202453703703702 53 11.93 11.8328703703704 11.8141666666667 0.0187037037037035 0.0971296296296291 54 11.89 11.8812037037037 11.7970833333333 0.0841203703703704 0.00879629629629619 55 11.83 11.9123842592593 11.79 0.122384259259259 -0.0823842592592623 56 11.59 11.8487731481481 11.7954166666667 0.0533564814814811 -0.258773148148146 57 12.04 11.8653009259259 11.8216666666667 0.0436342592592592 0.174699074074075 58 11.81 11.8209953703704 11.8504166666667 -0.0294212962962966 -0.0109953703703685 59 11.9 11.9424537037037 11.8625 0.0799537037037034 -0.0424537037037034 60 11.72 11.6546064814815 11.8658333333333 -0.211226851851851 0.065393518518519 61 11.91 11.9279398148148 11.8645833333333 0.0633564814814815 -0.0179398148148131 62 11.94 11.8183564814815 11.8679166666667 -0.0495601851851849 0.121643518518518 63 11.91 11.6722453703704 11.85 -0.177754629629629 0.237754629629629 64 11.84 11.8278703703704 11.8254166666667 0.00245370370370433 0.0121296296296300 65 12.01 11.8299537037037 11.81125 0.0187037037037035 0.180046296296297 66 11.89 11.8645370370370 11.7804166666667 0.0841203703703704 0.0254629629629637 67 11.8 11.8728009259259 11.7504166666667 0.122384259259259 -0.0728009259259235 68 11.7 11.7696064814815 11.71625 0.0533564814814811 -0.0696064814814807 69 11.5 11.7215509259259 11.6779166666667 0.0436342592592592 -0.221550925925923 70 11.76 11.6239120370370 11.6533333333333 -0.0294212962962966 0.136087962962964 71 11.61 11.7066203703704 11.6266666666667 0.0799537037037034 -0.0966203703703705 72 11.27 11.3791898148148 11.5904166666667 -0.211226851851851 -0.109189814814815 73 11.64 11.6146064814815 11.55125 0.0633564814814815 0.0253935185185181 74 11.39 11.4754398148148 11.525 -0.0495601851851849 -0.0854398148148157 75 11.54 11.3372453703704 11.515 -0.177754629629629 0.202754629629629 76 11.62 11.5028703703704 11.5004166666667 0.00245370370370433 0.117129629629629 77 11.59 11.5041203703704 11.4854166666667 0.0187037037037035 0.0858796296296287 78 11.44 11.5637037037037 11.4795833333333 0.0841203703703704 -0.123703703703704 79 11.31 11.6073842592593 11.485 0.122384259259259 -0.297384259259259 80 11.56 11.5650231481481 11.5116666666667 0.0533564814814811 -0.0050231481481493 81 11.4 11.5886342592593 11.545 0.0436342592592592 -0.188634259259260 82 11.51 11.5493287037037 11.57875 -0.0294212962962966 -0.0393287037037027 83 11.5 11.6928703703704 11.6129166666667 0.0799537037037034 -0.192870370370368 84 11.24 11.4641898148148 11.6754166666667 -0.211226851851851 -0.224189814814814 85 11.8 11.8721064814815 11.80875 0.0633564814814815 -0.0721064814814785 86 11.87 11.9166898148148 11.96625 -0.0495601851851849 -0.0466898148148172 87 11.86 11.9476620370370 12.1254166666667 -0.177754629629629 -0.08766203703704 88 12.11 12.2887037037037 12.28625 0.00245370370370433 -0.178703703703706 89 11.92 12.4495370370370 12.4308333333333 0.0187037037037035 -0.529537037037038 90 12.61 12.6682870370370 12.5841666666667 0.0841203703703704 -0.0582870370370401 91 13.34 NA NA 0.122384259259259 NA 92 13.31 NA NA 0.0533564814814811 NA 93 13.47 NA NA 0.0436342592592592 NA 94 13.3 NA NA -0.0294212962962966 NA 95 13.18 NA NA 0.0799537037037034 NA 96 13.24 NA NA -0.211226851851851 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/1pxiw1216201892.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/1pxiw1216201892.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/2wchl1216201892.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/2wchl1216201892.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/3ksrx1216201892.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/3ksrx1216201892.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/49cha1216201892.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Jul/16/t12162019414iun6h4ctgy997c/49cha1216201892.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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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