KDGP2W92

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 12 May 2011 16:32:53 +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/12/t13052177760yirg0dvbo24wgx.htm/, Retrieved Thu, 12 May 2011 18:29:37 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

7,24 7,52 7,57 7,59 7,58 7,55 7,52 7,55 7,62 7,64 7,68 7,69 7,7 7,6 7,51 7,66 7,69 7,66 7,7 7,72 7,74 7,76 7,72 7,73 7,75 8,1 8,22 8,32 8,07 8,18 8,33 8,34 8,25 8,36 8,36 8,34 8,41 8,39 8,43 8,44 8,49 8,47 8,53 8,52 8,51 8,53 8,54 8,53 8,47 8,63 8,67 8,73 8,57 8,55 8,63 8,65 8,44 8,62 8,37 8,59 8,84 8,72 8,8 8,69 8,68 8,57 8,85 8,85 8,85 8,93 8,75 8,78 8,77 9,03 9,01 9,07 8,99 9,02 8,99 8,98 8,94 8,94 8,75 8,86

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 7.24 NA NA -0.0279629629629631 NA 2 7.52 NA NA 0.0402314814814815 NA 3 7.57 NA NA 0.0494675925925928 NA 4 7.59 NA NA 0.076273148148148 NA 5 7.58 NA NA -0.0101851851851851 NA 6 7.55 NA NA -0.0324074074074065 NA 7 7.52 7.61273148148148 7.58166666666667 0.0310648148148149 -0.0927314814814819 8 7.55 7.62578703703704 7.60416666666667 0.0216203703703701 -0.0757870370370375 9 7.62 7.56946759259259 7.605 -0.0355324074074079 0.0505324074074087 10 7.64 7.62127314814815 7.60541666666667 0.0158564814814814 0.0187268518518522 11 7.68 7.5387037037037 7.61291666666667 -0.0742129629629631 0.141296296296297 12 7.69 7.56787037037037 7.62208333333333 -0.0542129629629631 0.122129629629631 13 7.7 7.6062037037037 7.63416666666667 -0.0279629629629631 0.093796296296298 14 7.6 7.68898148148148 7.64875 0.0402314814814815 -0.08898148148148 15 7.51 7.71030092592592 7.66083333333333 0.0494675925925928 -0.200300925925925 16 7.66 7.74710648148148 7.67083333333333 0.076273148148148 -0.0871064814814808 17 7.69 7.66731481481482 7.6775 -0.0101851851851851 0.0226851851851846 18 7.66 7.64842592592593 7.68083333333333 -0.0324074074074065 0.0115740740740735 19 7.7 7.71564814814815 7.68458333333333 0.0310648148148149 -0.0156481481481467 20 7.72 7.72912037037037 7.7075 0.0216203703703701 -0.00912037037037017 21 7.74 7.72238425925926 7.75791666666667 -0.0355324074074079 0.0176157407407418 22 7.76 7.83085648148148 7.815 0.0158564814814814 -0.0708564814814823 23 7.72 7.78412037037037 7.85833333333333 -0.0742129629629631 -0.0641203703703699 24 7.73 7.84162037037037 7.89583333333333 -0.0542129629629631 -0.111620370370369 25 7.75 7.91578703703704 7.94375 -0.0279629629629631 -0.165787037037038 26 8.1 8.03606481481481 7.99583333333333 0.0402314814814815 0.0639351851851861 27 8.22 8.09238425925926 8.04291666666667 0.0494675925925928 0.12761574074074 28 8.32 8.16543981481482 8.08916666666667 0.076273148148148 0.154560185185185 29 8.07 8.13064814814815 8.14083333333333 -0.0101851851851851 -0.0606481481481485 30 8.18 8.16050925925926 8.19291666666667 -0.0324074074074065 0.019490740740741 31 8.33 8.27689814814815 8.24583333333333 0.0310648148148149 0.0531018518518511 32 8.34 8.30703703703704 8.28541666666667 0.0216203703703701 0.032962962962964 33 8.25 8.2707175925926 8.30625 -0.0355324074074079 -0.0207175925925931 34 8.36 8.33585648148148 8.32 0.0158564814814814 0.0241435185185175 35 8.36 8.26828703703704 8.3425 -0.0742129629629631 0.0917129629629638 36 8.34 8.31787037037037 8.37208333333333 -0.0542129629629631 0.0221296296296298 37 8.41 8.36453703703704 8.3925 -0.0279629629629631 0.0454629629629633 38 8.39 8.44856481481481 8.40833333333333 0.0402314814814815 -0.0585648148148135 39 8.43 8.47613425925926 8.42666666666667 0.0494675925925928 -0.0461342592592597 40 8.44 8.52085648148148 8.44458333333333 0.076273148148148 -0.080856481481483 41 8.49 8.44898148148148 8.45916666666666 -0.0101851851851851 0.0410185185185199 42 8.47 8.44217592592593 8.47458333333333 -0.0324074074074065 0.0278240740740756 43 8.53 8.51606481481481 8.485 0.0310648148148149 0.0139351851851863 44 8.52 8.51912037037037 8.4975 0.0216203703703701 0.000879629629629619 45 8.51 8.4819675925926 8.5175 -0.0355324074074079 0.028032407407407 46 8.53 8.55543981481481 8.53958333333333 0.0158564814814814 -0.0254398148148152 47 8.54 8.48078703703704 8.555 -0.0742129629629631 0.0592129629629632 48 8.53 8.5074537037037 8.56166666666667 -0.0542129629629631 0.0225462962962943 49 8.47 8.5412037037037 8.56916666666667 -0.0279629629629631 -0.0712037037037039 50 8.63 8.61898148148148 8.57875 0.0402314814814815 0.0110185185185188 51 8.67 8.6307175925926 8.58125 0.0494675925925928 0.0392824074074039 52 8.73 8.65835648148148 8.58208333333333 0.076273148148148 0.0716435185185187 53 8.57 8.56856481481482 8.57875 -0.0101851851851851 0.00143518518518526 54 8.55 8.54175925925926 8.57416666666666 -0.0324074074074065 0.00824074074074232 55 8.63 8.62314814814815 8.59208333333333 0.0310648148148149 0.00685185185185233 56 8.65 8.63287037037037 8.61125 0.0216203703703701 0.0171296296296308 57 8.44 8.58488425925926 8.62041666666667 -0.0355324074074079 -0.144884259259261 58 8.62 8.64002314814815 8.62416666666667 0.0158564814814814 -0.0200231481481481 59 8.37 8.55287037037037 8.62708333333333 -0.0742129629629631 -0.18287037037037 60 8.59 8.57828703703704 8.6325 -0.0542129629629631 0.0117129629629638 61 8.84 8.61453703703704 8.6425 -0.0279629629629631 0.225462962962965 62 8.72 8.70023148148148 8.66 0.0402314814814815 0.0197685185185197 63 8.8 8.73488425925926 8.68541666666667 0.0494675925925928 0.0651157407407421 64 8.69 8.79168981481481 8.71541666666667 0.076273148148148 -0.101689814814815 65 8.68 8.73398148148148 8.74416666666667 -0.0101851851851851 -0.0539814814814825 66 8.57 8.73550925925926 8.76791666666667 -0.0324074074074065 -0.16550925925926 67 8.85 8.80398148148148 8.77291666666667 0.0310648148148149 0.0460185185185171 68 8.85 8.80453703703703 8.78291666666667 0.0216203703703701 0.045462962962965 69 8.85 8.76905092592593 8.80458333333333 -0.0355324074074079 0.0809490740740735 70 8.93 8.84502314814815 8.82916666666666 0.0158564814814814 0.0849768518518541 71 8.75 8.7837037037037 8.85791666666667 -0.0742129629629631 -0.0337037037037025 72 8.78 8.83537037037037 8.88958333333333 -0.0542129629629631 -0.0553703703703725 73 8.77 8.8862037037037 8.91416666666667 -0.0279629629629631 -0.116203703703704 74 9.03 8.96564814814815 8.92541666666667 0.0402314814814815 0.0643518518518515 75 9.01 8.98405092592593 8.93458333333333 0.0494675925925928 0.0259490740740738 76 9.07 9.01502314814815 8.93875 0.076273148148148 0.054976851851853 77 8.99 8.92898148148148 8.93916666666667 -0.0101851851851851 0.0610185185185177 78 9.02 8.9100925925926 8.9425 -0.0324074074074065 0.109907407407407 79 8.99 NA NA 0.0310648148148149 NA 80 8.98 NA NA 0.0216203703703701 NA 81 8.94 NA NA -0.0355324074074079 NA 82 8.94 NA NA 0.0158564814814814 NA 83 8.75 NA NA -0.0742129629629631 NA 84 8.86 NA NA -0.0542129629629631 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/17ph21305217969.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/17ph21305217969.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/2eczx1305217969.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/2eczx1305217969.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/3ulmf1305217969.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/3ulmf1305217969.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/4gu5q1305217969.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/12/t13052177760yirg0dvbo24wgx/4gu5q1305217969.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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