SHW WS9

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 03 Dec 2009 11:31:33 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4.htm/, Retrieved Thu, 03 Dec 2009 19:32:40 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

1.59 1.26 1.13 1.92 2.61 2.26 2.41 2.26 2.03 2.86 2.55 2.27 2.26 2.57 3.07 2.76 2.51 2.87 3.14 3.11 3.16 2.47 2.57 2.89 2.63 2.38 1.69 1.96 2.19 1.87 1.6 1.63 1.22 1.21 1.49 1.64 1.66 1.77 1.82 1.78 1.28 1.29 1.37 1.12 1.51 2.24 2.94 3.09 3.46 3.64 4.39 4.15 5.21 5.8 5.91 5.39 5.46 4.72 3.14 2.63

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 1.59 NA NA 1.02798328731299 NA 2 1.26 NA NA 1.05144617211418 NA 3 1.13 NA NA 1.06342910796478 NA 4 1.92 NA NA 1.03020636421097 NA 5 2.61 NA NA 1.00853101297762 NA 6 2.26 NA NA 1.03219333961335 NA 7 2.41 2.09497431689147 2.12375 0.986450531791155 1.15037209791477 8 2.26 2.03856538585811 2.20625 0.923995642315291 1.10862276759824 9 2.03 2.03437026152375 2.34166666666667 0.868770218444308 0.997851786566876 10 2.86 2.32986145424387 2.4575 0.948061629397304 1.22754080281919 11 2.55 2.53524996449231 2.48833333333333 1.01885464078726 1.00581798075704 12 2.27 2.61016254735224 2.50958333333333 1.04007805307079 0.869677638391792 13 2.26 2.63720545832752 2.56541666666667 1.02798328731299 0.856967739416579 14 2.57 2.76661774037544 2.63125 1.05144617211418 0.928932090073002 15 3.07 2.88588074173943 2.71375 1.06342910796478 1.06380002319486 16 2.76 2.82748721710735 2.74458333333333 1.03020636421097 0.9761317339654 17 2.51 2.75244922291808 2.72916666666667 1.00853101297762 0.911915096961884 18 2.87 2.84455281175114 2.75583333333333 1.03219333961335 1.00894593629752 19 3.14 2.75918434163084 2.79708333333333 0.986450531791155 1.13801747589799 20 3.11 2.59142277851009 2.80458333333333 0.923995642315291 1.20011293633378 21 3.16 2.37970642335537 2.73916666666667 0.868770218444308 1.32789489030518 22 2.47 2.51078321518719 2.64833333333333 0.948061629397304 0.983756775598744 23 2.57 2.65072015711486 2.60166666666667 1.01885464078726 0.969547838953048 24 2.89 2.64873210848695 2.54666666666667 1.04007805307079 1.09108806841582 25 2.63 2.50913587378311 2.44083333333333 1.02798328731299 1.04816962185258 26 2.38 2.43409788844433 2.315 1.05144617211418 0.977774974169629 27 1.69 2.31029973705349 2.1725 1.06342910796478 0.731506813983969 28 1.96 2.10076247768686 2.03916666666667 1.03020636421097 0.932994577358477 29 2.19 1.95823105019820 1.94166666666667 1.00853101297762 1.11835628373799 30 1.87 1.90396663102847 1.84458333333333 1.03219333961335 0.98216007020558 31 1.6 1.72834353590909 1.75208333333333 0.986450531791155 0.925741883345211 32 1.63 1.55808765185416 1.68625 0.923995642315291 1.04615423789558 33 1.22 1.44758837648283 1.66625 0.868770218444308 0.842781014147272 34 1.21 1.57773256158868 1.66416666666667 0.948061629397304 0.766923387054650 35 1.49 1.64927094977438 1.61875 1.01885464078726 0.90342948210167 36 1.64 1.61905483594687 1.55666666666667 1.04007805307079 1.01293666130887 37 1.66 1.56553288130374 1.52291666666667 1.02798328731299 1.06034182981682 38 1.77 1.5688453093087 1.49208333333333 1.05144617211418 1.12821830775651 39 1.82 1.57697674801944 1.48291666666667 1.06342910796478 1.15410706104943 40 1.78 1.58437153762612 1.53791666666667 1.03020636421097 1.12347385554969 41 1.28 1.65525152504951 1.64125 1.00853101297762 0.773296372562902 42 1.29 1.81881068051036 1.76208333333333 1.03219333961335 0.709254687045285 43 1.37 1.87178988407372 1.8975 0.986450531791155 0.731919758545957 44 1.12 1.89457606493065 2.05041666666667 0.923995642315291 0.591161273876328 45 1.51 1.94206342581405 2.23541666666667 0.868770218444308 0.777523524684607 46 2.24 2.31445545276617 2.44125 0.948061629397304 0.967830250231353 47 2.94 2.75472823502856 2.70375 1.01885464078726 1.06725591389218 48 3.09 3.17787181798671 3.05541666666667 1.04007805307079 0.972348847587445 49 3.46 3.52855263370182 3.4325 1.02798328731299 0.98057202461795 50 3.64 3.99505735146217 3.79958333333333 1.05144617211418 0.911125843704792 51 4.39 4.40481198428246 4.14208333333333 1.06342910796478 0.99663731747568 52 4.15 4.54321006617036 4.41 1.03020636421097 0.913451048830367 53 5.21 4.56024106368045 4.52166666666667 1.00853101297762 1.14248346244116 54 5.8 4.65605212277257 4.51083333333333 1.03219333961335 1.24569052215555 55 5.91 NA NA 0.986450531791155 NA 56 5.39 NA NA 0.923995642315291 NA 57 5.46 NA NA 0.868770218444308 NA 58 4.72 NA NA 0.948061629397304 NA 59 3.14 NA NA 1.01885464078726 NA 60 2.63 NA NA 1.04007805307079 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/124e31259865090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/124e31259865090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/268y11259865090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/268y11259865090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/323al1259865090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/323al1259865090.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/4b94z1259865090.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t125986515585q4g3jc0cmacw4/4b94z1259865090.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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