*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: Sun, 19 Dec 2010 16:14:36 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9.htm/, Retrieved Sun, 19 Dec 2010 17:12:25 +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/2010/Dec/19/t1292775145dofdzxmggc1oza9.htm/}, year = {2010}, } @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 = {2010}, 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 «

41,85 41,75 41,75 41,75 41,58 41,61 41,42 41,37 41,37 41,33 41,37 41,34 41,33 41,29 41,29 41,27 41,04 40,90 40,89 40,72 40,72 40,58 40,24 40,07 40,12 40,10 40,10 40,08 40,06 39,99 40,05 39,66 39,66 39,67 39,56 39,64 39,73 39,70 39,70 39,68 39,76 40,00 39,96 40,01 40,01 40,01 40,00 39,91 39,86 39,79 39,79 39,80 39,64 39,55 39,36 39,28

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 41.85 NA NA -0.0292476851851864 NA 2 41.75 NA NA -0.0200810185185186 NA 3 41.75 NA NA 0.0176967592592580 NA 4 41.75 NA NA 0.0349189814814835 NA 5 41.58 NA NA 0.0156134259259242 NA 6 41.61 NA NA 0.064502314814816 NA 7 41.42 41.6272800925926 41.5191666666667 0.10811342592593 -0.207280092592590 8 41.37 41.4576967592593 41.4783333333333 -0.0206365740740760 -0.0876967592592592 9 41.37 41.4610300925926 41.44 0.0210300925925906 -0.0910300925925895 10 41.33 41.4197800925926 41.4008333333333 0.0189467592592568 -0.0897800925925907 11 41.37 41.2638078703704 41.3583333333333 -0.0945254629629617 0.106192129629626 12 41.34 41.1899189814815 41.30625 -0.116331018518516 0.150081018518520 13 41.33 41.2253356481481 41.2545833333333 -0.0292476851851864 0.104664351851852 14 41.29 41.1853356481482 41.2054166666667 -0.0200810185185186 0.104664351851845 15 41.29 41.1689467592593 41.15125 0.0176967592592580 0.121053240740736 16 41.27 41.1278356481481 41.0929166666667 0.0349189814814835 0.142164351851861 17 41.04 41.0301967592593 41.0145833333333 0.0156134259259242 0.00980324074073735 18 40.9 40.9790856481481 40.9145833333333 0.064502314814816 -0.0790856481481441 19 40.89 40.9193634259259 40.81125 0.10811342592593 -0.029363425925915 20 40.72 40.6906134259259 40.71125 -0.0206365740740760 0.0293865740740813 21 40.72 40.6331134259259 40.6120833333333 0.0210300925925906 0.0868865740740787 22 40.58 40.5318634259259 40.5129166666667 0.0189467592592568 0.0481365740740713 23 40.24 40.327974537037 40.4225 -0.0945254629629617 -0.0879745370370344 24 40.07 40.2274189814815 40.34375 -0.116331018518516 -0.157418981481484 25 40.12 40.2415856481481 40.2708333333333 -0.0292476851851864 -0.121585648148148 26 40.1 40.1715856481482 40.1916666666667 -0.0200810185185186 -0.071585648148151 27 40.1 40.1210300925926 40.1033333333333 0.0176967592592580 -0.0210300925925964 28 40.08 40.0561689814815 40.02125 0.0349189814814835 0.0238310185185213 29 40.06 39.9706134259259 39.955 0.0156134259259242 0.0893865740740765 30 39.99 39.9732523148148 39.90875 0.064502314814816 0.0167476851851802 31 40.05 39.9826967592593 39.8745833333333 0.10811342592593 0.0673032407407348 32 39.66 39.8210300925926 39.8416666666667 -0.0206365740740760 -0.161030092592597 33 39.66 39.8293634259259 39.8083333333333 0.0210300925925906 -0.169363425925930 34 39.67 39.7939467592593 39.775 0.0189467592592568 -0.123946759259255 35 39.56 39.6513078703704 39.7458333333333 -0.0945254629629617 -0.0913078703703718 36 39.64 39.6174189814815 39.73375 -0.116331018518516 0.0225810185185225 37 39.73 39.7011689814815 39.7304166666667 -0.0292476851851864 0.0288310185185097 38 39.7 39.7211689814815 39.74125 -0.0200810185185186 -0.0211689814814733 39 39.7 39.7881134259259 39.7704166666667 0.0176967592592580 -0.0881134259259184 40 39.68 39.8340856481482 39.7991666666667 0.0349189814814835 -0.154085648148154 41 39.76 39.8472800925926 39.8316666666667 0.0156134259259242 -0.0872800925926 42 40 39.9257523148148 39.86125 0.064502314814816 0.0742476851851848 43 39.96 39.9860300925926 39.8779166666667 0.10811342592593 -0.0260300925925918 44 40.01 39.8664467592593 39.8870833333333 -0.0206365740740760 0.143553240740736 45 40.01 39.9156134259259 39.8945833333333 0.0210300925925906 0.094386574074079 46 40.01 39.9222800925926 39.9033333333333 0.0189467592592568 0.0877199074074042 47 40 39.8088078703704 39.9033333333333 -0.0945254629629617 0.191192129629634 48 39.91 39.7632523148148 39.8795833333333 -0.116331018518516 0.146747685185183 49 39.86 NA 39.8358333333333 NA NA 50 39.79 NA 39.7804166666667 NA NA 51 39.79 NA NA NA NA 52 39.8 NA NA NA NA 53 39.64 NA NA NA NA 54 39.55 NA NA NA NA 55 39.36 NA NA NA NA 56 39.28 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/18cyc1292775272.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/18cyc1292775272.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/28cyc1292775272.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/28cyc1292775272.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/30lxe1292775272.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/30lxe1292775272.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/40lxe1292775272.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/19/t1292775145dofdzxmggc1oza9/40lxe1292775272.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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