*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: Wed, 02 Dec 2009 08:45:24 -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/02/t125976876201gjjtag6yxaq2n.htm/, Retrieved Wed, 02 Dec 2009 16:46:07 +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/02/t125976876201gjjtag6yxaq2n.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:

shwws9v1

Dataseries X:

» Textbox « » Textfile « » CSV «

6539 6699 6962 6981 7024 6940 6774 6671 6965 6969 6822 6878 6691 6837 7018 7167 7076 7171 7093 6971 7142 7047 6999 6650 6475 6437 6639 6422 6272 6232 6003 5673 6050 5977 5796 5752 5609 5839 6069 6006 5809 5797 5502 5568 5864 5764 5615 5615 5681 5915 6334 6494 6620 6578 6495 6538 6737 6651 6530 6563

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 6539 NA NA 0.962671169304688 NA 2 6699 NA NA 0.986662538249252 NA 3 6962 NA NA 1.02852630120775 NA 4 6981 NA NA 1.03001442499547 NA 5 7024 NA NA 1.01828681487641 NA 6 6940 NA NA 1.01891059927276 NA 7 6774 6770.70481426628 6858.33333333333 0.987223059188279 1.00048668282315 8 6671 6671.23016491152 6870.41666666667 0.971008089986516 0.999965498880141 9 6965 7006.90614641008 6878.5 1.01866775407575 0.994019308160486 10 6969 6955.62443687617 6888.58333333333 1.00973220476530 1.00192298523953 11 6822 6829.11401072893 6898.5 0.98994187297658 0.998958282038086 12 6878 6760.71958590112 6910.29166666667 0.978355171101236 1.01734732710161 13 6691 6674.39977328301 6933.20833333333 0.962671169304688 1.00248714899929 14 6837 6866.18460367654 6959 0.986662538249252 0.995749516600396 15 7018 7177.95649034124 6978.875 1.02852630120775 0.97771559488324 16 7167 7199.28582350586 6989.5 1.03001442499547 0.995515413014935 17 7076 7128.13498998674 7000.125 1.01828681487641 0.992686026560948 18 7171 7130.33637371076 6998 1.01891059927276 1.00570290434532 19 7093 6890.32334160459 6979.5 0.987223059188279 1.02941468032010 20 6971 6752.22842308457 6953.83333333333 0.971008089986516 1.03239990758717 21 7142 7050.58152636608 6921.375 1.01866775407575 1.01296608985969 22 7047 6941.44611383426 6874.54166666667 1.00973220476530 1.01520632508482 23 6999 6741.5041549705 6810 0.98994187297658 1.03819560725771 24 6650 6591.5456708982 6737.375 0.978355171101236 1.00886807617216 25 6475 6404.49084418921 6652.83333333333 0.962671169304688 1.01100933040989 26 6437 6465.9285006601 6553.33333333333 0.986662538249252 0.995526009813263 27 6639 6637.85161641952 6453.75 1.02852630120775 1.00017300531058 28 6422 6554.66846252952 6363.66666666667 1.03001442499547 0.979759699016367 29 6272 6383.59761384294 6268.95833333333 1.01828681487641 0.98251806887061 30 6232 6298.31096018795 6181.41666666667 1.01891059927276 0.98947162809091 31 6003 6029.87617693374 6107.91666666667 0.987223059188279 0.995542831039126 32 5673 5871.60500280763 6046.91666666667 0.971008089986516 0.966175346823796 33 6050 6110.2238558849 5998.25 1.01866775407575 0.990143756218212 34 5977 6015.14303248769 5957.16666666667 1.00973220476530 0.993658832004213 35 5796 5860.99210653588 5920.54166666667 0.98994187297658 0.98891107420817 36 5752 5755.78576598496 5883.125 0.978355171101236 0.999342267739127 37 5609 5625.97064731276 5844.125 0.962671169304688 0.996983516556229 38 5839 5741.26597725512 5818.875 0.986662538249252 1.01702307873073 39 6069 5972.3950995381 5806.75 1.02852630120775 1.01617523604046 40 6006 5963.91227252691 5790.125 1.03001442499547 1.00705706682960 41 5809 5879.2910687754 5773.70833333333 1.01828681487641 0.988044295144918 42 5797 5869.39205250242 5760.45833333333 1.01891059927276 0.987666175328745 43 5502 5684.18356904131 5757.75 0.987223059188279 0.967949034926745 44 5568 5596.80971334145 5763.91666666667 0.971008089986516 0.994852475818006 45 5864 5885.98961651897 5778.125 1.01866775407575 0.996264074870731 46 5764 5866.03924358401 5809.5 1.00973220476530 0.982605086780553 47 5615 5804.6479149323 5863.625 0.98994187297658 0.967328265605149 48 5615 5801.60539983154 5929.95833333333 0.978355171101236 0.96783555809622 49 5681 5779.75736660919 6003.875 0.962671169304688 0.982913233143708 50 5915 6004.4993202722 6085.66666666667 0.986662538249252 0.985094623964727 51 6334 6338.25047593021 6162.45833333333 1.02852630120775 0.99932939287484 52 6494 6422.95536793323 6235.79166666667 1.03001442499547 1.01106105024822 53 6620 6426.28080283318 6310.875 1.01828681487641 1.03014483853264 54 6578 6509.31036345402 6388.5 1.01891059927276 1.01055252134414 55 6495 NA NA 0.987223059188279 NA 56 6538 NA NA 0.971008089986516 NA 57 6737 NA NA 1.01866775407575 NA 58 6651 NA NA 1.00973220476530 NA 59 6530 NA NA 0.98994187297658 NA 60 6563 NA NA 0.978355171101236 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/1vyd71259768722.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/1vyd71259768722.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/2wujp1259768722.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/2wujp1259768722.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/3g84m1259768722.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/3g84m1259768722.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/49q8q1259768722.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t125976876201gjjtag6yxaq2n/49q8q1259768722.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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