*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 02 Jun 2010 12:03:42 +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/Jun/02/t1275480269u056xr091c2m0ty.htm/, Retrieved Wed, 02 Jun 2010 14:04:32 +0200

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/Jun/02/t1275480269u056xr091c2m0ty.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

8027,7 8059,6 8059,5 7988,9 7950,2 8003,8 8037,5 8069 8157,6 8244,3 8329,4 8417 8432,5 8486,4 8531,1 8643,8 8727,9 8847,3 8904,3 9003,2 9025,3 9044,7 9120,7 9184,3 9247,2 9407,1 9488,9 9592,5 9666,2 9809,6 9932,7 10008,9 10103,4 10194,3 10328,8 10507,6 10601,2 10684 10819,9 11014,3 11043 11258,5 11267,9 11334,5 11297,2 11371,3 11340,1 11380,1 11477,9 11538,8 11596,4 11598,8 11645,8 11738,7 11935,5 12042,8 12127,6 12213,8 12303,5 12410,3 12534,1 12587,5 12683,2 12748,7 12915,9 12962,5 12965,9 13060,7 13099,9 13204 13321,1 13391,2 13366,9 13415,3 13324,6 13141,9 12925,4 12901,5 12973 13155

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 4 seconds R Server 184.73.237.11 @ 184.73.237.11 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 8027.7 NA NA 0.998620064834709 NA 2 8059.6 NA NA 1.00018982747866 NA 3 8059.5 8028.39014401839 8024.2375 1.00051751260084 1.00387498059057 4 7988.9 8012.96085559406 8007.575 1.00067259508579 0.996997257814224 5 7950.2 7986.81348553828 7997.85 0.998620064834709 0.995415758036096 6 8003.8 8006.6320903223 8005.1125 1.00018982747866 0.999646281945973 7 8037.5 8045.21134469899 8041.05 1.00051751260084 0.999041498803624 8 8069 8102.48352763193 8097.0375 1.00067259508579 0.995867498216104 9 8157.6 8152.32227853382 8163.5875 0.998620064834709 1.0006473887177 10 8244.3 8245.13985705743 8243.575 1.00018982747866 0.99989813913748 11 8329.4 8325.74394876336 8321.4375 1.00051751260084 1.00043912607199 12 8417 8391.7029244266 8386.0625 1.00067259508579 1.00301453421328 13 8432.5 8429.88872555463 8441.5375 0.998620064834709 1.00030976380951 14 8486.4 8496.712603414 8495.1 1.00018982747866 0.998786283131448 15 8531.1 8564.80510193042 8560.375 1.00051751260084 0.996064697149638 16 8643.8 8648.22534417683 8642.4125 1.00067259508579 0.999488294534345 17 8727.9 8722.1224047777 8734.175 0.998620064834709 1.00066240703285 18 8847.3 8827.42536986982 8825.75 1.00018982747866 1.0022514639657 19 8904.3 8912.4599246214 8907.85 1.00051751260084 0.999084436318321 20 9003.2 8975.73297614097 8969.7 1.00067259508579 1.00306014271281 21 9025.3 9008.97601840146 9021.425 0.998620064834709 1.00181196859279 22 9044.7 9072.83444641456 9071.1125 1.00018982747866 0.996899045542964 23 9120.7 9126.20798471966 9121.4875 1.00051751260084 0.999396465133286 24 9184.3 9200.70919233114 9194.525 1.00067259508579 0.998216529618737 25 9247.2 9273.03612904538 9285.85 0.998620064834709 0.997213843590617 26 9407.1 9384.68113224956 9382.9 1.00018982747866 1.00238887900766 27 9488.9 9491.20927978535 9486.3 1.00051751260084 0.999756692775675 28 9592.5 9595.43700587016 9588.9875 1.00067259508579 0.999693916403352 29 9666.2 9681.39683905792 9694.775 0.998620064834709 0.998430305119133 30 9809.6 9804.16074589411 9802.3 1.00018982747866 1.00055479038409 31 9932.7 9914.12803236173 9909 1.00051751260084 1.00187328301366 32 10008.9 10018.4713454427 10011.7375 1.00067259508579 0.99904463015238 33 10103.4 10095.387269686 10109.3375 0.998620064834709 1.00079370212355 34 10194.3 10223.1277622521 10221.1875 1.00018982747866 0.997180142621466 35 10328.8 10351.1040559901 10345.75 1.00051751260084 0.99784524859672 36 10507.6 10476.2290240647 10469.1875 1.00067259508579 1.00299449122993 37 10601.2 10577.1715199655 10591.7875 0.998620064834709 1.00227173020587 38 10684 10718.546788548 10716.5125 1.00018982747866 0.996776914890658 39 10819.9 10840.6822878436 10835.075 1.00051751260084 0.99808293543785 40 11014.3 10969.4855629973 10962.1125 1.00067259508579 1.00408537271372 41 11043 11074.6216225121 11089.925 0.998620064834709 0.997144676938869 42 11258.5 11188.073400685 11185.95 1.00018982747866 1.00629479239122 43 11267.9 11263.5760274821 11257.75 1.00051751260084 1.00038388985055 44 11334.5 11311.2277626266 11303.625 1.00067259508579 1.00205744573992 45 11297.2 11311.1198193665 11326.75 0.998620064834709 0.998769368586945 46 11371.3 11343.6279236036 11341.475 1.00018982747866 1.00243943794549 47 11340.1 11375.6464953623 11369.7625 1.00051751260084 0.996875210971367 48 11380.1 11420.9640210852 11413.2875 1.00067259508579 0.996422016476917 49 11477.9 11450.4398011618 11466.2625 0.998620064834709 1.00239817852546 50 11538.8 11527.8253827066 11525.6375 1.00018982747866 1.00095201106271 51 11596.4 11579.9521714354 11573.9625 1.00051751260084 1.00142037102754 52 11598.8 11627.7530128596 11619.9375 1.00067259508579 0.99751000792435 53 11645.8 11671.1847664935 11687.3125 0.998620064834709 0.997825005172878 54 11738.7 11787.4371548016 11785.2 1.00018982747866 0.995865330676932 55 11935.5 11907.0838786492 11900.925 1.00051751260084 1.00238648871885 56 12042.8 12028.622454451 12020.5375 1.00067259508579 1.00117865080583 57 12127.6 12109.1920096808 12125.925 0.998620064834709 1.00152016668862 58 12213.8 12220.181786033 12217.8625 1.00018982747866 0.999477766685898 59 12303.5 12320.9854671432 12314.6125 1.00051751260084 0.998580838587153 60 12410.3 12420.4858426866 12412.1375 1.00067259508579 0.999179915921518 61 12534.1 12489.0545995931 12506.3125 0.998620064834709 1.00360679025363 62 12587.5 12598.4660811583 12596.075 1.00018982747866 0.99912957013277 63 12683.2 12692.6652166055 12686.1 1.00051751260084 0.999254276667351 64 12748.7 12789.2962360129 12780.7 1.00067259508579 0.996825764665722 65 12915.9 12845.1625147132 12862.9125 0.998620064834709 1.00550693579827 66 12962.5 12939.7058455484 12937.25 1.00018982747866 1.00176156666339 67 12965.9 13005.9772756765 12999.25 1.00051751260084 0.99691854946176 68 13060.7 13061.21650532 13052.4375 1.00067259508579 0.999960455037261 69 13099.9 13108.9105565868 13127.025 0.998620064834709 0.999312638792679 70 13204 13215.2456406459 13212.7375 1.00018982747866 0.999149040362043 71 13321.1 13294.3014098702 13287.425 1.00051751260084 1.00201579528729 72 13391.2 13356.1897695364 13347.2125 1.00067259508579 1.00262127381144 73 13366.9 13355.6071608535 13374.0625 0.998620064834709 1.00084555041269 74 13415.3 13345.8704321146 13343.3375 1.00018982747866 1.00520232593584 75 13324.6 13263.8481580804 13256.9875 1.00051751260084 1.00458025764435 76 13141.9 13146.4112683841 13137.575 1.00067259508579 0.999656844115702 77 12925.4 13011.4202727574 13029.4 0.998620064834709 0.993388863709409 78 12901.5 12989.5528060753 12987.0875 1.00018982747866 0.9932212596238 79 12973 NA NA 1.00051751260084 NA 80 13155 NA NA 1.00067259508579 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/1h0ss1275480218.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/1h0ss1275480218.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/2ar9v1275480218.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/2ar9v1275480218.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/384gn1275480218.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/384gn1275480218.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/484gn1275480218.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/02/t1275480269u056xr091c2m0ty/484gn1275480218.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 4 ;

Parameters (R input):

par1 = multiplicative ; par2 = 4 ;

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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