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*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 06 Jun 2010 15:51:45 +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/06/t1275839670tzoezwsm00qu4nm.htm/, Retrieved Sun, 06 Jun 2010 17:54:39 +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/06/t1275839670tzoezwsm00qu4nm.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 «
25000 25284 12434,5 33955 14980,5 50831 4198,5 34566 35000 11055,5 20807 21887,29 16977,5 19613,5 14570 24416,5 16825,5 13980 21450,5 27239,5 19078,5 20459,1 20373,5 19306,5 16723,16 11638 20917 17903,5 28218,5 15268 21555 23143 16691 17932,5 30512 41931,5 10853,5 25939,5 14900 25127,76 22063,5 25306,5 31217,5 23201,5 38148 26264 16359 27945,5 16218,5 36003,5 20323,5 20100,5 18741 24426,75 19174,5 13766 18999 21745 34469 13248 16218,5 36003,5 20323,5 20100,5 18741 24426,75 19174,5 13766 18999 21745 34469 13248
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
125000NANA-6301.67527777778NA
225284NANA4188.22605555555NA
312434.5NANA-3137.89894444444NA
433955NANA229.315555555554NA
514980.5NANA-585.465611111111NA
650831NANA-863.62152777778NA
74198.521405.087722222223832.3366666667-2427.24894444444-17206.5877222222
83456625682.396055555623261.7952420.601055555568883.60394444445
93500026580.133555555623114.50333333333465.630222222228419.86644444444
1011055.520229.307722222222806.045-2576.73727777778-9173.80772222222
112080725005.741888888922485.48252520.25938888889-4198.74188888889
1221887.2924095.514472222221026.89916666673068.61530555556-2208.22447222222
1316977.513908.598888888920210.2741666667-6301.675277777783068.90111111111
1419613.524812.062722222220623.83666666674188.22605555555-5198.56272222222
151457016517.271055555619655.17-3137.89894444444-1947.27105555556
1624416.519612.906388888919383.5908333333229.3155555555544803.59361111112
1716825.519171.879388888919757.345-585.465611111111-2346.37938888889
181398018768.128055555619631.7495833333-863.62152777778-4788.12805555555
1921450.517086.370222222219513.6191666667-2427.248944444444364.12977777778
2027239.521591.310222222219170.70916666672420.601055555565648.18977777778
2119078.522568.485222222219102.8553465.63022222222-3489.98522222222
2220459.116519.201055555619095.9383333333-2576.737277777783939.89894444444
2320373.521819.531055555619299.27166666672520.25938888889-1446.03105555556
2419306.522896.261972222219827.64666666673068.61530555556-3589.76197222222
2516723.1613583.992222222219885.6675-6301.675277777783139.16777777778
261163823907.560222222219719.33416666674188.22605555555-12269.5602222222
272091716311.268555555619449.1675-3137.898944444444605.73144444445
2817903.519473.728888888919244.4133333333229.315555555554-1570.22888888889
2928218.518976.110222222219561.5758333333-585.4656111111119242.38977777778
301526820063.100138888920926.7216666667-863.62152777778-4795.10013888889
312155519197.611888888921624.8608333333-2427.248944444442357.38811111111
322314324396.788555555621976.18752420.60105555556-1253.78855555555
331669125787.005222222222321.3753465.63022222222-9096.00522222222
3417932.519794.940222222222371.6775-2576.73727777778-1862.44022222222
353051224936.489388888922416.232520.259388888895575.51061111111
3641931.525646.657805555622578.04253068.6153055555616284.8421944444
3710853.517097.242222222223398.9175-6301.67527777778-6243.74222222222
3825939.527992.185222222223803.95916666674188.22605555555-2052.68522222223
391490021562.539388888924700.4383333333-3137.89894444444-6662.53938888889
4025127.7626170.941388888925941.6258333333229.315555555554-1043.18138888889
4122063.525113.597722222225699.0633333333-585.465611111111-3050.09772222222
4225306.523662.983472222224526.605-863.621527777781643.51652777778
4331217.521740.147722222224167.3966666667-2427.248944444449477.35227777778
4423201.527230.872722222224810.27166666672420.60105555556-4029.37272222222
453814828921.214388888925455.58416666673465.630222222229226.78561111111
462626422895.356888888925472.0941666667-2576.737277777783368.64311111112
471635927644.446888888925124.18752520.25938888889-11285.4468888889
4827945.528017.709055555524949.093753068.61530555556-72.2090555555478
4916218.518108.970555555624410.6458333333-6301.67527777778-1890.47055555556
5036003.527703.934388888923515.70833333334188.226055555558299.56561111111
5120323.519186.788555555622324.6875-3137.898944444441136.71144444445
5220100.521567.836388888921338.5208333333229.315555555554-1467.33638888889
531874121319.346888888921904.8125-585.465611111111-2578.34688888889
5424426.7521183.378472222222047-863.621527777783243.37152777778
5519174.519007.355222222221434.6041666667-2427.24894444444167.144777777779
561376623855.205222222221434.60416666672420.60105555556-10089.2052222222
571899924900.234388888921434.60416666673465.63022222222-5901.23438888889
582174518857.866888888921434.6041666667-2576.737277777782887.13311111111
593446923954.863555555621434.60416666672520.2593888888910514.1364444444
601324824503.219472222221434.60416666673068.61530555556-11255.2194722222
6116218.515132.928888888921434.6041666667-6301.675277777781085.57111111111
6236003.525622.830222222221434.60416666674188.2260555555510380.6697777778
6320323.518296.705222222221434.6041666667-3137.898944444442026.79477777778
6420100.521663.919722222221434.6041666667229.315555555554-1563.41972222222
651874120849.138555555621434.6041666667-585.465611111111-2108.13855555555
6624426.7520570.982638888921434.6041666667-863.621527777783855.76736111111
6719174.5NANA-2427.24894444444NA
6813766NANA2420.60105555556NA
6918999NANA3465.63022222222NA
7021745NANA-2576.73727777778NA
7134469NANA2520.25938888889NA
7213248NANA3068.61530555556NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/1w3ak1275839503.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/1w3ak1275839503.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/2w3ak1275839503.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/2w3ak1275839503.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/36c951275839503.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/36c951275839503.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/4h4q81275839503.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839670tzoezwsm00qu4nm/4h4q81275839503.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')
 





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