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Paper

*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: Tue, 28 Dec 2010 19:12:59 +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/28/t1293563544la1z0mhn91bkl87.htm/, Retrieved Tue, 28 Dec 2010 20:12:30 +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/28/t1293563544la1z0mhn91bkl87.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 «
961 935 956 951 986 980 1031 1059 1036 1023 1030 1075 1151 1220 1290 1330 1419 1443 1516 1546 1579 1591 1603 1606 1616 1628 1594 1596 1526 1535 1581 1611 1571 1535 1498 1493 1480 1448 1462 1428 1315 1186 1230 1271 1243 1220 1214 1227 1262 1274 1272 1249 1266 1307 1345 1369 1374 1400 1425 1465 1510 1508 1512 1539 1569 1571 1650 1736 1700 1731 1752
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1961NANA2.30902777777778NA
2935NANA11.0590277777778NA
3956NANA16.3090277777778NA
4951NANA5.11111111111115NA
5986NANA-22.1805555555557NA
6980NANA-44.107638888889NA
710311008.173611111111009.83333333333-1.6597222222222222.8263888888890
810591052.475694444441029.62522.85069444444456.52430555555566
910361065.454861111111055.4166666666710.0381944444444-29.4548611111109
1010231085.423611111111085.1250.298611111111152-62.4236111111112
1110301114.017361111111118.95833333333-4.94097222222216-84.0173611111111
1210751161.204861111111156.291666666674.91319444444447-86.2048611111113
1311511198.100694444441195.791666666672.30902777777778-47.1006944444443
1412201247.350694444441236.2916666666711.0590277777778-27.3506944444446
1512901295.517361111111279.2083333333316.3090277777778-5.51736111111109
1613301330.611111111111325.55.11111111111115-0.611111111110858
1714191350.861111111111373.04166666667-22.180555555555768.1388888888889
1814431374.934027777781419.04166666667-44.10763888888968.0659722222222
1915161458.881944444441460.54166666667-1.6597222222222257.1180555555559
2015461519.767361111111496.9166666666722.850694444444526.2326388888889
2115791536.621527777781526.5833333333310.038194444444442.3784722222226
2215911550.631944444441550.333333333330.29861111111115240.3680555555559
2316031560.934027777781565.875-4.9409722222221642.0659722222226
2416061579.079861111111574.166666666674.9131944444444726.9201388888891
2516161583.017361111111580.708333333332.3090277777777832.9826388888891
2616281597.184027777781586.12511.059027777777830.8159722222226
2715941604.809027777781588.516.3090277777778-10.8090277777776
2815961590.944444444441585.833333333335.111111111111155.05555555555566
2915261556.944444444441579.125-22.1805555555557-30.9444444444443
3015351525.934027777781570.04166666667-44.1076388888899.06597222222217
3115811558.006944444441559.66666666667-1.6597222222222222.9930555555557
3216111569.350694444441546.522.850694444444541.6493055555557
3315711543.538194444441533.510.038194444444427.4618055555554
3415351521.2986111111115210.29861111111115213.7013888888889
3514981500.267361111111505.20833333333-4.94097222222216-2.26736111111109
3614931486.788194444441481.8754.913194444444476.21180555555543
3714801455.017361111111452.708333333332.3090277777777824.9826388888887
3814481434.975694444441423.9166666666711.059027777777813.0243055555554
3914621412.392361111111396.0833333333316.309027777777849.6076388888889
4014281374.402777777781369.291666666675.1111111111111553.5972222222224
4113151322.152777777781344.33333333333-22.1805555555557-7.15277777777783
4211861277.309027777781321.41666666667-44.107638888889-91.3090277777776
4312301299.590277777781301.25-1.65972222222222-69.590277777778
4412711307.767361111111284.9166666666722.8506944444445-36.7673611111111
4512431279.788194444441269.7510.0381944444444-36.7881944444443
4612201254.673611111111254.3750.298611111111152-34.6736111111109
4712141239.934027777781244.875-4.94097222222216-25.9340277777778
4812271252.788194444441247.8754.91319444444447-25.7881944444446
4912621260.017361111111257.708333333332.309027777777781.98263888888891
5012741277.642361111111266.5833333333311.0590277777778-3.64236111111086
5112721292.434027777781276.12516.3090277777778-20.4340277777778
5212491294.194444444441289.083333333335.11111111111115-45.1944444444443
5312661283.194444444441305.375-22.1805555555557-17.1944444444443
5413071279.975694444441324.08333333333-44.10763888888927.0243055555554
5513451342.673611111111344.33333333333-1.659722222222222.32638888888891
5613691387.267361111111364.4166666666722.8506944444445-18.2673611111111
5713741394.204861111111384.1666666666710.0381944444444-20.2048611111109
5814001406.548611111111406.250.298611111111152-6.54861111111109
5914251426.017361111111430.95833333333-4.94097222222216-1.01736111111109
6014651459.496527777781454.583333333334.913194444444475.50347222222217
611510NA1478.29166666667NANA
621508NA1506.29166666667NANA
631512NA1535.16666666667NANA
641539NA1562.54166666667NANA
651569NA1589.95833333333NANA
661571NANANANA
671650NANANANA
681736NANANANA
691700NANANANA
701731NANANANA
711752NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/12as81293563576.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/12as81293563576.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/22as81293563576.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/22as81293563576.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/3d2sc1293563576.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/3d2sc1293563576.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/46b9e1293563576.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293563544la1z0mhn91bkl87/46b9e1293563576.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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