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*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 10:37:27 +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/t1293532937mcw3t35b1yfc9r9.htm/, Retrieved Tue, 28 Dec 2010 11:42:18 +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/t1293532937mcw3t35b1yfc9r9.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 «
655362 873127 1107897 1555964 1671159 1493308 2957796 2638691 1305669 1280496 921900 867888 652586 913831 1108544 1555827 1699283 1509458 3268975 2425016 1312703 1365498 934453 775019 651142 843192 1146766 1652601 1465906 1652734 2922334 2702805 1458956 1410363 1019279 936574 708917 885295 1099663 1576220 1487870 1488635 2882530 2677026 1404398 1344370 936865 872705 628151 953712 1160384 1400618 1661511 1495347 2918786 2775677 1407026 1370199 964526 850851 683118 847224 1073256 1514326 1503734 1507712 2865698 2788128 1391596 1366378 946295 859626
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1655362NANA-789446.804166667NA
2873127NANA-566056.629166667NA
31107897NANA-338946.195833333NA
41555964NANA81817.8625NA
51671159NANA104641.2875NA
61493308NANA71623.2458333334NA
729577962975054.11251443989.083333331531065.02916667-17258.1125000003
826386912630377.804166671445569.416666671184808.38758313.19583333307
913056691366512.695833331447292.375-80779.6791666667-60843.6958333333
1012804961343604.404166671447313.625-103709.220833333-63108.4041666666
11921900947732.1208333331448479.75-500747.629166667-25832.1208333331
12867888856054.8458333331450324.5-594269.65416666711833.1541666666
13652586674516.4041666671463963.20833333-789446.804166667-21930.4041666666
14913831901969.2458333331468025.875-566056.62916666711861.7541666669
1511085441120469.63751459415.83333333-338946.195833333-11925.6375
1615558271545068.529166671463250.6666666781817.862510758.4708333334
1716992831571956.745833331467315.45833333104641.2875127326.254166667
1815094581535592.204166671463968.9583333371623.2458333334-26134.2041666666
1932689752991104.279166671460039.251531065.02916667277870.720833333
2024250162641844.179166671457035.791666671184808.3875-216828.179166667
2113127031374905.404166671455685.08333333-80779.6791666667-62202.4041666666
2213654981357600.695833331461309.91666667-103709.2208333337897.3041666667
23934453954870.4958333331455618.125-500747.629166667-20417.4958333333
24775019857594.26251451863.91666667-594269.654166667-82575.2625
25651142653943.5708333341443390.375-789446.804166667-2801.57083333354
26843192874464.91251440521.54166667-566056.629166667-31272.9125000001
2711467661119243.76251458189.95833333-338946.19583333327522.2375
2816526011547971.070833331466153.2083333381817.8625104629.929166667
2914659061576198.28751471557104641.2875-110292.2875
3016527341553446.120833331481822.87571623.245833333499287.8791666671
3129223343022026.654166671490961.6251531065.02916667-99692.6541666666
3227028052679931.595833331495123.208333331184808.387522873.404166667
3314589561414135.195833331494914.875-80779.679166666744820.8041666669
3414103631386060.48751489769.70833333-103709.22083333324302.5125000004
351019279986754.7041666671487502.33333333-500747.62916666732524.2958333334
36936574887310.38751481580.04166667-594269.65416666749263.6125
37708917683637.2791666671473084.08333333-789446.80416666725279.7208333332
38885295904294.8291666671470351.45833333-566056.629166667-18999.8291666668
3910996631128057.88751467004.08333333-338946.195833333-28394.8875
4015762201543798.98751461981.12581817.862532421.0125000002
4114878701560438.78751455797.5104641.2875-72568.7874999996
4214886351521325.620833331449702.37571623.2458333334-32690.6208333333
4328825302974740.945833331443675.916666671531065.02916667-92210.945833333
4426770262627969.76251443161.3751184808.387549056.2374999998
4514043981367762.445833331448542.125-80779.679166666736635.5541666665
4613443701340046.195833331443755.41666667-103709.2208333334323.8041666667
47936865942926.0791666671443673.70833333-500747.629166667-6061.0791666666
48872705856918.76251451188.41666667-594269.65416666715786.2375
49628151663531.9458333331452978.75-789446.804166667-35380.9458333333
50953712892543.2458333331458599.875-566056.62916666761168.7541666669
5111603841123873.63751462819.83333333-338946.19583333336510.3625
5214006181545823.404166671464005.5416666781817.8625-145205.404166667
5316615111570875.579166671466234.29166667104641.287590635.4208333332
5414953471538099.495833331466476.2571623.2458333334-42752.4958333333
5529187862998920.98751467855.958333331531065.02916667-80134.9875
5627756772650517.63751465709.251184808.3875125159.3625
5714070261376862.23751457641.91666667-80779.679166666730163.7625000002
5813701991355040.195833331458749.41666667-103709.22083333315158.8041666669
59964526956165.5791666671456913.20833333-500747.6291666678360.4208333334
60850851856584.7208333331450854.375-594269.654166667-5733.72083333344
61683118659710.7791666671449157.58333333-789446.80416666723407.2208333334
62847224881407.7458333331447464.375-566056.629166667-34183.7458333333
6310732561108394.054166671447340.25-338946.195833333-35138.0541666667
6415143261528355.98751446538.12581817.8625-14029.9875
6515037341550260.579166671445619.29166667104641.2875-46526.5791666664
6615077121516848.53751445225.2916666771623.2458333334-9136.53749999986
672865698NANA1531065.02916667NA
682788128NANA1184808.3875NA
691391596NANA-80779.6791666667NA
701366378NANA-103709.220833333NA
71946295NANA-500747.629166667NA
72859626NANA-594269.654166667NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/1m3f51293532644.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/1m3f51293532644.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/2m3f51293532644.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/2m3f51293532644.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/3wcxq1293532644.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/3wcxq1293532644.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/4wcxq1293532644.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t1293532937mcw3t35b1yfc9r9/4wcxq1293532644.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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Software written by Ed van Stee & Patrick Wessa


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