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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: Thu, 19 May 2011 15:10:34 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4.htm/, Retrieved Thu, 19 May 2011 17:06:45 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
7989 41102 9123 22109 47115 9105 5496 23102 6994 84101 5884 6383 6292 36109 49127 26116 63120 60115 77117 18110 3999 79105 2286 7487 7789 3788 83108 52100 93102 598 8983 3488 2780 7974 9171 3071 4166 7968 6580 7183 2779 5273 1568 1860 9853 6757 6745 5144 9444 2248 7349 8149 7949 3545 1043 4038 4335 5432 8027 8626 24 4829 931 232 9034 1337 5138 9736 7730 7133 8126 424
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'George Udny Yule' @ 216.218.223.82


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
17989NANA0.573891570901684NA
241102NANA0.842147051649363NA
39123NANA1.42023255523095NA
422109NANA1.13979911660533NA
547115NANA1.9434857545525NA
69105NANA0.733959472132513NA
7549614902.249464918722304.54166666670.6681262357966120.368803381861115
82310212795.615908299522025.79166666670.5809378433222931.80546213371532
9699415478.798231159523484.58333333330.6591046565083960.451843863816299
108410143808.831961798125318.3751.73031768278171.91972705579864
11588423529.995281958526152.20833333330.8997326337434930.25006379854702
12638323394.838645293728944.50.8082654267751640.272837957840929
13629219543.375291932334054.1250.5738915709016840.321950528299858
143610931016.556627929936830.33333333330.8421470516493631.1641846783045
154912751834.996860898136497.54166666671.420232555230950.947757364234725
162611641220.360135733336164.58333333331.139799116605330.633570398560405
176312069589.42267038435806.51.94348575455250.90703439657738
186011526204.249217100435702.58333333330.7339594721325132.29409358390509
197711723926.240791519335810.95833333330.6681262357966123.22311393051491
201811020057.823064697634526.6250.5809378433222930.902889607789701
21399922802.247383094434595.79166666670.6591046565083960.17537744998613
227910564184.980897665337094.33333333331.73031768278171.23245343215296
23228635473.083751129439426.250.8997326337434930.0644432273223841
24748730872.203141569138195.6250.8082654267751640.242515895793612
25778918867.737527939532876.83333333330.5738915709016840.412821091477766
26378824783.264867305229428.66666666670.8421470516493630.152845075912385
278310840858.13779423128768.6251.420232555230952.03406235542469
285210029354.433940683625754.04166666671.139799116605331.77485963807983
299310244850.063693527323077.1251.94348575455252.0758498948004
3059817013.1805640317231800.7339594721325130.0351492184397466
31898315263.371695366822845.04166666670.6681262357966120.588533135357427
32348813285.03183555522868.250.5809378433222930.262551120928818
33278013085.699074153619853.750.6591046565083960.212445661805792
34797425597.526736801214793.54166666671.73031768278170.311514470987382
3591718240.238814999529158.541666666670.8997326337434931.11295318083576
3630714518.102702494825589.8750.8082654267751640.67971009120803
3741663142.462857216115475.708333333330.5738915709016841.3257117710822
3879684294.037637439135098.916666666670.8421470516493631.85559621800426
3965807563.86270737775325.791666666671.420232555230950.869925890323465
4071836348.443621342425569.791666666671.139799116605331.13145842169125
41277910529.805818165454181.94348575455250.263917497434362
4252733965.797099444675403.291666666670.7339594721325131.32961920839026
4315683814.722420467075709.583333333330.6681262357966120.411039081529821
4418603306.214089321055691.166666666670.5809378433222930.562576998872435
4598533615.106652866495484.8750.6591046565083962.7255074182078
4667579615.663749498385557.166666666671.73031768278170.702707600435019
4767455229.995844511975812.833333333330.8997326337434931.28967597690881
4851444814.230948229575956.250.8082654267751641.06849880184741
4994443364.367597964765862.3750.5738915709016842.80706543652158
5022484994.984700095285931.250.8421470516493630.450051428577373
5173498226.10531261065792.083333333331.420232555230950.893375384914414
5281496276.826243515725506.958333333331.139799116605331.29826757725185
53794910699.21299310395505.166666666671.94348575455250.742951841890002
5435454186.260175886485703.666666666670.7339594721325130.84681788781781
5510433645.463774065265456.250.6681262357966120.286109001389662
5640383004.199028023885171.291666666670.5809378433222931.34411866934666
5743353303.048060703795011.416666666670.6591046565083961.31242413683691
5854327637.838541508774414.1251.73031768278170.711195971278933
5980273715.408422184024129.458333333330.8997326337434932.16046234703896
6086263299.878315714074082.666666666670.8082654267751642.61403578396296
61242388.130211563424161.291666666670.5738915709016840.0100497032715348
6248293848.050594669824569.333333333330.8421470516493631.25492112985441
639317027.606565065084948.208333333331.420232555230950.132477535755643
642325881.980832871695160.541666666671.139799116605330.0394424950695961
65903410175.20064653275235.541666666671.94348575455250.887844899950786
6613373594.87233121574897.916666666670.7339594721325130.371918632100033
675138NANA0.668126235796612NA
689736NANA0.580937843322293NA
697730NANA0.659104656508396NA
707133NANA1.7303176827817NA
718126NANA0.899732633743493NA
72424NANA0.808265426775164NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/1ufi31305817830.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/1ufi31305817830.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/257n01305817830.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/257n01305817830.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/38ec71305817830.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/38ec71305817830.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/4j51m1305817830.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/19/t1305817599zylsdwilyhkw3h4/4j51m1305817830.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

Creative Commons License

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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