Home » date » 2011 » May » 10 »

Alexander De Raeymaeker - Opdracht 9.2

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 10 May 2011 17:24:57 +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/10/t1305048116dx5h0yuman64ka8.htm/, Retrieved Tue, 10 May 2011 19:22:00 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2435 1379 1511 2021 1614 1680 1630 870 1877 2428 1711 127 3192 1934 2075 1700 1198 1582 1705 911 1817 1168 920 84 2254 1485 1886 1358 1167 1781 1218 779 1418 1641 1196 132 2926 1777 2094 1648 1646 1537 1917 977 1475 2124 1209 135 2917 1981 1398 1171 903 1390 1280 781 1828 1631 1063 186 2275 1342 1070 950 1121 1305 1586 548 1225 1419 880 124 2044 1143 897 1264 1326 1529 1373 587 1137 1426 1016 176 2614
 
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'Herman Ole Andreas Wold' @ www.yougetit.org


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12435NANA1169.67650462963NA
21379NANA182.42650462963NA
31511NANA149.197337962963NA
42021NANA-60.2054398148148NA
51614NANA-170.087384259259NA
61680NANA128.232060185185NA
716301762.864004629631638.45833333333124.405671296296-132.86400462963
88701179.683449074071693.125-513.441550925926-309.683449074074
918771732.037615740741739.75-7.7123842592592144.962384259259
1024281950.100115740741749.875200.225115740741477.899884259259
1117111559.176504629631719.16666666667-159.990162037037151.82349537037
12127655.0237268518521697.75-1042.72627314815-528.023726851852
1331922866.46817129631696.791666666671169.67650462963325.531828703704
1419341884.051504629631701.625182.4265046296349.9484953703707
1520751850.03067129631700.83333333333149.197337962963224.969328703704
1617001585.627893518521645.83333333333-60.2054398148148114.372106481482
1711981390.287615740741560.375-170.087384259259-192.287615740741
1815821653.857060185191525.625128.232060185185-71.8570601851852
1917051609.15567129631484.75124.40567129629695.8443287037037
20911913.5167824074071426.95833333333-513.441550925926-2.51678240740716
2118171392.662615740741400.375-7.7123842592592424.337384259259
2211681578.475115740741378.25200.225115740741-410.47511574074
239201202.71817129631362.70833333333-159.990162037037-282.718171296296
2484326.9820601851851369.70833333333-1042.72627314815-242.982060185185
2522542527.384837962961357.708333333331169.67650462963-273.384837962963
2614851514.34317129631331.91666666667182.42650462963-29.3431712962963
2718861458.989004629631309.79166666667149.197337962963427.01099537037
2813581252.669560185191312.875-60.2054398148148105.330439814815
2911671173.995949074071344.08333333333-170.087384259259-6.9959490740739
3017811485.815393518521357.58333333333128.232060185185295.184606481482
3112181511.989004629631387.58333333333124.405671296296-293.98900462963
32779914.3084490740741427.75-513.441550925926-135.308449074074
3314181440.870949074071448.58333333333-7.7123842592592-22.8709490740741
3416411669.558449074071469.33333333333200.225115740741-28.5584490740741
3511961341.384837962961501.375-159.990162037037-145.384837962963
36132468.4403935185181511.16666666667-1042.72627314815-336.440393518518
3729262699.801504629631530.1251169.67650462963226.19849537037
3817771749.926504629631567.5182.4265046296327.0734953703704
3920941727.322337962961578.125149.197337962963366.677662037037
4016481540.419560185181600.625-60.2054398148148107.580439814815
4116461451.204282407411621.29166666667-170.087384259259194.795717592593
4215371750.190393518521621.95833333333128.232060185185-213.190393518518
4319171746.114004629631621.70833333333124.405671296296170.88599537037
449771116.391782407411629.83333333333-513.441550925926-139.391782407407
4514751601.620949074071609.33333333333-7.7123842592592-126.620949074074
4621241760.683449074071560.45833333333200.225115740741363.316550925926
4712091349.634837962961509.625-159.990162037037-140.634837962963
48135429.8153935185181472.54166666667-1042.72627314815-294.815393518518
4929172609.551504629631439.8751169.67650462963307.44849537037
5019811587.59317129631405.16666666667182.42650462963393.406828703704
5113981560.90567129631411.70833333333149.197337962963-162.905671296296
5211711345.669560185191405.875-60.2054398148148-174.669560185185
539031209.162615740741379.25-170.087384259259-306.162615740741
5413901503.523726851851375.29166666667128.232060185185-113.523726851852
5512801475.072337962961350.66666666667124.405671296296-195.072337962963
56781783.850115740741297.29166666667-513.441550925926-2.85011574074065
5718281249.287615740741257-7.7123842592592578.712384259259
5816311434.350115740741234.125200.225115740741196.649884259259
5910631074.009837962961234-159.990162037037-11.0098379629628
60186196.8153935185181239.54166666667-1042.72627314815-10.8153935185185
6122752418.426504629631248.751169.67650462963-143.426504629629
6213421434.21817129631251.79166666667182.42650462963-92.2181712962963
6310701366.15567129631216.95833333333149.197337962963-296.155671296296
649501122.794560185181183-60.2054398148148-172.794560185185
651121996.4542824074081166.54166666667-170.087384259259124.545717592592
6613051284.565393518521156.33333333333128.23206018518520.4346064814815
6715861268.53067129631144.125124.405671296296317.469328703704
68548612.7667824074071126.20833333333-513.441550925926-64.7667824074072
6912251102.995949074071110.70833333333-7.7123842592592122.004050925926
7014191316.808449074071116.58333333333200.225115740741102.191550925926
71880978.2181712962961138.20833333333-159.990162037037-98.2181712962963
72124113.3570601851851156.08333333333-1042.7262731481510.6429398148148
7320442326.21817129631156.541666666671169.67650462963-282.218171296296
7411431331.71817129631149.29166666667182.42650462963-188.718171296296
758971296.447337962961147.25149.197337962963-399.447337962963
7612641083.669560185191143.875-60.2054398148148180.330439814815
771326979.7459490740741149.83333333333-170.087384259259346.254050925926
7815291285.898726851851157.66666666667128.232060185185243.101273148148
7913731307.989004629631183.58333333333124.40567129629665.0109953703704
80587NANA-513.441550925926NA
811137NANA-7.7123842592592NA
821426NANA200.225115740741NA
831016NANA-159.990162037037NA
84176NANA-1042.72627314815NA
852614NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/1hg931305048295.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/1hg931305048295.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/2kqyn1305048295.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/2kqyn1305048295.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/33obo1305048295.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/33obo1305048295.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/4bhga1305048295.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/10/t1305048116dx5h0yuman64ka8/4bhga1305048295.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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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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