Home » date » 2009 » Jun » 01 »

Aantal bouwevergunningen in Vlaanderen-Wendy van Weelde-MAR202B

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 01 Jun 2009 14:19:07 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz.htm/, Retrieved Mon, 01 Jun 2009 22:19:55 +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/2009/Jun/01/t1243887591ojk9itzkfwysyzz.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
1528 1816 1420 1757 1544 1678 1655 1391 1403 1744 1266 1358 1596 1819 1416 1521 1638 1543 1623 1530 1336 1700 1615 1494 1578 1607 1767 1505 1938 1862 2571 2082 1781 1869 1785 1682 1556 2080 2027 1887 1935 1798 1590 1592 1387 1849 1470 1437 1500 2081 1552 1586 1914 1639 1633 1693 1224 1417 1577 1225 1510 1515 1393 1455 1532 1268 1365 1282 1063 1296 1639 1247 1515 1547 1299
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
11528NANA-79.7520833333332NA
21816NANA195.972916666667NA
31420NANA10.3145833333333NA
41757NANA-23.3187500000001NA
51544NANA177.90625NA
61678NANA6.32291666666671NA
716551691.822916666671549.5142.322916666667-36.8229166666667
813911577.122916666671552.4583333333324.6645833333333-186.122916666666
914031302.722916666671552.41666666667-249.69375100.277083333333
1017441599.281251542.4166666666756.8645833333334144.71875
1112661463.806251536.5-72.69375-197.80625
1213581345.881251534.79166666667-188.91041666666712.1187499999996
1315961448.081251527.83333333333-79.7520833333332147.918750000000
1418191728.264583333331532.29166666667195.97291666666790.7354166666669
1514161545.606251535.2916666666710.3145833333333-129.606250000000
1615211507.347916666671530.66666666667-23.318750000000113.6520833333334
1716381721.281251543.375177.90625-83.28125
1815431569.906251563.583333333336.32291666666671-26.9062499999998
1916231710.822916666671568.5142.322916666667-87.8229166666667
2015301583.581251558.9166666666724.6645833333333-53.58125
2113361315.014583333331564.70833333333-249.6937520.9854166666669
2217001635.531251578.6666666666756.864583333333464.46875
2316151517.806251590.5-72.6937597.1937500000001
2414941427.381251616.29166666667-188.91041666666766.6187499999999
2515781589.331251669.08333333333-79.7520833333332-11.3312500000000
2616071927.556251731.58333333333195.972916666667-320.55625
2717671783.439583333331773.12510.3145833333333-16.4395833333333
2815051775.389583333331798.70833333333-23.3187500000001-270.389583333333
2919381990.739583333331812.83333333333177.90625-52.7395833333335
3018621834.072916666671827.756.3229166666667127.927083333333
3125711976.989583333331834.66666666667142.322916666667594.010416666667
3220821878.122916666671853.4583333333324.6645833333333203.877083333334
3317811634.306251884-249.69375146.6937500
3418691967.614583333331910.7556.8645833333334-98.6145833333335
3517851853.847916666671926.54166666667-72.69375-68.8479166666668
3616821734.839583333331923.75-188.910416666667-52.8395833333332
3715561800.456251880.20833333333-79.7520833333332-244.45625
3820802014.889583333331818.91666666667195.97291666666765.1104166666667
3920271792.397916666671782.0833333333310.3145833333333234.602083333333
4018871741.514583333331764.83333333333-23.3187500000001145.485416666667
4119351928.781251750.875177.906256.21875
4217981733.864583333331727.541666666676.3229166666667164.1354166666667
4315901857.322916666671715142.322916666667-267.322916666667
4415921737.372916666671712.7083333333324.6645833333333-145.372916666667
4513871443.264583333331692.95833333333-249.69375-56.2645833333329
4618491717.489583333331660.62556.8645833333334131.510416666667
4714701574.514583333331647.20833333333-72.69375-104.514583333333
4814371450.797916666671639.70833333333-188.910416666667-13.7979166666667
4915001555.122916666671634.875-79.7520833333332-55.1229166666665
5020811836.847916666671640.875195.972916666667244.152083333334
5115521648.606251638.2916666666710.3145833333333-96.60625
5215861590.181251613.5-23.3187500000001-4.18124999999986
5319141777.864583333331599.95833333333177.90625136.135416666667
5416391601.906251595.583333333336.3229166666667137.09375
5516331729.489583333331587.16666666667142.322916666667-96.4895833333333
5616931588.66458333333156424.6645833333333104.335416666667
5712241284.097916666671533.79166666667-249.69375-60.0979166666666
5814171578.572916666671521.7083333333356.8645833333334-161.572916666667
5915771427.639583333331500.33333333333-72.69375149.360416666667
6012251280.047916666671468.95833333333-188.910416666667-55.0479166666667
6115101362.581251442.33333333333-79.7520833333332147.41875
6215151610.014583333331414.04166666667195.972916666667-95.0145833333333
6313931400.522916666671390.2083333333310.3145833333333-7.52291666666656
6414551355.139583333331378.45833333333-23.318750000000199.8604166666669
6515321553.906251376177.90625-21.9062499999998
6612681385.822916666671379.56.32291666666671-117.822916666667
6713651522.947916666671380.625142.322916666667-157.947916666667
6812821406.831251382.1666666666724.6645833333333-124.831250000000
6910631129.889583333331379.58333333333-249.69375-66.8895833333333
701296NANA56.8645833333334NA
711639NANA-72.69375NA
721247NANA-188.910416666667NA
731515NANANANA
741547NANANANA
751299NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/1o9xv1243887545.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/1o9xv1243887545.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/2zy091243887545.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/2zy091243887545.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/3bw061243887545.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/3bw061243887545.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/47dy21243887545.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243887591ojk9itzkfwysyzz/47dy21243887545.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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