Home » date » 2010 » Dec » 27 »

CD Werkloosheid België 2000 - 2010

*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: Mon, 27 Dec 2010 19:14:29 +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/27/t1293477203qe725sldn44iatu.htm/, Retrieved Mon, 27 Dec 2010 20:13:24 +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/27/t1293477203qe725sldn44iatu.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:
Data Paper Statistiek
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
464 460 467 460 448 443 436 431 484 510 513 503 471 471 476 475 470 461 455 456 517 525 523 519 509 512 519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516 528 533 536 537 524 536 587 597 581 564 558 575 580 575 563 552 537 545 601 604 586 564 549
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1464NANA-4.29861111111112NA
2460NANA-0.84953703703702NA
3467NANA1.96990740740739NA
4460NANA-2.3402777777778NA
5448NANA-11.3356481481482NA
6443NANA-18.2893518518519NA
7436439.386574074074468.541666666667-29.1550925925926-3.38657407407408
8431441.928240740741469.291666666667-27.3634259259259-10.9282407407408
9484488.9375470.12518.8125-4.93749999999994
10510503.233796296296471.12532.10879629629636.7662037037037
11513499.548611111111472.66666666666726.881944444444413.4513888888889
12503488.19212962963474.33333333333313.858796296296314.8078703703704
13471471.576388888889475.875-4.29861111111112-0.576388888888857
14471476.858796296296477.708333333333-0.84953703703702-5.8587962962963
15476482.094907407407480.1251.96990740740739-6.09490740740739
16475479.784722222222482.125-2.3402777777778-4.78472222222223
17470471.831018518519483.166666666667-11.3356481481482-1.83101851851853
18461465.960648148148484.25-18.2893518518519-4.96064814814815
19455457.344907407408486.5-29.1550925925926-2.3449074074075
20456462.428240740741489.791666666667-27.3634259259259-6.42824074074076
21517512.104166666667493.29166666666718.81254.89583333333337
22525528.94212962963496.83333333333332.1087962962963-3.94212962962968
23523527.131944444444500.2526.8819444444444-4.1319444444444
24519517.775462962963503.91666666666713.85879629629631.22453703703707
25509503.534722222222507.833333333333-4.298611111111125.46527777777783
26512511.025462962963511.875-0.849537037037020.974537037037067
27519518.136574074074516.1666666666671.969907407407390.863425925925981
28517518.284722222222520.625-2.3402777777778-1.28472222222217
29510513.872685185185525.208333333333-11.3356481481482-3.87268518518522
30509511.127314814815529.416666666667-18.2893518518519-2.12731481481489
31501503.761574074074532.916666666667-29.1550925925926-2.76157407407402
32507508.928240740741536.291666666667-27.3634259259259-1.92824074074076
33569558.6875539.87518.812510.3125
34580575.608796296296543.532.10879629629634.3912037037037
35578574.090277777778547.20833333333326.88194444444443.90972222222229
36565564.400462962963550.54166666666713.85879629629630.599537037037067
37547549.201388888889553.5-4.29861111111112-2.20138888888891
38555555.650462962963556.5-0.84953703703702-0.65046296296282
39562561.011574074074559.0416666666671.969907407407390.98842592592598
40561559.034722222222561.375-2.34027777777781.96527777777794
41555552.789351851852564.125-11.33564814814822.21064814814815
42544549.210648148148567.5-18.2893518518519-5.21064814814804
43537542.219907407407571.375-29.1550925925926-5.21990740740728
44543547.636574074074575-27.3634259259259-4.63657407407402
45594596.6875577.87518.8125-2.68749999999989
46611612.358796296296580.2532.1087962962963-1.3587962962963
47613609.506944444445582.62526.88194444444443.49305555555543
48611598.900462962963585.04166666666713.858796296296312.0995370370371
49594583.201388888889587.5-4.2986111111111210.7986111111111
50595588.983796296296589.833333333333-0.849537037037026.01620370370381
51591594.011574074074592.0416666666671.96990740740739-3.01157407407402
52589591.576388888889593.916666666667-2.3402777777778-2.57638888888891
53584583.956018518518595.291666666667-11.33564814814820.0439814814816373
54573577.668981481482595.958333333333-18.2893518518519-4.66898148148152
55567566.886574074074596.041666666667-29.15509259259260.113425925925981
56569568.803240740741596.166666666667-27.36342592592590.196759259259238
57621615.145833333333596.33333333333318.81255.85416666666663
58629628.56712962963596.45833333333332.10879629629630.432870370370438
59628623.215277777778596.33333333333326.88194444444444.78472222222217
60612610.06712962963596.20833333333313.85879629629631.93287037037044
61595592.201388888889596.5-4.298611111111122.7986111111112
62597596.06712962963596.916666666667-0.849537037037020.932870370370438
63593599.011574074074597.0416666666671.96990740740739-6.01157407407402
64590594.534722222222596.875-2.3402777777778-4.53472222222217
65580585.081018518518596.416666666667-11.3356481481482-5.08101851851848
66574576.793981481482595.083333333333-18.2893518518519-2.79398148148152
67573563.719907407407592.875-29.15509259259269.28009259259272
68573562.636574074074590-27.363425925925910.363425925926
69620605.812558718.812514.1875
70626616.06712962963583.95833333333332.10879629629639.93287037037032
71620607.131944444444580.2526.881944444444412.8680555555555
72588590.108796296296576.2513.8587962962963-2.1087962962963
73566567.368055555556571.666666666667-4.29861111111112-1.36805555555566
74557565.150462962963566-0.84953703703702-8.15046296296293
75561562.178240740741560.2083333333331.96990740740739-1.17824074074076
76549552.618055555556554.958333333333-2.3402777777778-3.61805555555554
77532537.831018518518549.166666666667-11.3356481481482-5.83101851851848
78526525.085648148148543.375-18.28935185185190.914351851851961
79511509.344907407407538.5-29.15509259259261.65509259259272
80499507.011574074074534.375-27.3634259259259-8.01157407407402
81555549.5625530.7518.81255.43750000000011
82565559.31712962963527.20833333333332.10879629629635.68287037037044
83542550.756944444444523.87526.8819444444444-8.75694444444434
84527534.608796296296520.7513.8587962962963-7.60879629629619
85510513.201388888889517.5-4.29861111111112-3.2013888888888
86514514.025462962963514.875-0.84953703703702-0.0254629629628198
87517514.844907407407512.8751.969907407407392.15509259259261
88508508.118055555556510.458333333333-2.3402777777778-0.118055555555543
89493496.831018518518508.166666666667-11.3356481481482-3.83101851851842
90490488.002314814815506.291666666667-18.28935185185191.99768518518533
91469475.928240740741505.083333333333-29.1550925925926-6.92824074074065
92478477.469907407407504.833333333333-27.36342592592590.530092592592666
93528524.1875505.37518.81253.81250000000011
94534538.983796296296506.87532.1087962962963-4.98379629629625
95518536.590277777778509.70833333333326.8819444444444-18.5902777777777
96506527.31712962963513.45833333333313.8587962962963-21.3171296296296
97502513.409722222222517.708333333333-4.29861111111112-11.4097222222222
98516521.56712962963522.416666666667-0.84953703703702-5.56712962962956
99528529.261574074074527.2916666666671.96990740740739-1.26157407407413
100533530.034722222222532.375-2.34027777777782.96527777777783
101536526.289351851852537.625-11.33564814814829.71064814814815
102537524.377314814815542.666666666667-18.289351851851912.6226851851851
103524518.261574074074547.416666666667-29.15509259259265.73842592592598
104536524.844907407408552.208333333333-27.363425925925911.1550925925924
105587575.645833333333556.83333333333318.812511.3541666666666
106597592.858796296296560.7532.10879629629634.14120370370358
107581590.506944444445563.62526.8819444444444-9.50694444444457
108564579.233796296296565.37513.8587962962963-15.2337962962963
109558562.243055555556566.541666666667-4.29861111111112-4.24305555555566
110575566.608796296296567.458333333333-0.849537037037028.3912037037037
111580570.386574074074568.4166666666671.969907407407399.61342592592598
112575566.951388888889569.291666666667-2.34027777777788.0486111111112
113563558.456018518519569.791666666667-11.33564814814824.54398148148141
114552551.710648148148570-18.28935185185190.289351851851848
115537540.469907407407569.625-29.1550925925926-3.46990740740739
116545NANA-27.3634259259259NA
117601NANA18.8125NA
118604NANA32.1087962962963NA
119586NANA26.8819444444444NA
120564NANA13.8587962962963NA
121549NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/194m01293477265.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/194m01293477265.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/294m01293477265.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/294m01293477265.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/3kw331293477265.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/3kw331293477265.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/4kw331293477265.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/27/t1293477203qe725sldn44iatu/4kw331293477265.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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