Home » date » 2009 » May » 28 »

opgave 9 oef 2 decomposition werkloosheid belgie. umran celik

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 28 May 2009 10:25:52 -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/May/28/t1243527978rxp4hcjaajoh8x4.htm/, Retrieved Thu, 28 May 2009 18:26:23 +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/May/28/t1243527978rxp4hcjaajoh8x4.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 «
580 579 572 560 551 537 541 588 607 599 578 563 566 561 554 540 526 512 505 554 584 569 540 522 526 527 516 503 489 479 475 524 552 532 511 492 492 493 481 462 457 442 439 488 521 501 485 464 460 467 460 448 443 436 431 484 510 513 503 471 471 476 475 470 461 455 456 517 525 523 519 509 512 519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516
 
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
1580NANA-0.90625NA
2579NANA1.55965909090909NA
3572NANA-4.02746212121215NA
4560NANA-14.6259469696970NA
5551NANA-21.7736742424242NA
6537NANA-31.2698863636363NA
7541539.180871212121570.666666666667-31.48579545454541.81912878787875
8588586.044507575758569.33333333333316.71117424242431.95549242424249
9607605.370265151515567.83333333333337.53693181818181.62973484848487
10599598.366477272727566.2532.11647727272720.633522727272748
11578581.858901515152564.37517.4839015151515-3.8589015151515
12563560.972537878788562.291666666667-1.319128787878792.02746212121212
13566558.84375559.75-0.906257.15625
14561558.392992424242556.8333333333331.559659090909092.60700757575751
15554550.430871212121554.458333333333-4.027462121212153.56912878787875
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18512514.855113636364546.125-31.2698863636363-2.85511363636363
19505511.264204545455542.75-31.4857954545454-6.26420454545462
20554556.377840909091539.66666666666716.7111742424243-2.37784090909088
21584574.203598484848536.66666666666737.53693181818189.79640151515162
22569565.658143939394533.54166666666732.11647727272723.34185606060601
23540547.942234848485530.45833333333317.4839015151515-7.94223484848487
24522526.222537878788527.541666666667-1.31912878787879-4.22253787878799
25526524.010416666667524.916666666667-0.906251.98958333333326
26527523.976325757576522.4166666666671.559659090909093.02367424242425
27516515.805871212121519.833333333333-4.027462121212150.194128787878753
28503502.332386363636516.958333333333-14.62594696969700.66761363636374
29489492.434659090909514.208333333333-21.7736742424242-3.43465909090901
30479480.480113636364511.75-31.2698863636363-1.48011363636368
31475477.597537878788509.083333333333-31.4857954545454-2.59753787878793
32524522.961174242424506.2516.71117424242431.03882575757575
33552540.911931818182503.37537.536931818181811.0880681818182
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37492490.34375491.25-0.906251.65625000000006
38493489.809659090909488.251.559659090909093.19034090909105
39481481.430871212121485.458333333333-4.02746212121215-0.430871212121133
40462468.249053030303482.875-14.6259469696970-6.24905303030295
41457458.726325757576480.5-21.7736742424242-1.72632575757569
42442446.980113636364478.25-31.2698863636363-4.98011363636357
43439444.264204545454475.75-31.4857954545454-5.26420454545445
44488490.044507575758473.33333333333316.7111742424243-2.04450757575751
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46501502.033143939394469.91666666666732.1164772727272-1.03314393939388
47485486.233901515152468.7517.4839015151515-1.23390151515150
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79507504.805871212121536.291666666667-31.48579545454542.19412878787875
80569556.586174242424539.87516.711174242424312.4138257575758
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85555555.59375556.5-0.90625-0.593749999999886
86562560.601325757576559.0416666666671.559659090909091.39867424242425
87561557.347537878788561.375-4.027462121212153.65246212121224
88555549.499053030303564.125-14.62594696969705.500946969697
89544545.726325757576567.5-21.7736742424242-1.72632575757564
90537540.105113636364571.375-31.2698863636363-3.10511363636351
91543543.514204545455575-31.4857954545454-0.514204545454504
92594594.586174242424577.87516.7111742424243-0.586174242424136
93611617.786931818182580.2537.5369318181818-6.78693181818176
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95611602.525568181818585.04166666666717.48390151515158.47443181818187
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97595588.927083333333589.833333333333-0.906256.07291666666674
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100584580.66571969697595.291666666667-14.62594696969703.33428030303048
101573574.184659090909595.958333333333-21.7736742424242-1.18465909090912
102567564.77178030303596.041666666667-31.26988636363632.22821969696975
103569564.680871212121596.166666666667-31.48579545454544.31912878787875
104621613.044507575758596.33333333333316.71117424242437.95549242424238
105629633.995265151515596.45833333333337.5369318181818-4.99526515151501
106628628.44981060606596.33333333333332.1164772727272-0.449810606060623
107612613.692234848485596.20833333333317.4839015151515-1.69223484848476
108595595.180871212121596.5-1.31912878787879-0.180871212121133
109597596.010416666667596.916666666667-0.906250.989583333333371
110593598.601325757576597.0416666666671.55965909090909-5.60132575757575
111590592.847537878788596.875-4.02746212121215-2.84753787878788
112580581.79071969697596.416666666667-14.6259469696970-1.79071969696963
113574573.309659090909595.083333333333-21.77367424242420.690340909090878
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120566570.347537878788571.666666666667-1.31912878787879-4.34753787878799
121557565.09375566-0.90625-8.09375
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123549550.930871212121554.958333333333-4.02746212121215-1.93087121212125
124532534.54071969697549.166666666667-14.6259469696970-2.54071969696963
125526521.601325757576543.375-21.77367424242424.39867424242436
126511507.230113636364538.5-31.26988636363633.76988636363649
127499502.889204545454534.375-31.4857954545454-3.8892045454545
128555547.461174242424530.7516.71117424242437.53882575757586
129565564.745265151515527.20833333333337.53693181818180.254734848484986
130542555.991477272727523.87532.1164772727272-13.9914772727271
131527538.233901515151520.7517.4839015151515-11.2339015151514
132510516.180871212121517.5-1.31912878787879-6.18087121212108
133514513.96875514.875-0.906250.0312500000001137
134517514.434659090909512.8751.559659090909092.56534090909088
135508506.430871212121510.458333333333-4.027462121212151.56912878787881
136493493.54071969697508.166666666667-14.6259469696970-0.540719696969632
137490484.517992424242506.291666666667-21.77367424242425.48200757575773
138469473.813446969697505.083333333333-31.2698863636363-4.81344696969688
139478473.347537878788504.833333333333-31.48579545454544.65246212121218
140528NANA16.7111742424243NA
141534NANA37.5369318181818NA
142518NANA32.1164772727272NA
143506NANA17.4839015151515NA
144502NANA-1.31912878787879NA
145516NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/132ew1243527950.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/132ew1243527950.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/23kcd1243527950.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/23kcd1243527950.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/3qaq11243527950.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/3qaq11243527950.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/4q0cp1243527950.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/28/t1243527978rxp4hcjaajoh8x4/4q0cp1243527950.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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