Home » date » 2010 » Nov » 30 »

Births

*The author of this computation has been verified*
R Software Module: /rwasp_decomposeloess.wasp (opens new window with default values)
Title produced by software: Decomposition by Loess
Date of computation: Tue, 30 Nov 2010 13:47:21 +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/Nov/30/t1291124755x1taod0rtp7pmno.htm/, Retrieved Tue, 30 Nov 2010 14:46:03 +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/Nov/30/t1291124755x1taod0rtp7pmno.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:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
9700 9081 9084 9743 8587 9731 9563 9998 9437 10038 9918 9252 9737 9035 9133 9487 8700 9627 8947 9283 8829 9947 9628 9318 9605 8640 9214 9567 8547 9185 9470 9123 9278 10170 9434 9655 9429 8739 9552 9687 9019 9672 9206 9069 9788 10312 10105 9863 9656 9295 9946 9701 9049 10190 9706 9765 9893 9994 10433 10073 10112 9266 9820 10097 9115 10411 9678 10408 10153 10368 10581 10597 10680 9738 9556
 
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'George Udny Yule' @ 72.249.76.132


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal751076
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
197009665.38145341098216.6597249533519517.95882163567-34.6185465890176
290819165.77198053844-524.3262785862859520.5542980478484.7719805384404
390848819.30488240033-174.4546568603459523.14977446002-264.695117599675
497439815.13667288808148.1342581676269522.7290689442972.1366728880839
585878390.3144979431-738.6228613716649522.30836342856-196.685502056896
697319725.13807465836217.6548137857949519.20711155585-5.86192534164002
795639776.79463081457-166.9004904976999516.10585968313213.794630814569
8999810477.39159839494.899093226308989513.70930837882479.391598394866
994379409.98788403842-47.3006411129379511.31275707452-27.0121159615828
101003810051.5200681786519.336152915619505.1437789057613.5200681786337
1199189947.88590851772389.1392907452919498.974800737029.8859085177173
1292528874.86319978722155.7816679040929473.35513230869-377.136800212778
1397379809.60481116627216.6597249533519447.7354638803872.6048111662658
1490359183.97766975102-524.3262785862859410.34860883526148.977669751022
1591339067.49290307021-174.4546568603459372.96175379014-65.5070969297922
1694879479.52410990384148.1342581676269346.34163192853-7.47589009615695
1787008818.90135130474-738.6228613716649319.72151006693118.901351304739
1896279732.83020877482217.6548137857949303.51497743939105.830208774820
1989478773.59204568585-166.9004904976999287.30844481185-173.407954314149
2092839285.245003453764.899093226308989275.855903319932.24500345375964
2188298440.89727928493-47.3006411129379264.40336182801-388.102720715075
22994710115.1181643594519.336152915619259.54568272501168.118164359381
2396289612.1727056327389.1392907452919254.688003622-15.8272943672982
2493189223.30241590875155.7816679040929256.91591618715-94.6975840912455
2596059734.19644629435216.6597249533519259.1438287523129.196446294351
2686408534.35586053478-524.3262785862859269.9704180515-105.644139465216
2792149321.65764950964-174.4546568603459280.7970073507107.657649509643
2895679693.8923956418148.1342581676269291.97334619057126.892395641804
2985478529.47317634123-738.6228613716649303.14968503044-17.5268236587726
3091858843.67047554607217.6548137857949308.67471066814-341.329524453933
3194709792.70075419186-166.9004904976999314.19973630584322.700754191857
3291238918.073575426264.899093226308989323.02733134744-204.926424573745
3392789271.4457147239-47.3006411129379331.85492638903-6.55428527609365
341017010468.1371071333519.336152915619352.52673995109298.137107133301
3594349105.66215574156389.1392907452919373.19855351315-328.33784425844
3696559760.51674400656155.7816679040929393.70158808935105.516744006556
3794299227.1356523811216.6597249533519414.20462266556-201.864347618908
3887398573.70419384917-524.3262785862859428.62208473712-165.295806150831
3995529835.41511005167-174.4546568603459443.03954680867283.415110051672
4096879758.56511370315148.1342581676269467.3006281292271.5651137031527
4190199285.0611519219-738.6228613716649491.56170944977266.061151921896
4296729605.97213280895217.6548137857949520.37305340526-66.0278671910492
4392069029.71609313695-166.9004904976999549.18439736074-176.283906863046
4490698558.394271843354.899093226308989574.70663493034-510.605728156652
45978810023.071768613-47.3006411129379600.22887249994235.071768612997
461031210480.8282002468519.336152915619623.8356468376168.828200246793
471010510173.4182880795389.1392907452919647.4424211752568.4182880794542
4898639890.35914461412155.7816679040929679.8591874817927.3591446141218
4996569383.06432125833216.6597249533519712.27595378832-272.935678741667
5092959374.16734837944-524.3262785862859740.1589302068479.1673483794402
51994610298.4127502350-174.4546568603459768.04190662537352.412750234975
5297019472.4536957728148.1342581676269781.41204605957-228.546304227195
5390499041.8406758779-738.6228613716649794.78218549377-7.15932412210532
541019010354.4579532181217.6548137857949807.88723299606164.457953218149
5597069757.90820999935-166.9004904976999820.9922804983551.908209999352
5697659692.770733741144.899093226308989832.33017303255-72.229266258857
5798939989.63257554619-47.3006411129379843.6680655667596.6325755461912
5899949615.54519313482519.336152915619853.11865394957-378.454806865182
591043310614.2914669223389.1392907452919862.5692423324181.291466922312
601007310111.7158882826155.7816679040929878.5024438133138.715888282597
611011210112.9046297524216.6597249533519894.435645294230.904629752423716
6292669136.50938088752-524.3262785862859919.81689769877-129.490619112481
6398209869.25650675704-174.4546568603459945.198150103349.2565067570413
641009710071.7159847449148.1342581676269974.14975708747-25.2840152550962
6591158965.52149730003-738.62286137166410003.1013640716-149.478502699973
661041110567.8772587978217.65481378579410036.4679274164156.877258797795
6796789453.06599973651-166.90049049769910069.8344907612-224.934000263487
681040810720.29864439034.8990932263089810090.8022623834312.29864439033
691015310241.5306071074-47.30064111293710111.770034005588.5306071074046
701036810087.1032512669519.3361529156110129.5605958175-280.896748733074
711058110625.5095516253389.13929074529110147.351157629444.5095516253132
721059710873.4076819219155.78166790409210164.8106501740276.407681921863
731068010961.0701323280216.65972495335110182.2701427187281.070132327954
7497389801.4948913326-524.32627858628510198.831387253763.4948913326098
7595569071.0620250717-174.45465686034510215.3926317887-484.937974928309
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/1i4511291124838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/1i4511291124838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/2i4511291124838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/2i4511291124838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/3td4m1291124838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/3td4m1291124838.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/4td4m1291124838.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/30/t1291124755x1taod0rtp7pmno/4td4m1291124838.ps (open in new window)


 
Parameters (Session):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
Parameters (R input):
par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;
 
R code (references can be found in the software module):
par1 <- as.numeric(par1) #seasonal period
if (par2 != 'periodic') par2 <- as.numeric(par2) #s.window
par3 <- as.numeric(par3) #s.degree
if (par4 == '') par4 <- NULL else par4 <- as.numeric(par4)#t.window
par5 <- as.numeric(par5)#t.degree
if (par6 != '') par6 <- as.numeric(par6)#l.window
par7 <- as.numeric(par7)#l.degree
if (par8 == 'FALSE') par8 <- FALSE else par9 <- TRUE #robust
nx <- length(x)
x <- ts(x,frequency=par1)
if (par6 != '') {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.window=par6, l.degree=par7, robust=par8)
} else {
m <- stl(x,s.window=par2, s.degree=par3, t.window=par4, t.degre=par5, l.degree=par7, robust=par8)
}
m$time.series
m$win
m$deg
m$jump
m$inner
m$outer
bitmap(file='test1.png')
plot(m,main=main)
dev.off()
mylagmax <- nx/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$time.series[,'trend']),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$time.series[,'seasonal']),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$time.series[,'remainder']),na.action=na.pass,lag.max = mylagmax,main='Remainder')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
spectrum(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'trend']),'trend']),main='Trend')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'seasonal']),'seasonal']),main='Seasonal')
cpgram(as.numeric(m$time.series[!is.na(m$time.series[,'remainder']),'remainder']),main='Remainder')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Parameters',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Component',header=TRUE)
a<-table.element(a,'Window',header=TRUE)
a<-table.element(a,'Degree',header=TRUE)
a<-table.element(a,'Jump',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,m$win['s'])
a<-table.element(a,m$deg['s'])
a<-table.element(a,m$jump['s'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,m$win['t'])
a<-table.element(a,m$deg['t'])
a<-table.element(a,m$jump['t'])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Low-pass',header=TRUE)
a<-table.element(a,m$win['l'])
a<-table.element(a,m$deg['l'])
a<-table.element(a,m$jump['l'])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Seasonal Decomposition by Loess - Time Series Components',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observed',header=TRUE)
a<-table.element(a,'Fitted',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Remainder',header=TRUE)
a<-table.row.end(a)
for (i in 1:nx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]+m$time.series[i,'remainder'])
a<-table.element(a,m$time.series[i,'seasonal'])
a<-table.element(a,m$time.series[i,'trend'])
a<-table.element(a,m$time.series[i,'remainder'])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.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


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


FreeStatistics.org is powered by