Home » date » 2010 » Dec » 19 »

Loess maandelijke huwelijken

*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: Sun, 19 Dec 2010 08:54:03 +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/19/t1292748831arbwfbs52dd9wrp.htm/, Retrieved Sun, 19 Dec 2010 09:53:56 +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/19/t1292748831arbwfbs52dd9wrp.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 «
3111 3995 5245 5588 10681 10516 7496 9935 10249 6271 3616 3724 2886 3318 4166 6401 9209 9820 7470 8207 9564 5309 3385 3706 2733 3045 3449 5542 10072 9418 7516 7840 10081 4956 3641 3970 2931 3170 3889 4850 8037 12370 6712 7297 10613 5184 3506 3810 2692 3073 3713 4555 7807 10869 9682 7704 9826 5456 3677 3431 2765 3483 3445 6081 8767 9407 6551 12480 9530 5960 3252 3717 2642 2989 3607 5366 8898 9435 7328 8594 11349 5797 3621 3851
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal841085
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
131112638.39974468971-3404.723021087746988.32327639803-472.600255310289
239953970.0486562366-2919.886913697916939.83825746131-24.9513437633977
352455872.55472144438-2273.907959968976891.35323852459627.554721444378
455885050.82404606856-717.9273080145636843.103261946-537.175953931435
51068111697.80812372532869.338590907256794.85328536741016.80812372535
61051610213.76528746814069.666450335976748.56826219594-302.234712531907
774966940.295355634291349.421405341246702.28323902448-555.704644365714
8993510532.15527639432683.214994950856654.62972865481597.155276394338
9102499895.013558478883996.010223235976606.97621828515-353.986441521116
1062716593.36248062063-606.9417926507736555.57931203014322.362480620634
1136163359.99657114579-2632.178976920926504.18240577513-256.00342885421
1237243403.63028060175-2412.085523341636456.45524273988-320.36971939825
1328862767.99494138311-3404.723021087746408.72807970463-118.005058616885
1433183211.18130213619-2919.886913697916344.70561156172-106.818697863808
1541664325.22481655016-2273.907959968976280.68314341881159.224816550155
1664017297.59923673975-717.9273080145636222.32807127481896.59923673975
1792099384.688409961942869.338590907256163.97299913081175.688409961941
1898209442.960719980654069.666450335976127.37282968339-377.039280019356
1974707499.80593442281349.421405341246090.7726602359729.8059344227950
2082077677.015473010252683.214994950856053.7695320389-529.984526989749
2195649115.22337292223996.010223235976016.76640384183-448.776627077798
2253095225.21833592984-606.9417926507735999.72345672093-83.7816640701576
2333853419.49846732089-2632.178976920925982.6805096000334.498467320891
2437063831.8588822095-2412.085523341635992.22664113213125.858882209502
2527332868.95024842352-3404.723021087746001.77277266423135.950248423515
2630453002.61311384363-2919.886913697916007.27379985428-42.3868861563651
2734493159.13313292464-2273.907959968976012.77482704433-289.866867075360
2855425793.05152337138-717.9273080145636008.87578464319251.051523371378
291007211269.68466685072869.338590907256004.976742242041197.68466685071
3094188756.145875052744069.666450335976010.18767461129-661.854124947257
3175167667.179987678221349.421405341246015.39860698054151.179987678222
3278406977.854990198482683.214994950856018.93001485067-862.145009801523
331008110143.52835404323996.010223235976022.461422720862.5283540432256
3449564504.2797321239-606.9417926507736014.66206052688-451.720267876103
3536413907.31627858798-2632.178976920926006.86269833295266.316278587976
3639704334.52031498036-2412.085523341636017.56520836127364.52031498036
3729313238.45530269815-3404.723021087746028.2677183896307.455302698148
3831703220.79249355622-2919.886913697916039.0944201416950.7924935562232
3938894001.98683807518-2273.907959968976049.92112189378112.986838075184
4048504376.19082569711-717.9273080145636041.73648231745-473.809174302888
4180377171.109566351632869.338590907256033.55184274112-865.890433648366
421237014648.21391529194069.666450335976022.11963437212278.21391529193
4367126063.891168655671349.421405341246010.68742600309-648.108831344326
4472975908.899550911862683.214994950856001.8854541373-1388.10044908814
451061311236.90629449253996.010223235975993.0834822715623.906294492532
4651845004.3935967558-606.9417926507735970.54819589498-179.606403244205
4735063696.16606740246-2632.178976920925948.01290951846190.166067402463
4838104073.22771377964-2412.085523341635958.857809562263.227713779637
4926922819.02031148221-3404.723021087745969.70270960553127.020311482213
5030733062.60276129952-2919.886913697916003.28415239839-10.3972387004760
5137133663.04236477772-2273.907959968976036.86559519125-49.9576352222812
5245553780.61088895828-717.9273080145636047.31641905628-774.389111041721
5378076686.894166171432869.338590907256057.76724292132-1120.10583382857
541086911606.73246480984069.666450335976061.60108485422737.73246480981
55968211949.14366787161349.421405341246065.434926787132267.14366787163
5677046634.466528894762683.214994950856090.3184761544-1069.53347110524
5798269540.787751242373996.010223235976115.20202552166-285.212248757629
5854565387.72642867117-606.9417926507736131.2153639796-68.2735713288312
5936773838.95027448337-2632.178976920926147.22870243755161.950274483374
6034313140.58083547041-2412.085523341636133.50468787122-290.419164529589
6127652814.94234778285-3404.723021087746119.7806733048949.9423477828523
6234833737.24610405082-2919.886913697916148.64080964709254.246104050820
6334452986.40701397967-2273.907959968976177.5009459893-458.592986020328
6460816654.83029148447-717.9273080145636225.09701653009573.830291484469
6587678391.968322021862869.338590907256272.69308707089-375.031677978140
6694078462.159678280964069.666450335976282.17387138307-944.840321719038
6765515460.923938963511349.421405341246291.65465569525-1090.07606103649
681248015995.70554250842683.214994950856281.079462540783515.70554250837
6995308793.485507377723996.010223235976270.50426938631-736.514492622278
7059606274.26978471478-606.9417926507736252.67200793599314.269784714784
7132522901.33923043525-2632.178976920926234.83974648567-350.660769564748
7237173654.70121866792-2412.085523341636191.38430467371-62.2987813320788
7326422540.79415822599-3404.723021087746147.92886286175-101.205841774006
7429892796.02660948497-2919.886913697916101.86030421295-192.973390515031
7536073432.11621440483-2273.907959968976055.79174556414-174.883785595172
7653665362.37094846243-717.9273080145636087.55635955214-3.62905153757310
7788988807.340435552622869.338590907256119.32097354013-90.659564447381
7894358653.731357507984069.666450335976146.60219215605-781.268642492017
7973287132.69518388681349.421405341246173.88341077197-195.304816113205
8085948298.627687069772683.214994950856206.15731797938-295.372312930234
811134912463.55855157723996.010223235976238.43122518681114.55855157723
8257975921.87184796467-606.9417926507736279.0699446861124.871847964675
8336213554.47031273553-2632.178976920926319.7086641854-66.5296872644722
8438513749.06606878746-2412.085523341636365.01945455417-101.933931212538
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/1ehhy1292748837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/1ehhy1292748837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/2ehhy1292748837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/2ehhy1292748837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/378gj1292748837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/378gj1292748837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/478gj1292748837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/19/t1292748831arbwfbs52dd9wrp/478gj1292748837.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