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Paper

*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, 28 Dec 2010 20:47:39 +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/28/t12935691825h5445dk1lwexiw.htm/, Retrieved Tue, 28 Dec 2010 21:46:27 +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/28/t12935691825h5445dk1lwexiw.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 «
235243 230354 227184 221678 217142 219452 256446 265845 248624 241114 229245 231805 219277 219313 212610 214771 211142 211457 240048 240636 230580 208795 197922 194596 194581 185686 178106 172608 167302 168053 202300 202388 182516 173476 166444 171297 169701 164182 161914 159612 151001 158114 186530 187069 174330 169362 166827 178037 186413 189226 191563 188906 186005 195309 223532 226899 214126 206903 204442 220375 214320 212588 205816 202196 195722 198563 229139 229527 211868 203555 195770
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework
error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal711072
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
1235243236187.292507137-1294.69126405537235593.398756919944.292507136532
2230354229096.343287018-3809.21855692986235420.875269912-1257.65671298173
3227184226438.392547427-7318.7443303309235248.351782904-745.6074525734
4221678218114.168396543-9722.83435020938234964.665953666-3563.83160345655
5217142214069.112070695-14466.0921951226234680.980124428-3072.88792930503
6219452215002.421815873-10350.8923151265234252.470499254-4449.57818412723
7256446257901.56740562721166.4717202927233823.960874081455.56740562731
8265845274513.52621626623856.0974246686233320.3763590668668.52621626557
9248624255333.6481990719097.55995687696232816.7918440526709.6481990712
10241114250397.621119635-293.004224221963232123.3831045879283.62111963515
11229245234029.591150434-6969.56551555605231429.9743651224784.59115043428
12231805233327.727634864104.906636401442230177.3657287341522.72763486445
13219277210923.934171709-1294.69126405537228924.757092346-8353.06582829109
14219313215268.66753126-3809.21855692986227166.551025670-4044.33246873989
15212610207130.399371338-7318.7443303309225408.344958993-5479.60062866213
16214771215969.76047706-9722.83435020938223295.0738731501198.76047705981
17211142215568.289407816-14466.0921951226221181.8027873064426.28940781645
18211457214456.773164468-10350.8923151265218808.1191506592999.77316446783
19240048242495.09276569621166.4717202927216434.4355140112447.09276569591
20240636243831.42260103123856.0974246686213584.47997433195.42260103131
21230580241327.9156085349097.55995687696210734.52443458910747.9156085341
22208795210619.235727912-293.004224221963207263.7684963101824.23572791234
23197922199020.552957526-6969.56551555605203793.0125580301098.55295752574
24194596188983.465177797104.906636401442200103.628185802-5612.53482220334
25194581194042.447450482-1294.69126405537196414.243813573-538.552549518092
26185686182230.911623547-3809.21855692986192950.306933382-3455.08837645251
27178106174044.374277140-7318.7443303309189486.370053191-4061.62572286031
28172608168370.782524241-9722.83435020938186568.051825968-4237.21747575901
29167302165420.358596377-14466.0921951226183649.733598746-1881.64140362304
30168053164987.905316137-10350.8923151265181468.986998989-3065.09468386252
31202300204145.28788047521166.4717202927179288.2403992331845.2878804747
32202388203283.10819555523856.0974246686177636.794379777895.108195554698
33182516179949.0916828029097.55995687696175985.348360321-2566.90831719787
34173476172565.880029883-293.004224221963174679.124194339-910.119970116968
35166444166484.665487199-6969.56551555605173372.90002835740.6654871991486
36171297170258.404139043104.906636401442172230.689224556-1038.59586095711
37169701169608.212843301-1294.69126405537171088.478420754-92.7871566990798
38164182162044.944186807-3809.21855692986170128.274370123-2137.05581319294
39161914161978.674010840-7318.7443303309169168.07031949164.6740108397789
40159612160197.224262798-9722.83435020938168749.610087412585.224262797798
41151001148136.942339791-14466.0921951226168331.149855332-2864.05766020948
42158114157710.479040687-10350.8923151265168868.413274439-403.520959312649
43186530182487.85158616121166.4717202927169405.676693546-4042.14841383908
44187069179122.87491714323856.0974246686171159.027658189-7946.12508285732
45174330166650.0614202929097.55995687696172912.378622831-7679.93857970813
46169362163371.550045738-293.004224221963175645.454178483-5990.44995426151
47166827162245.035781420-6969.56551555605178378.529734136-4581.9642185797
48178037174266.733244236104.906636401442181702.360119363-3770.26675576437
49186413189094.500759465-1294.69126405537185026.190504592681.50075946527
50189226193804.816267151-3809.21855692986188456.4022897794578.81626715086
51191563198558.130255363-7318.7443303309191886.6140749686995.13025536304
52188906192488.368215586-9722.83435020938195046.4661346243582.36821558571
53186005188269.774000843-14466.0921951226198206.3181942792264.77400084308
54195309200093.909393610-10350.8923151265200874.9829215174784.90939360965
55223532222353.88063095321166.4717202927203543.647648754-1178.11936904705
56226899224457.49287815023856.0974246686205484.409697181-2441.50712184969
57214126211729.2682975159097.55995687696207425.171745608-2396.73170248495
58206903205447.607166136-293.004224221963208651.397058086-1455.39283386400
59204442205975.943144992-6969.56551555605209877.6223705641533.94314499211
60220375230105.223079418104.906636401442210539.8702841819730.2230794176
61214320218732.573066257-1294.69126405537211202.1181977984412.57306625735
62212588218004.134858948-3809.21855692986210981.0836979825416.13485894824
63205816208190.695132166-7318.7443303309210760.0491981652374.69513216571
64202196204110.929594937-9722.83435020938210003.9047552731914.92959493652
65195722196662.331882742-14466.0921951226209247.760312381940.331882742
66198563199076.110803032-10350.8923151265208400.781512094513.110803032061
67229139229557.72556789921166.4717202927207553.802711808418.725567898859
68229527228599.37008662423856.0974246686206598.532488707-927.629913376004
69211868208995.1777775179097.55995687696205643.262265607-2872.82222248349
70203555202807.626943079-293.004224221963204595.377281143-747.373056921206
71195770194962.073218876-6969.56551555605203547.492296680-807.926781123708
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/18pgx1293569253.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/18pgx1293569253.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/28pgx1293569253.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/28pgx1293569253.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/3ojn71293569253.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/3ojn71293569253.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/4ojn71293569253.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/28/t12935691825h5445dk1lwexiw/4ojn71293569253.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')
 





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