Home » date » 2010 » Dec » 29 »

*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: Wed, 29 Dec 2010 19:58:29 +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/29/t1293652615pajpa2bukstopox.htm/, Retrieved Wed, 29 Dec 2010 20:56:58 +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/29/t1293652615pajpa2bukstopox.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 «
60178 53200 59909 55970 47682 50173 43090 36031 42143 48478 36046 31060 54874 60051 71622 66526 50140 55973 40393 38483 42879 47875 40578 31027 62027 56493 65566 62653 53470 59600 42542 42018 44038 44988 43309 26843 69770 64886 79354 63025 54003 55926 45629 40361 43039 44570 43269 25563 68707 60223 74283 61232 61531 65305 51699 44599 35221 55066 45335 28702 69517 69240 71525 77740 62107 65450 51493 43067 49172 54483 38158 27898 58648 56000 62381 59849 48345 55376 45400 38389 44098 48290 41267 31238
 
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'Herman Ole Andreas Wold' @ www.yougetit.org


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


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
16017864171.2108466212543.876579488343640.91257389173993.21084662004
25320053174.42539108219049.6117050843644175.9629038335-25.5746089178865
35990956955.207527663618151.779238561144711.0132337754-2953.79247233642
45597054069.394263958812633.974787145545236.6309488957-1900.60573604122
54768247067.28931972252534.4620162613645762.2486640161-614.710680277472
65017347255.25548112856826.0299501761746264.7145686953-2917.74451887148
74309045164.0807055767-5751.2611789512346767.18047337452074.08070557673
83603135900.1697531761-11124.816209773147286.6464565971-130.830246823934
94214345129.6923090188-8649.8047488384147806.11243981962986.69230901879
104847851124.7593778742-2577.4262724900648408.66689461592646.75937787417
113604633721.2528794459-10640.47422885849011.2213494122-2324.74712055412
123106035815.0212944893-22995.95179573849300.93050124874755.02129448932
135487447613.483767426612543.876579488349590.6396530851-7260.51623257342
146005161407.92361785329049.6117050843649644.46467706241356.92361785319
157162275393.931060399218151.779238561149698.28970103973771.93106039919
166652670629.52308810512633.974787145549788.50212474964103.52308810495
175014047866.82343527932534.4620162613649878.7145484594-2273.17656472074
185597355104.90842034216826.0299501761750015.0616294817-868.091579657863
194039336385.8524684472-5751.2611789512350151.408710504-4007.14753155276
203848337981.5211330534-11124.816209773150109.2950767198-501.478866946636
214287944340.6233059029-8649.8047488384150067.18144293551461.62330590288
224787548332.959325439-2577.4262724900649994.4669470511457.959325438962
234057841874.7217776914-10640.47422885849921.75245116671296.72177769138
243102735008.6041476369-22995.95179573850041.34764810113981.60414763691
256202761349.180575476312543.876579488350160.9428450355-677.819424523739
265649353659.66574405449049.6117050843650276.7225508612-2833.33425594559
276556662587.71850475218151.779238561150392.502256687-2978.28149524807
286265362249.456408331312633.974787145550422.5688045232-403.543591668727
295347053952.90263137922534.4620162613650452.6353523595482.902631379155
305960061708.2411962596826.0299501761750665.72885356492108.24119625897
314254239956.438824181-5751.2611789512350878.8223547702-2585.56117581899
324201843755.0954683068-11124.816209773151405.72074146631737.0954683068
334403844793.185620676-8649.8047488384151932.6191281624755.185620675977
344498840211.3521146213-2577.4262724900652342.0741578687-4776.64788537868
354330944506.945041283-10640.47422885852751.52918757511197.94504128299
362684323830.8832284781-22995.95179573852851.0685672599-3012.11677152193
376977074045.51547356712543.876579488352950.60794694484275.51547356698
386488667806.80768115959049.6117050843652915.58061375622920.80768115948
397935487675.667480871418151.779238561152880.55328056768321.66748087136
406302560673.634517577112633.974787145552742.3906952774-2351.36548242292
415400352867.30987375132534.4620162613652604.2281099873-1135.69012624866
425592652673.77444462436826.0299501761752352.1956051995-3252.22555537571
434562944909.0980785394-5751.2611789512352100.1631004118-719.90192146055
444036139972.724498104-11124.816209773151874.0917116691-388.275501896016
454303943079.7844259119-8649.8047488384151648.020322926540.7844259119156
464457039959.5415192846-2577.4262724900651757.8847532055-4610.45848071545
474326945310.7250453735-10640.47422885851867.74918348452041.72504537353
482556321783.3452614713-22995.95179573852338.6065342667-3779.65473852873
496870772060.659535462812543.876579488352809.46388504893353.65953546282
506022358209.96841920329049.6117050843653186.4198757124-2013.0315807968
517428376850.84489506318151.779238561153563.3758663762567.84489506296
526123256012.887596374712633.974787145553817.1376164798-5219.11240362525
536153166456.63861715512534.4620162613654070.89936658364925.63861715508
546530569500.11508412836826.0299501761754283.85496569564195.11508412826
555169954652.4506141437-5751.2611789512354496.81056480762953.45061414365
564459945552.8338369694-11124.816209773154769.9823728038953.83383696936
573522124048.6505680385-8649.8047488384155043.1541808-11172.3494319615
585506657315.4407682337-2577.4262724900655393.98550425642249.4407682337
594533545565.6574011453-10640.47422885855744.8168277128230.657401145283
602870224340.2355702283-22995.95179573856059.7162255096-4361.76442977165
616951770115.507797205212543.876579488356374.6156233065598.507797205224
626924072728.466246139049.6117050843656701.92204878573488.46624612999
637152567868.992287174118151.779238561157029.2284742648-3656.00771282586
647774085721.278365393212633.974787145557124.74684746137981.27836539323
656210764459.27276308092534.4620162613657220.26522065782352.27276308086
666545067321.25754680526826.0299501761756752.71250301871871.25754680517
675149352452.1013935717-5751.2611789512356285.1597853795959.101393571706
684306741961.7926657524-11124.816209773155297.0235440207-1105.20733424762
694917252684.9174461764-8649.8047488384154308.8873026623512.91744617644
705448358428.8089670637-2577.4262724900653114.61730542643945.80896706365
713815835036.1269206672-10640.47422885851920.3473081908-3121.87307933279
722789827841.1365044749-22995.95179573850950.815291263-56.8634955250527
735864854770.840146176512543.876579488349981.2832743352-3877.15985382349
745600053512.96559097169049.6117050843649437.422703944-2487.0344090284
756238157716.658627886118151.779238561148893.5621335529-4664.34137211392
765984957948.806650747412633.974787145549115.2185621071-1900.19334925262
774834544818.66299307722534.4620162613649336.8749906614-3526.33700692276
785537654293.33718656826.0299501761749632.6328633239-1082.66281350004
794540046622.8704429649-5751.2611789512349928.39073598631222.87044296489
803838937595.339093027-11124.816209773150307.4771167462-793.660906973033
814409846159.2412513324-8649.8047488384150686.5634975062061.24125133242
824829048012.5906242537-2577.4262724900651144.8356482364-277.409375746342
834126741571.3664298912-10640.47422885851603.1077989668304.366429891226
843123833360.7682359183-22995.95179573852111.18355981972122.76823591825
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/1aki51293652707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/1aki51293652707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/2aki51293652707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/2aki51293652707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/33tzq1293652707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/33tzq1293652707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/4d3gs1293652707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/29/t1293652615pajpa2bukstopox/4d3gs1293652707.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


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