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*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, 26 Dec 2010 11:27:23 +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/26/t1293363279odap3bmkw3wyhsz.htm/, Retrieved Sun, 26 Dec 2010 12:34:44 +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/26/t1293363279odap3bmkw3wyhsz.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 «
2030 1855 1834 2092 2164 2368 2072 2521 1823 1947 2226 1754 1786 2072 1846 2137 2466 2154 2289 2628 2074 2798 2194 2442 2565 2063 2069 2539 1898 2139 2408 2725 2201 2311 2548 2276 2351 2280 2057 2479 2379 2295 2456 2546 2844 2260 2981 2678 3440 2842 2450 2669 2570 2540 2318 2930 2947 2799 2695 2498 2260 2160 2058 2533 2150 2172 2155 3016 2333 2355 2825 2214 2360 2299 1746 2069 2267 1878 2266 2282 2085 2277 2251 1828 1954 1851 1570 1852 2187 1855 2218 2253 2028 2169 1997 2034 1791 1627 1631 2319 1707 1747 2397 2059 2251 2558 2406 2049 2074 1734 1983 2121 1905 2126 2363 2173 2710 2137 2742 2419 2194 2660 2189 2310 2349 2540 2434 2916 2446 2375 3032 2218 1920 2039 1889 2014 2105 2153 2309 2955 2225 2160 2386 1653 1099 5010 2672 2729 2955 2409 3086 3384 2458 2913 2448 2215 2179 2461 2098 2621 2703 2388 3880 3310 3093 3237 3002 2670 2311 2062 2059 etc...
 
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'Gwilym Jenkins' @ 72.249.127.135


Seasonal Decomposition by Loess - Parameters
ComponentWindowDegreeJump
Seasonal24110242
Trend1912
Low-pass1312


Seasonal Decomposition by Loess - Time Series Components
tObservedFittedSeasonalTrendRemainder
120302073.17374531016-187.4402628931342174.2665175829743.1737453101623
218551566.57982236058-13.46089228490192156.88106992432-288.420177639422
318341890.55303815545-362.0486604211332139.4956222656856.5530381554549
420922062.42835560095-0.6432558832784562122.21490028233-29.5716443990468
521642308.25350968789-85.18768798686462104.93417829897144.253509687891
623682825.24399799669-179.655888570822090.41189057413457.243997996689
720721989.0844044994279.02599265129262075.88960284929-82.9155955005813
825212577.57626000955399.138682120792065.2850578696656.5762600095472
918231535.4178950358855.90159207407872054.68051289004-287.582104964116
1019471780.9888560062763.43306767434682049.57807631938-166.011143993727
1122262105.40973693981302.1146233114652044.47563974872-120.590263060188
1217541522.14529741021-71.17729169589532057.03199428569-231.854702589794
1317861689.85191407048-187.4402628931342069.58834882266-96.1480859295214
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1621372115.23652671537-0.6432558832784562159.40672916791-21.76347328463
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1821542257.70508563-179.655888570822229.95080294082103.705085629998
1922892233.6003318248879.02599265129262265.37367552383-55.3996681751246
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3822802201.79755973937-13.46089228490192371.66333254553-78.2024402606266
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4024792553.91703160751-0.6432558832784562404.7262242757774.9170316075101
4123792419.65404550276-85.18768798686462423.533642484140.6540455027634
4222952308.29876339088-179.655888570822461.3571251799413.2987633908824
4324562333.7933994729379.02599265129262499.18060787577-122.206600527067
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5729473270.0940454004855.90159207407872568.00436252544323.094045400479
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6521502015.46785283527-85.18768798686462369.71983515159-134.532147164728
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6830163278.23681791498399.138682120792354.62449996423262.236817914976
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7023552307.6801520330263.43306767434682338.88678029263-47.3198479669791
7128253020.22074926355302.1146233114652327.66462742499195.220749263547
7222142187.94533996727-71.17729169589532311.23195172863-26.0546600327320
7323602612.64098686087-187.4402628931342294.79927603227252.640986860868
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7620691927.52232356464-0.6432558832784562211.12093231863-141.477676435356
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7922662338.9675105782179.02599265129262114.006496770572.9675105782076
8022822078.31650365210399.138682120792086.54481422711-203.683496347904
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8322512173.05557377263302.1146233114652026.82980291590-77.9444262273682
8418281707.00422051287-71.17729169589532020.17307118302-120.995779487127
8519542081.92392344299-187.4402628931342013.51633945014127.923923442993
8618511704.28516486404-13.46089228490192011.17572742086-146.714835135959
8715701493.21354502955-362.0486604211332008.83511539158-76.7864549704477
8818521700.73473005038-0.6432558832784562003.9085258329-151.265269949621
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9018551895.04183140414-179.655888570821994.6140571666840.0418314041385
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9320282019.5660476793255.90159207407871980.53236024660-8.43395232068269
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10117071484.21128333225-85.18768798686462014.97640465461-222.788716667746
10217471636.34709071423-179.655888570822037.30879785659-110.652909285765
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10820492044.55277457225-71.17729169589532124.62451712364-4.44722542774798
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19829873219.24063827067-179.655888570822934.41525030015232.240638270670
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20426602538.68417249760-71.17729169589532852.4931191983-121.315827502402
20522261813.47272685569-187.4402628931342825.96753603744-412.527273144305
20629423096.98210875761-13.46089228490192800.47878352729154.982108757613
20724202427.05862940399-362.0486604211332774.990031017147.05862940399311
20825162269.39910332464-0.6432558832784562763.24415255864-246.600896675363
20924212175.68941388672-85.18768798686462751.49827410014-245.310586113279
21026312687.13169170265-179.655888570822754.5241968681756.1316917026502
21128872937.4238877125179.02599265129262757.5501196362050.4238877125117
21233283493.41687201369399.138682120792763.44444586552165.416872013695
21325872348.7596358310955.90159207407872769.33877209483-238.240364168913
21426952561.6308523045263.43306767434682764.93608002113-133.369147695479
21536694275.35198874111302.1146233114652760.53338794743606.351988741105
21627732875.00172124534-71.17729169589532742.17557045055102.001721245344
21725272517.62250993946-187.4402628931342723.81775295367-9.3774900605381
21827502819.58823396263-13.46089228490192693.8726583222769.5882339626341
21920141726.12109673027-362.0486604211332663.92756369086-287.878903269731
22027632900.50134582906-0.6432558832784562626.14191005422137.501345829057
22127262948.83143156928-85.18768798686462588.35625641758222.831431569285
22218261274.87505862095-179.655888570822556.78082994987-551.124941379047
22327132821.7686038665579.02599265129262525.20540348216108.768603866552
22430403163.08408755016399.138682120792517.77723032905123.084087550159
22524052243.7493507499855.90159207407872510.34905717595-161.250649250024
22625262474.3439499951263.43306767434682514.22298233053-51.6560500048786
22725262231.78846920342302.1146233114652518.09690748512-294.211530796583
22825292608.02353109467-71.17729169589532521.1537606012279.0235310946746
22924742611.22964917581-187.4402628931342524.21061371732137.229649175810
23025762630.46601980629-13.46089228490192534.9948724786154.4660198062875
23122192254.26952918123-362.0486604211332545.7791312399135.2695291812274
23229003209.40932587808-0.6432558832784562591.2339300052309.409325878079
23322741996.49895921637-85.18768798686462636.68872877049-277.501040783630
23421841839.07544318066-179.655888570822708.58044539016-344.924556819345
23526292398.5018453388779.02599265129262780.47216200983-230.498154661128
23627392226.65083635869399.138682120792852.21048152052-512.34916364131
23729332886.1496068947255.90159207407872923.94880103121-46.8503931052842
23831443225.2479046138563.43306767434682999.3190277118181.2479046138451
23933543331.19612229612302.1146233114653074.68925439241-22.8038777038755
24033573628.43149463896-71.17729169589533156.74579705693271.431494638963
24133293606.63792317168-187.4402628931343238.80233972145277.637923171680
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/1kbfp1293362837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/1kbfp1293362837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/2kbfp1293362837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/2kbfp1293362837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/3kbfp1293362837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/3kbfp1293362837.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/48z731293362837.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/26/t1293363279odap3bmkw3wyhsz/48z731293362837.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

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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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