*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: Fri, 04 Dec 2009 11:25:08 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb.htm/, Retrieved Fri, 04 Dec 2009 19:26:51 +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/2009/Dec/04/t1259951206zo0e9glgfljs0yb.htm/}, year = {2009}, } @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 = {2009}, 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 «

15836,8 17570,4 18252,1 16196,7 16643 17729 16446,1 15993,8 16373,5 17842,2 22321,5 22786,7 18274,1 22392,9 23899,3 21343,5 22952,3 21374,4 21164,1 20906,5 17877,4 20664,3 22160 19813,6 17735,4 19640,2 20844,4 19823,1 18594,6 21350,6 18574,1 18924,2 17343,4 19961,2 19932,1 19464,6 16165,4 17574,9 19795,4 19439,5 17170 21072,4 17751,8 17515,5 18040,3 19090,1 17746,5 19202,1 15141,6 16258,1 18586,5 17209,4 17838,7 19123,5 16583,6 15991,2 16704,4 17420,4 17872 17823,2

Output produced by software:

Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 601 0 61 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 15836.8 17569.463151726 -1978.60794768726 16082.7447959612 1732.66315172602 2 17570.4 18748.9625347058 15.7449624615287 16376.0925028327 1178.56253470582 3 18252.1 18293.0623780954 1541.69741220056 16669.4402097041 40.9623780953771 4 16196.7 15377.8886726176 21.7486566083146 16993.7626707741 -818.811327382422 5 16643 16155.7359055258 -187.82103736997 17318.0851318442 -487.264094474187 6 17729 16498.6987025737 1299.11279908322 17660.1884983430 -1230.30129742626 7 16446.1 15620.1620921189 -730.253956960823 18002.2918648419 -825.937907881093 8 15993.8 14580.6670745375 -944.914790201036 18351.8477156636 -1413.13292546253 9 16373.5 15565.9133832385 -1520.31694972372 18701.4035664852 -807.586616761495 10 17842.2 16319.5774116049 214.353862512309 19150.4687258828 -1522.62258839515 11 22321.5 23811.5033313294 1231.96278339019 19599.5338852805 1490.00333132935 12 22786.7 24488.2628464014 1037.29507331068 20047.8420802879 1701.56284640144 13 18274.1 18030.6576723920 -1978.60794768726 20496.1502752953 -243.442327608038 14 22392.9 23967.9445587562 15.7449624615287 20802.1104787823 1575.04455875620 15 23899.3 25148.8319055302 1541.69741220056 21108.0706822693 1249.53190553018 16 21343.5 21462.4980739464 21.7486566083146 21202.7532694453 118.998073946434 17 22952.3 24794.9851807487 -187.82103736997 21297.4358566212 1842.68518074872 18 21374.4 20263.1225332106 1299.11279908322 21186.5646677062 -1111.27746678937 19 21164.1 21982.7604781698 -730.253956960823 21075.6934787911 818.660478169768 20 20906.5 21885.1698933967 -944.914790201036 20872.7448968043 978.669893396727 21 17877.4 16605.3206349062 -1520.31694972372 20669.7963148176 -1272.07936509384 22 20664.3 20665.4653054229 214.353862512309 20448.7808320648 1.16530542285909 23 22160 22860.2718672977 1231.96278339019 20227.7653493121 700.271867297717 24 19813.6 18536.8889650068 1037.29507331068 20053.0159616826 -1276.71103499324 25 17735.4 17571.1413736342 -1978.60794768726 19878.2665740530 -164.258626365765 26 19640.2 19506.2979364906 15.7449624615287 19758.3571010479 -133.902063509413 27 20844.4 20508.6549597567 1541.69741220056 19638.4476280427 -335.745040243295 28 19823.1 20066.6146890772 21.7486566083146 19557.8366543145 243.514689077205 29 18594.6 17899.7953567837 -187.82103736997 19477.2256805862 -694.804643216255 30 21350.6 22018.3265262875 1299.11279908322 19383.7606746293 667.726526287512 31 18574.1 18588.1582882885 -730.253956960823 19290.2956686723 14.0582882885174 32 18924.2 19614.4677819306 -944.914790201036 19178.8470082705 690.267781930557 33 17343.4 17139.7186018551 -1520.31694972372 19067.3983478687 -203.681398144930 34 19961.2 20747.1634421385 214.353862512309 18960.8826953492 785.963442138458 35 19932.1 19777.87017378 1231.96278339019 18854.3670428298 -154.229826219995 36 19464.6 19120.2046361767 1037.29507331068 18771.7002905126 -344.395363823274 37 16165.4 15620.3744094919 -1978.60794768726 18689.0335381954 -545.025590508121 38 17574.9 16496.2086676654 15.7449624615287 18637.8463698731 -1078.69133233463 39 19795.4 19462.4433862486 1541.69741220056 18586.6592015508 -332.95661375139 40 19439.5 20309.0077215795 21.7486566083146 18548.2436218121 869.50772157955 41 17170 16017.9929952965 -187.82103736997 18509.8280420734 -1152.00700470348 42 21072.4 22394.3951527597 1299.11279908322 18451.2920481571 1321.99515275971 43 17751.8 17841.0979027201 -730.253956960823 18392.7560542407 89.297902720129 44 17515.5 17689.4446909968 -944.914790201036 18286.4700992042 173.944690996803 45 18040.3 19420.7328055559 -1520.31694972372 18180.1841441678 1380.43280555594 46 19090.1 19914.3074938003 214.353862512309 18051.5386436874 824.207493800335 47 17746.5 16338.1440734029 1231.96278339019 17922.8931432069 -1408.35592659711 48 19202.1 19567.7630274083 1037.29507331068 17799.1418992810 365.663027408351 49 15141.6 14586.4172923323 -1978.60794768726 17675.390655355 -555.182707667747 50 16258.1 14929.4808481936 15.7449624615287 17570.9741893449 -1328.61915180640 51 18586.5 18164.7448644647 1541.69741220056 17466.5577233347 -421.755135535288 52 17209.4 16977.0019101013 21.7486566083146 17420.0494332903 -232.398089898652 53 17838.7 18491.6798941240 -187.82103736997 17373.5411432460 652.979894124015 54 19123.5 19620.9912849885 1299.11279908322 17326.8959159283 497.491284988519 55 16583.6 16617.2032683503 -730.253956960823 17280.2506886106 33.6032683502563 56 15991.2 15685.7194030493 -944.914790201036 17241.5953871518 -305.480596950740 57 16704.4 17726.1768640307 -1520.31694972372 17202.940085693 1021.77686403073 58 17420.4 17459.1217802215 214.353862512309 17167.3243572662 38.7217802215309 59 17872 17380.3285877705 1231.96278339019 17131.7086288393 -491.671412229516 60 17823.2 17514.7868628612 1037.29507331068 17094.3180638281 -308.413137138799

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/12cit1259951107.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/12cit1259951107.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/2b43v1259951107.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/2b43v1259951107.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/3v1at1259951107.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/3v1at1259951107.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/4u0ts1259951107.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t1259951206zo0e9glgfljs0yb/4u0ts1259951107.ps (open in new window)

Parameters (Session):

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

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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