*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, 18 Dec 2009 02:45:11 -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/18/t1261129538a8os1i9yq81odtu.htm/, Retrieved Fri, 18 Dec 2009 10:45:43 +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/18/t1261129538a8os1i9yq81odtu.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 «

20366 22782 19169 13807 29743 25591 29096 26482 22405 27044 17970 18730 19684 19785 18479 10698 31956 29506 34506 27165 26736 23691 18157 17328 18205 20995 17382 9367 31124 26551 30651 25859 25100 25778 20418 18688 20424 24776 19814 12738 31566 30111 30019 31934 25826 26835 20205 17789 20520 22518 15572 11509 25447 24090 27786 26195 20516 22759 19028 16971

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 2 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 20366 20310.7182265013 -3160.06232189109 23581.3440953898 -55.2817734987475 2 22782 22919.3072431086 -774.93067742053 23419.6234343119 137.307243108586 3 19169 19889.2954207419 -4809.19819397593 23257.9027732340 720.295420741888 4 13807 15748.1001516722 -11251.0101368225 23116.9099851503 1941.10015167219 5 29743 29400.1001918492 7109.9826110842 22975.9171970666 -342.899808150847 6 25591 23990.5055188459 4341.58064973423 22849.9138314199 -1600.49448115409 7 29096 27855.7107492536 7612.37878497333 22723.9104657731 -1240.28925074642 8 26482 25588.2132539114 4784.81481111817 22590.9719349704 -893.786746088583 9 22405 20920.5133984882 1431.45319734403 22458.0334041677 -1484.48660151178 10 27044 29058.5085201918 2621.48379083908 22408.0076889691 2014.50852019184 11 17970 16941.1013977475 -3359.08337151792 22357.9819737704 -1028.89860225249 12 18730 19445.3156580625 -4547.4087114491 22562.0930533866 715.315658062533 13 19684 19761.8581888884 -3160.06232189109 22766.2041330027 77.858188888371 14 19785 17351.0010142016 -774.93067742053 22993.9296632189 -2433.99898579836 15 18479 18545.5430005409 -4809.19819397593 23221.6551934351 66.5430005408634 16 10698 9353.40375696946 -11251.0101368225 23293.6063798531 -1344.59624303054 17 31956 33436.4598226447 7109.9826110842 23365.5575662711 1480.45982264473 18 29506 31342.7024054356 4341.58064973423 23327.7169448301 1836.70240543563 19 34506 38109.7448916374 7612.37878497333 23289.8763233892 3603.74489163745 20 27165 26351.6278086329 4784.81481111817 23193.5573802489 -813.37219136711 21 26736 28943.3083655473 1431.45319734403 23097.2384371087 2207.30836554730 22 23691 21835.7951548499 2621.48379083908 22924.7210543110 -1855.20484515012 23 18157 16920.8797000045 -3359.08337151792 22752.2036715134 -1236.12029999548 24 17328 16656.6426895392 -4547.4087114491 22546.7660219099 -671.357310460804 25 18205 17228.7339495847 -3160.06232189109 22341.3283723064 -976.266050415314 26 20995 20522.3490206071 -774.93067742053 22242.5816568134 -472.650979392904 27 17382 17429.3632526555 -4809.19819397593 22143.8349413205 47.3632526554757 28 9367 7769.0562190461 -11251.0101368225 22215.9539177764 -1597.9437809539 29 31124 32849.9444946834 7109.9826110842 22288.0728942324 1725.94449468339 30 26551 26297.2149182142 4341.58064973423 22463.2044320516 -253.785081785798 31 30651 31051.2852451559 7612.37878497333 22638.3359698707 400.28524515593 32 25859 24077.8978245236 4784.81481111817 22855.2873643582 -1781.10217547641 33 25100 25696.3080438102 1431.45319734403 23072.2387588458 596.308043810212 34 25778 25649.0891932686 2621.48379083908 23285.4270158923 -128.910806731423 35 20418 20696.468098579 -3359.08337151792 23498.6152729389 278.468098579 36 18688 18234.0900174134 -4547.4087114491 23689.3186940357 -453.909982586636 37 20424 20128.0402067585 -3160.06232189109 23880.0221151325 -295.959793241451 38 24776 26261.7117690936 -774.93067742053 24065.2189083269 1485.71176909362 39 19814 20186.7824924547 -4809.19819397593 24250.4157015213 372.782492454662 40 12738 12384.9474255085 -11251.0101368225 24342.062711314 -353.052574491470 41 31566 31588.3076678091 7109.9826110842 24433.7097211067 22.3076678090656 42 30111 31480.6240295679 4341.58064973423 24399.7953206979 1369.62402956789 43 30019 28059.7402947376 7612.37878497333 24365.8809202890 -1959.25970526237 44 31934 34880.4873142936 4784.81481111817 24202.6978745882 2946.48731429361 45 25826 26181.0319737686 1431.45319734403 24039.5148288874 355.031973768560 46 26835 27307.8901235159 2621.48379083908 23740.626085645 472.890123515892 47 20205 20327.3460291153 -3359.08337151792 23441.7373424026 122.346029115281 48 17789 17089.8278556267 -4547.4087114491 23035.5808558224 -699.172144373311 49 20520 21570.6379526489 -3160.06232189109 22629.4243692422 1050.63795264891 50 22518 23599.4892436202 -774.93067742053 22211.4414338003 1081.48924362024 51 15572 14159.7396956175 -4809.19819397593 21793.4584983584 -1412.26030438247 52 11509 12694.1366357147 -11251.0101368225 21574.8735011078 1185.13663571471 53 25447 22427.7288850586 7109.9826110842 21356.2885038572 -3019.27111494145 54 24090 22666.5503935865 4341.58064973423 21171.8689566793 -1423.44960641351 55 27786 26972.1718055253 7612.37878497333 20987.4494095013 -813.828194474656 56 26195 26789.2677051588 4784.81481111817 20815.917483723 594.267705158822 57 20516 18956.1612447113 1431.45319734403 20644.3855579447 -1559.83875528874 58 22759 22398.1368456782 2621.48379083908 20498.3793634828 -360.863154321836 59 19028 21062.7102024971 -3359.08337151792 20352.3731690208 2034.71020249712 60 16971 18257.0035512726 -4547.4087114491 20232.4051601765 1286.00355127260

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/1dmoo1261129509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/1dmoo1261129509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/2i1h21261129509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/2i1h21261129509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/36ytq1261129509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/36ytq1261129509.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/4nlfy1261129509.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/18/t1261129538a8os1i9yq81odtu/4nlfy1261129509.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ; par9 = 1 ;

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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