WS 9 ADC2

*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 09:34:22 -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/t12599445190w58j749cjs3hee.htm/, Retrieved Fri, 04 Dec 2009 17:35:25 +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/t12599445190w58j749cjs3hee.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 «

100 96.21064363 96.31280765 107.1793443 114.9066592 92.56060184 114.9995356 107.1236185 117.7765394 107.3650971 106.2970187 114.5072908 98.0031578 103.0649206 100.2879168 104.6066685 111.1544534 104.9874617 109.9284852 111.5352466 132.4974459 100.3436426 123.0983561 114.2379493 104.569518 109.0833101 106.9843039 133.6769759 124.8537197 122.5132349 116.8013374 116.0118882 129.7575926 125.1973623 143.7912139 127.9465032 130.2962757 108.4424631 129.3675118 143.6797622 131.8844618 117.6186496 118.9560695 104.8202842 134.624315 140.401226 143.8005015 153.4317823 153.2924677 127.3149438 153.5525216 136.9276493 131.7730101 144.3391845 107.4208229 113.6249652 124.2221603 102.0618557 96.36853348 111.6838488

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 1 100 98.1874632812171 -1.22827288696546 103.040809605748 -1.81253671878287 2 96.21064363 98.630586043644 -9.83925378432986 103.629955000686 2.41994241364404 3 96.31280765 89.9699777164132 -1.56346281203655 104.219100395623 -6.34282993358678 4 107.1793443 103.1411109765 6.51743637118031 104.700141252320 -4.03823332350005 5 114.9066592 120.246502209291 4.38563408169314 105.181182109016 5.33984300929072 6 92.56060184 81.705020909012 -2.14583051444291 105.562013285431 -10.8555809309881 7 114.9995356 129.005460066352 -4.94923332819754 105.942844461846 14.0059244663518 8 107.1236185 115.896881448223 -7.96658501707058 106.316940568848 8.77326294822275 9 117.7765394 119.695513069621 9.1665290545296 106.69103667585 1.91897366962053 10 107.3650971 111.242463468923 -3.36348626690186 106.851216997979 3.87736636892298 11 106.2970187 101.177073600707 4.40556647918489 107.011397320108 -5.11994509929279 12 114.5072908 115.523035605565 6.58094976092526 106.91059623351 1.01574480556481 13 98.0031578 90.4247933400535 -1.22827288696546 106.809795146912 -7.57836445994654 14 103.0649206 108.871621033629 -9.83925378432986 107.097473950701 5.80670043362893 15 100.2879168 94.7541436575467 -1.56346281203655 107.385152754490 -5.53377314245328 16 104.6066685 94.6289441065743 6.51743637118031 108.066956522245 -9.97772439342567 17 111.1544534 109.174512428306 4.38563408169314 108.748760290001 -1.97994097169403 18 104.9874617 102.684860076734 -2.14583051444291 109.435893837709 -2.30260162326589 19 109.9284852 114.683176342781 -4.94923332819754 110.123027385417 4.75469114278083 20 111.5352466 120.145683324927 -7.96658501707058 110.891394892144 8.61043672492706 21 132.4974459 144.1686003466 9.1665290545296 111.659762398870 11.6711544466001 22 100.3436426 91.3423078996572 -3.36348626690186 112.708463567245 -9.00133470034282 23 123.0983561 128.033980985196 4.40556647918489 113.757164735619 4.93562488519606 24 114.2379493 107.207698137868 6.58094976092526 114.687250701207 -7.03025116213203 25 104.569518 94.749972220171 -1.22827288696546 115.617336666795 -9.81954577982904 26 109.0833101 111.602463429372 -9.83925378432986 116.403410554958 2.51915332937182 27 106.9843039 98.342586168915 -1.56346281203655 117.189484443122 -8.64171773108502 28 133.6769759 142.364885839931 6.51743637118031 118.471629588888 8.68790993993129 29 124.8537197 125.568030583652 4.38563408169314 119.753774734655 0.71431088365162 30 122.5132349 125.911131716708 -2.14583051444291 121.261168597734 3.39789681670845 31 116.8013374 115.783345667384 -4.94923332819754 122.768562460814 -1.01799173261614 32 116.0118882 116.055200519008 -7.96658501707058 123.935160898062 0.0433123190083364 33 129.7575926 125.246896810160 9.1665290545296 125.101759335311 -4.5106957898404 34 125.1973623 127.911404397113 -3.36348626690186 125.846806469789 2.71404209711258 35 143.7912139 156.585007716547 4.40556647918489 126.591853604268 12.7937938165474 36 127.9465032 122.487983847767 6.58094976092526 126.824072791308 -5.45851935223307 37 130.2962757 134.764532308618 -1.22827288696546 127.056291978348 4.46825660861754 38 108.4424631 99.7896090805199 -9.83925378432986 126.93457090381 -8.6528540194801 39 129.3675118 133.485636582765 -1.56346281203655 126.812849829272 4.11812478276454 40 143.6797622 153.709496546585 6.51743637118031 127.132591482235 10.0297343465851 41 131.8844618 131.930956383110 4.38563408169314 127.452333135197 0.0464945831096912 42 117.6186496 108.706608844477 -2.14583051444291 128.676520869966 -8.91204075552338 43 118.9560695 112.960663723462 -4.94923332819754 129.900708604735 -5.99540577653786 44 104.8202842 86.0557232806419 -7.96658501707058 131.551430136429 -18.7645609193581 45 134.624315 126.879949277348 9.1665290545296 133.202151668122 -7.74436572265154 46 140.401226 149.666649281305 -3.36348626690186 134.499288985597 9.26542328130472 47 143.8005015 147.399010217743 4.40556647918489 135.796426303072 3.59850871774273 48 153.4317823 163.681435558471 6.58094976092526 136.601179280604 10.2496532584711 49 153.2924677 170.407276028831 -1.22827288696546 137.405932258135 17.1148083288306 50 127.3149438 127.585662220552 -9.83925378432986 136.883479163778 0.270718420551560 51 153.5525216 172.307479942615 -1.56346281203655 136.361026069422 18.7549583426149 52 136.9276493 134.588881671533 6.51743637118031 132.748980557287 -2.33876762846734 53 131.7730101 130.023451073154 4.38563408169314 129.136935045152 -1.74955902684552 54 144.3391845 165.440826143392 -2.14583051444291 125.383373371051 21.1016416433922 55 107.4208229 98.1610674312486 -4.94923332819754 121.629811696949 -9.25975546875138 56 113.6249652 117.530294181302 -7.96658501707058 117.686221235769 3.90532898130171 57 124.2221603 125.535160770882 9.1665290545296 113.742630774589 1.31300047088159 58 102.0618557 97.9037301976577 -3.36348626690186 109.583467469244 -4.15812550234233 59 96.36853348 82.9071963169155 4.40556647918489 105.424304163900 -13.4613371630845 60 111.6838488 115.670384134192 6.58094976092526 101.116363704883 3.98653533419166

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/13d8s1259944460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/13d8s1259944460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/2kwoc1259944460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/2kwoc1259944460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/3cmyo1259944460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/3cmyo1259944460.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/4ih581259944460.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/04/t12599445190w58j749cjs3hee/4ih581259944460.ps (open in new window)

Parameters (Session):

par1 = TRUE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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')

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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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