Paper Statistiek

*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: Mon, 27 Dec 2010 13:47:55 +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/27/t1293457606zbg28gecv0vt1vg.htm/, Retrieved Mon, 27 Dec 2010 14:46:51 +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/2010/Dec/27/t1293457606zbg28gecv0vt1vg.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:

Decomposition by Loess - Handelsbalans België (1995-2009)

Dataseries X:

» Textbox « » Textfile « » CSV «

2540,9 2370,3 1807,5 1834,8 786,8 1561,4 1347,2 1549,8 1553,8 1822,5 3078,7 1589,1 1791,5 2558,1 2111,8 2083,1 2052,1 2243,5 2622 1952,6 808,9 1709,8 1582,1 865,6 1116,1 1119,4 2350 1975,6 2536,5 2785 2819,7 1829,5 758,3 2921,6 2482 1892,7 1855,1 2151,3 1642,2 1640,5 1366,1 1532,8 824,4 -518,7 -978,5 1162,5 1243,4 1199,5 883,1 1437,2 534,5 -1901,9 -2521,1 -1721,1 -3094,5 -3694,8 -2492,1 -464,6 -626,1 -1711,4

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 601 0 61 Trend 7 1 1 Low-pass 5 1 1 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 2540.9 2880.02732339672 -398.683162080154 2600.45583868343 339.127323396724 2 2370.3 2054.99149348091 421.267200346618 2264.34130617247 -315.308506519086 3 1807.5 1377.05303409893 306.876546593396 1931.07041930767 -430.446965901069 4 1834.8 2379.89671672659 -329.461791827352 1619.16507510076 545.096716726588 5 786.8 530.476221608482 -398.683162080154 1441.80694047167 -256.323778391518 6 1561.4 1398.94021212128 421.267200346618 1302.59258753210 -162.459787878720 7 1347.2 1000.72919002207 306.876546593396 1386.79426338453 -346.470809977926 8 1549.8 1867.90208983971 -329.461791827352 1561.15970198764 318.102089839709 9 1553.8 1723.19346995828 -398.683162080154 1783.08969212187 169.393469958283 10 1822.5 1230.17713463162 421.267200346618 1993.55566502176 -592.322865368381 11 3078.7 3789.036938585 306.876546593396 2061.48651482161 710.336938584998 12 1589.1 1330.61969741937 -329.461791827352 2177.04209440798 -258.480302580628 13 1791.5 1855.17033263248 -398.683162080154 2126.51282944767 63.6703326324823 14 2558.1 2623.76883427618 421.267200346618 2071.1639653772 65.6688342761836 15 2111.8 1767.74561938603 306.876546593396 2148.97783402057 -344.054380613969 16 2083.1 2315.74409098944 -329.461791827352 2179.91770083792 232.644090989435 17 2052.1 2301.24565051652 -398.683162080154 2201.63751156364 249.145650516518 18 2243.5 1850.90572669130 421.267200346618 2214.82707296208 -392.594273308695 19 2622 2854.08684143253 306.876546593396 2083.03661197408 232.086841432525 20 1952.6 2365.01949764427 -329.461791827352 1869.64229418308 412.419497644267 21 808.9 395.084205603886 -398.683162080154 1621.39895647627 -413.815794396114 22 1709.8 1643.91203547041 421.267200346618 1354.42076418297 -65.8879645295924 23 1582.1 1582.03855317312 306.876546593396 1275.28490023348 -0.0614468268772725 24 865.6 803.444028334514 -329.461791827352 1257.21776349284 -62.1559716654856 25 1116.1 1378.55644703613 -398.683162080154 1252.32671504403 262.456447036126 26 1119.4 354.687124609232 421.267200346618 1462.84567504415 -764.712875390768 27 2350 2591.28727890290 306.876546593396 1801.83617450371 241.287278902896 28 1975.6 2036.11295824872 -329.461791827352 2244.54883357863 60.5129582487207 29 2536.5 2976.07926539425 -398.683162080154 2495.60389668591 439.579265394248 30 2785 2626.622409509 421.267200346618 2522.11039014438 -158.377590490999 31 2819.7 3042.04808433743 306.876546593396 2290.47536906918 222.348084337427 32 1829.5 1941.02429185059 -329.461791827352 2047.43749997677 111.524291850585 33 758.3 -80.5338373597629 -398.683162080154 1995.81699943992 -838.833837359763 34 2921.6 3411.38266486958 421.267200346618 2010.5501347838 489.782664869581 35 2482 2480.55981825065 306.876546593396 2176.56363515595 -1.44018174934945 36 1892.7 1918.05988551401 -329.461791827352 2196.80190631334 25.3598855140076 37 1855.1 2097.25568413316 -398.683162080154 2011.62747794699 242.155684133160 38 2151.3 2044.97128368617 421.267200346618 1836.36151596721 -106.328716313827 39 1642.2 1243.42546373594 306.876546593396 1734.09798967066 -398.774536264057 40 1640.5 1965.7116024689 -329.461791827352 1644.75018935845 325.211602468898 41 1366.1 1648.74214230281 -398.683162080154 1482.14101977734 282.642142302815 42 1532.8 1561.87065184999 421.267200346618 1082.46214780339 29.0706518499890 43 824.4 838.308144216057 306.876546593396 503.615309190548 13.9081442160567 44 -518.7 -823.010105517499 -329.461791827352 115.071897344851 -304.310105517499 45 -978.5 -1678.84538899553 -398.683162080154 120.528551075688 -700.345388995534 46 1162.5 1465.41165203573 421.267200346618 438.32114761765 302.911652035732 47 1243.4 1258.74850123841 306.876546593396 921.174952168197 15.3485012384079 48 1199.5 1544.91068791836 -329.461791827352 1183.55110390899 345.410687918363 49 883.1 1027.90511159386 -398.683162080154 1136.97805048630 144.805111593856 50 1437.2 1778.79881504442 421.267200346618 674.333984608964 341.598815044418 51 534.5 923.556928221235 306.876546593396 -161.433474814630 389.056928221235 52 -1901.9 -2424.35766572906 -329.461791827352 -1049.98054244359 -522.457665729055 53 -2521.1 -2764.15704080058 -398.683162080154 -1879.35979711927 -243.057040800580 54 -1721.1 -1338.08178074098 421.267200346618 -2525.38541960563 383.018219259016 55 -3094.5 -3685.36280761438 306.876546593396 -2810.51373897902 -590.862807614378 56 -3694.8 -4382.10815696718 -329.461791827352 -2678.03005120547 -687.308156967177 57 -2492.1 -2460.8357329274 -398.683162080154 -2124.68110499245 31.2642670726023 58 -464.6 293.905241180022 421.267200346618 -1644.37244152664 758.505241180022 59 -626.1 -346.63271595239 306.876546593396 -1212.44383064101 279.46728404761 60 -1711.4 -2265.006843383 -329.461791827352 -828.33136478965 -553.606843382998

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/1bi8x1293457672.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/1bi8x1293457672.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/2bi8x1293457672.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/2bi8x1293457672.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/3y70b1293457672.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/3y70b1293457672.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/4y70b1293457672.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/27/t1293457606zbg28gecv0vt1vg/4y70b1293457672.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

Parameters (R input):

par1 = 4 ; 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')

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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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