*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: Thu, 03 Dec 2009 13:08:59 -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/03/t12598710231dd15nirs8wepwn.htm/, Retrieved Thu, 03 Dec 2009 21:10:28 +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/03/t12598710231dd15nirs8wepwn.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 «

1915 1843 1761 2858 3968 5061 4661 4269 3857 3568 3274 2987 1683 1381 1071 2772 4485 6181 5479 4782 4067 3489 2903 2330 1736 1483 1242 2334 3423 4523 3986 3462 2908 2575 2237 1904 1610 1251 941 2450 3946 5409 4741 4069 3539 3189 2960 2704 1697 1598 1456 2316 3083 4158 3469 2892 2578 2233 1947 2049

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 1915 1851.17829054527 -1350.68353120853 3329.50524066326 -63.8217094547267 2 1843 1909.20899242879 -1547.31074275725 3324.10175032846 66.2089924287925 3 1761 1947.23967196232 -1743.93793195598 3318.69825999366 186.239671962323 4 2858 2879.0193344964 -476.148220083375 3313.12888558697 21.0193344964009 5 3968 3853.59897703312 774.841511786592 3307.55951118029 -114.401022966880 6 5061 4748.4844149681 2073.64145666020 3299.87412837169 -312.515585031897 7 4661 4541.97002137749 1487.84123305941 3292.1887455631 -119.029978622510 8 4269 4324.72909429795 932.346403235027 3280.92450246702 55.7290942979507 9 3857 4000.08822237454 444.251518254517 3269.66025937095 143.088222374538 10 3568 3774.69555157128 92.9297557416354 3268.37469268709 206.695551571277 11 3274 3506.90288142153 -225.992007424762 3267.08912600323 232.902881421531 12 2987 3130.13085887445 -461.779601393112 3305.64874251866 143.130858874453 13 1683 1372.47517217445 -1350.68353120853 3344.20835903409 -310.524827825554 14 1381 922.95540744311 -1547.31074275725 3386.35533531414 -458.044592556889 15 1071 457.435620361788 -1743.93793195598 3428.50231159420 -613.564379638212 16 2772 2576.1574147591 -476.148220083375 3443.99080532427 -195.842585240900 17 4485 4735.67918915905 774.841511786592 3459.47929905435 250.679189159055 18 6181 6831.58616255906 2073.64145666020 3456.77238078074 650.586162559058 19 5479 6016.09330443347 1487.84123305941 3454.06546250712 537.093304433466 20 4782 5193.54921327172 932.346403235027 3438.10438349325 411.549213271725 21 4067 4267.60517726611 444.251518254517 3422.14330447937 200.605177266109 22 3489 3524.52238067743 92.9297557416354 3360.54786358093 35.5223806774311 23 2903 2733.03958474227 -225.992007424762 3298.95242268249 -169.960415257731 24 2330 1931.4863353627 -461.779601393112 3190.29326603041 -398.513664637299 25 1736 1741.04942183020 -1350.68353120853 3081.63410937833 5.04942183020239 26 1483 1536.87306627477 -1547.31074275725 2976.43767648248 53.8730662747671 27 1242 1356.69668836934 -1743.93793195598 2871.24124358664 114.696688369344 28 2334 2347.64347308338 -476.148220083375 2796.50474699999 13.6434730833803 29 3423 3349.39023780006 774.841511786592 2721.76825041335 -73.6097621999411 30 4523 4294.90807238648 2073.64145666020 2677.45047095332 -228.091927613523 31 3986 3851.0260754473 1487.84123305941 2633.13269149329 -134.973924552701 32 3462 3377.56210468257 932.346403235027 2614.0914920824 -84.4378953174287 33 2908 2776.69818907397 444.251518254517 2595.05029267151 -131.301810926030 34 2575 2444.4583635924 92.9297557416354 2612.61188066596 -130.541636407600 35 2237 2069.81853876435 -225.992007424762 2630.17346866041 -167.181461235653 36 1904 1585.91707864178 -461.779601393112 2683.86252275133 -318.082921358223 37 1610 1833.13195436628 -1350.68353120853 2737.55157684226 223.131954366277 38 1251 1246.73154924542 -1547.31074275725 2802.57919351183 -4.26845075458232 39 941 758.33112177457 -1743.93793195598 2867.60681018141 -182.66887822543 40 2450 2448.96298072975 -476.148220083375 2927.18523935362 -1.03701927024758 41 3946 4130.39481968758 774.841511786592 2986.76366852583 184.394819687576 42 5409 5712.40813663022 2073.64145666020 3031.95040670957 303.408136630222 43 4741 4917.02162204727 1487.84123305941 3077.13714489332 176.021622047272 44 4069 4105.3738703115 932.346403235027 3100.27972645348 36.3738703114973 45 3539 3510.32617373185 444.251518254517 3123.42230801364 -28.673826268152 46 3189 3180.66620064556 92.9297557416354 3104.4040436128 -8.333799354436 47 2960 3060.60622821280 -225.992007424762 3085.38577921197 100.606228212796 48 2704 2858.53507166367 -461.779601393112 3011.24452972944 154.535071663669 49 1697 1807.58025096161 -1350.68353120853 2937.10328024692 110.580250961611 50 1598 1905.13734235451 -1547.31074275725 2838.17340040274 307.137342354511 51 1456 1916.69441139742 -1743.93793195598 2739.24352055856 460.694411397424 52 2316 2459.37748636043 -476.148220083375 2648.77073372295 143.377486360426 53 3083 2832.86054132607 774.841511786592 2558.29794688734 -250.139458673930 54 4158 3766.68741040071 2073.64145666020 2475.67113293909 -391.312589599294 55 3469 3057.11444794975 1487.84123305941 2393.04431899084 -411.885552050254 56 2892 2543.88744444452 932.346403235027 2307.76615232045 -348.112555555482 57 2578 2489.26049609542 444.251518254517 2222.48798565007 -88.7395039045837 58 2233 2234.03508440723 92.9297557416354 2139.03515985114 1.03508440722590 59 1947 2064.40967337255 -225.992007424762 2055.58233405221 117.409673372552 60 2049 2583.41338589509 -461.779601393112 1976.36621549802 534.413385895094

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/1c4qm1259870937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/1c4qm1259870937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/23aum1259870937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/23aum1259870937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/3i3rd1259870937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/3i3rd1259870937.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/4j2c31259870937.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598710231dd15nirs8wepwn/4j2c31259870937.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; 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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