workshop 9 berekening 6

*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 10:54:46 -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/t12598629279gv7ix2is1qr9fq.htm/, Retrieved Thu, 03 Dec 2009 18:55:33 +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/t12598629279gv7ix2is1qr9fq.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 «

4716.99 4926.65 4920.10 5170.09 5246.24 5283.61 4979.05 4825.20 4695.12 4711.54 4727.22 4384.96 4378.75 4472.93 4564.07 4310.54 4171.38 4049.38 3591.37 3720.46 4107.23 4101.71 4162.34 4136.22 4125.88 4031.48 3761.36 3408.56 3228.47 3090.45 2741.14 2980.44 3104.33 3181.57 2863.86 2898.01 3112.33 3254.33 3513.47 3587.61 3727.45 3793.34 3817.58 3845.13 3931.86 4197.52 4307.13 4229.43 4362.28 4217.34 4361.28 4327.74 4417.65 4557.68 4650.35 4967.18 5123.42 5290.85 5535.66 5514.06 5493.88 5694.83 5850.41 6116.64 6175.00 6513.58 6383.78 6673.66 6936.61 7300.68 7392.93 7497.31 7584.71 7160.79 7196.19 7245.63 7347.51 7425.75 7778.51 7822.33 8181.22 8371.47 8347.71 8672.11 8802.79 9138.46 9123.29 9023.21 8850.41 8864.58 9163.74 8516.66 8553.44 7555.20 7851.22 7442.00 7992.53 8264.04 7517.39 7200.40 7193.69 6193.58 5104.21 4800.46 4461.61 4398.59 4243.63 4293.82

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 1081 0 109 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 4716.99 4338.42057841314 55.4288651778108 5040.13055640905 -378.569421586862 2 4926.65 4713.49299084938 125.204254565742 5014.60275458488 -213.157009150625 3 4920.1 4761.03390866272 90.091138576565 4989.07495276072 -159.066091337280 4 5170.09 5329.28653134827 53.8260939005422 4957.06737475119 159.196531348271 5 5246.24 5507.13472829504 60.285474963302 4925.05979674166 260.894728295041 6 5283.61 5675.57562359494 1.37605228989992 4890.26832411516 391.965623594941 7 4979.05 5268.64204696551 -166.01889845417 4855.47685148866 289.59204696551 8 4825.2 4995.98984245107 -165.566275684191 4819.97643323312 170.789842451069 9 4695.12 4659.59658761483 -53.8326025924161 4784.47601497758 -35.5234123851687 10 4711.54 4738.37007576279 -33.8451719361991 4718.55509617341 26.8300757627912 11 4727.22 4781.41037572748 20.3954469032896 4652.63417736923 54.1903757274804 12 4384.96 4198.36259386931 12.6554468129424 4558.90195931774 -186.597406130687 13 4378.75 4236.90139355593 55.4288651778108 4465.16974126626 -141.848606444069 14 4472.93 4439.18323198675 125.204254565742 4381.47251344751 -33.7467680132495 15 4564.07 4740.27357579468 90.091138576565 4297.77528562876 176.203575794676 16 4310.54 4324.74794507955 53.8260939005422 4242.50596101991 14.2079450795454 17 4171.38 4095.23788862563 60.285474963302 4187.23663641107 -76.1421113743672 18 4049.38 3944.74445042916 1.37605228989992 4152.63949728094 -104.635549570839 19 3591.37 3230.71654030336 -166.01889845417 4118.04235815081 -360.653459696643 20 3720.46 3525.28946838467 -165.566275684191 4081.19680729952 -195.170531615332 21 4107.23 4223.94134614418 -53.8326025924161 4044.35125644823 116.711346144183 22 4101.71 4248.67005716257 -33.8451719361991 3988.59511477363 146.960057162572 23 4162.34 4371.44557999769 20.3954469032896 3932.83897309902 209.105579997688 24 4136.22 4400.52225677638 12.6554468129424 3859.26229641067 264.302256776384 25 4125.88 4410.64551509986 55.4288651778108 3785.68561972233 284.765515099863 26 4031.48 4238.97659647825 125.204254565742 3698.77914895601 207.496596478247 27 3761.36 3820.75618323374 90.091138576565 3611.8726781897 59.3961832337382 28 3408.56 3249.45053657998 53.8260939005422 3513.84336951948 -159.109463420021 29 3228.47 2980.84046418744 60.285474963302 3415.81406084926 -247.629535812563 30 3090.45 2855.09183539569 1.37605228989992 3324.43211231441 -235.358164604309 31 2741.14 2415.24873467461 -166.01889845417 3233.05016377956 -325.891265325388 32 2980.44 2945.535761135 -165.566275684191 3180.91051454919 -34.9042388650009 33 3104.33 3133.72173727359 -53.8326025924161 3128.77086531883 29.3917372735891 34 3181.57 3260.21780175005 -33.8451719361991 3136.76737018615 78.6478017500535 35 2863.86 2562.56067804325 20.3954469032896 3144.76387505346 -301.299321956754 36 2898.01 2583.15820426976 12.6554468129424 3200.20634891729 -314.851795730237 37 3112.33 2913.58231204107 55.4288651778108 3255.64882278112 -198.747687958934 38 3254.33 3046.86195523638 125.204254565742 3336.59379019788 -207.46804476362 39 3513.47 3519.3101038088 90.091138576565 3417.53875761463 5.84010380880181 40 3587.61 3603.54211901509 53.8260939005422 3517.85178708437 15.9321190150881 41 3727.45 3776.44970848259 60.285474963302 3618.16481655411 48.9997084825918 42 3793.34 3860.11197333555 1.37605228989992 3725.19197437455 66.7719733355507 43 3817.58 3968.95976625918 -166.01889845417 3832.21913219499 151.379766259177 44 3845.13 3937.16721505402 -165.566275684191 3918.65906063017 92.0372150540247 45 3931.86 3912.45361352708 -53.8326025924161 4005.09898906534 -19.4063864729246 46 4197.52 4360.70853490986 -33.8451719361991 4068.17663702634 163.188534909864 47 4307.13 4462.61026810938 20.3954469032896 4131.25428498733 155.480268109380 48 4229.43 4254.47624125442 12.6554468129424 4191.72831193264 25.0462412544202 49 4362.28 4416.92879594424 55.4288651778108 4252.20233887794 54.6487959442447 50 4217.34 3980.54554489313 125.204254565742 4328.93020054113 -236.794455106868 51 4361.28 4226.81079921913 90.091138576565 4405.65806220431 -134.469200780874 52 4327.74 4098.47967587008 53.8260939005422 4503.17423022938 -229.260324129918 53 4417.65 4174.32412678226 60.285474963302 4600.69039825444 -243.325873217742 54 4557.68 4400.74794850388 1.37605228989992 4713.23599920622 -156.932051496117 55 4650.35 4640.93729829617 -166.01889845417 4825.781600158 -9.4127017038254 56 4967.18 5148.32611854529 -165.566275684191 4951.6001571389 181.146118545291 57 5123.42 5223.25388847261 -53.8326025924161 5077.41871411980 99.8338884726118 58 5290.85 5401.43454954525 -33.8451719361991 5214.11062239095 110.584549545245 59 5535.66 5700.1220224346 20.3954469032896 5350.80253066210 164.462022434605 60 5514.06 5522.57478632909 12.6554468129424 5492.88976685797 8.5147863290913 61 5493.88 5297.35413176836 55.4288651778108 5634.97700305383 -196.52586823164 62 5694.83 5482.9956371608 125.204254565742 5781.46010827346 -211.834362839200 63 5850.41 5682.78564793035 90.091138576565 5927.94321349309 -167.624352069654 64 6116.64 6090.58167557525 53.8260939005422 6088.87223052421 -26.0583244247518 65 6175 6039.91327748137 60.285474963302 6249.80124755533 -135.086722518632 66 6513.58 6607.76803308135 1.37605228989992 6418.01591462875 94.1880330813465 67 6383.78 6347.348316752 -166.01889845417 6586.23058170218 -36.4316832480063 68 6673.66 6782.66155087675 -165.566275684191 6730.22472480744 109.001550876751 69 6936.61 7052.83373467971 -53.8326025924161 6874.2188679127 116.223734679712 70 7300.68 7656.92867258841 -33.8451719361991 6978.27649934779 356.248672588407 71 7392.93 7683.13042231383 20.3954469032896 7082.33413078288 290.200422313831 72 7497.31 7816.12717792523 12.6554468129424 7165.83737526183 318.81717792523 73 7584.71 7864.65051508141 55.4288651778108 7249.34061974078 279.940515081414 74 7160.79 6864.25997175526 125.204254565742 7332.115773679 -296.530028244738 75 7196.19 6887.39793380622 90.091138576565 7414.89092761722 -308.792066193782 76 7245.63 6933.31536361391 53.8260939005422 7504.11854248554 -312.314636386085 77 7347.51 7041.38836768283 60.285474963302 7593.34615735387 -306.12163231717 78 7425.75 7143.95729427198 1.37605228989992 7706.16665343812 -281.792705728024 79 7778.51 7904.0517489318 -166.01889845417 7818.98714952238 125.541748931792 80 7822.33 7844.79889990724 -165.566275684191 7965.42737577695 22.4688999072423 81 8181.22 8304.4050005609 -53.8326025924161 8111.86760203152 123.185000560897 82 8371.47 8517.06250446234 -33.8451719361991 8259.72266747386 145.592504462338 83 8347.71 8267.44682018051 20.3954469032896 8407.5777329162 -80.2631798194907 84 8672.11 8807.25636780334 12.6554468129424 8524.30818538372 135.146367803338 85 8802.79 8909.11249697095 55.4288651778108 8641.03863785124 106.322496970946 86 9138.46 9449.24260553694 125.204254565742 8702.47313989732 310.782605536941 87 9123.29 9392.58121948005 90.091138576565 8763.9076419434 269.291219480045 88 9023.21 9244.82072794575 53.8260939005422 8747.77317815371 221.610727945746 89 8850.41 8908.89581067267 60.285474963302 8731.63871436403 58.4858106726679 90 8864.58 9074.99260674841 1.37605228989992 8652.79134096169 210.412606748410 91 9163.74 9919.55493089482 -166.01889845417 8573.94396755935 755.814930894818 92 8516.66 8737.89146462115 -165.566275684191 8460.99481106305 221.231464621145 93 8553.44 8812.66694802568 -53.8326025924161 8348.04565456674 259.226948025675 94 7555.2 6946.3414598409 -33.8451719361991 8197.9037120953 -608.858540159095 95 7851.22 7634.28278347287 20.3954469032896 8047.76176962385 -216.937216527135 96 7442 7042.27754202814 12.6554468129424 7829.06701115892 -399.722457971861 97 7992.53 8319.2588821282 55.4288651778108 7610.372252694 326.728882128196 98 8264.04 9075.98855241598 125.204254565742 7326.88719301828 811.948552415976 99 7517.39 7901.28672808086 90.091138576565 7043.40213334257 383.896728080863 100 7200.4 7646.97358391077 53.8260939005422 6700.00032218869 446.573583910772 101 7193.69 7970.4960140019 60.285474963302 6356.5985110348 776.8060140019 102 6193.58 6365.03708373216 1.37605228989992 6020.74686397794 171.457083732164 103 5104.21 4689.5436815331 -166.01889845417 5684.89521692107 -414.666318466902 104 4800.46 4428.98922579075 -165.566275684191 5337.49704989344 -371.470774209246 105 4461.61 3986.95371972661 -53.8326025924161 4990.0988828658 -474.656280273386 106 4398.59 4199.34538734006 -33.8451719361991 4631.67978459614 -199.24461265994 107 4243.63 4193.60386677023 20.3954469032896 4273.26068632648 -50.0261332297659 108 4293.82 4664.77505176349 12.6554468129424 3910.20950142356 370.955051763493

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/1pe9b1259862884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/1pe9b1259862884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/2y7sl1259862884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/2y7sl1259862884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/3iyvn1259862884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/3iyvn1259862884.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/4ngy61259862884.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598629279gv7ix2is1qr9fq/4ngy61259862884.ps (open in new window)

Parameters (Session):

par1 = FALSE ; par2 = 0.2 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;

Parameters (R input):

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

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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