*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: Wed, 29 Dec 2010 13:40:12 +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/29/t1293629938uaxnoui8eoo1aij.htm/, Retrieved Wed, 29 Dec 2010 14:38:58 +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/29/t1293629938uaxnoui8eoo1aij.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:

Dataseries X:

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3106.54 3125.67 3039.71 3051.67 3112.83 3228.01 3223.98 3328.8 3264.26 3394.14 3549.25 3744.63 3839.25 3912.28 3911.06 3675.8 3703.32 3795.91 3906.01 4070.78 4144.38 4140.3 4388.53 4433.57 4305.23 4471.65 4614.76 4697.86 4639.4 4384.47 4350.83 4325.29 4441.82 4162.5 4127.47 3722.23 3757.12 3719.52 3925.43 3751.41 3168.22 2994.38 3136 2672.2 2100.18 1881.46 1908.64 1900.09 1696.58 1748.74 1953.35 2071.37 2030.98 2169.14 2229.85 2480.93 2525.93 2475.14 2529.66 2453.37 2386.53 2517.3 2457.46 2589.73 2679.07 2506.13 2592.31

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Seasonal Decomposition by Loess - Parameters Component Window Degree Jump Seasonal 671 0 68 Trend 19 1 2 Low-pass 13 1 2 Seasonal Decomposition by Loess - Time Series Components t Observed Fitted Seasonal Trend Remainder 1 3106.54 3313.64273568123 -44.8766538118925 2944.31391813066 207.102735681234 2 3125.67 3230.19715382961 19.3735322707297 3001.76931389966 104.527153829613 3 3039.71 2936.12141275922 84.0738775721264 3059.22470966866 -103.588587240782 4 3051.67 2911.99700038362 72.8276116733257 3118.51538794306 -139.672999616381 5 3112.83 3059.62106046762 -11.7671266850794 3177.80606621746 -53.208939532376 6 3228.01 3267.60503416839 -49.4113385700273 3237.82630440164 39.5950341683865 7 3223.98 3134.38225056952 15.7312068446568 3297.84654258583 -89.5977494304843 8 3328.8 3250.1317624485 48.907418231687 3358.56081931981 -78.668237551502 9 3264.26 3127.10372324219 -17.8588192959930 3419.2750960538 -137.156276757810 10 3394.14 3396.96570055723 -91.931576499104 3483.24587594187 2.82570055723136 11 3549.25 3542.67960961081 8.60373455924511 3547.21665582994 -6.57039038918629 12 3744.63 3920.43707309817 -33.6718115204286 3602.49473842226 175.807073098170 13 3839.25 4065.60383279732 -44.8766538118925 3657.77282101458 226.353832797317 14 3912.28 4090.92657781871 19.3735322707297 3714.25988991056 178.646577818709 15 3911.06 3967.29916362133 84.0738775721264 3770.74695880655 56.2391636213265 16 3675.8 3450.38992637623 72.8276116733257 3828.38246195044 -225.410073623769 17 3703.32 3532.38916159074 -11.7671266850794 3886.01796509434 -170.930838409261 18 3795.91 3699.57536174568 -49.4113385700273 3941.65597682435 -96.3346382543223 19 3906.01 3798.99480460098 15.7312068446568 3997.29398855436 -107.015195399015 20 4070.78 4033.88138593381 48.907418231687 4058.7711958345 -36.8986140661905 21 4144.38 4186.37041618135 -17.8588192959930 4120.24840311465 41.9904161813456 22 4140.3 4181.82342715688 -91.931576499104 4190.70814934223 41.5234271568779 23 4388.53 4507.28836987095 8.60373455924511 4261.16789556980 118.758369870950 24 4433.57 4583.46681768584 -33.6718115204286 4317.34499383459 149.896817685842 25 4305.23 4281.81456171252 -44.8766538118925 4373.52209209937 -23.4154382874767 26 4471.65 4522.71559238523 19.3735322707297 4401.21087534404 51.0655923852291 27 4614.76 4716.54646383916 84.0738775721264 4428.89965858871 101.786463839162 28 4697.86 4897.67392463173 72.8276116733257 4425.21846369494 199.813924631734 29 4639.4 4869.02985788391 -11.7671266850794 4421.53726880117 229.629857883911 30 4384.47 4434.86788302483 -49.4113385700273 4383.4834555452 50.3978830248252 31 4350.83 4340.49915086611 15.7312068446568 4345.42964228924 -10.3308491338948 32 4325.29 4320.33500019259 48.907418231687 4281.33758157572 -4.95499980740806 33 4441.82 4684.25329843379 -17.8588192959930 4217.24552086221 242.433298433788 34 4162.5 4287.08651778034 -91.931576499104 4129.84505871876 124.586517780341 35 4127.47 4203.89166886543 8.60373455924511 4042.44459657532 76.4216688654333 36 3722.23 3546.22494240807 -33.6718115204286 3931.90686911236 -176.005057591927 37 3757.12 3737.7475121625 -44.8766538118925 3821.36914164939 -19.3724878374969 38 3719.52 3741.91434985683 19.3735322707297 3677.75211787244 22.394349856831 39 3925.43 4232.65102833238 84.0738775721264 3534.13509409549 307.221028332384 40 3751.41 4069.61144470173 72.8276116733257 3360.38094362494 318.201444701733 41 3168.22 3161.58033353069 -11.7671266850794 3186.62679315439 -6.63966646931385 42 2994.38 3033.26585269513 -49.4113385700273 3004.9054858749 38.8858526951303 43 3136 3433.08461455994 15.7312068446568 2823.1841785954 297.084614559942 44 2672.2 2645.32593663878 48.907418231687 2650.16664512954 -26.8740633612233 45 2100.18 1741.06970763232 -17.8588192959930 2477.14911166367 -359.110292367677 46 1881.46 1515.82140764941 -91.931576499104 2339.03016884969 -365.638592350585 47 1908.64 1607.76503940505 8.60373455924511 2200.91122603571 -300.874960594954 48 1900.09 1709.96163044289 -33.6718115204286 2123.89018107754 -190.128369557114 49 1696.58 1391.16751769252 -44.8766538118925 2046.86913611938 -305.412482307484 50 1748.74 1440.58592582803 19.3735322707297 2037.52054190124 -308.154074171974 51 1953.35 1794.45417474476 84.0738775721264 2028.17194768311 -158.89582525524 52 2071.37 1997.51055481474 72.8276116733257 2072.40183351194 -73.859445185263 53 2030.98 1957.09540734432 -11.7671266850794 2116.63171934076 -73.8845926556814 54 2169.14 2210.05363392969 -49.4113385700273 2177.63770464034 40.9136339296879 55 2229.85 2205.32510321542 15.7312068446568 2238.64368993992 -24.5248967845750 56 2480.93 2618.16517482536 48.907418231687 2294.78740694295 137.235174825358 57 2525.93 2718.78769535 -17.8588192959930 2350.93112394599 192.857695350002 58 2475.14 2653.890958687 -91.931576499104 2388.32061781211 178.750958686998 59 2529.66 2625.00615376253 8.60373455924511 2425.71011167822 95.3461537625344 60 2453.37 2488.24481988848 -33.6718115204286 2452.16699163195 34.8748198884832 61 2386.53 2339.31278222622 -44.8766538118925 2478.62387158567 -47.2172177737771 62 2517.3 2513.06131105358 19.3735322707297 2502.16515667569 -4.23868894642146 63 2457.46 2305.13968066216 84.0738775721264 2525.70644176571 -152.320319337841 64 2589.73 2559.94821976687 72.8276116733257 2546.68416855980 -29.7817802331297 65 2679.07 2802.24523133119 -11.7671266850794 2567.66189535389 123.175231331186 66 2506.13 2474.46263382958 -49.4113385700273 2587.20870474044 -31.6673661704172 67 2592.31 2562.13327902835 15.7312068446568 2606.75551412700 -30.1767209716527

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/12vf71293630006.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/12vf71293630006.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/2d4fa1293630006.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/2d4fa1293630006.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/3d4fa1293630006.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/3d4fa1293630006.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/4nvwd1293630006.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629938uaxnoui8eoo1aij/4nvwd1293630006.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 12 ; 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')

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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