workshop 9 berekening 5

*The author of this computation has been verified*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Thu, 03 Dec 2009 10:49:36 -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/t12598626335e3kzvm4ujkeaco.htm/, Retrieved Thu, 03 Dec 2009 18:50:39 +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/t12598626335e3kzvm4ujkeaco.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 1 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 4716.99 NA NA 1.01621417709746 NA 2 4926.65 NA NA 1.02316103477131 NA 3 4920.1 NA NA 1.02124609026921 NA 4 5170.09 NA NA 1.00268517095746 NA 5 5246.24 NA NA 1.00029270143372 NA 6 5283.61 NA NA 0.986143298146215 NA 7 4979.05 4697.6208070479 4868.1375 0.96497290946443 1.05990887824106 8 4825.2 4712.67850282078 4835.13916666667 0.974672773704192 1.02387633637896 9 4695.12 4806.53208319738 4801.39958333333 1.00106895911806 0.976820692909373 10 4711.54 4784.89123581612 4750.75041666667 1.00718640554757 0.984670239677118 11 4727.22 4708.4768868923 4670.15 1.00820677856007 1.00398071681309 12 4384.96 4547.17901192797 4573.93791666667 0.99414970093031 0.964325351717528 13 4378.75 4537.08296803556 4464.69166666667 1.01621417709746 0.965102474618376 14 4472.93 4461.84241950632 4360.84083333333 1.02316103477131 1.00248497805418 15 4564.07 4381.46699425412 4290.31458333333 1.02124609026921 1.04167622533397 16 4310.54 4251.79580799426 4240.40958333333 1.00268517095746 1.01381632483274 17 4171.38 4192.69018066036 4191.46333333333 1.00029270143372 0.994917301364489 18 4049.38 4099.95239599902 4157.5625 0.986143298146215 0.987665126051616 19 3591.37 3991.76684602536 4136.66208333333 0.96497290946443 0.899694330488256 20 3720.46 4003.6946232962 4107.73208333333 0.974672773704192 0.929256686649339 21 4107.23 4060.22766615767 4055.89208333333 1.00106895911806 1.01157628037317 22 4101.71 4013.50017729831 3984.86333333333 1.00718640554757 1.02197827801295 23 4162.34 3940.06494914807 3907.99291666667 1.00820677856007 1.05641405756522 24 4136.22 3806.35025320788 3828.74958333333 0.99414970093031 1.08666300388781 25 4125.88 3814.22568877943 3753.36791666667 1.01621417709746 1.08170840863911 26 4031.48 3772.50472501306 3687.1075 1.02316103477131 1.06864809824355 27 3761.36 3691.2795256251 3614.48583333333 1.02124609026921 1.01898541518961 28 3408.56 3543.84952525425 3534.35916666667 1.00268517095746 0.96182413381546 29 3228.47 3442.92412060973 3441.91666666667 1.00029270143372 0.937711633164965 30 3090.45 3289.99222682049 3336.22125 0.986143298146215 0.939348723928953 31 2741.14 3128.82615128724 3242.39791666667 0.96497290946443 0.876092140457296 32 2980.44 3087.55419856219 3167.78541666667 0.974672773704192 0.965307751160426 33 3104.33 3128.41599452793 3125.07541666667 1.00106895911806 0.992300897780198 34 3181.57 3144.64452963766 3122.20708333333 1.00718640554757 1.01174233526693 35 2863.86 3176.31344723772 3150.45833333333 1.00820677856007 0.901630159482702 36 2898.01 3181.81215575411 3200.53625 0.99414970093031 0.91080486783581 37 3112.33 3327.77116038663 3274.675 1.01621417709746 0.935259622731511 38 3254.33 3433.27355234914 3355.55541666667 1.02316103477131 0.947879611216327 39 3513.47 3498.85506073896 3426.06458333333 1.02124609026921 1.00417706335568 40 3587.61 3512.28207157409 3502.87625 1.00268517095746 1.02144700422428 41 3727.45 3606.39903928466 3605.34375 1.00029270143372 1.03356560363862 42 3793.34 3669.39565773973 3720.95583333333 0.986143298146215 1.03377786257496 43 3817.58 3694.41124811798 3828.51291666667 0.96497290946443 1.03333920985238 44 3845.13 3821.41863120384 3920.71958333333 0.974672773704192 1.00620486031092 45 3931.86 4000.44215947088 3996.17041666667 1.00106895911806 0.98285635518851 46 4197.52 4091.52816711909 4062.33458333333 1.00718640554757 1.02590519447787 47 4307.13 4155.75944709474 4121.93166666667 1.00820677856007 1.03642428173052 48 4229.43 4158.06840475480 4182.5375 0.99414970093031 1.01716219847745 49 4362.28 4317.97914642444 4249.08375 1.01621417709746 1.01025962656912 50 4217.34 4430.83424539628 4330.53458333333 1.02316103477131 0.95181624191469 51 4361.28 4520.99006062591 4426.935 1.02124609026921 0.964673653672267 52 4327.74 4534.28146563713 4522.13875 1.00268517095746 0.954448909446316 53 4417.65 4620.23487031854 4618.88291666667 1.00029270143372 0.956152690067773 54 4557.68 4658.14442865826 4723.59791666667 0.986143298146215 0.978432521748323 55 4650.35 4655.29387866242 4824.27416666667 0.96497290946443 0.998938009330607 56 4967.18 4808.04739093214 4932.98625 0.974672773704192 1.03309713822039 57 5123.42 5062.00071044365 5056.59541666667 1.00106895911806 1.01213340200242 58 5290.85 5230.50029756153 5193.18 1.00718640554757 1.01153803632639 59 5535.66 5384.77233196877 5340.94041666667 1.00820677856007 1.02802117874797 60 5514.06 5463.50791695658 5495.65916666667 0.99414970093031 1.00925267864745 61 5493.88 5740.98131807857 5649.38125 1.01621417709746 0.956958348340128 62 5694.83 5926.87601036431 5792.71083333333 1.02316103477131 0.960848512781686 63 5850.41 6065.55200837415 5939.36375 1.02124609026921 0.964530514604916 64 6116.64 6115.03218464206 6098.65625 1.00268517095746 1.00026292835579 65 6175 6261.61766483287 6259.78541666667 1.00029270143372 0.986166886982042 66 6513.58 6330.84973062076 6419.80708333333 0.986143298146215 1.02886346654153 67 6383.78 6358.74728736247 6589.56041666667 0.96497290946443 1.00393673651527 68 6673.66 6567.11122775316 6737.76 0.974672773704192 1.01622460295732 69 6936.61 6862.2434581169 6854.91583333333 1.00106895911806 1.01083705967837 70 7300.68 7008.03448437516 6958.03125 1.00718640554757 1.04175857243244 71 7392.93 7111.81710088513 7053.92708333333 1.00820677856007 1.03952757714760 72 7497.31 7099.01300021902 7140.78875 0.99414970093031 1.05610596850135 73 7584.71 7354.25011695581 7236.90958333333 1.01621417709746 1.03133696561568 74 7160.79 7512.95338848963 7342.88458333333 1.02316103477131 0.953125838764131 75 7196.19 7600.73083214884 7442.60458333333 1.02124609026921 0.946776061265352 76 7245.63 7559.32330087651 7539.07958333333 1.00268517095746 0.958502462668829 77 7347.51 7625.7097363714 7623.47833333333 1.00029270143372 0.96351818440656 78 7425.75 7605.3450271823 7712.21083333333 0.986143298146215 0.976385683155674 79 7778.51 7538.28554189473 7811.91416666667 0.96497290946443 1.03186725373697 80 7822.33 7743.84382028762 7945.07041666667 0.974672773704192 1.01013529992776 81 8181.22 8116.43604044449 8107.76916666667 1.00106895911806 1.00798182345462 82 8371.47 8321.50585618875 8262.13083333333 1.00718640554757 1.00600421902895 83 8347.71 8467.74473538893 8398.8175 1.00820677856007 0.98582447403176 84 8672.11 8471.53690578149 8521.38958333333 0.99414970093031 1.02367611644135 85 8802.79 8779.13397852787 8639.05875 1.01621417709746 1.00269457346590 86 9138.46 8927.80348849468 8725.70708333333 1.02316103477131 1.02359555872582 87 9123.29 8956.47799442084 8770.14666666667 1.02124609026921 1.01862473236501 88 9023.21 8775.14424519855 8751.64458333333 1.00268517095746 1.02826913699307 89 8850.41 8699.49185863632 8696.94625 1.00029270143372 1.01734792604166 90 8864.58 8505.49046633455 8625.00458333333 0.986143298146215 1.04221855695292 91 9163.74 8240.85819295304 8539.98916666667 0.96497290946443 1.11198855573515 92 8516.66 8255.27777312859 8469.79416666667 0.974672773704192 1.03166243875188 93 8553.44 8375.39089034089 8366.4475 1.00106895911806 1.02125860297033 94 7555.2 8282.6825972039 8223.58458333333 1.00718640554757 0.912168239134324 95 7851.22 8144.90348213695 8078.60416666667 1.00820677856007 0.96394266883812 96 7442 7852.0751852381 7898.2825 0.99414970093031 0.947774928848231 97 7992.53 7741.36081766328 7617.84375 1.01621417709746 1.03244509437716 98 8264.04 7462.7882292719 7293.855 1.02316103477131 1.10736627465661 99 7517.39 7116.57423048197 6968.52041666667 1.02124609026921 1.05632144857019 100 7200.4 6684.40278111538 6666.50208333333 1.00268517095746 1.07719421402050 101 7193.69 6386.52921592441 6384.66041666667 1.00029270143372 1.12638488869087 102 6193.58 6018.60019294703 6103.17 0.986143298146215 1.02907317340301 103 5104.21 NA NA 0.96497290946443 NA 104 4800.46 NA NA 0.974672773704192 NA 105 4461.61 NA NA 1.00106895911806 NA 106 4398.59 NA NA 1.00718640554757 NA 107 4243.63 NA NA 1.00820677856007 NA 108 4293.82 NA NA 0.99414970093031 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/12x341259862574.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/12x341259862574.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/2gemz1259862574.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/2gemz1259862574.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/3jneh1259862574.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/3jneh1259862574.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/4xdiu1259862574.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/03/t12598626335e3kzvm4ujkeaco/4xdiu1259862574.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 = multiplicative ; par2 = 12 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2) x <- ts(x,freq=par2) m <- decompose(x,type=par1) m$figure bitmap(file='test1.png') plot(m) dev.off() mylagmax <- length(x)/2 bitmap(file='test2.png') op <- par(mfrow = c(2,2)) acf(as.numeric(x),lag.max = mylagmax,main='Observed') acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend') acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal') acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random') par(op) dev.off() bitmap(file='test3.png') op <- par(mfrow = c(2,2)) spectrum(as.numeric(x),main='Observed') spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() bitmap(file='test4.png') op <- par(mfrow = c(2,2)) cpgram(as.numeric(x),main='Observed') cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend') cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal') cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random') par(op) dev.off() load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'t',header=TRUE) a<-table.element(a,'Observations',header=TRUE) a<-table.element(a,'Fit',header=TRUE) a<-table.element(a,'Trend',header=TRUE) a<-table.element(a,'Seasonal',header=TRUE) a<-table.element(a,'Random',header=TRUE) a<-table.row.end(a) for (i in 1:length(m$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i]) a<-table.element(a,m$trend[i]) a<-table.element(a,m$seasonal[i]) a<-table.element(a,m$random[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable.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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