opdracht 9

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 11 Dec 2010 12:39:10 +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/11/t12920712680krghx4k23ouagu.htm/, Retrieved Sat, 11 Dec 2010 13:41:13 +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/11/t12920712680krghx4k23ouagu.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:

KDGP2W92 - Evi Van Dingenen

Dataseries X:

» Textbox « » Textfile « » CSV «

84,9 81,9 95,9 81 89,2 102,5 89,8 88,8 83,2 90,2 100,4 187,1 87,6 85,4 86,1 86,7 89,1 103,7 86,9 85,2 80,8 91,2 102,8 182,5 80,9 83,1 88,3 86,6 93 105,3 93,8 86,4 87 96,7 100,5 196,7 86,8 88,2 93,8 85 90,4 115,9 94,9 87,7 91,7 95,9 106,8 204,5 90,2 90,5 93,2 97,8 99,4 120 108,2 98,5 104,3 102,9 111,1 188,1 93,8 94,5 112,4 102,5 115,8 136,5 122,1 110,6 116,4 112,6 121,5 199,3 102,1 100,6 119 106,8 121,3 145,5 129,7 117,7 121,3 124,3 135,2 210,1 106,8 110,5 111,5 122,1 126,3 143,2 137,3 121,5 121,9 123,9 131,6 220,9

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 84.9 NA NA -17.6618055555556 NA 2 81.9 NA NA -17.4820436507937 NA 3 95.9 NA NA -10.5499007936508 NA 4 81 NA NA -13.3808531746032 NA 5 89.2 NA NA -6.93859126984127 NA 6 102.5 NA NA 11.9316468253968 NA 7 89.8 93.7423611111111 98.0208333333333 -4.27847222222222 -3.94236111111111 8 88.8 86.4858134920635 98.2791666666667 -11.7933531746032 2.31418650793651 9 83.2 87.3602182539683 98.0166666666667 -10.6564484126984 -4.16021825396825 10 90.2 91.0090277777778 97.8458333333333 -6.83680555555556 -0.809027777777786 11 100.4 99.9911706349206 98.0791666666667 1.91200396825397 0.408829365079356 12 187.1 183.859623015873 98.125 85.734623015873 3.24037698412700 13 87.6 80.3923611111111 98.0541666666667 -17.6618055555556 7.20763888888888 14 85.4 80.3012896825397 97.7833333333333 -17.4820436507937 5.09871031746033 15 86.1 86.9834325396825 97.5333333333333 -10.5499007936508 -0.88343253968253 16 86.7 84.0941468253968 97.475 -13.3808531746032 2.60585317460317 17 89.1 90.6780753968254 97.6166666666667 -6.93859126984127 -1.57807539682540 18 103.7 109.456646825397 97.525 11.9316468253968 -5.75664682539683 19 86.9 92.7756944444445 97.0541666666667 -4.27847222222222 -5.87569444444445 20 85.2 84.8858134920635 96.6791666666667 -11.7933531746032 0.314186507936526 21 80.8 86.0185515873016 96.675 -10.6564484126984 -5.21855158730158 22 91.2 89.9256944444444 96.7625 -6.83680555555556 1.27430555555556 23 102.8 98.8328373015873 96.9208333333333 1.91200396825397 3.96716269841272 24 182.5 182.884623015873 97.15 85.734623015873 -0.384623015872990 25 80.9 79.8423611111111 97.5041666666666 -17.6618055555556 1.05763888888892 26 83.1 80.359623015873 97.8416666666667 -17.4820436507937 2.74037698412700 27 88.3 87.6000992063492 98.15 -10.5499007936508 0.699900793650812 28 86.6 85.2566468253968 98.6375 -13.3808531746032 1.34335317460318 29 93 91.832242063492 98.7708333333333 -6.93859126984127 1.16775793650794 30 105.3 111.198313492063 99.2666666666667 11.9316468253968 -5.89831349206349 31 93.8 95.8256944444444 100.104166666667 -4.27847222222222 -2.02569444444444 32 86.4 88.7691468253968 100.5625 -11.7933531746032 -2.36914682539683 33 87 90.3477182539682 101.004166666667 -10.6564484126984 -3.34771825396824 34 96.7 94.3298611111111 101.166666666667 -6.83680555555556 2.37013888888889 35 100.5 102.903670634921 100.991666666667 1.91200396825397 -2.40367063492064 36 196.7 187.059623015873 101.325 85.734623015873 9.64037698412697 37 86.8 84.1506944444445 101.8125 -17.6618055555556 2.64930555555553 38 88.2 84.4304563492063 101.9125 -17.4820436507937 3.76954365079366 39 93.8 91.6125992063492 102.1625 -10.5499007936508 2.18740079365080 40 85 88.9441468253968 102.325 -13.3808531746032 -3.94414682539681 41 90.4 95.6155753968254 102.554166666667 -6.93859126984127 -5.21557539682539 42 115.9 115.073313492063 103.141666666667 11.9316468253968 0.826686507936529 43 94.9 99.3298611111111 103.608333333333 -4.27847222222222 -4.42986111111108 44 87.7 92.0524801587301 103.845833333333 -11.7933531746032 -4.35248015873013 45 91.7 93.2602182539682 103.916666666667 -10.6564484126984 -1.56021825396824 46 95.9 97.5881944444444 104.425 -6.83680555555556 -1.68819444444443 47 106.8 107.245337301587 105.333333333333 1.91200396825397 -0.445337301587287 48 204.5 191.613789682540 105.879166666667 85.734623015873 12.8862103174603 49 90.2 88.9423611111111 106.604166666667 -17.6618055555556 1.25763888888889 50 90.5 90.1262896825397 107.608333333333 -17.4820436507937 0.373710317460322 51 93.2 98.0334325396826 108.583333333333 -10.5499007936508 -4.83343253968256 52 97.8 96.0191468253968 109.4 -13.3808531746032 1.78085317460317 53 99.4 102.932242063492 109.870833333333 -6.93859126984127 -3.53224206349205 54 120 121.298313492063 109.366666666667 11.9316468253968 -1.29831349206347 55 108.2 104.554861111111 108.833333333333 -4.27847222222222 3.64513888888889 56 98.5 97.3566468253968 109.15 -11.7933531746032 1.14335317460321 57 104.3 99.4602182539683 110.116666666667 -10.6564484126984 4.83978174603175 58 102.9 104.275694444444 111.1125 -6.83680555555556 -1.37569444444442 59 111.1 113.903670634921 111.991666666667 1.91200396825397 -2.80367063492062 60 188.1 199.097123015873 113.3625 85.734623015873 -10.9971230158730 61 93.8 96.967361111111 114.629166666667 -17.6618055555556 -3.16736111111109 62 94.5 98.2304563492063 115.7125 -17.4820436507937 -3.73045634920635 63 112.4 106.170932539683 116.720833333333 -10.5499007936508 6.22906746031748 64 102.5 104.248313492063 117.629166666667 -13.3808531746032 -1.74831349206349 65 115.8 111.528075396825 118.466666666667 -6.93859126984127 4.27192460317461 66 136.5 131.298313492063 119.366666666667 11.9316468253968 5.2016865079365 67 122.1 115.900694444444 120.179166666667 -4.27847222222222 6.19930555555554 68 110.6 108.985813492063 120.779166666667 -11.7933531746032 1.61418650793651 69 116.4 110.651884920635 121.308333333333 -10.6564484126984 5.74811507936509 70 112.6 114.925694444444 121.7625 -6.83680555555556 -2.32569444444445 71 121.5 124.082837301587 122.170833333333 1.91200396825397 -2.58283730158730 72 199.3 208.509623015873 122.775 85.734623015873 -9.209623015873 73 102.1 105.804861111111 123.466666666667 -17.6618055555556 -3.7048611111111 74 100.6 106.597123015873 124.079166666667 -17.4820436507937 -5.99712301587303 75 119 114.029265873016 124.579166666667 -10.5499007936508 4.97073412698413 76 106.8 111.889980158730 125.270833333333 -13.3808531746032 -5.08998015873016 77 121.3 119.390575396825 126.329166666667 -6.93859126984127 1.9094246031746 78 145.5 139.281646825397 127.35 11.9316468253968 6.21835317460318 79 129.7 123.717361111111 127.995833333333 -4.27847222222222 5.98263888888889 80 117.7 116.810813492064 128.604166666667 -11.7933531746032 0.8891865079365 81 121.3 118.047718253968 128.704166666667 -10.6564484126984 3.25228174603174 82 124.3 122.192361111111 129.029166666667 -6.83680555555556 2.10763888888889 83 135.2 131.787003968254 129.875 1.91200396825397 3.41299603174602 84 210.1 215.722123015873 129.9875 85.734623015873 -5.62212301587303 85 106.8 112.546527777778 130.208333333333 -17.6618055555556 -5.74652777777777 86 110.5 113.201289682540 130.683333333333 -17.4820436507937 -2.70128968253965 87 111.5 120.316765873016 130.866666666667 -10.5499007936508 -8.81676587301585 88 122.1 117.494146825397 130.875 -13.3808531746032 4.6058531746032 89 126.3 123.769742063492 130.708333333333 -6.93859126984127 2.53025793650795 90 143.2 142.93998015873 131.008333333333 11.9316468253968 0.260019841269838 91 137.3 NA NA -4.27847222222222 NA 92 121.5 NA NA -11.7933531746032 NA 93 121.9 NA NA -10.6564484126984 NA 94 123.9 NA NA -6.83680555555556 NA 95 131.6 NA NA 1.91200396825397 NA 96 220.9 NA NA 85.734623015873 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/1rivz1292071145.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/1rivz1292071145.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/2jru11292071145.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/2jru11292071145.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/3jru11292071145.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/3jru11292071145.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/4c1tm1292071145.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/11/t12920712680krghx4k23ouagu/4c1tm1292071145.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

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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