Tijdreeks 2 - stap 24

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 05 Aug 2010 14:30:18 +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/Aug/05/t12810186039z9ci8ga2g153cr.htm/, Retrieved Thu, 05 Aug 2010 16:30:09 +0200

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/Aug/05/t12810186039z9ci8ga2g153cr.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:

Ghielens Nick

Dataseries X:

» Textbox « » Textfile « » CSV «

80 79 78 76 74 73 74 76 77 77 78 80 90 90 89 82 78 76 74 78 81 82 88 99 117 113 106 100 97 96 100 104 104 111 117 118 140 147 134 126 116 114 120 122 117 119 132 134 154 152 132 130 123 129 124 128 128 129 141 138 155 160 142 133 131 140 134 134 134 136 145 137 152 168 160 157 147 161 159 164 163 158 175 163

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 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 80 NA NA 1.14172484370715 NA 2 79 NA NA 1.15429284448547 NA 3 78 NA NA 1.05574572378660 NA 4 76 NA NA 0.994151779339418 NA 5 74 NA NA 0.935829420138376 NA 6 73 NA NA 0.95056944521912 NA 7 74 71.9432468665797 77.25 0.931304166557666 1.02858855032265 8 76 73.9554872235954 78.125 0.94663023646202 1.02764518027206 9 77 74.0874371027308 79.0416666666667 0.937321291758324 1.03931250710199 10 77 75.4361787758065 79.75 0.945908197815755 1.02073038758818 11 78 80.2084545393566 80.1666666666667 1.00052126244520 0.972466062935137 12 80 80.9411467574224 80.4583333333333 1.00600078828490 0.988372455850631 13 90 92.0039936554015 80.5833333333333 1.14172484370715 0.978218405791086 14 90 93.1129561218276 80.6666666666667 1.15429284448547 0.966567959481872 15 89 85.4274248163994 80.9166666666667 1.05574572378660 1.04182000325163 16 82 80.8162550621335 81.2916666666667 0.994151779339418 1.01464736193178 17 78 76.6600266663353 81.9166666666667 0.935829420138376 1.01747942691824 18 76 79.0160851338394 83.125 0.95056944521912 0.961829479039227 19 74 79.1996584976748 85.0416666666667 0.931304166557666 0.934347463154434 20 78 82.4751593517536 87.125 0.94663023646202 0.945739306393248 21 81 83.2263196973745 88.7916666666667 0.937321291758324 0.973249812012957 22 82 85.368214852872 90.25 0.945908197815755 0.960544860183888 23 88 91.839514215282 91.7916666666667 1.00052126244520 0.958193221642247 24 99 93.9772403056142 93.4166666666667 1.00600078828490 1.05344655448544 25 117 108.844435100082 95.3333333333333 1.14172484370715 1.07492863454543 26 113 112.543552337333 97.5 1.15429284448547 1.00405574244981 27 106 105.090688921925 99.5416666666667 1.05574572378660 1.00865263219228 28 100 101.11352055698 101.708333333333 0.994151779339418 0.988987421752836 29 97 97.4432383719084 104.125 0.935829420138376 0.995451317307244 30 96 100.879182373879 106.125 0.95056944521912 0.951633406823264 31 100 100.464436967408 107.875 0.931304166557666 0.995377100778867 32 104 104.365983569938 110.25 0.94663023646202 0.996493267658494 33 104 105.761085753398 112.833333333333 0.937321291758324 0.98334845240239 34 111 108.858268431963 115.083333333333 0.945908197815755 1.01967449601107 35 117 117.019299320153 116.958333333333 1.00052126244520 0.999835075750197 36 118 119.211093411760 118.5 1.00600078828490 0.989840765845698 37 140 137.102124981834 120.083333333333 1.14172484370715 1.02113661636207 38 147 140.438962745732 121.666666666667 1.15429284448547 1.04671806972932 39 134 129.812734620595 122.958333333333 1.05574572378660 1.03225619883630 40 126 123.109128674865 123.833333333333 0.994151779339418 1.02348218492205 41 116 116.783713054768 124.791666666667 0.935829420138376 0.993289192180414 42 114 119.850964218044 126.083333333333 0.95056944521912 0.951181333782184 43 120 118.586063875009 127.333333333333 0.931304166557666 1.01192329080490 44 122 121.286999046696 128.125 0.94663023646202 1.00587862638954 45 117 120.211455668005 128.25 0.937321291758324 0.973284944848565 46 119 121.391552053022 128.333333333333 0.945908197815755 0.980298859248646 47 132 128.858800925754 128.791666666667 1.00052126244520 1.02437706273594 48 134 130.486685580454 129.708333333333 1.00600078828490 1.02692469659964 49 154 148.995092103784 130.5 1.14172484370715 1.03359109233430 50 152 151.116171557222 130.916666666667 1.15429284448547 1.00584866883319 51 132 138.962530893412 131.625 1.05574572378660 0.949896343649985 52 130 131.725110762473 132.5 0.994151779339418 0.98690370611581 53 123 124.738263125944 133.291666666667 0.935829420138376 0.986064715970998 54 129 127.217877418492 133.833333333333 0.95056944521912 1.01400842882833 55 124 124.833562659000 134.041666666667 0.931304166557666 0.99332260778876 56 128 127.242880951103 134.416666666667 0.94663023646202 1.00595018788664 57 128 126.694594602667 135.166666666667 0.937321291758324 1.01030356031705 58 129 128.367625011913 135.708333333333 0.945908197815755 1.00492628096865 59 141 136.237645236288 136.166666666667 1.00052126244520 1.03495623221799 60 138 137.780191295519 136.958333333333 1.00600078828490 1.0015953578117 61 155 157.367740957636 137.833333333333 1.14172484370715 0.984954089426286 62 160 159.869558961237 138.5 1.15429284448547 1.00081592167771 63 142 146.748655606338 139 1.05574572378660 0.967640891926965 64 133 138.725596208655 139.541666666667 0.994151779339418 0.95872718254501 65 131 131.016118819373 140 0.935829420138376 0.999876970715375 66 140 133.198543511329 140.125 0.95056944521912 1.05106254399916 67 134 130.3437789778 139.958333333333 0.931304166557666 1.02805059858532 68 134 132.68600481076 140.166666666667 0.94663023646202 1.00990304283496 69 134 132.396632460863 141.25 0.937321291758324 1.01211033475199 70 136 135.264872287653 143 0.945908197815755 1.00543472743451 71 145 144.742075967072 144.666666666667 1.00052126244520 1.00178195615342 72 137 147.085698587154 146.208333333333 1.00600078828490 0.931429780841825 73 152 169.117992474122 148.125 1.14172484370715 0.898780772975758 74 168 173.624882024689 150.416666666667 1.15429284448547 0.967603249263035 75 160 161.397127523877 152.875 1.05574572378660 0.991343541577774 76 157 154.093525797610 155 0.994151779339418 1.01886175416745 77 147 147.081190531748 157.166666666667 0.935829420138376 0.999447988342666 78 161 151.615826512450 159.5 0.95056944521912 1.06189441896278 79 159 NA NA 0.931304166557666 NA 80 164 NA NA 0.94663023646202 NA 81 163 NA NA 0.937321291758324 NA 82 158 NA NA 0.945908197815755 NA 83 175 NA NA 1.00052126244520 NA 84 163 NA NA 1.00600078828490 NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/14vea1281018615.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/14vea1281018615.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/24vea1281018615.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/24vea1281018615.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/34vea1281018615.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/34vea1281018615.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/4xmvu1281018615.png (open in new window) http://www.freestatistics.org/blog/date/2010/Aug/05/t12810186039z9ci8ga2g153cr/4xmvu1281018615.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = multiplicative ; 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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