Decompostion Brutoschuld/Samira Allouch

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 04 Jun 2009 04:08:45 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy.htm/, Retrieved Thu, 04 Jun 2009 12:09:32 +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/2009/Jun/04/t1244110167bn2qaj8ppyrteoy.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 «

260288 261544 259886 257006 259670 258873 264416 263596 262586 260237 261690 259295 264170 264451 265538 261723 266189 265073 267007 266376 267406 262742 260300 263074 265940 264771 268403 264264 264118 266817 269296 269001 266707 267507 267510 267420 270845 270671 273653 271567 268372 268160 267879 271142 271323 269478 271008 269145 271684 273582 279475 276188 278422 281084 278618 280738 288897 282129 286406 284288 286139 288275 287670 286864 288798 288316 286915 288006 293338 303730 306248 305700 314849

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 260288 NA NA 1.00124512670782 NA 2 261544 NA NA 1.0019083064631 NA 3 259886 NA NA 1.00986447944717 NA 4 257006 NA NA 0.99713458050041 NA 5 259670 NA NA 0.99837746530059 NA 6 258873 NA NA 0.998294829991328 NA 7 264416 259839.282098998 260919 0.995861865555968 1.01761364896035 8 263596 262509.510777159 261201.875 1.00500622660982 1.00413885660609 9 262586 263582.06048691 261558.5 1.00773655028191 0.996221061156172 10 260237 260990.718131727 261990.541666667 0.996183741868773 0.997112088364206 11 261690 261125.119285603 262458.708333333 0.994918861499398 1.00216325689364 12 259295 261270.815694873 262988.666666667 0.993467965773707 0.992437671656446 13 264170 263682.868625592 263354.958333333 1.00124512670782 1.00184741381549 14 264451 264081.739032161 263578.75 1.0019083064631 1.00139828285436 15 265538 266498.607580579 263895.416666667 1.00986447944717 0.996395449907602 16 261723 263443.579377321 264200.625 0.99713458050041 0.99346888855144 17 266189 263818.333271407 264247.083333333 0.99837746530059 1.00898598175190 18 265073 263895.869063156 264346.625 0.998294829991328 1.00446058872017 19 267007 263482.974688089 264577.833333333 0.995861865555968 1.01337477427558 20 266376 265989.889215168 264664.916666667 1.00500622660982 1.00145159947986 21 267406 266846.245140342 264797.625 1.00773655028191 1.00209766811357 22 262742 264011.47929832 265022.875 0.996183741868773 0.995191575375078 23 260300 263695.740894002 265042.458333333 0.994918861499398 0.987122503827748 24 263074 263297.655923046 265028.833333333 0.993467965773707 0.999150558624377 25 265940 265527.078711894 265196.875 1.00124512670782 1.00155510048206 26 264771 265908.092636305 265401.625 1.0019083064631 0.995723738134363 27 268403 268100.715499535 265481.875 1.00986447944717 1.00112750352009 28 264264 264890.089275434 265651.291666667 0.99713458050041 0.99763641864765 29 264118 265718.411984119 266150.25 0.99837746530059 0.993977037676207 30 266817 266177.09753654 266631.75 0.998294829991328 1.00240404779142 31 269296 265912.25522638 267017.208333333 0.995861865555968 1.01272504259249 32 269001 268806.419165242 267467.416666667 1.00500622660982 1.00072386974746 33 266707 270004.869390132 267932 1.00773655028191 0.987785889204217 34 267507 267430.547931037 268455.041666667 0.996183741868773 1.00028587635015 35 267510 267570.079305538 268936.583333333 0.994918861499398 0.999775463289116 36 267420 267411.565374816 269169.791666667 0.993467965773707 1.00003154173669 37 270845 269501.854990736 269166.708333333 1.00124512670782 1.00498380617569 38 270671 269710.585136409 269196.875 1.0019083064631 1.00356090905036 39 273653 272136.680969332 269478.416666667 1.00986447944717 1.00557190241781 40 271567 268979.919851905 269752.875 0.99713458050041 1.00961811628734 41 268372 269542.696864952 269980.75 0.99837746530059 0.995656729421465 42 268160 269737.640834558 270198.375 0.998294829991328 0.994151202517836 43 267879 269186.649040328 270305.208333333 0.995861865555968 0.995142221781839 44 271142 271815.449682971 270461.458333333 1.00500622660982 0.997522401012317 45 271323 272920.587142281 270825.333333333 1.00773655028191 0.994146329674104 46 269478 270225.258403538 271260.458333333 0.996183741868773 0.99723468336027 47 271008 270490.331983849 271871.75 0.994918861499398 1.00191381337867 48 269145 271046.871634075 272829 0.993467965773707 0.992983237096193 49 271684 274155.892650956 273814.958333333 1.00124512670782 0.99098362385337 50 273582 275186.389746845 274662.25 1.0019083064631 0.994169807059424 51 279475 278514.900866147 275794.333333333 1.00986447944717 1.00344720921885 52 276188 276259.833235041 277053.708333333 0.99713458050041 0.999739979445437 53 278422 277770.991141471 278222.416666667 0.99837746530059 1.00234368915146 54 281084 279018.371912808 279494.958333333 0.998294829991328 1.00740319740607 55 278618 279566.517265018 280728.208333333 0.995861865555968 0.996607185744927 56 280738 283354.177422235 281942.708333333 1.00500622660982 0.990767111866727 57 288897 285085.017029757 282896.375 1.00773655028191 1.01337139008552 58 282129 282600.060383312 283682.666666667 0.996183741868773 0.998333120018895 59 286406 283113.945408459 284559.833333333 0.994918861499398 1.01162801990129 60 284288 283429.953093461 285293.5 0.993467965773707 1.00302736848090 61 286139 286296.573871946 285940.541666667 1.00124512670782 0.999449613141315 62 288275 287135.983133312 286589.083333333 1.0019083064631 1.00396682036942 63 287670 289908.82308857 287076.958333333 1.00986447944717 0.992277492403583 64 286864 287336.336533433 288162.041666667 0.99713458050041 0.998356154536068 65 288798 289418.478642279 289888.833333333 0.99837746530059 0.997856119466907 66 288316 291110.509210404 291607.75 0.998294829991328 0.99040052103243 67 286915 292480.812443303 293696.166666667 0.995861865555968 0.980970333073107 68 288006 NA NA 1.00500622660982 NA 69 293338 NA NA 1.00773655028191 NA 70 303730 NA NA 0.996183741868773 NA 71 306248 NA NA 0.994918861499398 NA 72 305700 NA NA 0.993467965773707 NA 73 314849 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/12dpv1244110123.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/12dpv1244110123.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/2g5mk1244110123.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/2g5mk1244110123.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/3jvp11244110123.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/3jvp11244110123.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/4nsds1244110123.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/04/t1244110167bn2qaj8ppyrteoy/4nsds1244110123.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

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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