multiplicative instead of additive

*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: Tue, 22 Sep 2009 02:36:12 -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/Sep/22/t1253608661mz64605c9fweodo.htm/, Retrieved Tue, 22 Sep 2009 10:37:47 +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/Sep/22/t1253608661mz64605c9fweodo.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 «

13328 12873 14000 13477 14237 13674 13529 14058 12975 14326 14008 16193 14483 14011 15057 14884 15414 14440 14900 15074 14442 15307 14938 17193 15528 14765 15838 15723 16150 15486 15986 15983 15692 16490 15686 18897 16316 15636 17163 16534 16518 16375 16290 16352 15943 16362 16393 19051 16747 16320 17910 16961 17480 17049 16879 17473 16998 17307 17418 20169 17871 17226 19062 17804 19100 18522 18060 18869 18127 18871 18890 21263 19547 18450 20254 19240 20216 19420 19415 20018 18652 19978 19509 21971

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 13328 NA NA 0.997446154479086 NA 2 12873 NA NA 0.952612175250179 NA 3 14000 NA NA 1.03439377187718 NA 4 13477 NA NA 0.990786621874973 NA 5 14237 NA NA 1.02155910700688 NA 6 13674 NA NA 0.981559624901186 NA 7 13529 13938.9369024288 13937.9583333333 0.978569095450089 0.991916327070772 8 14058 14034.4952714517 14033.5 0.995271451725292 1.00650513059493 9 12975 14125.9107947601 14124.9583333333 0.952461426798343 0.964434626586157 10 14326 14228.6196634146 14227.625 0.99466341461539 1.01231667993762 11 14008 14336.2670351651 14335.2916666667 0.975368498391713 1.00184578306806 12 16193 14417.3753086576 14416.25 1.12530865762970 0.9981673285668 13 14483 14506.2891128211 14505.2916666667 0.997446154479086 1.00101965408011 14 14011 14605.7026121752 14604.75 0.952612175250179 1.00706818930871 15 15057 14709.2427271052 14708.2083333333 1.03439377187718 0.98967541219641 16 14884 14811.1991199552 14810.2083333333 0.990786621874973 1.01432787297848 17 15414 14890.8548924403 14889.8333333333 1.02155910700688 1.01335594167248 18 14440 14971.2315596249 14970.25 0.981559624901186 0.982701127233388 19 14900 15056.4369024288 15055.4583333333 0.978569095450089 1.01134839856085 20 15074 15131.4119381184 15130.4166666667 0.995271451725292 1.00100460640068 21 14442 15195.3274614268 15194.375 0.952461426798343 0.997923163673607 22 15307 15262.8696634146 15261.875 0.99466341461539 1.00833779477249 23 14938 15328.4753684984 15327.5 0.975368498391713 0.999199953807792 24 17193 15402.8753086576 15401.75 1.12530865762970 0.991996028282445 25 15528 15491.5807794878 15490.5833333333 0.997446154479086 1.00498201475996 26 14765 15574.6609455086 15573.7083333333 0.952612175250179 0.995234188326474 27 15838 15664.7010604385 15663.6666666667 1.03439377187718 0.977509550329222 28 15723 15766.0324532885 15765.0416666667 0.990786621874973 1.00660749009184 29 16150 15846.521559107 15845.5 1.02155910700688 0.997707137407307 30 15486 15948.6482262916 15947.6666666667 0.981559624901186 0.989294100759252 31 15986 16052.4785690954 16051.5 0.978569095450089 1.01773026464026 32 15983 16121.6202714517 16120.625 0.995271451725292 0.996173253302707 33 15692 16213.0774614268 16212.125 0.952461426798343 1.01622753806839 34 16490 16302.1196634146 16301.125 0.99466341461539 1.01701400570137 35 15686 16351.2253684984 16350.25 0.975368498391713 0.983601286553775 36 18897 16403.7503086576 16402.625 1.12530865762970 1.02378284218309 37 16316 16453.3307794878 16452.3333333333 0.997446154479086 0.994252602326266 38 15636 16481.3276121752 16480.375 0.952612175250179 0.995961256229526 39 17163 16507.2427271052 16506.2083333333 1.03439377187718 1.00521736703713 40 16534 16512.3241199552 16511.3333333333 0.990786621874973 1.01068461395776 41 16518 16536.4798924403 16535.4583333333 1.02155910700688 0.977862349098764 42 16375 16572.3148929582 16571.3333333333 0.981559624901186 1.00671645989863 43 16290 16596.6869024288 16595.7083333333 0.978569095450089 1.00307589670141 44 16352 16643.1619381184 16642.1666666667 0.995271451725292 0.987232546377255 45 15943 16702.7441280935 16701.7916666667 0.952461426798343 1.00221197474349 46 16362 16751.7029967479 16750.7083333333 0.99466341461539 0.982035228512496 47 16393 16809.5587018317 16808.5833333333 0.975368498391713 0.999904682671003 48 19051 16877.8753086576 16876.75 1.12530865762970 1.00313018702050 49 16747 16930.3724461545 16929.375 0.997446154479086 0.99176010671743 50 16320 17001.5776121752 17000.625 0.952612175250179 1.00771828465007 51 17910 17092.3260604385 17091.2916666667 1.03439377187718 1.01305914959772 52 16961 17175.6157866219 17174.625 0.990786621874973 0.99674497998709 53 17480 17257.7298924403 17256.7083333333 1.02155910700688 0.991562216520764 54 17049 17346.9815596249 17346 0.981559624901186 1.00134303814751 55 16879 17440.3952357621 17439.4166666667 0.978569095450089 0.989061422914232 56 17473 17524.9952714517 17524 0.995271451725292 1.00182689237015 57 16998 17610.7024614268 17609.75 0.952461426798343 1.01343812451058 58 17307 17693.8696634146 17692.875 0.99466341461539 0.983438577181112 59 17418 17796.4753684984 17795.5 0.975368498391713 1.00350459703245 60 20169 17925.5003086576 17924.375 1.12530865762970 0.999927896016888 61 17871 18035.9557794878 18034.9583333333 0.997446154479086 0.993445967410447 62 17226 18143.2859455086 18142.3333333333 0.952612175250179 0.99672459029201 63 19062 18248.5760604385 18247.5416666667 1.03439377187718 1.00989960942047 64 17804 18360.7407866219 18359.75 0.990786621874973 0.97874755126211 65 19100 18487.271559107 18486.25 1.02155910700688 1.01139556637101 66 18522 18594.1482262916 18593.1666666667 0.981559624901186 1.01488733202451 67 18060 18709.5619024288 18708.5833333333 0.978569095450089 0.98647332754123 68 18869 18830.4119381184 18829.4166666667 0.995271451725292 1.00686320838303 69 18127 18931.0357947601 18930.0833333333 0.952461426798343 1.00537020984653 70 18871 19040.5779967479 19039.5833333333 0.99466341461539 0.996463351323665 71 18890 19146.8920351651 19145.9166666667 0.975368498391713 1.01154933429567 72 21263 19230.9586419910 19229.8333333333 1.12530865762970 0.982601355359033 73 19547 19324.7057794878 19323.7083333333 0.997446154479086 1.01414529213454 74 18450 19428.9942788419 19428.0416666667 0.952612175250179 0.996899128621962 75 20254 19498.8260604385 19497.7916666667 1.03439377187718 1.00424454884781 76 19240 19566.7824532885 19565.7916666667 0.990786621874973 0.99249312915148 77 20216 19638.7298924403 19637.7083333333 1.02155910700688 1.00772242635843 78 19420 19693.9815596249 19693 0.981559624901186 1.00466357936546 79 19415 NA NA 0.978569095450089 NA 80 20018 NA NA 0.995271451725292 NA 81 18652 NA NA 0.952461426798343 NA 82 19978 NA NA 0.99466341461539 NA 83 19509 NA NA 0.975368498391713 NA 84 21971 NA NA 1.12530865762970 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/1xpr81253608570.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/1xpr81253608570.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/2a2h71253608570.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/2a2h71253608570.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/3l9gu1253608570.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/3l9gu1253608570.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/4lt7s1253608570.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Sep/22/t1253608661mz64605c9fweodo/4lt7s1253608570.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$trend)) { a<-table.row.start(a) a<-table.element(a,i,header=TRUE) a<-table.element(a,x[i]) a<-table.element(a,m$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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