*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: Fri, 11 Dec 2009 06:04:40 -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/11/t12605367083qzh1uuyl72ias1.htm/, Retrieved Fri, 11 Dec 2009 14:05:14 +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/11/t12605367083qzh1uuyl72ias1.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 «

15859.4 15258.9 15498.6 15106.5 15023.6 12083.0 15761.3 16942.6 15070.3 13659.6 14768.9 14725.1 15998.1 15370.6 14956.9 15469.7 15101.8 11703.7 16283.6 16726.5 14968.9 14861.0 14583.3 15305.8 17903.9 16379.4 15420.3 17870.5 15912.8 13866.5 17823.2 17872.0 17422.0 16704.5 15991.2 16583.6 19123.5 17838.7 17209.4 18586.5 16258.1 15141.6 19202.1 17746.5 19090.1 18040.3 17515.5 17751.8 21072.4 17170.0 19439.5 19795.4 17574.9 16165.4 19464.6 19932.1 19961.2 17343.4 18924.2 18574.1 21350.6

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 15859.4 NA NA 1.11033803352377 NA 2 15258.9 NA NA 1.00030595448686 NA 3 15498.6 NA NA 0.99531399743087 NA 4 15106.5 NA NA 1.06043625305120 NA 5 15023.6 NA NA 0.956804451872642 NA 6 12083 NA NA 0.830761475963053 NA 7 15761.3 15900.9893411254 14985.5958333333 1.06108489231745 0.99121505347066 8 16942.6 15717.5466506426 14996.0291666667 1.04811390241756 1.07794176639567 9 15070.3 15681.564751243 14978.1125 1.04696534701839 0.96102016852658 10 13659.6 14728.8550086994 14970.675 0.98384708830426 0.927404064466122 11 14768.9 14004.0085687924 14989.0666666667 0.934281558700059 1.05461946323798 12 14725.1 14553.3898628828 14976.5208333333 0.971747044913876 1.01179863514515 13 15998.1 16635.6164552275 14982.4791666667 1.11033803352377 0.96167761760177 14 15370.6 14999.8253601946 14995.2375 1.00030595448686 1.02471859711042 15 14956.9 14911.8026037926 14982.0083333333 0.99531399743087 1.00302427529425 16 15469.7 15936.0681084467 15027.8416666667 1.06043625305120 0.970735058028551 17 15101.8 14419.2025571294 15070.1666666667 0.956804451872642 1.04733947249622 18 11703.7 12533.3903138072 15086.6291666667 0.830761475963053 0.933801605708135 19 16283.6 16118.127100777 15190.2333333333 1.06108489231745 1.01026626097365 20 16726.5 16048.3794367995 15311.675 1.04811390241756 1.04225476883015 21 14968.9 16095.0157291362 15373.0166666667 1.04696534701839 0.930033263210946 22 14861 15242.1116372162 15492.3583333333 0.98384708830426 0.9749961392301 23 14583.3 14599.2549211996 15626.1833333333 0.934281558700059 0.998907141406485 24 15305.8 15305.105034206 15750.0916666667 0.971747044913876 1.00004540745016 25 17903.9 17659.2139562907 15904.3583333333 1.11033803352377 1.01385599859172 26 16379.4 16021.1377397257 16016.2375 1.00030595448686 1.02236184883337 27 15420.3 16090.4244095586 16166.1791666667 0.99531399743087 0.958352595773636 28 17870.5 17333.0470618569 16345.2041666667 1.06043625305120 1.03100741238543 29 15912.8 15768.7871965514 16480.6791666667 0.956804451872642 1.00913277613894 30 13866.5 13784.4790200400 16592.5833333333 0.830761475963053 1.00595024156087 31 17823.2 17716.5542249381 16696.6416666667 1.06108489231745 1.00601955514079 32 17872 17616.9736017338 16808.2625 1.04811390241756 1.01447617530863 33 17422 17739.3751408076 16943.6125 1.04696534701839 0.982109001118222 34 16704.5 16772.6169626853 17047.9916666667 0.98384708830426 0.99593879936346 35 15991.2 15968.9389361326 17092.2125 0.934281558700059 1.00139402273103 36 16583.6 16674.9161092308 17159.7291666667 0.971747044913876 0.994523744009708 37 19123.5 19175.8848195910 17270.3125 1.11033803352377 0.997268192832622 38 17838.7 17327.8374080719 17322.5375 1.00030595448686 1.02948218983692 39 17209.4 17305.3378519560 17386.8125 0.99531399743087 0.994456169953066 40 18586.5 18570.3331525263 17511.975 1.06043625305120 1.00087057390629 41 16258.1 16869.5588249491 17631.1458333333 0.956804451872642 0.963753715713964 42 15141.6 14740.4777885044 17743.3333333333 0.830761475963053 1.02721229374318 43 19202.1 18964.9957609295 17873.2125 1.06108489231745 1.01250220364188 44 17746.5 18789.0706445249 17926.5541666667 1.04811390241756 0.944511856693206 45 19090.1 18836.5948244829 17991.6125 1.04696534701839 1.01345812116676 46 18040.3 17841.9726610518 18134.9041666667 0.98384708830426 1.01111577417564 47 17515.5 17041.4279872432 18240.1416666667 0.934281558700059 1.02781879623654 48 17751.8 17819.5733939490 18337.6666666667 0.971747044913876 0.996196688189402 49 21072.4 20420.5182382694 18391.2625 1.11033803352377 1.0319228804149 50 17170 18498.92476458 18493.2666666667 1.00030595448686 0.928162053660302 51 19439.5 18533.3728505528 18620.6291666667 0.99531399743087 1.04889164842006 52 19795.4 19753.6872227593 18627.8875 1.06043625305120 1.00211164512074 53 17574.9 17851.6229143512 18657.5458333333 0.956804451872642 0.984498725091895 54 16165.4 15577.1965165514 18750.5041666667 0.830761475963053 1.03776054842883 55 19464.6 19944.5318580853 18796.3583333333 1.06108489231745 0.975936669684693 56 19932.1 NA NA 1.04811390241756 NA 57 19961.2 NA NA 1.04696534701839 NA 58 17343.4 NA NA 0.98384708830426 NA 59 18924.2 NA NA 0.934281558700059 NA 60 18574.1 NA NA 0.971747044913876 NA 61 21350.6 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/132yu1260536678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/132yu1260536678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/2yz8m1260536678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/2yz8m1260536678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/3i4cn1260536678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/3i4cn1260536678.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/4e8wo1260536678.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/11/t12605367083qzh1uuyl72ias1/4e8wo1260536678.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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