Multiplicative model HPC

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

Title produced by software: Classical Decomposition

Date of computation: Thu, 03 Apr 2008 04:35:14 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l.htm/, Retrieved Thu, 03 Apr 2008 12:37:04 +0200

User-defined keywords:

HPC

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

Text written by user:

Output produced by software:

Summary of compuational 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 13328 NA NA 0.994578556481803 NA 2 12873 NA NA 0.953159362044224 NA 3 14000 NA NA 1.0334682729003 NA 4 13477 NA NA 0.992228846279956 NA 5 14237 NA NA 1.01993449653857 NA 6 13674 NA NA 0.980599134598712 NA 7 13529 13938.9384397608 13937.9583333333 0.980106427460313 0.990360470810323 8 14058 14034.4939309631 14033.5 0.993930963120608 1.00786257764928 9 12975 14125.9175263021 14124.9583333333 0.959192968805225 0.957666299030709 10 14326 14228.6171675996 14227.625 0.992167599595114 1.01486318032334 11 14008 14336.2666304291 14335.2916666667 0.974963762458175 1.00226167851349 12 16193 14417.3756696097 14416.25 1.12566960971700 0.997847260779928 13 14483 14506.2862452231 14505.2916666667 0.994578556481803 1.00390582323847 14 14011 14605.7031593620 14604.75 0.953159362044224 1.00649005470097 15 15057 14709.2418016062 14708.2083333333 1.0334682729003 0.990561693474166 16 14884 14811.2005621796 14810.2083333333 0.992228846279956 1.01285352719771 17 15414 14890.8532678299 14889.8333333333 1.01993449653857 1.01497007343933 18 14440 14971.2305991346 14970.25 0.980599134598712 0.983663676423608 19 14900 15056.4384397608 15055.4583333333 0.980106427460313 1.00976206240078 20 15074 15131.4105976298 15130.4166666667 0.993930963120608 1.00235463504241 21 14442 15195.3341929688 15194.375 0.959192968805225 0.990919816157124 22 15307 15262.8671675996 15261.875 0.992167599595114 1.01087428620270 23 14938 15328.4749637625 15327.5 0.974963762458175 0.999614750892224 24 17193 15402.8756696097 15401.75 1.12566960971700 0.9916779393566 25 15528 15491.5779118898 15490.5833333333 0.994578556481803 1.00787960831257 26 14765 15574.6614926954 15573.7083333333 0.953159362044224 0.994662847345605 27 15838 15664.7001349396 15663.6666666667 1.0334682729003 0.978384936746434 28 15723 15766.0338955129 15765.0416666667 0.992228846279956 1.00514436604148 29 16150 15846.5199344965 15845.5 1.01993449653857 0.999296342856524 30 15486 15948.6472658013 15947.6666666667 0.980599134598712 0.990263107723003 31 15986 16052.4801064275 16051.5 0.980106427460313 1.01613391829484 32 15983 16121.6189309631 16120.625 0.993930963120608 0.997516766025312 33 15692 16213.0841929688 16212.125 0.959192968805225 1.00909573186929 34 16490 16302.1171675996 16301.125 0.992167599595114 1.01957232229254 35 15686 16351.2249637625 16350.25 0.974963762458175 0.98400960817584 36 18897 16403.7506696097 16402.625 1.12566960971700 1.02345456064235 37 16316 16453.3279118898 16452.3333333333 0.994578556481803 0.997119260522989 38 15636 16481.3281593620 16480.375 0.953159362044224 0.995389497855752 39 17163 16507.2418016062 16506.2083333333 1.0334682729003 1.00611756655861 40 16534 16512.3255621796 16511.3333333333 0.992228846279956 1.0092155637266 41 16518 16536.4782678299 16535.4583333333 1.01993449653857 0.979419944625052 42 16375 16572.3139324679 16571.3333333333 0.980599134598712 1.00770253194678 43 16290 16596.6884397608 16595.7083333333 0.980106427460313 1.00150253625658 44 16352 16643.1605976298 16642.1666666667 0.993930963120608 0.988564001003074 45 15943 16702.7508596355 16701.7916666667 0.959192968805225 0.995178528682899 46 16362 16751.7005009329 16750.7083333333 0.992167599595114 0.98450555537538 47 16393 16809.5582970958 16808.5833333333 0.974963762458175 1.00031977230897 48 19051 16877.8756696097 16876.75 1.12566960971700 1.00280852786605 49 16747 16930.3695785565 16929.375 0.994578556481803 0.994619578477879 50 16320 17001.5781593620 17000.625 0.953159362044224 1.00713977683759 51 17910 17092.3251349396 17091.2916666667 1.0334682729003 1.01396637164899 52 16961 17175.6172288463 17174.625 0.992228846279956 0.99529619129175 53 17480 17257.7282678299 17256.7083333333 1.01993449653857 0.99314163398572 54 17049 17346.9805991346 17346 0.980599134598712 1.00232384696495 55 16879 17440.3967730941 17439.4166666667 0.980106427460313 0.987510044673132 56 17473 17524.9939309631 17524 0.993930963120608 1.00317803000739 57 16998 17610.7091929688 17609.75 0.959192968805225 1.00632589419991 58 17307 17693.8671675996 17692.875 0.992167599595114 0.985912434192216 59 17418 17796.4749637625 17795.5 0.974963762458175 1.00392118110001 60 20169 17925.5006696097 17924.375 1.12566960971700 0.999607263694481 61 17871 18035.9529118898 18034.9583333333 0.994578556481803 0.996310299893777 62 17226 18143.2864926954 18142.3333333333 0.953159362044224 0.996152393705766 63 19062 18248.5751349396 18247.5416666667 1.0334682729003 1.01080400201749 64 17804 18360.7422288463 18359.75 0.992228846279956 0.977324922188142 65 19100 18487.2699344965 18486.25 1.01993449653857 1.01300657553906 66 18522 18594.1472658013 18593.1666666667 0.980599134598712 1.01588140738734 67 18060 18709.5634397608 18708.5833333333 0.980106427460313 0.984926008820351 68 18869 18830.4105976298 18829.4166666667 0.993930963120608 1.00822113836750 69 18127 18931.0425263021 18930.0833333333 0.959192968805225 0.998314599536455 70 18871 19040.5755009329 19039.5833333333 0.992167599595114 0.998969972382853 71 18890 19146.8916304291 19145.9166666667 0.974963762458175 1.01196925796864 72 21263 19230.9590029430 19229.8333333333 1.12566960971700 0.982286278886196 73 19547 19324.7029118898 19323.7083333333 0.994578556481803 1.01706930551662 74 18450 19428.9948260287 19428.0416666667 0.953159362044224 0.99632683183729 75 20254 19498.8251349396 19497.7916666667 1.0334682729003 1.00514387718412 76 19240 19566.7838955129 19565.7916666667 0.992228846279956 0.991050520606075 77 20216 19638.7282678299 19637.7083333333 1.01993449653857 1.00932758473729 78 19420 19693.9805991346 19693 0.980599134598712 1.00564764063085 79 19415 NA NA 0.980106427460313 NA 80 20018 NA NA 0.993930963120608 NA 81 18652 NA NA 0.959192968805225 NA 82 19978 NA NA 0.992167599595114 NA 83 19509 NA NA 0.974963762458175 NA 84 21971 NA NA 1.12566960971700 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/1j6451207218912.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/1j6451207218912.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/2bsse1207218912.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/2bsse1207218912.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/31nvw1207218912.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/31nvw1207218912.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/4x7ue1207218912.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/03/t1207219018m2hq9onrh4n7i2l/4x7ue1207218912.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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