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

Title produced by software: Classical Decomposition

Date of computation: Thu, 22 May 2008 12:04:43 -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/May/22/t12114795128ebwy4qo7qa8ld9.htm/, Retrieved Thu, 22 May 2008 20:05:17 +0200

User-defined keywords:

Dataseries X:

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12,23 12,25 12,33 12,37 12,38 12,41 12,41 12,41 12,43 12,44 12,46 12,46 12,46 12,47 12,55 12,58 12,6 12,62 12,62 12,66 12,7 12,76 12,79 12,81 12,86 13,03 13,09 13,15 13,17 13,18 13,18 13,19 13,19 13,19 13,25 13,24 13,3 13,35 13,35 13,35 13,36 13,37 13,38 13,39 13,4 13,4 13,44 13,44 13,44 13,47 13,52 13,58 13,65 13,66 13,68 13,68 13,77 13,78 13,79 13,79 13,82 13,86 13,94 14 14,02 14,03 14,07 14,1 14,1 14,16 14,17 14,17

Text written by user:

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 12.23 NA NA 0.997399534356366 NA 2 12.25 NA NA 1.00035195171650 NA 3 12.33 NA NA 1.00194448072618 NA 4 12.37 NA NA 1.00266432985917 NA 5 12.38 NA NA 1.00279338979910 NA 6 12.41 NA NA 1.00163740479876 NA 7 12.41 12.3919797185655 12.39125 1.00005888982673 1.00145418906775 8 12.41 12.3981821821109 12.41 0.999047718139473 1.00095318956566 9 12.43 12.4216797868015 12.4283333333333 0.999464646919797 1.00066981385298 10 12.44 12.4289354506033 12.44625 0.998608854120981 1.00089022502697 11 12.46 12.4519761042026 12.4641666666667 0.99902195126316 1.00064438734304 12 12.46 12.4447225665538 12.4820833333333 0.997006848473782 1.00122762346565 13 12.46 12.4670785963153 12.4995833333333 0.997399534356366 0.999432216917495 14 12.47 12.5231559955510 12.51875 1.00035195171650 0.99575538342173 15 12.55 12.5648012651733 12.5404166666667 1.00194448072618 0.998822005628188 16 12.58 12.5984773046804 12.565 1.00266432985917 0.99853337000706 17 12.6 12.6272579304661 12.5920833333333 1.00279338979910 0.997841342069976 18 12.62 12.641081397479 12.6204166666667 1.00163740479876 0.998332310597793 19 12.62 12.6524117211245 12.6516666666667 1.00005888982673 0.997438296995157 20 12.66 12.6795806227201 12.6916666666667 0.999047718139473 0.998455735776856 21 12.7 12.7306809401409 12.7375 0.999464646919797 0.997590000072645 22 12.76 12.7659659388691 12.78375 0.998608854120981 0.999532668432795 23 12.79 12.8187004121454 12.83125 0.99902195126316 0.997761051337292 24 12.81 12.8397865302615 12.8783333333333 0.997006848473782 0.99768013820235 25 12.86 12.8913889815560 12.925 0.997399534356366 0.997565120282932 26 13.03 12.9749816270763 12.9704166666667 1.00035195171650 1.00424034303131 27 13.09 13.0382200323164 13.0129166666667 1.00194448072618 1.00397139851569 28 13.15 13.0860228350744 13.05125 1.00266432985917 1.00488896937839 29 13.17 13.1248941501539 13.0883333333333 1.00279338979910 1.00343666389459 30 13.18 13.1469082869024 13.1254166666667 1.00163740479876 1.00251707187541 31 13.18 13.1624417549361 13.1616666666667 1.00005888982673 1.00133396564184 32 13.19 13.1807695613201 13.1933333333333 0.999047718139473 1.00070029588462 33 13.19 13.2104239706624 13.2175 0.999464646919797 0.998453950402517 34 13.19 13.2182525323814 13.2366666666667 0.998608854120981 0.997862612148453 35 13.25 13.2399546682614 13.2529166666667 0.99902195126316 1.00075871345411 36 13.24 13.2290346206865 13.26875 0.997006848473782 1.00082888733970 37 13.3 13.2504528139243 13.285 0.997399534356366 1.00373928248125 38 13.35 13.3063482110824 13.3016666666667 1.00035195171650 1.00328052356854 39 13.35 13.3446480526719 13.31875 1.00194448072618 1.00040105571215 40 13.35 13.3717821690843 13.33625 1.00266432985917 0.998371034705107 41 13.36 13.3902165678715 13.3529166666667 1.00279338979910 0.997743384678031 42 13.37 13.3910574043221 13.3691666666667 1.00163740479876 0.998427502497652 43 13.38 13.3841214755144 13.3833333333333 1.00005888982673 0.999692062305177 44 13.39 13.3814116447131 13.3941666666667 0.999047718139473 1.00064181235246 45 13.4 13.3990729227685 13.40625 0.999464646919797 1.00006918965490 46 13.4 13.4042434314614 13.4229166666667 0.998608854120981 0.99968342626101 47 13.44 13.4314338755868 13.4445833333333 0.99902195126316 1.00063776693483 48 13.44 13.4284359903813 13.46875 0.997006848473782 1.00086115833795 49 13.44 13.4582443835819 13.4933333333333 0.997399534356366 0.998644371207574 50 13.47 13.5226743206410 13.5179166666667 1.00035195171650 0.996104740867667 51 13.52 13.5717554683031 13.5454166666667 1.00194448072618 0.996186531033219 52 13.58 13.6128393850546 13.5766666666667 1.00266432985917 0.997587616798692 53 13.65 13.6450932211121 13.6070833333333 1.00279338979910 1.00035960024665 54 13.66 13.6585780611871 13.63625 1.00163740479876 1.00010410591839 55 13.68 13.6674714942986 13.6666666666667 1.00005888982673 1.00091666594707 56 13.68 13.6857049288631 13.69875 0.999047718139473 0.999583146875316 57 13.77 13.7251482638261 13.7325 0.999464646919797 1.00326785075919 58 13.78 13.7483473991106 13.7675 0.998608854120981 1.00230228404699 59 13.79 13.7869191865780 13.8004166666667 0.99902195126316 1.00022345916302 60 13.79 13.789850972953 13.83125 0.997006848473782 1.00001080700925 61 13.82 13.8268666281544 13.8629166666667 0.997399534356366 0.999503385087952 62 13.86 13.9015576223537 13.8966666666667 1.00035195171650 0.997010577988265 63 13.94 13.9549992321809 13.9279166666667 1.00194448072618 0.998925171407656 64 14 13.9946873840093 13.9575 1.00266432985917 1.00037961662486 65 14.02 14.0282438621312 13.9891666666667 1.00279338979910 0.9994123382647 66 14.03 14.0437911131159 14.0208333333333 1.00163740479876 0.999017992150064 67 14.07 NA NA 1.00005888982673 NA 68 14.1 NA NA 0.999047718139473 NA 69 14.1 NA NA 0.999464646919797 NA 70 14.16 NA NA 0.998608854120981 NA 71 14.17 NA NA 0.99902195126316 NA 72 14.17 NA NA 0.997006848473782 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/1bh1m1211479481.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/1bh1m1211479481.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/2ihu61211479481.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/2ihu61211479481.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/3at7r1211479481.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/3at7r1211479481.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/4rcr01211479481.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t12114795128ebwy4qo7qa8ld9/4rcr01211479481.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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