ws9

*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: Wed, 02 Dec 2009 14:43:47 -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/02/t12597902870uvirpgc0cw0lyz.htm/, Retrieved Wed, 02 Dec 2009 22:44:53 +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/02/t12597902870uvirpgc0cw0lyz.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 «

216234 213587 209465 204045 200237 203666 241476 260307 243324 244460 233575 237217 235243 230354 227184 221678 217142 219452 256446 265845 248624 241114 229245 231805 219277 219313 212610 214771 211142 211457 240048 240636 230580 208795 197922 194596 194581 185686 178106 172608 167302 168053 202300 202388 182516 173476 166444 171297 169701 164182 161914 159612 151001 158114 186530 187069 174330 169362 166827 178037 186412 189226 191563 188906 186005 195309 223532 226899 214126

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 216234 NA NA 0.989027628127167 NA 2 213587 NA NA 0.971034667675954 NA 3 209465 NA NA 0.954245136014163 NA 4 204045 NA NA 0.947232772757375 NA 5 200237 NA NA 0.925127924881793 NA 6 203666 NA NA 0.945608265017215 NA 7 241476 252144.743102093 226424.791666667 1.11359158706124 0.95768802089293 8 260307 257566.085282959 227915.458333333 1.13009484817946 1.01064159791857 9 243324 242371.204810767 229352.375 1.05676344014649 1.00393114021105 10 244460 232141.720978206 230825.375 1.00570277846708 1.05306361549267 11 233575 225026.544112493 232264.458333333 0.968837616083074 1.03798865561048 12 237217 231928.897354762 233626.583333333 0.992733335589003 1.02280053372198 13 235243 232330.584487066 234908.083333333 0.989027628127167 1.01253565267511 14 230354 228933.641757508 235762.583333333 0.971034667675954 1.00620423556620 15 227184 225406.219599306 236214.166666667 0.954245136014163 1.0078870068619 16 221678 223826.920591155 236295.583333333 0.947232772757375 0.99039918618601 17 217142 218307.755919925 235975.75 0.925127924881793 0.994660034339997 18 219452 222756.781388728 235569.833333333 0.945608265017215 0.985164171577068 19 256446 261336.652859243 234679.083333333 1.11359158706124 0.981286004830417 20 265845 263937.936735278 233553.791666667 1.13009484817946 1.00722542309875 21 248624 245683.233527616 232486.5 1.05676344014649 1.01196974832250 22 241114 232912.173115076 231591.458333333 1.00570277846708 1.03521424739304 23 229245 223853.483600586 231053.666666667 0.968837616083074 1.02408502343896 24 231805 228795.789583754 230470.541666667 0.992733335589003 1.01315238546007 25 219277 226936.510222229 229454.166666667 0.989027628127167 0.9662482241631 26 219313 221124.54050028 227720.541666667 0.971034667675954 0.991807600838055 27 212610 215581.470719760 225918.333333333 0.954245136014163 0.986216483680908 28 214771 212009.520794459 223819.875 0.947232772757375 1.01302526035243 29 211142 204608.808531247 221168.125 0.925127924881793 1.03193015743384 30 211457 206438.222557604 218312.625 0.945608265017215 1.02431128005375 31 240048 240238.732249378 215733.25 1.11359158706124 0.999206072028467 32 240636 241052.762663078 213303.125 1.13009484817946 0.998271072861916 33 230580 222411.01292147 210464.333333333 1.05676344014649 1.03672923822983 34 208795 208451.889180024 207269.875 1.00570277846708 1.00164599525255 35 197922 197339.062351837 203686.416666667 0.968837616083074 1.00295399015895 36 194596 198597.544701249 200051.25 0.992733335589003 0.979850986036766 37 194581 194511.981204801 196669.916666667 0.989027628127167 1.00035483055990 38 185686 187898.525897078 193503.416666667 0.971034667675954 0.988224889543358 39 178106 181217.910565470 189907.083333333 0.954245136014163 0.982827798004296 40 172608 176595.250183315 186432.791666667 0.947232772757375 0.97742153212402 41 167302 169899.357934573 183649.583333333 0.925127924881793 0.984712373453626 42 168053 171502.331203099 181367.208333333 0.945608265017215 0.97988755500347 43 202300 199733.508657407 179359.75 1.11359158706124 1.01284957821972 44 202388 200509.432802507 177427.083333333 1.13009484817946 1.00936897167997 45 182516 185838.631848500 175856.416666667 1.05676344014649 0.982120876507479 46 173476 175636.184657185 174640.25 1.00570277846708 0.987700799459965 47 166444 168015.375330553 173419.541666667 0.968837616083074 0.99064743135882 48 171297 171073.971608155 172326.208333333 0.992733335589003 1.00130369564550 49 169701 169375.926454918 171255 0.989027628127167 1.00191924290474 50 164182 165036.687980205 169959.625 0.971034667675954 0.994821224355234 51 161914 161248.581644957 168980.25 0.954245136014163 1.00412666175575 52 159612 159578.173952696 168467.75 0.947232772757375 1.00021197164039 53 151001 155710.401121682 168312.291666667 0.925127924881793 0.969755385075387 54 158114 159438.142756976 168609.083333333 0.945608265017215 0.991694943668563 55 186530 188849.774881614 169586.208333333 1.11359158706124 0.987716295224242 56 187069 193614.629959193 171326 1.13009484817946 0.96619248266222 57 174330 183459.284931201 173604.875 1.05676344014649 0.95023808724304 58 169362 177064.869262563 176060.833333333 1.00570277846708 0.956496908197298 59 166827 173169.954762221 178739.916666667 0.968837616083074 0.96337150534612 60 178037 180427.505096075 181748.208333333 0.992733335589003 0.986750883160513 61 186412 NA 184839.75 NA NA 62 189226 NA 188041.083333333 NA NA 63 191563 NA 191358.833333333 NA NA 64 188906 NA NA NA NA 65 186005 NA NA NA NA 66 195309 NA NA NA NA 67 223532 NA NA NA NA 68 226899 NA NA NA NA 69 214126 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/1xqqg1259790225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/1xqqg1259790225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/2bt9o1259790225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/2bt9o1259790225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/336pf1259790225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/336pf1259790225.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/4jnv41259790225.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/02/t12597902870uvirpgc0cw0lyz/4jnv41259790225.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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