Decompositiemodel-Consumptieprijzen Kabeljauw-Toon Baeten

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 17 May 2011 14:00:14 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62.htm/, Retrieved Tue, 17 May 2011 16:03:35 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

12,32 12,34 12,36 12,54 12,77 12,79 12,96 12,96 13 13,19 13,25 13,61 13,8 13,83 14,04 14,16 14,2 14,27 14,31 14,69 14,9 14,92 15,01 15,09 15,14 15,24 15,33 15,36 15,44 15,5 15,58 15,65 15,72 15,82 15,87 16,07 16,18 16,19 16,39 16,54 16,61 16,62 16,66 16,71 16,72 16,79 16,82 16,83 16,91 16,97 17,02 17,03 17,04 17,07 17,11 17,12 17,14 17,18 17,24 17,26 17,26 17,29 17,36 17,44 17,48 17,48 17,52 17,54 17,58 17,64 17,69 17,69 17,76 17,79 17,82 17,89 17,95 18 18,03 18,06 18,08 18,13 18,16 18,18 18,18 18,27 18,31 18,35 18,45 18,5

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 'Herman Ole Andreas Wold' @ www.yougetit.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 12.32 NA NA 1.00113412263928 NA 2 12.34 NA NA 0.999267247570566 NA 3 12.36 NA NA 1.00166550197341 NA 4 12.54 NA NA 1.00195798670682 NA 5 12.77 NA NA 1.00065377558901 NA 6 12.79 NA NA 0.998793776768194 NA 7 12.96 12.8722666792337 12.9025 0.997656785834811 1.00681568545405 8 12.96 13.0260394286136 13.02625 0.999983834842232 0.99493019893149 9 13 13.1608340434794 13.1583333333333 1.00019004763618 0.987779342635274 10 13.19 13.2948967471175 13.2958333333333 0.999929557915448 0.992109999113741 11 13.25 13.4157758653151 13.4229166666667 0.999468014178368 0.987643214452945 12 13.61 13.5346769238853 13.5441666666667 0.999299348345681 1.00556519202773 13 13.8 13.6775778113413 13.6620833333333 1.00113412263928 1.00895057519301 14 13.83 13.7803117053513 13.7904166666667 0.999267247570566 1.00360574533517 15 14.04 13.9648865400127 13.9416666666667 1.00166550197341 1.00537873757671 16 14.16 14.1205104101604 14.0929166666667 1.00195798670682 1.00279661206944 17 14.2 14.2476420080948 14.2383333333333 1.00065377558901 0.996656147868696 18 14.27 14.3559958847482 14.3733333333333 0.998793776768194 0.994009758331045 19 14.31 14.4568782074013 14.4908333333333 0.997656785834811 0.989840254217119 20 14.69 14.605180567802 14.6054166666667 0.999983834842232 1.00580748945925 21 14.9 14.7207137719386 14.7179166666667 1.00019004763618 1.01217918036034 22 14.92 14.8206225975701 14.8216666666667 0.999929557915448 1.0067053459985 23 15.01 14.9153943315885 14.9233333333333 0.999468014178368 1.0063428204651 24 15.09 15.0157218330793 15.02625 0.999299348345681 1.00494669305588 25 15.14 15.14757641475 15.1304166666667 1.00113412263928 0.99949982660311 26 15.24 15.2121783988492 15.2233333333333 0.999267247570566 1.00182890316043 27 15.33 15.3229780164383 15.2975 1.00166550197341 1.00045826493741 28 15.36 15.3992592906949 15.3691666666667 1.00195798670682 0.997450572787052 29 15.44 15.4525959295332 15.4425 1.00065377558901 0.999184866439873 30 15.5 15.5004470872951 15.5191666666667 0.998793776768194 0.999971156490355 31 15.58 15.5667713816425 15.6033333333333 0.997656785834811 1.00084979846066 32 15.65 15.685996429294 15.68625 0.999983834842232 0.997705186950908 33 15.72 15.7729970512225 15.77 1.00019004763618 0.99664001387622 34 15.82 15.8622158870654 15.8633333333333 0.999929557915448 0.99733858829271 35 15.87 15.9527588413045 15.96125 0.999468014178368 0.994812255226337 36 16.07 16.0454165366038 16.0566666666667 0.999299348345681 1.00153211749537 37 16.18 16.1666475237532 16.1483333333333 1.00113412263928 1.00082592734376 38 16.19 16.2256019324271 16.2375 0.999267247570566 0.997805817462099 39 16.39 16.3505198772127 16.3233333333333 1.00166550197341 1.00241460963222 40 16.54 16.4375382544199 16.4054166666667 1.00195798670682 1.00623339967301 41 16.61 16.496194429658 16.4854166666667 1.00065377558901 1.00689889845972 42 16.62 16.5366956306921 16.5566666666667 0.998793776768194 1.00503754626489 43 16.66 16.5798087095923 16.61875 0.997656785834811 1.00483668369234 44 16.71 16.6813970048932 16.6816666666667 0.999983834842232 1.00171466425135 45 16.72 16.7435981432828 16.7404166666667 1.00019004763618 0.998590616958143 46 16.79 16.7859008161898 16.7870833333333 0.999929557915448 1.00024420398137 47 16.82 16.816465783557 16.8254166666667 0.999468014178368 1.0002101640433 48 16.83 16.8502688867506 16.8620833333333 0.999299348345681 0.998797117904361 49 16.91 16.918749533386 16.8995833333333 1.00113412263928 0.999482849877958 50 16.97 16.9230071989607 16.9354166666667 0.999267247570566 1.00277685877497 51 17.02 16.9982635684888 16.97 1.00166550197341 1.00127874423311 52 17.03 17.0370431164661 17.00375 1.00195798670682 0.9995865998332 53 17.04 17.0486387015977 17.0375 1.00065377558901 0.999493290828147 54 17.07 17.0523229179486 17.0729166666667 0.998793776768194 1.00103663777284 55 17.11 17.0653350120319 17.1054166666667 0.997656785834811 1.00261729335736 56 17.12 17.1330563702969 17.1333333333333 0.999983834842232 0.99923794272226 57 17.14 17.1640947091432 17.1608333333333 1.00019004763618 0.99859621439106 58 17.18 17.1908722871455 17.1920833333333 0.999929557915448 0.999367554655521 59 17.24 17.2183352142578 17.2275 0.999468014178368 1.00125823928229 60 17.26 17.2508213755458 17.2629166666667 0.999299348345681 1.00053206883628 61 17.26 17.3167003471351 17.2970833333333 1.00113412263928 0.996725684108492 62 17.29 17.3189668458105 17.3316666666667 0.999267247570566 0.998327449548901 63 17.36 17.3964256055233 17.3675 1.00166550197341 0.997906144264962 64 17.44 17.4390787586322 17.405 1.00195798670682 1.00005282626339 65 17.48 17.4543204197844 17.4429166666667 1.00065377558901 1.00147124491805 66 17.48 17.4584990538344 17.4795833333333 0.998793776768194 1.00123154608534 67 17.52 17.4772841265162 17.5183333333333 0.997656785834811 1.00244407959352 68 17.54 17.5597161398296 17.56 0.999983834842232 0.998877194843437 69 17.58 17.6033448383967 17.6 1.00019004763618 0.998673840760886 70 17.64 17.6366742150495 17.6379166666667 0.999929557915448 1.00018857211456 71 17.69 17.6668464856204 17.67625 0.999468014178368 1.00131056294616 72 17.69 17.7050862043146 17.7175 0.999299348345681 0.999147916923956 73 17.76 17.7805591572913 17.7604166666667 1.00113412263928 0.998843728304074 74 17.79 17.7902878975813 17.8033333333333 0.999267247570566 0.99998381714883 75 17.82 17.8755556039672 17.8458333333333 1.00166550197341 0.996892090785984 76 17.89 17.9221060047238 17.8870833333333 1.00195798670682 0.998208580804323 77 17.95 17.9388036227988 17.9270833333333 1.00065377558901 1.00062414291592 78 18 17.9454110200089 17.9670833333333 0.998793776768194 1.00304194648594 79 18.03 17.9628104289558 18.005 0.997656785834811 1.00374048210941 80 18.06 18.042208340141 18.0425 0.999983834842232 1.0009861132032 81 18.08 18.0863532822344 18.0829166666667 1.00019004763618 0.999648725083756 82 18.13 18.1212234133227 18.1225 0.999929557915448 1.00048432638775 83 18.16 18.1528378075146 18.1625 0.999468014178368 1.00039454946721 84 18.18 18.1914118871762 18.2041666666667 0.999299348345681 0.999372677214559 85 18.18 NA NA NA NA 86 18.27 NA NA NA NA 87 18.31 NA NA NA NA 88 18.35 NA NA NA NA 89 18.45 NA NA NA NA 90 18.5 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/1hu1y1305640812.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/1hu1y1305640812.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/22sk81305640812.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/22sk81305640812.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/30gbk1305640812.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/30gbk1305640812.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/4rjk31305640812.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/17/t1305641011k7l6glhkfccdm62/4rjk31305640812.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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