Gemiddelde consumptieprijs - broodje - Elisa Somers

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 17 May 2008 10:12:03 -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/17/t1211040769ouqmrsqwy0iz3xb.htm/, Retrieved Sat, 17 May 2008 18:12:55 +0200

User-defined keywords:

Dataseries X:

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4.07 4.07 4.08 4.09 4.08 4.09 4.12 4.14 4.14 4.14 4.14 4.14 4.23 4.29 4.32 4.33 4.35 4.35 4.35 4.35 4.36 4.36 4.38 4.4 4.4 4.4 4.43 4.44 4.46 4.47 4.49 4.49 4.57 4.62 4.64 4.66 4.67 4.68 4.72 4.74 4.75 4.76 4.77 4.76 4.77 4.77 4.78 4.81 4.81 4.85 4.92 4.96 4.95 4.96 4.97 5 5 5.01 5.01 5.02 5.04 5.04 5.19 5.22 5.22 5.22 5.24 5.28 5.34 5.36 5.38 5.39 5.41 5.44 5.51 5.55 5.56 5.57 5.58 5.58 5.59 5.61 5.63 5.64 5.64

Text written by user:

Multiplicatief

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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 4.07 NA NA 0.996062039909238 NA 2 4.07 NA NA 0.996949949031497 NA 3 4.08 NA NA 1.00598615846656 NA 4 4.09 NA NA 1.00624873224116 NA 5 4.08 NA NA 1.00374164108387 NA 6 4.09 NA NA 1.00063900939266 NA 7 4.12 4.11041795535326 4.115 0.998886501908446 1.00233116066318 8 4.14 4.11922122652862 4.13083333333333 0.997188919070878 1.00504434511494 9 4.14 4.14933126918529 4.15 0.999838860044648 0.997751139019777 10 4.14 4.16604080025560 4.17 0.999050551620047 0.993749269029243 11 4.14 4.1822959889082 4.19125 0.997863641851048 0.989886897287907 12 4.14 4.20298536720088 4.21333333333333 0.997543995379955 0.985014135977632 13 4.23 4.21707766146573 4.23375 0.996062039909238 1.00306428754024 14 4.29 4.23911426244434 4.25208333333333 0.996949949031497 1.01200386080802 15 4.32 4.2955608966522 4.27 1.00598615846656 1.00568938584175 16 4.33 4.31512998009419 4.28833333333333 1.00624873224116 1.00344601900161 17 4.35 4.32361711896875 4.3075 1.00374164108387 1.00610203917352 18 4.35 4.33109917898788 4.32833333333333 1.00063900939266 1.00436397787975 19 4.35 4.34141045891958 4.34625 0.998886501908446 1.00197851393267 20 4.35 4.3456662102343 4.35791666666667 0.997188919070878 1.00099726706011 21 4.36 4.36637962171998 4.36708333333333 0.999838860044648 0.998538921882044 22 4.36 4.37209497652723 4.37625 0.999050551620047 0.997233597030219 23 4.38 4.37604784603428 4.38541666666667 0.997863641851048 1.00090313317056 24 4.4 4.3842058596949 4.395 0.997543995379955 1.00360250882613 25 4.4 4.38848333750012 4.40583333333333 0.996062039909238 1.00262429217891 26 4.4 4.40402639984664 4.4175 0.996949949031497 0.999085745751484 27 4.43 4.45861448650365 4.43208333333333 1.00598615846656 0.993582202141435 28 4.44 4.47948393969358 4.45166666666667 1.00624873224116 0.991185605256063 29 4.46 4.49007094111516 4.47333333333333 1.00374164108387 0.993302791535029 30 4.47 4.49787234721999 4.495 1.00063900939266 0.993803215149666 31 4.49 4.51205356966228 4.51708333333333 0.998886501908446 0.995112298796593 32 4.49 4.52723769258179 4.54 0.997188919070878 0.991774743207585 33 4.57 4.56301459752876 4.56375 0.999838860044648 1.00153087445195 34 4.62 4.58397694768332 4.58833333333333 0.999050551620047 1.00785847152545 35 4.64 4.6030618245554 4.61291666666667 0.997863641851048 1.00802469678933 36 4.66 4.62569463524313 4.63708333333333 0.997543995379955 1.0074162623048 37 4.67 4.64247915767697 4.66083333333333 0.996062039909238 1.00592804865425 38 4.68 4.66946432377627 4.68375 0.996949949031497 1.00225629226249 39 4.72 4.7314882319877 4.70333333333333 1.00598615846656 0.997571962261253 40 4.74 4.74739766465279 4.71791666666667 1.00624873224116 0.998441743208522 41 4.75 4.74769796232668 4.73 1.00374164108387 1.00048487449951 42 4.76 4.74511356912409 4.74208333333333 1.00063900939266 1.00313721276826 43 4.77 4.7488729111564 4.75416666666667 0.998886501908446 1.00444886381229 44 4.76 4.75368267628746 4.76708333333333 0.997188919070878 1.00132893256507 45 4.77 4.78172934816353 4.7825 0.999838860044648 0.997547048921111 46 4.77 4.79544264777623 4.8 0.999050551620047 0.99469441099707 47 4.78 4.80720809461742 4.8175 0.997863641851048 0.99434014627994 48 4.81 4.82229393099926 4.83416666666667 0.997543995379955 0.997450605215034 49 4.81 4.83173094525973 4.85083333333333 0.996062039909238 0.995502451294179 50 4.85 4.8543154601592 4.86916666666667 0.996949949031497 0.999111005414746 51 4.92 4.91801483220338 4.88875 1.00598615846656 1.00040365225896 52 4.96 4.93900419408372 4.90833333333333 1.00624873224116 1.00425102006219 53 4.95 4.94635516212453 4.92791666666667 1.00374164108387 1.00073687346662 54 4.96 4.94941070020842 4.94625 1.00063900939266 1.00213950719247 55 4.97 4.9590552792663 4.96458333333333 0.998886501908446 1.00220701728804 56 5 4.9680782938877 4.98208333333333 0.997188919070878 1.00642536293189 57 5 5.0004440987983 5.00125 0.999838860044648 0.99991118812859 58 5.01 5.01856393763804 5.02333333333333 0.999050551620047 0.99829354816548 59 5.01 5.03463784965597 5.04541666666667 0.997863641851048 0.995106331300938 60 5.02 5.05505419658792 5.0675 0.997543995379955 0.993065515180513 61 5.04 5.06954075728806 5.08958333333333 0.996062039909238 0.994172892831448 62 5.04 5.09690661442353 5.1125 0.996949949031497 0.988835068262289 63 5.19 5.16909221092066 5.13833333333333 1.00598615846656 1.00404476999562 64 5.22 5.19937105355112 5.16708333333333 1.00624873224116 1.00396758497065 65 5.22 5.21652895384961 5.19708333333333 1.00374164108387 1.00066539382434 66 5.22 5.23125735452069 5.22791666666667 1.00063900939266 0.997848059508875 67 5.24 5.25289439191104 5.25875 0.998886501908446 0.997545278669433 68 5.28 5.27596037265084 5.29083333333333 0.997188919070878 1.00076566673436 69 5.34 5.31997593448757 5.32083333333333 0.999838860044648 1.00376393911533 70 5.36 5.34283909585138 5.34791666666667 0.999050551620047 1.00321194478081 71 5.38 5.36434862798426 5.37583333333333 0.997863641851048 1.00291766495826 72 5.39 5.39130965169725 5.40458333333333 0.997543995379955 0.999757080972554 73 5.41 5.41193708350686 5.43333333333333 0.996062039909238 0.9996420720572 74 5.44 5.44334672171197 5.46 0.996949949031497 0.999385172048912 75 5.51 5.51573827469226 5.48291666666667 1.00598615846656 0.998959654282622 76 5.55 5.53814146007231 5.50375 1.00624873224116 1.00214124901164 77 5.56 5.54525434130457 5.52458333333333 1.00374164108387 1.00265914920901 78 5.57 5.54896024000286 5.54541666666667 1.00063900939266 1.00379165809217 79 5.58 5.55921958582963 5.56541666666667 0.998886501908446 1.00373800923844 80 5.58 NA NA 0.997188919070878 NA 81 5.59 NA NA 0.999838860044648 NA 82 5.61 NA NA 0.999050551620047 NA 83 5.63 NA NA 0.997863641851048 NA 84 5.64 NA NA 0.997543995379955 NA 85 5.64 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/18zc01211040719.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/18zc01211040719.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/2bqir1211040719.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/2bqir1211040719.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/307ew1211040719.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/307ew1211040719.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/4o2na1211040719.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/17/t1211040769ouqmrsqwy0iz3xb/4o2na1211040719.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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