Paper: Gemiddeld aantal bouwaanvragen in Belgie

*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: Fri, 10 Dec 2010 08:22:09 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n.htm/, Retrieved Fri, 10 Dec 2010 09:22:08 +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/2010/Dec/10/t1291969326a3x42wmlm0yja4n.htm/}, year = {2010}, } @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 = {2010}, 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:

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2.560 2.800 3.095 3.191 3.320 3.309 2.621 2.864 2.923 3.061 2.777 3.218 3.769 3.037 3.394 2.598 3.266 2.936 2.826 2.662 2.785 2.897 2.403 2.630 2.681 2.594 2.930 2.648 2.573 3.147 2.836 2.565 2.294 2.997 2.384 2.400 2.951 2.794 2.577 3.237 2.471 3.081 2.652 2.519 2.940 2.510 2.249 2.456 2.150 2.115 2.645 2.299 2.186 2.931 2.433 2.751 2.506 2.464 2.195 2.173 2.403 2.422 3.312 3.641 3.797 4.108 2.783

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 'RServer@AstonUniversity' @ vre.aston.ac.uk Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 2.56 NA NA 0.159563368055556 NA 2 2.8 NA NA -0.0900512152777778 NA 3 3.095 NA NA 0.166969618055556 NA 4 3.191 NA NA -0.0134678819444444 NA 5 3.32 NA NA -0.0726866319444444 NA 6 3.309 NA NA 0.344011284722222 NA 7 2.621 3.06075086805556 3.028625 0.0321258680555555 -0.439750868055555 8 2.864 3.07913628472222 3.088875 -0.00973871527777792 -0.215136284722222 9 2.923 3.11573003472222 3.11120833333333 0.00452170138888863 -0.192730034722222 10 3.061 3.17921961805556 3.09895833333333 0.080261284722222 -0.118219618055555 11 2.777 2.72661545138889 3.072 -0.345384548611111 0.0503845486111119 12 3.218 2.79808420138889 3.05420833333333 -0.256124131944444 0.419915798611111 13 3.769 3.20677170138889 3.04720833333333 0.159563368055556 0.562228298611112 14 3.037 2.95728211805556 3.04733333333333 -0.0900512152777778 0.0797178819444442 15 3.394 3.20013628472222 3.03316666666667 0.166969618055556 0.193863715277778 16 2.598 3.00711545138889 3.02058333333333 -0.0134678819444444 -0.409115451388889 17 3.266 2.92548003472222 2.99816666666667 -0.0726866319444444 0.340519965277778 18 2.936 3.30209461805556 2.95808333333333 0.344011284722222 -0.366094618055556 19 2.826 2.92037586805556 2.88825 0.0321258680555555 -0.0943758680555553 20 2.662 2.81471961805556 2.82445833333333 -0.00973871527777792 -0.152719618055556 21 2.785 2.79118836805556 2.78666666666667 0.00452170138888863 -0.00618836805555523 22 2.897 2.84967795138889 2.76941666666667 0.080261284722222 0.047322048611111 23 2.403 2.39724045138889 2.742625 -0.345384548611111 0.00575954861111061 24 2.63 2.46641753472222 2.72254166666667 -0.256124131944444 0.163582465277778 25 2.681 2.89131336805556 2.73175 0.159563368055556 -0.210313368055556 26 2.594 2.63807378472222 2.728125 -0.0900512152777778 -0.0440737847222223 27 2.93 2.87059461805556 2.703625 0.166969618055556 0.0594053819444444 28 2.648 2.67386545138889 2.68733333333333 -0.0134678819444444 -0.025865451388889 29 2.573 2.61802170138889 2.69070833333333 -0.0726866319444444 -0.0450217013888885 30 3.147 3.02434461805556 2.68033333333333 0.344011284722222 0.122655381944444 31 2.836 2.71412586805556 2.682 0.0321258680555555 0.121874131944444 32 2.565 2.69184461805556 2.70158333333333 -0.00973871527777792 -0.126844618055556 33 2.294 2.69973003472222 2.69520833333333 0.00452170138888863 -0.405730034722222 34 2.997 2.78530295138889 2.70504166666667 0.080261284722222 0.211697048611112 35 2.384 2.37994878472222 2.72533333333333 -0.345384548611111 0.00405121527777741 36 2.4 2.46220920138889 2.71833333333333 -0.256124131944444 -0.0622092013888889 37 2.951 2.86748003472222 2.70791666666667 0.159563368055556 0.0835199652777776 38 2.794 2.60828211805556 2.69833333333333 -0.0900512152777778 0.185717881944444 39 2.577 2.89030295138889 2.72333333333333 0.166969618055556 -0.313302951388889 40 3.237 2.71649045138889 2.72995833333333 -0.0134678819444444 0.520509548611111 41 2.471 2.63135503472222 2.70404166666667 -0.0726866319444444 -0.160355034722222 42 3.081 3.04476128472222 2.70075 0.344011284722222 0.0362387152777779 43 2.652 2.70183420138889 2.66970833333333 0.0321258680555555 -0.0498342013888893 44 2.519 2.59830295138889 2.60804166666667 -0.00973871527777792 -0.0793029513888892 45 2.94 2.58710503472222 2.58258333333333 0.00452170138888863 0.352894965277777 46 2.51 2.62659461805556 2.54633333333333 0.080261284722222 -0.116594618055556 47 2.249 2.14999045138889 2.495375 -0.345384548611111 0.0990095486111113 48 2.456 2.22112586805556 2.47725 -0.256124131944444 0.234874131944445 49 2.15 2.62143836805556 2.461875 0.159563368055556 -0.471438368055555 50 2.115 2.37236545138889 2.46241666666667 -0.0900512152777778 -0.257365451388889 51 2.645 2.62096961805556 2.454 0.166969618055556 0.0240303819444447 52 2.299 2.42053211805556 2.434 -0.0134678819444444 -0.121532118055555 53 2.186 2.35714670138889 2.42983333333333 -0.0726866319444444 -0.171146701388889 54 2.931 2.75980295138889 2.41579166666667 0.344011284722222 0.171197048611111 55 2.433 2.44666753472222 2.41454166666667 0.0321258680555555 -0.0136675347222219 56 2.751 2.42813628472222 2.437875 -0.00973871527777792 0.322863715277779 57 2.506 2.48298003472222 2.47845833333333 0.00452170138888863 0.0230199652777774 58 2.464 2.64242795138889 2.56216666666667 0.080261284722222 -0.178427951388889 59 2.195 2.33982378472222 2.68520833333333 -0.345384548611111 -0.144823784722222 60 2.173 2.54525086805556 2.801375 -0.256124131944444 -0.372250868055555 61 2.403 NA 2.865 NA NA 62 2.422 NA NA NA NA 63 3.312 NA NA NA NA 64 3.641 NA NA NA NA 65 3.797 NA NA NA NA 66 4.108 NA NA NA NA 67 2.783 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/19vpg1291969326.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/19vpg1291969326.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/29vpg1291969326.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/29vpg1291969326.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/3kn611291969326.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/3kn611291969326.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/4izca1291969326.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/10/t1291969326a3x42wmlm0yja4n/4izca1291969326.ps (open in new window)

Parameters (Session):

par1 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; 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')

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Software written by Ed van Stee & Patrick Wessa

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