Verbruik zonne-energie

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 06 Jun 2010 15:57:00 +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/Jun/06/t1275839925vpcov2o81ilgui2.htm/, Retrieved Sun, 06 Jun 2010 17:58:51 +0200

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/Jun/06/t1275839925vpcov2o81ilgui2.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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

5,074 4,643 5,451 5,397 5,635 5,708 5,578 5,574 5,352 5,302 4,923 4,982 5,101 4,763 5,505 5,385 5,794 5,695 5,798 5,705 5,422 5,311 4,968 5,053 5,236 4,782 5,531 5,566 5,961 5,868 5,872 5,908 5,594 5,526 5,111 5,177 5,835 5,348 6,038 6,039 6,408 6,214 6,138 6,529 6,058 6,026 5,678 5,733 6,488 5,936 6,84 6,694 7,193 6,991 7,209 7,104 6,83 6,848 6,396 6,414 7,151 6,882 7,698 7,626 7,936 8,054 8,128 8,062 7,708 7,574 7,039 7,146 7,07 6,607 7,699 7,663 7,988 7,723 8,087 8,028 7,362

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 5.074 NA NA 0.986490971516826 NA 2 4.643 NA NA 0.90952257528937 NA 3 5.451 NA NA 1.03225635717524 NA 4 5.397 NA NA 1.01661998319296 NA 5 5.635 NA NA 1.07622189976605 NA 6 5.708 NA NA 1.05398534932221 NA 7 5.578 5.61083828506803 5.30270833333333 1.05810803317199 0.994147347793747 8 5.574 5.61933994771643 5.30883333333333 1.05848867253629 0.991931446017098 9 5.352 5.30811489987963 5.31608333333333 0.998501070627742 1.00826754901658 10 5.302 5.22262816292635 5.31783333333333 0.98209699995481 1.01519768105206 11 4.923 4.85065769828212 5.32395833333333 0.91109986115258 1.01491391605380 12 4.982 4.88556003815607 5.33004166666667 0.916608226293928 1.01973979668467 13 5.101 5.26654646660449 5.33866666666667 0.986490971516826 0.968566409191634 14 4.763 4.86893962294179 5.35329166666667 0.90952257528937 0.978241746428193 15 5.505 5.53461450172126 5.36166666666667 1.03225635717524 0.994649220517156 16 5.385 5.45412385066427 5.36495833333333 1.01661998319296 0.987326314444463 17 5.794 5.77630714894018 5.36720833333333 1.07622189976605 1.00306300385413 18 5.695 5.66205321261514 5.37204166666667 1.05398534932221 1.00581887632413 19 5.798 5.69328253598601 5.380625 1.05810803317199 1.01839316130055 20 5.705 5.7021225826477 5.38704166666667 1.05848867253629 1.00050462214914 21 5.422 5.38083906119035 5.38891666666667 0.998501070627742 1.00764953910377 22 5.311 5.30090947796441 5.39754166666667 0.98209699995481 1.00190354543452 23 4.968 4.93091041105198 5.41204166666667 0.91109986115258 1.00752185415190 24 5.053 4.973707195918 5.42620833333333 0.916608226293928 1.01594239486938 25 5.236 5.36305816665122 5.4365 0.986490971516826 0.976308635352624 26 4.782 4.95511688695046 5.44804166666667 0.90952257528937 0.965063006403266 27 5.531 5.63990465015313 5.46366666666667 1.03225635717524 0.980690338417303 28 5.566 5.5708657120676 5.47979166666667 1.01661998319296 0.999126578826509 29 5.961 5.91352544116035 5.49470833333333 1.07622189976605 1.00802813132572 30 5.868 5.80306766914321 5.50583333333333 1.05398534932221 1.01118931133649 31 5.872 5.8576419838054 5.53595833333333 1.05810803317199 1.00245115973873 32 5.908 5.91112999177893 5.5845 1.05848867253629 0.999470491803887 33 5.594 5.62077054761994 5.62920833333333 0.998501070627742 0.995237210380118 34 5.526 5.5685309104521 5.67004166666667 0.98209699995481 0.992362274514402 35 5.111 5.20089966990686 5.708375 0.91109986115258 0.982714592548857 36 5.177 5.26262974724773 5.74141666666667 0.916608226293928 0.983728715231673 37 5.835 5.68901122515657 5.76691666666667 0.986490971516826 1.02566153749141 38 5.348 5.27875533665759 5.803875 0.90952257528937 1.01311761180927 39 6.038 6.03775345448109 5.84908333333333 1.03225635717524 1.00004083398250 40 6.039 5.98712923601915 5.88925 1.01661998319296 1.00866371209574 41 6.408 6.38598685515765 5.93370833333333 1.07622189976605 1.00344710149608 42 6.214 6.30335938162149 5.9805 1.05398534932221 0.985823530563396 43 6.138 6.3813172845561 6.030875 1.05810803317199 0.961870367244555 44 6.529 6.43834555809139 6.08258333333333 1.05848867253629 1.01408039396001 45 6.058 6.13129582418965 6.1405 0.998501070627742 0.988045622607136 46 6.026 6.09018810026143 6.20120833333333 0.98209699995481 0.989460407592555 47 5.678 5.70458604314737 6.26120833333333 0.91109986115258 0.995339531572267 48 5.733 5.79873098360139 6.32629166666667 0.916608226293928 0.988664591651643 49 6.488 6.3167894171556 6.40329166666667 0.986490971516826 1.02710405105154 50 5.936 5.88631641695089 6.471875 0.90952257528937 1.00844052197161 51 6.84 6.73856949963998 6.528 1.03225635717524 1.01505223035326 52 6.694 6.70401576083405 6.59441666666667 1.01661998319296 0.998506005774544 53 7.193 7.16611320475056 6.65858333333333 1.07622189976605 1.00375193560040 54 6.991 7.07948784322863 6.716875 1.05398534932221 0.987500812885318 55 7.209 7.16643344516971 6.772875 1.05810803317199 1.00593971257195 56 7.104 7.23997431275887 6.83991666666667 1.05848867253629 0.981218950940303 57 6.83 6.90471811181339 6.91508333333333 0.998501070627742 0.989178687586746 58 6.848 6.86453066401746 6.98966666666667 0.98209699995481 0.997591872652836 59 6.396 6.43187150731242 7.05945833333333 0.91109986115258 0.994422850756326 60 6.414 6.53973235054118 7.13470833333333 0.916608226293928 0.980774083127305 61 7.151 7.11979306797029 7.21729166666667 0.986490971516826 1.00438312346044 62 6.882 6.6354219480236 7.2955 0.90952257528937 1.03716086993531 63 7.698 7.60979386509589 7.372 1.03225635717524 1.01159113327743 64 7.626 7.56246661830857 7.43883333333333 1.01661998319296 1.00840114540640 65 7.936 8.06722483290885 7.495875 1.07622189976605 0.983733584271317 66 8.054 7.96092700765555 7.55316666666667 1.05398534932221 1.01169122543831 67 8.128 8.02076750628666 7.58029166666667 1.05810803317199 1.01336935569187 68 8.062 8.00795194837864 7.56545833333333 1.05848867253629 1.00674929769431 69 7.708 7.54271869173324 7.55404166666667 0.998501070627742 1.02191269686988 70 7.574 7.42035664528355 7.555625 0.98209699995481 1.02070565635334 71 7.039 6.88730755040606 7.55933333333333 0.91109986115258 1.0220249275183 72 7.146 6.91829154800057 7.54770833333333 0.916608226293928 1.03291397166765 73 7.07 NA 7.53220833333333 NA NA 74 6.607 NA 7.52908333333333 NA NA 75 7.699 NA 7.51325 NA NA 76 7.663 NA NA NA NA 77 7.988 NA NA NA NA 78 7.723 NA NA NA NA 79 8.087 NA NA NA NA 80 8.028 NA NA NA NA 81 7.362 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/18q5j1275839818.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/18q5j1275839818.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/28q5j1275839818.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/28q5j1275839818.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/3j04m1275839818.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/3j04m1275839818.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/4j04m1275839818.png (open in new window) http://www.freestatistics.org/blog/date/2010/Jun/06/t1275839925vpcov2o81ilgui2/4j04m1275839818.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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