Additief decompositiemodel - consumptieprijs bloemkool

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 18 Aug 2008 07:25:11 -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/Aug/18/t1219065958rqtvum1nexie59t.htm/, Retrieved Mon, 18 Aug 2008 13:26:03 +0000

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

100.58 118.48 79.58 81.97 127.13 120.76 120.26 74.9 67.59 87.73 102.87 144.94 110.48 96.34 100.43 90.88 128.28 101.21 73.76 73.64 66.4 57.34 113.59 123.53 102.87 102.99 95.8 98.43 102.65 129.55 100.37 101.93 101.94 93.87 100.91 92.64 101.67 88.67 129.86 98.07 166.45 176.52 82.07 92.18 95.02 84.69 103.01 107.9 204.13 101.99 119.23 95.65 160.95 111.06 150.41 94.79 160.34 104.08 101.07 111.5 136.9 141.71 153.98 134.27 124.71 72.89 101.2 73.28 174.05 111.9 97.06 105.23 109.13 84.04 118.82 90.84 144.28 110.16 86.09 59.87 108.97 94.93 87.36 143.52 108.7 121.13 210.25 110.2 161.46 99.41 132.72 174.29 69.93 83.43 127.53 187.58

Text written by user:

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 'George Udny Yule' @ 72.249.76.132 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 100.58 NA NA 18.9308680555555 NA 2 118.48 NA NA -5.63413194444445 NA 3 79.58 NA NA 11.2462152777778 NA 4 81.97 NA NA -7.42114583333333 NA 5 127.13 NA NA 29.1665625 NA 6 120.76 NA NA 8.29579861111112 NA 7 120.26 93.0480208333333 102.645 -9.59697916666667 27.2119791666667 8 74.9 76.0098958333333 102.135 -26.1251041666667 -1.10989583333333 9 67.59 110.193020833333 102.08125 8.11177083333333 -42.6030208333333 10 87.73 83.8845486111111 103.32125 -19.4367013888889 3.84545138888890 11 102.87 93.3041319444444 103.740416666667 -10.4362847222222 9.56586805555555 12 144.94 105.872881944444 102.97375 2.89913194444445 39.0671180555556 13 110.48 119.152534722222 100.221666666667 18.9308680555555 -8.67253472222222 14 96.34 92.5975347222222 98.2316666666667 -5.63413194444445 3.74246527777780 15 100.43 109.375798611111 98.1295833333333 11.2462152777778 -8.9457986111111 16 90.88 89.3926041666667 96.81375 -7.42114583333333 1.48739583333332 17 128.28 125.160729166667 95.9941666666667 29.1665625 3.11927083333335 18 101.21 103.844548611111 95.54875 8.29579861111112 -2.63454861111111 19 73.76 84.7426041666667 94.3395833333333 -9.59697916666667 -10.9826041666667 20 73.64 68.1744791666667 94.2995833333333 -26.1251041666667 5.46552083333333 21 66.4 102.495520833333 94.38375 8.11177083333333 -36.0955208333333 22 57.34 75.0687152777778 94.5054166666667 -19.4367013888889 -17.7287152777778 23 113.59 83.3157986111111 93.7520833333333 -10.4362847222222 30.2742013888889 24 123.53 96.7641319444444 93.865 2.89913194444445 26.7658680555556 25 102.87 115.085451388889 96.1545833333333 18.9308680555555 -12.2154513888889 26 102.99 92.8079513888889 98.4420833333333 -5.63413194444445 10.1820486111111 27 95.8 112.347881944444 101.101666666667 11.2462152777778 -16.5478819444445 28 98.43 96.6834375 104.104583333333 -7.42114583333333 1.74656250000000 29 102.65 134.264895833333 105.098333333333 29.1665625 -31.6148958333333 30 129.55 111.578715277778 103.282916666667 8.29579861111112 17.9712847222222 31 100.37 92.3488541666666 101.945833333333 -9.59697916666667 8.02114583333336 32 101.93 75.1740625 101.299166666667 -26.1251041666667 26.7559375 33 101.94 110.2334375 102.121666666667 8.11177083333333 -8.29343749999997 34 93.87 84.0891319444444 103.525833333333 -19.4367013888889 9.78086805555557 35 100.91 95.7328819444445 106.169166666667 -10.4362847222222 5.17711805555554 36 92.64 113.683715277778 110.784583333333 2.89913194444445 -21.0437152777778 37 101.67 130.910034722222 111.979166666667 18.9308680555555 -29.2400347222222 38 88.67 105.176284722222 110.810416666667 -5.63413194444445 -16.5062847222222 39 129.86 121.362048611111 110.115833333333 11.2462152777778 8.4979513888889 40 98.07 102.023854166667 109.445 -7.42114583333333 -3.95385416666666 41 166.45 138.3165625 109.15 29.1665625 28.1334375 42 176.52 118.169131944444 109.873333333333 8.29579861111112 58.3508680555556 43 82.07 105.181354166667 114.778333333333 -9.59697916666667 -23.1113541666667 44 92.18 93.4773958333333 119.6025 -26.1251041666667 -1.29739583333333 45 95.02 127.826354166667 119.714583333333 8.11177083333333 -32.8063541666667 46 84.69 99.7341319444445 119.170833333333 -19.4367013888889 -15.0441319444445 47 103.01 108.404548611111 118.840833333333 -10.4362847222222 -5.39454861111111 48 107.9 118.783298611111 115.884166666667 2.89913194444445 -10.8832986111111 49 204.13 134.935034722222 116.004166666667 18.9308680555555 69.1949652777778 50 101.99 113.326284722222 118.960416666667 -5.63413194444445 -11.3362847222222 51 119.23 133.037048611111 121.790833333333 11.2462152777778 -13.8070486111111 52 95.65 117.899270833333 125.320416666667 -7.42114583333333 -22.2492708333333 53 160.95 155.2140625 126.0475 29.1665625 5.73593749999999 54 111.06 134.412465277778 126.116666666667 8.29579861111112 -23.3524652777778 55 150.41 113.8684375 123.465416666667 -9.59697916666667 36.5415625 56 94.79 96.1940625 122.319166666667 -26.1251041666667 -1.40406249999997 57 160.34 133.533854166667 125.422083333333 8.11177083333333 26.8061458333333 58 104.08 109.042465277778 128.479166666667 -19.4367013888889 -4.96246527777777 59 101.07 118.142048611111 128.578333333333 -10.4362847222222 -17.0720486111111 60 111.5 128.377048611111 125.477916666667 2.89913194444445 -16.8770486111111 61 136.9 140.767951388889 121.837083333333 18.9308680555555 -3.86795138888887 62 141.71 113.256284722222 118.890416666667 -5.63413194444445 28.4537152777778 63 153.98 129.811631944444 118.565416666667 11.2462152777778 24.1683680555556 64 134.27 112.041354166667 119.4625 -7.42114583333333 22.2286458333333 65 124.71 148.7878125 119.62125 29.1665625 -24.0778125 66 72.89 127.488715277778 119.192916666667 8.29579861111112 -54.5987152777778 67 101.2 108.177604166667 117.774583333333 -9.59697916666667 -6.97760416666667 68 73.28 88.0894791666667 114.214583333333 -26.1251041666667 -14.8094791666667 69 174.05 118.4584375 110.346666666667 8.11177083333333 55.5915625 70 111.9 87.6353819444444 107.072083333333 -19.4367013888889 24.2646180555556 71 97.06 95.6416319444444 106.077916666667 -10.4362847222222 1.41836805555558 72 105.23 111.345381944444 108.44625 2.89913194444445 -6.11538194444445 73 109.13 128.300451388889 109.369583333333 18.9308680555555 -19.1704513888889 74 84.04 102.547118055556 108.18125 -5.63413194444445 -18.5071180555555 75 118.82 116.157048611111 104.910833333333 11.2462152777778 2.66295138888889 76 90.84 94.0709375 101.492083333333 -7.42114583333333 -3.23093750000000 77 144.28 129.547395833333 100.380833333333 29.1665625 14.7326041666667 78 110.16 109.867881944444 101.572083333333 8.29579861111112 0.292118055555534 79 86.09 93.5526041666667 103.149583333333 -9.59697916666667 -7.46260416666665 80 59.87 78.5519791666667 104.677083333333 -26.1251041666667 -18.6819791666667 81 108.97 118.143854166667 110.032083333333 8.11177083333333 -9.17385416666667 82 94.93 95.2116319444445 114.648333333333 -19.4367013888889 -0.281631944444456 83 87.36 105.734548611111 116.170833333333 -10.4362847222222 -18.3745486111111 84 143.52 119.337881944444 116.43875 2.89913194444445 24.1821180555556 85 108.7 136.864618055556 117.93375 18.9308680555555 -28.1646180555556 86 121.13 119.010034722222 124.644166666667 -5.63413194444445 2.11996527777778 87 210.25 139.031215277778 127.785 11.2462152777778 71.2187847222222 88 110.2 118.258020833333 125.679166666667 -7.42114583333333 -8.05802083333333 89 161.46 156.0403125 126.87375 29.1665625 5.41968750000002 90 99.41 138.679131944444 130.383333333333 8.29579861111112 -39.2691319444445 91 132.72 NA NA -9.59697916666667 NA 92 174.29 NA NA -26.1251041666667 NA 93 69.93 NA NA 8.11177083333333 NA 94 83.43 NA NA -19.4367013888889 NA 95 127.53 NA NA -10.4362847222222 NA 96 187.58 NA NA 2.89913194444445 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/1qoix1219065909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/1qoix1219065909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/2ge4h1219065909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/2ge4h1219065909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/3ha5u1219065909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/3ha5u1219065909.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/4uj0t1219065909.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Aug/18/t1219065958rqtvum1nexie59t/4uj0t1219065909.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')

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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