Opgave 9 oef 2 Classical Deomposition- Michiel Van Schaik

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 25 May 2008 11:08:40 -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/25/t1211735389xassivgkxarc48g.htm/, Retrieved Sun, 25 May 2008 19:09:49 +0200

User-defined keywords:

Dataseries X:

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65.05 65.84 66.6 67.55 68.07 69.06 69.06 69.11 69.29 69.38 69.28 69.75 69.9 70.21 70.48 71.55 72.18 72.64 72.77 72.74 73.13 73.44 73.34 73.34 73.81 74.26 74.72 75.11 75.26 75.89 75.91 76.43 76.56 76.76 76.76 76.56 76.82 77.09 77.51 77.76 77.86 77.89 77.94 77.99 78.17 78.91 78.87 78.88 79.08 79.41 79.51 79.73 80.38 80.56 80.46 80.45 80.58 80.68 80.52 81.49 81.66 81.95 82.3 82.4 83.14 83.17 83.11 83.21 83.33 83.88 83.8 83.73

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 6 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 65.05 NA NA -0.295564236111109 NA 2 65.84 NA NA -0.192439236111114 NA 3 66.6 NA NA -0.115668402777769 NA 4 67.55 NA NA 0.13151909722223 NA 5 68.07 NA NA 0.279227430555557 NA 6 69.06 NA NA 0.364852430555543 NA 7 69.06 68.5171440972222 68.3720833333333 0.145060763888888 0.542855902777788 8 69.11 68.7890190972222 68.75625 0.0327690972222274 0.320980902777762 9 69.29 69.0948524305556 69.1 -0.00514756944445101 0.195147569444444 10 69.38 69.5245399305555 69.4283333333333 0.0962065972222265 -0.144539930555553 11 69.28 69.5602690972222 69.76625 -0.205980902777779 -0.280269097222202 12 69.75 69.8518315972222 70.0866666666667 -0.234835069444448 -0.101831597222215 13 69.9 70.0948524305556 70.3904166666667 -0.295564236111109 -0.194852430555557 14 70.21 70.5038107638889 70.69625 -0.192439236111114 -0.293810763888899 15 70.48 70.8918315972222 71.0075 -0.115668402777769 -0.411831597222232 16 71.55 71.4681857638889 71.3366666666667 0.13151909722223 0.0818142361111143 17 72.18 71.9542274305555 71.675 0.279227430555557 0.225772569444459 18 72.64 72.3586024305555 71.99375 0.364852430555543 0.281397569444451 19 72.77 72.4513107638889 72.30625 0.145060763888888 0.318689236111112 20 72.74 72.6706857638889 72.6379166666667 0.0327690972222274 0.0693142361111114 21 73.13 72.9781857638889 72.9833333333333 -0.00514756944445101 0.151814236111107 22 73.44 73.4045399305555 73.3083333333333 0.0962065972222265 0.0354600694444542 23 73.34 73.3790190972222 73.585 -0.205980902777779 -0.0390190972221944 24 73.34 73.6139149305556 73.84875 -0.234835069444448 -0.273914930555549 25 73.81 73.8194357638889 74.115 -0.295564236111109 -0.0094357638888738 26 74.26 74.2071440972222 74.3995833333333 -0.192439236111114 0.0528559027777931 27 74.72 74.5805815972222 74.69625 -0.115668402777769 0.139418402777778 28 75.11 75.1090190972222 74.9775 0.13151909722223 0.000980902777783399 29 75.26 75.5375607638889 75.2583333333333 0.279227430555557 -0.277560763888886 30 75.89 75.8998524305556 75.535 0.364852430555543 -0.0098524305555685 31 75.91 75.9396440972222 75.7945833333333 0.145060763888888 -0.0296440972222314 32 76.43 76.0706857638889 76.0379166666667 0.0327690972222274 0.359314236111118 33 76.56 76.2669357638889 76.2720833333333 -0.00514756944445101 0.293064236111121 34 76.76 76.5949565972222 76.49875 0.0962065972222265 0.165043402777798 35 76.76 76.5115190972222 76.7175 -0.205980902777779 0.248480902777771 36 76.56 76.6743315972222 76.9091666666667 -0.234835069444448 -0.114331597222218 37 76.82 76.7815190972222 77.0770833333333 -0.295564236111109 0.0384809027777635 38 77.09 77.0342274305555 77.2266666666667 -0.192439236111114 0.0557725694444571 39 77.51 77.2430815972222 77.35875 -0.115668402777769 0.26691840277779 40 77.76 77.6469357638889 77.5154166666667 0.13151909722223 0.113064236111128 41 77.86 77.9721440972222 77.6929166666667 0.279227430555557 -0.112144097222213 42 77.89 78.2423524305556 77.8775 0.364852430555543 -0.352352430555555 43 77.94 78.2133940972222 78.0683333333333 0.145060763888888 -0.273394097222223 44 77.99 78.2919357638889 78.2591666666667 0.0327690972222274 -0.301935763888892 45 78.17 78.4340190972222 78.4391666666667 -0.00514756944445101 -0.264019097222231 46 78.91 78.7007899305555 78.6045833333333 0.0962065972222265 0.209210069444453 47 78.87 78.5856857638889 78.7916666666667 -0.205980902777779 0.284314236111115 48 78.88 78.7730815972222 79.0079166666667 -0.234835069444448 0.106918402777765 49 79.08 78.9286024305556 79.2241666666667 -0.295564236111109 0.151397569444441 50 79.41 79.2392274305556 79.4316666666667 -0.192439236111114 0.170772569444438 51 79.51 79.5189149305555 79.6345833333333 -0.115668402777769 -0.00891493055553383 52 79.73 79.9402690972222 79.80875 0.13151909722223 -0.210269097222209 53 80.38 80.2304774305555 79.95125 0.279227430555557 0.149522569444457 54 80.56 80.4936024305555 80.12875 0.364852430555543 0.0663975694444616 55 80.46 80.4900607638889 80.345 0.145060763888888 -0.0300607638888835 56 80.45 80.5911024305556 80.5583333333333 0.0327690972222274 -0.141102430555549 57 80.58 80.7752690972222 80.7804166666667 -0.00514756944445101 -0.195269097222223 58 80.68 81.1041232638889 81.0079166666667 0.0962065972222265 -0.424123263888887 59 80.52 81.0281857638889 81.2341666666667 -0.205980902777779 -0.508185763888889 60 81.49 81.2230815972222 81.4579166666667 -0.234835069444448 0.266918402777776 61 81.66 81.3815190972222 81.6770833333333 -0.295564236111109 0.278480902777758 62 81.95 81.7100607638889 81.9025 -0.192439236111114 0.239939236111113 63 82.3 82.0164149305556 82.1320833333333 -0.115668402777769 0.283585069444442 64 82.4 82.5115190972222 82.38 0.13151909722223 -0.111519097222214 65 83.14 82.9292274305556 82.65 0.279227430555557 0.210772569444444 66 83.17 83.2448524305555 82.88 0.364852430555543 -0.0748524305555236 67 83.11 NA NA 0.145060763888888 NA 68 83.21 NA NA 0.0327690972222274 NA 69 83.33 NA NA -0.00514756944445101 NA 70 83.88 NA NA 0.0962065972222265 NA 71 83.8 NA NA -0.205980902777779 NA 72 83.73 NA NA -0.234835069444448 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/1tfo51211735312.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/1tfo51211735312.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/2r4nr1211735312.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/2r4nr1211735312.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/3efnk1211735312.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/3efnk1211735312.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/4u1oh1211735312.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211735389xassivgkxarc48g/4u1oh1211735312.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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