Joeri Van de Velde opgave 9 oef 2

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 10:17:20 -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/t1211732311vmn50t6o52bi338.htm/, Retrieved Sun, 25 May 2008 18:18:31 +0200

User-defined keywords:

Dataseries X:

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105,18 105,18 105,18 105,18 105,18 105,18 105,18 105,18 105,18 105,18 97,82 97,83 97,82 97,83 97,82 97,82 97,8 97,8 97,44 97,44 97,44 97,44 97,7 97,7 97,7 97,7 97,7 97,7 97,7 97,7 97,7 97,7 97,7 89,38 89,38 89,38 89,38 87,69 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 89,21 92,07 92,07 92,07 92,07 92,07 92,07 92,07 92,07 92,07 92,07 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94 94

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 1 105.18 NA NA -0.385312500000002 NA 2 105.18 NA NA -0.483229166666662 NA 3 105.18 NA NA -0.0628819444444433 NA 4 105.18 NA NA 0.0923958333333353 NA 5 105.18 NA NA 0.193229166666665 NA 6 105.18 NA NA 0.246354166666664 NA 7 105.18 103.886979166667 103.6475 0.239479166666668 1.29302083333333 8 105.18 103.3271875 103.034583333333 0.292604166666669 1.85281250000001 9 105.18 103.0890625 102.421666666667 0.667395833333333 2.09093750000000 10 105.18 101.142118055556 101.808333333333 -0.666215277777782 4.03788194444445 11 97.82 101.100868055556 101.194166666667 -0.0932986111111131 -3.28086805555556 12 97.83 100.538645833333 100.579166666667 -0.0405208333333312 -2.70864583333332 13 97.82 99.5638541666667 99.9491666666667 -0.385312500000002 -1.74385416666667 14 97.83 98.8209375 99.3041666666666 -0.483229166666662 -0.990937499999987 15 97.82 98.5962847222222 98.6591666666667 -0.0628819444444433 -0.776284722222229 16 97.82 98.1065625 98.0141666666667 0.0923958333333353 -0.286562500000002 17 97.8 97.8798958333333 97.6866666666667 0.193229166666665 -0.0798958333333388 18 97.8 97.9226041666667 97.67625 0.246354166666664 -0.122604166666662 19 97.44 97.9053125 97.6658333333333 0.239479166666668 -0.46531250000001 20 97.44 97.9480208333333 97.6554166666667 0.292604166666669 -0.508020833333319 21 97.44 98.3123958333333 97.645 0.667395833333333 -0.872395833333329 22 97.44 96.9687847222222 97.635 -0.666215277777782 0.471215277777773 23 97.7 97.5325347222222 97.6258333333333 -0.0932986111111131 0.167465277777765 24 97.7 97.5769791666667 97.6175 -0.0405208333333312 0.123020833333328 25 97.7 97.2388541666667 97.6241666666667 -0.385312500000002 0.461145833333333 26 97.7 97.1626041666667 97.6458333333333 -0.483229166666662 0.537395833333335 27 97.7 97.6046180555555 97.6675 -0.0628819444444433 0.0953819444444548 28 97.7 97.4348958333333 97.3425 0.0923958333333353 0.265104166666674 29 97.7 96.8532291666667 96.66 0.193229166666665 0.846770833333338 30 97.7 96.2130208333333 95.9666666666667 0.246354166666664 1.48697916666667 31 97.7 95.5128125 95.2733333333333 0.239479166666668 2.18718750000002 32 97.7 94.8021875 94.5095833333333 0.292604166666669 2.89781250000001 33 97.7 94.4061458333333 93.73875 0.667395833333333 3.29385416666669 34 89.38 92.3650347222222 93.03125 -0.666215277777782 -2.98503472222222 35 89.38 92.2304513888889 92.32375 -0.0932986111111131 -2.85045138888889 36 89.38 91.5757291666666 91.61625 -0.0405208333333312 -2.19572916666665 37 89.38 90.5234375 90.90875 -0.385312500000002 -1.14343750000000 38 87.69 89.7180208333333 90.20125 -0.483229166666662 -2.02802083333333 39 89.21 89.4308680555556 89.49375 -0.0628819444444433 -0.22086805555557 40 89.21 89.2253125 89.1329166666667 0.0923958333333353 -0.0153125000000216 41 89.21 89.3119791666667 89.11875 0.193229166666665 -0.101979166666695 42 89.21 89.3509375 89.1045833333334 0.246354166666664 -0.140937500000021 43 89.21 89.3298958333334 89.0904166666667 0.239479166666668 -0.119895833333359 44 89.21 89.4392708333334 89.1466666666667 0.292604166666669 -0.229270833333359 45 89.21 89.8773958333334 89.21 0.667395833333333 -0.667395833333359 46 89.21 88.5437847222222 89.21 -0.666215277777782 0.666215277777752 47 89.21 89.116701388889 89.21 -0.0932986111111131 0.0932986111110807 48 89.21 89.1694791666667 89.21 -0.0405208333333312 0.0405208333333036 49 89.21 88.8246875 89.21 -0.385312500000002 0.385312499999969 50 89.21 88.7267708333334 89.21 -0.483229166666662 0.483229166666632 51 89.21 89.1471180555556 89.21 -0.0628819444444433 0.0628819444444133 52 89.21 89.3023958333334 89.21 0.0923958333333353 -0.09239583333337 53 89.21 89.4032291666667 89.21 0.193229166666665 -0.193229166666697 54 89.21 89.4563541666667 89.21 0.246354166666664 -0.246354166666691 55 89.21 89.4494791666667 89.21 0.239479166666668 -0.239479166666698 56 89.21 89.5026041666667 89.21 0.292604166666669 -0.292604166666692 57 89.21 89.8773958333334 89.21 0.667395833333333 -0.667395833333359 58 89.21 88.5437847222222 89.21 -0.666215277777782 0.666215277777752 59 89.21 89.116701388889 89.21 -0.0932986111111131 0.0932986111110807 60 89.21 89.1694791666667 89.21 -0.0405208333333312 0.0405208333333036 61 89.21 88.8246875 89.21 -0.385312500000002 0.385312499999969 62 89.21 88.7267708333334 89.21 -0.483229166666662 0.483229166666632 63 89.21 89.1471180555556 89.21 -0.0628819444444433 0.0628819444444133 64 89.21 89.3023958333334 89.21 0.0923958333333353 -0.09239583333337 65 89.21 89.5223958333333 89.3291666666667 0.193229166666665 -0.312395833333355 66 89.21 89.8138541666667 89.5675 0.246354166666664 -0.603854166666693 67 89.21 90.0453125 89.8058333333334 0.239479166666668 -0.835312500000029 68 89.21 90.3367708333333 90.0441666666667 0.292604166666669 -1.12677083333335 69 89.21 90.9498958333333 90.2825 0.667395833333333 -1.73989583333335 70 89.21 89.8546180555556 90.5208333333333 -0.666215277777782 -0.644618055555569 71 92.07 90.6658680555556 90.7591666666667 -0.0932986111111131 1.40413194444443 72 92.07 90.9569791666667 90.9975 -0.0405208333333312 1.11302083333332 73 92.07 90.8505208333333 91.2358333333333 -0.385312500000002 1.21947916666664 74 92.07 90.9909375 91.4741666666667 -0.483229166666662 1.07906249999998 75 92.07 91.7300347222222 91.7929166666667 -0.0628819444444433 0.339965277777779 76 92.07 92.2844791666667 92.1920833333333 0.0923958333333353 -0.214479166666678 77 92.07 92.6653125 92.4720833333333 0.193229166666665 -0.595312500000006 78 92.07 92.8792708333333 92.6329166666667 0.246354166666664 -0.809270833333329 79 92.07 93.0332291666667 92.79375 0.239479166666668 -0.963229166666665 80 92.07 93.2471875 92.9545833333333 0.292604166666669 -1.17718749999999 81 94 93.7828125 93.1154166666667 0.667395833333333 0.217187499999994 82 94 92.6100347222222 93.27625 -0.666215277777782 1.38996527777778 83 94 93.3437847222222 93.4370833333333 -0.0932986111111131 0.656215277777775 84 94 93.5573958333333 93.5979166666667 -0.0405208333333312 0.442604166666669 85 94 93.3734375 93.75875 -0.385312500000002 0.626562500000006 86 94 93.4363541666667 93.9195833333333 -0.483229166666662 0.563645833333339 87 94 93.9371180555556 94 -0.0628819444444433 0.0628819444444417 88 94 94.0923958333333 94 0.0923958333333353 -0.0923958333333417 89 94 94.1932291666667 94 0.193229166666665 -0.193229166666669 90 94 94.2463541666667 94 0.246354166666664 -0.246354166666663 91 94 NA NA 0.239479166666668 NA 92 94 NA NA 0.292604166666669 NA 93 94 NA NA 0.667395833333333 NA 94 94 NA NA -0.666215277777782 NA 95 94 NA NA -0.0932986111111131 NA 96 94 NA NA -0.0405208333333312 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/1cy8v1211732232.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/1cy8v1211732232.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/2jdev1211732232.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/2jdev1211732232.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/369er1211732232.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/369er1211732232.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/42lge1211732232.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/25/t1211732311vmn50t6o52bi338/42lge1211732232.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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