decompositie van additieve tijdsreeks - prijsindex van ijzererts - Karen De Maere

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 24 May 2008 10:16:31 -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/24/t1211645847i6cfaxls6xlb8qj.htm/, Retrieved Sat, 24 May 2008 18:17:27 +0200

User-defined keywords:

Dataseries X:

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229,7 231,5 226,4 242,1 228,3 209,9 209,3 220,8 239,6 241,1 241,9 240,8 179,9 190,8 174,2 170 170,3 159,3 147,9 154,2 164,5 173,9 163,6 149,7 128,2 124,7 125,1 120,9 117,5 114 113,4 118,9 121,7 121,9 120,3 115,6 105,7 105,1 104,6 105 104,9 105,1 103,9 101,9 99 97 95,8 94,7 97,6 97,9 99,3 99,7 99,7 100 99,1 98,2 98,1 98,6 100,2 101,9 97,5 97,1 98,1 98,5 98,3 99,3 100,9 100,4 101,7 102 103,2 103,1

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 3 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 229.7 NA NA -7.76536458333334 NA 2 231.5 NA NA -3.56536458333334 NA 3 226.4 NA NA -4.63932291666668 NA 4 242.1 NA NA -3.58098958333333 NA 5 228.3 NA NA -1.42057291666665 NA 6 209.9 NA NA -1.99765625 NA 7 209.3 224.82421875 228.041666666667 -3.21744791666667 -15.52421875 8 220.8 225.112760416667 224.270833333333 0.84192708333333 -4.31276041666666 9 239.6 225.535677083333 220.4 5.13567708333333 14.0643229166666 10 241.1 223.919010416667 215.220833333333 8.69817708333334 17.1809895833333 11 241.9 217.11796875 209.8 7.31796875 24.78203125 12 240.8 209.46796875 205.275 4.19296875 31.33203125 13 179.9 192.84296875 200.608333333333 -7.76536458333334 -12.9429687500000 14 190.8 191.709635416667 195.275 -3.56536458333334 -0.909635416666646 15 174.2 184.731510416667 189.370833333333 -4.63932291666668 -10.5315104166667 16 170 179.860677083333 183.441666666667 -3.58098958333333 -9.86067708333334 17 170.3 175.95859375 177.379166666667 -1.42057291666665 -5.65859374999999 18 159.3 168.323177083333 170.320833333333 -1.99765625 -9.0231770833333 19 147.9 161.153385416667 164.370833333333 -3.21744791666667 -13.2533854166667 20 154.2 160.304427083333 159.4625 0.84192708333333 -6.10442708333332 21 164.5 159.798177083333 154.6625 5.13567708333333 4.70182291666671 22 173.9 159.269010416667 150.570833333333 8.69817708333334 14.6309895833333 23 163.6 153.64296875 146.325 7.31796875 9.95703125 24 149.7 146.43046875 142.2375 4.19296875 3.26953125 25 128.2 131.147135416667 138.9125 -7.76536458333334 -2.94713541666667 26 124.7 132.438802083333 136.004166666667 -3.56536458333334 -7.73880208333335 27 125.1 128.110677083333 132.75 -4.63932291666668 -3.01067708333332 28 120.9 125.219010416667 128.8 -3.58098958333333 -4.31901041666664 29 117.5 123.40859375 124.829166666667 -1.42057291666665 -5.90859375 30 114 119.606510416667 121.604166666667 -1.99765625 -5.60651041666667 31 113.4 116.028385416667 119.245833333333 -3.21744791666667 -2.62838541666666 32 118.9 118.33359375 117.491666666667 0.84192708333333 0.56640625 33 121.7 120.956510416667 115.820833333333 5.13567708333333 0.743489583333329 34 121.9 123.00234375 114.304166666667 8.69817708333334 -1.10234374999999 35 120.3 120.434635416667 113.116666666667 7.31796875 -0.134635416666669 36 115.6 116.413802083333 112.220833333333 4.19296875 -0.813802083333343 37 105.7 103.688802083333 111.454166666667 -7.76536458333334 2.01119791666667 38 105.1 106.784635416667 110.35 -3.56536458333334 -1.68463541666667 39 104.6 104.056510416667 108.695833333333 -4.63932291666668 0.54348958333334 40 105 103.131510416667 106.7125 -3.58098958333333 1.86848958333334 41 104.9 103.23359375 104.654166666667 -1.42057291666665 1.66640625000001 42 105.1 100.76484375 102.7625 -1.99765625 4.33515625000001 43 103.9 98.33671875 101.554166666667 -3.21744791666667 5.56328125000003 44 101.9 101.75859375 100.916666666667 0.84192708333333 0.141406250000017 45 99 105.531510416667 100.395833333333 5.13567708333333 -6.53151041666669 46 97 108.65234375 99.9541666666667 8.69817708333334 -11.6523437500000 47 95.8 106.834635416667 99.5166666666667 7.31796875 -11.0346354166667 48 94.7 103.28046875 99.0875 4.19296875 -8.58046874999998 49 97.6 90.9096354166667 98.675 -7.76536458333334 6.69036458333333 50 97.9 94.75546875 98.3208333333333 -3.56536458333334 3.14453125000001 51 99.3 93.48984375 98.1291666666667 -4.63932291666668 5.81015625 52 99.7 94.57734375 98.1583333333333 -3.58098958333333 5.12265624999999 53 99.7 96.9877604166666 98.4083333333333 -1.42057291666665 2.71223958333336 54 100 96.8940104166667 98.8916666666667 -1.99765625 3.10598958333334 55 99.1 95.9700520833333 99.1875 -3.21744791666667 3.12994791666667 56 98.2 99.9919270833334 99.15 0.84192708333333 -1.79192708333335 57 98.1 104.20234375 99.0666666666667 5.13567708333333 -6.10234375 58 98.6 107.66484375 98.9666666666667 8.69817708333334 -9.06484375 59 100.2 106.176302083333 98.8583333333333 7.31796875 -5.97630208333334 60 101.9 102.963802083333 98.7708333333333 4.19296875 -1.06380208333333 61 97.5 91.0513020833333 98.8166666666667 -7.76536458333334 6.44869791666667 62 97.1 95.41796875 98.9833333333333 -3.56536458333334 1.68203124999999 63 98.1 94.5856770833333 99.225 -4.63932291666668 3.51432291666667 64 98.5 95.9356770833333 99.5166666666667 -3.58098958333333 2.56432291666667 65 98.3 98.3627604166667 99.7833333333333 -1.42057291666665 -0.0627604166666629 66 99.3 97.9606770833333 99.9583333333333 -1.99765625 1.33932291666667 67 100.9 NA NA -3.21744791666667 NA 68 100.4 NA NA 0.84192708333333 NA 69 101.7 NA NA 5.13567708333333 NA 70 102 NA NA 8.69817708333334 NA 71 103.2 NA NA 7.31796875 NA 72 103.1 NA NA 4.19296875 NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/1um3k1211645786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/1um3k1211645786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/2ts5f1211645786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/2ts5f1211645786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/3k5e51211645786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/3k5e51211645786.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/4irs01211645786.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/24/t1211645847i6cfaxls6xlb8qj/4irs01211645786.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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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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