Neerslag Nottigham (Kelly de Wachter)

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 21 May 2008 01:01:23 -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/21/t12113533399lcwab82k08ecjl.htm/, Retrieved Wed, 21 May 2008 09:02:19 +0200

User-defined keywords:

Dataseries X:

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29.90 28.77 15.64 23.73 25.65 21.81 28.97 24.29 25.33 28.84 19.99 19.75 22.70 23.28 24.15 20.38 27.75 27.31 25.61 22.64 26.05 28.07 21.02 25.00 17.93 35.45 17.70 28.53 26.55 26.51 30.78 26.83 27.49 25.89 20.44 19.79 18.14 27.98 35.90 34.38 21.58 21.53 31.14 28.25 25.16 20.51 30.05 20.17 32.37 22.46 25.40 19.82 18.14 20.10 20.25 19.73 24.74 26.17 20.14 31.71 26.66 20.75 20.01 26.67 23.91 26.81 29.31 31.76 22.99 23.94 27.04 20.28 23.32

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 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 29.9 NA NA 0.920573621034835 NA 2 28.77 NA NA 1.09506210839953 NA 3 15.64 NA NA 1.04324646871271 NA 4 23.73 NA NA 1.03512427767241 NA 5 25.65 NA NA 0.950996363204017 NA 6 21.81 NA NA 0.962337631454808 NA 7 28.97 25.8427785497119 24.0891666666667 1.07279670182497 1.12100948991505 8 24.29 22.8689195684731 23.5604166666667 0.970650048003102 1.06214025228748 9 25.33 24.6348221123275 23.68625 1.04004737399662 1.02821931834956 10 28.84 24.2234197321074 23.90125 1.01347920013001 1.19058334120238 11 19.99 21.9359283453819 23.8491666666667 0.919777560867198 0.911290358231334 12 19.75 23.5836456563744 24.1658333333333 0.97590864469979 0.837444739789916 13 22.7 22.3285131781999 24.255 0.920573621034835 1.01663732908839 14 23.28 26.3321372241021 24.04625 1.09506210839953 0.884090789967915 15 24.15 25.0457395976204 24.0075 1.04324646871271 0.96423584960911 16 20.38 24.8485895873086 24.0054166666667 1.03512427767241 0.820167274621053 17 27.75 22.8393664077985 24.01625 0.950996363204017 1.21500743516794 18 27.31 23.3635528216572 24.2779166666667 0.962337631454808 1.16891468555607 19 25.61 26.0667248612179 24.2979166666667 1.07279670182497 0.982478625003735 20 22.64 23.8840577436763 24.60625 0.970650048003102 0.94791263038184 21 26.05 25.8395436538736 24.8445833333333 1.04004737399662 1.00814473927812 22 28.07 25.2512565542393 24.9154166666667 1.01347920013001 1.11162784868571 23 21.02 23.1829934216577 25.205 0.919777560867198 0.906699131457414 24 25 24.5164516692666 25.1216666666667 0.97590864469979 1.01972342234744 25 17.93 23.2939647632602 25.30375 0.920573621034835 0.769727274091168 26 35.45 28.1362520476904 25.69375 1.09506210839953 1.25994037656163 27 17.7 27.0496421896060 25.9283333333333 1.04324646871271 0.654352463368307 28 28.53 26.8071309810213 25.8975 1.03512427767241 1.06426905662521 29 26.55 24.5190637343076 25.7825 0.950996363204017 1.08283090609413 30 26.51 24.5793060293951 25.54125 0.962337631454808 1.07854957207888 31 30.78 27.1770694476067 25.3329166666667 1.07279670182497 1.13257244528661 32 26.83 24.2957751390377 25.0304166666667 0.970650048003102 1.10430722405273 33 27.49 26.4978069709989 25.4775 1.04004737399662 1.03744434511456 34 25.89 26.8365069364426 26.4795833333333 1.01347920013001 0.964730620915597 35 20.44 24.3890517483448 26.51625 0.919777560867198 0.838080963987752 36 19.79 25.4728421410724 26.1016666666667 0.97590864469979 0.776905847035052 37 18.14 23.8512953763284 25.9091666666667 0.920573621034835 0.760545694218493 38 27.98 28.4533637832477 25.9833333333333 1.09506210839953 0.983363521204251 39 35.9 27.0674643167799 25.9454166666667 1.04324646871271 1.32631559350554 40 34.38 26.5241970117909 25.6241666666667 1.03512427767241 1.29617495997021 41 21.58 24.5361024191483 25.8004166666667 0.950996363204017 0.879520293457802 42 21.53 25.2292849046402 26.2166666666667 0.962337631454808 0.8533733746865 43 31.14 28.7782185250804 26.8254166666667 1.07279670182497 1.08206836961993 44 28.25 26.3903570551243 27.1883333333333 0.970650048003102 1.07046675954369 45 25.16 27.5829230645354 26.5208333333333 1.04004737399662 0.91215858236393 46 20.51 25.8200717553122 25.4766666666667 1.01347920013001 0.794343261101909 47 30.05 22.7430331550429 24.7266666666667 0.919777560867198 1.32128374413141 48 20.17 23.9329396254565 24.52375 0.97590864469979 0.842771523918691 49 32.37 22.1033562133885 24.0104166666667 0.920573621034835 1.46448347877562 50 22.46 25.4072660183831 23.2016666666667 1.09506210839953 0.883999088439874 51 25.4 23.8164475086539 22.8291666666667 1.04324646871271 1.06648986969071 52 19.82 23.8570267896549 23.0475 1.03512427767241 0.830782484957199 53 18.14 21.7496830749605 22.8704166666667 0.950996363204017 0.834035141453799 54 20.1 22.0744213695209 22.9383333333333 0.962337631454808 0.910556143852222 55 20.25 24.8687685441800 23.18125 1.07279670182497 0.81427433626339 56 19.73 22.2007887854310 22.8720833333333 0.970650048003102 0.888707162195409 57 24.74 23.4803695271912 22.57625 1.04004737399662 1.05364610941706 58 26.17 22.9422131099431 22.6370833333333 1.01347920013001 1.14069204547045 59 20.14 21.3047309942368 23.1629166666667 0.919777560867198 0.945329936597092 60 31.71 23.1123631067047 23.6829166666667 0.97590864469979 1.37199298287249 61 26.66 22.4067619359879 24.34 0.920573621034835 1.18981939809790 62 20.75 27.6160975462006 25.21875 1.09506210839953 0.751373359877734 63 20.01 26.7562291202806 25.6470833333333 1.04324646871271 0.747863232522287 64 26.67 26.3762605004402 25.48125 1.03512427767241 1.01113651040696 65 23.91 24.4176241222325 25.6758333333333 0.950996363204017 0.979210748773454 66 26.81 24.5271794076913 25.4870833333333 0.962337631454808 1.09307309880046 67 29.31 26.6822419688899 24.8716666666667 1.07279670182497 1.09848340458699 68 31.76 NA NA 0.970650048003102 NA 69 22.99 NA NA 1.04004737399662 NA 70 23.94 NA NA 1.01347920013001 NA 71 27.04 NA NA 0.919777560867198 NA 72 20.28 NA NA 0.97590864469979 NA 73 23.32 NA NA NA NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/1ebc11211353278.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/1ebc11211353278.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/2cfoh1211353278.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/2cfoh1211353278.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/3oi541211353278.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/3oi541211353278.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/414l31211353278.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/21/t12113533399lcwab82k08ecjl/414l31211353278.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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