| Decompositie volgens multiplicatief model van gemiddelde consumptieprijzen van mineraalwater - Rebecca De Cauwer | | R Software Module: rwasp_decompose.wasp (opens new window with default values) | | Title produced by software: Classical Decomposition | | Date of computation: Thu, 22 May 2008 12:38:50 -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/22/t1211481581jcz12uf44gq1x1a.htm/, Retrieved Thu, 22 May 2008 20:39:45 +0200 | | | | User-defined keywords: | | | | | Dataseries X: | | » Textbox « » Textfile « » CSV « | | 1,12
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| | | | Text written by user: | | | | | Output produced by software: |
| Classical Decomposition by Moving Averages | | t | Observations | Fit | Trend | Seasonal | Random | | 1 | 1.12 | NA | NA | 0.996983561636156 | NA | | 2 | 1.12 | NA | NA | 1.00252020632735 | NA | | 3 | 1.12 | NA | NA | 1.00464164641206 | NA | | 4 | 1.13 | NA | NA | 1.00535407190813 | NA | | 5 | 1.13 | NA | NA | 1.00271498068435 | NA | | 6 | 1.13 | NA | NA | 1.00149837283367 | NA | | 7 | 1.14 | 1.13166007634640 | 1.13458333333333 | 0.997423497330647 | 1.00736963672036 | | 8 | 1.14 | 1.13202517029800 | 1.13791666666667 | 0.994822559031567 | 1.00704474592194 | | 9 | 1.14 | 1.14189097599192 | 1.14208333333333 | 0.999831573287347 | 0.99834399602792 | | 10 | 1.14 | 1.14528713052127 | 1.14625 | 0.999159983006563 | 0.995383576414705 | | 11 | 1.14 | 1.14986697783412 | 1.15041666666667 | 0.999522182832989 | 0.991419026701069 | | 12 | 1.15 | 1.14941930317047 | 1.15458333333333 | 0.995527364709178 | 1.00050520887193 | | 13 | 1.15 | 1.15483929222855 | 1.15833333333333 | 0.996983561636156 | 0.995809553536052 | | 14 | 1.17 | 1.16459430635027 | 1.16166666666667 | 1.00252020632735 | 1.00464169678681 | | 15 | 1.17 | 1.17040751807005 | 1.165 | 1.00464164641206 | 0.999651815232084 | | 16 | 1.18 | 1.17500757154263 | 1.16875 | 1.00535407190813 | 1.00424884790386 | | 17 | 1.18 | 1.17610111276101 | 1.17291666666667 | 1.00271498068435 | 1.00331509527258 | | 18 | 1.18 | 1.17842975203428 | 1.17666666666667 | 1.00149837283367 | 1.00133249178664 | | 19 | 1.18 | 1.17695972685016 | 1.18 | 0.997423497330647 | 1.00258315818331 | | 20 | 1.18 | 1.17637767605483 | 1.1825 | 0.994822559031567 | 1.00307921853577 | | 21 | 1.18 | 1.18438381785664 | 1.18458333333333 | 0.999831573287347 | 0.996298651002704 | | 22 | 1.19 | 1.18608616316071 | 1.18708333333333 | 0.999159983006563 | 1.00329979133123 | | 23 | 1.19 | 1.18901492999508 | 1.18958333333333 | 0.999522182832989 | 1.00082847572396 | | 24 | 1.19 | 1.18675157934707 | 1.19208333333333 | 0.995527364709178 | 1.00273723726976 | | 25 | 1.19 | 1.19097994633786 | 1.19458333333333 | 0.996983561636156 | 0.999177193250927 | | 26 | 1.19 | 1.20010023032437 | 1.19708333333333 | 1.00252020632735 | 0.991583844358035 | | 27 | 1.2 | 1.20598857638047 | 1.20041666666667 | 1.00464164641206 | 0.995034300906525 | | 28 | 1.21 | 1.21103275911934 | 1.20458333333333 | 1.00535407190813 | 0.999147207941685 | | 29 | 1.21 | 1.21203173290220 | 1.20875 | 1.00271498068435 | 0.99832369661037 | | 30 | 1.21 | 1.21473406804950 | 1.21291666666667 | 1.00149837283367 | 0.99610279469884 | | 31 | 1.21 | 1.21519429424784 | 1.21833333333333 | 0.997423497330647 | 0.995725544242245 | | 32 | 1.21 | 1.21907214421327 | 1.22541666666667 | 0.994822559031567 | 0.992558156417296 | | 33 | 1.23 | 1.23270901056553 | 1.23291666666667 | 0.999831573287347 | 0.997802392501145 | | 34 | 1.24 | 1.23895837892814 | 1.24 | 0.999159983006563 | 1.00084072321522 | | 35 | 1.24 | 1.24648745550797 | 1.24708333333333 | 0.999522182832989 | 0.99479541051191 | | 36 | 1.24 | 1.24938684271002 | 1.255 | 0.995527364709178 | 0.992486840433138 | | 37 | 1.27 | 1.25952256620034 | 1.26333333333333 | 0.996983561636156 | 1.00831857568957 | | 38 | 1.28 | 1.27487152904628 | 1.27166666666667 | 1.00252020632735 | 1.00402273549677 | | 39 | 1.29 | 1.28552270672143 | 1.27958333333333 | 1.00464164641206 | 1.00348285818303 | | 40 | 1.29 | 1.29355557252180 | 1.28666666666667 | 1.00535407190813 | 0.997251318306438 | | 41 | 1.3 | 1.29726250626037 | 1.29375 | 1.00271498068435 | 1.00211020801605 | | 42 | 1.31 | 1.30319975764981 | 1.30125 | 1.00149837283367 | 1.00521811204328 | | 43 | 1.31 | 1.30454681588371 | 1.30791666666667 | 0.997423497330647 | 1.00418013677232 | | 44 | 1.31 | 1.30694813692772 | 1.31375 | 0.994822559031567 | 1.00233510648667 | | 45 | 1.32 | 1.31936108025043 | 1.31958333333333 | 0.999831573287347 | 1.00048426451192 | | 46 | 1.32 | 1.32471961080287 | 1.32583333333333 | 0.999159983006563 | 0.996437275658652 | | 47 | 1.33 | 1.33144684104878 | 1.33208333333333 | 0.999522182832989 | 0.998913331719923 | | 48 | 1.33 | 1.33151785029853 | 1.3375 | 0.995527364709178 | 0.99886006011997 | | 49 | 1.34 | 1.33845043149654 | 1.3425 | 0.996983561636156 | 1.00115773320177 | | 50 | 1.35 | 1.35089597802611 | 1.3475 | 1.00252020632735 | 0.999336752762107 | | 51 | 1.36 | 1.35877782677231 | 1.3525 | 1.00464164641206 | 1.00089946509548 | | 52 | 1.37 | 1.36476815261529 | 1.3575 | 1.00535407190813 | 1.00383350635394 | | 53 | 1.37 | 1.36619916118242 | 1.3625 | 1.00271498068435 | 1.00278205325078 | | 54 | 1.37 | 1.36954902485004 | 1.3675 | 1.00149837283367 | 1.00032928733603 | | 55 | 1.37 | 1.36854815696243 | 1.37208333333333 | 0.997423497330647 | 1.00106086368257 | | 56 | 1.37 | 1.36953905626679 | 1.37666666666667 | 0.994822559031567 | 1.00033656851997 | | 57 | 1.38 | 1.38143395709202 | 1.38166666666667 | 0.999831573287347 | 0.99896197926462 | | 58 | 1.38 | 1.38508552644285 | 1.38625 | 0.999159983006563 | 0.996328366482964 | | 59 | 1.39 | 1.38975230171403 | 1.39041666666667 | 0.999522182832989 | 1.00017823196670 | | 60 | 1.39 | 1.38876067376930 | 1.395 | 0.995527364709178 | 1.00089239726765 | | 61 | 1.39 | 1.39577698629062 | 1.4 | 0.996983561636156 | 0.995861096473606 | | 62 | 1.41 | 1.40895860664257 | 1.40541666666667 | 1.00252020632735 | 1.00073912274819 | | 63 | 1.42 | 1.41821912418502 | 1.41166666666667 | 1.00464164641206 | 1.00125571273480 | | 64 | 1.42 | 1.4259271919897 | 1.41833333333333 | 1.00535407190813 | 0.995843271645989 | | 65 | 1.42 | 1.42886884747519 | 1.425 | 1.00271498068435 | 0.993793099002148 | | 66 | 1.43 | 1.43381183710687 | 1.43166666666667 | 1.00149837283367 | 0.997341466287126 | | 67 | 1.43 | 1.43462746366058 | 1.43833333333333 | 0.997423497330647 | 0.99677444927147 | | 68 | 1.44 | 1.43710408840102 | 1.44458333333333 | 0.994822559031567 | 1.00201510219222 | | 69 | 1.46 | 1.45017237775552 | 1.45041666666667 | 0.999831573287347 | 1.00677686487153 | | 70 | 1.46 | 1.45502672525331 | 1.45625 | 0.999159983006563 | 1.00341799546385 | | 71 | 1.47 | 1.46180119239325 | 1.4625 | 0.999522182832989 | 1.00560870222943 | | 72 | 1.47 | 1.46218081691661 | 1.46875 | 0.995527364709178 | 1.0053476170614 | | 73 | 1.47 | 1.47055075341333 | 1.475 | 0.996983561636156 | 0.999625478133243 | | 74 | 1.48 | 1.48540077237503 | 1.48166666666667 | 1.00252020632735 | 0.996364097504546 | | 75 | 1.49 | 1.49482304972394 | 1.48791666666667 | 1.00464164641206 | 0.996773497890048 | | 76 | 1.49 | 1.50174764491277 | 1.49375 | 1.00535407190813 | 0.992177350866794 | | 77 | 1.5 | 1.50323687520928 | 1.49916666666667 | 1.00271498068435 | 0.997846729771826 | | 78 | 1.5 | 1.50683776012599 | 1.50458333333333 | 1.00149837283367 | 0.995462178937289 | | 79 | 1.51 | NA | NA | 0.997423497330647 | NA | | 80 | 1.52 | NA | NA | 0.994822559031567 | NA | | 81 | 1.53 | NA | NA | 0.999831573287347 | NA | | 82 | 1.53 | NA | NA | 0.999159983006563 | NA | | 83 | 1.53 | NA | NA | 0.999522182832989 | NA | | 84 | 1.54 | NA | NA | 0.995527364709178 | NA |
| | | Charts produced by software: |  | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/1n7171211481528.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/1n7171211481528.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/28pgo1211481528.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/28pgo1211481528.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/30sbr1211481528.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/30sbr1211481528.ps (open in new window) |
 | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/407ba1211481528.png (open in new window) | | http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/May/22/t1211481581jcz12uf44gq1x1a/407ba1211481528.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')
| | |
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