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tijdreeks 2 stap 24

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Wed, 18 Aug 2010 11:32:14 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu.htm/, Retrieved Wed, 18 Aug 2010 13:32:39 +0200
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
120 119 118 116 114 113 114 116 117 117 118 120 123 125 120 116 111 108 113 112 126 124 124 118 119 122 114 108 104 101 107 104 123 125 134 131 127 124 123 117 112 118 123 124 144 148 152 154 146 132 136 128 120 124 126 121 140 142 142 139 131 117 122 112 98 103 108 102 126 129 126 126 112 99 106 104 90 98 99 91 118 115 119 123
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1120NANA1.04862904746646NA
2119NANA0.998181977520622NA
3118NANA1.00195374377330NA
4116NANA0.952879404183104NA
5114NANA0.88215913453607NA
6113NANA0.905731532582667NA
7114110.716189228869116.9583333333330.9466293343401661.02965971637936
8116109.297998843949117.3333333333330.9315170356018371.06131860808925
9117125.496497569159117.6666666666671.066542472259140.932296934705477
10117127.203929567348117.751.080288149191920.919782906062301
11118129.063951627413117.6251.097249323081080.914275430994455
12120127.641347915838117.2916666666671.088238845463630.940134227343973
13123122.733291430554117.0416666666671.048629047466461.00217307436587
14125116.620927706993116.8333333333330.9981819775206221.07184878784415
15120117.270336094134117.0416666666671.001953743773301.02327667845750
16116112.161846534053117.7083333333330.9528794041831041.03421977779923
17111104.315317658890118.250.882159134536071.06408150299622
18108107.253708983331118.4166666666670.9057315325826671.00695818376580
19113111.860033007863118.1666666666670.9466293343401661.01019101247768
20112109.802570571567117.8750.9315170356018371.02001254995211
21126125.318740490449117.51.066542472259141.00543621414391
22124126.303689443022116.9166666666671.080288149191920.981760711399796
23124127.600952529971116.2916666666671.097249323081080.971779579551922
24118125.918303077188115.7083333333331.088238845463630.937115551244891
25119120.767111966554115.1666666666671.048629047466460.985367605983297
26122114.375018257571114.5833333333330.9981819775206221.06666649639572
27114114.347971008128114.1251.001953743773300.996956911390205
28108108.667955385382114.0416666666670.9528794041831040.99385324419685
29104101.00722090438114.50.882159134536071.0296293578699
30101104.574253199440115.4583333333330.9057315325826670.965820906293027
31107110.124545894906116.3333333333330.9466293343401660.971627162050795
32104108.754613906514116.750.9315170356018370.956281267196613
33123125.007665602707117.2083333333331.066542472259140.98393966007583
34125127.428989598430117.9583333333331.080288149191920.98093848498615
35134130.206919672289118.6666666666671.097249323081081.02913117319155
36131130.271258459042119.7083333333331.088238845463631.00559403163505
37127126.971500497397121.0833333333331.048629047466461.00022445590145
38124122.360474077736122.5833333333330.9981819775206221.01339914653503
39123124.53450073649124.2916666666671.001953743773300.987678107452834
40117120.181914852594126.1250.9528794041831040.973524179103847
41112112.769342698194127.8333333333330.882159134536070.993177731821554
42118117.329972283313129.5416666666670.9057315325826671.00571062707719
43123124.284543021078131.2916666666670.9466293343401660.9896644989807
44124123.34838079761132.4166666666670.9315170356018371.00528275440810
45144142.161223698208133.2916666666671.066542472259141.01293444340136
46148145.073696035231134.2916666666671.080288149191921.02017115469408
47152148.220096059536135.0833333333331.097249323081081.02550196660880
48154147.637736701233135.6666666666671.088238845463631.04309374717415
49146142.657243332416136.0416666666671.048629047466461.02343208511183
50132135.794339858535136.0416666666670.9981819775206220.972058188415752
51136136.015220717226135.751.001953743773300.999888095485598
52128128.95634603278135.3333333333330.9528794041831040.992583955251516
53120118.797430117524134.6666666666670.882159134536071.01012286108619
54124121.028376041359133.6250.9057315325826671.02455311767238
55126125.310058133279132.3750.9466293343401661.00550587779623
56121122.145171293291131.1250.9315170356018370.990624506223491
57140138.561642854333129.9166666666671.066542472259141.01038062999281
58142138.997075196027128.6666666666671.080288149191921.02160423015907
59142139.442101474888127.0833333333331.097249323081081.01834380361496
60139136.347258679548125.2916666666671.088238845463631.01945577304702
61131129.680458870019123.6666666666671.048629047466461.01017532742773
62117121.902974004706122.1250.9981819775206220.959779701481962
63122120.985914560626120.751.001953743773301.00838184711879
64112113.988198725404119.6250.9528794041831040.98255785469342
6598104.462344181313118.4166666666670.882159134536070.9381370939743
66103106.15928338146117.2083333333330.9057315325826670.970240159119124
67108109.690674116667115.8750.9466293343401660.984586892821276
68102106.503447737143114.3333333333330.9315170356018370.957715474636482
69126120.430420825928112.9166666666671.066542472259141.04624727818665
70129120.902248697062111.9166666666671.080288149191921.06697767320464
71126122.068987192771111.251.097249323081081.03220320654436
72126120.477108849870110.7083333333331.088238845463631.04584183006095
73112115.480273852244110.1251.048629047466460.96986261171586
7499109.092971959858109.2916666666670.9981819775206220.907482839833424
75106108.711981199403108.51.001953743773300.975053520601112
76104102.513942566699107.5833333333330.9528794041831041.01449614946117
779094.1337309811198106.7083333333330.882159134536070.956086612757877
789896.271714150766106.2916666666670.9057315325826671.01795216657852
7999NANA0.946629334340166NA
8091NANA0.931517035601837NA
81118NANA1.06654247225914NA
82115NANA1.08028814919192NA
83119NANA1.09724932308108NA
84123NANA1.08823884546363NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/1ssgm1282131130.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/1ssgm1282131130.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/2ssgm1282131130.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/2ssgm1282131130.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/3ljgp1282131130.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/3ljgp1282131130.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/4ljgp1282131130.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/18/t1282131150agcrf2ztf13xkiu/4ljgp1282131130.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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Software written by Ed van Stee & Patrick Wessa


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