Home » date » 2010 » Aug » 19 »

aantal bezoekers

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 19 Aug 2010 13:51:40 +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/19/t1282226023hao5xlza5e44hph.htm/, Retrieved Thu, 19 Aug 2010 15:53:49 +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/19/t1282226023hao5xlza5e44hph.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:
Trouillard Olivier
 
Dataseries X:
» Textbox « » Textfile « » CSV «
80 79 78 76 74 73 74 76 77 77 78 80 90 90 89 82 78 76 74 78 81 82 88 99 117 113 106 100 97 96 100 104 104 111 117 118 140 147 134 126 116 114 120 122 117 119 132 134 154 152 132 130 123 129 124 128 128 129 141 138 155 160 142 133 131 140 134 134 134 136 145 137 152 168 160 157 147 161 159 164 163 158 175 163
 
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'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
180NANA16.0202546296296NA
279NANA18.4855324074074NA
378NANA6.1105324074074NA
476NANA-0.882523148148146NA
574NANA-8.11863425925925NA
673NANA-5.36863425925926NA
77469.763310185185277.25-7.486689814814814.23668981481482
87672.186921296296378.125-5.93807870370373.81307870370370
97771.749421296296379.0416666666667-7.292245370370375.25057870370371
107773.492476851851879.75-6.257523148148153.50752314814815
117880.673032407407480.16666666666670.50636574074073-2.67303240740739
128080.679976851851880.45833333333330.221643518518511-0.679976851851833
139096.60358796296380.583333333333316.0202546296296-6.60358796296296
149099.15219907407480.666666666666718.4855324074074-9.15219907407408
158987.02719907407480.91666666666676.11053240740741.97280092592594
168280.409143518518581.2916666666667-0.8825231481481461.59085648148150
177873.798032407407481.9166666666667-8.118634259259254.20196759259258
187677.756365740740783.125-5.36863425925926-1.75636574074075
197477.554976851851985.0416666666667-7.48668981481481-3.55497685185186
207881.186921296296387.125-5.9380787037037-3.18692129629630
218181.499421296296388.7916666666667-7.29224537037037-0.499421296296291
228283.992476851851990.25-6.25752314814815-1.99247685185186
238892.298032407407491.79166666666670.50636574074073-4.29803240740739
249993.638310185185293.41666666666670.2216435185185115.36168981481482
25117111.35358796296395.333333333333316.02025462962965.64641203703705
26113115.98553240740797.518.4855324074074-2.98553240740738
27106105.65219907407499.54166666666676.11053240740740.347800925925938
28100100.825810185185101.708333333333-0.882523148148146-0.825810185185176
299796.0063657407407104.125-8.118634259259250.993634259259267
3096100.756365740741106.125-5.36863425925926-4.75636574074073
31100100.388310185185107.875-7.48668981481481-0.388310185185176
32104104.311921296296110.25-5.9380787037037-0.311921296296276
33104105.541087962963112.833333333333-7.29224537037037-1.54108796296296
34111108.825810185185115.083333333333-6.257523148148152.17418981481482
35117117.464699074074116.9583333333330.50636574074073-0.464699074074076
36118118.721643518519118.50.221643518518511-0.721643518518505
37140136.103587962963120.08333333333316.02025462962963.89641203703702
38147140.152199074074121.66666666666718.48553240740746.84780092592592
39134129.068865740741122.9583333333336.11053240740744.93113425925927
40126122.950810185185123.833333333333-0.8825231481481463.04918981481482
41116116.673032407407124.791666666667-8.11863425925925-0.673032407407419
42114120.714699074074126.083333333333-5.36863425925926-6.71469907407409
43120119.846643518519127.333333333333-7.486689814814810.153356481481481
44122122.186921296296128.125-5.9380787037037-0.186921296296305
45117120.957754629630128.25-7.29224537037037-3.95775462962963
46119122.075810185185128.333333333333-6.25752314814815-3.07581018518516
47132129.298032407407128.7916666666670.506365740740732.70196759259258
48134129.929976851852129.7083333333330.2216435185185114.07002314814815
49154146.520254629630130.516.02025462962967.47974537037038
50152149.402199074074130.91666666666718.48553240740742.59780092592590
51132137.735532407407131.6256.1105324074074-5.7355324074074
52130131.617476851852132.5-0.882523148148146-1.61747685185185
53123125.173032407407133.291666666667-8.11863425925925-2.17303240740739
54129128.464699074074133.833333333333-5.368634259259260.535300925925924
55124126.554976851852134.041666666667-7.48668981481481-2.55497685185185
56128128.478587962963134.416666666667-5.9380787037037-0.478587962962962
57128127.874421296296135.166666666667-7.292245370370370.125578703703724
58129129.450810185185135.708333333333-6.25752314814815-0.450810185185190
59141136.673032407407136.1666666666670.506365740740734.32696759259258
60138137.179976851852136.9583333333330.2216435185185110.820023148148152
61155153.853587962963137.83333333333316.02025462962961.14641203703707
62160156.985532407407138.518.48553240740743.01446759259258
63142145.1105324074071396.1105324074074-3.11053240740739
64133138.659143518519139.541666666667-0.882523148148146-5.6591435185185
65131131.881365740741140-8.11863425925925-0.881365740740733
66140134.756365740741140.125-5.368634259259265.2436342592593
67134132.471643518519139.958333333333-7.486689814814811.52835648148147
68134134.228587962963140.166666666667-5.9380787037037-0.228587962962962
69134133.957754629630141.25-7.292245370370370.0422453703703809
70136136.742476851852143-6.25752314814815-0.742476851851848
71145145.173032407407144.6666666666670.50636574074073-0.173032407407419
72137146.429976851852146.2083333333330.221643518518511-9.42997685185185
73152164.145254629630148.12516.0202546296296-12.1452546296296
74168168.902199074074150.41666666666718.4855324074074-0.902199074074076
75160158.985532407407152.8756.11053240740741.01446759259258
76157154.117476851852155-0.8825231481481462.88252314814815
77147149.048032407407157.166666666667-8.11863425925925-2.04803240740736
78161154.131365740741159.5-5.368634259259266.86863425925927
79159NANA-7.48668981481481NA
80164NANA-5.9380787037037NA
81163NANA-7.29224537037037NA
82158NANA-6.25752314814815NA
83175NANA0.50636574074073NA
84163NANA0.221643518518511NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/1dwgi1282225896.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/1dwgi1282225896.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/2dwgi1282225896.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/2dwgi1282225896.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/355xl1282225896.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/355xl1282225896.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/455xl1282225896.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/19/t1282226023hao5xlza5e44hph/455xl1282225896.ps (open in new window)


 
Parameters (Session):
 
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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