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ws 8 classical decomposition

*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 29 Nov 2010 18:48:39 +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/Nov/29/t1291056438rgrt7l9oys2bxwz.htm/, Retrieved Mon, 29 Nov 2010 19:47:18 +0100
 
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/Nov/29/t1291056438rgrt7l9oys2bxwz.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 «
167,16 179,84 174,44 180,35 193,17 195,16 202,43 189,91 195,98 212,09 205,81 204,31 196,07 199,98 199,10 198,31 195,72 223,04 238,41 259,73 326,54 335,15 321,81 368,62 369,59 425,00 439,72 362,23 328,76 348,55 328,18 329,34 295,55 237,38 226,85 220,14 239,36 224,69 230,98 233,47 256,70 253,41 224,95 210,37 191,09 198,85 211,04 206,25 201,19 194,37 191,08 192,87 181,61 157,67 196,14 246,35 271,90
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1167.16NANA0.999982204104269NA
2179.84NANA1.03716151365447NA
3174.44NANA1.05682814473984NA
4180.35NANA0.985682997956348NA
5193.17NANA0.983051682033842NA
6195.16NANA1.03398001321637NA
7202.43193.914943701812192.9254166666670.9895270351457431.06037081029508
8189.91195.964984695419194.9691666666670.9958180287521330.978142014360496
9195.98197.842953812578196.8358333333331.007120479244960.988612649212912
10212.09199.571854037366198.6116666666670.9601873706993531.11213996332746
11205.81200.42208125752199.466250.9558312575201361.07948303406991
12204.31201.728995939599200.7341666666670.9948292729325421.02310396663975
13196.07204.394982204104203.3950.9999822041042690.964003487319606
14199.98208.840494846988207.8033333333331.037161513654470.927871128448571
15199.1217.20932814474216.15251.056828144739840.871578736689774
16198.31227.705682997956226.720.9856829979563480.887396100908084
17195.72237.663885015367236.6808333333330.9830516820338420.8411932260439
18223.04249.394396679883248.3604166666671.033980013216370.868536816829029
19238.41263.426193701812262.4366666666670.9895270351457430.91806258970965
20259.73280.038318028752279.04250.9958180287521330.9346989969479
21326.54299.451287145912298.4441666666671.007120479244961.08640527725365
22335.15316.260187370699315.30.9601873706993531.10702967716330
23321.81328.629164590853327.6733333333330.9558312575201361.02748906244151
24368.62339.441079272932338.446250.9948292729325421.09481473118860
25369.59348.416232204104347.416250.9999822041042691.06384366678920
26425355.094244846988354.0570833333331.037161513654471.15736208221459
27439.72356.72307814474355.666251.056828144739841.16984734235277
28362.23351.286932997956350.301250.9856829979563481.04907240486157
29328.76343.253885015367342.2708333333330.9830516820338420.97708586117988
30348.55333.161480013216332.12751.033980013216371.01495808317140
31328.18321.504110368479320.5145833333330.9895270351457431.03475289920842
32329.34307.737901362085306.7420833333330.9958180287521331.07817965762691
33295.55290.705453812578289.6983333333331.007120479244961.01298622530989
34237.38276.596020704033275.6358333333330.9601873706993530.896917421734397
35226.85268.224164590853267.2683333333330.9558312575201360.88799403709152
36220.14261.296495939599260.3016666666670.9948292729325420.85010673591638
37239.36253.036232204104252.036250.9999822041042690.949721556726096
38224.69243.815078180321242.7779166666671.037161513654470.892335500171742
39230.98234.525161478073233.4683333333331.056828144739840.936142631792353
40233.47228.496099664623227.5104166666670.9856829979563481.04110020506525
41256.7226.229301682034225.246250.9830516820338421.15928962361112
42253.41225.042730013216224.008751.033980013216371.09407381083447
43224.95222.829110368479221.8395833333330.9895270351457431.02475322191683
44210.37219.981651362085218.9858333333330.9958180287521330.964690056260092
45191.09217.067120479245216.061.007120479244960.878177208271354
46198.85213.666020704033212.7058333333330.9601873706993530.973621611937199
47211.04208.841247924187207.8854166666670.9558312575201361.06208561130187
48206.25201.762329272933200.76750.9948292729325421.03264724372992
49201.19NA195.577916666667NANA
50194.37NA195.876666666667NANA
51191.08NA200.742916666667NANA
52192.87NANANANA
53181.61NANANANA
54157.67NANANANA
55196.14NANANANA
56246.35NANANANA
57271.9NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/1k6p51291056515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/1k6p51291056515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/2k6p51291056515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/2k6p51291056515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/3cf7p1291056515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/3cf7p1291056515.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/4cf7p1291056515.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Nov/29/t1291056438rgrt7l9oys2bxwz/4cf7p1291056515.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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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