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Classic Decomposition omzet product Y

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 15 Aug 2010 16:13:21 +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/15/t1281888931o2wnu6sljtdzdjs.htm/, Retrieved Sun, 15 Aug 2010 18:15:37 +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/15/t1281888931o2wnu6sljtdzdjs.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 «
118 117 116 114 112 111 112 114 115 115 116 118 126 131 122 124 119 112 109 108 117 122 127 124 129 141 127 133 114 98 93 101 111 128 126 134 140 158 144 146 138 119 113 120 127 141 144 150 156 174 163 167 160 141 132 144 155 164 162 181 187 209 189 201 193 177 159 158 155 164 163 185 191 217 193 192 184 166 145 146 138 149 145 166
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1118NANA1.06687655489819NA
2117NANA1.17574840608764NA
3116NANA1.06926790388455NA
4114NANA1.09427414764089NA
5112NANA1.02447744766666NA
6111NANA0.91268261338734NA
711299.288798170502115.1666666666670.8621313878770071.12802251677646
8114102.971899080396116.0833333333330.887051535509511.10709815996493
9115107.938070050847116.9166666666670.9232051608055331.06542575706446
10115115.147815045085117.5833333333330.9792868749404840.998716301781086
11116115.692538086344118.2916666666670.9780277964326391.00265757773787
12118121.824336519403118.6251.026970170869570.96860777880129
13126126.469324945223118.5416666666671.066876554898190.996289021504415
14131138.934269986022118.1666666666671.175748406087640.94289191581875
15122126.1736126583761181.069267903884550.9669216679269
16124129.534702226990118.3751.094274147640890.957272436406335
17119122.040875953290119.1251.024477447666660.975083135633554
18112109.369799837583119.8333333333330.912682613387341.02404868772114
19109103.635377251049120.2083333333330.8621313878770071.05176439639869
20108107.111472912773120.750.887051535509511.00829534935021
21117112.054026392772121.3750.9232051608055331.04413918684092
22122119.432195122950121.9583333333330.9792868749404841.02150010618499
23127119.441644639336122.1250.9780277964326391.06328073749727
24124124.605714065508121.3333333333331.026970170869570.99513895434049
25129128.114092967358120.0833333333331.066876554898191.00691498501159
26141140.061028875190119.1251.175748406087641.00670401418832
27127126.797352268976118.5833333333331.069267903884551.00159820159805
28133129.762676007749118.5833333333331.094274147640891.02494803661461
29114121.699383470735118.7916666666671.024477447666660.936734408579922
3098108.761344761991119.1666666666670.912682613387340.901055427500083
3193103.491688686402120.0416666666670.8621313878770070.8986228863441
32101107.518038199882121.2083333333330.887051535509510.939377258839448
33111113.208032843778122.6250.9232051608055330.980495793555344
34128121.309161633252123.8750.9792868749404841.05515526013588
35126122.660986135927125.4166666666670.9780277964326391.02722148230875
36134130.724744666939127.2916666666671.026970170869571.02505459346205
37140137.6270755818661291.066876554898191.01724169759549
38158153.582135545197130.6251.175748406087641.02876548394851
39144141.232468971417132.0833333333331.069267903884551.01959557209995
40146145.857624929300133.2916666666671.094274147640891.00097612360525
41138137.877589831804134.5833333333331.024477447666661.00088781772546
42119124.1248354206781360.912682613387340.95871224800976
43113118.399377268442137.3333333333330.8621313878770070.95439691159692
44120123.004479590652138.6666666666670.887051535509510.975574226234275
45127129.364123157875140.1250.9232051608055330.981725047871347
46141138.854718142603141.7916666666670.9792868749404841.01544983048537
47144140.428491104453143.5833333333330.9780277964326391.02543293648929
48150149.338579013950145.4166666666671.026970170869571.00442900280970
49156156.964213139396147.1251.066876554898190.993857114815466
50174175.088533473217148.9166666666671.175748406087640.993782953962638
51163161.548559145223151.0833333333331.069267903884551.00898454843829
52167167.651918369814153.2083333333331.094274147640890.996111476825595
53160158.708631267693154.9166666666671.024477447666661.00813672654091
54141143.253141859588156.9583333333330.912682613387340.984271605981275
55132137.545878507544159.5416666666670.8621313878770070.95967979144326
56144143.961072117064162.2916666666670.887051535509511.00027040561982
57155152.174984006112164.8333333333330.9232051608055331.01856426016627
58164163.867337073374167.3333333333330.9792868749404841.00080957516608
59162166.386978868103170.1250.9780277964326390.973633881100875
60181177.6658395604361731.026970170869571.01876646882604
61187187.370194953994175.6251.066876554898190.998024259119305
62209208.499384012874177.3333333333331.175748406087641.00240104300306
63189190.240581232792177.9166666666671.069267903884550.99347888224083
64201194.689608767775177.9166666666671.094274147640891.03241257338881
65193182.314299124345177.9583333333331.024477447666661.05861142503346
66177162.609618951844178.1666666666670.912682613387341.08849649326352
67159153.890452736046178.50.8621313878770071.03320249679633
68158158.7822248562021790.887051535509510.99507359934709
69155165.715326364593179.50.9232051608055330.935338953857423
70164175.577975952871179.2916666666670.9792868749404840.934057925602362
71163174.618712821411178.5416666666670.9780277964326390.93346238422171
72185182.501157448280177.7083333333331.026970170869571.01369220111619
73191188.48152469868176.6666666666671.066876554898191.01336192130951
74217206.441824302221175.5833333333331.175748406087641.05114358843450
75193186.453590739868174.3751.069267903884551.03511012705175
76192189.355022298025173.0416666666671.094274147640891.01396835251516
77184175.868628516109171.6666666666671.024477447666661.04623548584247
78166155.270129602521170.1250.912682613387341.06910453687999
79145NANA0.862131387877007NA
80146NANA0.88705153550951NA
81138NANA0.923205160805533NA
82149NANA0.979286874940484NA
83145NANA0.978027796432639NA
84166NANA1.02697017086957NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/1eahv1281888799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/1eahv1281888799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/2eahv1281888799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/2eahv1281888799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/361yg1281888799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/361yg1281888799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/4hsy11281888799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/15/t1281888931o2wnu6sljtdzdjs/4hsy11281888799.ps (open in new window)


 
Parameters (Session):
 
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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