Home » date » 2010 » Jul » 26 »

Kelly Janbroers - 2de zit - Stap 29/A

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 26 Jul 2010 15:40:02 +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/Jul/26/t1280158806fzeywadj0n415h6.htm/, Retrieved Mon, 26 Jul 2010 17:40:07 +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/Jul/26/t1280158806fzeywadj0n415h6.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 «
33 32 31 29 49 48 33 23 24 24 25 27 24 21 15 21 49 48 35 36 51 50 61 63 61 62 58 65 93 94 86 88 102 107 121 127 125 128 117 127 160 162 153 160 177 178 196 212 212 211 204 216 248 250 240 249 275 277 286 302 290 290 277 285 311 300 291 299 332 337 343 360 353 351 341 348 381 358 353 358 399 409 407 419 418 421 414 424 463 437 430 436 474 489 482 492 502 500 493 504 538 516 502 501 541 571 559 569 576 573 562 570 597 573 562 556 600 630 624 634
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
133NANA1.01361422051094NA
232NANA0.978955013411664NA
331NANA0.896542980583883NA
429NANA0.928941589519901NA
549NANA1.11492877818024NA
648NANA1.05184921376385NA
73329.963425255503831.1250.9626803294940971.10134271094185
82327.913385379215830.29166666666670.9214872752423390.823977446215667
92429.684044780159129.16666666666671.017738678176880.80851515276118
102428.519179738600628.16666666666671.012515256991740.841538929940403
112528.952179323172827.83333333333331.040198059515190.863492855613479
122729.518602828291227.83333333333331.060548604609260.914677437718113
132428.296730322597227.91666666666671.013614220510940.848154529742049
142127.941007674457928.54166666666670.9789550134116640.751583487777966
151527.083069205138130.20833333333330.8965429805838830.553851555242278
162130.113189860270132.41666666666670.9289415895199010.697368830650066
174939.0225072363085351.114928778180241.25568558942845
184839.9702701230264381.051849213763851.20089255970146
193539.510005189653541.04166666666670.9626803294940970.885851566761257
203640.814207232608644.29166666666670.9214872752423390.882045798288537
215148.639427661203547.79166666666671.017738678176881.04853207474477
225052.060159463658551.41666666666671.012515256991740.96042733090173
236157.297576444961755.08333333333331.040198059515191.06461745478179
246362.395609571178358.83333333333331.060548604609261.00968642558307
256163.730994114625662.8751.013614220510940.957148101130924
266265.753145067483467.16666666666670.9789550134116640.942920676058438
275864.065467154223371.45833333333330.8965429805838830.905323922174452
286570.560854903949175.95833333333330.9289415895199010.921190652926203
299390.123409569569780.83333333333331.114928778180241.03191834889702
309490.4590323836914861.051849213763851.03914443392772
318687.924803427127591.33333333333330.9626803294940970.978108527376774
328889.153893879696296.750.9214872752423390.987057280063916
33102103.766939395785101.9583333333331.017738678176880.982972039012877
34107108.3391324981161071.012515256991740.987639438610612
35121116.89225693802112.3751.040198059515191.03514127598852
36127125.1447353438931181.060548604609261.014824951693
37125125.308058010665123.6251.013614220510940.997541594566574
38128126.69309465236129.4166666666670.9789550134116641.01031552154619
39117121.518929826641135.5416666666670.8965429805838830.962812955700917
40127131.561352615756141.6250.9289415895199010.965329083921187
41160164.684271610374147.7083333333331.114928778180240.971556047432046
42162162.379222374795154.3751.051849213763850.997664588059675
43153155.512984893692161.5416666666670.9626803294940970.983840674813038
44160155.385791787739168.6250.9214872752423391.02969517456631
45177178.82516691133175.7083333333331.017738678176880.989793567970023
46178185.332480165196183.0416666666671.012515256991740.960436075971895
47196198.071047166018190.4166666666671.040198059515190.989543917722202
48212209.723486561482197.751.060548604609261.01085483307493
49212207.833149130598205.0416666666671.013614220510941.02004901954685
50211207.905570973302212.3750.9789550134116641.01488381966973
51204197.388879558552220.1666666666670.8965429805838831.03349287181848
52216212.147035506607228.3750.9289415895199011.01816176447713
53248263.401923845083236.251.114928778180240.941526912103569
54250256.388245854939243.751.051849213763850.975083702321699
55240241.392092620645250.750.9626803294940970.994233064531934
56249237.090996859227257.2916666666670.9214872752423391.05022967256679
57275268.301359034381263.6251.017738678176881.02496685439734
58277272.915049894982269.5416666666671.012515256991741.01496784478024
59286286.097807952491275.0416666666671.040198059515190.999658131066468
60302296.688472139441279.751.060548604609261.0179027106185
61290287.824204699254283.9583333333331.013614220510941.00755945909073
62290282.102203031461288.1666666666670.9789550134116641.027996225778
63277262.350889693359292.6250.8965429805838831.05583785259415
64285276.360122882171297.50.9289415895199011.03126311071121
65311337.126589302251302.3751.114928778180240.922502139756092
66300323.093016827797307.1666666666671.051849213763850.928525175026902
67291300.556821204136312.2083333333330.9626803294940970.968202946897535
68299292.457023980037317.3750.9214872752423391.02237243589133
69332328.305535268559322.5833333333331.017738678176881.01125312958375
70337331.978439886166327.8751.012515256991741.01512616336035
71343346.81936967669333.4166666666671.040198059515190.988987438388316
72360359.260839811388338.751.060548604609261.00205744714342
73353348.429888300637343.751.013614220510941.01311630216814
74351341.451350719543348.7916666666670.9789550134116641.02796488946474
75341317.413571084219354.0416666666670.8965429805838831.07430819304674
76348334.264148628911359.8333333333330.9289415895199011.04109280467986
77381407.506468424879365.51.114928778180240.934954484213873
78358389.841614851228370.6251.051849213763850.918321662854338
79353361.767245487802375.7916666666670.9626803294940970.975765507803282
80358351.470604898682381.4166666666670.9214872752423391.0185773575665
81399394.24652045877387.3751.017738678176881.01205712490677
82409398.509129897665393.5833333333331.012515256991741.02632529424114
83407416.252590149329400.1666666666671.040198059515190.977771693514245
84419431.510713500394406.8751.060548604609260.971007177553235
85418419.002778403712413.3751.013614220510940.997606749989745
86421410.997946463997419.8333333333330.9789550134116641.02433601827468
87414382.114089516356426.2083333333330.8965429805838831.08344604755088
88424401.922061065611432.6666666666670.9289415895199011.05493089599475
89463489.5930997184439.1251.114928778180240.945683262828469
90437468.379689478929445.2916666666671.051849213763850.933003735678977
91430434.971062209749451.8333333333330.9626803294940970.988571510517286
92436422.617101608017458.6250.9214872752423391.03166672229084
93474473.460514243537465.2083333333331.017738678176881.00113945247858
94489477.738448757269471.8333333333331.012515256991741.02357262906518
95482497.518063548953478.2916666666671.040198059515190.968809044965608
96492514.056746559148484.7083333333331.060548604609260.957092778751013
97502497.6845822708744911.013614220510941.00867098938335
98500486.255113120019496.7083333333330.9789550134116641.02826682231018
99493450.251356040731502.2083333333330.8965429805838831.09494395382876
100504472.289386471743508.4166666666670.9289415895199011.06714233780512
101538574.234776128583515.0416666666671.114928778180240.936899021733108
102516548.495537927276521.4583333333331.051849213763850.94075514624955
103502508.054543890509527.750.9626803294940970.98808288605364
104501491.959019070003533.8750.9214872752423391.0183775082467
105541549.366857324229539.7916666666671.017738678176880.984769999841306
106571552.242696417577545.4166666666671.012515256991741.03396568882504
107559572.759056520552550.6251.040198059515190.975977583655968
108569589.090560335253555.4583333333331.060548604609260.965895633561299
109576567.961834892966560.3333333333331.013614220510941.01415265007824
110573553.231951954267565.1250.9789550134116641.03573193481668
111562510.91743106024569.8750.8965429805838831.09998204373993
112570533.947884476127574.7916666666670.9289415895199011.06751991453107
113597646.612235978784579.9583333333331.114928778180240.923273589303974
114573615.726233507016585.3751.051849213763850.930608391876276
115562NANA0.962680329494097NA
116556NANA0.921487275242339NA
117600NANA1.01773867817688NA
118630NANA1.01251525699174NA
119624NANA1.04019805951519NA
120634NANA1.06054860460926NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/1107i1280158799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/1107i1280158799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/2107i1280158799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/2107i1280158799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/3cr731280158799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/3cr731280158799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/4cr731280158799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/26/t1280158806fzeywadj0n415h6/4cr731280158799.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')
 




Copyright

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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