Home » date » 2010 » Dec » 20 »

*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, 20 Dec 2010 09:43:49 +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/Dec/20/t12928380993g87lbrb6l6qw7x.htm/, Retrieved Mon, 20 Dec 2010 10:41:40 +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/Dec/20/t12928380993g87lbrb6l6qw7x.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 «
377 370 358 357 349 348 369 381 368 361 351 351 358 354 347 345 343 340 362 370 373 371 354 357 363 364 363 358 357 357 380 378 376 380 379 384 392 394 392 396 392 396 419 421 420 418 410 418 426 428 430 424 423 427 441 449 452 462 455 461 461 463 462 456 455 456 472 472 471 465 459 465 468 467 463 460 462 461 476 476 471 453 443 442 444 438 427 424 416 406 431 434 418 412 404 409 412 406 398 397 385 390 413 413 401 397 397 409 419 424 428 430 424 433 456 459 446 441 439 454 460 457 451 444 437 443 471 469 454 444 436
 
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'RServer@AstonUniversity' @ vre.aston.ac.uk


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1377NANA2.90470679012346NA
2370NANA1.585262345679NA
3358NANA-2.24807098765432NA
4357NANA-5.20177469135803NA
5349NANA-9.64621913580245NA
6348NANA-9.53047839506172NA
7369370.839891975309360.8759.96489197530865-1.8398919753086
8381370.876929012346359.41666666666711.46026234567910.1230709876543
9368363.904706790123358.2916666666675.613040123456814.09529320987656
10361358.784336419753357.3333333333331.451003086419722.21566358024694
11351350.585262345679356.583333333333-5.998070987654330.414737654321016
12351355.645447530864356-0.354552469135815-4.6454475308642
13358358.279706790123355.3752.90470679012346-0.279706790123441
14354356.210262345679354.6251.585262345679-2.21026234567898
15347352.126929012346354.375-2.24807098765432-5.12692901234561
16345349.798225308642355-5.20177469135803-4.79822530864197
17343345.895447530864355.541666666667-9.64621913580245-2.89544753086426
18340346.386188271605355.916666666667-9.53047839506172-6.38618827160491
19362366.339891975309356.3759.96489197530865-4.33989197530872
20370368.46026234567935711.4602623456791.53973765432096
21373363.69637345679358.0833333333335.613040123456819.30362654320993
22371360.742669753086359.2916666666671.4510030864197210.2573302469136
23354354.418595679012360.416666666667-5.99807098765433-0.418595679012299
24357361.353780864198361.708333333333-0.354552469135815-4.35378086419752
25363366.07137345679363.1666666666672.90470679012346-3.07137345679007
26364365.835262345679364.251.585262345679-1.83526234567904
27363362.460262345679364.708333333333-2.248070987654320.539737654320959
28358360.006558641975365.208333333333-5.20177469135803-2.00655864197529
29357356.978780864198366.625-9.646219135802450.0212191358024825
30357359.261188271605368.791666666667-9.53047839506172-2.26118827160491
31380381.089891975309371.1259.96489197530865-1.0898919753086
32378385.043595679012373.58333333333311.460262345679-7.04359567901236
33376381.654706790123376.0416666666675.61304012345681-5.65470679012344
34380380.284336419753378.8333333333331.45100308641972-0.284336419753117
35379375.876929012346381.875-5.998070987654333.12307098765433
36384384.603780864198384.958333333333-0.354552469135815-0.603780864197518
37392391.113040123457388.2083333333332.904706790123460.886959876543244
38394393.210262345679391.6251.5852623456790.789737654321073
39392393.001929012346395.25-2.24807098765432-1.00192901234567
40396393.464891975309398.666666666667-5.201774691358032.5351080246914
41392391.895447530864401.541666666667-9.646219135802450.10455246913574
42396394.719521604938404.25-9.530478395061721.28047839506172
43419417.048225308642407.0833333333339.964891975308651.95177469135808
44421421.376929012346409.91666666666711.460262345679-0.376929012345727
45420418.529706790123412.9166666666675.613040123456811.47029320987656
46418417.117669753086415.6666666666671.451003086419720.882330246913568
47410412.126929012346418.125-5.99807098765433-2.12692901234561
48418420.353780864198420.708333333333-0.354552469135815-2.35378086419752
49426425.82137345679422.9166666666672.904706790123460.17862654320993
50428426.5852623456794251.5852623456791.41473765432102
51430425.251929012346427.5-2.248070987654324.74807098765439
52424425.464891975309430.666666666667-5.20177469135803-1.4648919753086
53423424.728780864198434.375-9.64621913580245-1.72878086419752
54427428.511188271605438.041666666667-9.53047839506172-1.51118827160491
55441451.256558641975441.2916666666679.96489197530865-10.2565586419753
56449455.668595679012444.20833333333311.460262345679-6.6685956790123
57452452.6130401234574475.61304012345681-0.613040123456756
58462451.117669753086449.6666666666671.4510030864197210.8823302469136
59455446.335262345679452.333333333333-5.998070987654338.66473765432102
60461454.520447530864454.875-0.3545524691358156.47955246913574
61461460.279706790123457.3752.904706790123460.720293209876559
62463461.210262345679459.6251.5852623456791.78973765432096
63462459.126929012346461.375-2.248070987654322.87307098765433
64456457.089891975309462.291666666667-5.20177469135803-1.08989197530866
65455452.937114197531462.583333333333-9.646219135802452.06288580246917
66456453.386188271605462.916666666667-9.530478395061722.61381172839509
67472473.339891975309463.3759.96489197530865-1.3398919753086
68472475.293595679012463.83333333333311.460262345679-3.29359567901236
69471469.654706790123464.0416666666675.613040123456811.34529320987656
70465465.70100308642464.251.45100308641972-0.70100308641969
71459458.710262345679464.708333333333-5.998070987654330.289737654321073
72465464.853780864197465.208333333333-0.3545524691358150.146219135802539
73468468.488040123457465.5833333333332.90470679012346-0.488040123456699
74467467.501929012346465.9166666666671.585262345679-0.50192901234567
75463463.835262345679466.083333333333-2.24807098765432-0.835262345678984
76460460.381558641975465.583333333333-5.20177469135803-0.381558641975289
77462454.770447530864464.416666666667-9.646219135802457.2295524691358
78461453.261188271605462.791666666667-9.530478395061727.73881172839515
79476470.798225308642460.8333333333339.964891975308655.20177469135803
80476470.085262345679458.62511.4602623456795.91473765432102
81471461.529706790123455.9166666666675.613040123456819.4702932098765
82453454.367669753086452.9166666666671.45100308641972-1.36766975308649
83443443.501929012346449.5-5.99807098765433-0.501929012345727
84442444.937114197531445.291666666667-0.354552469135815-2.93711419753089
85444444.029706790123441.1252.90470679012346-0.0297067901234414
86438439.085262345679437.51.585262345679-1.08526234567893
87427431.293595679012433.541666666667-2.24807098765432-4.2935956790123
88424424.423225308642429.625-5.20177469135803-0.423225308641918
89416416.645447530864426.291666666667-9.64621913580245-0.645447530864146
90406413.761188271605423.291666666667-9.53047839506172-7.76118827160491
91431430.548225308642420.5833333333339.964891975308650.451774691358082
92434429.376929012346417.91666666666711.4602623456794.62307098765439
93418420.988040123457415.3755.61304012345681-2.98804012345681
94412414.492669753086413.0416666666671.45100308641972-2.49266975308643
95404404.626929012346410.625-5.99807098765433-0.626929012345613
96409408.312114197531408.666666666667-0.3545524691358150.687885802469168
97412410.154706790123407.252.904706790123461.84529320987662
98406407.210262345679405.6251.585262345679-1.21026234567887
99398401.793595679012404.041666666667-2.24807098765432-3.7935956790123
100397397.506558641975402.708333333333-5.20177469135803-0.506558641975289
101385392.145447530864401.791666666667-9.64621913580245-7.14544753086415
102390391.969521604938401.5-9.53047839506172-1.96952160493822
103413411.756558641975401.7916666666679.964891975308651.24344135802471
104413414.293595679012402.83333333333311.460262345679-1.2935956790123
105401410.44637345679404.8333333333335.61304012345681-9.44637345679007
106397408.909336419753407.4583333333331.45100308641972-11.9093364197531
107397404.460262345679410.458333333333-5.99807098765433-7.46026234567904
108409413.520447530864413.875-0.354552469135815-4.5204475308642
109419420.363040123457417.4583333333332.90470679012346-1.36304012345676
110424422.751929012346421.1666666666671.5852623456791.24807098765439
111428422.710262345679424.958333333333-2.248070987654325.28973765432107
112430423.464891975309428.666666666667-5.201774691358036.5351080246914
113424422.603780864198432.25-9.646219135802451.39621913580248
114433426.344521604938435.875-9.530478395061726.65547839506172
115456449.423225308642439.4583333333339.964891975308656.57677469135808
116459454.001929012346442.54166666666711.4602623456794.99807098765439
117446450.488040123457444.8755.61304012345681-4.4880401234567
118441447.867669753086446.4166666666671.45100308641972-6.86766975308637
119439441.543595679012447.541666666667-5.99807098765433-2.5435956790123
120454448.145447530864448.5-0.3545524691358155.85455246913585
121460NA449.541666666667NANA
122457NA450.583333333333NANA
123451NA451.333333333333NANA
124444NA451.791666666667NANA
125437NA451.791666666667NANA
126443NANANANA
127471NANANANA
128469NANANANA
129454NANANANA
130444NANANANA
131436NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/1jvxg1292838225.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/1jvxg1292838225.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/2u4w11292838225.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/2u4w11292838225.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/3u4w11292838225.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/3u4w11292838225.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/44vd31292838225.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928380993g87lbrb6l6qw7x/44vd31292838225.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
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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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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