Home » date » 2010 » Jul » 20 »

tijdreeks 1 - stap 29

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 20 Jul 2010 11:58:56 +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/20/t1279627143u2uppzaaoqzw4xs.htm/, Retrieved Tue, 20 Jul 2010 13:59:04 +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/20/t1279627143u2uppzaaoqzw4xs.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:
Vanhille Olivier
 
Dataseries X:
» Textbox « » Textfile « » CSV «
568 567 566 564 584 583 568 558 559 559 560 562 563 552 552 555 575 567 548 541 544 546 551 550 546 532 523 528 555 543 525 517 519 521 520 516 509 494 484 482 508 500 480 467 471 482 481 477 471 455 441 434 459 448 432 414 415 423 425 427 415 399 386 377 397 379 361 350 348 363 367 365 354 327 312 307 335 317 298 286 288 303 310 301 293 264 255 251 279 253 233 226 232 245 250 242 230 196 188 181 212 186 166 155 157 173 182 182 168 131 114 106 134 103 83 74 83 96 95 100
 
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
1568NANA10.5158179012346NA
2567NANA-7.10918209876543NA
3566NANA-13.2202932098765NA
4564NANA-12.6508487654321NA
5584NANA17.5343364197531NA
6583NANA4.27044753086419NA
7568558.525077160494566.291666666667-7.766589506172869.47492283950623
8558550.784336419753565.458333333333-14.67399691358027.215663580247
9559555.798225308642564.25-8.451774691358033.20177469135808
10559568.163966049383563.2916666666674.87229938271606-9.16396604938268
11560575.062114197531562.54166666666712.5204475308642-15.0621141975308
12562575.659336419753561.514.1593364197531-13.659336419753
13563570.51581790123456010.5158179012346-7.51581790123441
14552551.349151234568558.458333333333-7.109182098765430.650848765432102
15552543.904706790123557.125-13.22029320987658.09529320987656
16555543.307484567901555.958333333333-12.650848765432111.6925154320988
17575572.57600308642555.04166666666717.53433641975312.42399691358014
18567558.437114197531554.1666666666674.270447530864198.56288580246917
19548545.191743827161552.958333333333-7.766589506172862.80825617283949
20541536.742669753086551.416666666667-14.67399691358024.25733024691351
21544540.923225308642549.375-8.451774691358033.07677469135808
22546551.913966049383547.0416666666674.87229938271606-5.91396604938268
23551557.603780864198545.08333333333312.5204475308642-6.60378086419757
24550557.409336419753543.2514.1593364197531-7.409336419753
25546551.807484567901541.29166666666710.5158179012346-5.80748456790116
26532532.224151234568539.333333333333-7.10918209876543-0.224151234567785
27523524.07137345679537.291666666667-13.2202932098765-1.07137345679007
28528522.557484567901535.208333333333-12.65084876543215.44251543209884
29555550.409336419753532.87517.53433641975314.590663580247
30543534.437114197531530.1666666666674.270447530864198.56288580246917
31525519.44174382716527.208333333333-7.766589506172865.5582561728396
32517509.409336419753524.083333333333-14.67399691358027.590663580247
33519512.423225308642520.875-8.451774691358036.57677469135808
34521522.205632716049517.3333333333334.87229938271606-1.20563271604931
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36516523.867669753086509.70833333333314.1593364197531-7.86766975308637
37509516.557484567901506.04166666666710.5158179012346-7.55748456790121
38494494.974151234568502.083333333333-7.10918209876543-0.974151234567955
39484484.779706790123498-13.2202932098765-0.779706790123441
40482481.724151234568494.375-12.65084876543210.275848765432102
41508508.659336419753491.12517.5343364197531-0.659336419753117
42500492.145447530864487.8754.270447530864197.8545524691358
43480476.900077160494484.666666666667-7.766589506172863.09992283950623
44467466.784336419753481.458333333333-14.67399691358020.21566358024694
45471469.589891975309478.041666666667-8.451774691358031.41010802469134
46482479.122299382716474.254.872299382716062.87770061728395
47481482.728780864198470.20833333333312.5204475308642-1.72878086419757
48477480.15933641975346614.1593364197531-3.15933641975312
49471472.349151234568461.83333333333310.5158179012346-1.3491512345679
50455450.515817901235457.625-7.109182098765434.48418209876547
51441439.863040123457453.083333333333-13.22029320987651.13695987654324
52434435.640817901235448.291666666667-12.6508487654321-1.64081790123453
53459461.034336419753443.517.5343364197531-2.03433641975312
54448443.353780864198439.0833333333334.270447530864194.64621913580243
55432426.900077160494434.666666666667-7.766589506172865.09992283950618
56414415.32600308642430-14.6739969135802-1.32600308641969
57415416.923225308642425.375-8.45177469135803-1.92322530864197
58423425.580632716049420.7083333333334.87229938271606-2.58063271604931
59425428.270447530864415.7512.5204475308642-3.27044753086415
60427424.45100308642410.29166666666714.15933641975312.54899691358025
61415414.974151234568404.45833333333310.51581790123460.0258487654321016
62399391.724151234568398.833333333333-7.109182098765437.2758487654321
63386380.154706790123393.375-13.22029320987655.84529320987656
64377375.432484567901388.083333333333-12.65084876543211.56751543209884
65397400.70100308642383.16666666666717.5343364197531-3.70100308641963
66379382.437114197531378.1666666666674.27044753086419-3.43711419753089
67361365.275077160494373.041666666667-7.76658950617286-4.27507716049388
68350352.82600308642367.5-14.6739969135802-2.8260030864198
69348352.964891975309361.416666666667-8.45177469135803-4.9648919753086
70363360.288966049383355.4166666666674.872299382716062.71103395061738
71367362.437114197531349.91666666666712.52044753086424.56288580246917
72365358.909336419753344.7514.15933641975316.09066358024688
73354350.057484567901339.54166666666710.51581790123463.94251543209873
74327327.140817901235334.25-7.10918209876543-0.140817901234527
75312315.863040123457329.083333333333-13.2202932098765-3.86304012345676
76307311.432484567901324.083333333333-12.6508487654321-4.43248456790121
77335336.742669753086319.20833333333317.5343364197531-1.74266975308643
78317318.437114197531314.1666666666674.27044753086419-1.43711419753083
79298301.191743827161308.958333333333-7.76658950617286-3.19174382716051
80286289.117669753086303.791666666667-14.6739969135802-3.11766975308637
81288290.339891975309298.791666666667-8.45177469135803-2.3398919753086
82303298.955632716049294.0833333333334.872299382716064.04436728395058
83310301.937114197531289.41666666666712.52044753086428.06288580246917
84301298.57600308642284.41666666666714.15933641975312.4239969135802
85293289.557484567901279.04166666666710.51581790123463.44251543209873
86264266.724151234568273.833333333333-7.10918209876543-2.7241512345679
87255255.779706790123269-13.2202932098765-0.779706790123441
88251251.599151234568264.25-12.6508487654321-0.599151234567898
89279276.867669753086259.33333333333317.53433641975312.13233024691363
90253258.645447530864254.3754.27044753086419-5.64544753086417
91233241.525077160494249.291666666667-7.76658950617286-8.5250771604938
92226229.159336419753243.833333333333-14.6739969135802-3.15933641975306
93232229.756558641975238.208333333333-8.451774691358032.24344135802471
94245237.372299382716232.54.872299382716067.62770061728392
95250239.312114197531226.79166666666712.520447530864210.6878858024691
96242235.367669753086221.20833333333314.15933641975316.63233024691357
97230226.140817901235215.62510.51581790123463.85918209876542
98196202.765817901235209.875-7.10918209876543-6.76581790123453
99188190.57137345679203.791666666667-13.2202932098765-2.57137345679007
100181185.015817901235197.666666666667-12.6508487654321-4.01581790123458
101212209.367669753086191.83333333333317.53433641975312.63233024691363
102186190.770447530864186.54.27044753086419-4.7704475308642
103166173.650077160494181.416666666667-7.76658950617286-7.65007716049382
104155161.45100308642176.125-14.6739969135802-6.45100308641972
105157161.881558641975170.333333333333-8.45177469135803-4.88155864197532
106173168.997299382716164.1254.872299382716064.00270061728398
107182170.270447530864157.7512.520447530864211.7295524691358
108182165.20100308642151.04166666666714.159336419753116.7989969135802
109168154.640817901235144.12510.515817901234613.3591820987654
110131130.182484567901137.291666666667-7.109182098765430.817515432098759
111114117.613040123457130.833333333333-13.2202932098765-3.6130401234568
112106111.890817901235124.541666666667-12.6508487654321-5.89081790123458
113134135.242669753086117.70833333333317.5343364197531-1.2426697530864
114103114.937114197531110.6666666666674.27044753086419-11.9371141975308
11583NANA-7.76658950617286NA
11674NANA-14.6739969135802NA
11783NANA-8.45177469135803NA
11896NANA4.87229938271606NA
11995NANA12.5204475308642NA
120100NANA14.1593364197531NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/1h18b1279627132.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/1h18b1279627132.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/2h18b1279627132.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/2h18b1279627132.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/3sb8w1279627132.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/3sb8w1279627132.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/4sb8w1279627132.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Jul/20/t1279627143u2uppzaaoqzw4xs/4sb8w1279627132.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')
 




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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