Home » date » 2010 » Aug » 16 »

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: Mon, 16 Aug 2010 11:51:12 +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/16/t1281959463n8cprwe2j7mcc5m.htm/, Retrieved Mon, 16 Aug 2010 13:51:08 +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/16/t1281959463n8cprwe2j7mcc5m.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:
Magali De Reu
 
Dataseries X:
» Textbox « » Textfile « » CSV «
333 332 331 329 349 348 333 323 324 324 325 327 329 333 329 333 355 358 338 326 320 322 322 324 326 330 331 333 364 363 341 327 313 321 312 312 312 314 312 319 356 351 329 313 298 303 278 275 276 276 273 287 320 313 281 266 258 259 237 231 237 236 229 243 271 262 227 208 212 222 200 193 204 203 190 209 240 234 210 195 202 204 180 169 178 181 163 174 194 187 160 143 151 154 141 127 134 138 120 129 151 152 124 99 104 109 96 87 94 89 63 76 100 104 80 55 60 71 62 61
 
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
1333NANA0.966781825994328NA
2332NANA0.9820088306483NA
3331NANA0.924076445690105NA
4329NANA0.995886048486366NA
5349NANA1.15000771660726NA
6348NANA1.15756053087607NA
7333338.89293311812331.3333333333331.022815693515450.982611224542513
8323316.933412159207331.2083333333330.9569004770186161.01914152187194
9324321.312605422447331.1666666666670.9702444048991851.00836380064834
10324332.139501389371331.251.002685287213200.975493726716267
11325313.732932955014331.6666666666670.9459284410703951.03591292421507
12327307.442995028928332.3333333333330.9251042979807281.06361180865165
13329321.898065480028332.9583333333330.9667818259943281.02206268158021
14333327.295359848156333.2916666666670.98200883064831.01742963956009
15329307.948475526228333.250.9240764456901051.06836054128146
16333331.630054145963330.9958860484863661.00413094602529
17355382.712984689258332.7916666666671.150007716607260.927588073052292
18358384.937108205079332.5416666666671.157560530876070.930022053912433
19338339.873131491073332.2916666666671.022815693515450.994488733243329
20326317.730829223390332.0416666666670.9569004770186161.02602571112417
21320322.1211424265293320.9702444048991850.993415078530546
22322332.975072462049332.0833333333331.002685287213200.96703935708791
23322314.481792970862332.4583333333330.9459284410703951.02390665277667
24324308.098277239998333.0416666666670.9251042979807281.05161250138252
25326322.300891240859333.3750.9667818259943281.01147719059944
26330327.540862055818333.5416666666670.98200883064831.00750788139454
27331307.986978711465333.2916666666670.9240764456901051.07472076054908
28333331.588558893940332.9583333333330.9958860484863661.00425660375849
29364382.377565771914332.51.150007716607260.95193869249412
30363383.827779362989331.5833333333331.157560530876070.945736654607033
31341338.040586706857330.51.022815693515451.00875460938573
32327315.059482058379329.250.9569004770186161.03789924957538
33313318.038030555912327.7916666666670.9702444048991850.984159031084723
34321327.293189167840326.4166666666671.002685287213200.980772013057035
35312307.899707568413325.50.9459284410703951.01331697410163
36312300.350528744410324.6666666666670.9251042979807281.03878625186475
37312312.915051013498323.6666666666670.9667818259943280.997075720677117
38314316.779681953297322.5833333333330.98200883064830.99122518863534
39312296.975067733658321.3750.9240764456901051.05059324468213
40319318.6835355156373200.9958860484863661.00099303681896
41356365.510785928341317.8333333333331.150007716607260.973979465738104
42351364.486872159602314.8751.157560530876070.96299764630838
43329318.948027094569311.8333333333331.022815693515451.03151602158194
44313295.443022279498308.750.9569004770186161.05942593460167
45298296.450092546905305.5416666666670.9702444048991851.00522822388004
46303303.395856489259302.5833333333331.002685287213200.998695247542798
47278283.542050210851299.750.9459284410703950.980454221140287
48275274.447608400949296.6666666666670.9251042979807281.00201273970748
49276283.347640168504293.0833333333330.9667818259943280.97406846175202
50276283.92330316119289.1250.98200883064830.972093508799834
51273263.823825244525285.50.9240764456901051.03478144836605
52287280.8398656731552820.9958860484863661.02193468620304
53320320.22923208693278.4583333333331.150007716607260.999284162518718
54313318.232682613346274.9166666666671.157560530876070.9835570546357
55281277.651843468882271.4583333333331.022815693515451.01205883054579
56266256.608811253826268.1666666666670.9569004770186161.03659729648521
57258256.791352496651264.6666666666670.9702444048991851.00470672976951
58259261.7008599626442611.002685287213200.98967959080062
59237243.221850410225257.1250.9459284410703950.974419031843844
60231234.012841376708252.9583333333330.9251042979807280.98712531603401
61237240.325848911757248.5833333333330.9667818259943280.98616108534801
62236239.528320608965243.9166666666670.98200883064830.985269714245087
63229221.393315113254239.5833333333330.9240764456901051.03435824104650
64243235.153593198843236.1250.9958860484863661.03336715673539
65271267.999714957684233.0416666666671.150007716607261.01119510534849
66262266.142458723923229.9166666666671.157560530876070.984435182782242
67227232.136545107445226.9583333333331.022815693515450.977872742505635
68208214.545061118216224.2083333333330.9569004770186160.969493303252461
69212214.626147733741221.2083333333330.9702444048991850.987764082981173
70222218.752506827012218.1666666666671.002685287213201.01484551295019
71200203.808165365625215.4583333333330.9459284410703950.981314951936328
72193197.0472154698952130.9251042979807280.979460681744507
73204204.111813013053211.1250.9667818259943280.999452197247176
74203206.099103332312209.8750.98200883064830.984963043107883
75190193.054970778758208.9166666666670.9240764456901050.984175642997254
76209206.895326573042207.750.9958860484863661.01017264846828
77240237.093257573864206.1666666666671.150007716607261.01225991179960
78234236.52820180901204.3333333333331.157560530876070.989311203528063
79210206.864474013500202.251.022815693515451.01515739230456
80195191.619320522978200.250.9569004770186161.01764268586171
81202192.310526421059198.2083333333330.9702444048991851.05038452007419
82204196.150309311081195.6251.002685287213201.04001875253977
83180181.854742795783192.250.9459284410703950.989800965499887
84169174.266522132120188.3750.9251042979807280.969778922149334
85178178.210116591621184.3333333333330.9667818259943280.998820961482772
86181176.843423585915180.0833333333330.98200883064831.02350427474090
87163162.444938515273175.7916666666670.9240764456901051.00341692077205
88174170.877447819452171.5833333333330.9958860484863661.01827363540593
89194193.057545425444167.8751.150007716607261.00488172877408
90187190.418707329113164.51.157560530876070.982046368358103
91160164.588092014862160.9166666666671.022815693515450.972123791225143
92143150.512470864387157.2916666666670.9569004770186160.950087385973782
93151149.134650403046153.7083333333330.9702444048991851.01250782156872
94154150.444571635613150.0416666666671.002685287213201.02363281257497
95141138.460275561679146.3750.9459284410703951.01834262157878
96127132.405552648492143.1250.9251042979807280.959174275244769
97134135.510585943538140.1666666666670.9667818259943280.988852635142706
98138134.371541660376136.8333333333330.98200883064831.02700317563369
99120122.940670462021133.0416666666670.9240764456901050.976080572434087
100129128.676776514843129.2083333333330.9958860484863661.00251190225550
101151144.278051446019125.4583333333331.150007716607261.04659023660640
102152141.125921389307121.9166666666671.157560530876071.07705231259887
103124121.288894322708118.5833333333331.022815693515451.02235246427492
10499109.923942297514114.8750.9569004770186160.900622720863236
105104107.171579891156110.4583333333330.9702444048991850.970406521072313
106109106.159304783697105.8751.002685287213201.02675879634
1079696.0511504536897101.5416666666670.9459284410703950.999467466517079
1088790.120577028289297.41666666666670.9251042979807280.965373312830547
1099490.474665882635993.58333333333330.9667818259943281.03896487578011
1108988.298960689126389.91666666666670.98200883064831.0079393834922
1116379.701593440771686.250.9240764456901050.790448437480952
1127682.492561016287382.83333333333330.9958860484863660.921295193938694
11310091.80894937581379.83333333333331.150007716607261.08921843327776
11410489.518014387749277.33333333333331.157560530876071.16177733287874
11580NANA1.02281569351545NA
11655NANA0.956900477018616NA
11760NANA0.970244404899185NA
11871NANA1.00268528721320NA
11962NANA0.945928441070395NA
12061NANA0.925104297980728NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/1vfn41281959470.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/1vfn41281959470.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/25om71281959470.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/25om71281959470.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/35om71281959470.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/35om71281959470.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/4gyma1281959470.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Aug/16/t1281959463n8cprwe2j7mcc5m/4gyma1281959470.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')
 




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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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