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*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 13:11:51 +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/t12928506763ot7axpc3wh8o19.htm/, Retrieved Mon, 20 Dec 2010 14:11:17 +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/t12928506763ot7axpc3wh8o19.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 «
313737 312276 309391 302950 300316 304035 333476 337698 335932 323931 313927 314485 313218 309664 302963 298989 298423 301631 329765 335083 327616 309119 295916 291413 291542 284678 276475 272566 264981 263290 296806 303598 286994 276427 266424 267153 268381 262522 255542 253158 243803 250741 280445 285257 270976 261076 255603 260376 263903 264291 263276 262572 256167 264221 293860 300713 287224 275902 271115 277509 279681
 
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
1313737NANA-2121.01649305556NA
2312276NANA-5295.33940972223NA
3309391NANA-10127.4539930556NA
4302950NANA-11862.5269097222NA
5300316NANA-16894.0164930556NA
6304035NANA-11935.6414930556NA
7333476335871.660590278316824.54166666719047.1189236111-2395.66059027781
8337698340169.275173611316694.08333333323475.1918402778-2471.27517361107
9335932332178.764756944316317.41666666715861.34809027783753.23524305562
10323931322450.306423611315884.5416666676565.764756944461480.69357638893
11313927312964.118923611315640.625-2676.5060763889962.881076388818
12314485311424.660590278315461.583333333-4036.922743055573060.33940972219
13313218313085.775173611315206.791666667-2121.01649305556132.224826388876
14309664309647.868923611314943.208333333-5295.3394097222316.1310763889342
15302963304360.296006944314487.75-10127.4539930556-1397.29600694444
16298989301661.556423611313524.083333333-11862.5269097222-2672.55642361112
17298423295262.441840278312156.458333333-16894.01649305563160.55815972225
18301631298509.025173611310444.666666667-11935.64149305563121.97482638888
19329765327627.285590278308580.16666666719047.11892361112137.71440972225
20335083330111.108506944306635.91666666723475.19184027784971.8914930555
21327616320352.514756944304491.16666666715861.34809027787263.48524305562
22309119308852.306423611302286.5416666676565.76475694446266.693576388934
23295916297115.660590278299792.166666667-2676.5060763889-1199.66059027781
24291413292764.285590278296801.208333333-4036.92274305557-1351.28559027775
25291542291709.358506944293830.375-2121.01649305556-167.35850694438
26284678285849.868923611291145.208333333-5295.33940972223-1171.86892361107
27276475278013.296006944288140.75-10127.4539930556-1538.29600694444
28272566273223.473090278285086-11862.5269097222-657.473090277752
29264981265600.983506944282495-16894.0164930556-619.983506944438
30263290268319.691840278280255.333333333-11935.6414930556-5029.69184027775
31296806297326.577256944278279.45833333319047.1189236111-520.577256944438
32303598299866.441840278276391.2523475.19184027783731.55815972225
33286994290457.223090278274595.87515861.3480902778-3463.22309027781
34276427279480.7647569442729156565.76475694446-3053.76475694444
35266424268547.410590278271223.916666667-2676.5060763889-2123.41059027781
36267153265781.702256944269818.625-4036.922743055571371.29774305556
37268381266493.025173611268614.041666667-2121.016493055561887.97482638888
38262522261872.785590278267168.125-5295.33940972223649.214409722248
39255542255609.046006944265736.5-10127.4539930556-67.046006944438
40253158252566.931423611264429.458333333-11862.5269097222591.068576388876
41243803246444.941840278263338.958333333-16894.0164930556-2641.94184027781
42250741250670.066840278262605.708333333-11935.641493055670.9331597222481
43280445281183.868923611262136.7519047.1189236111-738.868923611095
44285257285499.066840278262023.87523475.1918402778-242.066840277694
45270976278281.181423611262419.83333333315861.3480902778-7305.1814236111
46261076269700.098090278263134.3333333336565.76475694446-8624.09809027778
47255603261365.243923611264041.75-2676.5060763889-5762.2439236111
48260376261081.660590278265118.583333333-4036.92274305557-705.660590277752
49263903264118.191840278266239.208333333-2121.01649305556-215.191840277752
50264291262146.827256944267442.166666667-5295.339409722232144.17274305556
51263276258635.712673611268763.166666667-10127.45399305564640.28732638893
52262572258195.389756944270057.916666667-11862.52690972224376.61024305556
53256167254427.983506944271322-16894.01649305561739.01649305556
54264221260746.566840278272682.208333333-11935.64149305563474.43315972225
55293860293100.618923611274053.519047.1189236111759.381076388934
56300713NANA23475.1918402778NA
57287224NANA15861.3480902778NA
58275902NANA6565.76475694446NA
59271115NANA-2676.5060763889NA
60277509NANA-4036.92274305557NA
61279681NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/1ak071292850707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/1ak071292850707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/2ak071292850707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/2ak071292850707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/3kbia1292850707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/3kbia1292850707.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/4d2hd1292850707.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/20/t12928506763ot7axpc3wh8o19/4d2hd1292850707.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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