Home » date » 2010 » Dec » 14 »

oefening 2 opg 9 eigen reeks

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 14 Dec 2010 12:35:24 +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/14/t1292330046fg8304yh8xj8w9p.htm/, Retrieved Tue, 14 Dec 2010 13:34:11 +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/14/t1292330046fg8304yh8xj8w9p.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:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
2834 4683 4120 3849 8435 12854 15883 10520 12562 5060 4520 2150 2905 4820 3950 4053 8700 13520 15400 11100 11950 4900 4633 2300 2945 3960 3900 3767 8820 11980 14085 11600 9814 4930 4360 2640 3050 5485 4366 4790 10100 14830 17930 13580 12490 6400 4980 4930 5856 5120 5100 5623 12035 19846 17030 15860 14890 8053 6080 5987 5682 4980 5450 6035 13240 18400 17689 16490 14062 9556 7555 4328
 
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'Gwilym Jenkins' @ 72.249.127.135


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12834NANA0.475773725786274NA
24683NANA0.58514577630885NA
34120NANA0.53684694070527NA
43849NANA0.565522987595239NA
58435NANA1.22764661933862NA
612854NANA1.80910670161637NA
71588314622.66164936387292.1252.005267552238041.08619076204167
81052011258.56344000157300.791666666671.542101727324290.934399850927928
91256211122.92295761617299.416666666671.523809842012411.12937939495468
1050605203.472772603587300.833333333330.7127231283100450.972427496236942
1145204391.90177406267320.3750.5999558457131771.02916691504667
1221503062.143017164187359.166666666670.4160991530514120.702122659832882
1329053504.925918341277366.791666666670.4757737257862740.828833495395192
1448204313.011992876487370.833333333330.585145776308851.11754848072783
1539503956.293529527497369.50.536846940705270.998409235947606
1640534149.43066764887337.333333333330.5655229875952390.976760506350757
1787009005.248320331017335.3751.227646619338620.966103286719828
181352013290.30086564107346.333333333331.809106701616371.01728321553297
191540014747.23889604667354.252.005267552238041.04426327589556
201110011288.31315249117320.083333333331.542101727324290.983317866013528
211195011096.63723784147282.166666666671.523809842012411.07690282595240
2249005180.190483745457268.166666666670.7127231283100450.945911162026832
2346334356.429384684817261.250.5999558457131771.06348561881606
2423002996.780775205697202.083333333330.4160991530514120.767490241204626
2529453369.96477145997083.1250.4757737257862740.873896375695405
2639604124.790101497137049.166666666670.585145776308850.96004885159191
2739003747.7284930634969810.536846940705271.04063034641339
2837673898.291334240886893.250.5655229875952390.966320799811016
2988208450.045136735136883.1251.227646619338621.04378140675918
301198012457.35798843856885.916666666671.809106701616370.961680639756718
311408513845.28626161296904.458333333332.005267552238041.01731374374337
321160010752.11153105276972.3751.542101727324291.07885785657064
33981410750.98637201167055.333333333331.523809842012410.912846473840683
3449305072.71777535577117.3750.7127231283100450.9718656188505
3543604327.681500411057213.333333333330.5999558457131771.00746785538304
3626403073.065619931787385.416666666670.4160991530514120.859077002091028
3730503646.508249573177664.3750.4757737257862740.836416591229982
3854854626.79641542217907.083333333330.585145776308851.18548548661387
3943664349.041803898458101.083333333330.536846940705271.00389929480244
4047904679.042945531748273.833333333330.5655229875952391.02371362172989
411010010264.25108040528360.916666666671.227646619338620.98399775306366
421483015345.14456089368482.166666666671.809106701616370.966429474883772
431793017434.79873293368694.52.005267552238041.02840303892530
441358013564.64806473778796.208333333331.542101727324291.00113176067592
451249013427.17740704598811.583333333331.523809842012410.930202947452375
4664006326.754119617238876.8750.7127231283100451.01157716563627
4749805394.927955454088992.208333333330.5999558457131770.92308924996216
4849303862.162988764369281.833333333330.4160991530514121.27648678068278
4958564497.647621099579453.333333333330.4757737257862741.30201396226064
5051205565.223954177429510.833333333330.585145776308850.919998915076325
5151005210.546931995249705.833333333330.536846940705270.978784006086496
5256235584.374558298279874.708333333330.5655229875952391.00691669967666
531203512263.47359999829989.416666666671.227646619338620.98136958520478
541984618234.514101712710079.29166666671.809106701616371.08837558759715
551703020285.453664069410116.08333333332.005267552238040.83951782799733
561586015579.8537511573101031.542101727324291.01798131441522
571489015408.38416996910111.751.523809842012410.96635700640309
5880537229.5070520129410143.50.7127231283100451.11390720585268
5960806126.0741460965410210.8750.5999558457131770.992479009395292
6059874244.5581104186110200.83333333330.4160991530514121.41051196479191
6156824837.6870677000710168.04166666670.4757737257862741.17452822402201
6249805981.2138389849910221.750.585145776308850.832606914593294
6354505483.0862288932810213.50.536846940705270.993965765353292
6460355791.8743678300910241.6250.5655229875952391.04197702103490
651324012725.426792466810365.70833333331.227646619338621.04043661685577
661840018738.802594788210358.04166666671.809106701616370.981919730832616
6717689NANA2.00526755223804NA
6816490NANA1.54210172732429NA
6914062NANA1.52380984201241NA
709556NANA0.712723128310045NA
717555NANA0.599955845713177NA
724328NANA0.416099153051412NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/1vfz11292330121.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/1vfz11292330121.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/2vfz11292330121.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/2vfz11292330121.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/3o6y41292330121.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/3o6y41292330121.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/4o6y41292330121.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/14/t1292330046fg8304yh8xj8w9p/4o6y41292330121.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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