Home » date » 2009 » Jun » 01 »

datareeks-cinemaprijs-seda hovhannesian

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Mon, 01 Jun 2009 10:16:34 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq.htm/, Retrieved Mon, 01 Jun 2009 18:17:12 +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/2009/Jun/01/t1243873028685kvd1ujx7wzcq.htm/},
    year = {2009},
}
@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 = {2009},
    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 «
5,44 5,44 5,44 5,44 5,44 5,49 5,49 5,49 5,49 5,49 5,49 5,60 5,60 5,60 5,60 5,60 5,60 5,60 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,67 5,82 5,82 5,95 5,95 5,95 5,95 5,95 5,95 6,02 6,02 6,05 6,05 6,05 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,12 6,17 6,17 6,17 6,17 6,17 6,28 6,27 6,28 6,28 6,27 6,27 6,28 6,59 6,59 6,59 6,59 6,59 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,63 6,79 6,79 6,79 6,81 6,80 6,80 6,85 6,85 6,85 6,85 6,85 6,85 6,86 6,86 6,88 6,88 6,88 6,91 6,91 6,91 6,91 6,99 6,99 6,99 7,02 7,02 7,05 7,05 7,05 7,05 7,10 7,10 7,10 7,10 7,12 7,13 7,18
 
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
15.44NANA0.0195008680555553NA
25.44NANA0.0105425347222223NA
35.44NANA-0.00633246527777766NA
45.44NANA0.000542534722222525NA
55.44NANA-0.000238715277777875NA
65.49NANA0.0193967013888889NA
75.495.496948784722225.4850.0119487847222221-0.00694878472222094
85.495.506427951388895.498333333333330.00809461805555554-0.0164279513888888
95.495.504657118055565.51166666666667-0.00700954861111127-0.0146571180555553
105.495.501636284722225.525-0.0233637152777777-0.0116362847222202
115.495.524605034722225.53833333333333-0.0137282986111113-0.0346050347222207
125.65.530230034722225.54958333333333-0.01935329861111090.0697699652777777
135.65.581167534722225.561666666666670.01950086805555530.0188324652777787
145.65.587209201388895.576666666666670.01054253472222230.0127907986111113
155.65.585334201388895.59166666666667-0.006332465277777660.0146657986111105
165.65.607209201388895.606666666666670.000542534722222525-0.00720920138889003
175.65.621427951388895.62166666666667-0.000238715277777875-0.0214279513888895
185.65.651480034722225.632083333333330.0193967013888889-0.0514800347222231
195.675.649865451388895.637916666666670.01194878472222210.0201345486111109
205.675.651844618055565.643750.008094618055555540.0181553819444433
215.675.642573784722225.64958333333333-0.007009548611111270.0274262152777771
225.675.638302951388895.66166666666667-0.02336371527777770.031697048611111
235.675.666271701388895.68-0.01372829861111130.00372829861111068
245.675.684396701388895.70375-0.0193532986111109-0.0143967013888897
255.675.749500868055565.730.0195008680555553-0.0795008680555567
265.675.763875868055565.753333333333330.0105425347222223-0.093875868055556
275.675.770334201388895.77666666666667-0.00633246527777766-0.100334201388890
285.825.800542534722225.80.0005425347222225250.0194574652777773
295.825.823094618055565.82333333333333-0.000238715277777875-0.003094618055556
305.955.868980034722225.849583333333330.01939670138888890.0810199652777772
315.955.890698784722225.878750.01194878472222210.0593012152777765
325.955.917261284722225.909166666666670.008094618055555540.0327387152777758
335.955.933823784722225.94083333333333-0.007009548611111270.0161762152777767
345.955.942886284722225.96625-0.02336371527777770.00711371527777782
355.955.974605034722225.98833333333333-0.0137282986111113-0.0246050347222226
366.025.988563368055566.00791666666667-0.01935329861111090.0314366319444428
376.026.041584201388896.022083333333330.0195008680555553-0.0215842013888894
386.056.046792534722226.036250.01054253472222230.00320746527777782
396.056.044084201388896.05041666666667-0.006332465277777660.00591579861111047
406.056.065125868055566.064583333333330.000542534722222525-0.0151258680555557
416.126.078511284722226.07875-0.0002387152777778750.0414887152777785
426.126.109396701388896.090.01939670138888890.0106032986111124
436.126.112365451388896.100416666666670.01194878472222210.00763454861111335
446.126.119761284722226.111666666666670.008094618055555540.000238715277778745
456.126.114657118055556.12166666666667-0.007009548611111270.0053428819444461
466.126.108302951388896.13166666666667-0.02336371527777770.0116970486111132
476.126.125021701388896.13875-0.0137282986111113-0.00502170138888669
486.126.128146701388896.1475-0.0193532986111109-0.00814670138888829
496.176.179917534722226.160416666666670.0195008680555553-0.0099175347222209
506.176.183875868055566.173333333333330.0105425347222223-0.0138758680555551
516.176.180334201388896.18666666666667-0.00633246527777766-0.0103342013888881
526.176.200125868055556.199583333333330.000542534722222525-0.0301258680555545
536.176.211844618055566.21208333333333-0.000238715277777875-0.0418446180555554
546.286.244396701388896.2250.01939670138888890.0356032986111128
556.276.261115451388896.249166666666660.01194878472222210.0088845486111131
566.286.292261284722226.284166666666660.00809461805555554-0.0122612847222205
576.286.312157118055556.31916666666667-0.00700954861111127-0.0321571180555535
586.276.330802951388896.35416666666667-0.0233637152777777-0.0608029513888875
596.276.375438368055556.38916666666667-0.0137282986111113-0.105438368055555
606.286.401896701388896.42125-0.0193532986111109-0.121896701388889
616.596.470334201388896.450833333333330.01950086805555530.119665798611111
626.596.490959201388896.480416666666670.01054253472222230.099040798611112
636.596.503250868055556.50958333333333-0.006332465277777660.0867491319444449
646.596.539709201388896.539166666666660.0005425347222225250.0502907986111127
656.596.568927951388896.56916666666667-0.0002387152777778750.0210720486111118
666.636.618146701388896.598750.01939670138888890.0118532986111122
676.636.626948784722226.6150.01194878472222210.00305121527777885
686.636.626427951388896.618333333333330.008094618055555540.00357204861111082
696.636.614657118055566.62166666666667-0.007009548611111270.0153428819444441
706.636.601636284722226.625-0.02336371527777770.0283637152777771
716.636.614605034722226.62833333333333-0.01372829861111130.0153949652777765
726.636.610646701388896.63-0.01935329861111090.0193532986111089
736.636.649500868055566.630.0195008680555553-0.0195008680555571
746.636.640542534722226.630.0105425347222223-0.0105425347222239
756.636.623667534722226.63-0.006332465277777660.00633246527777587
766.636.630542534722226.630.000542534722222525-0.000542534722224097
776.636.636427951388896.63666666666667-0.000238715277777875-0.00642795138889074
786.636.669396701388896.650.0193967013888889-0.0393967013888901
796.636.675282118055566.663333333333330.0119487847222221-0.0452821180555567
806.636.685594618055566.67750.00809461805555554-0.0555946180555571
816.636.685073784722226.69208333333333-0.00700954861111127-0.0550737847222234
826.636.682886284722226.70625-0.0233637152777777-0.0528862847222236
836.796.708771701388896.7225-0.01372829861111130.0812282986111104
846.796.721480034722226.74083333333333-0.01935329861111090.068519965277777
856.796.778667534722226.759166666666670.01950086805555530.0113324652777775
866.816.788042534722226.77750.01054253472222230.0219574652777785
876.86.789500868055556.79583333333333-0.006332465277777660.0104991319444458
886.86.814709201388896.814166666666670.000542534722222525-0.0147092013888876
896.856.826011284722226.82625-0.0002387152777778750.0239887152777785
906.856.851480034722226.832083333333330.0193967013888889-0.00148003472222147
916.856.850698784722226.838750.0119487847222221-0.000698784722222179
926.856.853511284722226.845416666666670.00809461805555554-0.0035112847222214
936.856.844657118055556.85166666666666-0.007009548611111270.0053428819444461
946.856.836219618055556.85958333333333-0.02336371527777770.0137803819444464
956.866.852938368055556.86666666666667-0.01372829861111130.00706163194444631
966.866.852313368055556.87166666666666-0.01935329861111090.00768663194444663
976.886.896167534722226.876666666666670.0195008680555553-0.0161675347222205
986.886.895542534722226.8850.0105425347222223-0.0155425347222202
996.886.890334201388896.89666666666666-0.00633246527777766-0.0103342013888872
1006.916.908875868055556.908333333333330.0005425347222225250.00112413194444638
1016.916.920594618055556.92083333333333-0.000238715277777875-0.0105946180555545
1026.916.953563368055566.934166666666670.0193967013888889-0.0435633680555556
1036.916.959865451388896.947916666666670.0119487847222221-0.0498654513888876
1046.996.970177951388896.962083333333330.008094618055555540.0198220486111103
1056.996.969240451388896.97625-0.007009548611111270.0207595486111094
1066.996.965802951388896.98916666666667-0.02336371527777770.0241970486111116
1077.026.989188368055567.00291666666667-0.01372829861111130.0308116319444443
1087.026.999396701388897.01875-0.01935329861111090.0206032986111122
1097.05NA7.03458333333333NANA
1107.05NA7.04708333333333NANA
1117.05NA7.05708333333333NANA
1127.05NA7.06833333333333NANA
1137.1NA7.08083333333333NANA
1147.1NANANANA
1157.1NANANANA
1167.1NANANANA
1177.12NANANANA
1187.13NANANANA
1197.18NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/1rjj21243872990.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/1rjj21243872990.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/2pdez1243872990.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/2pdez1243872990.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/3ri271243872990.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/3ri271243872990.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/4vg8v1243872990.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/01/t1243873028685kvd1ujx7wzcq/4vg8v1243872990.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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