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additief model, seasonal period is 6

R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Tue, 29 Apr 2008 07:26:34 -0600
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z.htm/, Retrieved Tue, 29 Apr 2008 15:30:41 +0200
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
56421 53152 53536 52408 41454 38271 35306 26414 31917 38030 27534 18387 50556 43901 48572 43899 37532 40357 35489 29027 34485 42598 30306 26451 47460 50104 61465 53726 39477 43895 31481 29896 33842 39120 33702 25094 51442 45594 52518 48564 41745 49585 32747 33379 35645 37034 35681 20972 58552 54955 65540 51570 51145 46641 35704 33253 35193 41668 34865 21210 56126 49231 59723 48103 47472 50497 40059 34149 36860 46356 36577
 
Text written by user:
 
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
156421NANA2306.00771604938NA
253152NANA-1112.87191358025NA
353536NANA4956.55401234568NA
45240852039.38734567947447.41666666674591.97067901235368.61265432099
54145440511.813271604943459.6666666667-2947.85339506173942.186728395063
63827131636.109567901239429.9166666667-7793.807098765436634.89043209877
73530638736.174382716036430.16666666672306.00771604938-3430.17438271605
82641432959.128086419834072-1112.87191358025-6545.12808641975
93191736211.5540123457312554956.55401234568-4294.55401234568
103803035460.804012345730868.83333333334591.970679012352569.19598765432
112753430649.063271604933596.9166666667-2947.85339506173-3115.06327160493
121838728648.276234567936442.0833333333-7793.80709876543-10261.2762345679
135055640625.091049382738319.08333333332306.007716049389930.9089506173
144390138528.461419753139641.3333333333-1112.871913580255372.53858024692
154857247261.88734567942305.33333333334956.554012345681310.11265432099
164389947472.554012345742880.58333333334591.97067901235-3573.55401234567
173753237437.646604938340385.5-2947.8533950617394.3533950617275
184035730178.276234567937972.0833333333-7793.8070987654310178.7237654321
193548938995.757716049436689.752306.00771604938-3506.75771604938
202902734866.294753086435979.1666666667-1112.87191358025-5839.29475308642
213448539174.720679012334218.16666666674956.55401234568-4689.72067901235
224259838648.88734567934056.91666666674591.970679012353949.11265432099
233030633863.063271604936810.9166666667-2947.85339506173-3557.06327160494
242645133021.859567901240815.6666666667-7793.80709876543-6570.85956790123
254746046297.341049382743991.33333333332306.007716049381162.65895061728
265010444570.044753086445682.9166666667-1112.871913580255533.95524691358
276146552857.38734567947900.83333333334956.554012345688607.61265432099
285372652614.88734567948022.91666666674591.970679012351111.11265432099
293947742059.479938271645007.3333333333-2947.85339506173-2582.47993827161
304389533227.609567901241021.4166666667-7793.8070987654310667.3904320988
313148139808.341049382737502.33333333332306.00771604938-8327.34104938271
322989634691.044753086435803.9166666667-1112.87191358025-4795.04475308642
333384238712.470679012333755.91666666674956.55401234568-4870.47067901235
343912038444.554012345733852.58333333334591.97067901235675.445987654326
353370233876.313271604936824.1666666667-2947.85339506173-174.313271604944
362509431894.859567901239688.6666666667-7793.80709876543-6800.85956790123
375144244338.0077160494420322306.007716049387103.99228395062
384559442376.378086419843489.25-1112.871913580253217.62191358025
395251851156.970679012346200.41666666674956.554012345681361.02932098766
404856451275.38734567946683.41666666674591.97067901235-2711.38734567901
414174541159.729938271644107.5833333333-2947.85339506173585.270061728399
424958533889.776234567941683.5833333333-7793.8070987654315695.2237654321
433274741622.674382716039316.66666666672306.00771604938-8875.67438271605
443337936737.628086419837850.5-1112.87191358025-3358.62808641975
453564539917.304012345734960.754956.55401234568-4272.30401234567
463703439318.720679012334726.754591.97067901235-2284.72067901234
473568135727.313271604938675.1666666667-2947.85339506173-46.3132716049367
482097235170.609567901242964.4166666667-7793.80709876543-14198.6095679012
495855248973.0077160494466672306.007716049389578.99228395062
505495548054.128086419749167-1112.871913580256900.87191358025
516554057551.304012345752594.754956.554012345687988.69598765433
525157057421.804012345752829.83333333334591.97067901235-5851.80401234567
535114546169.479938271649117.3333333333-2947.853395061734975.52006172839
544664136986.109567901244779.9166666667-7793.807098765439654.89043209877
553570443731.841049382741425.83333333332306.00771604938-8027.84104938271
563325338131.128086419839244-1112.87191358025-4878.12808641975
573519340724.63734567935768.08333333334956.55401234568-5531.63734567901
584166839942.63734567935350.66666666674591.970679012351725.36265432099
593486535436.146604938338384-2947.85339506173-571.146604938273
602121033965.859567901241759.6666666667-7793.80709876543-12755.8595679012
615612646646.091049382744340.08333333332306.007716049389479.9089506173
624923144814.044753086445926.9166666667-1112.871913580254416.95524691358
635972354374.63734567949418.08333333334956.554012345685348.36265432099
644810355111.720679012350519.754591.97067901235-7008.72067901233
654747244976.146604938347924-2947.853395061732495.85339506173
665049736968.109567901244761.9166666667-7793.8070987654313528.8904320988
6740059NA42711.0833333333NANA
6834149NA41657.5833333333NANA
6936860NANANANA
7046356NANANANA
7136577NANANANA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/1i3ku1209475592.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/1i3ku1209475592.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/234qe1209475592.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/234qe1209475592.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/34od41209475592.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/34od41209475592.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/4f6j01209475592.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Apr/29/t1209475835s0ls4g0bnc5g71z/4f6j01209475592.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 6 ;
 
Parameters (R input):
par1 = additive ; par2 = 6 ;
 
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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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