Home » date » 2010 » Dec » 16 »

*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: Thu, 16 Dec 2010 21:27:39 +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/16/t1292534750qak9dpjqeeegnw3.htm/, Retrieved Thu, 16 Dec 2010 22:25:51 +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/16/t1292534750qak9dpjqeeegnw3.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 «
-0,9 1,2 1,4 1,5 1,3 1,4 -0,4 1,9 1,6 1,4 1,3 1,6 1,7 1,5 1,4 1,7 1,9 2,0 2,3 2,0 2,2 2,5 2,8 2,7 2,7 3,0 3,0 2,3 2,4 2,3 2,1 2,2 1,9 1,5 1,4 1,4 1,2 1,1 1,1 1,8 1,5 1,5 1,4 1,4 1,5 1,7 1,7 1,7 1,5 1,7 1,4 1,3 1,3 1,2 1,2 1,3 1,2 1,2 1,1 0,9 1,2 1,0 1,3 0,8 1,2 1,3 1,0 1,3 1,3 1,5 1,6 1,6 1,1 1,7 1,4 1,8 1,6 1,7 1,8 1,7 1,7 1,4 1,2 1,4 1,3 1,4 1,7 1,5 1,5 1,4 1,4 1,5 1,2 1,3 1,3 1,6 1,5 1,4 1,6 1,2 1,4 1,8 1,8 1,9 2,1 2,4 2,4 2,4 1,9 2,4 2,2 2,7 2,4 2,3 2,0 2,0 2,2 1,8 1,7 1,6 1,4 1,2 1,1 1,0 1,3 1,1 0,7 1,1 1,1 1,2 1,4
 
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
1-0.9NANA-0.1NA
21.2NANA0.010648148148148NA
31.4NANA-0.00370370370370366NA
41.5NANA-0.0083333333333333NA
51.3NANA-0.000925925925926012NA
61.4NANA0.0305555555555555NA
7-0.41.193055555555561.21666666666667-0.0236111111111111-1.59305555555556
81.91.351.33750.01250.55
91.61.365277777777781.350.01527777777777790.234722222222222
101.41.378240740740741.358333333333330.01990740740740750.0217592592592593
111.31.406481481481481.391666666666670.0148148148148148-0.106481481481481
121.61.474537037037041.441666666666670.03287037037037040.125462962962963
131.71.479166666666671.57916666666667-0.10.220833333333333
141.51.706481481481481.695833333333330.010648148148148-0.206481481481481
151.41.72129629629631.725-0.00370370370370366-0.321296296296296
161.71.78751.79583333333333-0.0083333333333333-0.0875
171.91.903240740740741.90416666666667-0.000925925925926012-0.00324074074074043
1822.043055555555562.01250.0305555555555555-0.0430555555555556
192.32.076388888888892.1-0.02361111111111110.223611111111111
2022.216666666666672.204166666666670.0125-0.216666666666667
212.22.348611111111112.333333333333330.0152777777777779-0.148611111111111
222.52.444907407407412.4250.01990740740740750.0550925925925929
232.82.485648148148152.470833333333330.01481481481481480.314351851851852
242.72.537037037037042.504166666666670.03287037037037040.162962962962963
252.72.408333333333332.50833333333333-0.10.291666666666667
2632.518981481481482.508333333333330.0106481481481480.481018518518518
2732.500462962962962.50416666666667-0.003703703703703660.499537037037037
282.32.441666666666672.45-0.0083333333333333-0.141666666666667
292.42.349074074074072.35-0.0009259259259260120.050925925925926
302.32.268055555555562.23750.03055555555555550.0319444444444446
312.12.097222222222222.12083333333333-0.02361111111111110.00277777777777821
322.21.991666666666671.979166666666670.01250.208333333333333
331.91.836111111111111.820833333333330.01527777777777790.0638888888888889
341.51.740740740740741.720833333333330.0199074074074075-0.240740740740741
351.41.677314814814821.66250.0148148148148148-0.277314814814815
361.41.624537037037041.591666666666670.0328703703703704-0.224537037037037
371.21.429166666666671.52916666666667-0.1-0.229166666666667
381.11.477314814814811.466666666666670.010648148148148-0.377314814814815
391.11.412962962962961.41666666666667-0.00370370370370366-0.312962962962963
401.81.41.40833333333333-0.00833333333333330.4
411.51.428240740740741.42916666666667-0.0009259259259260120.0717592592592591
421.51.484722222222221.454166666666670.03055555555555550.0152777777777777
431.41.455555555555561.47916666666667-0.0236111111111111-0.0555555555555558
441.41.529166666666671.516666666666670.0125-0.129166666666667
451.51.569444444444441.554166666666670.0152777777777779-0.0694444444444446
461.71.565740740740741.545833333333330.01990740740740750.134259259259259
471.71.531481481481481.516666666666670.01481481481481480.168518518518519
481.71.52870370370371.495833333333330.03287037037037040.171296296296297
491.51.3751.475-0.10.125
501.71.473148148148151.46250.0106481481481480.226851851851852
511.41.442129629629631.44583333333333-0.00370370370370366-0.0421296296296294
521.31.404166666666671.4125-0.0083333333333333-0.104166666666667
531.31.365740740740741.36666666666667-0.000925925925926012-0.0657407407407404
541.21.338888888888891.308333333333330.0305555555555555-0.138888888888889
551.21.238888888888891.2625-0.0236111111111111-0.038888888888889
561.31.233333333333331.220833333333330.01250.0666666666666667
571.21.202777777777781.18750.0152777777777779-0.00277777777777799
581.21.182407407407411.16250.01990740740740750.0175925925925924
591.11.152314814814811.13750.0148148148148148-0.0523148148148147
600.91.170370370370371.13750.0328703703703704-0.27037037037037
611.21.033333333333331.13333333333333-0.10.166666666666667
6211.135648148148151.1250.010648148148148-0.135648148148148
631.31.125462962962961.12916666666667-0.003703703703703660.174537037037037
640.81.13751.14583333333333-0.0083333333333333-0.3375
651.21.178240740740741.17916666666667-0.0009259259259260120.0217592592592593
661.31.259722222222221.229166666666670.03055555555555550.0402777777777779
6711.230555555555561.25416666666667-0.0236111111111111-0.230555555555556
681.31.291666666666671.279166666666670.01250.00833333333333308
691.31.327777777777781.31250.0152777777777779-0.0277777777777779
701.51.378240740740741.358333333333330.01990740740740750.121759259259259
711.61.431481481481481.416666666666670.01481481481481480.168518518518519
721.61.482870370370371.450.03287037037037040.11712962962963
731.11.41.5-0.1-0.3
741.71.560648148148151.550.0106481481481480.139351851851852
751.41.579629629629631.58333333333333-0.00370370370370366-0.17962962962963
761.81.58751.59583333333333-0.00833333333333330.2125
771.61.574074074074071.575-0.0009259259259260120.0259259259259261
781.71.580555555555561.550.03055555555555550.119444444444444
791.81.526388888888891.55-0.02361111111111110.273611111111111
801.71.558333333333331.545833333333330.01250.141666666666667
811.71.561111111111111.545833333333330.01527777777777790.138888888888889
821.41.565740740740741.545833333333330.0199074074074075-0.165740740740741
831.21.543981481481481.529166666666670.0148148148148148-0.343981481481481
841.41.545370370370371.51250.0328703703703704-0.14537037037037
851.31.383333333333331.48333333333333-0.1-0.0833333333333333
861.41.468981481481481.458333333333330.010648148148148-0.0689814814814815
871.71.425462962962961.42916666666667-0.003703703703703660.274537037037037
881.51.395833333333331.40416666666667-0.00833333333333330.104166666666667
891.51.403240740740741.40416666666667-0.0009259259259260120.0967592592592592
901.41.447222222222221.416666666666670.0305555555555555-0.0472222222222223
911.41.409722222222221.43333333333333-0.0236111111111111-0.00972222222222263
921.51.454166666666671.441666666666670.01250.0458333333333334
931.21.452777777777781.43750.0152777777777779-0.252777777777778
941.31.440740740740741.420833333333330.0199074074074075-0.140740740740741
951.31.418981481481481.404166666666670.0148148148148148-0.118981481481482
961.61.449537037037041.416666666666670.03287037037037040.150462962962963
971.51.351.45-0.10.15
981.41.493981481481481.483333333333330.010648148148148-0.0939814814814817
991.61.53379629629631.5375-0.003703703703703660.0662037037037038
1001.21.61251.62083333333333-0.0083333333333333-0.4125
1011.41.711574074074071.7125-0.000925925925926012-0.311574074074074
1021.81.822222222222221.791666666666670.0305555555555555-0.0222222222222221
1031.81.818055555555561.84166666666667-0.0236111111111111-0.0180555555555553
1041.91.91251.90.0125-0.0124999999999997
1052.11.981944444444441.966666666666670.01527777777777790.118055555555556
1062.42.074074074074072.054166666666670.01990740740740750.325925925925926
1072.42.173148148148152.158333333333330.01481481481481480.226851851851852
1082.42.25370370370372.220833333333330.03287037037037040.146296296296296
1091.92.152.25-0.1-0.25
1102.42.273148148148152.26250.0106481481481480.126851851851852
1112.22.267129629629632.27083333333333-0.00370370370370366-0.0671296296296293
1122.72.241666666666672.25-0.00833333333333330.458333333333333
1132.42.194907407407412.19583333333333-0.0009259259259260120.205092592592592
1142.32.163888888888892.133333333333330.03055555555555550.136111111111111
11522.055555555555562.07916666666667-0.0236111111111111-0.0555555555555554
11622.020833333333332.008333333333330.0125-0.0208333333333333
1172.21.927777777777781.91250.01527777777777790.272222222222222
1181.81.815740740740741.795833333333330.0199074074074075-0.0157407407407408
1191.71.693981481481481.679166666666670.01481481481481480.0060185185185182
1201.61.61620370370371.583333333333330.0328703703703704-0.0162037037037037
1211.4NA1.47916666666667NANA
1221.2NA1.3875NANA
1231.1NA1.30416666666667NANA
1241NA1.23333333333333NANA
1251.3NA1.19583333333333NANA
1261.1NANANANA
1270.7NANANANA
1281.1NANANANA
1291.1NANANANA
1301.2NANANANA
1311.4NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/17qhq1292534854.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/17qhq1292534854.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/2ihgt1292534854.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/2ihgt1292534854.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/3ihgt1292534854.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/3ihgt1292534854.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/4tqge1292534854.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/16/t1292534750qak9dpjqeeegnw3/4tqge1292534854.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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Software written by Ed van Stee & Patrick Wessa


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