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Maandelijkse melkproductie per koe

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sun, 30 May 2010 13:13:48 +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/May/30/t1275225411kuen6sdg48qza7c.htm/, Retrieved Sun, 30 May 2010 15:16:52 +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/2010/May/30/t1275225411kuen6sdg48qza7c.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:
KDGP2W52
 
Dataseries X:
» Textbox « » Textfile « » CSV «
589 561 640 656 727 697 640 599 568 577 553 582 600 566 653 673 742 716 660 617 583 587 565 598 628 618 688 705 770 736 678 639 604 611 594 634 658 622 709 722 782 756 702 653 615 621 602 635 677 635 736 755 811 798 735 697 661 667 645 688 713 667 762 784 837 817 767 722 681 687 660 698 717 696 775 796 858 826 783 740 701 706 677 711 734 690 785 805 871 845 801 764 725 723 690 734 750 707 807 824 886 859 819 783 740 747 711 751 804 756 860 878 942 913 869 834 790 800 763 800 826 799 890 900 961 935 894 855 809 810 766 805 821 773 883 898 957 924 881 837 784 791 760 802 828 778 889 902 969 947 908 867 815 812 773 813 834 782 892 903 966 937 896 858 817 827 797 843
 
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
1589NANA0.975653951005714NA
2561NANA0.92293780007675NA
3640NANA1.0466924826735NA
4656NANA1.06678204740884NA
5727NANA1.14686690043049NA
6697NANA1.10955434590767NA
7640643.151676210915616.2083333333331.043724405237810.995099637725455
8599608.377772404085616.8750.9862253655993270.984585609748648
9568574.325186791425617.6250.9298930366993320.988986750125375
10577578.382574118755618.8750.9345709135427270.997609585453259
11553554.654550672393620.2083333333330.8943036087428580.997016970886857
12582586.065030565343621.6250.9427951426749940.993063857501578
13600608.076324964311623.250.9756539510057140.986718238101466
14566576.682302081289624.8333333333330.922937800076750.98147627897937
15653655.447555087501626.2083333333331.04669248267350.996265826199972
16673669.139039237192627.251.066782047408841.00577004260162
17742720.423557953752628.1666666666671.146866900430491.02994966198431
18716698.279535024558629.3333333333331.109554345907671.0253773225286
19660658.764053772597631.1666666666671.043724405237811.0018761591807
20617625.759994472773634.50.9862253655993270.9860010314655
21583593.387994043761638.1250.9298930366993320.982493757629017
22587598.982074671426640.9166666666670.9345709135427270.979995937811663
23565575.4098469253643.4166666666670.8943036087428580.981908813377239
24598608.495698334819645.4166666666670.9427951426749940.982751400932593
25628631.2481063006976470.9756539510057140.994854469632025
26618598.678986316451648.6666666666670.922937800076751.03227274403337
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28705695.897488926364652.3333333333331.066782047408841.0130802470457
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31678688.8581074569546601.043724405237810.984237526800638
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33604616.015391270112662.4583333333330.9298930366993320.980494982040403
34611620.594027047102664.0416666666670.9345709135427270.98454057462855
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38622618.3683260514226700.922937800076751.00587299477606
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41782771.459135022909672.6666666666671.146866900430491.01366354288718
42756746.776306226939673.0416666666671.109554345907671.01235134764741
43702703.339783579628673.8751.043724405237810.998095111906212
44653665.907585397379675.2083333333330.9862253655993270.980616551484879
45615629.42134921586676.8750.9298930366993320.977087924910989
46621634.92411438809679.3750.9345709135427270.978069640020667
47602609.877798512265681.9583333333330.8943036087428580.987082988540521
48635645.736106470481684.9166666666670.9427951426749940.983373848290498
49677671.29057053989688.0416666666670.9756539510057141.00850515367066
50635637.980754303053691.250.922937800076750.995327830372706
51736727.4512754580816951.04669248267351.01175161118047
52755745.502854130874698.8333333333331.066782047408841.01273924816854
53811805.72178367327702.5416666666671.146866900430491.0065509167478
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55735741.305258820154710.251.043724405237810.99149438271868
56697703.260871119453713.0833333333330.9862253655993270.99109737029804
57661665.338467758372715.50.9298930366993320.993479307196848
58667670.827213650023717.7916666666670.9345709135427270.994294784749117
59645643.973123595586720.0833333333330.8943036087428581.00159459512639
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80740739.6690241994957500.9862253655993271.00044746473041
81701697.574759697282750.1666666666670.9298930366993321.00491021249709
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83677672.404525823536751.8750.8943036087428581.00683438912139
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87785793.916248107849758.51.04669248267350.988769283751153
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90845846.54373449647762.9583333333331.109554345907670.998176426765017
91801798.014284838074764.5833333333331.043724405237811.00374143072205
92764755.407537325518765.9583333333330.9862253655993271.01137460542809
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95690689.247243788193770.7083333333330.8943036087428581.0010921425056
96734727.759283883206771.9166666666670.9427951426749941.00857524768835
97750754.424417615169773.250.9756539510057140.994135373256933
98707715.084516351131774.7916666666670.922937800076750.988694320508597
99807812.451427488525776.2083333333331.04669248267350.993290149658082
100824829.778635876173777.8333333333331.066782047408840.99303593075752
101886894.221679489822779.7083333333331.146866900430490.990805770338164
102859866.885564171444781.2916666666671.109554345907670.990903569632076
103819818.540864807751784.251.043724405237811.00056091908418
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105740737.211450386591792.7916666666670.9298930366993321.00378256416385
106747745.08666082194797.250.9345709135427271.00256794179613
107711717.082443610315801.8333333333330.8943036087428580.991517790367742
108751760.285716305493806.4166666666670.9427951426749940.987786543786965
109804791.011440777883810.750.9756539510057141.0164201913557
110756752.15585132088814.9583333333330.922937800076751.00511084062215
111860857.415592056708819.1666666666671.04669248267351.00301418351525
112878878.4505667892823.4583333333331.066782047408840.99948708919291
113942949.41464907304827.8333333333331.146866900430490.992190294219413
114913923.195447226258832.0416666666661.109554345907670.98895635018902
115869871.509878373578351.043724405237810.997120080407747
116834826.16920730727837.7083333333330.9862253655993271.00947843689098
117790781.807570604963840.750.9298930366993321.01047883098484
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119763755.351185534436844.6250.8943036087428581.01012616993532
120800797.918955750603846.3333333333330.9427951426749941.00260808974946
121826827.639116188556848.2916666666670.9756539510057140.998019527887826
122799784.689408773586850.2083333333330.922937800076751.01823726823175
123890891.651158677487851.8751.04669248267350.998148201052152
124900910.053984943688853.0833333333331.066782047408840.988952320290856
125961978.994257879976853.6251.146866900430490.98161964921128
126935947.513179974068853.9583333333331.109554345907670.98679366130357
127894891.297153556204853.9583333333331.043724405237811.00303248634085
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130810795.241966515399850.9166666666670.9345709135427271.01855791583695
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132805801.41515440469850.0416666666670.9427951426749941.00447314425689
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139881879.729208064818842.8751.043724405237811.00144452625141
140837831.757817712332843.3750.9862253655993271.00630253443495
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151908896.211355964198858.6666666666671.043724405237811.01315386594618
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155773768.616689064122859.4583333333330.8943036087428581.0057028568313
156813809.78246129593858.9166666666670.9427951426749941.00397333710948
157834837.1110899629038580.9756539510057140.996283539902642
158782791.073061890783857.1250.922937800076750.988530690364936
159892896.841008904076856.8333333333331.04669248267350.994602154834566
160903914.810054905052857.5416666666671.066782047408840.987090156211414
161966985.349811953195859.1666666666671.146866900430490.980362494904384
162937955.788606137296861.4166666666671.109554345907670.98034229952455
163896NANA1.04372440523781NA
164858NANA0.986225365599327NA
165817NANA0.929893036699332NA
166827NANA0.934570913542727NA
167797NANA0.894303608742858NA
168843NANA0.942795142674994NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/19d9j1275225224.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/19d9j1275225224.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/29d9j1275225224.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/29d9j1275225224.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/3ve8p1275225224.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/3ve8p1275225224.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/4ve8p1275225224.png (open in new window)
http://www.freestatistics.org/blog/date/2010/May/30/t1275225411kuen6sdg48qza7c/4ve8p1275225224.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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Software written by Ed van Stee & Patrick Wessa


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As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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