Home » date » 2009 » Aug » 20 »

multiplicatief model hotelkamers

*Unverified author*
R Software Module: rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 20 Aug 2009 00:40:36 -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/Aug/20/t1250750527bvtfojx6jyrptuh.htm/, Retrieved Thu, 20 Aug 2009 08:42:07 +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/Aug/20/t1250750527bvtfojx6jyrptuh.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 «
613.20 614.70 618.40 628.20 629.00 629.70 630.40 630.40 639.30 639.40 640.90 640.80 642.10 645.30 647.60 648.40 648.80 648.90 648.90 648.90 650.30 650.30 650.00 650.00 650.50 658.40 666.00 675.50 680.70 690.60 690.60 691.10 692.90 693.80 692.80 697.50 699.00 702.10 704.80 715.50 721.80 726.40 727.70 727.40 731.30 734.40 733.40 733.40 738.10 742.60 747.20 751.10 752.60 758.90 759.10 764.30 765.60 767.60 767.60 765.60 768.20 770.90 775.10 777.60 778.60 778.90 779.40 779.90 781.70 789.10 788.70 788.80 790.80 794.10 795.10 797.30 803.80 805.60 804.60 804.50 805.80 806.80 805.20 814.90 816.60 819.50 823.00 824.00 831.40 831.70 831.10 832.10 833.30 838.80 838.00 837.30 994.20 994.20 994.20 994.20 994.20 1092.60 1100.00 1100.00 1092.60 1000.70 1000.70 1000.50 1000.50 1000.50 1000.50 1000.50 1000.50 1087.70 1113.20 1116.00 1085.20 1031.30 1028.70 1027.50 1027.50 1027.50 1027.50 1027.50 1027.50 etc...
 
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'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1613.2NANA0.997750168919923NA
2614.7NANA0.99639281368466NA
3618.4NANA0.995082668826342NA
4628.2NANA0.994939006026579NA
5629NANA0.994525984508865NA
6629.7NANA1.02330751731143NA
7630.4640.527087627878630.73751.015520858721540.984189446748642
8630.4641.000569382561633.2166666666671.012292637142460.98346246495105
9639.3640.444718602846635.7083333333331.007450563444210.998212619185395
10639.4632.510576891013637.7666666666660.9917586006758161.01089218640873
11640.9631.148882517095639.4333333333330.9870440742071.01544978966613
12640.8630.759799501027641.0583333333330.9839351065311691.01591762903552
13642.1641.183359594536642.6291666666660.9977501689199231.00142960729056
14645.3641.847189118591644.1708333333330.996392813684661.00537949053910
15647.6642.226354460521645.40.9950826688263421.00836721430405
16648.4643.041516332553646.31250.9949390060265791.00833302909897
17648.8643.603347016643647.1458333333330.9945259845088651.00807431006605
18648.9663.009468028723647.9083333333331.023307517311430.978719054992271
19648.9658.709142335905648.6416666666671.015520858721540.985108537736216
20648.9657.522028797923649.53751.012292637142460.986887087549468
21650.3655.699199217663650.851.007450563444210.991765737667355
22650.3647.366294263636652.7458333333330.9917586006758161.00453175545647
23650646.715390104069655.2041666666670.9870440742071.00507891098030
24650647.695782522195658.2708333333330.9839351065311691.00355756134281
25650.5660.257016990388661.7458333333330.9977501689199230.985222395613661
26658.4662.842016030272665.2416666666670.996392813684660.993298529781085
27666665.486411844337668.7750.9950826688263421.00077174852337
28675.5668.959677439546672.36250.9949390060265791.00977685618584
29680.7672.258126945305675.9583333333330.9945259845088651.01255748754285
30690.6695.563438423193679.7208333333331.023307517311430.99286414703676
31690.6694.332767792474683.7208333333331.015520858721540.99462394983267
32691.1696.014456325266687.56251.012292637142460.99293914619071
33692.9696.1483393399486911.007450563444210.995333840280322
34693.8688.561467139208694.2833333333330.9917586006758161.00760793786872
35692.8688.62363642144697.66250.9870440742071.00606479847288
36697.5689.607318330812700.8666666666670.9839351065311691.01144518258346
37699702.320501195104703.9041666666670.9977501689199230.99527209986117
38702.1704.412354544541706.96250.996392813684660.996717328238747
39704.8706.583326066865710.0750.9950826688263420.997476127724679
40715.5709.756322265827713.3666666666670.9949390060265791.00809246434866
41721.8712.826499396729716.750.9945259845088651.01258861814322
42726.4736.717455744401719.93751.023307517311430.985995369508415
43727.7734.285050909344723.06251.015520858721540.991032023733577
44727.4735.308282190346726.3791666666671.012292637142460.989244943404162
45731.3735.271002887032729.8333333333331.007450563444210.994599266295774
46734.4727.04170084543733.0833333333330.9917586006758161.01012087634865
47733.4726.31638200522735.850.9870440742071.00975279942774
48733.4726.623776984437738.48750.9839351065311691.00932562796622
49738.1739.482537695741.150.9977501689199230.998130398454965
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51747.2743.289438013196746.96250.9950826688263421.00526115640397
52751.1745.980393243578749.7750.9949390060265791.00686292401622
53752.6748.463680508296752.5833333333330.9945259845088651.00552641310383
54758.9772.955333201192755.351.023307517311430.981816112008721
55759.1769.70980353108757.9458333333331.015520858721540.98621583942103
56764.3769.72623185319760.3791666666671.012292637142460.992950439222884
57765.6768.403533292303762.7208333333331.007450563444210.996351483080392
58767.6758.682932534491764.98750.9917586006758161.01175335187220
59767.6757.235537629755767.1750.9870440742071.01368723713455
60765.6756.7362909739769.0916666666660.9839351065311691.01171307512514
61768.2769.036729156883770.7708333333330.9977501689199230.998911977640132
62770.9769.480956914873772.2666666666670.996392813684661.00184415620994
63775.1769.783514070698773.58750.9950826688263421.00690646893850
64777.6771.231116100694775.1541666666670.9949390060265791.00825807435196
65778.6772.676244372818776.9291666666670.9945259845088651.00766654296715
66778.9796.926311794212778.7751.023307517311430.977380202501248
67779.4792.800209056261780.6833333333331.015520858721540.983097621691835
68779.9792.211782055716782.5916666666671.012292637142460.984458976331091
69781.7790.235826544275784.3916666666671.007450563444210.98919838071427
70789.1779.567715733722786.0458333333330.9917586006758161.01222765395987
71788.7777.708476802265787.9166666666670.9870440742071.01413321768451
72788.8777.386629022224790.0791666666670.9839351065311691.01468171763146
73790.8790.459256742068792.2416666666670.9977501689199231.00043106998245
74794.1791.45141845662794.3166666666660.996392813684661.00334648657089
75795.1792.42993714207796.3458333333330.9950826688263421.00336946237488
76797.3794.048383972237798.08750.9949390060265791.00409498475584
77803.8795.135956189643799.51250.9945259845088651.01089630489341
78805.6819.963522277686801.28751.023307517311430.98248273016123
79804.6815.920233939822803.451.015520858721540.986125808052143
80804.5815.486076938017805.5833333333331.012292637142460.98652818576711
81805.8813.822762860913807.8041666666671.007450563444210.990141879501244
82806.8803.402980769965810.0791666666670.9917586006758161.00422828805885
83805.2801.81702831477812.3416666666670.9870440742071.00421913175421
84814.9801.493039132237814.5791666666670.9839351065311691.01672748260207
85816.6814.933236927199816.7708333333330.9977501689199231.00204527560943
86819.5816.070624228078819.0250.996392813684661.00420230268081
87823817.282126796008821.3208333333330.9950826688263421.00699620488020
88824819.630753164695823.80.9949390060265791.00533075024142
89831.4821.975726196576826.50.9945259845088651.01146539186386
90831.7848.117270347717828.81.023307517311430.980642688314806
91831.1850.126361531092837.1333333333331.015520858721540.977619372375625
92832.1862.283521975915851.81251.012292637142460.964995826538874
93833.3872.67886431946866.2251.007450563444210.954875881690834
94838.8873.193859965022880.450.9917586006758160.960611427150438
95838882.738191665175894.3250.9870440742070.949318844378103
96837.3897.328318508373911.9791666666670.9839351065311690.933103283079087
97994.2931.952702572024934.0541666666670.9977501689199231.06679233533653
98994.2952.970845191626956.4208333333330.996392813684661.04326381548439
99994.2973.576444646333978.38750.9950826688263421.02118329327612
100994.2990.897066314596995.93750.9949390060265791.00333327627832
101994.21003.936686637281009.46250.9945259845088650.990301493344274
1021092.61046.886228022821023.041666666671.023307517311431.04366641833040
10311001046.092267905971030.104166666671.015520858721541.05153248307813
10411001043.298317040941030.629166666671.012292637142461.05434848502380
1051092.61038.836846206181031.154166666671.007450563444211.05175322187518
1061000.71023.176686679731031.679166666670.9917586006758160.978032448381263
1071000.71018.831006080111032.204166666670.9870440742070.982204108461653
1081000.51015.679312905631032.26250.9839351065311690.98505501420305
1091000.51030.285139011451032.608333333330.9977501689199230.97109039247132
1101000.51030.095800607541033.8250.996392813684660.971268885291944
1111000.51029.097911389061034.183333333330.9950826688263420.972210699222531
1121000.51029.911112088411035.150.9949390060265790.971443057810325
1131000.51031.911873809861037.591666666670.9945259845088650.969559538360687
1141087.71064.120432126871039.883333333331.023307517311431.02215873989563
1151113.21058.308137569011042.133333333331.015520858721541.05186756151860
11611161057.221558687641044.383333333331.012292637142461.05559708920931
1171085.21054.431341386161046.633333333331.007450563444211.02918033389770
1181031.31040.239066938851048.883333333330.9917586006758160.991406718683275
1191028.71037.514927868121051.133333333330.9870440742070.991503806228379
1201027.51037.998240905451054.945833333330.9839351065311690.98988607061965
1211027.51057.004057076651059.38750.9977501689199230.972087091928243
1221027.51058.891552923031062.7250.996392813684660.970354326808657
1231027.51060.513500479461065.754166666670.9950826688263420.96887026854016
1241027.51063.788785243621069.20.9949390060265790.965887227100907
1251027.51067.225833976461073.10.9945259845088650.962776543903135
1261152.21101.859160609051076.76251.023307517311431.04568718143898
1271155.3NANA1.01552085872154NA
1281154NANA1.01229263714246NA
1291119.9NANA1.00745056344421NA
1301079.3NANA0.991758600675816NA
1311074.3NANA0.987044074207NA
1321069.8NANA0.983935106531169NA
 
Charts produced by software:
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/185p51250750431.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/185p51250750431.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/2sxwl1250750431.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/2sxwl1250750431.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/3lt2o1250750431.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/3lt2o1250750431.ps (open in new window)


http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/44zqj1250750431.png (open in new window)
http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Aug/20/t1250750527bvtfojx6jyrptuh/44zqj1250750431.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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