Home » date » 2011 » May » 14 »

Daphné Van den Buys-Opgave9

*Unverified author*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Sat, 14 May 2011 13:51:44 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma.htm/, Retrieved Sat, 14 May 2011 15:49:13 +0200
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
KDGP2W92
 
Dataseries X:
» Textbox « » Textfile « » CSV «
476 475 470 461 455 456 517 525 523 519 509 512 519 517 510 509 501 507 569 580 578 565 547 555 562 561 555 544 537 543 594 611 613 611 594 595 591 589 584 573 567 569 621 629 628 612 595 597 593 590 580 574 573 573 620 626 620 588 566 557 561 549 532 526 511 499 555 565 542 527 510 514 517 508 493 490 469 478 528 534 518 506 502 516
 
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
1476NANA0.508680555555534NA
2475NANA-4.4635416666667NA
3470NANA-14.4913194444444NA
4461NANA-20.6996527777778NA
5455NANA-30.2274305555555NA
6456NANA-28.3732638888888NA
7517518.008680555556493.29166666666724.7170138888889-1.00868055555554
8525531.036458333333496.83333333333334.203125-6.03645833333337
9523528.730902777778500.2528.4809027777777-5.73090277777771
10519518.369791666667503.91666666666714.4531250.630208333333371
11509505.154513888889507.833333333333-2.678819444444453.8454861111112
12512510.446180555556511.875-1.428819444444431.55381944444446
13519516.675347222222516.1666666666670.5086805555555342.32465277777783
14517516.161458333333520.625-4.46354166666670.838541666666742
15510510.717013888889525.208333333333-14.4913194444444-0.717013888888914
16509508.717013888889529.416666666667-20.69965277777780.282986111111086
17501502.689236111111532.916666666667-30.2274305555555-1.68923611111109
18507507.918402777778536.291666666667-28.3732638888888-0.918402777777828
19569564.592013888889539.87524.71701388888894.40798611111109
20580577.703125543.534.2031252.296875
21578575.689236111111547.20833333333328.48090277777772.31076388888903
22565564.994791666667550.54166666666714.4531250.00520833333337123
23547550.821180555556553.5-2.67881944444445-3.82118055555554
24555555.071180555555556.5-1.42881944444443-0.0711805555554292
25562559.550347222222559.0416666666670.5086805555555342.44965277777783
26561556.911458333333561.375-4.46354166666674.08854166666686
27555549.633680555556564.125-14.49131944444445.36631944444446
28544546.800347222222567.5-20.6996527777778-2.80034722222206
29537541.147569444444571.375-30.2274305555555-4.14756944444434
30543546.626736111111575-28.3732638888888-3.62673611111109
31594602.592013888889577.87524.7170138888889-8.5920138888888
32611614.453125580.2534.203125-3.453125
33613611.105902777778582.62528.48090277777771.89409722222217
34611599.494791666667585.04166666666714.45312511.5052083333334
35594584.821180555556587.5-2.678819444444459.17881944444446
36595588.404513888889589.833333333333-1.428819444444436.5954861111112
37591592.550347222222592.0416666666670.508680555555534-1.55034722222217
38589589.453125593.916666666667-4.4635416666667-0.453125
39584580.800347222222595.291666666667-14.49131944444443.19965277777794
40573575.258680555556595.958333333333-20.6996527777778-2.25868055555554
41567565.814236111111596.041666666667-30.22743055555551.18576388888891
42569567.793402777778596.166666666667-28.37326388888881.20659722222217
43621621.050347222222596.33333333333324.7170138888889-0.0503472222222854
44629630.661458333333596.45833333333334.203125-1.66145833333326
45628624.814236111111596.33333333333328.48090277777773.18576388888891
46612610.661458333333596.20833333333314.4531251.33854166666674
47595593.821180555555596.5-2.678819444444451.17881944444457
48597595.487847222222596.916666666667-1.428819444444431.51215277777783
49593597.550347222222597.0416666666670.508680555555534-4.55034722222217
50590592.411458333333596.875-4.4635416666667-2.41145833333326
51580581.925347222222596.416666666667-14.4913194444444-1.92534722222217
52574574.383680555556595.083333333333-20.6996527777778-0.383680555555543
53573562.647569444444592.875-30.227430555555510.3524305555557
54573561.626736111111590-28.373263888888811.3732638888889
55620611.71701388888958724.71701388888898.28298611111109
56626618.161458333333583.95833333333334.2031257.83854166666663
57620608.730902777778580.2528.480902777777711.2690972222223
58588590.703125576.2514.453125-2.703125
59566568.987847222222571.666666666667-2.67881944444445-2.98784722222229
60557564.571180555556566-1.42881944444443-7.57118055555554
61561560.717013888889560.2083333333330.5086805555555340.282986111111086
62549550.494791666667554.958333333333-4.4635416666667-1.49479166666663
63532534.675347222222549.166666666667-14.4913194444444-2.67534722222217
64526522.675347222222543.375-20.69965277777783.32465277777794
65511508.272569444444538.5-30.22743055555552.72743055555566
66499506.001736111111534.375-28.3732638888888-7.00173611111109
67555555.467013888889530.7524.7170138888889-0.4670138888888
68565561.411458333333527.20833333333334.2031253.58854166666674
69542552.355902777778523.87528.4809027777777-10.3559027777776
70527535.203125520.7514.453125-8.20312499999989
71510514.821180555555517.5-2.67881944444445-4.82118055555543
72514513.446180555555514.875-1.428819444444430.553819444444571
73517513.383680555556512.8750.5086805555555343.61631944444446
74508505.994791666667510.458333333333-4.46354166666672.00520833333343
75493493.675347222222508.166666666667-14.4913194444444-0.675347222222115
76490485.592013888889506.291666666667-20.69965277777784.40798611111126
77469474.855902777778505.083333333333-30.2274305555555-5.85590277777771
78478476.460069444444504.833333333333-28.37326388888881.5399305555556
79528NANA24.7170138888889NA
80534NANA34.203125NA
81518NANA28.4809027777777NA
82506NANA14.453125NA
83502NANA-2.67881944444445NA
84516NANA-1.42881944444443NA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/1faws1305381100.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/1faws1305381100.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/229uo1305381100.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/229uo1305381100.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/3phxh1305381100.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/3phxh1305381100.ps (open in new window)


http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/4v75r1305381100.png (open in new window)
http://www.freestatistics.org/blog/date/2011/May/14/t1305380953izrgel1wz7b2fma/4v75r1305381100.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')
 





Copyright

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This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa


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