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TIJDREEKS A - STAP 29

*Unverified author*

R Software Module: /rwasp_decompose.wasp (opens new window with default values)

Title produced by software: Classical Decomposition

Date of computation: Thu, 12 Aug 2010 08:55:32 +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/Aug/12/t1281603932y5vh9adt9nz05hx.htm/, Retrieved Thu, 12 Aug 2010 11:05:33 +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/Aug/12/t1281603932y5vh9adt9nz05hx.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:

Hoes Isabelle

Dataseries X:

» Textbox « » Textfile « » CSV «

698 697 696 694 714 713 698 688 689 689 690 692 688 679 677 673 694 690 673 659 657 654 644 643 638 626 621 615 640 633 620 610 601 595 585 584 580 574 560 550 580 569 551 536 535 526 517 512 510 501 496 491 524 514 495 479 479 467 451 459 461 460 452 449 483 470 442 419 419 406 393 396 390 389 373 371 407 391 357 327 321 317 300 304 296 296 283 279 319 295 255 227 228 233 210 219 212 209 201 198 245 216 173 144 143 152 127 141 129 127 113 117 174 143 103 81 92 104 81 89

Output produced by software:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	698	NA	NA	-4.68171296296297	NA
2	697	NA	NA	-3.8946759259259	NA
3	696	NA	NA	-7.76504629629628	NA
4	694	NA	NA	-5.95949074074074	NA
5	714	NA	NA	35.4571759259259	NA
6	713	NA	NA	24.9571759259259	NA
7	698	699.716435185185	696.083333333333	3.63310185185186	-1.71643518518522
8	688	684.378472222222	694.916666666667	-10.5381944444445	3.62152777777771
9	689	686.28587962963	693.375	-7.08912037037038	2.71412037037032
10	689	686.322916666667	691.708333333333	-5.38541666666665	2.67708333333337
11	690	676.230324074074	690	-13.7696759259259	13.7696759259259
12	692	683.244212962963	688.208333333333	-4.96412037037038	8.75578703703707
13	688	681.52662037037	686.208333333333	-4.68171296296297	6.47337962962956
14	679	680.063657407407	683.958333333333	-3.8946759259259	-1.06365740740728
15	677	673.65162037037	681.416666666667	-7.76504629629628	3.34837962962968
16	673	672.66550925926	678.625	-5.95949074074074	0.334490740740762
17	694	710.707175925926	675.25	35.4571759259259	-16.7071759259259
18	690	696.248842592593	671.291666666667	24.9571759259259	-6.24884259259261
19	673	670.799768518518	667.166666666667	3.63310185185186	2.20023148148152
20	659	652.336805555556	662.875	-10.5381944444445	6.66319444444446
21	657	651.244212962963	658.333333333333	-7.08912037037038	5.75578703703695
22	654	648.197916666667	653.583333333333	-5.38541666666665	5.80208333333337
23	644	635.146990740741	648.916666666667	-13.7696759259259	8.85300925925924
24	643	639.327546296296	644.291666666667	-4.96412037037038	3.67245370370381
25	638	635.02662037037	639.708333333333	-4.68171296296297	2.97337962962968
26	626	631.563657407408	635.458333333333	-3.8946759259259	-5.5636574074075
27	621	623.318287037037	631.083333333333	-7.76504629629628	-2.31828703703707
28	615	620.332175925926	626.291666666667	-5.95949074074074	-5.33217592592587
29	640	656.832175925926	621.375	35.4571759259259	-16.8321759259259
30	633	641.415509259259	616.458333333333	24.9571759259259	-8.41550925925912
31	620	615.216435185185	611.583333333333	3.63310185185186	4.7835648148149
32	610	596.461805555556	607	-10.5381944444445	13.5381944444445
33	601	595.202546296296	602.291666666667	-7.08912037037038	5.7974537037037
34	595	591.65625	597.041666666667	-5.38541666666665	3.34375
35	585	578.063657407407	591.833333333333	-13.7696759259259	6.93634259259261
36	584	581.702546296296	586.666666666667	-4.96412037037038	2.29745370370381
37	580	576.443287037037	581.125	-4.68171296296297	3.55671296296293
38	574	571.271990740741	575.166666666667	-3.8946759259259	2.72800925925924
39	560	561.568287037037	569.333333333333	-7.76504629629628	-1.56828703703695
40	550	557.748842592592	563.708333333333	-5.95949074074074	-7.7488425925925
41	580	593.457175925926	558	35.4571759259259	-13.4571759259258
42	569	577.123842592593	552.166666666667	24.9571759259259	-8.1238425925925
43	551	549.883101851852	546.25	3.63310185185186	1.11689814814827
44	536	529.753472222222	540.291666666667	-10.5381944444445	6.24652777777783
45	535	527.494212962963	534.583333333333	-7.08912037037038	7.50578703703707
46	526	524.072916666667	529.458333333333	-5.38541666666665	1.92708333333337
47	517	510.896990740741	524.666666666667	-13.7696759259259	6.10300925925924
48	512	515.077546296296	520.041666666667	-4.96412037037038	-3.07754629629619
49	510	510.734953703704	515.416666666667	-4.68171296296297	-0.734953703703695
50	501	506.813657407407	510.708333333333	-3.8946759259259	-5.81365740740733
51	496	498.234953703704	506	-7.76504629629628	-2.23495370370364
52	491	495.248842592593	501.208333333333	-5.95949074074074	-4.24884259259255
53	524	531.457175925926	496	35.4571759259259	-7.45717592592598
54	514	515.998842592593	491.041666666667	24.9571759259259	-1.99884259259255
55	495	490.424768518518	486.791666666667	3.63310185185186	4.57523148148152
56	479	472.503472222222	483.041666666667	-10.5381944444445	6.49652777777783
57	479	472.41087962963	479.5	-7.08912037037038	6.58912037037038
58	467	470.53125	475.916666666667	-5.38541666666665	-3.53125
59	451	458.688657407407	472.458333333333	-13.7696759259259	-7.68865740740739
60	459	463.952546296296	468.916666666667	-4.96412037037038	-4.95254629629636
61	461	460.193287037037	464.875	-4.68171296296297	0.80671296296299
62	460	456.271990740741	460.166666666667	-3.8946759259259	3.7280092592593
63	452	447.40162037037	455.166666666667	-7.76504629629628	4.59837962962968
64	449	444.165509259259	450.125	-5.95949074074074	4.83449074074082
65	483	480.623842592593	445.166666666667	35.4571759259259	2.37615740740739
66	470	465.082175925926	440.125	24.9571759259259	4.91782407407408
67	442	438.174768518519	434.541666666667	3.63310185185186	3.82523148148147
68	419	418.086805555556	428.625	-10.5381944444445	0.9131944444444
69	419	415.28587962963	422.375	-7.08912037037038	3.71412037037044
70	406	410.447916666667	415.833333333333	-5.38541666666665	-4.44791666666663
71	393	395.646990740741	409.416666666667	-13.7696759259259	-2.64699074074076
72	396	397.994212962963	402.958333333333	-4.96412037037038	-1.99421296296293
73	390	391.443287037037	396.125	-4.68171296296297	-1.44328703703701
74	389	384.855324074074	388.75	-3.8946759259259	4.14467592592598
75	373	373.068287037037	380.833333333333	-7.76504629629628	-0.0682870370369528
76	371	367.082175925926	373.041666666667	-5.95949074074074	3.91782407407408
77	407	400.915509259259	365.458333333333	35.4571759259259	6.08449074074076
78	391	382.707175925926	357.75	24.9571759259259	8.29282407407413
79	357	353.633101851852	350	3.63310185185186	3.36689814814821
80	327	331.670138888889	342.208333333333	-10.5381944444445	-4.67013888888886
81	321	327.494212962963	334.583333333333	-7.08912037037038	-6.49421296296299
82	317	321.614583333333	327	-5.38541666666665	-4.61458333333337
83	300	305.730324074074	319.5	-13.7696759259259	-5.73032407407402
84	304	306.869212962963	311.833333333333	-4.96412037037038	-2.86921296296293
85	296	298.90162037037	303.583333333333	-4.68171296296297	-2.90162037037032
86	296	291.271990740741	295.166666666667	-3.8946759259259	4.7280092592593
87	283	279.359953703704	287.125	-7.76504629629628	3.6400462962963
88	279	273.790509259259	279.75	-5.95949074074074	5.20949074074082
89	319	307.957175925926	272.5	35.4571759259259	11.0428240740741
90	295	290.165509259259	265.208333333333	24.9571759259259	4.83449074074076
91	255	261.799768518519	258.166666666667	3.63310185185186	-6.79976851851853
92	227	240.503472222222	251.041666666667	-10.5381944444445	-13.5034722222222
93	228	236.91087962963	244	-7.08912037037038	-8.91087962962962
94	233	231.822916666667	237.208333333333	-5.38541666666665	1.17708333333331
95	210	216.980324074074	230.75	-13.7696759259259	-6.98032407407405
96	219	219.41087962963	224.375	-4.96412037037038	-0.410879629629591
97	212	212.984953703704	217.666666666667	-4.68171296296297	-0.984953703703667
98	209	206.896990740741	210.791666666667	-3.8946759259259	2.10300925925924
99	201	196.02662037037	203.791666666667	-7.76504629629628	4.97337962962965
100	198	190.915509259259	196.875	-5.95949074074074	7.08449074074073
101	245	225.498842592593	190.041666666667	35.4571759259259	19.5011574074074
102	216	208.290509259259	183.333333333333	24.9571759259259	7.70949074074076
103	173	180.258101851852	176.625	3.63310185185186	-7.25810185185185
104	144	159.211805555556	169.75	-10.5381944444445	-15.2118055555555
105	143	155.577546296296	162.666666666667	-7.08912037037038	-12.5775462962963
106	152	150.239583333333	155.625	-5.38541666666665	1.76041666666666
107	127	135.521990740741	149.291666666667	-13.7696759259259	-8.52199074074076
108	141	138.327546296296	143.291666666667	-4.96412037037038	2.67245370370372
109	129	132.65162037037	137.333333333333	-4.68171296296297	-3.65162037037038
110	127	127.896990740741	131.791666666667	-3.8946759259259	-0.89699074074079
111	113	119.27662037037	127.041666666667	-7.76504629629628	-6.27662037037037
112	117	116.957175925926	122.916666666667	-5.95949074074074	0.0428240740740762
113	174	154.457175925926	119	35.4571759259259	19.5428240740741
114	143	139.873842592593	114.916666666667	24.9571759259259	3.12615740740742
115	103	NA	NA	3.63310185185186	NA
116	81	NA	NA	-10.5381944444445	NA
117	92	NA	NA	-7.08912037037038	NA
118	104	NA	NA	-5.38541666666665	NA
119	81	NA	NA	-13.7696759259259	NA
120	89	NA	NA	-4.96412037037038	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/1o5wm1281603328.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/1o5wm1281603328.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/2o5wm1281603328.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/2o5wm1281603328.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/3zev71281603328.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/3zev71281603328.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/4zev71281603328.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/12/t1281603932y5vh9adt9nz05hx/4zev71281603328.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')

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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