Home » date » 2010 » Jul » 29 »

Tijdreeks 1 - 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, 29 Jul 2010 11:30:26 +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/Jul/29/t1280403021lunfhvdb007w75v.htm/, Retrieved Thu, 29 Jul 2010 13:30:27 +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/Jul/29/t1280403021lunfhvdb007w75v.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:

Habimana Christelle

Dataseries X:

» Textbox « » Textfile « » CSV «

900 899 898 896 916 915 900 890 891 891 892 894 896 889 878 883 901 897 881 866 867 866 862 871 865 856 847 859 870 872 856 839 829 825 822 827 822 812 810 816 820 823 810 793 777 772 765 765 753 742 736 740 742 742 728 707 699 696 689 692 673 653 642 648 654 653 630 609 598 601 592 591 568 538 523 530 529 534 513 491 480 478 462 461 437 411 400 405 395 407 385 366 349 343 332 327 306 276 269 268 260 274 247 226 212 199 188 179 155 124 117 116 105 112 86 64 53 42 32 24

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	900	NA	NA	-1.15354938271603	NA
2	899	NA	NA	-12.8942901234568	NA
3	898	NA	NA	-13.9683641975309	NA
4	896	NA	NA	-1.38040123456790	NA
5	916	NA	NA	7.75385802469136	NA
6	915	NA	NA	19.9853395061728	NA
7	900	906.165895061728	898.333333333333	7.83256172839507	-6.16589506172829
8	890	894.508487654321	897.75	-3.24151234567901	-4.50848765432102
9	891	891.01774691358	896.5	-5.48225308641977	-0.0177469135801402
10	891	893.425154320988	895.125	-1.69984567901233	-2.42515432098753
11	892	892.179783950617	893.958333333333	-1.77854938271604	-0.179783950617207
12	894	898.610339506173	892.583333333333	6.02700617283953	-4.61033950617275
13	896	889.88811728395	891.041666666667	-1.15354938271603	6.11188271604942
14	889	876.355709876543	889.25	-12.8942901234568	12.6442901234568
15	878	873.28163580247	887.25	-13.9683641975309	4.71836419753083
16	883	883.827932098765	885.208333333333	-1.38040123456790	-0.82793209876536
17	901	890.670524691358	882.916666666667	7.75385802469136	10.3294753086421
18	897	900.693672839506	880.708333333333	19.9853395061728	-3.69367283950612
19	881	886.290895061728	878.458333333333	7.83256172839507	-5.29089506172841
20	866	872.550154320988	875.791666666667	-3.24151234567901	-6.55015432098753
21	867	867.64274691358	873.125	-5.48225308641977	-0.642746913580254
22	866	869.13348765432	870.833333333333	-1.69984567901233	-3.13348765432079
23	862	866.76311728395	868.541666666667	-1.77854938271604	-4.76311728395046
24	871	872.235339506173	866.208333333333	6.02700617283953	-1.23533950617264
25	865	862.971450617284	864.125	-1.15354938271603	2.02854938271616
26	856	849.064043209877	861.958333333333	-12.8942901234568	6.93595679012344
27	847	845.281635802469	859.25	-13.9683641975309	1.71836419753083
28	859	854.577932098766	855.958333333333	-1.38040123456790	4.42206790123441
29	870	860.337191358025	852.583333333333	7.75385802469136	9.66280864197518
30	872	869.068672839506	849.083333333333	19.9853395061728	2.93132716049388
31	856	853.290895061728	845.458333333333	7.83256172839507	2.70910493827159
32	839	838.591820987654	841.833333333333	-3.24151234567901	0.408179012345613
33	829	832.976080246914	838.458333333333	-5.48225308641977	-3.97608024691351
34	825	833.425154320988	835.125	-1.69984567901233	-8.42515432098753
35	822	829.471450617284	831.25	-1.77854938271604	-7.47145061728395
36	827	833.15200617284	827.125	6.02700617283953	-6.15200617283949
37	822	822.01311728395	823.166666666667	-1.15354938271603	-0.0131172839504643
38	812	806.439043209877	819.333333333333	-12.8942901234568	5.56095679012344
39	810	801.28163580247	815.25	-13.9683641975309	8.71836419753095
40	816	809.494598765432	810.875	-1.38040123456790	6.50540123456801
41	820	814.045524691358	806.291666666667	7.75385802469136	5.95447530864215
42	823	821.318672839506	801.333333333333	19.9853395061728	1.68132716049399
43	810	803.707561728395	795.875	7.83256172839507	6.29243827160508
44	793	786.841820987654	790.083333333333	-3.24151234567901	6.15817901234561
45	777	778.601080246914	784.083333333333	-5.48225308641977	-1.60108024691363
46	772	776.133487654321	777.833333333333	-1.69984567901233	-4.13348765432102
47	765	769.63811728395	771.416666666667	-1.77854938271604	-4.63811728395058
48	765	770.818672839506	764.791666666667	6.02700617283953	-5.81867283950612
49	753	756.846450617284	758	-1.15354938271603	-3.84645061728395
50	742	738.105709876543	751	-12.8942901234568	3.89429012345681
51	736	730.198302469136	744.166666666667	-13.9683641975309	5.8016975308642
52	740	736.369598765432	737.75	-1.38040123456790	3.63040123456801
53	742	739.170524691358	731.416666666667	7.75385802469136	2.82947530864203
54	742	745.193672839506	725.208333333333	19.9853395061728	-3.19367283950612
55	728	726.665895061728	718.833333333333	7.83256172839507	1.33410493827159
56	707	708.550154320988	711.791666666667	-3.24151234567901	-1.55015432098764
57	699	698.684413580247	704.166666666667	-5.48225308641977	0.315586419753117
58	696	694.716820987654	696.416666666667	-1.69984567901233	1.28317901234561
59	689	687.138117283951	688.916666666667	-1.77854938271604	1.86188271604942
60	692	687.568672839506	681.541666666667	6.02700617283953	4.43132716049388
61	673	672.596450617284	673.75	-1.15354938271603	0.403549382716051
62	653	652.689043209877	665.583333333333	-12.8942901234568	0.310956790123441
63	642	643.323302469136	657.291666666667	-13.9683641975309	-1.32330246913580
64	648	647.744598765432	649.125	-1.38040123456790	0.255401234568012
65	654	648.878858024691	641.125	7.75385802469136	5.12114197530866
66	653	652.860339506173	632.875	19.9853395061728	0.139660493827137
67	630	632.124228395062	624.291666666667	7.83256172839507	-2.12422839506155
68	609	611.883487654321	615.125	-3.24151234567901	-2.88348765432102
69	598	599.89274691358	605.375	-5.48225308641977	-1.89274691358025
70	601	593.800154320988	595.5	-1.69984567901233	7.19984567901247
71	592	583.596450617284	585.375	-1.77854938271604	8.40354938271616
72	591	581.235339506173	575.208333333333	6.02700617283953	9.76466049382725
73	568	564.221450617284	565.375	-1.15354938271603	3.77854938271616
74	538	542.689043209876	555.583333333333	-12.8942901234568	-4.68904320987644
75	523	531.781635802469	545.75	-13.9683641975309	-8.78163580246905
76	530	534.327932098765	535.708333333333	-1.38040123456790	-4.32793209876536
77	529	532.920524691358	525.166666666667	7.75385802469136	-3.92052469135797
78	534	534.318672839506	514.333333333333	19.9853395061728	-0.318672839506007
79	513	511.290895061728	503.458333333333	7.83256172839507	1.70910493827159
80	491	489.466820987654	492.708333333333	-3.24151234567901	1.53317901234573
81	480	476.809413580247	482.291666666667	-5.48225308641977	3.19058641975306
82	478	470.258487654321	471.958333333333	-1.69984567901233	7.74151234567904
83	462	459.388117283951	461.166666666667	-1.77854938271604	2.61188271604937
84	461	456.318672839506	450.291666666667	6.02700617283953	4.68132716049382
85	437	438.513117283951	439.666666666667	-1.15354938271603	-1.51311728395058
86	411	416.230709876543	429.125	-12.8942901234568	-5.23070987654313
87	400	404.489969135802	418.458333333333	-13.9683641975309	-4.48996913580243
88	405	405.994598765432	407.375	-1.38040123456790	-0.994598765432158
89	395	404.087191358025	396.333333333333	7.75385802469136	-9.08719135802465
90	407	405.318672839506	385.333333333333	19.9853395061728	1.68132716049394
91	385	382.124228395062	374.291666666667	7.83256172839507	2.87577160493834
92	366	359.966820987654	363.208333333333	-3.24151234567901	6.03317901234573
93	349	346.64274691358	352.125	-5.48225308641977	2.35725308641969
94	343	339.258487654321	340.958333333333	-1.69984567901233	3.74151234567898
95	332	327.846450617284	329.625	-1.77854938271604	4.15354938271605
96	327	324.485339506173	318.458333333333	6.02700617283953	2.51466049382719
97	306	306.013117283951	307.166666666667	-1.15354938271603	-0.0131172839506348
98	276	282.689043209877	295.583333333333	-12.8942901234568	-6.6890432098765
99	269	270.073302469136	284.041666666667	-13.9683641975309	-1.07330246913580
100	268	270.952932098765	272.333333333333	-1.38040123456790	-2.95293209876542
101	260	268.087191358025	260.333333333333	7.75385802469136	-8.08719135802468
102	274	268.152006172839	248.166666666667	19.9853395061728	5.84799382716054
103	247	243.540895061728	235.708333333333	7.83256172839507	3.45910493827162
104	226	219.841820987654	223.083333333333	-3.24151234567901	6.1581790123457
105	212	204.934413580247	210.416666666667	-5.48225308641977	7.06558641975312
106	199	196.050154320988	197.75	-1.69984567901233	2.94984567901236
107	188	183.179783950617	184.958333333333	-1.77854938271604	4.82021604938268
108	179	177.777006172840	171.75	6.02700617283953	1.22299382716048
109	155	157.138117283951	158.291666666667	-1.15354938271603	-2.13811728395063
110	124	131.939043209877	144.833333333333	-12.8942901234568	-7.9390432098765
111	117	117.489969135802	131.458333333333	-13.9683641975309	-0.489969135802482
112	116	116.911265432099	118.291666666667	-1.38040123456790	-0.911265432098787
113	105	113.003858024691	105.25	7.75385802469136	-8.00385802469135
114	112	112.277006172839	92.2916666666667	19.9853395061728	-0.277006172839464
115	86	NA	NA	7.83256172839507	NA
116	64	NA	NA	-3.24151234567901	NA
117	53	NA	NA	-5.48225308641977	NA
118	42	NA	NA	-1.69984567901233	NA
119	32	NA	NA	-1.77854938271604	NA
120	24	NA	NA	6.02700617283953	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/1acg41280403023.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/1acg41280403023.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/2acg41280403023.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/2acg41280403023.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/3llxp1280403023.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/3llxp1280403023.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/4llxp1280403023.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/29/t1280403021lunfhvdb007w75v/4llxp1280403023.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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