Home » date » 2010 » Aug » 19 »

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, 19 Aug 2010 20:59:38 +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/19/t1282251625tdva5mm39nmp8ju.htm/, Retrieved Thu, 19 Aug 2010 23:00:26 +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/19/t1282251625tdva5mm39nmp8ju.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:

Aerts Ellen

Dataseries X:

» Textbox « » Textfile « » CSV «

25 24 23 21 41 40 25 15 16 16 17 19 18 19 20 21 46 47 30 16 15 18 30 31 32 36 30 31 61 57 45 33 31 36 46 49 34 40 41 48 75 77 71 54 50 56 66 66 48 63 71 70 88 92 91 80 81 81 98 106 85 93 96 92 115 109 119 107 107 106 132 143 120 123 132 136 158 151 155 138 143 139 168 182 154 158 167 170 197 190 196 174 180 171 200 215 184 186 197 186 211 205 218 199 213 207 236 248 211 220 235 223 245 236 253 246 255 248 274 288

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	25	NA	NA	0.88010480829017	NA
2	24	NA	NA	0.936399030973389	NA
3	23	NA	NA	0.946881990031835	NA
4	21	NA	NA	0.937204017852464	NA
5	41	NA	NA	1.28524329475686	NA
6	40	NA	NA	1.22892471969396	NA
7	25	25.9257058360181	23.2083333333333	1.1170860683383	0.964293900352289
8	15	19.5810009561197	22.7083333333333	0.862282610911693	0.766048683293283
9	16	19.0152577550292	22.375	0.849843922012479	0.841429561782736
10	16	19.0517334457544	22.25	0.856257682955254	0.839818594226948
11	17	23.0920971557112	22.4583333333333	1.02821953940087	0.73618259464994
12	19	24.6010552268868	22.9583333333333	1.07155231478273	0.77232459440336
13	18	20.6457919611402	23.4583333333333	0.88010480829017	0.871848366673453
14	19	22.2004603593274	23.7083333333333	0.936399030973389	0.855838108420902
15	20	22.4489938470048	23.7083333333333	0.946881990031835	0.89090852517956
16	21	22.258595423996	23.75	0.937204017852464	0.94345575720204
17	46	31.3278053096985	24.375	1.28524329475686	1.46834416089018
18	47	31.2351699588881	25.4166666666667	1.22892471969396	1.50471407909295
19	30	29.602780810965	26.5	1.1170860683383	1.01341830659665
20	16	23.9642708949208	27.7916666666667	0.862282610911693	0.667660621520982
21	15	24.5746534115275	28.9166666666667	0.849843922012479	0.610385007219015
22	18	25.4736660679188	29.75	0.856257682955254	0.706612073503976
23	30	31.6605933173851	30.7916666666667	1.02821953940087	0.947550151674723
24	31	34.1110820205835	31.8333333333333	1.07155231478273	0.908795563309713
25	32	28.9334455725393	32.875	0.88010480829017	1.10598649302837
26	36	32.032650184548	34.2083333333333	0.936399030973389	1.12385331193626
27	30	33.6932174786328	35.5833333333333	0.946881990031835	0.89038691597278
28	31	34.6765486605412	37	0.937204017852464	0.893975934671816
29	61	49.3747632402427	38.4166666666667	1.28524329475686	1.23544896211841
30	57	48.9521680011426	39.8333333333333	1.22892471969396	1.16440195250739
31	45	45.428166779091	40.6666666666667	1.1170860683383	0.99057486116107
32	33	35.2817301631368	40.9166666666667	0.862282610911693	0.935328280314304
33	31	35.3039329269351	41.5416666666667	0.849843922012479	0.878089137098621
34	36	36.5693385428807	42.7083333333333	0.856257682955254	0.98443125947676
35	46	45.2416597336383	44	1.02821953940087	1.016761990405
36	49	48.6663342963822	45.4166666666667	1.07155231478273	1.00685619141943
37	34	41.658294259068	47.3333333333333	0.88010480829017	0.816163998183843
38	40	46.15666890173	49.2916666666667	0.936399030973389	0.86661366497574
39	41	48.2515280753723	50.9583333333333	0.946881990031835	0.849714022236874
40	48	49.2813112720754	52.5833333333333	0.937204017852464	0.974000057242766
41	75	69.7244487405597	54.25	1.28524329475686	1.07566286080038
42	77	68.563758319592	55.7916666666667	1.22892471969396	1.1230422877504
43	71	63.7669964009781	57.0833333333333	1.1170860683383	1.1134286387513
44	54	50.551318064698	58.625	0.862282610911693	1.0682214048482
45	50	51.6988385890924	60.8333333333333	0.849843922012479	0.967139714634695
46	56	53.944234026181	63	0.856257682955254	1.03810909564164
47	66	66.2773178105478	64.4583333333333	1.02821953940087	0.995815796116848
48	66	70.3206206576165	65.625	1.07155231478273	0.938558269008274
49	48	59.0403642227989	67.0833333333333	0.88010480829017	0.8130031145957
50	63	64.6115331371638	69	0.936399030973389	0.97505811951958
51	71	67.5837020385222	71.375	0.946881990031835	1.05054913919232
52	70	69.0797461492087	73.7083333333333	0.937204017852464	1.01332161598862
53	88	97.7855940094178	76.0833333333333	1.28524329475686	0.899928060891307
54	92	97.1874632491304	79.0833333333333	1.22892471969396	0.946624152172458
55	91	91.9268743736728	82.2916666666667	1.1170860683383	0.989917264347473
56	80	73.3658788117365	85.0833333333333	0.862282610911693	1.09042515806683
57	81	74.2551126858403	87.375	0.849843922012479	1.09083397856651
58	81	76.4923530106694	89.3333333333333	0.856257682955254	1.0589293806755
59	98	93.9535604127546	91.375	1.02821953940087	1.04306850713766
60	106	99.8776053403734	93.2083333333333	1.07155231478273	1.06129897326595
61	85	83.6832988549236	95.0833333333333	0.88010480829017	1.01573433603949
62	93	91.1818556410337	97.375	0.936399030973389	1.01993976045107
63	96	94.2936648406702	99.5833333333333	0.946881990031835	1.01809596818846
64	92	95.3214586490777	101.708333333333	0.937204017852464	0.965155184402859
65	115	133.879509870506	104.166666666667	1.28524329475686	0.858981334120753
66	109	131.648560597215	107.125	1.22892471969396	0.827961958000366
67	119	123.019103275756	110.125	1.1170860683383	0.967329437715486
68	107	97.294221264536	112.833333333333	0.862282610911693	1.09975699079881
69	107	98.2277933192757	115.583333333333	0.849843922012479	1.08930473122013
70	106	101.823309464762	118.916666666667	0.856257682955254	1.04101900200644
71	132	125.999736057415	122.541666666667	1.02821953940087	1.04762124215761
72	143	135.104887688856	126.083333333333	1.07155231478273	1.05843691110071
73	120	113.826888538862	129.333333333333	0.88010480829017	1.05423245368804
74	123	123.721721967359	132.125	0.936399030973389	0.994166570300813
75	132	127.750161821795	134.916666666667	0.946881990031835	1.03326679291517
76	136	129.138903626587	137.791666666667	0.937204017852464	1.05312958512682
77	158	180.790890129132	140.666666666667	1.28524329475686	0.873937839938433
78	151	176.70913365266	143.791666666667	1.22892471969396	0.854511574352494
79	155	164.025471034341	146.833333333333	1.1170860683383	0.944975186003574
80	138	129.090892541905	149.708333333333	0.862282610911693	1.06901422155094
81	143	129.707428597155	152.625	0.849843922012479	1.1024811882142
82	139	133.148069699542	155.5	0.856257682955254	1.04395054553673
83	168	163.015639475846	158.541666666667	1.02821953940087	1.03057596522751
84	182	173.368234929222	161.791666666667	1.07155231478273	1.04978861943367
85	154	145.327306468914	165.125	0.88010480829017	1.05967697153281
86	158	157.627170213854	168.333333333333	0.936399030973389	1.00236526346086
87	167	162.271901041706	171.375	0.946881990031835	1.02913689263478
88	170	163.307800110792	174.25	0.937204017852464	1.04097905846915
89	197	227.380959564068	176.916666666667	1.28524329475686	0.86638740718522
90	190	220.745602775027	179.625	1.22892471969396	0.860719296835275
91	196	203.588935954656	182.25	1.1170860683383	0.962724222124006
92	174	159.234855481693	184.666666666667	0.862282610911693	1.09272558117783
93	180	158.991633743168	187.083333333333	0.849843922012479	1.13213504234297
94	171	161.832702078543	189	0.856257682955254	1.05664675806382
95	200	195.618767371016	190.25	1.02821953940087	1.02239679090031
96	215	205.157620267776	191.458333333333	1.07155231478273	1.04797472167681
97	184	169.860228000003	193	0.88010480829017	1.08324357129673
98	186	182.55879441352	194.958333333333	0.936399030973389	1.01884984833261
99	197	186.890832782533	197.375	0.946881990031835	1.05409129526021
100	186	187.675104574956	200.25	0.937204017852464	0.991074444430179
101	211	261.225699659332	203.25	1.28524329475686	0.8077306339888
102	205	253.312107846917	206.125	1.22892471969396	0.809278331550922
103	218	233.052081007078	208.625	1.1170860683383	0.935413230630533
104	199	182.085344670852	211.166666666667	0.862282610911693	1.09289410611119
105	213	182.008239964339	214.166666666667	0.849843922012479	1.17027668660349
106	207	186.057659025485	217.291666666667	0.856257682955254	1.11255833855056
107	236	226.465353553042	220.25	1.02821953940087	1.04210200941278
108	248	238.911518183432	222.958333333333	1.07155231478273	1.03804120406447
109	211	198.646989437827	225.708333333333	0.88010480829017	1.06218574264393
110	220	214.552427971778	229.125	0.936399030973389	1.02539040028454
111	235	220.465690012412	232.833333333333	0.946881990031835	1.06592549610223
112	223	221.453499385055	236.291666666667	0.937204017852464	1.00698341014813
113	245	307.922872702165	239.583333333333	1.28524329475686	0.795653787748901
114	236	298.423886099016	242.833333333333	1.22892471969396	0.790821415420132
115	253	NA	NA	1.1170860683383	NA
116	246	NA	NA	0.862282610911693	NA
117	255	NA	NA	0.849843922012479	NA
118	248	NA	NA	0.856257682955254	NA
119	274	NA	NA	1.02821953940087	NA
120	288	NA	NA	1.07155231478273	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/1qoxv1282251574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/1qoxv1282251574.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/2qoxv1282251574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/2qoxv1282251574.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/3jfxy1282251574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/3jfxy1282251574.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/4jfxy1282251574.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/19/t1282251625tdva5mm39nmp8ju/4jfxy1282251574.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')

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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