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opgave 9 decompositie oef 2

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Mon, 13 Dec 2010 12:26:26 +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/Dec/13/t1292243102z21n78q9arhlb6x.htm/, Retrieved Mon, 13 Dec 2010 13:25:04 +0100

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/Dec/13/t1292243102z21n78q9arhlb6x.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:

opgave 9 decompositie oef 2 boeken

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

90 69,3 87,3 57,4 56,2 61,6 77,7 177,2 97,6 81,6 96,8 191,3 106 75,1 72 63,5 57,4 62,3 79,4 178,1 109,3 85,2 102,7 193,7 108,4 73,4 85,9 58,5 58,6 62,7 77,5 180,5 102,2 82,6 97,8 197,8 93,8 72,4 77,7 58,7 53,1 64,3 76,4 188,4 105,5 79,8 96,1 202,5 97,3 89,5 64,7 61,2 57,8 62 76,3 195 110,9 81,4 101,7 202,2 97,4 68,5 86,8 59,1 62,4 66,2 68 198,5 120,4 90,2 103,2 207,3 106,4 75,5 97,3 60 67,5 71,2 73,7 213,3 114,6 96,1 117 229,2 105,6 99,9 79,3 72,5 67,4 78,3 85,7 177,4 113,6 94,1 105,7 228,3 100,3 70,3 94,2 66,5 64,4 73,7 87,9 152,2 97,3 89,3 107,6 228,4

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	2 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	90	NA	NA	0.999031223454888	NA
2	69.3	NA	NA	0.764165145223747	NA
3	87.3	NA	NA	0.805903656286252	NA
4	57.4	NA	NA	0.612591703228792	NA
5	56.2	NA	NA	0.597377249099274	NA
6	61.6	NA	NA	0.659589734874162	NA
7	77.7	72.6982277541793	96	0.757273205772701	1.06880184566168
8	177.2	179.674332527954	96.9083333333333	1.8540648295945	0.986228792431613
9	97.6	103.612820449088	96.5125	1.07356892059669	0.94196837396157
10	81.6	81.4553896824661	96.1291666666666	0.847353540106275	1.00177533148018
11	96.8	97.03814911671	96.4333333333333	1.00627185395828	0.997545819671153
12	191.3	195.226347609851	96.5125	2.02280893780444	0.979888228930569
13	106	96.5189040760354	96.6125	0.999031223454888	1.09823045562655
14	75.1	73.9106896503285	96.7208333333333	0.764165145223747	1.01609118187502
15	72	78.3707726419368	97.2458333333333	0.805903656286252	0.91870984006954
16	63.5	59.9625178843782	97.8833333333333	0.612591703228792	1.05899488948151
17	57.4	58.7097382271024	98.2791666666667	0.597377249099274	0.977691295061885
18	62.3	65.0520376019642	98.625	0.659589734874162	0.95769482858011
19	79.4	74.8375245604872	98.825	0.757273205772701	1.06096507689569
20	178.1	183.28203367554	98.8541666666667	1.8540648295945	0.971726450369306
21	109.3	106.672491872789	99.3625	1.07356892059669	1.02463154353181
22	85.2	84.5093930665991	99.7333333333333	0.847353540106275	1.00817195471818
23	102.7	100.199519857896	99.575	1.00627185395828	1.02495501121812
24	193.7	201.556053911064	99.6416666666667	2.02280893780444	0.961022982150017
25	108.4	99.4826967056182	99.5791666666667	0.999031223454888	1.08963672668393
26	73.4	76.1108484642852	99.6	0.764165145223747	0.964382889969263
27	85.9	80.1101813667546	99.4041666666667	0.805903656286252	1.07227319342518
28	58.5	60.6465786196504	99	0.612591703228792	0.964605115927267
29	58.6	58.9536672704846	98.6875	0.597377249099274	0.99400092840939
30	62.7	65.071275635898	98.6541666666667	0.659589734874162	0.963558795909176
31	77.5	74.3768500269754	98.2166666666667	0.757273205772701	1.04199088791596
32	180.5	180.894925207437	97.5666666666667	1.8540648295945	0.997816825391956
33	102.2	104.333006266655	97.1833333333333	1.07356892059669	0.979555786390322
34	82.6	82.0661903592927	96.85	0.847353540106275	1.00650462313859
35	97.8	97.2352106881101	96.6291666666666	1.00627185395828	1.00580848550533
36	197.8	195.133635533535	96.4666666666667	2.02280893780444	1.01366429964355
37	93.8	96.3940251731035	96.4875	0.999031223454888	0.973089357266229
38	72.4	73.9488979075897	96.7708333333333	0.764165145223747	0.979054482873765
39	77.7	78.3640567781344	97.2375	0.805903656286252	0.991526003049912
40	58.7	59.5796480698602	97.2583333333333	0.612591703228792	0.985235762574012
41	53.1	57.9879073844408	97.0708333333333	0.597377249099274	0.915708160461188
42	64.3	64.1093739392066	97.1958333333333	0.659589734874162	1.00297345066845
43	76.4	73.8625353080548	97.5375	0.757273205772701	1.03435388023661
44	188.4	182.432253961976	98.3958333333333	1.8540648295945	1.03271212139531
45	105.5	105.818109940147	98.5666666666667	1.07356892059669	0.996993804365553
46	79.8	83.1500967626787	98.1291666666667	0.847353540106275	0.959710248176376
47	96.1	99.046500025235	98.4291666666667	1.00627185395828	0.970251346342533
48	202.5	199.305678967756	98.5291666666667	2.02280893780444	1.01602724542917
49	97.3	98.3338107986451	98.4291666666667	0.999031223454888	0.989486720892349
50	89.5	75.4230998335838	98.7	0.764165145223747	1.18663910920495
51	64.7	79.9456427035962	99.2	0.805903656286252	0.809299891926313
52	61.2	60.9477695404045	99.4916666666667	0.612591703228792	1.00413846907766
53	57.8	59.613271316365	99.7916666666667	0.597377249099274	0.969582757726176
54	62	65.9672183591021	100.0125	0.659589734874162	0.939860760271776
55	76.3	75.7304758822941	100.004166666667	0.757273205772701	1.00752040854188
56	195	183.799626773802	99.1333333333333	1.8540648295945	1.06093795413406
57	110.9	106.475670904013	99.1791666666667	1.07356892059669	1.04155248854901
58	81.4	84.7459459298788	100.0125	0.847353540106275	0.960517923386597
59	101.7	100.74458377879	100.116666666667	1.00627185395828	1.00948354924279
60	202.2	203.258584767049	100.483333333333	2.02280893780444	0.994791930838925
61	97.4	100.215319602818	100.3125	0.999031223454888	0.971907293076783
62	68.5	76.5024831012123	100.1125	0.764165145223747	0.89539577309373
63	86.8	81.1175609371124	100.654166666667	0.805903656286252	1.07005189748362
64	59.1	62.12700856912	101.416666666667	0.612591703228792	0.95127709125489
65	62.4	60.8403837488898	101.845833333333	0.597377249099274	1.02563455644112
66	66.2	67.3578533834618	102.120833333333	0.659589734874162	0.98281041741532
67	68	77.7782688429045	102.708333333333	0.757273205772701	0.874280194347672
68	198.5	191.663951759332	103.375	1.8540648295945	1.03566684385832
69	120.4	111.762997837952	104.104166666667	1.07356892059669	1.07727962142328
70	90.2	88.6155270963641	104.579166666667	0.847353540106275	1.01788030783717
71	103.2	105.486639890568	104.829166666667	1.00627185395828	0.97832294314294
72	207.3	212.900640703917	105.25	2.02280893780444	0.973693640914375
73	106.4	105.593437689084	105.695833333333	0.999031223454888	1.00763837534384
74	75.5	81.4217962235902	106.55	0.764165145223747	0.927270135292416
75	97.3	86.1712484484075	106.925	0.805903656286252	1.12914692257541
76	60	65.5039203331687	106.929166666667	0.612591703228792	0.915975710992954
77	67.5	64.3673985904468	107.75	0.597377249099274	1.04866751613632
78	71.2	72.0519336633163	109.2375	0.659589734874162	0.988176116587
79	73.7	83.3884011756706	110.116666666667	0.757273205772701	0.88381596194343
80	213.3	205.986602567949	111.1	1.8540648295945	1.03550423833821
81	114.6	119.559792123785	111.366666666667	1.07356892059669	0.958516219912376
82	96.1	94.172754063561	111.1375	0.847353540106275	1.02046500556985
83	117	112.354445293833	111.654166666667	1.00627185395828	1.0413473155781
84	229.2	226.445032216632	111.945833333333	2.02280893780444	1.0121661656977
85	105.6	112.632445184343	112.741666666667	0.999031223454888	0.937562882765855
86	99.9	85.3922709573153	111.745833333333	0.764165145223747	1.16989510736793
87	79.3	88.8172987865473	110.208333333333	0.805903656286252	0.892844086494681
88	72.5	67.4361366637695	110.083333333333	0.612591703228792	1.07509124316357
89	67.4	65.4302322794692	109.529166666667	0.597377249099274	1.03010485599558
90	78.3	71.9090225540935	109.020833333333	0.659589734874162	1.08887587703058
91	85.7	82.3629270428534	108.7625	0.757273205772701	1.04051668726404
92	177.4	198.956606755737	107.308333333333	1.8540648295945	0.891651716888184
93	113.6	114.545330623831	106.695833333333	1.07356892059669	0.991747104673032
94	94.1	90.7233190273785	107.066666666667	0.847353540106275	1.03721954850001
95	105.7	107.360821218565	106.691666666667	1.00627185395828	0.984530472105981
96	228.3	215.176300758947	106.375	2.02280893780444	1.06099044920265
97	100.3	106.172043272668	106.275	0.999031223454888	0.94469313115141
98	70.3	80.4793258778143	105.316666666667	0.764165145223747	0.873516263129878
99	94.2	83.4815449955521	103.5875	0.805903656286252	1.12839310778231
100	66.5	62.9182728524572	102.708333333333	0.612591703228792	1.0569266603351
101	64.4	61.2834385419718	102.5875	0.597377249099274	1.0508548725753
102	73.7	67.7206277376426	102.670833333333	0.659589734874162	1.08829469634455
103	87.9	NA	NA	0.757273205772701	NA
104	152.2	NA	NA	1.8540648295945	NA
105	97.3	NA	NA	1.07356892059669	NA
106	89.3	NA	NA	0.847353540106275	NA
107	107.6	NA	NA	1.00627185395828	NA
108	228.4	NA	NA	2.02280893780444	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/17d8s1292243181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/17d8s1292243181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/27d8s1292243181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/27d8s1292243181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/3z47d1292243181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/3z47d1292243181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/4z47d1292243181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/13/t1292243102z21n78q9arhlb6x/4z47d1292243181.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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