Home » date » 2010 » Aug » 13 »

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: Fri, 13 Aug 2010 14:20:58 +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/13/t1281709270n4vp03odvhb89p7.htm/, Retrieved Fri, 13 Aug 2010 16:21:15 +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/13/t1281709270n4vp03odvhb89p7.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:

Jacobs Jeff

Dataseries X:

» Textbox « » Textfile « » CSV «

130 129 128 126 146 145 130 120 121 121 122 124 123 125 120 124 146 149 138 133 135 149 146 141 139 141 138 139 166 179 167 154 151 162 148 143 145 143 148 139 169 186 174 161 151 158 144 135 139 137 149 136 169 185 177 164 145 147 142 126 130 136 139 120 151 166 156 150 141 141 130 110 110 123 133 108 136 148 146 142 132 128 116 90 94 112 130 106 124 139 140 129 113 110 102 78 79 94 121 99 126 137 141 119 96 96 88 64 66 92 120 101 135 146 149 134 101 100 91 70

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	6 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	130	NA	NA	-18.8329475308642	NA
2	129	NA	NA	-10.3190586419753	NA
3	128	NA	NA	0.264274691358037	NA
4	126	NA	NA	-13.5459104938272	NA
5	146	NA	NA	14.4726080246914	NA
6	145	NA	NA	27.4216820987654	NA
7	130	146.648533950617	128.208333333333	18.4402006172840	-16.6485339506173
8	120	135.880015432099	127.75	8.13001543209876	-15.8800154320987
9	121	125.921682098765	127.25	-1.32831790123458	-4.9216820987654
10	121	128.657793209877	126.833333333333	1.82445987654320	-7.65779320987652
11	122	120.518904320988	126.75	-6.23109567901234	1.48109567901238
12	124	106.620756172839	126.916666666667	-20.2959104938272	17.3792438271605
13	123	108.583719135802	127.416666666667	-18.8329475308642	14.4162808641976
14	125	117.972608024691	128.291666666667	-10.3190586419753	7.02739197530866
15	120	129.680941358025	129.416666666667	0.264274691358037	-9.6809413580247
16	124	117.620756172839	131.166666666667	-13.5459104938272	6.37924382716051
17	146	147.805941358025	133.333333333333	14.4726080246914	-1.80594135802471
18	149	162.463348765432	135.041666666667	27.4216820987654	-13.4633487654321
19	138	154.856867283951	136.416666666667	18.4402006172840	-16.8568672839506
20	133	145.880015432099	137.75	8.13001543209876	-12.8800154320988
21	135	137.838348765432	139.166666666667	-1.32831790123458	-2.83834876543207
22	149	142.36612654321	140.541666666667	1.82445987654320	6.63387345679013
23	146	135.768904320988	142	-6.23109567901234	10.2310956790124
24	141	123.787422839506	144.083333333333	-20.2959104938272	17.2125771604938
25	139	127.708719135802	146.541666666667	-18.8329475308642	11.2912808641975
26	141	138.305941358025	148.625	-10.3190586419753	2.69405864197535
27	138	150.430941358025	150.166666666667	0.264274691358037	-12.4309413580247
28	139	137.829089506173	151.375	-13.5459104938272	1.17091049382717
29	166	166.472608024691	152	14.4726080246914	-0.472608024691368
30	179	179.588348765432	152.166666666667	27.4216820987654	-0.588348765432073
31	167	170.940200617284	152.5	18.4402006172840	-3.94020061728392
32	154	160.963348765432	152.833333333333	8.13001543209876	-6.96334876543207
33	151	152.005015432099	153.333333333333	-1.32831790123458	-1.00501543209876
34	162	155.574459876543	153.75	1.82445987654320	6.42554012345678
35	148	147.643904320988	153.875	-6.23109567901234	0.356095679012384
36	143	133.995756172839	154.291666666667	-20.2959104938272	9.0042438271605
37	145	136.042052469136	154.875	-18.8329475308642	8.9579475308642
38	143	145.139274691358	155.458333333333	-10.3190586419753	-2.13927469135800
39	148	156.014274691358	155.75	0.264274691358037	-8.014274691358
40	139	142.037422839506	155.583333333333	-13.5459104938272	-3.03742283950615
41	169	169.722608024691	155.25	14.4726080246914	-0.72260802469134
42	186	182.171682098765	154.75	27.4216820987654	3.82831790123458
43	174	172.606867283951	154.166666666667	18.4402006172840	1.39313271604942
44	161	161.796682098765	153.666666666667	8.13001543209876	-0.796682098765444
45	151	152.130015432099	153.458333333333	-1.32831790123458	-1.13001543209876
46	158	155.199459876543	153.375	1.82445987654320	2.80054012345678
47	144	147.018904320988	153.25	-6.23109567901234	-3.01890432098762
48	135	132.912422839506	153.208333333333	-20.2959104938272	2.08757716049385
49	139	134.458719135802	153.291666666667	-18.8329475308642	4.54128086419755
50	137	143.222608024691	153.541666666667	-10.3190586419753	-6.22260802469137
51	149	153.680941358025	153.416666666667	0.264274691358037	-4.68094135802465
52	136	139.162422839506	152.708333333333	-13.5459104938272	-3.16242283950618
53	169	166.639274691358	152.166666666667	14.4726080246914	2.36072530864197
54	185	179.130015432099	151.708333333333	27.4216820987654	5.86998456790124
55	177	169.398533950617	150.958333333333	18.4402006172840	7.60146604938274
56	164	158.671682098765	150.541666666667	8.13001543209876	5.32831790123458
57	145	148.755015432099	150.083333333333	-1.32831790123458	-3.75501543209876
58	147	150.824459876543	149	1.82445987654320	-3.82445987654322
59	142	141.352237654321	147.583333333333	-6.23109567901234	0.647762345679041
60	126	125.745756172839	146.041666666667	-20.2959104938272	0.254243827160508
61	130	125.542052469136	144.375	-18.8329475308642	4.45794753086423
62	136	132.597608024691	142.916666666667	-10.3190586419753	3.40239197530866
63	139	142.430941358025	142.166666666667	0.264274691358037	-3.43094135802465
64	120	128.204089506173	141.75	-13.5459104938272	-8.2040895061728
65	151	155.472608024691	141	14.4726080246914	-4.47260802469134
66	166	167.255015432099	139.833333333333	27.4216820987654	-1.25501543209873
67	156	156.773533950617	138.333333333333	18.4402006172840	-0.773533950617264
68	150	145.088348765432	136.958333333333	8.13001543209876	4.91165123456793
69	141	134.838348765432	136.166666666667	-1.32831790123458	6.16165123456793
70	141	137.24112654321	135.416666666667	1.82445987654320	3.75887345679013
71	130	128.060570987654	134.291666666667	-6.23109567901234	1.9394290123457
72	110	112.620756172839	132.916666666667	-20.2959104938272	-2.62075617283949
73	110	112.917052469136	131.75	-18.8329475308642	-2.91705246913580
74	123	120.680941358025	131	-10.3190586419753	2.31905864197532
75	133	130.555941358025	130.291666666667	0.264274691358037	2.44405864197532
76	108	115.829089506173	129.375	-13.5459104938272	-7.82908950617283
77	136	142.722608024691	128.25	14.4726080246914	-6.72260802469137
78	148	154.255015432099	126.833333333333	27.4216820987654	-6.25501543209874
79	146	143.773533950617	125.333333333333	18.4402006172840	2.22646604938272
80	142	132.338348765432	124.208333333333	8.13001543209876	9.66165123456793
81	132	122.296682098765	123.625	-1.32831790123458	9.70331790123458
82	128	125.241126543210	123.416666666667	1.82445987654320	2.75887345679014
83	116	116.602237654321	122.833333333333	-6.23109567901234	-0.602237654320987
84	90	101.662422839506	121.958333333333	-20.2959104938272	-11.6624228395062
85	94	102.500385802469	121.333333333333	-18.8329475308642	-8.50038580246911
86	112	110.222608024691	120.541666666667	-10.3190586419753	1.77739197530866
87	130	119.472608024691	119.208333333333	0.264274691358037	10.5273919753087
88	106	104.120756172839	117.666666666667	-13.5459104938272	1.87924382716052
89	124	130.805941358025	116.333333333333	14.4726080246914	-6.80594135802467
90	139	142.671682098765	115.25	27.4216820987654	-3.6716820987654
91	140	132.565200617284	114.125	18.4402006172840	7.43479938271606
92	129	120.880015432099	112.75	8.13001543209876	8.11998456790126
93	113	110.296682098765	111.625	-1.32831790123458	2.7033179012346
94	110	112.782793209877	110.958333333333	1.82445987654320	-2.78279320987653
95	102	104.518904320988	110.75	-6.23109567901234	-2.51890432098766
96	78	90.4540895061728	110.75	-20.2959104938272	-12.4540895061728
97	79	91.8753858024691	110.708333333333	-18.8329475308642	-12.8753858024691
98	94	100.014274691358	110.333333333333	-10.3190586419753	-6.01427469135801
99	121	109.472608024691	109.208333333333	0.264274691358037	11.5273919753086
100	99	94.3707561728395	107.916666666667	-13.5459104938272	4.62924382716052
101	126	121.222608024691	106.75	14.4726080246914	4.77739197530865
102	137	133.005015432099	105.583333333333	27.4216820987654	3.99498456790126
103	141	122.898533950617	104.458333333333	18.4402006172840	18.1014660493827
104	119	111.963348765432	103.833333333333	8.13001543209876	7.03665123456791
105	96	102.380015432099	103.708333333333	-1.32831790123458	-6.38001543209876
106	96	105.574459876543	103.75	1.82445987654320	-9.57445987654322
107	88	97.977237654321	104.208333333333	-6.23109567901234	-9.977237654321
108	64	84.6624228395062	104.958333333333	-20.2959104938272	-20.6624228395062
109	66	86.8337191358025	105.666666666667	-18.8329475308642	-20.8337191358025
110	92	96.3059413580247	106.625	-10.3190586419753	-4.30594135802467
111	120	107.722608024691	107.458333333333	0.264274691358037	12.2773919753086
112	101	94.2874228395062	107.833333333333	-13.5459104938272	6.71257716049384
113	135	122.597608024691	108.125	14.4726080246914	12.4023919753086
114	146	135.921682098765	108.5	27.4216820987654	10.0783179012346
115	149	NA	NA	18.4402006172840	NA
116	134	NA	NA	8.13001543209876	NA
117	101	NA	NA	-1.32831790123458	NA
118	100	NA	NA	1.82445987654320	NA
119	91	NA	NA	-6.23109567901234	NA
120	70	NA	NA	-20.2959104938272	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/18lfy1281709252.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/18lfy1281709252.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/28lfy1281709252.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/28lfy1281709252.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/3jufj1281709252.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/3jufj1281709252.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/4tlwm1281709252.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/13/t1281709270n4vp03odvhb89p7/4tlwm1281709252.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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