Home » date » 2010 » Jul » 02 »

additieve decompositie

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 02 Jul 2010 14:55:49 +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/02/t12780826094a0kcfryn45zltq.htm/, Retrieved Fri, 02 Jul 2010 16:56:55 +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/02/t12780826094a0kcfryn45zltq.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:

Thomas Talboom

Dataseries X:

» Textbox « » Textfile « » CSV «

68 67 66 64 84 83 68 58 59 59 60 62 58 58 59 62 87 83 68 58 68 63 65 68 62 69 74 72 94 102 92 81 99 95 92 93 85 92 99 107 125 137 125 115 135 128 120 123 119 128 139 155 164 176 162 155 174 171 162 160 156 163 180 195 203 212 203 184 200 198 195 177 176 180 194 204 206 219 213 196 214 209 213 194 197 211 240 251 254 273 271 245 264 264 262 237 237 251 272 282 278 291 293 271 284 290 288 262 263 275 297 301 296 309 310 292 300 314 310 288

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	2 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	68	NA	NA	-18.460262345679	NA
2	67	NA	NA	-12.4417438271605	NA
3	66	NA	NA	-0.529706790123453	NA
4	64	NA	NA	5.50733024691358	NA
5	84	NA	NA	11.8360339506173	NA
6	83	NA	NA	20.1878858024691	NA
7	68	75.738811728395	66.0833333333333	9.65547839506172	-7.73881172839505
8	58	58.414737654321	65.2916666666667	-6.87692901234567	-0.414737654320987
9	59	70.6045524691358	64.625	5.97955246913581	-11.6045524691358
10	59	65.8406635802469	64.25	1.59066358024692	-6.8406635802469
11	60	61.5814043209876	64.2916666666667	-2.71026234567901	-1.58140432098764
12	62	50.6786265432099	64.4166666666667	-13.7380401234568	11.3213734567901
13	58	45.9564043209876	64.4166666666667	-18.460262345679	12.0435956790124
14	58	51.9749228395062	64.4166666666667	-12.4417438271605	6.02507716049385
15	59	64.2619598765432	64.7916666666667	-0.529706790123453	-5.2619598765432
16	62	70.8406635802469	65.3333333333333	5.50733024691358	-8.84066358024691
17	87	77.5443672839506	65.7083333333333	11.8360339506173	9.45563271604938
18	83	86.3545524691358	66.1666666666667	20.1878858024691	-3.35455246913579
19	68	76.238811728395	66.5833333333333	9.65547839506172	-8.23881172839505
20	58	60.3314043209877	67.2083333333333	-6.87692901234567	-2.33140432098766
21	68	74.2712191358025	68.2916666666667	5.97955246913581	-6.27121913580247
22	63	70.9239969135803	69.3333333333333	1.59066358024692	-7.92399691358025
23	65	67.3314043209876	70.0416666666667	-2.71026234567901	-2.33140432098764
24	68	57.3869598765432	71.125	-13.7380401234568	10.6130401234568
25	62	54.4564043209876	72.9166666666666	-18.460262345679	7.54359567901237
26	69	62.4332561728395	74.875	-12.4417438271605	6.56674382716051
27	74	76.5952932098765	77.125	-0.529706790123453	-2.59529320987653
28	72	85.2573302469136	79.75	5.50733024691358	-13.2573302469136
29	94	94.0443672839506	82.2083333333333	11.8360339506173	-0.0443672839506206
30	102	104.562885802469	84.375	20.1878858024691	-2.56288580246914
31	92	96.0304783950617	86.375	9.65547839506172	-4.03047839506172
32	81	81.414737654321	88.2916666666667	-6.87692901234567	-0.414737654320987
33	99	96.2712191358025	90.2916666666667	5.97955246913581	2.72878086419753
34	95	94.3823302469136	92.7916666666667	1.59066358024692	0.617669753086417
35	92	92.8314043209877	95.5416666666667	-2.71026234567901	-0.831404320987659
36	93	84.5536265432099	98.2916666666667	-13.7380401234568	8.44637345679013
37	85	82.664737654321	101.125	-18.460262345679	2.33526234567901
38	92	91.4749228395062	103.916666666667	-12.4417438271605	0.525077160493836
39	99	106.303626543210	106.833333333333	-0.529706790123453	-7.30362654320989
40	107	115.215663580247	109.708333333333	5.50733024691358	-8.21566358024691
41	125	124.086033950617	112.25	11.8360339506173	0.913966049382708
42	137	134.854552469136	114.666666666667	20.1878858024691	2.14544753086419
43	125	126.988811728395	117.333333333333	9.65547839506172	-1.98881172839508
44	115	113.373070987654	120.25	-6.87692901234567	1.62692901234566
45	135	129.396219135802	123.416666666667	5.97955246913581	5.6037808641975
46	128	128.673996913580	127.083333333333	1.59066358024692	-0.673996913580254
47	120	127.998070987654	130.708333333333	-2.71026234567901	-7.99807098765433
48	123	120.220293209877	133.958333333333	-13.7380401234568	2.77970679012347
49	119	118.664737654321	137.125	-18.460262345679	0.335262345678984
50	128	127.891589506173	140.333333333333	-12.4417438271605	0.108410493827165
51	139	143.095293209877	143.625	-0.529706790123453	-4.09529320987656
52	155	152.548996913580	147.041666666667	5.50733024691358	2.45100308641975
53	164	162.419367283951	150.583333333333	11.8360339506173	1.58063271604937
54	176	174.062885802469	153.875	20.1878858024691	1.93711419753086
55	162	166.613811728395	156.958333333333	9.65547839506172	-4.61381172839506
56	155	153.081404320988	159.958333333333	-6.87692901234567	1.91859567901233
57	174	169.104552469136	163.125	5.97955246913581	4.8954475308642
58	171	168.090663580247	166.5	1.59066358024692	2.90933641975312
59	162	167.081404320988	169.791666666667	-2.71026234567901	-5.08140432098764
60	160	159.17862654321	172.916666666667	-13.7380401234568	0.821373456790155
61	156	157.664737654321	176.125	-18.460262345679	-1.66473765432096
62	163	166.599922839506	179.041666666667	-12.4417438271605	-3.59992283950615
63	180	180.80362654321	181.333333333333	-0.529706790123453	-0.803626543209901
64	195	189.048996913580	183.541666666667	5.50733024691358	5.95100308641975
65	203	197.877700617284	186.041666666667	11.8360339506173	5.12229938271605
66	212	208.312885802469	188.125	20.1878858024691	3.68711419753089
67	203	199.322145061728	189.666666666667	9.65547839506172	3.67785493827162
68	184	184.331404320988	191.208333333333	-6.87692901234567	-0.331404320987616
69	200	198.479552469136	192.5	5.97955246913581	1.52044753086420
70	198	195.048996913580	193.458333333333	1.59066358024692	2.95100308641977
71	195	191.248070987654	193.958333333333	-2.71026234567901	3.75192901234567
72	177	180.636959876543	194.375	-13.7380401234568	-3.63695987654319
73	176	176.623070987654	195.083333333333	-18.460262345679	-0.62307098765433
74	180	183.558256172839	196	-12.4417438271605	-3.55825617283949
75	194	196.55362654321	197.083333333333	-0.529706790123453	-2.55362654320987
76	204	203.632330246914	198.125	5.50733024691358	0.36766975308646
77	206	211.169367283951	199.333333333333	11.8360339506173	-5.16936728395061
78	219	220.979552469136	200.791666666667	20.1878858024691	-1.97955246913577
79	213	212.030478395062	202.375	9.65547839506172	0.969521604938308
80	196	197.664737654321	204.541666666667	-6.87692901234567	-1.66473765432096
81	214	213.729552469136	207.75	5.97955246913581	0.270447530864203
82	209	213.215663580247	211.625	1.59066358024692	-4.21566358024691
83	213	212.873070987654	215.583333333333	-2.71026234567901	0.12692901234567
84	194	206.095293209877	219.833333333333	-13.7380401234568	-12.0952932098765
85	197	206.039737654321	224.5	-18.460262345679	-9.03973765432099
86	211	216.516589506173	228.958333333333	-12.4417438271605	-5.51658950617281
87	240	232.55362654321	233.083333333333	-0.529706790123453	7.44637345679016
88	251	242.965663580247	237.458333333333	5.50733024691358	8.03433641975312
89	254	253.627700617284	241.791666666667	11.8360339506173	0.372299382716079
90	273	265.812885802469	245.625	20.1878858024691	7.18711419753089
91	271	258.738811728395	249.083333333333	9.65547839506172	12.2611882716049
92	245	245.539737654321	252.416666666667	-6.87692901234567	-0.539737654320959
93	264	261.396219135802	255.416666666667	5.97955246913581	2.60378086419757
94	264	259.632330246914	258.041666666667	1.59066358024692	4.36766975308643
95	262	257.623070987654	260.333333333333	-2.71026234567901	4.37692901234573
96	237	248.345293209877	262.083333333333	-13.7380401234568	-11.3452932098766
97	237	245.289737654321	263.75	-18.460262345679	-8.28973765432099
98	251	253.308256172839	265.75	-12.4417438271605	-2.30825617283949
99	272	267.136959876543	267.666666666667	-0.529706790123453	4.86304012345681
100	282	275.090663580247	269.583333333333	5.50733024691358	6.90933641975312
101	278	283.586033950617	271.75	11.8360339506173	-5.58603395061726
102	291	294.062885802469	273.875	20.1878858024691	-3.06288580246911
103	293	285.655478395062	276	9.65547839506172	7.34452160493828
104	271	271.206404320988	278.083333333333	-6.87692901234567	-0.206404320987644
105	284	286.104552469136	280.125	5.97955246913581	-2.10455246913574
106	290	283.54899691358	281.958333333333	1.59066358024692	6.45100308641975
107	288	280.789737654321	283.5	-2.71026234567901	7.2102623456791
108	262	271.261959876543	285	-13.7380401234568	-9.26195987654319
109	263	267.998070987654	286.458333333333	-18.460262345679	-4.99807098765427
110	275	275.599922839506	288.041666666667	-12.4417438271605	-0.599922839506178
111	297	289.05362654321	289.583333333333	-0.529706790123453	7.94637345679013
112	301	296.757330246914	291.25	5.50733024691358	4.24266975308649
113	296	305.002700617284	293.166666666667	11.8360339506173	-9.00270061728395
114	309	315.354552469136	295.166666666667	20.1878858024691	-6.3545524691358
115	310	NA	NA	9.65547839506172	NA
116	292	NA	NA	-6.87692901234567	NA
117	300	NA	NA	5.97955246913581	NA
118	314	NA	NA	1.59066358024692	NA
119	310	NA	NA	-2.71026234567901	NA
120	288	NA	NA	-13.7380401234568	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/1q9jz1278082546.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/1q9jz1278082546.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/2q9jz1278082546.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/2q9jz1278082546.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/31j0k1278082546.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/31j0k1278082546.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/41j0k1278082546.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Jul/02/t12780826094a0kcfryn45zltq/41j0k1278082546.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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