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

Title produced by software: Classical Decomposition

Date of computation: Sat, 11 Dec 2010 20:48:41 +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/11/t1292100528mle7igrd4kpyqkq.htm/, Retrieved Sat, 11 Dec 2010 21:48:53 +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/11/t1292100528mle7igrd4kpyqkq.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:

KDGP2W92

Dataseries X:

» Textbox « » Textfile « » CSV «

24.3 29.4 31.8 36.7 37.1 37.7 39.4 43.3 39.6 34.3 32 29.6 22.3 28.9 31.7 34.2 38.6 37.2 38.8 43.4 38.8 36.3 33 29.2 22.64 28.44 30.14 34.39 36.82 36.74 38.9 42.8 39.09 37.49 33.17 30.98 21.2 27.8 29 35.4 37.5 34.7 38.4 39.9 35.9 34.7 30.4 29 21.5 28 29.3 34.3 36.6 36.2 37.5 41.6 39.4 37.3 32.7 30.7 22.9 29.1 29.5 37.1 37.7 38.4 39.4 40.6 39.7 36.6 32.8 31.6 24.1 30.3 31.8 38.7 37.8 38.4 40.7 43.8 41.5 39.3 35.9 33.4

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	3 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	24.3	NA	NA	-11.6793865740741	NA
2	29.4	NA	NA	-5.37521990740741	NA
3	31.8	NA	NA	-3.90855324074074	NA
4	36.7	NA	NA	1.48519675925926	NA
5	37.1	NA	NA	3.24505787037037	NA
6	37.7	NA	NA	2.62825231481481	NA
7	39.4	39.197974537037	34.5166666666667	4.68130787037037	0.202025462962965
8	43.3	42.2889467592593	34.4125	7.87644675925926	1.01105324074074
9	39.6	39.0726967592593	34.3875	4.68519675925926	0.527303240740736
10	34.3	36.3171412037037	34.2791666666667	2.03797453703704	-2.01714120370371
11	32	32.4867245370370	34.2375	-1.75077546296296	-0.486724537037041
12	29.6	30.3536689814815	34.2791666666667	-3.92549768518518	-0.753668981481482
13	22.3	22.5539467592593	34.2333333333333	-11.6793865740741	-0.253946759259257
14	28.9	28.8372800925926	34.2125	-5.37521990740741	0.0627199074074056
15	31.7	30.2747800925926	34.1833333333333	-3.90855324074074	1.42521990740741
16	34.2	35.7185300925926	34.2333333333333	1.48519675925926	-1.51853009259259
17	38.6	37.6033912037037	34.3583333333333	3.24505787037037	0.996608796296293
18	37.2	37.0115856481481	34.3833333333333	2.62825231481481	0.188414351851854
19	38.8	39.0621412037037	34.3808333333333	4.68130787037037	-0.262141203703706
20	43.4	42.2522800925926	34.3758333333333	7.87644675925926	1.14771990740741
21	38.8	38.9768634259259	34.2916666666667	4.68519675925926	-0.17686342592593
22	36.3	36.2725578703704	34.2345833333333	2.03797453703704	0.0274421296296268
23	33	32.4175578703704	34.1683333333333	-1.75077546296296	0.582442129629626
24	29.2	30.1495023148148	34.075	-3.92549768518518	-0.949502314814815
25	22.64	22.3806134259259	34.06	-11.6793865740741	0.259386574074078
26	28.44	28.6639467592592	34.0391666666667	-5.37521990740741	-0.223946759259249
27	30.14	30.1176967592593	34.02625	-3.90855324074074	0.0223032407407331
28	34.39	35.5731134259259	34.0879166666667	1.48519675925926	-1.18311342592593
29	36.82	37.3896412037037	34.1445833333333	3.24505787037037	-0.56964120370371
30	36.74	36.8540856481482	34.2258333333333	2.62825231481481	-0.114085648148155
31	38.9	38.9213078703704	34.24	4.68130787037037	-0.0213078703703715
32	42.8	42.0297800925926	34.1533333333333	7.87644675925926	0.770219907407402
33	39.09	38.7643634259259	34.0791666666667	4.68519675925926	0.325636574074075
34	37.49	36.1117245370370	34.07375	2.03797453703704	1.37827546296297
35	33.17	32.3933912037037	34.1441666666667	-1.75077546296296	0.776608796296301
36	30.98	30.1620023148148	34.0875	-3.92549768518518	0.817997685185183
37	21.2	22.3022800925926	33.9816666666667	-11.6793865740741	-1.10228009259259
38	27.8	28.4647800925926	33.84	-5.37521990740741	-0.664780092592594
39	29	29.6776967592593	33.58625	-3.90855324074074	-0.677696759259263
40	35.4	34.8222800925926	33.3370833333333	1.48519675925926	0.577719907407406
41	37.5	36.3504745370370	33.1054166666667	3.24505787037037	1.14952546296296
42	34.7	35.5357523148148	32.9075	2.62825231481481	-0.835752314814812
43	38.4	37.5188078703704	32.8375	4.68130787037037	0.881192129629632
44	39.9	40.7347800925926	32.8583333333333	7.87644675925926	-0.834780092592595
45	35.9	37.5643634259259	32.8791666666667	4.68519675925926	-1.66436342592592
46	34.7	34.8838078703704	32.8458333333333	2.03797453703704	-0.183807870370366
47	30.4	31.0117245370370	32.7625	-1.75077546296296	-0.611724537037034
48	29	28.8620023148148	32.7875	-3.92549768518518	0.137997685185191
49	21.5	21.1331134259259	32.8125	-11.6793865740741	0.366886574074073
50	28	27.4706134259259	32.8458333333333	-5.37521990740741	0.529386574074074
51	29.3	29.1539467592593	33.0625	-3.90855324074074	0.146053240740741
52	34.3	34.8018634259259	33.3166666666667	1.48519675925926	-0.501863425925933
53	36.6	36.7658912037037	33.5208333333333	3.24505787037037	-0.165891203703708
54	36.2	36.3157523148148	33.6875	2.62825231481481	-0.115752314814813
55	37.5	38.4979745370370	33.8166666666667	4.68130787037037	-0.997974537037038
56	41.6	41.7972800925926	33.9208333333333	7.87644675925926	-0.197280092592585
57	39.4	38.6601967592593	33.975	4.68519675925926	0.739803240740741
58	37.3	36.1379745370370	34.1	2.03797453703704	1.16202546296297
59	32.7	32.5117245370370	34.2625	-1.75077546296296	0.188275462962963
60	30.7	30.4745023148148	34.4	-3.92549768518518	0.225497685185182
61	22.9	22.8914467592593	34.5708333333333	-11.6793865740741	0.00855324074073138
62	29.1	29.2331134259259	34.6083333333333	-5.37521990740741	-0.133113425925927
63	29.5	30.6706134259259	34.5791666666667	-3.90855324074074	-1.17061342592592
64	37.1	36.0476967592593	34.5625	1.48519675925926	1.05230324074075
65	37.7	37.7825578703704	34.5375	3.24505787037037	-0.0825578703703584
66	38.4	37.2074189814815	34.5791666666667	2.62825231481481	1.19258101851852
67	39.4	39.347974537037	34.6666666666667	4.68130787037037	0.0520254629629662
68	40.6	42.6431134259259	34.7666666666667	7.87644675925926	-2.04311342592592
69	39.7	39.5976967592593	34.9125	4.68519675925926	0.102303240740753
70	36.6	37.1129745370370	35.075	2.03797453703704	-0.512974537037039
71	32.8	33.3950578703704	35.1458333333333	-1.75077546296296	-0.595057870370375
72	31.6	31.2245023148148	35.15	-3.92549768518518	0.375497685185181
73	24.1	23.5247800925926	35.2041666666667	-11.6793865740741	0.575219907407408
74	30.3	30.0164467592593	35.3916666666667	-5.37521990740741	0.283553240740737
75	31.8	31.6914467592593	35.6	-3.90855324074074	0.108553240740747
76	38.7	37.2726967592593	35.7875	1.48519675925926	1.42730324074075
77	37.8	39.274224537037	36.0291666666667	3.24505787037037	-1.47422453703704
78	38.4	38.8615856481482	36.2333333333333	2.62825231481481	-0.461585648148152
79	40.7	NA	NA	4.68130787037037	NA
80	43.8	NA	NA	7.87644675925926	NA
81	41.5	NA	NA	4.68519675925926	NA
82	39.3	NA	NA	2.03797453703704	NA
83	35.9	NA	NA	-1.75077546296296	NA
84	33.4	NA	NA	-3.92549768518518	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/1xye71292100517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/1xye71292100517.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/2qpeb1292100517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/2qpeb1292100517.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/3qpeb1292100517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/3qpeb1292100517.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/4izvv1292100517.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/11/t1292100528mle7igrd4kpyqkq/4izvv1292100517.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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