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Paper Decomposition Vacatures

*The author of this computation has been verified*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 18 Dec 2010 18:40:43 +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/18/t1292697579xhflrkrt66wrvsu.htm/, Retrieved Sat, 18 Dec 2010 19:39:44 +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/18/t1292697579xhflrkrt66wrvsu.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:

Dataseries X:

» Textbox « » Textfile « » CSV «

21.454 23.899 24.939 23.580 24.562 24.696 23.785 23.812 21.917 19.713 19.282 18.788 21.453 24.482 27.474 27.264 27.349 30.632 29.429 30.084 26.290 24.379 23.335 21.346 21.106 24.514 28.353 30.805 31.348 34.556 33.855 34.787 32.529 29.998 29.257 28.155 30.466 35.704 39.327 39.351 42.234 43.630 43.722 43.121 37.985 37.135 34.646 33.026 35.087 38.846 42.013 43.908 42.868 44.423 44.167 43.636 44.382 42.142 43.452 36.912 42.413 45.344 44.873 47.510 49.554 47.369 45.998 48.140 48.441 44.928 40.454 38.661 37.246 36.843 36.424 37.594 38.144 38.737 34.560 36.080 33.508 35.462 33.374 32.110

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	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	21.454	NA	NA	-3.95605497685186	NA
2	23.899	NA	NA	-1.12240914351852	NA
3	24.939	NA	NA	0.833736689814813	NA
4	23.58	NA	NA	1.97187557870370	NA
5	24.562	NA	NA	2.60881307870370	NA
6	24.696	NA	NA	3.72677141203704	NA
7	23.785	25.2123964120370	22.5355416666667	2.67685474537037	-1.42739641203704
8	23.812	25.4744241898148	22.5597916666667	2.91463252314815	-1.66242418981481
9	21.917	23.4286950231481	22.6897083333333	0.738986689814819	-1.51169502314815
10	19.713	21.3025769675926	22.9488333333333	-1.64625636574074	-1.58957696759259
11	19.282	20.0690630787037	23.2184583333333	-3.14939525462963	-0.7870630787037
12	18.788	17.9843616898148	23.5819166666667	-5.59755497685185	0.803638310185189
13	21.453	20.1083616898148	24.0644166666667	-3.95605497685186	1.34463831018519
14	24.482	23.4385075231481	24.5609166666667	-1.12240914351852	1.04349247685186
15	27.474	25.8381950231481	25.0044583333333	0.833736689814813	1.63580497685185
16	27.264	27.3529589120370	25.3810833333333	1.97187557870370	-0.0889589120370324
17	27.349	28.3531880787037	25.744375	2.60881307870370	-1.00418807870370
18	30.632	29.7466047453704	26.0198333333333	3.72677141203704	0.885395254629632
19	29.429	28.7888130787037	26.1119583333333	2.67685474537037	0.640186921296298
20	30.084	29.0134658564815	26.0988333333333	2.91463252314815	1.07053414351852
21	26.29	26.8757783564815	26.1367916666667	0.738986689814819	-0.585778356481484
22	24.379	24.6747019675926	26.3209583333333	-1.64625636574074	-0.295701967592592
23	23.335	23.4857297453704	26.635125	-3.14939525462963	-0.150729745370366
24	21.346	21.3676950231481	26.96525	-5.59755497685185	-0.0216950231481476
25	21.106	23.3571116898148	27.3131666666667	-3.95605497685186	-2.25111168981481
26	24.514	26.5711325231481	27.6935416666667	-1.12240914351852	-2.05713252314815
27	28.353	28.9831950231481	28.1494583333333	0.833736689814813	-0.630195023148147
28	30.805	30.6154172453704	28.6435416666667	1.97187557870370	0.189582754629630
29	31.348	31.7332297453704	29.1244166666667	2.60881307870370	-0.385229745370374
30	34.556	33.3816464120370	29.654875	3.72677141203704	1.17435358796296
31	33.855	33.0054380787037	30.3285833333333	2.67685474537037	0.849561921296299
32	34.787	34.0994658564815	31.1848333333333	2.91463252314815	0.68753414351852
33	32.529	32.8473200231481	32.1083333333333	0.738986689814819	-0.318320023148143
34	29.998	31.2754103009259	32.9216666666667	-1.64625636574074	-1.27741030092592
35	29.257	30.5819380787037	33.7313333333333	-3.14939525462963	-1.32493807870371
36	28.155	28.9654450231482	34.563	-5.59755497685185	-0.810445023148148
37	30.466	31.3961533564815	35.3522083333333	-3.95605497685186	-0.930153356481483
38	35.704	34.9881741898148	36.1105833333333	-1.12240914351852	0.715825810185187
39	39.327	37.5189033564815	36.6851666666667	0.833736689814813	1.80809664351852
40	39.351	39.1817505787037	37.209875	1.97187557870370	0.169249421296300
41	42.234	40.3406047453704	37.7317916666667	2.60881307870370	1.89339525462963
42	43.63	41.8860630787037	38.1592916666667	3.72677141203704	1.74393692129630
43	43.722	41.231646412037	38.5547916666667	2.67685474537037	2.49035358796296
44	43.121	41.7928825231482	38.87825	2.91463252314815	1.32811747685185
45	37.985	39.8600700231481	39.1210833333333	0.738986689814819	-1.87507002314814
46	37.135	37.7766186342593	39.422875	-1.64625636574074	-0.64161863425926
47	34.646	36.4897714120370	39.6391666666667	-3.14939525462963	-1.84377141203703
48	33.026	34.1010700231482	39.698625	-5.59755497685185	-1.07507002314815
49	35.087	35.7941533564815	39.7502083333333	-3.95605497685186	-0.707153356481484
50	38.846	38.6677991898148	39.7902083333333	-1.12240914351852	0.178200810185182
51	42.013	40.9119450231481	40.0782083333333	0.833736689814813	1.10105497685185
52	43.908	42.5252505787037	40.553375	1.97187557870370	1.38274942129630
53	42.868	43.7377297453704	41.1289166666667	2.60881307870370	-0.869729745370371
54	44.423	45.3845214120370	41.65775	3.72677141203704	-0.961521412037037
55	44.167	44.8017714120370	42.1249166666667	2.67685474537037	-0.63477141203704
56	43.636	45.6155491898148	42.7009166666667	2.91463252314815	-1.97954918981481
57	44.382	43.8298200231481	43.0908333333333	0.738986689814819	0.552179976851853
58	42.142	41.7138269675926	43.3600833333333	-1.64625636574074	0.428173032407408
59	43.452	40.6393547453704	43.78875	-3.14939525462963	2.81264525462963
60	36.912	38.5925283564815	44.1900833333333	-5.59755497685185	-1.68052835648147
61	42.413	40.4330700231481	44.389125	-3.95605497685186	1.97992997685185
62	45.344	43.5306741898148	44.6530833333333	-1.12240914351852	1.81332581018518
63	44.873	45.8436116898148	45.009875	0.833736689814813	-0.970611689814817
64	47.51	47.266958912037	45.2950833333333	1.97187557870370	0.243041087962965
65	49.554	47.8950630787037	45.28625	2.60881307870370	1.65893692129631
66	47.369	48.9609797453704	45.2342083333333	3.72677141203704	-1.59197974537037
67	45.998	47.768646412037	45.0917916666667	2.67685474537037	-1.77064641203704
68	48.14	47.4369241898148	44.5222916666667	2.91463252314815	0.703075810185183
69	48.441	44.5550283564815	43.8160416666667	0.738986689814819	3.88597164351852
70	44.928	41.4045769675926	43.0508333333333	-1.64625636574074	3.52342303240741
71	40.454	39.0128547453704	42.16225	-3.14939525462963	1.44114525462963
72	38.661	35.7296116898148	41.3271666666667	-5.59755497685185	2.93138831018519
73	37.246	36.5348616898148	40.4909166666667	-3.95605497685186	0.711138310185177
74	36.843	38.3894241898148	39.5118333333333	-1.12240914351852	-1.54642418981481
75	36.424	39.2208616898148	38.387125	0.833736689814813	-2.79686168981481
76	37.594	39.3423755787037	37.3705	1.97187557870370	-1.74837557870371
77	38.144	39.289896412037	36.6810833333333	2.60881307870370	-1.14589641203705
78	38.737	39.839896412037	36.113125	3.72677141203704	-1.10289641203704
79	34.56	NA	NA	2.67685474537037	NA
80	36.08	NA	NA	2.91463252314815	NA
81	33.508	NA	NA	0.738986689814819	NA
82	35.462	NA	NA	-1.64625636574074	NA
83	33.374	NA	NA	-3.14939525462963	NA
84	32.11	NA	NA	-5.59755497685185	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/1gjhg1292697640.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/1gjhg1292697640.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/2gjhg1292697640.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/2gjhg1292697640.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/3rtyj1292697640.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/3rtyj1292697640.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/4rtyj1292697640.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292697579xhflrkrt66wrvsu/4rtyj1292697640.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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