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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: Mon, 17 Jan 2011 00:05:57 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a.htm/, Retrieved Mon, 17 Jan 2011 01:03:38 +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/2011/Jan/17/t12952226142wexo6rr7rfmh5a.htm/},
    year = {2011},
}
@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 = {2011},
    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 «

1590 1798 1935 1887 2027 2080 1556 1682 1785 1869 1781 2082 2571 1862 1938 1505 1767 1607 1578 1495 1615 1700 1337 1531 1623 1543 1640 1524 1429 1827 1603 1351 1267 1742 1384 1392 1649 1665 1526 1717 1391 1790 1472 1350 1704 1391 1190 1351 1160 1236 1444 1257 1193 1701 1428 1611 1431 1472 1240 1276

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	'Gwilym Jenkins' @ www.wessa.org

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1590	NA	NA	1.09622001036115	NA
2	1798	NA	NA	1.00399468896838	NA
3	1935	NA	NA	1.04797549003747	NA
4	1887	NA	NA	0.970299290984255	NA
5	2027	NA	NA	0.932171041876886	NA
6	2080	NA	NA	1.14166900207308	NA
7	1556	1800.60068305899	1880.20833333333	0.957660197084008	0.864156064495406
8	1682	1745.46545526489	1923.75	0.90732447317213	0.963639810187332
9	1785	1916.61482360505	1926.54166666667	0.994847325010731	0.931329538943308
10	1869	2005.51096404142	1910.75	1.04959359625352	0.931932077914784
11	1781	1684.66666530889	1884	0.894196743794527	1.05718243061066
12	2082	1860.96139286232	1853.45833333333	1.00404814038387	1.11877656784577
13	2571	2011.1983123426	1834.66666666667	1.09622001036115	1.2783423614777
14	1862	1835.09312587399	1827.79166666667	1.00399468896838	1.01466240254875
15	1938	1899.89223214709	1812.91666666667	1.04797549003747	1.02005785760272
16	1505	1745.36627879505	1798.79166666667	0.970299290984255	0.862283188511586
17	1767	1652.97230000819	1773.25	0.932171041876886	1.06898343063054
18	1607	1977.1328638818	1731.79166666667	1.14166900207308	0.812793125518585
19	1578	1598.6540889989	1669.33333333333	0.957660197084008	0.987080326418932
20	1495	1466.72781606913	1616.54166666667	0.90732447317213	1.01927568538697
21	1615	1582.63628620457	1590.83333333333	0.994847325010731	1.0204492428725
22	1700	1657.52695381686	1579.20833333333	1.04959359625352	1.02562434721519
23	1337	1400.23758438691	1565.91666666667	0.894196743794527	0.954837961006024
24	1531	1567.31914713922	1561	1.00404814038387	0.976827216584756
25	1623	1722.3900154462	1571.20833333333	1.09622001036115	0.942295290523704
26	1543	1572.50668159672	1566.25	1.00399468896838	0.981235894294098
27	1640	1619.90811372541	1545.75	1.04797549003747	1.01240310243794
28	1524	1487.46881307886	1533	0.970299290984255	1.02455929603359
29	1429	1432.47500814423	1536.70833333333	0.932171041876886	0.997574123021715
30	1827	1750.03587155277	1532.875	1.14166900207308	1.04397860049517
31	1603	1463.46439117721	1528.16666666667	0.957660197084008	1.09534609086767
32	1351	1392.13818333711	1534.33333333333	0.90732447317213	0.970449640826248
33	1267	1526.75902811647	1534.66666666667	0.994847325010731	0.82986245810059
34	1742	1614.23121797141	1537.95833333333	1.04959359625352	1.0791514750837
35	1384	1381.01235439533	1544.41666666667	0.894196743794527	1.00216337355358
36	1392	1547.53103170582	1541.29166666667	1.00404814038387	0.89949730989602
37	1649	1681.92122673036	1534.29166666667	1.09622001036115	0.980426415811184
38	1665	1534.89871387244	1528.79166666667	1.00399468896838	1.08476213117628
39	1526	1621.17441744254	1546.95833333333	1.04797549003747	0.941292919245122
40	1717	1504.48947980821	1550.54166666667	0.970299290984255	1.1412509180316
41	1391	1424.20199014757	1527.83333333333	0.932171041876886	0.976687302519407
42	1790	1733.10111468868	1518.04166666667	1.14166900207308	1.03283067838863
43	1472	1432.61975232946	1495.95833333333	0.957660197084008	1.0274882763598
44	1350	1322.61444558029	1457.70833333333	0.90732447317213	1.02070562174126
45	1704	1429.01527843416	1436.41666666667	0.994847325010731	1.19242951822541
46	1391	1483.95041283644	1413.83333333333	1.04959359625352	0.937362857928136
47	1190	1239.7292688758	1386.41666666667	0.894196743794527	0.959886992971546
48	1351	1380.02233361845	1374.45833333333	1.00404814038387	0.978969663815258
49	1160	1500.63384251689	1368.91666666667	1.09622001036115	0.77300669032922
50	1236	1383.46284828638	1377.95833333333	1.00399468896838	0.893410330122681
51	1444	1443.54257188119	1377.45833333333	1.04797549003747	1.00031687885603
52	1257	1328.78444986581	1369.45833333333	0.970299290984255	0.945977355565034
53	1193	1281.65750166056	1374.91666666667	0.932171041876886	0.93082590197015
54	1701	1568.51050022315	1373.875	1.14166900207308	1.08446835373943
55	1428	NA	NA	0.957660197084008	NA
56	1611	NA	NA	0.90732447317213	NA
57	1431	NA	NA	0.994847325010731	NA
58	1472	NA	NA	1.04959359625352	NA
59	1240	NA	NA	0.894196743794527	NA
60	1276	NA	NA	1.00404814038387	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/1k4ii1295222755.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/1k4ii1295222755.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/25goj1295222755.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/25goj1295222755.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/32u5p1295222755.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/32u5p1295222755.ps (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/4zqo11295222755.png (open in new window)

http://www.freestatistics.org/blog/date/2011/Jan/17/t12952226142wexo6rr7rfmh5a/4zqo11295222755.ps (open in new window)

Parameters (Session):

par1 = 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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