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*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: Wed, 29 Dec 2010 13:26:25 +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/29/t1293629078vapu55eczxwvrnl.htm/, Retrieved Wed, 29 Dec 2010 14:24:39 +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/29/t1293629078vapu55eczxwvrnl.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 «

1203.6 1180.59 1156.85 1191.5 1191.33 1234.18 1220.33 1228.81 1207.01 1249.48 1248.29 1280.08 1280.66 1294.87 1310.61 1270.09 1270.2 1276.66 1303.82 1335.85 1377.94 1400.63 1418.3 1438.24 1406.82 1420.86 1482.37 1530.62 1503.35 1455.27 1473.99 1526.75 1549.38 1481.14 1468.36 1378.55 1330.63 1322.7 1385.59 1400.38 1280 1267.38 1282.83 1166.36 968.75 896.24 903.25 825.88 735.09 797.87 872.81 919.14 919.32 987.48 1020.62 1057.08 1036.19 1095.63 1115.1 1073.87 1104.49 1169.43 1186.69 1089.41 1030.71 1101.6 1049.33 1141.2 1183.26 1180.55 1258.51

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	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1203.6	NA	NA	-66.6014149305556	NA
2	1180.59	NA	NA	-41.9572482638889	NA
3	1156.85	NA	NA	15.3809809027778	NA
4	1191.5	NA	NA	35.9754600694444	NA
5	1191.33	NA	NA	2.12546006944454	NA
6	1234.18	NA	NA	9.14087673611125	NA
7	1220.33	1255.95650173611	1219.215	36.7415017361111	-35.6265017361111
8	1228.81	1268.26577256944	1227.1875	41.0782725694444	-39.4557725694447
9	1207.01	1243.58660590278	1238.35583333333	5.23077256944445	-36.5766059027781
10	1249.48	1241.78577256944	1248.03708333333	-6.2513107638887	7.69422743055566
11	1248.29	1260.56587673611	1254.59791666667	5.96796006944434	-12.2758767361111
12	1280.08	1222.82285590278	1259.65416666667	-36.831310763889	57.2571440972224
13	1280.66	1198.30150173611	1264.90291666667	-66.6014149305556	82.358498263889
14	1294.87	1230.88441840278	1272.84166666667	-41.9572482638889	63.9855815972223
15	1310.61	1299.80473090278	1284.42375	15.3809809027778	10.8052690972222
16	1270.09	1333.81921006944	1297.84375	35.9754600694444	-63.7292100694444
17	1270.2	1313.35087673611	1311.22541666667	2.12546006944454	-43.1508767361108
18	1276.66	1334.04004340278	1324.89916666667	9.14087673611125	-57.3800434027776
19	1303.82	1373.48733506944	1336.74583333333	36.7415017361111	-69.6673350694446
20	1335.85	1388.33035590278	1347.25208333333	41.0782725694444	-52.4803559027778
21	1377.94	1364.88910590278	1359.65833333333	5.23077256944445	13.0508940972222
22	1400.63	1371.41910590278	1377.67041666667	-6.2513107638887	29.2108940972225
23	1418.3	1404.20837673611	1398.24041666667	5.96796006944434	14.091623263889
24	1438.24	1378.56577256944	1415.39708333333	-36.831310763889	59.6742274305557
25	1406.82	1363.32816840278	1429.92958333333	-66.6014149305556	43.491831597222
26	1420.86	1403.01691840278	1444.97416666667	-41.9572482638889	17.8430815972222
27	1482.37	1475.45264756944	1460.07166666667	15.3809809027778	6.91735243055564
28	1530.62	1506.54504340278	1470.56958333333	35.9754600694444	24.0749565972224
29	1503.35	1478.13546006944	1476.01	2.12546006944454	25.2145399305557
30	1455.27	1484.74962673611	1475.60875	9.14087673611125	-29.4796267361112
31	1473.99	1506.68858506944	1469.94708333333	36.7415017361111	-32.6985850694443
32	1526.75	1503.76077256944	1462.6825	41.0782725694444	22.9892274305557
33	1549.38	1459.79077256944	1454.56	5.23077256944445	89.5892274305556
34	1481.14	1438.84952256944	1445.10083333333	-6.2513107638887	42.2904774305555
35	1468.36	1436.33587673611	1430.36791666667	5.96796006944434	32.0241232638887
36	1378.55	1376.40160590278	1413.23291666667	-36.831310763889	2.14839409722208
37	1330.63	1330.83775173611	1397.43916666667	-66.6014149305556	-0.207751736111049
38	1322.7	1332.50066840278	1374.45791666667	-41.9572482638889	-9.80066840277755
39	1385.59	1350.62973090278	1335.24875	15.3809809027778	34.9602690972224
40	1400.38	1322.66046006944	1286.685	35.9754600694444	77.7195399305558
41	1280	1240.89337673611	1238.76791666667	2.12546006944454	39.1066232638891
42	1267.38	1201.33462673611	1192.19375	9.14087673611125	66.0453732638889
43	1282.83	1181.09316840278	1144.35166666667	36.7415017361111	101.736831597222
44	1166.36	1138.74785590278	1097.66958333333	41.0782725694444	27.6121440972222
45	968.75	1059.66660590278	1054.43583333333	5.23077256944445	-90.9166059027779
46	896.24	1006.76702256944	1013.01833333333	-6.2513107638887	-110.527022569445
47	903.25	983.906293402778	977.938333333333	5.96796006944434	-80.6562934027776
48	825.88	914.416189236111	951.2475	-36.831310763889	-88.536189236111
49	735.09	862.058168402778	928.659583333333	-66.6014149305556	-126.968168402778
50	797.87	871.223585069444	913.180833333333	-41.9572482638889	-73.3535850694443
51	872.81	926.818480902778	911.4375	15.3809809027778	-54.0084809027777
52	919.14	958.530876736111	922.555416666667	35.9754600694444	-39.3908767361112
53	919.32	941.815876736111	939.690416666667	2.12546006944454	-22.4958767361112
54	987.48	967.991293402778	958.850416666667	9.14087673611125	19.4887065972222
55	1020.62	1021.31650173611	984.575	36.7415017361111	-0.696501736110918
56	1057.08	1056.52660590278	1015.44833333333	41.0782725694444	0.553394097222281
57	1036.19	1049.23910590278	1044.00833333333	5.23077256944445	-13.0491059027777
58	1095.63	1057.92993923611	1064.18125	-6.2513107638887	37.7000607638893
59	1115.1	1081.88504340278	1075.91708333333	5.96796006944434	33.2149565972222
60	1073.87	1048.48202256944	1085.31333333333	-36.831310763889	25.3879774305556
61	1104.49	NA	1091.26458333333	NA	NA
62	1169.43	NA	1095.96583333333	NA	NA
63	1186.69	NA	1105.59875	NA	NA
64	1089.41	NA	1115.265	NA	NA
65	1030.71	NA	1124.77875	NA	NA
66	1101.6	NA	NA	NA	NA
67	1049.33	NA	NA	NA	NA
68	1141.2	NA	NA	NA	NA
69	1183.26	NA	NA	NA	NA
70	1180.55	NA	NA	NA	NA
71	1258.51	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/1cf1p1293629181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/1cf1p1293629181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/2cf1p1293629181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/2cf1p1293629181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/3cf1p1293629181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/3cf1p1293629181.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/4m60s1293629181.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/29/t1293629078vapu55eczxwvrnl/4m60s1293629181.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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