Home » date » 2009 » Jun » 07 »

Yelle Eyckmans

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 07 Jun 2009 02:55:04 -0600

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5.htm/, Retrieved Wed, 22 May 2013 04:00:00 +0000

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

System-generated keywords (parent):

(pk = 0)

Estimated Impact

Dataseries X:

» Textfile « » CSV « » Stem and Leaf « » Histogram « » Kernel Density « » Harrell-Davis Quantiles « » Central Tendency « » Variability «

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	41086	NA	NA	8179.45347222222	NA
2	39690	NA	NA	5047.23680555556	NA
3	43129	NA	NA	8069.20347222222	NA
4	37863	NA	NA	5544.53680555556	NA
5	35953	NA	NA	578.453472222222	NA
6	29133	NA	NA	1266.33680555556	NA
7	24693	26610.6701388889	29668.3333333333	-3057.66319444444	-1917.67013888889
8	22205	23213.1034722222	29138.75	-5925.64652777778	-1008.10347222222
9	21725	24050.0201388889	28311.8333333333	-4261.81319444444	-2325.02013888889
10	27192	28287.4784722222	27554.7083333333	732.770138888889	-1095.47847222222
11	21790	21855.5284722222	26986.4166666667	-5130.88819444444	-65.5284722222241
12	13253	15662.3951388889	26704.375	-11041.9798611111	-2409.39513888889
13	37702	34897.9951388889	26718.5416666667	8179.45347222222	2804.00486111111
14	30364	31821.1951388889	26773.9583333333	5047.23680555556	-1457.19513888889
15	32609	34801.0784722222	26731.875	8069.20347222222	-2192.07847222222
16	30212	32292.8701388889	26748.3333333333	5544.53680555556	-2080.87013888888
17	29965	27412.2868055556	26833.8333333333	578.453472222222	2552.71319444444
18	28352	28148.3368055556	26882	1266.33680555556	203.663194444449
19	25814	23818.5451388889	26876.2083333333	-3057.66319444444	1995.45486111111
20	22414	21122.1034722222	27047.75	-5925.64652777778	1291.89652777778
21	20506	23093.7284722222	27355.5416666667	-4261.81319444444	-2587.72847222222
22	28806	28379.0618055556	27646.2916666667	732.770138888889	426.938194444447
23	22228	22577.3618055556	27708.25	-5130.88819444444	-349.361805555556
24	13971	16355.4368055556	27397.4166666667	-11041.9798611111	-2384.43680555555
25	36845	35267.8701388889	27088.4166666667	8179.45347222222	1577.12986111112
26	35338	31798.2784722222	26751.0416666667	5047.23680555556	3539.72152777778
27	35022	34645.7868055556	26576.5833333333	8069.20347222222	376.213194444445
28	34777	31980.7451388889	26436.2083333333	5544.53680555556	2796.25486111111
29	26887	26636.9951388889	26058.5416666667	578.453472222222	250.004861111112
30	23970	27021.7118055556	25755.375	1266.33680555556	-3051.71180555555
31	22780	22373.2118055556	25430.875	-3057.66319444444	406.788194444449
32	17351	18938.6451388889	24864.2916666667	-5925.64652777778	-1587.64513888889
33	21382	20026.3534722222	24288.1666666667	-4261.81319444444	1355.64652777778
34	24561	24427.9368055556	23695.1666666667	732.770138888889	133.063194444447
35	17409	18015.8618055556	23146.75	-5130.88819444444	-606.861805555556
36	11514	11912.3951388889	22954.375	-11041.9798611111	-398.395138888885
37	31514	31083.7451388889	22904.2916666667	8179.45347222222	430.254861111109
38	27071	27937.4868055555	22890.25	5047.23680555556	-866.486805555549
39	29462	30964.4534722222	22895.25	8069.20347222222	-1502.45347222222
40	26105	28445.4951388889	22900.9583333333	5544.53680555556	-2340.49513888889
41	22397	23523.1618055556	22944.7083333333	578.453472222222	-1126.16180555556
42	23843	24391.4201388889	23125.0833333333	1266.33680555556	-548.420138888887
43	21705	20090.2951388889	23147.9583333333	-3057.66319444444	1614.70486111111
44	18089	17173.4368055556	23099.0833333333	-5925.64652777778	915.563194444447
45	20764	19224.2701388889	23486.0833333333	-4261.81319444444	1539.72986111111
46	25316	24756.5618055556	24023.7916666667	732.770138888889	559.438194444443
47	17704	19151.6951388889	24282.5833333333	-5130.88819444444	-1447.69513888889
48	15548	13272.1868055556	24314.1666666667	-11041.9798611111	2275.81319444445
49	28029	32359.2451388889	24179.7916666667	8179.45347222222	-4330.24513888889
50	29383	29049.8201388889	24002.5833333333	5047.23680555556	333.179861111112
51	36438	32032.2034722222	23963	8069.20347222222	4405.79652777778
52	32034	29385.3701388889	23840.8333333333	5544.53680555556	2648.62986111111
53	22679	24374.7451388889	23796.2916666667	578.453472222222	-1695.74513888889
54	24319	25051.1284722222	23784.7916666667	1266.33680555556	-732.128472222219
55	18004	20726.0034722222	23783.6666666667	-3057.66319444444	-2722.00347222222
56	17537	17771.4368055556	23697.0833333333	-5925.64652777778	-234.436805555557
57	20366	18971.3534722222	23233.1666666667	-4261.81319444444	1394.64652777778
58	22782	23428.6868055556	22695.9166666667	732.770138888889	-646.686805555553
59	19169	17322.2784722222	22453.1666666667	-5130.88819444444	1846.72152777778
60	13807	11513.3118055556	22555.2916666667	-11041.9798611111	2293.68819444444
61	29743	30846.8701388889	22667.4166666667	8179.45347222222	-1103.87013888889
62	25591	27762.9451388889	22715.7083333333	5047.23680555556	-2171.94513888889
63	29096	30806.2034722222	22737	8069.20347222222	-1710.20347222222
64	26482	28128.2451388889	22583.7083333333	5544.53680555556	-1646.24513888889
65	22405	23008.5368055556	22430.0833333333	578.453472222222	-603.536805555552
66	27044	23538.1284722222	22271.7916666667	1266.33680555556	3505.87152777778
67	17970	NA	NA	-3057.66319444444	NA
68	18730	NA	NA	-5925.64652777778	NA
69	19684	NA	NA	-4261.81319444444	NA
70	19785	NA	NA	732.770138888889	NA
71	18479	NA	NA	-5130.88819444444	NA
72	10698	NA	NA	-11041.9798611111	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/1wwax1244364899.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/1wwax1244364899.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/2yvrb1244364899.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/2yvrb1244364899.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/3olec1244364899.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/3olec1244364899.ps (opens in new window)

Click here to open pdf file.

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/4v5g41244364899.png (opens in new window)

http://www.freestatistics.org/blog/date/2009/Jun/07/t1244365625motvv0mp46608r5/4v5g41244364899.ps (opens in new window)

Click here to open pdf file.

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')