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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: Thu, 16 Dec 2010 19:30:36 +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/16/t1292527999srtf2jbrdl0euyd.htm/, Retrieved Thu, 16 Dec 2010 20:33:24 +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/16/t1292527999srtf2jbrdl0euyd.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 «

-2.0 2.6 0.2 0.1 -0.1 0.1 -1.6 2.3 -0.3 0.0 0.1 0.4 -1.9 2.4 0.0 0.4 0.1 0.2 -1.3 2.1 -0.1 0.3 0.3 0.2 -1.9 2.7 0.0 -0.2 0.2 0.1 -1.5 2.1 -0.3 -0.2 0.2 0.3 -2.0 2.6 0.0 0.5 -0.1 0.2 -1.6 2.1 -0.2 0.0 0.2 0.2 -2.2 2.7 -0.3 0.4 -0.1 0.0 -1.6 2.2 -0.3 0.0 0.1 0.1 -1.9 2.5 0.1 -0.1 0.3 0.1 -1.9 2.5 -0.3 0.2 0.2 0.1 -2.4 3.1 -0.3 0.2 0.1 0.2 -1.8 2.4 -0.4 0.0 0.0 0.2 -2.4 3.2 0.0 0.1 0.1 0.1 -1.8 2.5 -0.6 0.0 0.0 0.4 -2.5 3.1 0.2 -0.3 0.3 0.4 -1.8 2.6 -0.3 0.3 0.0 0.4 -2.9 3.6 -0.1 0.3 0.0 0.3 -2.1 2.6 -0.2 0.0 -0.2 0.3 -3.1 3.4 -0.1 0.1 0.3 0.1 -2.5 3.1 -0.1 0.1 0.0

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	4 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	-2	NA	NA	-2.38310185185185	NA
2	2.6	NA	NA	2.72893518518519	NA
3	0.2	NA	NA	-0.195138888888889	NA
4	0.1	NA	NA	-0.00671296296296297	NA
5	-0.1	NA	NA	-0.0497685185185185	NA
6	0.1	NA	NA	0.0298611111111111	NA
7	-1.6	-1.69884259259259	0.154166666666667	-1.85300925925926	0.0988425925925925
8	2.3	2.35347222222222	0.15	2.20347222222222	-0.0534722222222226
9	-0.3	-0.311805555555556	0.133333333333333	-0.445138888888889	0.0118055555555556
10	0	0.0608796296296296	0.1375	-0.0766203703703704	-0.0608796296296296
11	0.1	0.104398148148148	0.158333333333333	-0.0539351851851852	-0.00439814814814807
12	0.4	0.271990740740741	0.170833333333333	0.101157407407407	0.128009259259259
13	-1.9	-2.19560185185185	0.1875	-2.38310185185185	0.295601851851852
14	2.4	2.92060185185185	0.191666666666667	2.72893518518519	-0.520601851851852
15	0	-0.00347222222222227	0.191666666666667	-0.195138888888889	0.00347222222222227
16	0.4	0.205787037037037	0.2125	-0.00671296296296297	0.194212962962963
17	0.1	0.183564814814815	0.233333333333333	-0.0497685185185185	-0.0835648148148148
18	0.2	0.263194444444444	0.233333333333333	0.0298611111111111	-0.0631944444444444
19	-1.3	-1.62800925925926	0.225	-1.85300925925926	0.328009259259259
20	2.1	2.44097222222222	0.2375	2.20347222222222	-0.340972222222222
21	-0.1	-0.195138888888889	0.25	-0.445138888888889	0.0951388888888889
22	0.3	0.148379629629630	0.225	-0.0766203703703704	0.151620370370370
23	0.3	0.150231481481481	0.204166666666667	-0.0539351851851852	0.149768518518519
24	0.2	0.305324074074074	0.204166666666667	0.101157407407407	-0.105324074074074
25	-1.9	-2.19143518518519	0.191666666666667	-2.38310185185185	0.291435185185186
26	2.7	2.91226851851852	0.183333333333333	2.72893518518519	-0.212268518518518
27	0	-0.0201388888888889	0.175	-0.195138888888889	0.0201388888888889
28	-0.2	0.139120370370370	0.145833333333333	-0.00671296296296297	-0.33912037037037
29	0.2	0.0710648148148148	0.120833333333333	-0.0497685185185185	0.128935185185185
30	0.1	0.150694444444444	0.120833333333333	0.0298611111111111	-0.0506944444444444
31	-1.5	-1.73217592592593	0.120833333333333	-1.85300925925926	0.232175925925926
32	2.1	2.31597222222222	0.1125	2.20347222222222	-0.215972222222222
33	-0.3	-0.336805555555556	0.108333333333333	-0.445138888888889	0.0368055555555556
34	-0.2	0.0608796296296296	0.1375	-0.0766203703703704	-0.260879629629630
35	0.2	0.100231481481481	0.154166666666667	-0.0539351851851852	0.0997685185185186
36	0.3	0.246990740740741	0.145833333333333	0.101157407407407	0.0530092592592592
37	-2	-2.23726851851852	0.145833333333333	-2.38310185185185	0.237268518518519
38	2.6	2.87060185185185	0.141666666666667	2.72893518518519	-0.270601851851852
39	0	-0.0493055555555556	0.145833333333333	-0.195138888888889	0.0493055555555556
40	0.5	0.151620370370370	0.158333333333333	-0.00671296296296297	0.348379629629630
41	-0.1	0.116898148148148	0.166666666666667	-0.0497685185185185	-0.216898148148148
42	0.2	0.192361111111111	0.1625	0.0298611111111111	0.00763888888888889
43	-1.6	-1.70300925925926	0.15	-1.85300925925926	0.103009259259259
44	2.1	2.34930555555556	0.145833333333333	2.20347222222222	-0.249305555555556
45	-0.2	-0.307638888888889	0.1375	-0.445138888888889	0.107638888888889
46	0	0.0442129629629629	0.120833333333333	-0.0766203703703704	-0.0442129629629629
47	0.2	0.0627314814814814	0.116666666666667	-0.0539351851851852	0.137268518518519
48	0.2	0.209490740740741	0.108333333333333	0.101157407407407	-0.00949074074074066
49	-2.2	-2.28310185185185	0.1	-2.38310185185185	0.083101851851852
50	2.7	2.83310185185185	0.104166666666667	2.72893518518519	-0.133101851851852
51	-0.3	-0.0909722222222222	0.104166666666667	-0.195138888888889	-0.209027777777778
52	0.4	0.093287037037037	0.1	-0.00671296296296297	0.306712962962963
53	-0.1	0.0460648148148148	0.0958333333333334	-0.0497685185185185	-0.146064814814815
54	0	0.117361111111111	0.0875	0.0298611111111111	-0.117361111111111
55	-1.6	-1.75717592592593	0.0958333333333334	-1.85300925925926	0.157175925925926
56	2.2	2.30347222222222	0.1	2.20347222222222	-0.103472222222222
57	-0.3	-0.336805555555556	0.108333333333333	-0.445138888888889	0.0368055555555556
58	0	0.0275462962962963	0.104166666666667	-0.0766203703703704	-0.0275462962962963
59	0.1	0.0460648148148148	0.1	-0.0539351851851852	0.0539351851851852
60	0.1	0.221990740740741	0.120833333333333	0.101157407407407	-0.121990740740741
61	-1.9	-2.27060185185185	0.1125	-2.38310185185185	0.370601851851852
62	2.5	2.84143518518519	0.1125	2.72893518518519	-0.341435185185185
63	0.1	-0.0701388888888889	0.125	-0.195138888888889	0.170138888888889
64	-0.1	0.126620370370370	0.133333333333333	-0.00671296296296297	-0.226620370370370
65	0.3	0.0960648148148148	0.145833333333333	-0.0497685185185185	0.203935185185185
66	0.1	0.179861111111111	0.15	0.0298611111111111	-0.0798611111111111
67	-1.9	-1.72384259259259	0.129166666666667	-1.85300925925926	-0.176157407407407
68	2.5	2.33680555555556	0.133333333333333	2.20347222222222	0.163194444444444
69	-0.3	-0.303472222222222	0.141666666666667	-0.445138888888889	0.00347222222222232
70	0.2	0.0608796296296295	0.1375	-0.0766203703703704	0.139120370370370
71	0.2	0.0877314814814815	0.141666666666667	-0.0539351851851852	0.112268518518519
72	0.1	0.238657407407407	0.1375	0.101157407407407	-0.138657407407407
73	-2.4	-2.23726851851852	0.145833333333333	-2.38310185185185	-0.162731481481481
74	3.1	2.87476851851852	0.145833333333333	2.72893518518519	0.225231481481481
75	-0.3	-0.057638888888889	0.1375	-0.195138888888889	-0.242361111111111
76	0.2	0.118287037037037	0.125	-0.00671296296296297	0.081712962962963
77	0.1	0.0585648148148148	0.108333333333333	-0.0497685185185185	0.0414351851851852
78	0.2	0.134027777777778	0.104166666666667	0.0298611111111111	0.0659722222222223
79	-1.8	-1.74467592592593	0.108333333333333	-1.85300925925926	-0.055324074074074
80	2.4	2.31597222222222	0.1125	2.20347222222222	0.0840277777777774
81	-0.4	-0.315972222222222	0.129166666666667	-0.445138888888889	-0.0840277777777778
82	0	0.0608796296296296	0.1375	-0.0766203703703704	-0.0608796296296296
83	0	0.0793981481481481	0.133333333333333	-0.0539351851851852	-0.0793981481481481
84	0.2	0.230324074074074	0.129166666666667	0.101157407407407	-0.030324074074074
85	-2.4	-2.25810185185185	0.125	-2.38310185185185	-0.141898148148148
86	3.2	2.85810185185185	0.129166666666667	2.72893518518519	0.341898148148148
87	0	-0.0701388888888889	0.125	-0.195138888888889	0.0701388888888889
88	0.1	0.109953703703704	0.116666666666667	-0.00671296296296297	-0.00995370370370369
89	0.1	0.0668981481481481	0.116666666666667	-0.0497685185185185	0.0331018518518519
90	0.1	0.154861111111111	0.125	0.0298611111111111	-0.0548611111111111
91	-1.8	-1.72384259259259	0.129166666666667	-1.85300925925926	-0.0761574074074074
92	2.5	2.32430555555556	0.120833333333333	2.20347222222222	0.175694444444444
93	-0.6	-0.320138888888889	0.125	-0.445138888888889	-0.279861111111111
94	0	0.0400462962962962	0.116666666666667	-0.0766203703703704	-0.0400462962962962
95	0	0.0543981481481481	0.108333333333333	-0.0539351851851852	-0.0543981481481481
96	0.4	0.230324074074074	0.129166666666667	0.101157407407407	0.169675925925926
97	-2.5	-2.24143518518519	0.141666666666667	-2.38310185185185	-0.258564814814815
98	3.1	2.87476851851852	0.145833333333333	2.72893518518519	0.225231481481481
99	0.2	-0.0326388888888889	0.1625	-0.195138888888889	0.232638888888889
100	-0.3	0.180787037037037	0.1875	-0.00671296296296297	-0.480787037037037
101	0.3	0.150231481481481	0.2	-0.0497685185185185	0.149768518518519
102	0.4	0.229861111111111	0.2	0.0298611111111111	0.170138888888889
103	-1.8	-1.66967592592593	0.183333333333333	-1.85300925925926	-0.130324074074074
104	2.6	2.39097222222222	0.1875	2.20347222222222	0.209027777777778
105	-0.3	-0.249305555555556	0.195833333333333	-0.445138888888889	-0.0506944444444444
106	0.3	0.131712962962963	0.208333333333333	-0.0766203703703704	0.168287037037037
107	0	0.166898148148148	0.220833333333333	-0.0539351851851852	-0.166898148148148
108	0.4	0.305324074074074	0.204166666666667	0.101157407407407	0.094675925925926
109	-2.9	-2.19560185185185	0.1875	-2.38310185185185	-0.704398148148148
110	3.6	2.90393518518519	0.175	2.72893518518519	0.696064814814815
111	-0.1	-0.0159722222222222	0.179166666666667	-0.195138888888889	-0.0840277777777778
112	0.3	0.164120370370370	0.170833333333333	-0.00671296296296297	0.135879629629630
113	0	0.100231481481481	0.15	-0.0497685185185185	-0.100231481481481
114	0.3	0.167361111111111	0.1375	0.0298611111111111	0.132638888888889
115	-2.1	-1.72800925925926	0.125	-1.85300925925926	-0.371990740740741
116	2.6	2.31180555555556	0.108333333333333	2.20347222222222	0.288194444444444
117	-0.2	-0.345138888888889	0.1	-0.445138888888889	0.145138888888889
118	0	0.0150462962962963	0.0916666666666667	-0.0766203703703704	-0.0150462962962963
119	-0.2	0.0418981481481482	0.0958333333333334	-0.0539351851851852	-0.241898148148148
120	0.3	0.201157407407407	0.1	0.101157407407407	0.0988425925925926
121	-3.1	NA	0.075	NA	NA
122	3.4	NA	0.0791666666666667	NA	NA
123	-0.1	NA	0.104166666666667	NA	NA
124	0.1	NA	0.1125	NA	NA
125	0.3	NA	0.125	NA	NA
126	0.1	NA	NA	NA	NA
127	-2.5	NA	NA	NA	NA
128	3.1	NA	NA	NA	NA
129	-0.1	NA	NA	NA	NA
130	0.1	NA	NA	NA	NA
131	0	NA	NA	NA	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/1vda81292527832.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/1vda81292527832.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/26msb1292527832.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/26msb1292527832.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/36msb1292527832.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/36msb1292527832.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/4gv9w1292527832.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Dec/16/t1292527999srtf2jbrdl0euyd/4gv9w1292527832.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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As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

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