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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: Fri, 06 Aug 2010 12:20:37 +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/Aug/06/t1281097196ulzcatdxqxhcjhz.htm/, Retrieved Fri, 06 Aug 2010 14:20:01 +0200

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/Aug/06/t1281097196ulzcatdxqxhcjhz.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 «

94 93 92 90 88 87 88 90 91 91 92 94 88 90 82 83 88 83 85 81 79 71 70 85 88 84 81 93 99 96 90 95 93 86 77 89 90 84 76 96 104 101 95 101 95 90 88 99 81 79 70 95 100 105 107 106 99 86 81 95 82 78 68 96 98 107 102 98 93 69 65 90 87 80 67 85 85 92 87 85 79 58 47 67

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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	94	NA	NA	-2.59085648148148	NA
2	93	NA	NA	-6.04918981481482	NA
3	92	NA	NA	-14.4311342592593	NA
4	90	NA	NA	3.21469907407408	NA
5	88	NA	NA	8.08969907407408	NA
6	87	NA	NA	10.2563657407407	NA
7	88	95.8674768518519	90.5833333333333	5.28414351851852	-7.86747685185186
8	90	96.2980324074074	90.2083333333333	6.08969907407407	-6.29803240740742
9	91	92.5202546296296	89.6666666666667	2.85358796296297	-1.52025462962962
10	91	82.5202546296296	88.9583333333333	-6.4380787037037	8.47974537037038
11	92	78.9508101851852	88.6666666666667	-9.7158564814815	13.0491898148148
12	94	91.9369212962963	88.5	3.4369212962963	2.06307870370370
13	88	85.6174768518518	88.2083333333333	-2.59085648148148	2.38252314814815
14	90	81.6591435185185	87.7083333333333	-6.04918981481482	8.34085648148148
15	82	72.4021990740741	86.8333333333333	-14.4311342592593	9.59780092592592
16	83	88.714699074074	85.5	3.21469907407408	-5.71469907407408
17	88	91.839699074074	83.75	8.08969907407408	-3.83969907407406
18	83	92.7146990740741	82.4583333333333	10.2563657407407	-9.7146990740741
19	85	87.3674768518518	82.0833333333333	5.28414351851852	-2.36747685185185
20	81	87.9230324074074	81.8333333333333	6.08969907407407	-6.9230324074074
21	79	84.3952546296296	81.5416666666667	2.85358796296297	-5.39525462962963
22	71	75.478587962963	81.9166666666667	-6.4380787037037	-4.47858796296295
23	70	73.0758101851852	82.7916666666667	-9.7158564814815	-3.07581018518518
24	85	87.228587962963	83.7916666666667	3.4369212962963	-2.22858796296296
25	88	81.9508101851852	84.5416666666667	-2.59085648148148	6.04918981481481
26	84	79.2841435185185	85.3333333333333	-6.04918981481482	4.71585648148148
27	81	72.0688657407407	86.5	-14.4311342592593	8.93113425925927
28	93	90.9230324074074	87.7083333333333	3.21469907407408	2.07696759259261
29	99	96.714699074074	88.625	8.08969907407408	2.28530092592594
30	96	99.339699074074	89.0833333333333	10.2563657407407	-3.33969907407408
31	90	94.6174768518518	89.3333333333333	5.28414351851852	-4.61747685185185
32	95	95.5063657407407	89.4166666666667	6.08969907407407	-0.506365740740733
33	93	92.0619212962963	89.2083333333333	2.85358796296297	0.938078703703724
34	86	82.6869212962963	89.125	-6.4380787037037	3.31307870370371
35	77	79.7424768518518	89.4583333333333	-9.7158564814815	-2.74247685185183
36	89	93.3119212962963	89.875	3.4369212962963	-4.31192129629629
37	90	87.7008101851852	90.2916666666667	-2.59085648148148	2.29918981481481
38	84	84.7008101851852	90.75	-6.04918981481482	-0.70081018518519
39	76	76.6521990740741	91.0833333333333	-14.4311342592593	-0.652199074074076
40	96	94.5480324074074	91.3333333333333	3.21469907407408	1.45196759259260
41	104	100.048032407407	91.9583333333333	8.08969907407408	3.9519675925926
42	101	103.089699074074	92.8333333333333	10.2563657407407	-2.08969907407406
43	95	98.1591435185185	92.875	5.28414351851852	-3.15914351851850
44	101	98.3813657407407	92.2916666666667	6.08969907407407	2.61863425925927
45	95	94.6869212962963	91.8333333333333	2.85358796296297	0.313078703703709
46	90	85.103587962963	91.5416666666667	-6.4380787037037	4.89641203703704
47	88	81.6174768518518	91.3333333333333	-9.7158564814815	6.38252314814815
48	99	94.7702546296296	91.3333333333333	3.4369212962963	4.22974537037035
49	81	89.4091435185185	92	-2.59085648148148	-8.40914351851852
50	79	86.6591435185185	92.7083333333333	-6.04918981481482	-7.65914351851852
51	70	78.652199074074	93.0833333333333	-14.4311342592593	-8.65219907407405
52	95	96.2980324074074	93.0833333333333	3.21469907407408	-1.29803240740739
53	100	100.714699074074	92.625	8.08969907407408	-0.714699074074076
54	105	102.423032407407	92.1666666666667	10.2563657407407	2.57696759259260
55	107	97.3258101851852	92.0416666666667	5.28414351851852	9.67418981481482
56	106	98.1313657407407	92.0416666666667	6.08969907407407	7.86863425925927
57	99	94.7702546296296	91.9166666666667	2.85358796296297	4.22974537037037
58	86	85.4369212962963	91.875	-6.4380787037037	0.563078703703724
59	81	82.1174768518518	91.8333333333333	-9.7158564814815	-1.11747685185185
60	95	95.2702546296296	91.8333333333333	3.4369212962963	-0.270254629629633
61	82	89.1174768518518	91.7083333333333	-2.59085648148148	-7.11747685185185
62	78	85.1174768518519	91.1666666666667	-6.04918981481482	-7.11747685185186
63	68	76.1521990740741	90.5833333333333	-14.4311342592593	-8.15219907407406
64	96	92.839699074074	89.625	3.21469907407408	3.16030092592594
65	98	96.339699074074	88.25	8.08969907407408	1.66030092592592
66	107	97.6313657407407	87.375	10.2563657407407	9.36863425925927
67	102	92.6591435185185	87.375	5.28414351851852	9.34085648148148
68	98	93.7563657407407	87.6666666666667	6.08969907407407	4.24363425925925
69	93	90.5619212962963	87.7083333333333	2.85358796296297	2.43807870370371
70	69	80.7702546296296	87.2083333333333	-6.4380787037037	-11.7702546296296
71	65	76.4924768518518	86.2083333333333	-9.7158564814815	-11.4924768518518
72	90	88.478587962963	85.0416666666667	3.4369212962963	1.52141203703704
73	87	81.2008101851852	83.7916666666667	-2.59085648148148	5.79918981481481
74	80	76.5758101851852	82.625	-6.04918981481482	3.42418981481481
75	67	67.0688657407407	81.5	-14.4311342592593	-0.0688657407407476
76	85	83.6730324074074	80.4583333333333	3.21469907407408	1.32696759259258
77	85	87.339699074074	79.25	8.08969907407408	-2.33969907407408
78	92	87.7980324074074	77.5416666666667	10.2563657407407	4.20196759259258
79	87	NA	NA	5.28414351851852	NA
80	85	NA	NA	6.08969907407407	NA
81	79	NA	NA	2.85358796296297	NA
82	58	NA	NA	-6.4380787037037	NA
83	47	NA	NA	-9.7158564814815	NA
84	67	NA	NA	3.4369212962963	NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/1ykv61281097235.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/1ykv61281097235.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/2ykv61281097235.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/2ykv61281097235.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/3rtvr1281097235.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/3rtvr1281097235.ps (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/4rtvr1281097235.png (open in new window)

http://www.freestatistics.org/blog/date/2010/Aug/06/t1281097196ulzcatdxqxhcjhz/4rtvr1281097235.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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