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Classical Decomposition prijs citroenen - Lotte Kenis

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Fri, 09 Jan 2009 09:15:55 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r.htm/, Retrieved Fri, 09 Jan 2009 17:18:42 +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/2009/Jan/09/t1231517918qqs6vvqppbxrt6r.htm/},
    year = {2009},
}
@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 = {2009},
    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 «

1.82 1.76 1.79 1.74 1.78 1.80 1.80 1.80 1.79 1.82 1.82 1.83 1.77 1.77 1.77 1.77 1.74 1.78 1.78 1.78 1.78 1.81 1.84 1.80 1.78 1.76 1.74 1.72 1.73 1.77 1.81 1.83 1.87 1.89 1.82 1.79 1.79 1.82 1.82 1.81 1.81 1.78 1.80 1.79 1.83 1.82 1.80 1.82 1.84 1.82 1.81 1.79 1.87 1.89 1.92 1.9 1.91 1.95 2.04 1.99 1.94 1.93 1.89 1.87 1.89 1.9 1.93 1.94 1.88 1.89 1.92 1.91 1.89 1.89 1.98 2.02 2.02 1.99 1.97 1.96 1.95 1.98 2.00 2.00

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' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	1.82	NA	NA	-0.0124305555555555	NA
2	1.76	NA	NA	-0.0180555555555555	NA
3	1.79	NA	NA	-0.0169444444444444	NA
4	1.74	NA	NA	-0.0241666666666666	NA
5	1.78	NA	NA	-0.0131944444444444	NA
6	1.8	NA	NA	-0.00729166666666674	NA
7	1.8	1.80229166666667	1.79375	0.00854166666666663	-0.00229166666666658
8	1.8	1.79923611111111	1.79208333333333	0.00715277777777774	0.00076388888888923
9	1.79	1.79993055555556	1.79166666666667	0.00826388888888884	-0.00993055555555533
10	1.82	1.81708333333333	1.79208333333333	0.0249999999999998	0.00291666666666712
11	1.82	1.82305555555556	1.79166666666667	0.0313888888888889	-0.00305555555555515
12	1.83	1.80090277777778	1.78916666666667	0.0117361111111111	0.0290972222222226
13	1.77	1.77506944444444	1.7875	-0.0124305555555555	-0.00506944444444413
14	1.77	1.76777777777778	1.78583333333333	-0.0180555555555555	0.00222222222222235
15	1.77	1.76763888888889	1.78458333333333	-0.0169444444444444	0.00236111111111148
16	1.77	1.75958333333333	1.78375	-0.0241666666666666	0.0104166666666667
17	1.74	1.77097222222222	1.78416666666667	-0.0131944444444444	-0.0309722222222222
18	1.78	1.77645833333333	1.78375	-0.00729166666666674	0.003541666666667
19	1.78	1.79145833333333	1.78291666666667	0.00854166666666663	-0.0114583333333331
20	1.78	1.79006944444444	1.78291666666667	0.00715277777777774	-0.0100694444444442
21	1.78	1.78951388888889	1.78125	0.00826388888888884	-0.00951388888888882
22	1.81	1.80291666666667	1.77791666666667	0.0249999999999998	0.00708333333333311
23	1.84	1.80680555555556	1.77541666666667	0.0313888888888889	0.0331944444444443
24	1.8	1.78631944444444	1.77458333333333	0.0117361111111111	0.0136805555555553
25	1.78	1.76298611111111	1.77541666666667	-0.0124305555555555	0.0170138888888889
26	1.76	1.76069444444444	1.77875	-0.0180555555555555	-0.000694444444444553
27	1.74	1.76763888888889	1.78458333333333	-0.0169444444444444	-0.027638888888889
28	1.72	1.7675	1.79166666666667	-0.0241666666666666	-0.0474999999999997
29	1.73	1.78097222222222	1.79416666666667	-0.0131944444444444	-0.050972222222222
30	1.77	1.785625	1.79291666666667	-0.00729166666666674	-0.0156249999999998
31	1.81	1.80145833333333	1.79291666666667	0.00854166666666663	0.00854166666666667
32	1.83	1.80298611111111	1.79583333333333	0.00715277777777774	0.0270138888888891
33	1.87	1.80993055555556	1.80166666666667	0.00826388888888884	0.0600694444444445
34	1.89	1.83375	1.80875	0.0249999999999998	0.0562499999999997
35	1.82	1.84722222222222	1.81583333333333	0.0313888888888889	-0.0272222222222220
36	1.79	1.83131944444444	1.81958333333333	0.0117361111111111	-0.0413194444444445
37	1.79	1.80715277777778	1.81958333333333	-0.0124305555555555	-0.0171527777777776
38	1.82	1.79944444444444	1.8175	-0.0180555555555555	0.0205555555555559
39	1.82	1.79722222222222	1.81416666666667	-0.0169444444444444	0.022777777777778
40	1.81	1.78541666666667	1.80958333333333	-0.0241666666666666	0.0245833333333334
41	1.81	1.79263888888889	1.80583333333333	-0.0131944444444444	0.0173611111111112
42	1.78	1.79895833333333	1.80625	-0.00729166666666674	-0.0189583333333332
43	1.8	1.818125	1.80958333333333	0.00854166666666663	-0.0181249999999999
44	1.79	1.81881944444444	1.81166666666667	0.00715277777777774	-0.0288194444444447
45	1.83	1.81951388888889	1.81125	0.00826388888888884	0.0104861111111112
46	1.82	1.835	1.81	0.0249999999999998	-0.0150000000000001
47	1.8	1.84305555555556	1.81166666666667	0.0313888888888889	-0.0430555555555556
48	1.82	1.83048611111111	1.81875	0.0117361111111111	-0.0104861111111110
49	1.84	1.81590277777778	1.82833333333333	-0.0124305555555555	0.0240972222222224
50	1.82	1.81986111111111	1.83791666666667	-0.0180555555555555	0.000138888888888911
51	1.81	1.82888888888889	1.84583333333333	-0.0169444444444444	-0.0188888888888885
52	1.79	1.83041666666667	1.85458333333333	-0.0241666666666666	-0.0404166666666663
53	1.87	1.85680555555556	1.87	-0.0131944444444444	0.0131944444444450
54	1.89	1.87979166666667	1.88708333333333	-0.00729166666666674	0.0102083333333334
55	1.92	1.906875	1.89833333333333	0.00854166666666663	0.0131250000000001
56	1.9	1.91423611111111	1.90708333333333	0.00715277777777774	-0.0142361111111111
57	1.91	1.92326388888889	1.915	0.00826388888888884	-0.0132638888888885
58	1.95	1.94666666666667	1.92166666666667	0.0249999999999998	0.00333333333333363
59	2.04	1.95722222222222	1.92583333333333	0.0313888888888889	0.082777777777778
60	1.99	1.93881944444444	1.92708333333333	0.0117361111111111	0.0511805555555558
61	1.94	1.91548611111111	1.92791666666667	-0.0124305555555555	0.0245138888888889
62	1.93	1.91194444444444	1.93	-0.0180555555555555	0.0180555555555557
63	1.89	1.91347222222222	1.93041666666667	-0.0169444444444444	-0.0234722222222221
64	1.87	1.9025	1.92666666666667	-0.0241666666666666	-0.0324999999999998
65	1.89	1.90597222222222	1.91916666666667	-0.0131944444444444	-0.0159722222222218
66	1.9	1.90354166666667	1.91083333333333	-0.00729166666666674	-0.00354166666666678
67	1.93	1.91395833333333	1.90541666666667	0.00854166666666663	0.0160416666666667
68	1.94	1.90881944444444	1.90166666666667	0.00715277777777774	0.0311805555555555
69	1.88	1.91201388888889	1.90375	0.00826388888888884	-0.032013888888889
70	1.89	1.93875	1.91375	0.0249999999999998	-0.0487499999999998
71	1.92	1.95680555555556	1.92541666666667	0.0313888888888889	-0.0368055555555555
72	1.91	1.94631944444444	1.93458333333333	0.0117361111111111	-0.0363194444444443
73	1.89	1.92756944444444	1.94	-0.0124305555555555	-0.0375694444444443
74	1.89	1.92444444444444	1.9425	-0.0180555555555555	-0.0344444444444445
75	1.98	1.92930555555556	1.94625	-0.0169444444444444	0.0506944444444446
76	2.02	1.92875	1.95291666666667	-0.0241666666666666	0.09125
77	2.02	1.94680555555556	1.96	-0.0131944444444444	0.0731944444444443
78	1.99	1.95979166666667	1.96708333333333	-0.00729166666666674	0.0302083333333332
79	1.97	NA	NA	0.00854166666666663	NA
80	1.96	NA	NA	0.00715277777777774	NA
81	1.95	NA	NA	0.00826388888888884	NA
82	1.98	NA	NA	0.0249999999999998	NA
83	2	NA	NA	0.0313888888888889	NA
84	2	NA	NA	0.0117361111111111	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/1iqk61231517753.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/1iqk61231517753.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/23vfh1231517753.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/23vfh1231517753.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/3um5d1231517753.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/3um5d1231517753.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/41z7h1231517753.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jan/09/t1231517918qqs6vvqppbxrt6r/41z7h1231517753.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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