Home » date » 2009 » May » 31 »

cijderreeks - Verkoop eenmanswoningen - Anne-Sophie De Smedt

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 31 May 2009 07:53:17 -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/May/31/t1243778071zcgttl6pwfofrdu.htm/, Retrieved Sun, 31 May 2009 15:54:36 +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/2009/May/31/t1243778071zcgttl6pwfofrdu.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 «

53 59 73 72 62 58 55 56 52 52 43 37 43 55 68 68 64 65 57 59 54 57 43 42 52 51 58 60 61 58 62 61 49 51 47 40 45 50 58 52 50 50 46 46 38 37 34 29 30 40 46 46 47 47 43 46 37 41 39 36 48 55 56 53 52 53 52 56 51 48 42 42 44 50 60 66 58 59 55 57 57 56 53 51 45 58 74 65 65 55 52 59 54 57 45 40 47 47 60 58 63 64 64 63 55 54 44

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	53	NA	NA	-7.30357142857143	NA
2	59	NA	NA	0.124999999999999	NA
3	73	NA	NA	8.80952380952381	NA
4	72	NA	NA	7.33928571428572	NA
5	62	NA	NA	5.44047619047619	NA
6	58	NA	NA	3.98214285714286	NA
7	55	56.6666666666667	55.5833333333333	1.08333333333333	-1.66666666666666
8	56	58.5357142857143	55	3.53571428571429	-2.53571428571428
9	52	51.9702380952381	54.625	-2.65476190476191	0.0297619047619122
10	52	52.7023809523809	54.25	-1.54761904761905	-0.702380952380942
11	43	46.3988095238095	54.1666666666667	-7.76785714285714	-3.39880952380953
12	37	43.5	54.5416666666667	-11.0416666666667	-6.49999999999999
13	43	47.6130952380952	54.9166666666667	-7.30357142857143	-4.61309523809524
14	55	55.25	55.125	0.124999999999999	-0.250000000000014
15	68	64.1428571428571	55.3333333333333	8.80952380952381	3.85714285714285
16	68	62.9642857142857	55.625	7.33928571428572	5.03571428571428
17	64	61.2738095238095	55.8333333333333	5.44047619047619	2.72619047619047
18	65	60.0238095238095	56.0416666666667	3.98214285714286	4.97619047619047
19	57	57.7083333333333	56.625	1.08333333333333	-0.708333333333329
20	59	60.3690476190476	56.8333333333333	3.53571428571429	-1.36904761904761
21	54	53.5952380952381	56.25	-2.65476190476191	0.404761904761905
22	57	53.952380952381	55.5	-1.54761904761905	3.04761904761905
23	43	47.2738095238095	55.0416666666667	-7.76785714285714	-4.27380952380951
24	42	43.5833333333333	54.625	-11.0416666666667	-1.58333333333333
25	52	47.2380952380952	54.5416666666667	-7.30357142857143	4.76190476190476
26	51	54.9583333333333	54.8333333333333	0.124999999999999	-3.95833333333334
27	58	63.5178571428571	54.7083333333333	8.80952380952381	-5.51785714285715
28	60	61.5892857142857	54.25	7.33928571428572	-1.58928571428572
29	61	59.6071428571429	54.1666666666667	5.44047619047619	1.39285714285715
30	58	58.2321428571429	54.25	3.98214285714286	-0.232142857142861
31	62	54.9583333333333	53.875	1.08333333333333	7.04166666666666
32	61	57.077380952381	53.5416666666667	3.53571428571429	3.92261904761904
33	49	50.8452380952381	53.5	-2.65476190476191	-1.84523809523809
34	51	51.6190476190476	53.1666666666667	-1.54761904761905	-0.61904761904762
35	47	44.6071428571429	52.375	-7.76785714285714	2.39285714285715
36	40	40.5416666666667	51.5833333333333	-11.0416666666667	-0.541666666666657
37	45	43.2797619047619	50.5833333333333	-7.30357142857143	1.72023809523809
38	50	49.4166666666667	49.2916666666667	0.124999999999999	0.583333333333336
39	58	57.0178571428571	48.2083333333333	8.80952380952381	0.982142857142861
40	52	54.5059523809524	47.1666666666667	7.33928571428572	-2.50595238095237
41	50	51.4821428571429	46.0416666666667	5.44047619047619	-1.48214285714285
42	50	49.0238095238095	45.0416666666667	3.98214285714286	0.976190476190482
43	46	45.0416666666667	43.9583333333333	1.08333333333333	0.958333333333336
44	46	46.4523809523809	42.9166666666667	3.53571428571429	-0.452380952380949
45	38	39.3452380952381	42	-2.65476190476191	-1.34523809523809
46	37	39.7023809523809	41.25	-1.54761904761905	-2.70238095238094
47	34	33.1071428571429	40.875	-7.76785714285714	0.892857142857146
48	29	29.5833333333333	40.625	-11.0416666666667	-0.583333333333329
49	30	33.0714285714286	40.375	-7.30357142857143	-3.07142857142857
50	40	40.375	40.25	0.124999999999999	-0.374999999999993
51	46	49.0178571428571	40.2083333333333	8.80952380952381	-3.01785714285715
52	46	47.672619047619	40.3333333333333	7.33928571428572	-1.67261904761904
53	47	46.1488095238095	40.7083333333333	5.44047619047619	0.851190476190482
54	47	45.1904761904762	41.2083333333333	3.98214285714286	1.80952380952380
55	43	43.3333333333333	42.25	1.08333333333333	-0.333333333333343
56	46	47.1607142857143	43.625	3.53571428571429	-1.16071428571428
57	37	42.0119047619048	44.6666666666667	-2.65476190476191	-5.01190476190475
58	41	43.8273809523809	45.375	-1.54761904761905	-2.82738095238094
59	39	38.1071428571429	45.875	-7.76785714285714	0.892857142857139
60	36	35.2916666666667	46.3333333333333	-11.0416666666667	0.708333333333329
61	48	39.6547619047619	46.9583333333333	-7.30357142857143	8.3452380952381
62	55	47.875	47.75	0.124999999999999	7.12500
63	56	57.5595238095238	48.75	8.80952380952381	-1.55952380952380
64	53	56.9642857142857	49.625	7.33928571428572	-3.96428571428572
65	52	55.4821428571429	50.0416666666667	5.44047619047619	-3.48214285714285
66	53	54.3988095238095	50.4166666666667	3.98214285714286	-1.39880952380953
67	52	51.5833333333333	50.5	1.08333333333333	0.416666666666664
68	56	53.6607142857143	50.125	3.53571428571429	2.33928571428572
69	51	47.4285714285714	50.0833333333333	-2.65476190476191	3.57142857142857
70	48	49.2440476190476	50.7916666666667	-1.54761904761905	-1.24404761904762
71	42	43.8154761904762	51.5833333333333	-7.76785714285714	-1.81547619047618
72	42	41.0416666666667	52.0833333333333	-11.0416666666667	0.958333333333336
73	44	45.1547619047619	52.4583333333333	-7.30357142857143	-1.15476190476191
74	50	52.75	52.625	0.124999999999999	-2.75
75	60	61.7261904761905	52.9166666666667	8.80952380952381	-1.72619047619047
76	66	60.8392857142857	53.5	7.33928571428572	5.1607142857143
77	58	59.7321428571428	54.2916666666667	5.44047619047619	-1.73214285714285
78	59	59.1071428571429	55.125	3.98214285714286	-0.107142857142854
79	55	56.625	55.5416666666667	1.08333333333333	-1.625
80	57	59.452380952381	55.9166666666667	3.53571428571429	-2.45238095238096
81	57	54.1785714285714	56.8333333333333	-2.65476190476191	2.82142857142857
82	56	55.827380952381	57.375	-1.54761904761905	0.172619047619051
83	53	49.8571428571428	57.625	-7.76785714285714	3.14285714285715
84	51	46.7083333333333	57.75	-11.0416666666667	4.29166666666667
85	45	50.1547619047619	57.4583333333333	-7.30357142857143	-5.1547619047619
86	58	57.5416666666667	57.4166666666667	0.124999999999999	0.458333333333343
87	74	66.1845238095238	57.375	8.80952380952381	7.8154761904762
88	65	64.6309523809524	57.2916666666667	7.33928571428572	0.36904761904762
89	65	62.4404761904762	57	5.44047619047619	2.55952380952382
90	55	60.1904761904762	56.2083333333333	3.98214285714286	-5.19047619047619
91	52	56.9166666666667	55.8333333333333	1.08333333333333	-4.91666666666666
92	59	58.9940476190476	55.4583333333333	3.53571428571429	0.00595238095239381
93	54	51.7619047619048	54.4166666666667	-2.65476190476191	2.23809523809524
94	57	51.9940476190476	53.5416666666667	-1.54761904761905	5.00595238095238
95	45	45.3988095238095	53.1666666666667	-7.76785714285714	-0.398809523809526
96	40	42.4166666666667	53.4583333333333	-11.0416666666667	-2.41666666666666
97	47	NA	54.3333333333333	NA	NA
98	47	NA	55	NA	NA
99	60	NA	55.2083333333333	NA	NA
100	58	NA	55.125	NA	NA
101	63	NA	54.9583333333333	NA	NA
102	64	NA	NA	NA	NA
103	64	NA	NA	NA	NA
104	63	NA	NA	NA	NA
105	55	NA	NA	NA	NA
106	54	NA	NA	NA	NA
107	44	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/1y00m1243777993.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/1y00m1243777993.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/2fscd1243777993.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/2fscd1243777993.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/38alg1243777993.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/38alg1243777993.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/4bwar1243777993.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/31/t1243778071zcgttl6pwfofrdu/4bwar1243777993.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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