Home » date » 2009 » Jun » 02 »

Opgave 9 - Aantal nieuwe gebouwen - Christophe Morre

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Tue, 02 Jun 2009 01:27:26 -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/02/t1243927681queoa7y84nc7xyk.htm/, Retrieved Tue, 02 Jun 2009 09:28:06 +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/Jun/02/t1243927681queoa7y84nc7xyk.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 «

2194 2419 2742 2137 2710 2173 2363 2126 1905 2121 1983 1734 2074 2049 2406 2558 2251 2059 2397 1747 1707 2319 1631 1627 1791 2034 1997 2169 2028 2253 2218 1855 2187 1852 1570 1851 1954 1828 2251 2277 2085 2282 2266 1878 2267 2069 1746 2299 2360 2214 2825 2355 2333 3016 2155 2172 2150 2533 2058 2160 2260 2498 2695 2799 2945 2930 2318 2540 2570 2669 2450 2842 3440 2678 2981 2259 2844 2546 2456 2295 2379 2479 2057 2280 2351 2275 2543 2305 2188 2720 2398 2147 1898 2538 2081 2057 2497 2460 2195 2823 2100 2640 2342 2171 2482

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	2194	NA	NA	45.1602182539683	NA
2	2419	NA	NA	-48.6016865079365	NA
3	2742	NA	NA	254.457837301587	NA
4	2137	NA	NA	112.588789682540	NA
5	2710	NA	NA	102.666170634921	NA
6	2173	NA	NA	261.874503968254	NA
7	2363	2241.39831349206	2212.25	29.1483134920635	121.601686507937
8	2126	1991.16021825397	2191.83333333333	-200.673115079365	134.839781746032
9	1905	2035.41021825397	2162.41666666667	-127.006448412698	-130.410218253968
10	2121	2224.48759920635	2165.95833333333	58.529265873016	-103.487599206349
11	1983	1812.79712301587	2164.375	-351.577876984127	170.202876984127
12	1734	2003.93402777778	2140.5	-136.565972222222	-269.934027777778
13	2074	2182.32688492063	2137.16666666667	45.1602182539683	-108.326884920635
14	2049	2074.18998015873	2122.79166666667	-48.6016865079365	-25.1899801587301
15	2406	2353.20783730159	2098.75	254.457837301587	52.7921626984125
16	2558	2211.33878968254	2098.75	112.588789682540	346.661210317460
17	2251	2194.99950396825	2092.33333333333	102.666170634921	56.0004960317465
18	2059	2335.08283730159	2073.20833333333	261.874503968254	-276.082837301587
19	2397	2086.10664682540	2056.95833333333	29.1483134920635	310.893353174603
20	1747	1843.8685515873	2044.54166666667	-200.673115079365	-96.8685515873015
21	1707	1899.8685515873	2026.875	-127.006448412698	-192.868551587301
22	2319	2052.15426587302	1993.625	58.529265873016	266.845734126985
23	1631	1616.54712301587	1968.125	-351.577876984127	14.4528769841272
24	1627	1830.35069444444	1966.91666666667	-136.565972222222	-203.350694444445
25	1791	2012.70188492063	1967.54166666667	45.1602182539683	-221.701884920635
26	2034	1915.98164682540	1964.58333333333	-48.6016865079365	118.018353174603
27	1997	2243.54117063492	1989.08333333333	254.457837301587	-246.541170634920
28	2169	2102.21378968254	1989.625	112.588789682540	66.7862103174602
29	2028	2070.29117063492	1967.625	102.666170634921	-42.2911706349205
30	2253	2236.29117063492	1974.41666666667	261.874503968254	16.7088293650793
31	2218	2019.68998015873	1990.54166666667	29.1483134920635	198.31001984127
32	1855	1788.07688492063	1988.75	-200.673115079365	66.923115079365
33	2187	1863.7435515873	1990.75	-127.006448412698	323.256448412699
34	1852	2064.36259920635	2005.83333333333	58.529265873016	-212.362599206349
35	1570	1661.13045634921	2012.70833333333	-351.577876984127	-91.130456349206
36	1851	1879.72569444444	2016.29166666667	-136.565972222222	-28.7256944444441
37	1954	2064.66021825397	2019.5	45.1602182539683	-110.660218253968
38	1828	1973.85664682540	2022.45833333333	-48.6016865079365	-145.856646825397
39	2251	2281.20783730159	2026.75	254.457837301587	-30.2078373015868
40	2277	2151.71378968254	2039.125	112.588789682540	125.286210317460
41	2085	2158.16617063492	2055.5	102.666170634921	-73.1661706349203
42	2282	2343.37450396825	2081.5	261.874503968254	-61.374503968254
43	2266	2146.23164682540	2117.08333333333	29.1483134920635	119.768353174603
44	1878	1949.41021825397	2150.08333333333	-200.673115079365	-71.4102182539682
45	2267	2063.07688492063	2190.08333333333	-127.006448412698	203.923115079365
46	2069	2275.77926587302	2217.25	58.529265873016	-206.779265873016
47	1746	1879.25545634921	2230.83333333333	-351.577876984127	-133.255456349206
48	2299	2135.18402777778	2271.75	-136.565972222222	163.815972222222
49	2360	2342.8685515873	2297.70833333333	45.1602182539683	17.1314484126983
50	2214	2256.73164682540	2305.33333333333	-48.6016865079365	-42.7316468253971
51	2825	2567.16617063492	2312.70833333333	254.457837301587	257.833829365079
52	2355	2439.75545634921	2327.16666666667	112.588789682540	-84.7554563492067
53	2333	2462.16617063492	2359.5	102.666170634921	-129.166170634920
54	3016	2628.58283730159	2366.70833333333	261.874503968254	387.417162698413
55	2155	2385.89831349206	2356.75	29.1483134920635	-230.898313492064
56	2172	2163.7435515873	2364.41666666667	-200.673115079365	8.25644841269832
57	2150	2243.82688492064	2370.83333333333	-127.006448412698	-93.8268849206352
58	2533	2442.44593253968	2383.91666666667	58.529265873016	90.5540674603176
59	2058	2076.33878968254	2427.91666666667	-351.577876984127	-18.3387896825398
60	2160	2313.26736111111	2449.83333333333	-136.565972222222	-153.267361111111
61	2260	2498.20188492063	2453.04166666667	45.1602182539683	-238.201884920635
62	2498	2426.56498015873	2475.16666666667	-48.6016865079365	71.4350198412699
63	2695	2762.45783730159	2508	254.457837301587	-67.4578373015875
64	2799	2643.75545634921	2531.16666666667	112.588789682540	155.244543650794
65	2945	2655.83283730159	2553.16666666667	102.666170634921	289.167162698413
66	2930	2859.79117063492	2597.91666666667	261.874503968254	70.208829365079
67	2318	2704.64831349206	2675.5	29.1483134920635	-386.648313492064
68	2540	2531.4935515873	2732.16666666667	-200.673115079365	8.50644841269786
69	2570	2624.57688492064	2751.58333333333	-127.006448412698	-54.5768849206352
70	2669	2799.52926587302	2741	58.529265873016	-130.529265873016
71	2450	2362.71378968254	2714.29166666667	-351.577876984127	87.2862103174607
72	2842	2557.51736111111	2694.08333333333	-136.565972222222	284.482638888889
73	3440	2728.9935515873	2683.83333333333	45.1602182539683	711.006448412699
74	2678	2630.77331349206	2679.375	-48.6016865079365	47.2266865079364
75	2981	2915.66617063492	2661.20833333333	254.457837301587	65.333829365079
76	2259	2757.92212301587	2645.33333333333	112.588789682540	-498.922123015873
77	2844	2723.70783730159	2621.04166666667	102.666170634921	120.292162698413
78	2546	2843.12450396825	2581.25	261.874503968254	-297.124503968254
79	2456	2541.60664682540	2512.45833333333	29.1483134920635	-85.6066468253971
80	2295	2249.61855158730	2450.29166666667	-200.673115079365	45.3814484126979
81	2379	2288.24355158730	2415.25	-127.006448412698	90.7564484126979
82	2479	2457.44593253968	2398.91666666667	58.529265873016	21.5540674603176
83	2057	2021.92212301587	2373.5	-351.577876984127	35.0778769841272
84	2280	2216.85069444444	2353.41666666667	-136.565972222222	63.1493055555552
85	2351	2403.41021825397	2358.25	45.1602182539683	-52.4102182539687
86	2275	2301.06498015873	2349.66666666667	-48.6016865079365	-26.0649801587301
87	2543	2577.91617063492	2323.45833333333	254.457837301587	-34.9161706349209
88	2305	2418.46378968254	2305.875	112.588789682540	-113.463789682539
89	2188	2411.99950396825	2309.33333333333	102.666170634921	-223.999503968254
90	2720	2562.91617063492	2301.04166666667	261.874503968254	157.083829365080
91	2398	2326.98164682540	2297.83333333333	29.1483134920635	71.0183531746029
92	2147	2110.95188492063	2311.625	-200.673115079365	36.0481150793653
93	1898	2177.82688492064	2304.83333333333	-127.006448412698	-279.826884920635
94	2538	2370.44593253968	2311.91666666667	58.529265873016	167.554067460318
95	2081	1978.25545634921	2329.83333333333	-351.577876984127	102.744543650794
96	2057	2186.26736111111	2322.83333333333	-136.565972222222	-129.267361111112
97	2497	NA	2317.16666666667	NA	NA
98	2460	NA	2315.83333333333	NA	NA
99	2195	NA	2341.16666666667	NA	NA
100	2823	NA	NA	NA	NA
101	2100	NA	NA	NA	NA
102	2640	NA	NA	NA	NA
103	2342	NA	NA	NA	NA
104	2171	NA	NA	NA	NA
105	2482	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/1add81243927644.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/1add81243927644.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/2jca21243927644.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/2jca21243927644.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/3wlj71243927644.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/3wlj71243927644.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/40u221243927644.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/Jun/02/t1243927681queoa7y84nc7xyk/40u221243927644.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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