Home » date » 2009 » May » 30 »

Classical decomposition-ruwe aardolie-Katrijn Kempenaers

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sat, 30 May 2009 02:08:11 -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/30/t12436709725bdyaxrcfanosbb.htm/, Retrieved Sat, 30 May 2009 10:09:55 +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/30/t12436709725bdyaxrcfanosbb.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 «

88.6 95 96.3 83.3 96.9 103.4 99.3 103.8 113.4 111.5 114.2 90.6 90.8 96.4 90 92.1 97.2 95.1 88.5 91 90.5 75 66.3 66 68.4 70.6 83.9 90.1 90.6 87.1 90.8 94.1 99.8 96.8 87 96.3 107.1 115.2 106.1 89.5 91.3 97.6 100.7 104.6 94.7 101.8 102.5 105.3 110.3 109.8 117.3 118.8 131.3 125.9 133.1 147 145.8 164.4 149.8 137.7 151.7 156.8 180 180.4 170.4 191.6 199.5 218.2 217.5 205 194 199.3 219.3 211.1 215.2 240.2 242.2 240.7 255.4 253 218.2 203.7 205.6 215.6 188.5 202.9 214 230.3 230 241 259.6 247.8 270.3 289.7 322.7 315 320.2 329.5 360.6 382.2 435.4 464 468.8 403 351.6 252 188 146.5 152.9

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	'George Udny Yule' @ 72.249.76.132

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	88.6	NA	NA	-12.0525607638889	NA
2	95	NA	NA	-11.0353732638889	NA
3	96.3	NA	NA	-4.48433159722221	NA
4	83.3	NA	NA	0.605772569444436	NA
5	96.9	NA	NA	7.5896267361111	NA
6	103.4	NA	NA	13.7391059027778	NA
7	99.3	119.582855902778	99.7833333333333	19.7995225694445	-20.2828559027778
8	103.8	104.031814236111	99.9333333333333	4.09848090277779	-0.23181423611112
9	113.4	99.3854600694444	99.7291666666667	-0.343706597222223	14.0145399305556
10	111.5	97.7448350694444	99.8333333333333	-2.08849826388890	13.7551649305556
11	114.2	93.5385850694445	100.2125	-6.67391493055556	20.6614149305556
12	90.6	90.7250434027778	99.8791666666667	-9.1541232638889	-0.125043402777777
13	90.8	87.0307725694444	99.0833333333333	-12.0525607638889	3.76922743055556
14	96.4	87.0646267361111	98.1	-11.0353732638889	9.3353732638889
15	90	92.1281684027778	96.6125	-4.48433159722221	-2.12816840277777
16	92.1	94.7432725694444	94.1375	0.605772569444436	-2.64327256944443
17	97.2	98.2104600694444	90.6208333333333	7.5896267361111	-1.01046006944443
18	95.1	101.339105902778	87.6	13.7391059027778	-6.23910590277778
19	88.5	105.441189236111	85.6416666666667	19.7995225694445	-16.9411892361111
20	91	87.7318142361111	83.6333333333333	4.09848090277779	3.26818576388888
21	90.5	81.9604600694444	82.3041666666667	-0.343706597222223	8.53953993055556
22	75	79.8781684027778	81.9666666666667	-2.08849826388890	-4.87816840277777
23	66.3	74.9344184027778	81.6083333333333	-6.67391493055556	-8.63441840277777
24	66	71.8458767361111	81	-9.1541232638889	-5.84587673611111
25	68.4	68.7099392361111	80.7625	-12.0525607638889	-0.309939236111106
26	70.6	69.9521267361111	80.9875	-11.0353732638889	0.647873263888869
27	83.9	77.0198350694445	81.5041666666667	-4.48433159722221	6.88016493055554
28	90.1	83.4057725694444	82.8	0.605772569444436	6.69422743055556
29	90.6	92.1604600694444	84.5708333333333	7.5896267361111	-1.56046006944443
30	87.1	100.434939236111	86.6958333333333	13.7391059027778	-13.3349392361111
31	90.8	109.370355902778	89.5708333333333	19.7995225694445	-18.5703559027778
32	94.1	97.1401475694445	93.0416666666667	4.09848090277779	-3.04014756944446
33	99.8	95.4812934027778	95.825	-0.343706597222223	4.31870659722223
34	96.8	94.6365017361111	96.725	-2.08849826388890	2.16349826388891
35	87	90.0552517361111	96.7291666666667	-6.67391493055556	-3.05525173611110
36	96.3	88.0417100694444	97.1958333333333	-9.1541232638889	8.25828993055558
37	107.1	85.9932725694444	98.0458333333333	-12.0525607638889	21.1067274305556
38	115.2	87.8604600694444	98.8958333333333	-11.0353732638889	27.3395399305556
39	106.1	94.6365017361111	99.1208333333333	-4.48433159722221	11.4634982638889
40	89.5	99.7224392361111	99.1166666666666	0.605772569444436	-10.2224392361111
41	91.3	107.560460069444	99.9708333333333	7.5896267361111	-16.2604600694444
42	97.6	114.730772569444	100.991666666667	13.7391059027778	-17.1307725694444
43	100.7	121.299522569444	101.5	19.7995225694445	-20.5995225694444
44	104.6	105.506814236111	101.408333333333	4.09848090277779	-0.906814236111117
45	94.7	101.306293402778	101.65	-0.343706597222223	-6.60629340277777
46	101.8	101.249001736111	103.3375	-2.08849826388890	0.550998263888886
47	102.5	99.5510850694444	106.225	-6.67391493055556	2.94891493055557
48	105.3	99.9167100694444	109.070833333333	-9.1541232638889	5.38328993055556
49	110.3	99.5474392361111	111.6	-12.0525607638889	10.7525607638889
50	109.8	103.681293402778	114.716666666667	-11.0353732638889	6.11870659722221
51	117.3	114.128168402778	118.6125	-4.48433159722221	3.17183159722221
52	118.8	123.955772569444	123.35	0.605772569444436	-5.15577256944447
53	131.3	135.518793402778	127.929166666667	7.5896267361111	-4.21879340277778
54	125.9	144.989105902778	131.25	13.7391059027778	-19.0891059027778
55	133.1	154.124522569444	134.325	19.7995225694445	-21.0245225694445
56	147	142.106814236111	138.008333333333	4.09848090277779	4.89318576388888
57	145.8	142.235460069444	142.579166666667	-0.343706597222223	3.56453993055558
58	164.4	145.669835069444	147.758333333333	-2.08849826388890	18.7301649305556
59	149.8	145.280251736111	151.954166666667	-6.67391493055556	4.51974826388889
60	137.7	147.166710069444	156.320833333333	-9.1541232638889	-9.46671006944447
61	151.7	149.772439236111	161.825	-12.0525607638889	1.92756076388892
62	156.8	156.522960069444	167.558333333333	-11.0353732638889	0.277039930555588
63	180	169.028168402778	173.5125	-4.48433159722221	10.9718315972223
64	180.4	178.797439236111	178.191666666667	0.605772569444436	1.60256076388893
65	170.4	189.314626736111	181.725	7.5896267361111	-18.9146267361111
66	191.6	199.872439236111	186.133333333333	13.7391059027778	-8.2724392361111
67	199.5	211.316189236111	191.516666666667	19.7995225694445	-11.8161892361111
68	218.2	200.694314236111	196.595833333333	4.09848090277779	17.5056857638889
69	217.5	199.981293402778	200.325	-0.343706597222223	17.5187065972223
70	205	202.194835069444	204.283333333333	-2.08849826388890	2.80516493055561
71	194	203.092751736111	209.766666666667	-6.67391493055556	-9.09275173611113
72	199.3	205.650043402778	214.804166666667	-9.1541232638889	-6.35004340277777
73	219.3	207.126605902778	219.179166666667	-12.0525607638889	12.1733940972222
74	211.1	211.922960069444	222.958333333333	-11.0353732638889	-0.822960069444463
75	215.2	219.953168402778	224.4375	-4.48433159722221	-4.75316840277779
76	240.2	225.018272569444	224.4125	0.605772569444436	15.1817274305555
77	242.2	232.431293402778	224.841666666667	7.5896267361111	9.7687065972222
78	240.7	239.743272569444	226.004166666667	13.7391059027778	0.956727430555532
79	255.4	245.199522569444	225.4	19.7995225694445	10.2004774305556
80	253	227.873480902778	223.775	4.09848090277779	25.1265190972223
81	218.2	223.039626736111	223.383333333333	-0.343706597222223	-4.83962673611114
82	203.7	220.832335069444	222.920833333333	-2.08849826388890	-17.1323350694445
83	205.6	215.326085069444	222	-6.67391493055556	-9.72608506944445
84	215.6	212.350043402778	221.504166666667	-9.1541232638889	3.24995659722222
85	188.5	209.639105902778	221.691666666667	-12.0525607638889	-21.1391059027778
86	202.9	210.614626736111	221.65	-11.0353732638889	-7.71462673611109
87	214	219.119835069444	223.604166666667	-4.48433159722221	-5.11983506944443
88	230.3	229.964105902778	229.358333333333	0.605772569444436	0.335894097222223
89	230	245.410460069444	237.820833333333	7.5896267361111	-15.4104600694444
90	241	260.580772569444	246.841666666667	13.7391059027778	-19.5807725694445
91	259.6	276.270355902778	256.470833333333	19.7995225694445	-16.6703559027778
92	247.8	271.331814236111	267.233333333333	4.09848090277779	-23.5318142361111
93	270.3	278.272960069444	278.616666666667	-0.343706597222223	-7.97296006944441
94	289.7	288.965668402778	291.054166666667	-2.08849826388890	0.734331597222194
95	322.7	299.267751736111	305.941666666667	-6.67391493055556	23.4322482638889
96	315	314.637543402778	323.791666666667	-9.1541232638889	0.362456597222263
97	320.2	329.747439236111	341.8	-12.0525607638889	-9.54743923611107
98	329.5	345.947960069444	356.983333333333	-11.0353732638889	-16.4479600694444
99	360.6	362.353168402778	366.8375	-4.48433159722221	-1.75316840277770
100	382.2	369.259939236111	368.654166666667	0.605772569444436	12.9400607638889
101	435.4	369.060460069444	361.470833333333	7.5896267361111	66.3395399305556
102	464	362.576605902778	348.8375	13.7391059027778	101.423394097222
103	468.8	354.645355902778	334.845833333333	19.7995225694445	114.154644097222
104	403	NA	NA	4.09848090277779	NA
105	351.6	NA	NA	-0.343706597222223	NA
106	252	NA	NA	-2.08849826388890	NA
107	188	NA	NA	-6.67391493055556	NA
108	146.5	NA	NA	-9.1541232638889	NA
109	152.9	NA	NA	NA	NA

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/1j2h11243670888.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/1j2h11243670888.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/2y7y41243670888.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/2y7y41243670888.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/3f1k81243670889.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/3f1k81243670889.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/4gwwi1243670889.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2009/May/30/t12436709725bdyaxrcfanosbb/4gwwi1243670889.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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