opgave 10.1

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Wed, 18 May 2011 11:47:38 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i.htm/, Retrieved Wed, 18 May 2011 13:51:28 +0200

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

KDGP2W101

Dataseries X:

» Textbox « » Textfile « » CSV «

814 1150 1225 1691 1759 1754 2100 2062 2012 1897 1964 2186 966 1549 1538 1612 2078 2137 2907 2249 1883 1739 1828 1868 1138 1430 1809 1763 2200 2067 2503 2141 2103 1972 2181 2344 970 1199 1718 1683 2025 2051 2439 2353 2230 1852 2147 2286 1007 1665 1642 1518 1831 2207 2822 2393 2306 1785 2047 2171 1212 1335 2011 1860 1954 2152 2835 2224 2182 1992 2389 2724 891 1247 2017 2257 2255 2255 3057 3330 1896 2096 2374 2535 1041 1728 2201 2455 2204 2660 3670 2665 2639 2226 2586 2684 1185 1749 2459 2618 2585 3310 3923

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 'Gwilym Jenkins' @ www.wessa.org Classical Decomposition by Moving Averages t Observations Fit Trend Seasonal Random 1 814 NA NA 0.51723341036936 NA 2 1150 NA NA 0.71994479119866 NA 3 1225 NA NA 0.910429225840974 NA 4 1691 NA NA 0.918464905236503 NA 5 1759 NA NA 1.02317800979132 NA 6 1754 NA NA 1.0852626663327 NA 7 2100 2426.8478908688 1724.16666666667 1.40754831756528 0.865319993025276 8 2062 2108.19674829881 1747.125 1.20666623641629 0.97808707923675 9 2012 1884.38904891602 1776.79166666667 1.06055711779154 1.06772006617072 10 1897 1686.78799912834 1786.54166666667 0.944163816943353 1.12462265618459 11 1964 1920.60248785932 1796.54166666667 1.0690553542367 1.0225957804465 12 2186 2076.83098839017 1825.79166666667 1.13749614827732 1.05256518812561 13 966 970.00660697144 1875.375 0.51723341036936 0.995869505483114 14 1549 1379.98417622967 1916.79166666667 0.71994479119866 1.12247663899459 15 1538 1747.30335714421 1919.20833333333 0.910429225840974 0.880213497966205 16 1612 1751.74219051232 1907.25 0.918464905236503 0.920226736976945 17 2078 1938.92232855455 1895 1.02317800979132 1.07172936708049 18 2137 2036.04320059566 1876.08333333333 1.0852626663327 1.04958480221579 19 2907 2632.11535384707 1870 1.40754831756528 1.10443487811093 20 2249 2259.13058337055 1872.20833333333 1.20666623641629 0.995515715893042 21 1883 1992.30073565131 1878.54166666667 1.06055711779154 0.945138435329854 22 1739 1790.25261740171 1896.125 0.944163816943353 0.971371293132865 23 1828 2039.2230882065 1907.5 1.0690553542367 0.896419823104165 24 1868 2172.23847782693 1909.66666666667 1.13749614827732 0.859942413813015 25 1138 977.528042813893 1889.91666666667 0.51723341036936 1.16416097560145 26 1430 1345.27683775396 1868.58333333333 0.71994479119866 1.06297823605399 27 1809 1705.4615473066 1873.25 0.910429225840974 1.06070993090223 28 1763 1737.85040882062 1892.125 0.918464905236503 1.01447166629057 29 2200 1960.96328818214 1916.54166666667 1.02317800979132 1.12189759658349 30 2067 2117.43790057062 1951.08333333333 1.0852626663327 0.97617974980186 31 2503 2764.30760000508 1963.91666666667 1.40754831756528 0.905470867278084 32 2141 2349.73110662148 1947.29166666667 1.20666623641629 0.911168088113028 33 2103 2050.98489616911 1933.875 1.06055711779154 1.02536103699644 34 1972 1819.1676342956 1926.75 0.944163816943353 1.08401224979114 35 2181 2048.44369063679 1916.125 1.0690553542367 1.06471074111976 36 2344 2170.53223360451 1908.16666666667 1.13749614827732 1.07991946109338 37 970 985.243441185236 1904.83333333333 0.51723341036936 0.984528249011332 38 1199 1375.81449598064 1911 0.71994479119866 0.871483767254093 39 1718 1752.69006339711 1925.125 0.910429225840974 0.980207531199288 40 1683 1768.42763629078 1925.41666666667 0.918464905236503 0.95169288551158 41 2025 1963.47860078954 1919 1.02317800979132 1.03133285954108 42 2051 2078.45888313817 1915.16666666667 1.0852626663327 0.986788825431703 43 2439 2694.4580147459 1914.29166666667 1.40754831756528 0.905191317382619 44 2353 2335.20083402463 1935.25 1.20666623641629 1.00762211357414 45 2230 2069.67721537019 1951.5 1.06055711779154 1.0774626997095 46 1852 1833.05471043648 1941.45833333333 0.944163816943353 1.01033536503611 47 2147 2059.535139937 1926.5 1.0690553542367 1.04246825332909 48 2286 2189.58529408816 1924.91666666667 1.13749614827732 1.04403331816859 49 1007 1007.24741251803 1947.375 0.51723341036936 0.999754367680713 50 1665 1414.69151470537 1965 0.71994479119866 1.17693502978758 51 1642 1793.39383670241 1969.83333333333 0.910429225840974 0.915582493034109 52 1518 1809.56721017117 1970.20833333333 0.918464905236503 0.838874616796582 53 1831 2008.75422772281 1963.25 1.02317800979132 0.91151021599874 54 2207 2120.91978495843 1954.29166666667 1.0852626663327 1.04058626622848 55 2822 2756.03825363938 1958.04166666667 1.40754831756528 1.02393353803182 56 2393 2356.41804868162 1952.83333333333 1.20666623641629 1.01552438937516 57 2306 2072.81469684365 1954.45833333333 1.06055711779154 1.11249693641763 58 1785 1873.29969313369 1984.08333333333 0.944163816943353 0.95286408605236 59 2047 2141.80785824013 2003.45833333333 1.0690553542367 0.955734657581268 60 2171 2282.14904315423 2006.29166666667 1.13749614827732 0.951296325939955 61 1212 1036.81592247748 2004.54166666667 0.51723341036936 1.16896352932535 62 1335 1438.47969051456 1998.04166666667 0.71994479119866 0.92806315501226 63 2011 1807.96070431587 1985.83333333333 0.910429225840974 1.11230293623055 64 1860 1827.09458211277 1989.29166666667 0.918464905236503 1.01800969594534 65 1954 2058.80468536843 2012.16666666667 1.02317800979132 0.949094401176924 66 2152 2224.20061537109 2049.45833333333 1.0852626663327 0.967538622697913 67 2835 2898.31792940661 2059.125 1.40754831756528 0.97815355977197 68 2224 2464.11301028177 2042.08333333333 1.20666623641629 0.902556007261082 69 2182 2162.12244413768 2038.66666666667 1.06055711779154 1.00919353846782 70 1992 1940.68938556802 2055.45833333333 0.944163816943353 1.02643937500434 71 2389 2228.49042987949 2084.54166666667 1.0690553542367 1.07202614288507 72 2724 2390.30596858626 2101.375 1.13749614827732 1.13960306161604 73 891 1093.905560147 2114.91666666667 0.51723341036936 0.814512726199387 74 1247 1562.46018309889 2170.25 0.71994479119866 0.798100337844624 75 2017 2006.96535926427 2204.41666666667 0.910429225840974 1.00499990729257 76 2257 2017.71431932039 2196.83333333333 0.918464905236503 1.11859244809255 77 2255 2251.54584296287 2200.54166666667 1.02317800979132 1.00153412689683 78 2255 2378.94098387903 2192.04166666667 1.0852626663327 0.94790077403394 79 3057 3083.11729393362 2190.41666666667 1.40754831756528 0.991528932750951 80 3330 2674.82710181596 2216.70833333333 1.20666623641629 1.24494027959386 81 1896 2380.33207112329 2244.41666666667 1.06055711779154 0.796527519416762 82 2096 2134.12494756429 2260.33333333333 0.944163816943353 0.98213555977226 83 2374 2422.96941640438 2266.45833333333 1.0690553542367 0.97978950288318 84 2535 2594.8656925848 2281.20833333333 1.13749614827732 0.97692917488721 85 1041 1201.8564831695 2323.625 0.51723341036936 0.866159990463006 86 1728 1671.32183506806 2321.45833333333 0.71994479119866 1.03391217881721 87 2201 2116.48240822273 2324.70833333333 0.910429225840974 1.0399330471394 88 2455 2168.57218000549 2361.08333333333 0.918464905236503 1.13208129415078 89 2204 2430.38883259098 2375.33333333333 1.02317800979132 0.906850776486808 90 2660 2594.18474603502 2390.375 1.0852626663327 1.02537030335468 91 3670 3381.75212864372 2402.58333333333 1.40754831756528 1.08523625043799 92 2665 2907.4120188852 2409.45833333333 1.20666623641629 0.916622749954046 93 2639 2567.69716193313 2421.08333333333 1.06055711779154 1.02776917742635 94 2226 2302.46148809348 2438.625 0.944163816943353 0.966791414975286 95 2586 2631.25703458817 2461.29166666667 1.0690553542367 0.982800222861827 96 2684 2848.57472932349 2504.25 1.13749614827732 0.94222558824617 97 1185 NA 2541.875 NA NA 98 1749 NA NA NA NA 99 2459 NA NA NA NA 100 2618 NA NA NA NA 101 2585 NA NA NA NA 102 3310 NA NA NA NA 103 3923 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/1xr931305719254.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/1xr931305719254.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/2w8281305719254.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/2w8281305719254.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/3ggqi1305719254.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/3ggqi1305719254.ps (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/47ru71305719254.png (open in new window) http://www.freestatistics.org/blog/date/2011/May/18/t1305719484qxo9w46r4xo2v1i/47ru71305719254.ps (open in new window)

Parameters (Session):

par1 = multiplicative ; par2 = 12 ;

Parameters (R input):

par1 = multiplicative ; 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')

Copyright

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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