Earthquakes/year magnitude >= 7.0 1900 - 1998

*Unverified author*

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

Title produced by software: Classical Decomposition

Date of computation: Sun, 23 May 2010 12:33:37 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci.htm/, Retrieved Sun, 23 May 2010 14:37:14 +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/2010/May/23/t12746182280d0tednsitflnci.htm/}, year = {2010}, } @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 = {2010}, 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:

KDGP2W52

Dataseries X:

» Textbox « » Textfile « » CSV «

13 14 8 10 16 26 32 27 18 32 36 24 22 23 22 18 25 21 21 14 8 11 14 23 18 17 19 20 22 19 13 26 13 14 22 24 21 22 26 21 23 24 27 41 31 27 35 26 28 36 39 21 17 22 17 19 15 34 10 15 22 18 15 20 15 22 19 16 30 27 29 23 20 16 21 21 25 16 18 15 18 14 10 15 8 15 6 11 8 7 13 10 23 16 15 25 22 20 16

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 13 NA NA 0.96897629511439 NA 2 14 NA NA 1.05352889135862 NA 3 8 NA NA 1.00481560665762 NA 4 10 NA NA 0.957924403758919 NA 5 16 NA NA 0.980812860184474 NA 6 26 NA NA 0.93780313394856 NA 7 32 20.3643345513538 21.7083333333333 0.938088347855071 1.57137469526951 8 27 22.1950901912453 22.4583333333333 0.988278598497007 1.21648525720567 9 18 23.7563430379681 23.4166666666667 1.01450575251109 0.757692375936475 10 32 27.2051183808089 24.3333333333333 1.11801856359489 1.17624924663344 11 36 24.8820212265096 25.0416666666667 0.993624807714195 1.44682779876601 12 24 26.3079898740468 25.2083333333333 1.04362273880516 0.91227038306246 13 22 23.780293242599 24.5416666666667 0.96897629511439 0.925135774212832 14 23 24.8018259840675 23.5416666666667 1.05352889135862 0.927351075472228 15 22 22.6920857836846 22.5833333333333 1.00481560665762 0.969501006197402 16 18 20.3958070967003 21.2916666666667 0.957924403758919 0.88253433240757 17 25 19.1258507735973 19.5 0.980812860184474 1.30713139488215 18 21 17.3884331086295 18.5416666666667 0.93780313394856 1.20769938664445 19 21 17.1982863773430 18.3333333333333 0.938088347855071 1.22105188501021 20 14 17.7066582230714 17.9166666666667 0.988278598497007 0.790663027637724 21 8 17.7961217419655 17.5416666666667 1.01450575251109 0.44953614703225 22 11 19.5653248629105 17.5 1.11801856359489 0.56221913395634 23 14 17.3470331013437 17.4583333333333 0.993624807714195 0.807054435084671 24 23 18.0024922443891 17.25 1.04362273880516 1.27760088368701 25 18 16.3111009677589 16.8333333333333 0.96897629511439 1.10354292059006 26 17 17.9099911530965 17 1.05352889135862 0.949190865293132 27 19 17.7936097012287 17.7083333333333 1.00481560665762 1.06779907613057 28 20 17.2825527844838 18.0416666666667 0.957924403758919 1.15723644819161 29 22 18.1450379134128 18.5 0.980812860184474 1.21245268844204 30 19 17.7010341532791 18.875 0.93780313394856 1.07338361337947 31 13 17.8627656237403 19.0416666666667 0.938088347855071 0.727770843207084 32 26 19.1478978458795 19.375 0.988278598497007 1.35785140537477 33 13 20.163301831158 19.875 1.01450575251109 0.644735674189597 34 14 22.59329180598 20.2083333333333 1.11801856359489 0.619652953638852 35 22 20.1623033898672 20.2916666666667 0.993624807714195 1.09114517198746 36 24 21.4377504262894 20.5416666666667 1.04362273880516 1.11952044980282 37 21 20.6714942957737 21.3333333333333 0.96897629511439 1.01589172507444 38 22 23.7482970927088 22.5416666666667 1.05352889135862 0.926382212337844 39 26 24.0318399258948 23.9166666666667 1.00481560665762 1.08189801863587 40 21 24.1476776780894 25.2083333333333 0.957924403758919 0.86964884490961 41 23 25.7872047823501 26.2916666666667 0.980812860184474 0.89191520345556 42 24 25.2425343554487 26.9166666666667 0.93780313394856 0.950776164629424 43 27 25.6019944935447 27.2916666666667 0.938088347855071 1.05460533579944 44 41 27.8365138576657 28.1666666666667 0.988278598497007 1.47288558508591 45 31 29.7165643339708 29.2916666666667 1.01450575251109 1.04318923451598 46 27 33.3542204805808 29.8333333333333 1.11801856359489 0.80949276016568 47 35 29.3947338948783 29.5833333333333 0.993624807714195 1.19068946584675 48 26 30.525965110051 29.25 1.04362273880516 0.851733922458006 49 28 27.8580684845387 28.75 0.96897629511439 1.00509480818959 50 36 28.8842504380821 27.4166666666667 1.05352889135862 1.24635396293809 51 39 25.9577365053219 25.8333333333333 1.00481560665762 1.50244224846046 52 21 24.3871587790291 25.4583333333333 0.957924403758919 0.861108921719007 53 17 24.2342510870581 24.7083333333333 0.980812860184474 0.701486501024106 54 22 21.7648477337228 23.2083333333333 0.93780313394856 1.01080422289897 55 17 21.1069878267391 22.5 0.938088347855071 0.80542046736123 56 19 21.2479898676856 21.5 0.988278598497007 0.894202233637901 57 15 20.0364886120941 19.75 1.01450575251109 0.748634168910511 58 34 20.9162639605877 18.7083333333333 1.11801856359489 1.62552930408919 59 10 18.4648610100221 18.5833333333333 0.993624807714195 0.541569199712488 60 15 19.3070206678955 18.5 1.04362273880516 0.776919456296155 61 22 18.0068094842091 18.5833333333333 0.96897629511439 1.22176002468914 62 18 19.5341815272744 18.5416666666667 1.05352889135862 0.92146169394749 63 15 19.1333638434389 19.0416666666667 1.00481560665762 0.783970875311804 64 20 18.5597853228290 19.375 0.957924403758919 1.07759867111175 65 15 19.4936555961664 19.875 0.980812860184474 0.769481123024963 66 22 19.6938658129197 21 0.93780313394856 1.11709911141810 67 19 19.9343773919203 21.25 0.938088347855071 0.953127335077995 68 16 20.8362071183119 21.0833333333333 0.988278598497007 0.767894075401967 69 30 21.5582472408608 21.25 1.01450575251109 1.39157880809248 70 27 24.0839832241065 21.5416666666667 1.11801856359489 1.12107701407858 71 29 21.8597457697123 22 0.993624807714195 1.32663939944722 72 23 23.1336373768478 22.1666666666667 1.04362273880516 0.994223244072222 73 20 21.1963564556273 21.875 0.96897629511439 0.94355839136166 74 16 22.9581504241899 21.7916666666667 1.05352889135862 0.69692025291121 75 21 21.3523316414745 21.25 1.00481560665762 0.983499149067632 76 21 19.3580556592948 20.2083333333333 0.957924403758919 1.08481969313467 77 25 18.5128427359820 18.875 0.980812860184474 1.35041389140143 78 16 16.6460056275869 17.75 0.93780313394856 0.961191552974347 79 18 15.8693278845483 16.9166666666667 0.938088347855071 1.1342635385035 80 15 16.1830620503885 16.375 0.988278598497007 0.926895043304856 81 18 15.9361945290284 15.7083333333333 1.01450575251109 1.12950428455252 82 14 16.3976055993917 14.6666666666667 1.11801856359489 0.853783188962623 83 10 13.4553359377964 13.5416666666667 0.993624807714195 0.743199578682368 84 15 13.0017999542810 12.4583333333333 1.04362273880516 1.15368641670733 85 8 11.5065935044834 11.875 0.96897629511439 0.695253551529643 86 15 12.0716852134842 11.4583333333333 1.05352889135862 1.24257713274737 87 6 11.5135121596186 11.4583333333333 1.00481560665762 0.521126821843628 88 11 11.2556117441673 11.75 0.957924403758919 0.97729028417316 89 8 11.8106215247214 12.0416666666667 0.980812860184474 0.67735639341713 90 7 11.8788396966817 12.6666666666667 0.93780313394856 0.589283143702612 91 13 12.8205407540193 13.6666666666667 0.938088347855071 1.01399779068792 92 10 14.2888614032692 14.4583333333333 0.988278598497007 0.699845825204243 93 23 15.302128433709 15.0833333333333 1.01450575251109 1.50305887835403 94 16 NA NA 1.11801856359489 NA 95 15 NA NA 0.993624807714195 NA 96 25 NA NA 1.04362273880516 NA 97 22 NA NA NA NA 98 20 NA NA NA NA 99 16 NA NA NA NA

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/1ub3w1274618014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/1ub3w1274618014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/25k2z1274618014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/25k2z1274618014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/35k2z1274618014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/35k2z1274618014.ps (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/4xu121274618014.png (open in new window) http://www.freestatistics.org/blog/date/2010/May/23/t12746182280d0tednsitflnci/4xu121274618014.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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