Package 'extRemes' - What are R and CRAN?

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4 atdf taildep: Estimate chi and/or chibar; statistics that inform about tail dependence between two variables. atdf: Auto-tail dependence function and plot. Helps to inform about possible dependence in the extremes of a process. Note that a process that is highly correlated may or may not be dependent in the extremes. decluster: Decluster threshold exceedance in a data set to yield a new related process that is more closely independent in the extremes. Includes two methods for declustering both of which are based on runs declustering. extremalindex: Estimate the extremal index, a measure of dependence in the extremes. Two methods are available, one based on runs declustering and the other is the intervals estiamte of Ferro and Segers (2003). devd, pevd, qevd, revd: Functions for finding the density, cumulative probability distribution (cdf), quantiles and make random draws from EVDs. pextRemes, rextRemes, return.level: Functions for finding the cdf, make random draws from, and find return levels for fitted EVDs. To see how to cite extRemes in publications or elsewhere, use citation("extRemes"). Acknowledgements Funding for extRemes was provided by the Weather and Climate Impacts Assessment Science (WCIAS, http://www.assessment.ucar.edu/) Program at the National Center for Atmospheric Research (NCAR) in Boulder, Colorado. WCIAS is funded by the National Science Foundation (NSF). The National Center for Atmospheric Research (NCAR) is operated by the nonprofit University Corporation for Atmospheric Research (UCAR) under the sponsorship of the NSF. Any opinions, findings, conclusions, or recommendations expressed in this publication/software package are those of the author(s) and do not necessarily reflect the views of the NSF. References Coles, S. (2001) An introduction to statistical modeling of extreme values, London, U.K.: Springer- Verlag, 208 pp. Ferro, C. A. T. and Segers, J. (2003). Inference for clusters of extreme values. Journal of the Royal Statistical Society B, 65, 545–556. Gilleland, E. and Katz, R. W. (2011). New software to analyze how extremes change over time. Eos, 11 January, 92, (2), 13–14. atdf Auto-Tail Dependence Function Description Computes (and by default plots) estimates of the auto-tail dependence function(s) (atdf) based on either chi (rho) or chibar (rhobar), or both.
atdf 5 Usage atdf(x, u, lag.max = NULL, type = c("all", "rho", "rhobar"), plot = TRUE, na.action = na.fail, ...) ## S3 method for class atdf plot(x, type = NULL, ...) Arguments x u lag.max type plot Details na.action For atdf: a univariate time series object or a numeric vector. For the plot method function, a list object of class “atdf”. numeric between 0 and 1 (non-inclusive) determining the level F^(-1)(u) over which to compute the atdf. Typically, this should be close to 1, but low enough to incorporate enough data. The maximum lag for which to compute the atdf. Default is 10*log10(n), where n is the length of the data. Will be automatically limited to one less than the total number of observations in the series. character string stating which type of atdf to calculate/plot (rho, rhobar or both). If NULL the plot method function will take the type to be whatever was passed to the call to atdf. If “all”, then a 2 by 1 panel of two plots are graphed. logical, should the plot be made or not? If TRUE, output is returned invisibly. If FALSE, output is returned normally. function to be called to handle missing values. ... Further arguments to be passed to the plot method function or to plot. Note that if main, xlab or ylab are used with type “all”, then the labels/title will be applied to both plots, which is probably not desirable. The tail dependence functions are those described in, e.g., Reiss and Thomas (2007) Eq (2.60) for "chi" and Eq (13.25) "chibar", and estimated by Eq (2.62) and Eq (13.28), resp. See also, Sibuya (1960) and Coles (2001) sec. 8.4, as well as other texts on EVT such as Beirlant et al. (2004) sec. 9.4.1 and 10.3.4 and de Haan and Ferreira (2006). Specifically, for two series X and Y with associated df’s F and G, chi, a function of u, is defined as chi(u) = Pr[Y > G^(-1)(u) | X > F^(-1)(u)] = Pr[V > u | U > u], where (U,V) = (F(X),G(Y))–i.e., the copula. Define chi = limit as u goes to 1 of chi(u). The coefficient of tail dependence, chibar(u) was introduced by Coles et al. (1999), and is given by chibar(u) = 2*log(Pr[U > u])/log(Pr[U > u, V > u]) - 1. Define chibar = limit as u goes to 1 of chibar(u). The auto-tail dependence function using chi(u) and/or chibar(u) employs X against itself at different lags. The associated estimators for the auto-tail dependence functions employed by these functions are based on the above two coefficients of tail dependence, and are given by Reiss and Thomas (2007) Eq (2.65) and (13.28) for a lag h as
Page 1 and 2: Package ‘extRemes’ January 24,
Page 3: extRemes-package 3 extRemes (= 2.0)
Page 7 and 8: lockmaxxer 7 Examples z
Page 9 and 10: ci.fevd 9 Examples data(Fort) bmFor
Page 11 and 12: ci.fevd 11 1. Simulate a sample of
Page 13 and 14: ci.rl.ns.fevd.bayesian 13 ## End(No
Page 15 and 16: datagrabber.declustered 15 Usage da
Page 17 and 18: decluster 17 decluster Decluster Da
Page 19 and 20: decluster 19 data.name character st
Page 21 and 22: Denmint 21 Denmint Denver Minimum T
Page 23 and 24: devd 23 Usage devd(x, loc = 0, scal
Page 25 and 26: devd 25 Pickands, J. (1975) Statist
Page 27 and 28: distill.fevd 27 ## S3 method for cl
Page 29 and 30: erlevd 29 References Gilleland, E.
Page 31 and 32: extremalindex 31 Value There are ma
Page 33 and 34: fevd 33 fevd Fit An Extreme Value D
Page 35 and 36: fevd 35 method initial span units t
Page 37 and 38: fevd 37 iter weights blocks FUN Det
Page 39 and 40: fevd 39 For Bayesian estimation, it
Page 41 and 42: fevd 41 facilitate a more meaningfu
Page 43 and 44: fevd 43 proposalFun, proposalParams
Page 45 and 46: fevd 45 # Port Jervis, New York win
Page 47 and 48: fevd 47 ci(fit, type="parameter") p
Page 49 and 50: fevd 49 set.seed(0) FortSub 0.395,
Page 51 and 52: fevd 51 plot(fitB, "trace") # Above
Page 53 and 54: findpars 53 ## S3 method for class
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Flood 55 Format A data frame with 6
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fpois 57 Usage fpois(x, na.action =
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ftcanmax 59 ftcanmax Annual Maximum
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levd 61 Details This function is mo
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lr.test 63 When blocks is supplied,
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make.qcov 65 Author(s) Eric Gillela
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mrlplot 67 mrlplot Mean Residual Li
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parcov.fevd 69 References Coles, S.
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pextRemes 71 Details Source Peak st
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pextRemes 73 Value given. If not, t
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pextRemes 75 # Note that fit has a
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Potomac 77 References Thompson, D.
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qqnorm 79 gr, method, lower, upper,
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qqplot 81 qqplot qq-plot Between Tw
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eturn.level 83 return.level Return
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eturn.level 85 Value shape and loca
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evtrans.evd 87 revtrans.evd Reverse
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levd 89 Details The extreme value d
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shiftplot 91 Format A data frame wi
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taildep 93 taildep Tail Dependence
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taildep.test 95 taildep.test Tail D
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threshrange.plot 97 taildep.test(PO
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Tphap 99 Tphap Daily Maximum and Mi
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trans 101 Value For GP distributed
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INDEX 103 levd, 61 pextRemes, 71 th
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