Package 'extRemes' - What are R and CRAN?

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26 distill.fevd par(mfrow=c(1,2)) plot(x, devd(x, 1, 1, -0.5), type="l", col="blue", lwd=1.5, ylab="GEV df") # Note upper bound at 1 - 1/(-0.5) = 3 in above plot. lines(x, devd(x, 1, 1, 0), col="lightblue", lwd=1.5) lines(x, devd(x, 1, 1, 0.5), col="darkblue", lwd=1.5) legend("topright", legend=c("(reverse) Weibull", "Gumbel", "Frechet"), col=c("blue", "lightblue", "darkblue"), bty="n", lty=1, lwd=1.5) plot(x, devd(x, 1, 1, -0.5, 1, type="GP"), type="l", col="blue", lwd=1.5, ylab="GP df") lines(x, devd(x, 1, 1, 0, 1, type="GP"), col="lightblue", lwd=1.5) lines(x, devd(x, 1, 1, 0.5, 1, type="GP"), col="darkblue", lwd=1.5) legend("topright", legend=c("Beta", "Exponential", "Pareto"), col=c("blue", "lightblue", "darkblue"), bty="n", lty=1, lwd=1.5) # Emphasize the tail differences more by using different scale parameters. par(mfrow=c(1,2)) plot(x, devd(x, 1, 0.5, -0.5), type="l", col="blue", lwd=1.5, ylab="GEV df") lines(x, devd(x, 1, 1, 0), col="lightblue", lwd=1.5) lines(x, devd(x, 1, 2, 0.5), col="darkblue", lwd=1.5) legend("topright", legend=c("(reverse) Weibull", "Gumbel", "Frechet"), col=c("blue", "lightblue", "darkblue"), bty="n", lty=1, lwd=1.5) plot(x, devd(x, 1, 0.5, -0.5, 1, type="GP"), type="l", col="blue", lwd=1.5, ylab="GP df") lines(x, devd(x, 1, 1, 0, 1, type="GP"), col="lightblue", lwd=1.5) lines(x, devd(x, 1, 2, 0.5, 1, type="GP"), col="darkblue", lwd=1.5) legend("topright", legend=c("Beta", "Exponential", "Pareto"), col=c("blue", "lightblue", "darkblue"), bty="n", lty=1, lwd=1.5) ## End(Not run) distill.fevd Distill Parameter Information Description Usage Distill parameter information (and possibly other pertinent inforamtion) from fevd objects. ## S3 method for class fevd distill(x, ...) ## S3 method for class fevd.bayesian distill(x, cov = TRUE, FUN = "mean", burn.in = 499, ...)
distill.fevd 27 ## S3 method for class fevd.lmoments distill(x, ...) ## S3 method for class fevd.mle distill(x, cov = TRUE, ...) Arguments x ... Not used. cov FUN burn.in list object returned by fevd. logical; should the parameter covariance be returned with the parameters (if TRUE, they are returned as a vector concatenated to the end of the returned value). character string naming a function to use to estimate the parameters from the MCMC sample. The function is applied to each column of the results component of the returned fevd object. The first burn.in values are thrown out before calculating anything from the MCMC sample. Details Obtaining just the basic information from the fits: distill: The distill method function works on fevd output to obtain only pertinent information and output it in a very user-friendly format (i.e., a single vector). Mostly, this simply means returning the parameter estimates, but for some methods, more information (e.g., the optimized negative log-likelihood value and parameter covariances) can also be returned. In the case of the parameter covariances (returned if cov = TRUE), if np is the number of parameters in the model, the covariance matrix can be obtained by peeling off the last np^2 values of the vector, call it v, and using v
Page 1 and 2: Package ‘extRemes’ January 24,
Page 3 and 4: extRemes-package 3 extRemes (= 2.0)
Page 5 and 6: atdf 5 Usage atdf(x, u, lag.max = N
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: devd 25 Pickands, J. (1975) Statist
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
Page 55 and 56: Flood 55 Format A data frame with 6
Page 57 and 58: fpois 57 Usage fpois(x, na.action =
Page 59 and 60: ftcanmax 59 ftcanmax Annual Maximum
Page 61 and 62: levd 61 Details This function is mo
Page 63 and 64: lr.test 63 When blocks is supplied,
Page 65 and 66: make.qcov 65 Author(s) Eric Gillela
Page 67 and 68: mrlplot 67 mrlplot Mean Residual Li
Page 69 and 70: parcov.fevd 69 References Coles, S.
Page 71 and 72: pextRemes 71 Details Source Peak st
Page 73 and 74: pextRemes 73 Value given. If not, t
Page 75 and 76: pextRemes 75 # Note that fit has a
Page 77 and 78:
Potomac 77 References Thompson, D.
Page 79 and 80:
qqnorm 79 gr, method, lower, upper,
Page 81 and 82:
qqplot 81 qqplot qq-plot Between Tw
Page 83 and 84:
eturn.level 83 return.level Return
Page 85 and 86:
eturn.level 85 Value shape and loca
Page 87 and 88:
evtrans.evd 87 revtrans.evd Reverse
Page 89 and 90:
levd 89 Details The extreme value d
Page 91 and 92:
shiftplot 91 Format A data frame wi
Page 93 and 94:
taildep 93 taildep Tail Dependence
Page 95 and 96:
taildep.test 95 taildep.test Tail D
Page 97 and 98:
threshrange.plot 97 taildep.test(PO
Page 99 and 100:
Tphap 99 Tphap Daily Maximum and Mi
Page 101 and 102:
trans 101 Value For GP distributed
Page 103 and 104:
INDEX 103 levd, 61 pextRemes, 71 th
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