fbvpot | R Documentation |
Fit the bivariate POT model to data.
fbvpot(x, threshold, dep.model = "logistic", na.action = na.fail, init = 0.5, lower = 0, upper = 1, cutoff, parnames, tform = "tformRankFrechet", ...)
x |
|
threshold |
A single number, |
dep.model |
character string giving the name of a bivariate dependence model function. The user may make their own function; such functions must take arguments |
na.action |
function determining how missing values should be handled. Default is to error out. |
init |
vector giving an initial guess for each parameter in the dependence model defined by |
lower, upper |
Arguments to the |
cutoff |
|
parnames |
optional character vector giving the names of the parameters of the dependence model. |
tform |
character string giving the name of the function to be used to transform the component variables to the same scale. Default transforms to the Frechet scale using the rank transformation. |
... |
optional arguments to the |
See Beirlant et al. (2004) for more about bivariate threshold exceedance modeling, as well as Coles and Tawn (1994).
A list object of class “fbvpot” with components:
orig.data |
original matrix of the data from the x argument. |
threshold, cutoff, init, lower, upper, parnames |
Same as values passed in through the input arguments. |
Frechet.transformed.data |
matrix with the Frechet-transformed data. |
radial, angular |
vectors giving the radial and angular components, respectively. |
sorting, polar |
model, LH |
function giving the dependence model used and its companion likelihood function, respectively. |
model.name |
character naming the dependence model used. |
fit |
the result of nlminb giving the optimized parameter value(s). |
call |
original call to this function. |
data.name |
character string giving the name of the data used in the x argument. |
Dan Cooley and Eric Gilleland
Beirlant, J., Y. Goegebeur, J. Segers, and J. Teugels (2004) Statistics of Extremes: Theory and Applications. Wiley, West Sussex, England, United Kingdom, 514 pp.
Coles, S. G. and J. A. Tawn (1994) Statistical methods for multivariate extremes: an application to structural design (with Discussion). Appl. Statist., 43, 1–48, doi: 10.2307/2986112.
logistic
, mixbeta
, bvpotbooter
data( "SantaAna" ) Z <- SantaAna[,3:4] mfit1 <- fevd( x = temp, data = Z, threshold = 36.75, type = "GP" ) mfit2 <- fevd( x = windspeeds, data = Z, threshold = 7.09875, type = "GP" ) fit2 <- fbvpot( x = Z, threshold = apply( Z, 2, quantile, probs = 0.95 ), tform = "tf", fit = list( mfit1, mfit2 ) ) fit2 plot( fit2 ) hist( fit2 )
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