mc.quant: Compute GEV Quantiles from Markov Chains

In evdbayes: Bayesian Analysis in Extreme Value Theory

Compute GEV Quantiles from Markov Chains

Description

Compute gev quantiles from samples stored within a Markov chain, corresponding to specified probabilities in the upper tail.

Usage

mc.quant(post, p, lh = c("gev", "gpd"))

Arguments

post	A Markov chain generated using posterior, containing samples of gev parameters.
p	A numeric vector of upper tail probabilities.
lh	Specify “gev” or “gpd” likelihood.

Details

See the user's guide.

Value

A matrix with n rows and m columns, where n is the number of samples stored within the chain, and m is the length of the vector p. If m = 1 the dimension is dropped (i.e. a vector of length n is returned). The (i,j)th entry contains the gev quantile coresponding to the upper tail probability p[j], evaluated at the parameters within sample i.

If a linear trend on the location has been implemented, the quantiles correspond to the distribution obtained when the trend parameter is zero.

See Also

posterior

evdbayes documentation built on March 7, 2023, 5:34 p.m.