Fit a standard univariate threshold exceedance model.
1 2 | uni_excess(x, uq = 0.95, threshold, r = 0, m.ksi = 0, s.ksi = 10,
nburn = 10000, nmcmc = 10000, window = 200, chain_init)
|
x |
Numeric vector of data. |
uq |
numeric, within (0, 1), the quantile of the complete data (includes replicates if available) that determines the threshold |
threshold |
numeric, values greater than threshold are considered excess. Defaults to the threshold given by uq, but if specified, uq is ignored. |
r |
Numeric, nonnegative integer, specifies the degree of separation between clusters in a declustering scheme. Defaults to r = 0 (no clustering). |
m.ksi, s.ksi |
numeric, prior mean and sd for ksi, ksi is normal |
nburn |
numeric, nonnegative integer, the first nburn samples are discared. Defaults to 80000. |
nmcmc |
numeric, positive integer, the number of samples that are kept _post_ burn-in. That is, a total of nburn + nmcmc iterations of the chain are run. Defaults to 40000. |
window |
numeric, during the burn-in, the covariance matrix of the proposal distribution is changed every window iterations to improve the acceptance rate. Also, the iteration number is displayed every window iterations. Defaults to 500. |
chain_init |
numeric vector of length 2, specifying the starting location of the Markov chain for (sigma, ksi). Defaults to c(1, 1e-6). |
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