excessProb.pb {BMAmevt} R Documentation

## Estimates the probability of joint excess (Frechet margins)

### Description

Double Monte-Carlo integration.

### Usage

excessProb.pb(
post.sample,
Nmin.intern = 100,
precision = 0.05,
from = NULL,
to = NULL,
thin = 100,
displ = FALSE,
thres = rep(500, 5),
known.par = FALSE,
true.par
)


### Arguments

 post.sample The posterior sample. Nmin.intern The minimum number of MC iteration in the internal loop (excess probability, conditional to a parameter). precision The desired precision for the internal MC estimate from Integer or NULL. If NULL, the default value is used. Otherwise, should be greater than post.sample$Nbin. Indicates the index where the averaging process should start. Default to post.sample$Nbin +1 to Integer or NULL. If NULL, the default value is used. Otherwise, must be lower than Nsim+1. Indicates where the averaging process should stop. Default to post.sample\$Nsim. thin Thinning interval. displ logical. Should a plot be produced ? thres A multivariate threshold known.par Logical true.par The true parameter from which the data are issued.

### Value

whole

A vector of estimated excess probabilities, one for each element of the thinned posterior sample.

mean

the estimated threshold excess probability: mean estimate.

esterr

The estimated standard deviation of the mean estimate (where the Monte-Carlo error is neglected)

estsd

The estimated standard deviation of the posterior sample (where the Monte-Carlo error is neglected)

lowquants

The three lower 0.1 quantiles of, respectively, the conditional mean estimates and of the upper and lower bounds of the Gaussian (centered) 80 % confidence intervals around the conditional estimates.

upquants

The three upper 0.9 quantiles

true.est

the mean estimate conditional to the true parameter: a vector of size three: the mean estimate , and the latter +/- the standard deviation of the estimate

[Package BMAmevt version 1.0.5 Index]