# excessProb.nl: Posterior distribution the probability of joint threshold... In lbelzile/BMAmevt: Multivariate Extremes: Bayesian Estimation of the Spectral Measure

## Description

Posterior distribution the probability of joint threshold excess, in the NL model.

## Usage

 ```1 2 3``` ``` excessProb.nl(post.sample, from = NULL, to = NULL, thin = 100, thres = rep(100, 3), known.par = FALSE, true.par, displ = FALSE) ```

## Arguments

 `post.sample` The posterior sample, as returned by `posteriorMCMC` `known.par` logical. Is the true parameter known ? `true.par` The true parameter, only used if `known.par=TRUE` `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 positive vector of size three.

## Value

whole

The output of `posteriorMean` called with `FUN=excessProb.condit.nl`.

mean

The posterior mean of the excess probability

esterr

The standard deviation of the mean estimator

estsd

The standard deviation of the excess probability, in the posterior sample.

lowquant

The lower 0.1 quantile of the empirical posterior distribution of the excess probability

upquant

The upper 0.1 quantile of the empirical posterior distribution of the excess probability

true

`NULL` if `known.par=FALSE`, otherwise the excess probability in the true model.

lbelzile/BMAmevt documentation built on May 17, 2018, 12:16 p.m.