extrapol.return.level: Spatial extrapolation of a return level.

In hkevp: Spatial Extreme Value Analysis with the Hierarchical Model of Reich and Shaby (2012)

Spatial extrapolation of a return level.

Description

Predictive distribution of a T-years return level at ungauged positions (targets), given the output from the MCMC procedures hkevp.fit or latent.fit.

Usage

extrapol.return.level(period, fit, targets, targets.covariates)

Arguments

period	An integer indicating the wished return period T.
fit	Output from the hkevp.fit procedure.
targets	A matrix of real values giving the spatial coordinates of the ungauged positions. Each row corresponds to an ungauged position.
targets.covariates	A matrix of real values giving the spatial covariates of the ungauged positions. Must match with the covariates used in hkevp.fit or latent.fit.

Details

Spatial extrapolation of the return level at target positions (s^*_1, ..., s^*_k) is a two-step procedure:

• Estimation of the predictive distribution for GEV parameters at (s^*_1, ..., s^*_k), by using {extrapol.gev}.

• Computation of the associated return level for each state of the predictive distribution.

Value

A matrix of predictive sample. Each column corresponds to a target position and each row to a predictive draw.

Author(s)

Quentin Sebille

See Also

extrapol.gev

Examples

# Simulation of HKEVP:
sites <- as.matrix(expand.grid(1:3,1:3))
knots <- sites
loc <- sites[,1]*10
scale <- 1
shape <- .2
alpha <- .4
tau <- 1
ysim <- hkevp.rand(10, sites, knots, loc, scale, shape, alpha, tau)

# HKEVP fit:
fit <- hkevp.fit(ysim, sites, niter = 1000)

## Extrapolation of the 100-years return level (may need more iterations and burn-in/nthin):
targets <- as.matrix(expand.grid(1.5:2.5,1.5:2.5))
pred.sample <- extrapol.return.level(100, fit, targets)
pred.mean <- apply(pred.sample, 2, mean)
pred.sd <- apply(pred.sample, 2, sd)
true <- return.level(100, targets[,1]*10, scale, shape)
# cbind(true, pred.mean, pred.sd)

hkevp documentation built on April 18, 2023, 5:11 p.m.