Description Usage Arguments Details Value Author(s) References See Also Examples

Metropolis-within-Gibbs algorithm that returns posterior distribution (as Markov chains) for the marginal GEV parameters of an observed spatial process.
This function is close to `hkevp.fit`

but with less parameters since conditional independence is assumed and only the margins are estimated.
In `SpatialExtremes`

library, a similar function can be found under the name `latent`

.

1 2 | ```
latent.fit(y, sites, niter, nburn, nthin, quiet, trace, gev.vary,
spatial.covariates, log.scale, correlation, mcmc.init, mcmc.prior, mcmc.jumps)
``` |

`y` |
A matrix of observed block maxima. Each column corresponds to a site position. |

`sites` |
The coordinates of the sites where the data are observed. Each row corresponds to a site position. |

`niter` |
The number of MCMC iterations. |

`nburn` |
The number of first MCMC iterations that are discarded. Zero by default. |

`nthin` |
The size of the MCMC thinning. One by default (i.e. no thinning). |

`quiet` |
A logical indicating if the progression of the routine should be displayed. TRUE by default. |

`trace` |
If |

`gev.vary` |
A logical vector of size three indicating if the GEV parameters (respectively the location, the scale and the shape) are spatially-varying. If not (by default for the shape), the parameter is the same at each position. |

`spatial.covariates` |
A numerical matrix of spatial covariates. Each row corresponds to a site position. See details. |

`log.scale` |
A logical value indicating if the GEV scale parameter |

`correlation` |
A character string indicating the form of the correlation function associated to the latent Gaussian processes that describes the marginal parameters. Must be one of |

`mcmc.init` |
A named list indicating the initial states of the chains. See details. |

`mcmc.prior` |
A named list indicating the hyperparameters of the prior distributions. See details. |

`mcmc.jumps` |
A named list indicating the amplitude of the jumps to propose the MCMC candidates. See details. |

Details of the MCMC procedure are presented in Davison et al. (2012). This function follows the indications and the choices of the authors, with the exception of several small changes:

The scale parameter

*σ*can be modelled like the two other marginal parameters as in Davison et al. (2012) or by its logarithm as in Reich and Shaby (2012). For this, use the argument`log.scale`

, set to FALSE by default.The Inverse-Gamma prior distributions defined for the bandwith parameter

*τ*and for the ranges*λ*of the latent processes are replaced by a Beta distribution over the interval*[0,2D_{max}]*, where*D_{max}*stands for the maximum distance between two sites.

If the the parameters are assumed spatially-varying, the user can provide spatial covariates to fit the mean of the latent Gaussian processes. Recall for instance for the GEV location parameter that:

*μ(s) = β_{0,μ} + β_{1,μ} c_1(s) + ... + β_{p,μ} c_p(s) ~.*

The given matrix `spatial.covariates`

that represents the *c_i(s)* elements should have the first column filled with ones to account for the intercept *β_0*.

The arguments `mcmc.init`

, `mcmc.prior`

and `mcmc.jumps`

are named list that have default values. The user can make point changes in these arguments, by setting `mcmc.init = list(loc = .5)`

for instance, but must respect the constraints of each element:

`mcmc.init`

: all elements are of length one. The possible elements are:`loc`

,`scale`

and`shape`

(GEV parameters).`range`

and`sill`

of the correlation functions.

mcmc.prior: the possible elements are:

`constant.gev`

: a*2 \times 3*matrix of normal parameters for spatially-constant*μ*,*σ*and*ξ*. The first row are the means, the second are the standard deviations.`beta.sd`

: the normal sd prior of all*β*parameters (a single value).`range`

: the two Beta parameters.`sill`

: the two Inverse-Gamma parameters.

mcmc.jumps: the possible elements are

`gev`

and`range`

, vectors of length 3 (for each GEV parameter).

A named list with following elements (less elements if `fit.margins`

is `FALSE`

):

`GEV`

: the Markov chains associated to the GEV parameters. The dimensions of the array correspond respectively to the sites positions, the three GEV parameters and the states of the Markov chains.`llik`

: the log-likelihood of the model for each step of the algorithm.`time`

: time (in sec) spent for the fit.`spatial`

: a named list with four elements linked to the GEV spatially-varying parameters:`vary`

: the argument`gev.vary`

.`beta`

: the*β*parameters for each GEV parameter. The dimensions correspond respectively to the steps of the Markov chains, the*p*spatial covariates and the GEV parameters`sills`

: the Markov chains associated to the sills in the correlation functions of the latent Gaussian processes.`ranges`

: the Markov chains associated to the ranges in the correlation functions of the latent Gaussian processes.

`data`

: the data fitted.`sites`

: the sites where the data are observed.`spatial.covariates`

: the spatial covariates.`correlation`

: the type of correlation function for the marginal latent processes.`nstep`

: the number of steps at the end of the routine after burn-in and thinning.`log.scale`

: a boolean indicating if the scale parameter has been modelled via its logarithm.`fit.type`

: "latent" character string to specify the type of fit.

Quentin Sebille

Reich, B. J., & Shaby, B. A. (2012). A hierarchical max-stable spatial model for extreme precipitation. The annals of applied statistics, 6(4), 1430. <DOI:10.1214/12-AOAS591>

Davison, A. C., Padoan, S. A., & Ribatet, M. (2012). Statistical modeling of spatial extremes. Statistical Science, 27(2), 161-186. <DOI:10.1214/11-STS376>

hkevp.fit

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
# Simulation of HKEVP:
sites <- as.matrix(expand.grid(1:4,1:4))
loc <- sites[,1]*10
scale <- 3
shape <- .2
alpha <- 1
tau <- 2
ysim <- hkevp.rand(15, sites, sites, loc, scale, shape, alpha, tau)
# Latent Variable Model fit:
set.seed(1)
fit <- latent.fit(ysim, sites, niter = 10000, nburn = 5000, nthin = 5)
mcmc.plot(fit, TRUE)
par(mfrow = c(2,2))
apply(fit$GEV[,1,], 1, acf)
``` |

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