R: Two dimensional map from a Bayesian hierarchical model

map.latent {SpatialExtremes} | R Documentation

## Two dimensional map from a Bayesian hierarchical model

### Description

This function plots 2D maps from a Markov chain.

### Usage

    map.latent(fitted, x, y, covariates = NULL, param = "quant", ret.per =
    100, col = terrain.colors(64), plot.contour = TRUE, fun = mean, level =
    0.95, show.data = TRUE, control = list(nlines = 500), ...)

### Arguments

`fitted` | An object of class "latent". Typically this will be the output of `latent`.
`x,y` | Numeric vector specifying the coordinates of the grid points.
`covariates` | An array specifying the covariates at each grid point defined by `x` and `y`. If `NULL`, no covariate is needed.
`param` | A character string. Must be one of "loc", "scale", "shape" or "quant" for a map of the location, scale, shape parameters or for a map of a specified quantile.
`ret.per` | A numeric giving the return period for which the quantile map is plotted. It is only required if `param = "quant"`.
`col` | A list of colors such as that generated by 'rainbow', 'heat.colors', 'topo.colors', 'terrain.colors' or similar functions.
`plot.contour` | Logical. If `TRUE` (default), contour lines are added to the plot.
`fun` | A character string specifying the function to be used to get posterior point estimates. The default is to take posterior means.
`level` | A numeric specifying the significance level for the pointwise credible intervals.
`show.data` | Logical. Should the locations where have observed the process have to be plotted?
`control` | A list with named components specifying options to be passed to `rgp`. Typically one might want specify `nlines` to reduce the computational demand.
`...` | Several arguments to be passed to the `image` function.

### Value

A plot and a invisible list containing all the data required to do the plot.

### Author(s)

Mathieu Ribatet

### See Also

`condrgp`, `map`

### Examples

    ## Not run:
    ## Generate realizations from the model
    n.site <- 30
    n.obs <- 50
    coord <- cbind(lon = runif(n.site, -10, 10), lat = runif(n.site, -10 , 10))

    gp.loc <- rgp(1, coord, "powexp", sill = 4, range = 20, smooth = 1)
    gp.scale <- rgp(1, coord, "powexp", sill = 0.4, range = 5, smooth = 1)
    gp.shape <- rgp(1, coord, "powexp", sill = 0.01, range = 10, smooth = 1)

    locs <- 26 + 0.5 * coord[,"lon"] + gp.loc
    scales <- 10 + 0.2 * coord[,"lat"] + gp.scale
    shapes <- 0.15 + gp.shape

    data <- matrix(NA, n.obs, n.site)
    for (i in 1:n.site)
      data[,i] <- rgev(n.obs, locs[i], scales[i], shapes[i])

    loc.form <- y ~ lon
    scale.form <- y ~ lat
    shape.form <- y ~ 1

    hyper <- list()
    hyper$sills <- list(loc = c(1,8), scale = c(1,1), shape = c(1,0.02))
    hyper$ranges <- list(loc = c(2,20), scale = c(1,5), shape = c(1, 10))
    hyper$smooths <- list(loc = c(1,1/3), scale = c(1,1/3), shape = c(1, 1/3))
    hyper$betaMeans <- list(loc = rep(0, 2), scale = c(9, 0), shape = 0)
    hyper$betaIcov <- list(loc = solve(diag(c(400, 100))),
                           scale = solve(diag(c(400, 100))),
                           shape = solve(diag(c(10), 1, 1)))

    ## We will use an exponential covariance function so the jump sizes for
    ## the shape parameter of the covariance function are null.
    prop <- list(gev = c(2.5, 1.5, 0.2), ranges = c(0.7, 0.75, 0.9), smooths = c(0,0,0))
    start <- list(sills = c(4, .36, 0.009), ranges = c(24, 17, 16), smooths
                  = c(1, 1, 1),  beta = list(loc = c(26, 0.5), scale = c(10, 0.2),
                                   shape = c(0.15)))

    ## Generate a Markov chain
    mc <- latent(data, coord, loc.form = loc.form, scale.form = scale.form,
                 shape.form = shape.form, hyper = hyper, prop = prop, start = start,
                 n = 100)

    x.grid <- y.grid <- seq(-10, 10, length = 50)
    map.latent(mc, x.grid, y.grid, param = "shape")

    ## End(Not run)

[Package SpatialExtremes version 2.0-5 Index]