condmap {SpatialExtremes} | R Documentation |
Produces a conditional 2D map from a fitted max-stable process.
condmap(fitted, fix.coord, x, y, marg.cov = NULL, ..., ret.per1 = 100, ret.per2 = ret.per1, col = terrain.colors(length(x)), plot.contour = TRUE)
fitted |
An object of class maxstab . Most often, it will
be the output of the function fitmaxstab . |
fix.coord |
The spatial coordinates of the location from which the conditional quantile is computed. |
x,y |
Numeric vector defining the grid at which the levels are computed. |
marg.cov |
A matrix of the (currently only one) marginal
covariable value at points x[i], y[j]. See function
fitmaxstab . |
... |
Several arguments to be passed to the link{image}
and contour functions. |
ret.per1,ret.per2 |
Numerics giving the return period for which the quantile map is plotted. See details. |
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. |
The function solves the following equation:
Pr[Z(x_2) > z_2 | Z(x_1) > z_1] = 1 / T_2
where z_1 = -1 / log(1 - 1/T_1).
In other words, it computes, given that at location x_1 we exceed the level z_1, the levels which is expected to be exceeded in average every T_2 year.
A plot. Additionally, a list with the details for plotting the map is returned invisibly.
Mathieu Ribatet
map
, filled.contour
,
heatmap
, heat.colors
,
topo.colors
, terrain.colors
,
rainbow
## Not run: require(RandomFields) ##Define the coordinate of each location n.site <- 30 locations <- matrix(runif(2*n.site, 0, 10), ncol = 2) colnames(locations) <- c("lon", "lat") sigma <- matrix(c(4, 1, 1, 3), 2) sigma.inv <- solve(sigma) sqrtCinv <- t(chol(sigma.inv)) model <- list(list(model = "gauss", var = 1, aniso = sqrtCinv / 2)) ##Simulate a max-stable process - with unit Frechet margins ms0 <- MaxStableRF(locations[,1], locations[,2], grid=FALSE, model = model, maxstable = "Bool", n = 40) ms0 <- t(ms0) ms1 <- ms0 ##Now define the spatial model for the GEV parameters param.loc <- -10 - 4 * locations[,1] + locations[,2]^2 param.scale <- 5 + locations[,1] + locations[,2]^2 / 10 param.shape <- rep(.2, n.site) ##Transform the unit Frechet margins to GEV for (i in 1:n.site) ms1[,i] <- param.scale[i] * (ms1[,i]^param.shape[i] - 1) / param.shape[i] + param.loc[i] ##Define a model for the GEV margins to be fitted ##shape ~ 1 stands for the GEV shape parameter is constant ##over the region loc.form <- loc ~ lon + I(lat^2) scale.form <- scale ~ lon + I(lat^2) shape.form <- shape ~ lat + lon ## 1- Fit a max-stable process fitted <- fitmaxstab(ms1, locations, "gauss", loc.form, scale.form, shape.form, std.err.type = "none") condmap(fitted, c(1, 1), seq(0, 10, length = 25), seq(0,10, length = 25)) ## End(Not run)