R/plotting.r

In cmjt/lgcpSPDE: Fitting and Simulation for log-Gaussian Cox processes using INLA/SPDE approach

#' Plots fields eith spatio-temporal or spatial
#' @inheritParams find.fields
#' @export
plot.fields <- function(x = NULL, mesh = NULL, n.t = NULL, sd = FALSE, spatial.polygon = NULL,col = grey.colors(100,0.05,0.95),...){
    proj <- inla.mesh.projector(mesh,dims = dims)
    fields <- summary(x)$random.names[summary(x)$random.model=="SPDE2 model" | summary(x)$random.model=="Copy"]
    idx <- which(summary(x)$random.model=="SPDE2 model" | summary(x)$random.model=="Copy")
    n <- length(fields)
    par(...)
    if(!is.null(n.t)){
        rfs <- find.fields(x = x, mesh = mesh, n.t = n.t, sd = sd,spatial.polygon = spatial.polygon)
        t <- n.t
        for(i in idx[1]:n){
            for(j in 1:t){ image.plot(proj$x,proj$y,rfs[[i]][[j]],axes=FALSE,xlab="",ylab="", main = paste(fields[i], "time", j,  sep = " "), col = col)
            contour(proj$x,proj$y,rfs[[i]][[j]],add=TRUE)
            }
        }
    }else{
        rfs <- find.fields(x = x, mesh = mesh, sd = sd,spatial.polygon = spatial.polygon)
        for(i in 1:n){
           image.plot(proj$x,proj$y,rfs[[i]],axes=FALSE,xlab="",ylab="", main = fields[i], col = col)
           contour(proj$x,proj$y, rfs[[i]],add=TRUE)
        }
    }
}


#' plots a tidy mesh (internal function)
plot.mesh <- function(x,...){
    plot(x,main="",asp=1,draw.segment = FALSE,...)
    if (!is.null(x$segm$bnd)) 
                lines(x$segm$bnd, x$loc, lwd = 2,col = 1)
    if (!is.null(x$segm$int)) 
                lines(x$segm$int, x$loc, lwd = 2,col = 1)
}