R/localsummary.R

In stopp: Spatio-Temporal Point Pattern Methods, Model Fitting, Diagnostics, Simulation, Local Tests

#' Summary plots of the fitted coefficient of a local spatio-temporal Poisson process or a local LGCP model
#'
#' The function breaks up the contribution of the local estimates
#' to the fitted intensity, by plotting the overall intensity and the
#' density kernel smoothing of some artificial intensities, obtained by
#' imputing the quartiles of the local parameters' distributions.
#'
#' @param x An object of class \code{locstppm} or \code{stlgcppm}
#' @param scaler Optional. Controls the value for a scalar representation of the
#'  spatial scale of the data.
#'  Either a character string, \code{"silverman"} (default), \code{"IQR"},
#'   \code{"sd"}, or \code{"var"};
#'  or positive numeric value(s). See \link[sparr]{OS}.
#' @param do.points Add points to plot
#' @param print.bw It prints the estimated oversmoothing (\link[sparr]{OS}) bandwidth selector
#' @param zap Noise threshold factor (default to 0.00001). A numerical value greater than or equal to 1.
#'  If the range of pixel values is less than \code{zap * .Machine$double.eps},
#'   the image will be treated as constant. This avoids displaying images which
#'   should be constant but contain small numerical errors.
#' @param par Default to \code{TRUE}.
#'
#' @export
#'
#' @author Nicoletta D'Angelo and Giada Adelfio
#'
#' @seealso
#' \link{locstppm}, \link{stlgcppm}
#'
#'
#' @examples
#' \donttest{
#' # Local spatio-temporal Poisson process model
#' 
#' set.seed(2)
#' inh <- rstpp(lambda = function(x, y, t, a) {exp(a[1] + a[2]*x)}, 
#'              par = c(0.005, 5))
#' inh_local <- locstppm(inh, formula = ~ x)
#' 
#' localsummary(inh_local)
#' 
#' # Local LGCP
#' 
#' catsub <- stp(greececatalog$df[1:200, ])
#' 
#' lgcp_loc <- stlgcppm(catsub, formula = ~ x, first = "local")
#'
#' localsummary(lgcp_loc)
#' }
#'
#'
#' @references
#' D'Angelo, N., Adelfio, G., and Mateu, J. (2023). Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 180, 107679.
#'
#' Davies, T.M. and Hazelton, M.L. (2010). Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.
#'
#' Terrell, G.R. (1990). The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477.
#'
localsummary <- function(x, scaler = c("silverman", "IQR", "sd", "var"),
                                  do.points = TRUE,
                                  print.bw = FALSE,
                                  zap = 0.00001,
                                  par = TRUE){
  UseMethod("localsummary")
}