R/summary.locstppm.R

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

#' Summary of a fitted local spatio-temporal Poisson process model
#'
#' The function summarises the main information on the distribution of the parameters
#'  of a fitted local spatio-temporal Poisson process model.
#'
#' @param object An object of class \code{locstppm}
#' @param ... additional unused argument
#'
#' @export
#'
#' @author Nicoletta D'Angelo
#'
#' @seealso
#' \link{locstppm}, \link{print.locstppm},
#' \link{plot.locstppm}
#'
#'
#'
#' @examples
#' 
#' 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)
#' 
#' summary(inh_local)
#'
#'
#'
#' @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.
#'
#'
#'
summary.locstppm <- function(object, ...){

  if(ncol(object$IntCoefs_local) == 1){
    cat("Homogeneous Poisson process \n")
    cat("with median Intensity: ")
    cat(as.numeric((as.numeric((median(object$IntCoefs_local[, 1])), 5))))
    cat("\n\n")
  } else {
    cat("Inhomogeneous Poisson process \n")
    cat("with Trend: ")
    print(object$formula)
    cat("\n")
  }
  cat("Summary of estimated coefficients \n")
  print(summary(object$IntCoefs_local))
}