predict_marks: Predict values from the mark distribution

In ldmppr: Estimate and Simulate from Location Dependent Marked Point Processes

Predict values from the mark distribution

Description

Prefer using the S3 method predict() on an ldmppr_mark_model: predict(mark_model, sim_realization = ..., xy_bounds = ...). This wrapper is retained for backward compatibility and is deprecated.

Usage

predict_marks(
  sim_realization,
  raster_list = NULL,
  scaled_rasters = FALSE,
  mark_model = NULL,
  xy_bounds = NULL,
  include_comp_inds = FALSE,
  competition_radius = 15,
  edge_correction = "none",
  seed = NULL
)

Arguments

sim_realization	a data.frame containing a thinned or unthinned realization from simulate_mpp (or simulate_sc). Must contain x, y, and time.
raster_list	list of raster objects used for mark prediction.
scaled_rasters	TRUE or FALSE indicating whether rasters are already scaled.
mark_model	a mark model object. May be an ldmppr_mark_model, model_fit, or workflow.
xy_bounds	vector of domain bounds as c(a_x, b_x, a_y, b_y).
include_comp_inds	TRUE or FALSE indicating whether to generate and use competition indices as covariates.
competition_radius	positive numeric distance used when include_comp_inds = TRUE.
edge_correction	type of edge correction to apply ("none" or "toroidal").
seed	optional nonnegative integer seed for reproducibility.

Value

a vector of predicted mark values.

ldmppr documentation built on March 3, 2026, 9:06 a.m.