Predict values from the mark distribution — predict

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.