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

Predict values from the mark distribution

Usage

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

Arguments

sim_realization	a data frame containing a thinned or unthinned realization from simulate_sc.
raster_list	a list of raster objects.
scaled_rasters	'TRUE' or 'FALSE' indicating whether the rasters have been scaled.
mark_model	a model object (typically from train_mark_model).
xy_bounds	a vector of domain bounds (2 for x, 2 for y).
include_comp_inds	'TRUE' or 'FALSE' indicating whether to generate and use competition indices as covariates.
competition_radius	distance for competition radius if include_comp_inds is 'TRUE'.
correction	type of correction to apply ("none" or "toroidal").

Value

a vector of predicted mark values.

Examples

# Simulate a realization
generating_parameters <- c(2, 8, .02, 2.5, 3, 1, 2.5, .2)
M_n <- matrix(c(10, 14), ncol = 1)
generated_locs <- simulate_sc(
  t_min = 0,
  t_max = 1,
  sc_params = generating_parameters,
  anchor_point = M_n,
  xy_bounds = c(0, 25, 0, 25)
)

# Load the raster files
raster_paths <- list.files(system.file("extdata", package = "ldmppr"),
  pattern = "\\.tif$", full.names = TRUE
)
rasters <- lapply(raster_paths, terra::rast)

# Scale the rasters
scaled_raster_list <- scale_rasters(rasters)

# Load the example mark model
file_path <- system.file("extdata", "example_mark_model.rds", package = "ldmppr")
example_mark_model <- readRDS(file_path)

# Unbundle the model
mark_model <- bundle::unbundle(example_mark_model)

# Predict the mark values
predict_marks(
  sim_realization = generated_locs$thinned,
  raster_list = scaled_raster_list,
  scaled_rasters = TRUE,
  mark_model = mark_model,
  xy_bounds = c(0, 25, 0, 25),
  include_comp_inds = TRUE,
  competition_radius = 10,
  correction = "none"
)

ldmppr documentation built on April 4, 2025, 12:45 a.m.