ldmppr (version 1.1.0)

predict_marks: 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"
)

Value

a vector of predicted mark values.

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").

Examples

# Simulate a realization
generating_parameters <- c(2, 8, .02, 2.5, 3, 1, 2.5, .2)
M_n <- c(10, 14)
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$", full.names = TRUE
)
raster_paths <- raster_paths[!grepl("_med\\.tif$", raster_paths)]
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")
mark_model <- load_mark_model(file_path)

# 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"
)