Train a flexible model for the mark distribution

Trains a predictive model for the mark distribution of a spatio-temporal process. data may be either (1) a data.frame containing columns x, y, size and time, (2) a data.frame containing x, y, size (time will be derived via delta), or (3) a ldmppr_fit object returned by estimate_process_parameters. Allows the user to incorporate location specific information and competition indices as covariates in the mark model.

Usage

train_mark_model(
  data,
  raster_list = NULL,
  scaled_rasters = FALSE,
  model_type = "xgboost",
  xy_bounds = NULL,
  delta = NULL,
  save_model = FALSE,
  save_path = NULL,
  parallel = TRUE,
  n_cores = NULL,
  include_comp_inds = FALSE,
  competition_radius = 15,
  edge_correction = "none",
  selection_metric = "rmse",
  cv_folds = 5,
  tuning_grid_size = 200,
  verbose = TRUE
)

Arguments

data: a data.frame or a ldmppr_fit object. See Description.
raster_list: a list of raster objects.
scaled_rasters: TRUE or FALSE indicating whether the rasters have been scaled.
model_type: the machine learning model type ("xgboost" or "random_forest").
xy_bounds: a vector of domain bounds (2 for x, 2 for y). If data is an ldmppr_fit and xy_bounds is NULL, defaults to c(0, b_x, 0, b_y) derived from fit.
delta: (optional) numeric scalar used only when data contains (x,y,size) but not time. If data is an ldmppr_fit and time is missing, the function will infer the delta value from the fit.
save_model: TRUE or FALSE indicating whether to save the generated model.
save_path: path for saving the generated model.
parallel: TRUE or FALSE indicating whether to use parallelization in model training.
n_cores: number of cores to use in parallel model training (if parallel is TRUE).
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.
edge_correction: type of edge correction to apply ("none", "toroidal", or "truncation").
selection_metric: metric to use for identifying the optimal model ("rmse", "mae", or "rsq").
cv_folds: number of cross-validation folds to use in model training. If cv_folds <= 1, tuning is skipped and the model is fit once with default hyperparameters.
tuning_grid_size: size of the tuning grid for hyperparameter tuning.
verbose: TRUE or FALSE indicating whether to show progress of model training.

Value

an object of class "ldmppr_mark_model" containing the trained mark model.

Examples

# Load the small example data
data(small_example_data)

# Load example raster data
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)


# Train the model
mark_model <- train_mark_model(
  data = small_example_data,
  raster_list = scaled_raster_list,
  scaled_rasters = TRUE,
  model_type = "xgboost",
  xy_bounds = c(0, 25, 0, 25),
  delta = 1,
  parallel = FALSE,
  include_comp_inds = FALSE,
  competition_radius = 10,
  edge_correction = "none",
  selection_metric = "rmse",
  cv_folds = 3,
  tuning_grid_size = 2,
  verbose = TRUE
)
#> Processing data...
#> Training XGBoost model...
#> Training complete!

print(mark_model)
#> <ldmppr_mark_model>
#>   engine: xgboost
#>   has fit_engine: TRUE
#>   has xgb_raw: FALSE
#>   n_features: 7