ldmppr_mark_model: Mark model object

Description

ldmppr_mark_model objects store a fitted mark model and preprocessing information used to predict marks at new locations and times. These objects are typically returned by train_mark_model and can be saved/loaded with save_mark_model and load_mark_model.

Usage

ldmppr_mark_model(
  engine,
  fit_engine = NULL,
  xgb_raw = NULL,
  recipe = NULL,
  outcome = "size",
  feature_names = NULL,
  rasters = NULL,
  info = list()
)
# S3 method for ldmppr_mark_model
print(x, ...)
# S3 method for ldmppr_mark_model
summary(object, ...)
# S3 method for summary.ldmppr_mark_model
print(x, ...)
# S3 method for ldmppr_mark_model
predict(
  object,
  new_data = NULL,
  sim_realization = NULL,
  raster_list = NULL,
  scaled_rasters = FALSE,
  xy_bounds = NULL,
  include_comp_inds = FALSE,
  competition_radius = 15,
  edge_correction = "none",
  seed = NULL,
  ...
)
save_mark_model(object, path, ...)
# S3 method for ldmppr_mark_model
save_mark_model(object, path, ...)
load_mark_model(path)

Value

print(): prints a brief summary.

predict()

returns numeric predictions for new data.

an object of class "ldmppr_mark_model".

Arguments

engine: character string (currently "xgboost" and "ranger").
fit_engine: fitted engine object (e.g. xgb.Booster or a ranger fit).
xgb_raw: raw xgboost payload (e.g. UBJ) used for rehydration.
recipe: a prepped recipes object used for preprocessing new data.
outcome: outcome column name (default "size").
feature_names: (optional) vector of predictor names required at prediction time.
rasters: (optional) list of rasters used for prediction (e.g. for spatial covariates).
info: (optional) list of metadata.
x: an object of class summary.ldmppr_mark_model.
...: passed to methods.
object: a ldmppr_mark_model object.
new_data: a data frame of predictors (and possibly outcome columns). Ignored when sim_realization is supplied.
sim_realization: optional simulation realization containing x, y, and time. When supplied, predictors are built from rasters and optional competition indices.
raster_list: optional list of rasters used when sim_realization is supplied. If omitted, uses rasters stored in object when available.
scaled_rasters: TRUE or FALSE; whether supplied rasters are pre-scaled.
xy_bounds: domain bounds c(a_x, b_x, a_y, b_y) used for competition indices.
include_comp_inds: TRUE or FALSE; include competition-index features.
competition_radius: positive numeric distance used when include_comp_inds = TRUE.
edge_correction: edge correction for competition indices ("none" or "toroidal").
seed: optional nonnegative integer seed.
path: path to an .rds created by save_mark_model (or legacy objects).

Methods (by generic)

print(ldmppr_mark_model): Print a brief summary of the mark model.
summary(ldmppr_mark_model): Summarize a mark model.
predict(ldmppr_mark_model): Predict marks for new data.
save_mark_model(ldmppr_mark_model): Save method for ldmppr_mark_model.

Functions

ldmppr_mark_model(): Create a mark model container.
print(summary.ldmppr_mark_model): Print a summary produced by summary.ldmppr_mark_model.
save_mark_model(): Save a mark model to disk.
load_mark_model(): Load a saved mark model from disk.

Details

The model may be backed by different engines (currently "xgboost" and "ranger"). For "xgboost", the object can store a serialized booster payload to make saving/loading robust across R sessions.