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Mark model object

Source: R/mark_model_s3.R
ldmppr_mark_model.Rd

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 class 'ldmppr_mark_model'
print(x, ...)

# S3 method for class 'ldmppr_mark_model'
summary(object, ...)

# S3 method for class 'summary.ldmppr_mark_model'
print(x, ...)

# S3 method for class '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 class 'ldmppr_mark_model'
save_mark_model(object, path, ...)

load_mark_model(path)

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).

Value

print()

prints a brief summary.

predict()

returns numeric predictions for new data.

an object of class "ldmppr_mark_model".

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.

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.