`ldmppr_mark_model` objects store a fitted mark model and preprocessing information used to predict marks at new locations and times.
ldmppr_mark_model(
engine,
fit_engine = NULL,
xgb_raw = NULL,
recipe = NULL,
outcome = "size",
feature_names = NULL,
info = list()
)# S3 method for ldmppr_mark_model
print(x, ...)
# S3 method for ldmppr_mark_model
predict(object, new_data, ...)
save_mark_model(object, path, ...)
# S3 method for ldmppr_mark_model
save_mark_model(object, path, ...)
load_mark_model(path)
* `print()` prints a brief summary. * `predict()` returns numeric predictions for new data.
an object of class `"ldmppr_mark_model"`.
Character scalar. One of `"xgboost"` or `"ranger"`.
Fitted engine object (e.g. `xgb.Booster` or a ranger fit).
Raw xgboost payload (e.g. UBJ) used for rehydration.
A prepped recipes object used for preprocessing new data.
Outcome column name (default `"size"`).
Optional vector of predictor names required at prediction time.
Optional list of metadata.
a `ldmppr_mark_model` object.
passed to methods.
a `ldmppr_mark_model` object.
a data frame of predictors (and possibly outcome columns).
path to an `.rds` created by [save_mark_model()] (or legacy objects).
print(ldmppr_mark_model): Print a brief summary of the mark model.
predict(ldmppr_mark_model): Predict marks for new data.
save_mark_model(ldmppr_mark_model): Save method for `ldmppr_mark_model`.
ldmppr_mark_model(): Create a mark model container.
save_mark_model(): Save a mark model to disk.
load_mark_model(): Load a saved mark model from disk.
These objects are typically returned by [train_mark_model()] and can be saved/loaded with [save_mark_model()] and [load_mark_model()].
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.