ldmppr_mark_model: Mark model object
In ldmppr: Estimate and Simulate from Location Dependent Marked Point Processes

ldmppr_mark_model

R Documentation

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 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).

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.

Value

print(): prints a brief summary.
predict(): returns numeric predictions for new data.

an object of class "ldmppr_mark_model".

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.