kcde_predict: Make predictions from an estimated kcde model forward...

In reichlab/kcde: Kernel conditional density estimation with flexible kernel specifications

Description

Make predictions from an estimated kcde model forward prediction_horizon time steps from the end of predict_data, based on the weighting variables, lags, kernel functions, and bandwidths specified in the kcde_fit object.

Usage

kcde_predict(kcde_fit, prediction_data,
  leading_rows_to_drop = max(kcde_fit$vars_and_offsets$offset_value[kcde_fit$vars_and_offsets$offset_type
  == "lag"]),
  trailing_rows_to_drop = max(kcde_fit$vars_and_offsets$offset_value[kcde_fit$vars_and_offsets$offset_type
  == "horizon"]), additional_training_rows_to_drop = NULL,
  prediction_type = "distribution", n, p, q, prediction_test_lead_obs,
  log = FALSE)

Arguments

kcde_fit	is an object representing a fitted kcde model
prediction_data	is a vector of data points to use in prediction
prediction_type	character; either "distribution" or "point", indicating the type of prediction to perform.
normalize_weights	boolean, should the weights be normalized?

Value

an object with prediction results; the contents depend on the value of prediction_type

reichlab/kcde documentation built on May 27, 2019, 4:53 a.m.