kcde_predict_given_lagged_obs: 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 kernel functions and bandwidths specified in the kcde_fit object. This function requires that the lagged and lead observation vectors have already been computed.

Usage

kcde_predict_given_lagged_obs(train_lagged_obs, train_lead_obs,
  prediction_lagged_obs, prediction_test_lead_obs, kcde_fit,
  prediction_type = "distribution", n, p, q, log = FALSE)

Arguments

train_lagged_obs	is a matrix (with column names) containing the lagged observation vector computed from the training data. Each row corresponds to a time point. Each column is a (lagged) variable.
train_lead_obs	is a vector with length = nrow(train_lagged_obs) with the value of the prediction target variable corresponding to each row in the train_lagged_obs matrix.
prediction_lagged_obs	is a matrix (with column names) containing the lagged observation vector computed from the prediction data. There is only one row, representing one time point. Each column is a (lagged) variable.
kcde_fit	is an object representing a fitted kcde model
prediction_type	character; either "distribution" or "point", indicating the type of prediction to perform.

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.