kcde_quantile_predict_given_lagged_lead_obs: Draw a sample from the predictive distribution corresponding...

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

Description

Draw a sample from the predictive distribution corresponding to an estimated kcde model forward prediction_horizon time steps from the end of predict_data. This function requires that the kernel weights and centers have already been computed.

Usage

kcde_quantile_predict_given_lagged_lead_obs(p, n, train_lagged_obs,
  train_lead_obs, prediction_lagged_obs, kcde_fit)

Arguments

p	vector of probabilities which to compute quantiles
n	sample size for sample from predictive distribution used in approximating quantiles
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

Value

a vector of quantiles

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