compute_kernel_values: Compute kernel values measuring the similarity of each row in...

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

Description

Compute kernel values measuring the similarity of each row in the train_lagged_obs data frame to the prediction_lagged_obs.

Usage

compute_kernel_values(train_obs, prediction_obs, kernel_components, theta,
  log = TRUE)

Arguments

train_obs	a data frame with lagged observation vectors computed from the training data
prediction_obs	a data frame with the lagged observation vector computed from the prediction data. It is assumed that prediction_lagged_obs contains only one row.
theta	a list with one component for each component of the kernel function. This component is a named list with arguments to the corresponding kernel function.
log	boolean; if TRUE (default), return kernel values on the log scale
kcde_control	a list of kcde_control parameters for kcde

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