KernelComputer-class: An S4 class to represent input for kernel matrix computations

In jtimonen/lgpr: Longitudinal Gaussian Process Regression

An S4 class to represent input for kernel matrix computations

Description

An S4 class to represent input for kernel matrix computations

Usage

## S4 method for signature 'KernelComputer'
show(object)

## S4 method for signature 'KernelComputer'
num_components(object)

## S4 method for signature 'KernelComputer'
num_evalpoints(object)

## S4 method for signature 'KernelComputer'
num_paramsets(object)

## S4 method for signature 'KernelComputer'
component_names(object)

Arguments

object

The object for which to call a class method.

Methods (by generic)

• show(KernelComputer): Print a summary about the object.

• num_components(KernelComputer): Get number of components.

• num_evalpoints(KernelComputer): Get number of evaluation points.

• num_paramsets(KernelComputer): Get number of parameter sets.

• component_names(KernelComputer): Get component names.

Slots

input

Common input (for example parameter values).

K_input

Input for computing kernel matrices between data points (N x N). A list.

Ks_input

Input for computing kernel matrices between data and output points (P x N). A list.

Kss_input

Input for computing kernel matrices between output points (P x P). A list, empty if full_covariance=FALSE.

comp_names

Component names (character vector).

full_covariance

Boolean value determining if this can compute full predictive covariance matrices (or just marginal variance at each point).

no_separate_output_points

Boolean value determining if Ks_input and Kss_input are the same thing. Using this knowledge can reduce unnecessary computations of kernel matrices.

STREAM

external pointer (for calling 'Stan' functions)

jtimonen/lgpr documentation built on Sept. 15, 2024, 1:08 a.m.