GaussianPrediction-class: An S4 class to represent analytically computed predictive...

In jtimonen/lgpr: Longitudinal Gaussian Process Regression

An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model

Description

An S4 class to represent analytically computed predictive distributions (conditional on hyperparameters) of an additive GP model

Usage

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

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

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

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

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

Arguments

object

GaussianPrediction object for which to apply a class method.

Methods (by generic)

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

• component_names(GaussianPrediction): Get names of components.

• num_components(GaussianPrediction): Get number of components.

• num_paramsets(GaussianPrediction): Get number of parameter combinations (different parameter vectors) using which predictions were computed.

• num_evalpoints(GaussianPrediction): Get number of points where predictions were computed.

Slots

f_comp_mean

component means

f_comp_std

component standard deviations

f_mean

signal mean (on normalized scale)

f_std

signal standard deviation (on normalized scale)

y_mean

predictive mean (on original data scale)

y_std

predictive standard deviation (on original data scale)

x

a data frame of points (covariate values) where the function posteriors or predictive distributions have been evaluated

See Also

Prediction

jtimonen/lgpr documentation built on Oct. 12, 2023, 11:13 p.m.