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

Source: R/aaa.R, R/methods-Prediction.R

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

# S4 method for GaussianPrediction
show(object)

# S4 method for GaussianPrediction
component_names(object)

# S4 method for GaussianPrediction
num_components(object)

# S4 method for GaussianPrediction
num_paramsets(object)

# S4 method for 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