mgprPredict: Prediction of MGPR model

In GPFDA: Gaussian Process for Functional Data Analysis

Prediction of MGPR model

Description

Prediction of MGPR model

Usage

mgprPredict(
  train,
  DataObs = NULL,
  DataNew,
  noiseFreePred = F,
  meanModel = NULL,
  mu = 0
)

Arguments

train	A 'mgpr' object obtained from 'mgpr' function. If NULL, predictions are made based on DataObs informed by the user.
DataObs	List of observed data. Default to NULL. If NULL, predictions are made based on the trained data (included in the object of class 'mgpr') used for learning.
DataNew	List of test input data.
noiseFreePred	Logical. If TRUE, predictions will be noise-free.
meanModel	Type of mean function applied to all outputs. It can be 0 Zero mean function for each output. 1 Constant mean function to be estimated for each output. 't' Linear model for the mean function of each output. 'avg' The average across replications is used as the mean function of each output. This can only be used if there are more than two realisations observed at the same input values. Default to 0. If argument 'mu' is specified, then 'meanModel' will be set to 'userDefined'.
mu	Vector of concatenated mean function values defined by the user. Default to NULL.

Value

A list containing

pred.mean

Mean of predictions for the test set.

pred.sd

Standard deviation of predictions for the test set.

noiseFreePred

Logical. If TRUE, predictions are noise-free.

Examples

## See examples in vignette:
# vignette("mgpr", package = "GPFDA")

GPFDA documentation built on Sept. 11, 2023, 1:08 a.m.