gprPredict: Prediction of GPR model

In GPFDA: Gaussian Process for Functional Data Analysis

Prediction of GPR model

Description

Prediction of GPR model

Usage

gprPredict(
  train = NULL,
  inputNew = NULL,
  noiseFreePred = F,
  hyper = NULL,
  input = NULL,
  Y = NULL,
  mSR = NULL,
  Cov = NULL,
  gamma = NULL,
  nu = NULL,
  meanModel = 0,
  mu = 0
)

Arguments

train	A 'gpr' object obtained from 'gpr' function. Default to NULL. If NULL, learning is done based on the other given arguments; otherwise, prediction is made based on the trained model of class gpr'.
inputNew	Test input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate.
noiseFreePred	Logical. If TRUE, predictions will be noise-free.
hyper	The hyperparameters. Default to NULL. If not NULL, then it must be a list with appropriate names.
input	Input covariates. It must be either a matrix, where each column represents a covariate, or a vector if there is only one covariate.
Y	Training response. It should be a matrix, where each column is a realisation. It can be a vector if there is only one realisation.
mSR	Subset size m if Subset of Regressors method is used for prediction. It must be smaller than the total sample size.
Cov	Covariance function(s) to use. Options are: 'linear', 'pow.ex', 'rat.qu', and 'matern'. Default to 'power.ex'.
gamma	Power parameter used in powered exponential kernel function. It must be 0<gamma<=2.
nu	Smoothness parameter of the Matern class. It must be a positive value.
meanModel	Type of mean function. It can be 0 Zero mean function 1 Constant mean function to be estimated 't' Linear model for the mean function 'avg' The average across replications is used as the mean function. This is only used if there are more than two realisations observed at the same input coordinate values. Default to 0. If argument 'mu' is specified, then 'meanModel' will be set to 'userDefined'.
mu	Mean function specified by the user. It must be a vector. Its length must be the same as the sample size, that is, nrow(response).

Value

A list containing

pred.mean

Mean of predictions

pred.sd

Standard deviation of predictions

newdata

Test input data

noiseFreePred

Logical. If TRUE, predictions are noise-free.

...

Objects of 'gpr' class.

Examples

## See examples in vignettes:

# vignette("gpr_ex1", package = "GPFDA")
# vignette("gpr_ex2", package = "GPFDA")
# vignette("co2", package = "GPFDA")

GPFDA documentation built on May 7, 2022, 5:06 p.m.