gprPredict: Prediction of GPR model

Description Usage Arguments Value Examples

View source: R/gp.functions6.R

Description

Prediction of GPR model

Usage

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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

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## See examples in vignettes:

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

Example output

Loading required package: fda.usc
Loading required package: fda
Loading required package: splines
Loading required package: Matrix
Loading required package: fds
Loading required package: rainbow
Loading required package: MASS
Loading required package: pcaPP
Loading required package: RCurl

Attaching package:fdaThe following object is masked frompackage:graphics:

    matplot

Loading required package: mgcv
Loading required package: nlme
This is mgcv 1.8-33. For overview type 'help("mgcv-package")'.
----------------------------------------------------------------------------------
 Functional Data Analysis and Utilities for Statistical Computing
 fda.usc version 2.0.2 (built on 2020-02-17) is now loaded
 fda.usc is running sequentially usign foreach package
 Please, execute ops.fda.usc() once to run in local parallel mode
 Deprecated functions: min.basis, min.np, anova.hetero, anova.onefactor, anova.RPm
 New functions: optim.basis, optim.np, fanova.hetero, fanova.onefactor, fanova.RPm
----------------------------------------------------------------------------------

Loading required package: spam
Loading required package: dotCall64
Loading required package: grid
Spam version 2.5-1 (2019-12-12) is loaded.
Type 'help( Spam)' or 'demo( spam)' for a short introduction 
and overview of this package.
Help for individual functions is also obtained by adding the
suffix '.spam' to the function name, e.g. 'help( chol.spam)'.

Attaching package:spamThe following object is masked frompackage:Matrix:

    det

The following objects are masked frompackage:base:

    backsolve, forwardsolve

GPFDA documentation built on Jan. 29, 2021, 5:14 p.m.