mgprPredict: Prediction of MGPR model

Description Usage Arguments Value Examples

View source: R/mgp.functions.R

Description

Prediction of MGPR model

Usage

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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

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## See examples in vignette:
# vignette("mgpr", 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.