IGP_laGP_GauPro: IGP R6 object for fitting laGP_GauPro model

Description Format Value Methods Super class Public fields Active bindings Methods Examples

Description

Class providing object with methods for fitting a GP model. This mixes laGP and GauPro. It fits the model using laGP, then copies the parameters to a GauPro model for prediction.

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Methods

Documentation

For full documentation of each method go to https://github.com/CollinErickson/IGP/

new(X=NULL, Z=NULL, package=NULL, estimate.nugget=T, nugget0=F, ...)

This method is used to create object of this class with X and Z as the data. The package tells it which package to fit the GP model.

update(Xall=NULL, Zall=NULL, Xnew=NULL, Znew=NULL, ...)

This method updates the model, adding new data if given, then running optimization again.

Super class

IGP::IGP -> IGP_laGP_GauPro

Public fields

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

Active bindings

X

Design matrix

Z

Responses

N

Number of data points

D

Dimension of data

Methods

Public methods

Inherited methods

Method .init()

Usage
IGP_laGP_GauPro$.init(...)

Method .update()

Usage
IGP_laGP_GauPro$.update(..., no_update = FALSE)

Method .predict()

Usage
IGP_laGP_GauPro$.predict(XX, se.fit, ...)

Method .predict.se()

Usage
IGP_laGP_GauPro$.predict.se(XX, ...)

Method .predict.var()

Usage
IGP_laGP_GauPro$.predict.var(XX, ...)

Method .grad()

Usage
IGP_laGP_GauPro$.grad(XX)

Method .delete()

Usage
IGP_laGP_GauPro$.delete(...)

Method .theta()

Usage
IGP_laGP_GauPro$.theta()

Method .nugget()

Usage
IGP_laGP_GauPro$.nugget()

Method .s2()

Usage
IGP_laGP_GauPro$.s2()

Method .mean()

Usage
IGP_laGP_GauPro$.mean()

Method clone()

The objects of this class are cloneable with this method.

Usage
IGP_laGP_GauPro$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples

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n <- 40
d <- 2
n2 <- 20
f1 <- function(x) {sin(2*pi*x[1]) + sin(2*pi*x[2])}
X1 <- matrix(runif(n*d),n,d)
Z1 <- apply(X1,1,f1) + rnorm(n, 0, 1e-3)
X2 <- matrix(runif(n2*d),n2,d)
Z2 <- apply(X2,1,f1)
XX1 <- matrix(runif(10),5,2)
ZZ1 <- apply(XX1, 1, f1)
u <- IGP_laGP_GauPro$new(X=X1,Z=Z1)
cbind(u$predict(XX1), ZZ1)
u$predict.se(XX1)
u$update(Xnew=X2,Znew=Z2)
u$predict(XX1)
u$delete()

IGP documentation built on April 26, 2021, 5:08 p.m.