IgnoreIndsKernel: Kernel R6 class

In GauPro: Gaussian Process Fitting

Kernel R6 class

Description

Kernel R6 class

Kernel R6 class

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_IgnoreInds

Public fields

D

Number of input dimensions of data

kernel

Kernel to use on indices that aren't ignored

ignoreinds

Indices to ignore. For a matrix X, these are the columns to ignore. For example, when those dimensions will be given a different kernel, such as for factors.

Active bindings

s2_est

Is s2 being estimated?

s2

Value of s2 (variance)

Methods

Public methods

• IgnoreIndsKernel$new()

• IgnoreIndsKernel$k()

• IgnoreIndsKernel$kone()

• IgnoreIndsKernel$dC_dparams()

• IgnoreIndsKernel$C_dC_dparams()

• IgnoreIndsKernel$dC_dx()

• IgnoreIndsKernel$param_optim_start()

• IgnoreIndsKernel$param_optim_start0()

• IgnoreIndsKernel$param_optim_lower()

• IgnoreIndsKernel$param_optim_upper()

• IgnoreIndsKernel$set_params_from_optim()

• IgnoreIndsKernel$s2_from_params()

• IgnoreIndsKernel$print()

• IgnoreIndsKernel$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()

---

Method new()

Initialize kernel object

Usage

IgnoreIndsKernel$new(k, ignoreinds, useC = TRUE)

Arguments

k

Kernel to use on the non-ignored indices

ignoreinds

Indices of columns of X to ignore.

useC

Should C code used? Not implemented for IgnoreInds.

---

Method k()

Calculate covariance between two points

Usage

IgnoreIndsKernel$k(x, y = NULL, ...)

Arguments

x

vector.

y

vector, optional. If excluded, find correlation of x with itself.

...

Passed to kernel

---

Method kone()

Find covariance of two points

Usage

IgnoreIndsKernel$kone(x, y, ...)

Arguments

x

vector

y

vector

...

Passed to kernel

---

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

IgnoreIndsKernel$dC_dparams(params = NULL, X, ...)

Arguments

params

Kernel parameters

X

matrix of points in rows

...

Passed to kernel

---

Method C_dC_dparams()

Calculate covariance matrix and its derivative with respect to parameters

Usage

IgnoreIndsKernel$C_dC_dparams(params = NULL, X, nug)

Arguments

params

Kernel parameters

X

matrix of points in rows

nug

Value of nugget

---

Method dC_dx()

Derivative of covariance with respect to X

Usage

IgnoreIndsKernel$dC_dx(XX, X, ...)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

...

Additional arguments passed on to the kernel

---

Method param_optim_start()

Starting point for parameters for optimization

Usage

IgnoreIndsKernel$param_optim_start(...)

Arguments

...

Passed to kernel

---

Method param_optim_start0()

Starting point for parameters for optimization

Usage

IgnoreIndsKernel$param_optim_start0(...)

Arguments

...

Passed to kernel

---

Method param_optim_lower()

Lower bounds of parameters for optimization

Usage

IgnoreIndsKernel$param_optim_lower(...)

Arguments

...

Passed to kernel

---

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

IgnoreIndsKernel$param_optim_upper(...)

Arguments

...

Passed to kernel

---

Method set_params_from_optim()

Set parameters from optimization output

Usage

IgnoreIndsKernel$set_params_from_optim(...)

Arguments

...

Passed to kernel

---

Method s2_from_params()

Get s2 from params vector

Usage

IgnoreIndsKernel$s2_from_params(...)

Arguments

...

Passed to kernel

---

Method print()

Print this object

Usage

IgnoreIndsKernel$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

IgnoreIndsKernel$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

kg <- Gaussian$new(D=3)
kig <- GauPro::IgnoreIndsKernel$new(k = Gaussian$new(D=3), ignoreinds = 2)
Xtmp <- as.matrix(expand.grid(1:2, 1:2, 1:2))
cbind(Xtmp, kig$k(Xtmp))
cbind(Xtmp, kg$k(Xtmp))

GauPro documentation built on April 11, 2023, 6:06 p.m.