Exponential: Exponential Kernel R6 class

In GauPro: Gaussian Process Fitting

Exponential Kernel R6 class

Description

Exponential Kernel R6 class

Exponential Kernel R6 class

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Super classes

GauPro::GauPro_kernel -> GauPro::GauPro_kernel_beta -> GauPro_kernel_Exponential

Methods

Public methods

• Exponential$k()

• Exponential$kone()

• Exponential$dC_dparams()

• Exponential$dC_dx()

• Exponential$print()

• Exponential$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()
• GauPro::GauPro_kernel_beta$C_dC_dparams()
• GauPro::GauPro_kernel_beta$initialize()
• GauPro::GauPro_kernel_beta$param_optim_lower()
• GauPro::GauPro_kernel_beta$param_optim_start()
• GauPro::GauPro_kernel_beta$param_optim_start0()
• GauPro::GauPro_kernel_beta$param_optim_upper()
• GauPro::GauPro_kernel_beta$s2_from_params()
• GauPro::GauPro_kernel_beta$set_params_from_optim()

---

Method k()

Calculate covariance between two points

Usage

Exponential$k(x, y = NULL, beta = self$beta, s2 = self$s2, params = NULL)

Arguments

x

vector.

y

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

beta

Correlation parameters.

s2

Variance parameter.

params

parameters to use instead of beta and s2.

---

Method kone()

Find covariance of two points

Usage

Exponential$kone(x, y, beta, theta, s2)

Arguments

x

vector

y

vector

beta

correlation parameters on log scale

theta

correlation parameters on regular scale

s2

Variance parameter

---

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

Exponential$dC_dparams(params = NULL, X, C_nonug, C, nug)

Arguments

params

Kernel parameters

X

matrix of points in rows

C_nonug

Covariance without nugget added to diagonal

C

Covariance with nugget

nug

Value of nugget

---

Method dC_dx()

Derivative of covariance with respect to X

Usage

Exponential$dC_dx(XX, X, theta, beta = self$beta, s2 = self$s2)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

theta

Correlation parameters

beta

log of theta

s2

Variance parameter

---

Method print()

Print this object

Usage

Exponential$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

Exponential$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

k1 <- Exponential$new(beta=0)

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