GauPro (version 0.2.4)

kernel_product: Gaussian Kernel R6 class

Description

Gaussian Kernel R6 class

Gaussian Kernel R6 class

Value

Object of R6Class with methods for fitting GP model.

Format

R6Class object.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_product

Public fields

k1

kernel 1

k2

kernel 2

k1_param_length

param length of kernel 1

k2_param_length

param length of kernel 2

k1pl

param length of kernel 1

k2pl

param length of kernel 2

s2

Variance

Methods

Public methods

• kernel_product$new()

• kernel_product$k()

• kernel_product$param_optim_start()

• kernel_product$param_optim_start0()

• kernel_product$param_optim_lower()

• kernel_product$param_optim_upper()

• kernel_product$set_params_from_optim()

• kernel_product$dC_dparams()

• kernel_product$C_dC_dparams()

• kernel_product$dC_dx()

• kernel_product$s2_from_params()

• kernel_product$clone()

Method new()

Initialize kernel

Usage

kernel_product$new(k1, k2)

Arguments

k1

Kernel 1

k2

Kernel 2

Method k()

Calculate covariance between two points

Usage

kernel_product$k(x, y = NULL, params, ...)

Arguments

x

vector.

y

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

params

parameters to use instead of beta and s2.

...

Not used

Method param_optim_start()

Starting point for parameters for optimization

Usage

kernel_product$param_optim_start(jitter = F, y)

Arguments

jitter

Should there be a jitter?

y

Output

Method param_optim_start0()

Starting point for parameters for optimization

Usage

kernel_product$param_optim_start0(jitter = F, y)

Arguments

jitter

Should there be a jitter?

y

Output

Method param_optim_lower()

Lower bounds of parameters for optimization

Usage

kernel_product$param_optim_lower()

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

kernel_product$param_optim_upper()

Method set_params_from_optim()

Set parameters from optimization output

Usage

kernel_product$set_params_from_optim(optim_out)

Arguments

optim_out

Output from optimization

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

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

Arguments

params

Kernel parameters

C

Covariance with nugget

X

matrix of points in rows

C_nonug

Covariance without nugget added to diagonal

nug

Value of nugget

Method C_dC_dparams()

Calculate covariance matrix and its derivative with respect to parameters

Usage

kernel_product$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

kernel_product$dC_dx(XX, X)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

Method s2_from_params()

Get s2 from params vector

Usage

kernel_product$s2_from_params(params, s2_est = self$s2_est)

Arguments

params

parameter vector

s2_est

Is s2 being estimated?

Method clone()

The objects of this class are cloneable with this method.

Usage

kernel_product$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# NOT RUN {
k1 <- Exponential$new(beta=1)
k2 <- Matern32$new(beta=2)
k <- k1 + k2
k$k(matrix(c(2,1), ncol=1))
# }