GauPro (version 0.2.4)

White: White noise Kernel R6 class

Description

White noise Kernel R6 class

White noise Kernel R6 class

Value

Object of R6Class with methods for fitting GP model.

Format

R6Class object.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_White

Public fields

s2

variance

logs2

Log of s2

logs2_lower

Lower bound of logs2

logs2_upper

Upper bound of logs2

s2_est

Should s2 be estimated?

Methods

Public methods

• White$new()

• White$k()

• White$kone()

• White$dC_dparams()

• White$C_dC_dparams()

• White$dC_dx()

• White$param_optim_start()

• White$param_optim_start0()

• White$param_optim_lower()

• White$param_optim_upper()

• White$set_params_from_optim()

• White$s2_from_params()

• White$clone()

Method new()

Initialize kernel object

Usage

White$new(s2 = 1, D, s2_lower = 1e-08, s2_upper = 1e+08, s2_est = TRUE)

Arguments

s2

Initial variance

D

Number of input dimensions of data

s2_lower

Lower bound for s2

s2_upper

Upper bound for s2

s2_est

Should s2 be estimated?

Method k()

Calculate covariance between two points

Usage

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

Arguments

x

vector.

y

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

s2

Variance parameter.

params

parameters to use instead of beta and s2.

Method kone()

Find covariance of two points

Usage

White$kone(x, y, s2)

Arguments

x

vector

y

vector

s2

Variance parameter

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

White$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 C_dC_dparams()

Calculate covariance matrix and its derivative with respect to parameters

Usage

White$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

White$dC_dx(XX, X, s2 = self$s2)

Arguments

XX

matrix of points

X

matrix of points to take derivative with respect to

s2

Variance parameter

theta

Correlation parameters

beta

log of theta

Method param_optim_start()

Starting point for parameters for optimization

Usage

White$param_optim_start(jitter = F, y, s2_est = self$s2_est)

Arguments

jitter

Should there be a jitter?

y

Output

s2_est

Is s2 being estimated?

Method param_optim_start0()

Starting point for parameters for optimization

Usage

White$param_optim_start0(jitter = F, y, s2_est = self$s2_est)

Arguments

jitter

Should there be a jitter?

y

Output

s2_est

Is s2 being estimated?

Method param_optim_lower()

Lower bounds of parameters for optimization

Usage

White$param_optim_lower(s2_est = self$s2_est)

Arguments

s2_est

Is s2 being estimated?

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

White$param_optim_upper(s2_est = self$s2_est)

Arguments

s2_est

Is s2 being estimated?

Method set_params_from_optim()

Set parameters from optimization output

Usage

White$set_params_from_optim(optim_out, s2_est = self$s2_est)

Arguments

optim_out

Output from optimization

s2_est

s2 estimate

Method s2_from_params()

Get s2 from params vector

Usage

White$s2_from_params(params, s2_est = self$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

White$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# NOT RUN {
k1 <- White$new(s2=1e-8)
# }