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White noise Kernel R6 class

Source: R/kernel_White.R
White.Rd

Initialize kernel object

Usage

k_White(
  s2 = 1,
  D,
  s2_lower = 1e-08,
  s2_upper = 1e+08,
  s2_est = TRUE,
  useC = TRUE
)

Format

R6Class object.

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?

useC

Should C code used? Not implemented for White.

Value

Object of R6Class with methods for fitting GP model.

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

Inherited methods

Method new()

Initialize kernel object

Usage

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

useC

Should C code used? Not implemented for White.

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 print()

Print this object

Usage

White$print()

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

k1 <- White$new(s2=1e-8)