GauPro_kernel_beta {GauPro}

Beta Kernel R6 class

Description

Beta Kernel R6 class

Beta Kernel R6 class

Format

R6Class object.

Details

This is the base structure for a kernel that uses beta = log10(theta) for the lengthscale parameter. It standardizes the params because they all use the same underlying structure. Kernels that inherit this only need to implement kone and dC_dparams.

Value

Object of R6Class with methods for fitting GP model.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_beta

Public fields

beta

Parameter for correlation. Log of theta.

beta_est

Should beta be estimated?

beta_lower

Lower bound of beta

beta_upper

Upper bound of beta

beta_length

length of beta

s2

variance

logs2

Log of s2

logs2_lower

Lower bound of logs2

logs2_upper

Upper bound of logs2

s2_est

Should s2 be estimated?

useC

Should C code used? Much faster.

Methods

Public methods

• GauPro_kernel_beta$new()

• GauPro_kernel_beta$k()

• GauPro_kernel_beta$kone()

• GauPro_kernel_beta$param_optim_start()

• GauPro_kernel_beta$param_optim_start0()

• GauPro_kernel_beta$param_optim_lower()

• GauPro_kernel_beta$param_optim_upper()

• GauPro_kernel_beta$set_params_from_optim()

• GauPro_kernel_beta$C_dC_dparams()

• GauPro_kernel_beta$s2_from_params()

• GauPro_kernel_beta$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()
• GauPro::GauPro_kernel$print()

Method new()

Initialize kernel object

Usage

GauPro_kernel_beta$new(
  beta,
  s2 = 1,
  D,
  beta_lower = -8,
  beta_upper = 6,
  beta_est = TRUE,
  s2_lower = 1e-08,
  s2_upper = 1e+08,
  s2_est = TRUE,
  useC = TRUE
)

Arguments

beta

Initial beta value

s2

Initial variance

D

Number of input dimensions of data

beta_lower

Lower bound for beta

beta_upper

Upper bound for beta

beta_est

Should beta be estimated?

s2_lower

Lower bound for s2

s2_upper

Upper bound for s2

s2_est

Should s2 be estimated?

useC

Should C code used? Much faster.

Method k()

Calculate covariance between two points

Usage

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

Arguments

x

vector.

y

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

beta

Correlation parameters. Log of theta.

s2

Variance parameter.

params

parameters to use instead of beta and s2.

Method kone()

Calculate covariance between two points

Usage

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

Arguments

x

vector.

y

vector.

beta

Correlation parameters. Log of theta.

theta

Correlation parameters.

s2

Variance parameter.

Method param_optim_start()

Starting point for parameters for optimization

Usage

GauPro_kernel_beta$param_optim_start(
  jitter = F,
  y,
  beta_est = self$beta_est,
  s2_est = self$s2_est
)

Arguments

jitter

Should there be a jitter?

y

Output

beta_est

Is beta being estimated?

s2_est

Is s2 being estimated?

Method param_optim_start0()

Starting point for parameters for optimization

Usage

GauPro_kernel_beta$param_optim_start0(
  jitter = F,
  y,
  beta_est = self$beta_est,
  s2_est = self$s2_est
)

Arguments

jitter

Should there be a jitter?

y

Output

beta_est

Is beta being estimated?

s2_est

Is s2 being estimated?

Method param_optim_lower()

Upper bounds of parameters for optimization

Usage

GauPro_kernel_beta$param_optim_lower(
  beta_est = self$beta_est,
  s2_est = self$s2_est
)

Arguments

beta_est

Is beta being estimated?

s2_est

Is s2 being estimated?

p_est

Is p being estimated?

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

GauPro_kernel_beta$param_optim_upper(
  beta_est = self$beta_est,
  s2_est = self$s2_est
)

Arguments

beta_est

Is beta being estimated?

s2_est

Is s2 being estimated?

p_est

Is p being estimated?

Method set_params_from_optim()

Set parameters from optimization output

Usage

GauPro_kernel_beta$set_params_from_optim(
  optim_out,
  beta_est = self$beta_est,
  s2_est = self$s2_est
)

Arguments

optim_out

Output from optimization

beta_est

Is beta being estimated?

s2_est

Is s2 being estimated?

Method C_dC_dparams()

Calculate covariance matrix and its derivative with respect to parameters

Usage

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

Arguments

params

Kernel parameters

X

matrix of points in rows

nug

Value of nugget

Method s2_from_params()

Get s2 from params vector

Usage

GauPro_kernel_beta$s2_from_params(params, s2_est = self$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

GauPro_kernel_beta$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

#k1 <- Matern52$new(beta=0)