Gaussian Kernel R6 class

Gaussian Kernel R6 class

Gaussian Kernel R6 class

Format

R6Class object.

Value

Object of R6Class with methods for fitting GP model.

Super class

GauPro::GauPro_kernel -> GauPro_kernel_sum

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

s2_est

Is s2 being estimated?

Methods

Public methods

• kernel_sum$new()

• kernel_sum$k()

• kernel_sum$param_optim_start()

• kernel_sum$param_optim_start0()

• kernel_sum$param_optim_lower()

• kernel_sum$param_optim_upper()

• kernel_sum$set_params_from_optim()

• kernel_sum$dC_dparams()

• kernel_sum$C_dC_dparams()

• kernel_sum$dC_dx()

• kernel_sum$s2_from_params()

• kernel_sum$print()

• kernel_sum$clone()

Inherited methods

• GauPro::GauPro_kernel$plot()

---

Method new()

Initialize kernel

Usage

kernel_sum$new(k1, k2, useC = TRUE)

Arguments

k1

Kernel 1

k2

Kernel 2

useC

Should C code used? Not applicable for kernel sum.

---

Method k()

Calculate covariance between two points

Usage

kernel_sum$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_sum$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_sum$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_sum$param_optim_lower()

---

Method param_optim_upper()

Upper bounds of parameters for optimization

Usage

kernel_sum$param_optim_upper()

---

Method set_params_from_optim()

Set parameters from optimization output

Usage

kernel_sum$set_params_from_optim(optim_out)

Arguments

optim_out

Output from optimization

---

Method dC_dparams()

Derivative of covariance with respect to parameters

Usage

kernel_sum$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_sum$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_sum$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_sum$s2_from_params(params)

Arguments

params

parameter vector

s2_est

Is s2 being estimated?

---

Method print()

Print this object

Usage

kernel_sum$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

kernel_sum$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

k1 <- Exponential$new(beta=1)
k2 <- Matern32$new(beta=2)
k <- k1 + k2
k$k(matrix(c(2,1), ncol=1))
#>            [,1]       [,2]
#> [1,] 2.00000000 0.04232977
#> [2,] 0.04232977 2.00000000