White {GauPro} | R Documentation |
White noise Kernel R6 class
Description
White noise Kernel R6 class
White noise Kernel R6 class
Format
R6Class
object.
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)