Matern32 {GauPro} | R Documentation |
Matern 3/2 Kernel R6 class
Description
Matern 3/2 Kernel R6 class
Matern 3/2 Kernel R6 class
Format
R6Class
object.
Value
Object of R6Class
with methods for fitting GP model.
Super classes
GauPro::GauPro_kernel
-> GauPro::GauPro_kernel_beta
-> GauPro_kernel_Matern32
Public fields
sqrt3
Saved value of square root of 3
Methods
Public methods
Inherited methods
GauPro::GauPro_kernel$plot()
GauPro::GauPro_kernel_beta$C_dC_dparams()
GauPro::GauPro_kernel_beta$initialize()
GauPro::GauPro_kernel_beta$param_optim_lower()
GauPro::GauPro_kernel_beta$param_optim_start()
GauPro::GauPro_kernel_beta$param_optim_start0()
GauPro::GauPro_kernel_beta$param_optim_upper()
GauPro::GauPro_kernel_beta$s2_from_params()
GauPro::GauPro_kernel_beta$set_params_from_optim()
Method k()
Calculate covariance between two points
Usage
Matern32$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.
s2
Variance parameter.
params
parameters to use instead of beta and s2.
Method kone()
Find covariance of two points
Usage
Matern32$kone(x, y, beta, theta, s2)
Arguments
x
vector
y
vector
beta
correlation parameters on log scale
theta
correlation parameters on regular scale
s2
Variance parameter
Method dC_dparams()
Derivative of covariance with respect to parameters
Usage
Matern32$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 dC_dx()
Derivative of covariance with respect to X
Usage
Matern32$dC_dx(XX, X, theta, beta = self$beta, s2 = self$s2)
Arguments
XX
matrix of points
X
matrix of points to take derivative with respect to
theta
Correlation parameters
beta
log of theta
s2
Variance parameter
Method print()
Print this object
Usage
Matern32$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
Matern32$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
Examples
k1 <- Matern32$new(beta=0)
plot(k1)
n <- 12
x <- matrix(seq(0,1,length.out = n), ncol=1)
y <- sin(2*pi*x) + rnorm(n,0,1e-1)
gp <- GauPro_kernel_model$new(X=x, Z=y, kernel=Matern32$new(1),
parallel=FALSE)
gp$predict(.454)
gp$plot1D()
gp$cool1Dplot()