IgnoreIndsKernel | R Documentation |
Kernel R6 class
Kernel R6 class
k_IgnoreIndsKernel(k, ignoreinds, useC = TRUE)
k |
Kernel to use on the non-ignored indices |
ignoreinds |
Indices of columns of X to ignore. |
useC |
Should C code used? Not implemented for IgnoreInds. |
R6Class
object.
Object of R6Class
with methods for fitting GP model.
GauPro::GauPro_kernel
-> GauPro_kernel_IgnoreInds
D
Number of input dimensions of data
kernel
Kernel to use on indices that aren't ignored
ignoreinds
Indices to ignore. For a matrix X, these are the columns to ignore. For example, when those dimensions will be given a different kernel, such as for factors.
s2_est
Is s2 being estimated?
s2
Value of s2 (variance)
new()
Initialize kernel object
IgnoreIndsKernel$new(k, ignoreinds, useC = TRUE)
k
Kernel to use on the non-ignored indices
ignoreinds
Indices of columns of X to ignore.
useC
Should C code used? Not implemented for IgnoreInds.
k()
Calculate covariance between two points
IgnoreIndsKernel$k(x, y = NULL, ...)
x
vector.
y
vector, optional. If excluded, find correlation of x with itself.
...
Passed to kernel
kone()
Find covariance of two points
IgnoreIndsKernel$kone(x, y, ...)
x
vector
y
vector
...
Passed to kernel
dC_dparams()
Derivative of covariance with respect to parameters
IgnoreIndsKernel$dC_dparams(params = NULL, X, ...)
params
Kernel parameters
X
matrix of points in rows
...
Passed to kernel
C_dC_dparams()
Calculate covariance matrix and its derivative with respect to parameters
IgnoreIndsKernel$C_dC_dparams(params = NULL, X, nug)
params
Kernel parameters
X
matrix of points in rows
nug
Value of nugget
dC_dx()
Derivative of covariance with respect to X
IgnoreIndsKernel$dC_dx(XX, X, ...)
XX
matrix of points
X
matrix of points to take derivative with respect to
...
Additional arguments passed on to the kernel
param_optim_start()
Starting point for parameters for optimization
IgnoreIndsKernel$param_optim_start(...)
...
Passed to kernel
param_optim_start0()
Starting point for parameters for optimization
IgnoreIndsKernel$param_optim_start0(...)
...
Passed to kernel
param_optim_lower()
Lower bounds of parameters for optimization
IgnoreIndsKernel$param_optim_lower(...)
...
Passed to kernel
param_optim_upper()
Upper bounds of parameters for optimization
IgnoreIndsKernel$param_optim_upper(...)
...
Passed to kernel
set_params_from_optim()
Set parameters from optimization output
IgnoreIndsKernel$set_params_from_optim(...)
...
Passed to kernel
s2_from_params()
Get s2 from params vector
IgnoreIndsKernel$s2_from_params(...)
...
Passed to kernel
print()
Print this object
IgnoreIndsKernel$print()
clone()
The objects of this class are cloneable with this method.
IgnoreIndsKernel$clone(deep = FALSE)
deep
Whether to make a deep clone.
kg <- Gaussian$new(D=3)
kig <- GauPro::IgnoreIndsKernel$new(k = Gaussian$new(D=3), ignoreinds = 2)
Xtmp <- as.matrix(expand.grid(1:2, 1:2, 1:2))
cbind(Xtmp, kig$k(Xtmp))
cbind(Xtmp, kg$k(Xtmp))
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