Description Usage Arguments Details Value Author(s)
Computes maximum likelihood estimates for parameters in a universal kriging model with regionally-varying covariance parameters.
1 2 |
y |
Dependent variable to be modeled |
X |
A n x d matrix of kriging covariates for the mean component. |
coords |
N x 2 matrix of coordinates |
reg.ind |
N-vector of distinct kriging regions.
Defaults to |
cov.model |
Either a single character string or a named vector providing the covariance model to be used. Valid values are "exp" and "iid". If only one value is provided, it is used for all regions. |
init.pars |
Vector of initial starting values for log-covariance parameters. |
optim.args |
List of named arguments passed to |
The MLEs are obtained by maximizing the profile
likelihood (see prof.rlik
).
By default, the opimization is done using the 'L-BFGS-B' method of
optim
.
If provided, the initial (log) parameters should be given
as a vector of length 3 times the number of distinct regions.
Values should follow the order of nugget,
sill, and range for each region in succession.
For example, if there are three regions the vector
c(tau1, sigma1, rho1, tau2, sigma2, rho2, tau3, sigma3, rho3) should
be given. If unspecifed, default initial values of 0 (on the log scale)
are used.
The default upper bound for the nugget and sill is log(10), and
the default lower bound is log(0.0001). Default bounds for the range
are log(0.001) and log(5000). These can be modified by providing
'upper' and 'lower' values in optim.args
.
Regionally-varying coefficients for regression parameters
are not explicitly implemented. However, they can be obtained
by supplying the covariate matri X
with suitable block
structure.
Setting cov.model='iid'
for all regions is equivalent
to ordinary least squares regression, but this function is
is not currently optimized for this use case and will likely
be much slower and less precise than lm
.
An object of class 'rlikfit' containing:
log.cov.pars |
maximized log-covariance parameters |
beta |
kriging regression covariate estimates |
max.log.lik |
maximized log-likelihood |
hess.pd |
indicator of positive definite hessian, if hess=TRUE |
r |
Number of regions |
Joshua Keller, Paul Sampson
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