This auxiliary function defines options and model for
pois.krige and binom.krige.
krige.glm.control(type.krige = "sk", trend.d = "cte", trend.l = "cte",
obj.model = NULL, beta, cov.model, cov.pars, kappa,
nugget, micro.scale, dist.epsilon = 1e-10,
aniso.pars, lambda)type of prediction to be performed (minimal mean
square error prediction). Options are
"sk" and "ok" corresponding to prediction with fixed
parameters (type.krige = "sk"), which is the default, or prediction with a uniform
prior on \(\beta\) (type.krige = "ok").
Prediction using a model with covariates can be done by specifying the
covariate model using the arguments trend.d and
trend.l.
specifies the trend (covariate) values at the data
locations.
See documentation of trend.spatial for
further details.
Default is trend.d = "cte".
specifies the trend (covariate) values at prediction
locations. It must be of the same type as for trend.d.
Only used if prediction locations are provided in the argument
locations.
a list with the model parameters.
numerical value of the mean (vector) parameter.
Only used if type.krige="sk".
string indicating the name of the model for the
correlation function. Further details in the
documentation for cov.spatial.
a vector with the 2 covariance parameters \(\sigma^2\), and \(\phi\) for the underlying Gaussian field.
additional smoothness parameter required by the following correlation
functions: "matern", "powered.exponential", "cauchy" and
"gneiting.matern".
the value of the nugget parameter
\(\tau^2\) for the underlying Gaussian field. Default is
nugget = 0.
micro-scale variance. If specified, the
nugget is divided into 2 terms: micro-scale variance
and measurement error.
This has effect on prediction where the ``signal'' part of \(S\)
(without the measurement error part of the nugget) is predicted. The
default is micro.scale = nugget.
a numeric value. Locations which are separated by a distance less than this value are considered co-located.
parameters for geometric anisotropy
correction. If aniso.pars = FALSE no correction is made, otherwise
a two elements vector with values for the anisotropy parameters
must be provided. Anisotropy correction consists of a
transformation of the data and prediction coordinates performed
by the function coords.aniso.
numeric value of the Box-Cox transformation parameter
for pois.krige.
The value \(\lambda = 1\) corresponds to
no transformation and \(\lambda = 0\) corresponds to
the log-transformation.
Prediction results are back-transformed and
returned is the same scale as for the original data.
A list with processed arguments to be passed to the main function.
pois.krige and binom.krige.