krige.glm.control: Defines options and model for prediction
In geoRglm: A Package for Generalised Linear Spatial Models

Description Usage Arguments Value Author(s) See Also

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.krige`	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`.
`trend.d`	specifies the trend (covariate) values at the data locations. See documentation of `trend.spatial` for further details. Default is `trend.d = "cte"`.
`trend.l`	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`.
`obj.model`	a list with the model parameters.
`beta`	numerical value of the mean (vector) parameter. Only used if `type.krige="sk"`.
`cov.model`	string indicating the name of the model for the correlation function. Further details in the documentation for `cov.spatial`.
`cov.pars`	a vector with the 2 covariance parameters sigma^2, and phi for the underlying Gaussian field.
`kappa`	additional smoothness parameter required by the following correlation functions: `"matern"`, `"powered.exponential"`, `"cauchy"` and `"gneiting.matern"`.
`nugget`	the value of the nugget parameter tau^2 for the underlying Gaussian field. Default is `nugget = 0`.
`micro.scale`	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`.
`dist.epsilon`	a numeric value. Locations which are separated by a distance less than this value are considered co-located.
`aniso.pars`	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`.
`lambda`	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.