This function implements the variable selection for Gaussian process-based SVC models using a penalized maximum likelihood estimation (PMLE, Dambon et al., 2021, <arXiv:2101.01932>). It jointly selects the fixed and random effects of GP-based SVC models.
SVC_selection(obj.fun, mle.par, control = NULL, ...)Returns an object of class SVC_selection. It contains parameter estimates under PMLE and the optimization as well as choice of the shrinkage parameters.
(SVC_obj_fun)
Function of class SVC_obj_fun. This is the output of
SVC_mle with the SVC_mle_control parameter
extract_fun set to TRUE. This objective function comprises
of the whole SVC model on which the selection should be applied.
(numeric(2*q+1))
Numeric vector with estimated covariance parameters of unpenalized MLE.
(list or NULL)
List of control parameters for variable selection. Output of
SVC_selection_control. If NULL is given, the
default values of SVC_selection_control are used.
Further arguments.
Jakob Dambon
Dambon, J. A., Sigrist, F., Furrer, R. (2021). Joint Variable Selection of both Fixed and Random Effects for Gaussian Process-based Spatially Varying Coefficient Models, ArXiv Preprint https://arxiv.org/abs/2101.01932