SVC_selection: SVC Model Selection

In varycoef: Modeling Spatially Varying Coefficients

SVC Model Selection

Description

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.

Usage

SVC_selection(obj.fun, mle.par, control = NULL, ...)

Arguments

obj.fun	(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.
mle.par	(numeric(2*q+1)) Numeric vector with estimated covariance parameters of unpenalized MLE.
control	(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.

Value

Returns an object of class SVC_selection. It contains parameter estimates under PMLE and the optimization as well as choice of the shrinkage parameters.

Author(s)

Jakob Dambon

References

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

varycoef documentation built on Sept. 18, 2022, 1:07 a.m.