View source: R/SVC_selection.R
SVC_selection | R Documentation |
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, ...)
obj.fun |
( |
mle.par |
( |
control |
( |
... |
Further arguments. |
Returns an object of class SVC_selection
. It contains parameter estimates under PMLE and the optimization as well as choice of the shrinkage parameters.
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
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