Modeling Spatially Varying Coefficients
Check Lower Bound of Covariance Parameters
Extact Mean Effects
Extact Covariance Parameters
Extact Model Fitted Values
GLS Estimate using Cholesky Factor
Conditional Akaike's and Bayesian Information Criteria
Setting of Optimization Bounds and Initial Values
Extact the Likelihood
Extract Number of Unique Locations
Extract Number of Observations
Plotting Residuals of SVC_mle model
Prediction of SVCs (and response variable)
Printing Method for summary.SVC_mle
Print Method for SVC_mle
Extact Model Residuals
Sample Function for GP-based SVC Model for Given Locations
Summary Method for SVC_mle
MLE of SVC model
Set Parameters for SVC_mle
SVC Model Selection
SVC Selection Parameters
varycoef: Modeling Spatially Varying Coefficients
Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) <doi:10.1016/j.spasta.2020.100470>). Covariance tapering (Furrer et al. (2006) <doi:10.1198/106186006X132178>) can be applied such that the method scales to large data. Further, it implements a joint variable selection of the fixed and random effects (Dambon et al. (2021b) <doi:10.1080/13658816.2022.2097684>). The package and its capabilities are described in (Dambon et al. (2021c) <arXiv:2106.02364>).