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varycoef (version 0.3.4)

Modeling Spatially Varying Coefficients

Description

Implements a maximum likelihood estimation (MLE) method for estimation and prediction of Gaussian process-based spatially varying coefficient (SVC) models (Dambon et al. (2021a) ). Covariance tapering (Furrer et al. (2006) ) 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) ). The package and its capabilities are described in (Dambon et al. (2021c) ).

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Install

install.packages('varycoef')

Monthly Downloads

577

Version

0.3.4

License

GPL-2

Issues

3

Pull Requests

1

Stars

13

Forks

3

Maintainer

Jakob Dambon

Last Published

September 17th, 2022

Functions in varycoef (0.3.4)

cov_par

Extact Covariance Parameters
coef.SVC_mle

Extact Mean Effects
SVC_selection

SVC Model Selection
SVC_selection_control

SVC Selection Parameters
IC.SVC_mle

Conditional Akaike's and Bayesian Information Criteria
GLS_chol

GLS Estimate using Cholesky Factor
check_cov_lower

Check Lower Bound of Covariance Parameters
SVC_mle_control

Set Parameters for SVC_mle
SVCdata

Sampled SVC Data
print.SVC_mle

Print Method for SVC_mle
print.summary.SVC_mle

Printing Method for summary.SVC_mle
nobs.SVC_mle

Extract Number of Observations
init_bounds_optim

Setting of Optimization Bounds and Initial Values
plot.SVC_mle

Plotting Residuals of SVC_mle model
fitted.SVC_mle

Extact Model Fitted Values
logLik.SVC_mle

Extact the Likelihood
predict.SVC_mle

Prediction of SVCs (and response variable)
nlocs

Extract Number of Unique Locations
house

Lucas County House Price Data
summary.SVC_mle

Summary Method for SVC_mle
residuals.SVC_mle

Extact Model Residuals
varycoef

varycoef: Modeling Spatially Varying Coefficients
sample_SVCdata

Sample Function for GP-based SVC Model for Given Locations
SVC_mle

MLE of SVC model