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varycoef (version 0.3.0)

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) ).

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Install

install.packages('varycoef')

Monthly Downloads

313

Version

0.3.0

License

GPL-2

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Repository

https://github.com/jakobdambon/varycoef

Maintainer

Jakob Dambon

Last Published

January 13th, 2021

Functions in varycoef (0.3.0)

Conditional Akaike's and Bayesian Information Criteria

\(L^q\) Norm Penalty

MLE of SVC model

Smoothly Clipped Absolute Deviation Penalty

SVC_mle_control

Set Parameters for SVC_mle

SVC_selection_control

SVC Selection Parameters

Extact Mean Effects

Extact Covariance Parameters Function to extract the covariance parameters from an SVC_mle object.

SVC Model Selection

fullSVC_reggrid

Sample Function for SVCs

Extact the Likelihood

Derivative of \(L^q\) Norm Penalty

print.summary.SVC_mle

Printing Method for summary.SVC_mle

Extact Number of Unique Locations Function to extract the number of unique locations in the data set used in an MLE of the SVC_mle object.

residuals.SVC_mle

Extact Model Residuals

Print Method for SVC_mle

predict.SVC_mle

Prediction of SVCs (and response variable)

Derivative of Smoothly Clipped Absolute Deviation Penalty

Extact Model Fitted Values

summary.SVC_mle

Summary Method for SVC_mle

varycoef: Modeling Spatially Varying Coefficients

Lucas County House Price Data

Extact Number of Observations

Plotting Residuals of SVC_mle model