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hgwrr (version 0.6-2)

Hierarchical and Geographically Weighted Regression

Description

This model divides coefficients into three types, i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022). If data have spatial hierarchical structures (especially are overlapping on some locations), it is worth trying this model to reach better fitness.

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Install

install.packages('hgwrr')

Monthly Downloads

204

Version

0.6-2

License

GPL (>= 2)

Issues

1

Pull Requests

1

Stars

3

Forks

1

Maintainer

Yigong Hu

Last Published

September 28th, 2025

Functions in hgwrr (0.6-2)

coef.hgwrm

Get estimated coefficients.
fitted.hgwrm

Get fitted response.
logLik.hgwrm

Log likelihood function
residuals.hgwrm

Get residuals.
spatial_hetero_test

Generic method to test spatial heterogeneity
make_dummy

Make Dummy Variables
print.summary.hgwrm

Print summary of an hgwrm object.
print.hgwrm

Print description of a hgwrm object.
print.spahetbootres

Print the result of spatial heterogeneity test
multisampling

Large Scale Simulated Spatial Multisampling Data (DataFrame)
hgwrr-package

HGWR: Hierarchical and Geographically Weighted Regression
spatial_hetero_test.hgwrm

Hierarchical and Geographically Weighted Regression
mulsam.test

Simulated Spatial Multisampling Data For Test (DataFrame)
spatial_hetero_test_data

Test the spatial heterogeneity in data based on permutation.
wuhan.hp

Wuhan Second-hand House Price and POI Data (DataFrame)
summary.hgwrm

Summary an hgwrm object.
print_table_md

Print a character matrix as a table.