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hgwrr (version 0.5-0)

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

477

Version

0.5-0

License

GPL (>= 2)

Issues

1

Pull Requests

0

Stars

2

Forks

1

Maintainer

Yigong Hu

Last Published

July 28th, 2024

Functions in hgwrr (0.5-0)

print.shgt

Print the result of spatial heterogeneity test
hgwr

Hierarchical and Geographically Weighted Regression
multisampling

Simulated Spatial Multisampling Data (DataFrame)
hgwrr-package

HGWR: Hierarchical and Geographically Weighted Regression
coef.hgwrm

Get estimated coefficients.
logLik.hgwrm

Log likelihood function
fitted.hgwrm

Get fitted response.
print.hgwrm

Print description of a hgwrm object.
multisampling.large

Large Scale Simulated Spatial Multisampling Data (DataFrame)
make.dummy

Make Dummy Variables
wuhan.hp

Wuhan Second-hand House Price and POI Data (DataFrame)
print.table.md

Print a character matrix as a table.
spatial_hetero_test

Test the spatial heterogeneity in data based on permutation.
residuals.hgwrm

Get residuals.
summary.hgwrm

Summary an hgwrm object.
print.summary.hgwrm

Print summary of an hgwrm object.