⚠️There's a newer version (1.1) of this package.Take me there.

mgwrsar (version 1.0.5)

GWR and MGWR with Spatial Autocorrelation

Description

Functions for computing (Mixed) Geographically Weighted Regression with spatial autocorrelation, Geniaux and Martinetti (2017) .

Copy Link

Version

Install

install.packages('mgwrsar')

Monthly Downloads

403

Version

1.0.5

License

GPL (>= 2)

Maintainer

Ghislain Geniaux

Last Published

November 30th, 2023

Functions in mgwrsar (1.0.5)

bandwidths_mgwrsar

find_TP

Search of a suitable set of target points. find_TP is a wrapper function that identifies a set of target points based on spatial smoothed OLS residuals.

plot_effect

plot_effect plot_effect is a function that plots the effect of a variable X_k with spatially varying coefficient, i.e X_k * Beta_k(u_i,v_i) for comparing the magnitude of effects of between variables.

int_prems

int_prems to be documented

plot_mgwrsar

plot_mgwrsar plots the value of local paramaters of a mgwrsar models using a leaflet map.

PhWY_C

PhWY_C to be documented

kernel_matW

kernel_matW A function that returns a sparse weight matrix based computed with a specified kernel (gauss,bisq,tcub,epane,rectangle,triangle) considering coordinates provides in S and a given bandwidth. If NN<nrow(S) only NN firts neighbours are considered. If Type!='GD' then S should have additional columns and several kernels and bandwidths should be be specified by the user.

Proj_C

Proj_C to be documented

QRcpp2_C

QRcpp2_C to be documented

Sl_C

Sl_C to be documented

mgwrsar_bootstrap_test

A bootstrap test for Betas for mgwrsar class model.

predict_mgwrsar

mgwrsar Model Predictions predict_mgwrsar is a function for computing predictions of a mgwrsar models. It uses Best Linear Unbiased Predictor for mgwrsar models with spatial autocorrelation.

INST_C

INST_C to be documented

mgwrsar_bootstrap_test_all

A bootstrap test for testing nullity of all Betas for mgwrsar class model,

simu_multiscale

Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).

multiscale_gwr

multiscale_gwr This function adapts the multiscale Geographically Weighted Regression (GWR) methodology proposed by Fotheringam et al. in 2017, employing a backward fitting procedure within the MGWRSAR subroutines. The consecutive bandwidth optimizations are performed by minimizing the corrected Akaike criteria.

MGWRSAR

Estimation of linear and local linear model with spatial autocorrelation model (mgwrsar).

mydata

mydata is a simulated data set of a mgwrsar model

normW

normW row normalization of dgCMatrix

multiscale_gwr.cv

multiscale_gwr.cv to be documented (experimental)

summary_Matrix

summary_Matrix to be documented

summary_mgwrsar

Print a summary of mgwrsar models