Fast Spatial Regression using Moran Eigenvectors

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Documentation for package ‘spmoran’ version 0.2.3

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

addlearn_local	Additional learning of local processes and prediction for large samples
besf	Spatial regression with RE-ESF for very large samples
besf_vc	Spatially and non-spatially varying coefficient (SNVC) modeling for very large samples
coef_marginal	Marginal effects evaluation
coef_marginal_vc	Marginal effects evaluation from models with varying coefficients
esf	Spatial regression with eigenvector spatial filtering
lsem	Low rank spatial error model (LSEM) estimation
lslm	Low rank spatial lag model (LSLM) estimation
meigen	Extraction of Moran's eigenvectors
meigen0	Nystrom extension of Moran eigenvectors
meigen_f	Fast approximation of Moran eigenvectors
nongauss_y	Parameter setup for modeling non-Gaussian continuous data and count data
plot_n	Plot non-spatially varying coefficients (NVCs)
plot_qr	Plot quantile regression coefficients estimated from SF-UQR
plot_s	Mapping spatially (and non-spatially) varying coefficients (SVCs or SNVC)
predict0	Spatial predictions
predict0_vc	Spatial predictions for explained variables and spatially varying coefficients
resf	Gaussian and non-Gaussian spatial regression models
resf_qr	Spatial filter unconditional quantile regression
resf_vc	Gaussian and non-Gaussian spatial regression models with varying coefficients
weigen	Extract eigenvectors from a spatial weight matrix