me.gwar: Marginal effects for the GWalphaR model

In CompositionalSR: Spatial Regression Models with Compositional Data

Marginal effects for the GW\alphaR model

Description

Marginal effects for the GW\alphaR model.

Usage

me.gwar(be, mu, x)

Arguments

be	A matrix with the beta regression coefficients of the \alpha-regression model.
mu	The fitted values of the \alpha-regression.
x	A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here.

Details

The location-specific marginal effects for the GW\alphaR model are computed.

Value

A list including:

me	An array with the location-specific marginal effects of each component for each predictor variable.
ame	The average location-specific marginal effects of each component for each predictor variable.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Tsagris M. and Pantazis Y. (2026). The \alpha–regression for compositional data: a unified framework for standard, spatially-lagged, spatial autoregressive and geographically-weighted regression models. https://arxiv.org/pdf/2510.12663

Tsagris M. (2015). Regression analysis with compositional data containing zero values. Chilean Journal of Statistics, 6(2): 47-57. https://arxiv.org/pdf/1508.01913v1.pdf

Tsagris M.T., Preston S. and Wood A.T.A. (2011). A data-based power transformation for compositional data. In Proceedings of the 4th Compositional Data Analysis Workshop, Girona, Spain. https://arxiv.org/pdf/1106.1451.pdf

See Also

gwar, me.aslx, me.ar

Examples

data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- gwar(y, x, a = 1, coords, h = 0.001)
me <- me.gwar(mod$be, mod$est, x)

CompositionalSR documentation built on March 28, 2026, 5:07 p.m.