me.asar: Marginal effects for the alpha-SAR model

In CompositionalSR: Spatial Regression Models with Compositional Data

Marginal effects for the \alpha-SAR model

Description

Marginal effects for the \alpha-SAR model.

Usage

me.asar(be, rho, mu, x, coords, k, cov_theta = NULL)

Arguments

be	A matrix with the beta coefficients of the \alpha-SAR model.
rho	The spatial auto-regressive coefficient \rho of the \alpha-SAR model.
mu	The fitted values of the \alpha-SAR model.
x	A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here.
coords	A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude.
k	The number of nearest neighbours to consider for the contiguity matrix.
cov_theta	The covariance matrix of the beta and gamma regression coefficients. If you pass this argument, then the standard error of the average marginal effects will be returned.

Details

The marginal effects of the \alpha-SAR model are computed.

Value

A list including:

me.dir	An array with the direct marginal effects of each component for each predictor variable.
me.indir	An array with the indirect marginal effects of each component for each predictor variable.
me.total	An array with the total marginal effects of each component for each predictor variable.
ame.dir	An array with the average direct marginal effects of each component for each predictor variable.
ame.indir	An array with the average indirect marginal effects of each component for each predictor variable.
ame.total	An array with the aerage total marginal effects of each component for each predictor variable.
se.amedir	An array with the standard errors of the average direct marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta.
se.ameindir	An array with the standard errors of the average indirect marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta.
se.ametotal	An array with the standard errors of the average total marginal effects of each component for each predictor variable. This is returned if you supply the covariance matrix cov_theta.

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

me.ar, me.aslx, me.gwar

Examples

data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.sar(y, x, a = 0.5, coords, k = 8)
me <- me.asar(mod$be, mod$rho, mod$est, x, coords, k = 6)

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