Marginal effects for the \(\alpha\)-SLX model.
me.aslx(be, gama, mu, x, coords, k = 10, cov_theta = NULL)A list including:
An array with the direct marginal effects of each component for each predictor variable.
An array with the indirect marginal effects of each component for each predictor variable.
An array with the total marginal effects of each component for each predictor variable.
An array with the average direct marginal effects of each component for each predictor variable.
An array with the average indirect marginal effects of each component for each predictor variable.
An array with the aerage total marginal effects of each component for each predictor variable.
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.
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.
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.
A matrix with the beta coefficients of the \(\alpha\)-SLX model.
A matrix with the gamma coefficients of the \(\alpha\)-SLX model.
The fitted values of the \(\alpha\)-SLX model.
A matrix with the continuous predictor variables or a data frame. Categorical predictor variables are not suited here.
A matrix with the coordinates of the locations. The first column is the latitude and the second is the longitude.
The number of nearest neighbours to consider for the contiguity matrix.
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.
Michail Tsagris.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.
The \(\alpha\)-transformation is applied to the compositional data first and then the \(\alpha\)-SLX model is applied.
Tsagris M. (2025). The \(\alpha\)--regression for compositional data: a unified framework for standard, spatially-lagged, 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
me.gwar, me.ar, alfa.slx
data(fadn)
coords <- fadn[, 1:2]
y <- fadn[, 3:7]
x <- fadn[, 8]
mod <- alfa.slx(y, x, a = 0.5, coords, k = 10, xnew = x, coordsnew = coords)
me <- me.aslx(mod$be, mod$gama, mod$est, x, coords, k = 10)
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